A couple weeks ago, the #BusinessTimes asked me where I think #STEM needs to head. I shared my vision that technology becomes understandable to the layman so that everyone who has a problem to solve is able to understand how technology can exponentially help them.
This is particularly close to my heart. I was a STEM student most of my life. In the final years of high school in Singapore, all I studied was science – physics, chemistry, biology and math (back then they allowed us to be completely specialized!)
While I excelled at STEM subjects, the content felt increasingly irrelevant to the things I cared about, such as equity, fairness and inclusiveness in society (and the world). At Princeton, I completely dropped my plans to be a doctor or engineer and pursued a career in governance and public policy. (Although I could not resist the occasional engineering statistics and chemistry class).
In those years, technology progressed exponentially, becoming pervasive in all parts of life and work. As I plunged back into the field two years ago, I realized STEM applications are relevant to ANY problem you or I care about and want to solve. I’ve found my way back to the STEM world with a greater sense of purpose and relevance.
What bothers me however, is the divide between the STEM and non-STEM worlds. A young intern in my office recently shared that she was studying philosophy and ethics for undergrad, and immediately added (with some embarrassment): “I know… so much less useful than the engineers”. I have heard this sentiment echoed over the few years.
Yes, let us value technical experts, AI researchers, hardware engineers, for pushing us beyond what we thought was possible. But please, you don’t need to be a hard-core engineer to contribute to the STEM world. We know from history that technological innovation will continue marching forward. The question is whether it will be used to solve the world’s biggest problems – or exacerbate them.
What we really need now are people who can play the role as “bridges” between the STEM world and other domains, where inefficiencies, lack of transparency, and inequality affect the lives of millions of people every day. We need people who want to solve problems in healthcare, education, financial inclusion, gender equality, people who are curious and driven to know how STEM can help them fulfil their missions. People who have the trust of stakeholders, who can convince the users, who reach out to late-adopters who are most vulnerable in our tech-driven society.
I am all for STEM education – I deeply appreciated my own experience. We should raise the baseline of STEM education and importantly, help our students see the relevance of everything they are learning to daily life.
But let us be very careful not to give the impression that only “STEM” is in vogue, and everyone else is less relevant. For STEM to progress for social good, we need to create a movement which involves and values people with a wide variety of skills, interests and passions, not a few brilliant renegades who tell everyone else what the future should look like.
I was in DC for a day on 5 March to run a workshop for the World Bank on how to develop “smart cities”.
“Smart cities” is honestly a buzzword and when I get invited to speak, most people expect me to start with cool tech like AR, VR, AI, modeling and simulation, blockchain and the like.
The fact is that cities are complex ecosystems with very established ways of operating. If we want to disrupt them with technology in a way that benefits the masses (i.e. not just the upper middle class), we need dedicated work from the ground-up, coupled with political commitment. The aim is really to create a movement with many champions, not just a few bright sparks which fizzle out shortly.
If anyone is thinking of starting your smart city efforts, here are five tips I have, borne through many conversations and projects with smart city leaders worldwide.
Carve out space for ground-up innovation
When I first joined the public service, tech was really a downstream IT function, the proprietary territory of geeks. The realm of digital possibilities was beyond my imagination: I’d go about making policies, never thinking twice about the inefficiency of the data request and management process. It was just the ways things were done.
In 2014, a small group sprung up which touted new techniques for managing and analyzing data. They were eager to show us new things we could do with our data that we never imagined – natural language processing, k-means clustering, fancy visualizations. We didn’t have to wait 3 months and pay money to get data-sets; these should be available in real-time so we can make decisions on the go.
They made data science accessible. They were happy to experiment with small and large datasets, amorphous and specific problems. The more we worked together, the more I wanted to learn about these new techniques.
Technology, in the form of data science, became a way for me to solve the problems I cared about, such as the allocation of preschool places. It inspired me to take courses in R and Tableau (visualization) and apply these in my day-to-day work.
On reflection, it was so important that the small group of data scientists, user experience designers and machine learning scientists did not just stay in their box. They saw their role as tech evangelists, spreading enthusiasm and skills to the rest of us. They started a ground-up movement to make data science part of our work, and succeeded.
Build your core of “tech commandos”*
*term first used by my colleagues Daniel and Chi Ling here.
This is why, when people ask me what should be the first step in building a smart city, I never fail to raise the issue of building internal capabilities in the Government. In Singapore, we did not start with a large group of data scientists and software programmers. It was a small group of “tech commandos” who went about demonstrating value to the rest of the organization, before scaling up.
From my observations, three traits are important in picking “tech commandos”.
First, credibility with the organization (an outsider trying to shake things up often results in an allergic reaction);
Second, a strong HR instinct and the ability to assemble cross-functional teams – this does not mean that he/she must be the best technical executer;
Finally, the commitment to the organization’s long-term capabilities (not just his/her own shining). This does not mean that the person has to be an internal hire. However, there must be a personality fit – we had one “tech commando” who had no public sector experience, but an infectious, humble energy that won people over.
These “tech commandos” are effectively the bridge between the bureaucracy and the budding team of experts. They must be allowed to organize their teams, build a completely different culture as they wish, and buffer their team from the bureaucracy. To deliver early value, they must have high-level backers who are intent on opening up use cases and data for them to demonstrate their skills.
Nurturing a small core of “tech commandos” is always one of the first steps a city needs to take when it aims for digital transformation. Implementing projects is one benefit. Beyond this, their technical expertise is critical in assessing procurement decisions, such as the trade-offs between “building or buying” products and solutions. Great talent delivering social impact also attracts more talent, and so the cycle begins.
Integrate across agency boundaries so that you truly transform the citizen experience
If cities want to radically transform the living experience of their citizens using technology, integration across digital services is often necessary. This necessitates some form of central planning – you cannot have different agencies building their own systems and creating multiple, disconnected touchpoints with citizens.
A great example of a developing country that managed to achieve this is India, with its “JAM Trinity”.
–“J” for Jan Dhan, a free bank account for every citizen;
“A” for Aadhar, a biometrically verified Digital Identity for every citizen;
“M” for mobile, a mobile phone for every citizen.
For every individual, these three are linked. Hence, on your mobile phone, you can verify your identity and make a bank transfer.
The integration across identity-bank account-mobile is what explains widespread adoption of these technologies in India. “Aadhar” the digital identity, was first launched in 2009. However, take-up rate only spiked in 2014, when the Government linked digital identities to bank accounts, and used that to directly transfer subsidies and provide free insurance to people.
Simply put, people start adopting a new way of living life when they see the value and benefit of doing so. In the digital world, integration is necessary.
In my presentation at the World Bank, I laid out five elements of a nationwide technology project, gleaned from lessons across developing and developed countries.* <this section is partially attributable to my colleague Kevin Goh and Tan Chee Hau, who visited India to study the Digital ID system closely>
First, an ambitious, compelling goal. Modi himself championed the JAM trinity as the solution to financial, and hence social and economic exclusion if the poor. With a bank account, ID and mobile, everyone could connect to the formal economy and receive subsidies directly from Government. Almost S$20B of savings was to be yielded by solving tax evasion and the leaky pipe of subsidies due to inefficiency and corruption.
Second, a clear operational strategy. Ask any Indian official and citizen, and they simply understand that “JAM” represents digital transformation. The Government went for end-to-end integration of these components. The huge amount of savings generated from “JAM” justified distributing free services and a massive communications campaign.
Third, a clear governance structure. India designated agencies to set architectural standards for each of their digital identity and payments platforms. Setting standards ensures integration between components of a big system. In Singapore, we enforce standards not just by rules, but also by baking them into our platforms. For example, if developers use our NECTAR platform, they automatically comply with Government standards for development on the cloud, and other engineering best practices.
Fourth, an open ecosystem. One of the most amazing things India did was to create an open, interoperable India Stack to support “JAM”, The India stack consists of API-based platforms which the private sector can build applications upon. For example, if you are a start-up wanting to build a microfinance solution, you can build on their existing architecture for digital identity and mobile payments. You do not need to start from scratch.
Fifth, a massive focus on inclusiveness. When India went about getting every citizen to have JAM, they tried all means of reaching the unbanked: branch banking, mobile banking, online banking – you name it, they had it. They went on a massive campaign to reach the very last mile.
These are the five elements of any successful nation-wide technology project, which truly transforms the lives of citizens.
Setting the stage for public and private collaborations
When we talk about nationwide technology projects, does it mean that the Government has to execute on everything? By no means: some of the most cutting-edge innovation will always come from industry.
However, in developing smart cities, a new paradigm for the Government-private sector relationship is needed. Where in the past a Government simply procures digital infrastructure, products and service from the private sector (an out-sourcing model), what is needed now is more co-creation of possibilities and pilots between the public and private sectors, before deciding what to scale. The rapidly changing nature of technology means we are not quite certain which solution will work at the outset.
In working with private companies, Governments also need to lay out their expectations of an open ecosystem which enables maximal industry participation. This means that centralized platforms must have an open architecture and clear standards for interoperability, enabling other players can build applications upon it. Such a requirement runs against a traditional instinct for large companies to provide “closed ecosystems” which exclude all but those who use their proprietary operating systems.
Governments, start-ups and large corporates looking to build smart cities need to envision a new type of relationship, and build more platforms where trust and co-creation can be established. Some good examples include the Start-up in Residence Program, successfully run by the city of SF, and the Accreditation and Innoleap Programs run by the Singapore Government.
The advantages of developing countries in digital transformation
Friends from developing countries often tell me that “what Singapore does we’ll never be able to do”. They are surprised when I tell them that Singapore actually studies the smart city efforts of “developing” countries extremely closely.
Why has India raced ahead with their JAM trinity? Why does China light the path in e-payment adoption, while the U.S. and Singapore lag behind? Why was Estonia the first to develop a cutting-edge digital identity solution in the 1990s?
Users from developing countries can often more clearly see the value proposition of adopting the new digital solution. In contrast, people living in developed countries are typically wedded to the way things have always been done, such as using proprietary data centers instead of the cloud, paying with credit cards instead of e-payments, and using wired telephones instead of mobile. China is rapidly becoming the next innovation powerhouse because their people are cloud and mobile natives.
The lack of digital baggage is also a huge advantage. Estonia was able to leapfrog to the world’s most cutting-edge digital identity system because when they left Russia in the 1990s, they had zero legacy infrastructure to deal with. Just ask Taavi Kotkar, the ex CIO of Estonia, who told me a few years ago that he had to teach his kids what a “queue” was when he first took them on holiday outside Estonia.
“What in the world is a smart nation?” ask many of my (non-technical) friends when I first joined this team in the Government. Ultimately, people need to see, touch and feel how technology transforms their life in order to understand why it truly matters. If not, it remains in the realm of “esoteric”.
Building a smart city is ultimately about creating momentum throughout society to deploy tech for public good, not announcing a few superstar projects that fizzle out without momentum. I hope these five tips helped you think about what you need to do to build your own smart city which benefits the most people possible.
Hello readers! It’s been awhile. I’ve taken a short hiatus to invest more time in a personal passion of mine: Professional Coaching Certification. I’ve been a coach and received coaching in many situations over the years and have seen how useful coaching can be in raising professional effectiveness, navigating transitions and improving relationships.
This article gives an overview of how coaching may be relevant to you and what a coaching session typically looks like. Do shoot me an email if you have other questions! (karentay at gmail dot com). I’ve also started a personal website, www.karentayengage.com, where you can read more coaching and personal articles by me.
Want to make a shift in how your career is going, or make your current role more manageable or meaningful? Perhaps start a side-gig or become a better manager? A coaching relationship could help you. Here are some basics about coaching. Email me at firstname.lastname@example.org if you’re interested to learn more.
What is coaching and how can it be relevant to your life?
The aim of a coaching relationship is to help you obtain clarity in your professional and personal goals, and to create forward momentum towards achieving these. Together, we will achieve your hoped-for future.
Coaching can be helpful to you in all sorts of situations. These could include:
Transitions. Be it taking on a managerial position, moving city, company or role, having a new baby, starting and ending relationships, transitions disrupt our existing ways of being and doing. Working with a coach can turn transitions into the most fertile grounds for learning and growth, rather than a source of resentment.
Achieving Goals. You may have a clear goal in mind – such as getting that promotion, becoming a better manager, hitting that fitness level or improving a significant relationship – but you’re having some frustrations staying on the course. A coach will help you take a candid look at what hinders and helps you, and work with you to design a more effective path.
Decisions. Decisions may leave you tangled up in knots in your head. You may feel paralysed from the immensity of the decision and the breadth of possible options, or exhausted from trying to do it all and please everyone. You can work with a coach to carve a path that is true to who you are, giving yourself the right level of challenge without being overwhelmed.
Unspecified unease. You may feel a vague sense of unease about how work, family or a relationship is going, but you’re unable to pinpoint why, or what to do. With a coach, you can achieve better awareness and begin to take action.
How is coaching different from other conversational professions, such as consulting or therapy?
There is no definitive answer to this, but I would point to two distinctive traits of coaching:
Coaching conversations focus squarely on creating your future. Our conversations will help you clarify your goals, illuminate your possibilities, and design a path forward with you – one that is unique to who you are. At times we may explore the impact of your past on the way you perceive your situation, but it will always be in service of the goals you want to achieve.
A coach’s role is primarily to ask good questions, rather than to give you answers – quite unlike a consultant or advisor. In a coaching conversation, don’t be surprised if you find many of the answers within yourself – my job is to help you discover these. This approach is borne from a deep respect for your agency, experience and abilities.
Why have a coach, rather than talk to a family member, peer or boss?
Bosses, peers and family can all be incredible resources. However, here are two ways a coaching relationship could be more effective.
Want to be truly challenged? While I will support you, I am trained to challenge you. When we talk about the issue you want to tackle, we will challenge your cultural narratives, assumptions of what is possible and not possible, and your self-assessments – because these all limit your possibilities for action. In general, someone who is part of your day-to-day work and family settings is more likely to share your interpretations of a situation, which limits how much they can challenge you.
Ever felt that you don’t want to tell a boss or family member something because they’ll have a strong opinion on what you should do? Wanting to please the person you are talking to (or make sure their interests are met) can get in the way of delving deep into what really matters to you, and what you are willing to stake for that. As a coach, my role is to create a neutral space for you to discover things about yourself – I will have no judgment or vested interest in what you decide to do. This can be an avenue for the free-est and deepest conversations.
What will a typical coaching session look like?
There is no formula for a coaching session, but broadly, we will cover the following areas:
1. Clarifying your goals.
Come to each session with a problem or issue you want to work on. It need not be fully formed, so we will spend some time clarifying why, what’s at stake, and what you want to achieve from the coaching conversation.
2. Creating awareness: exploring your perspectives on the issue.
When faced with a problem, it’s tempting to go straight into developing new solutions. While it seems the most ‘efficient’ way to do things, sticking to this level of discussion narrows your options significantly. If you’ve come for a coaching session, chances are that you’ve tried several solutions, and you already feel tired thinking about the rest.
To open up new options – ones that motivate and inspire you – we’ll have to first go a little deeper and uncover your perspectives on the situation. Windows into this include:
Your language, which reveals beliefs, assumptions, interpretations and narratives
The emotions and moods you experience in this situation, how they impact your effectiveness, and what influences them
The way your body is responding to the situation, how it impacts your effectiveness, and what influences this
This step is all about becoming more aware of who you are in this situation.
3. Challenge: New perspectives and possibilities
As we uncover your perspective on the issue, we’ll also explore where it might not hold up. I will challenge you. For example, where are you turning your assumptions into “facts of life”? Which beliefs about yourself and others are grounded, and which are not? How does your mood affect your effectiveness, and can your mood be shifted? How?
These are springboards into a greater curiosity about what else you could do to achieve your goal. It typically opens up possibilities and realms of change which you never saw before.
4. Designing an action or practice, setting up accountability
We will end off by designing an action or practice which will serve your goal. You will commit to doing it between now and the next coaching session. We’ll scope it together to make sure it provides a challenge but is not overwhelming. The key is to take baby steps, assess our progress at the next session, and re-evaluate.
Finding an accountability mechanism is an essential part of this step. As your coach, you can ask me to be part of this. We will discuss the timelines for your commitment and conditions of satisfaction. Accountability is an essential part of helping you stay on course to achieve your goal.
How do I get started?
I recommend committing to four coaching sessions to start with (30min to 1 hour each).
I suggest committing to four because at the start of a coaching relationship, we will spend a large part of our sessions on steps 1) and 2) above – clarifying goals and creating greater awareness.
Having been coached myself, I know this is the territory that generates most impatience in the person being coached – why can’t we get to the solutions now? I have a decision to make and a life to live! No kidding – I was tempted to shut down a coaching relationship because I was so impatient.
Yet, I have seen time and again that if we skip the hard work of this part of coaching, we will be stuck in the cycle you know very well – of finding limited solutions because we are living within a narrow perspective.
Once you and I get into the groove of coaching after the initial sessions, the actions will come. They will feel more congruent with who you are and more energizing than you imagined.
Confidentiality and Ethics
All that you tell me within a coaching relationship will be strictly confidential. If a potential conflict of interest arises which I know of, I will inform you immediately. Please let me know if you see a potential conflict of interest as well. We can have a conversation to evaluate how we want to proceed.
A coaching relationship is always in service to you. If at any time you want to end the coaching relationship, I will be happy to refer you to a network of coaches.
In-person, online, on the phone?
I am based in Palo Alto and do coaching via Zoom/Skype or in person (in Palo Alto or Redwood City).
Ready to get started? New possibilities await in 2018.
Email me at karentay at gmail dot com for a free no-obligations 45 minute coaching session.
2017 marks the first full year I’ve lived in America since college. It has been one crazy year – writing this took almost a week. Here’s my 2017 in review: 5 meaningful things areas of work, challenges + learnings, and hopes for 2018. I’d love to hear about your 2017 too!
Engaging on the global stage: Technology and public good
2017 started off with the Consumer Electronics Show in Las Vegas, where I was part of a Supersession panel discussing how cities should capitalise on the sharing economy to improve public transportation.
Not many know that this website www.techandpublicgood.com has its roots in CES. Walking around the exhibitions, I understood first-hand the quantum leap in technological progress enabled by data, computing resources, ubiquitous connectivity and algorithmic progress.
I left CES feeling strangely disconnected. Over the December ’16 holidays, I had read Hillbilly Elegy, a story of middle class decay in America, and had been reflecting on my roots in social, educational and welfare policy. Questions, to which I had no easy answers, became fodder for articles on www.techandpublicgood.com.
2017 also brought opportunities to engage with these issues on the global stage.
From January to December, I spoke at 15 events, including:
“Artificial Intelligence and Social Good” at the AI Expo in SF;
“The Future of Smart Cities” at the WorldsFair Nano,
“Self Driving Cars and Society” at AI By the Bay,
“Data and Networks in Smart Cities” at Smart Cities Connect in Austin Texas,
“The Future of Intelligent Mobility” at Innovfest Unbound in Singapore,
“Self Driving Everything: The Impact on Cities” at the Singularity University Global Summit.
End-2017, I was appointed Faculty member at Singularity University, an amazing global community which is excited about using technology for social good. I like to think that by the efforts of us all, emerging technology will be used to make society just a little more equal, more cohesive, more inclusive of minorities than before.
What topics on tech and public good do you want to hear more about in 2018?
Connecting with smart city leaders
2017 also brought opportunities to engage inspiring thought-leaders at the intersection of technology and government. Many have become friends, not just collaborators. These included:
Smart city leaders in the U.S. (e.g. Seattle, D.C., Austin, Orlando New York, SF, San Jose)
Universities and non-profits examining technology governance (e.g. the Stanford Policy and Innovation Initiative, the World Economic Forum’s Center for the Fourth Industrial Revolution, the Global Foundations Challenge in Sweden)
Tech companies seeking to disrupt public services in transportation, healthcare, energy, etc, resulting in many link-ups across borders
A wide range of Singaporean leaders who visit the Silicon Valley periodically, including both our Deputy Prime Ministers and delegations from transportation, healthcare, defense, Singapore’s NSF-equivalent and so on
While a popular perception is that Governments are backward and arcane when it comes to emerging technology, my experience couldn’t be more different.
City leaders understand the huge potential of emerging technologies and their specific applications (e.g. AI and IOT applied to smart lamp-posts, self-driving cars, digital health) to solve existential governance challenges: improving outcomes for city dwellers while reducing costs and manpower, reducing traffic congestion as population explodes, moving healthcare systems towards disease prevention, rather than costly treatments.
However, there is tremendous uncertainty when it comes to adopting emerging technology in public services.
Which use cases are game-changing enough to justify the upfront capital investments?
If we need to develop public-private partnerships or purchase solutions from the tech companies, how do we reconcile the trade-offs in data ownership, privacy and algorithmic accountability?
How far ahead can we race with experimentation before some of these issues catch up with us?
In the next 5 years, we will see many successes and failures in the smart cities space.
Failures will be hard for Governments to stomach because ‘losing’ public monies is always more galling than losing private investments.
Yet it is better than standing still. Like it or not, emerging technology is going to disrupt traditional public services such as healthcare, education, city management and transportation.
Governments need to get their foot in early and help make self driving cars, AI, IOT and digital health work for the widest range of city-dwellers possible: not just those who can afford it.
Building a community among Singaporeans-in-technology
A group of 16-year old girls visited the Silicon Valley in October. They met big names, inspiring founders, judges and venture capitalists. I hosted one of their final sessions, and a question left me ruminating for months. In gist: “many Singaporeans think that living in the Silicon Valley is so much better than living in Singapore. Why are you such a huge champion for Singapore?”
Over the course of 2017, I’ve met over two hundred Singaporeans living the Bay Area. Sure, many reflect on the better career opportunities, weather, outdoor activities and family time available in the Silicon Valley (compared to Singapore).
But I also frequently get asked (1) What’s happening in the tech scene back home? (2) Is there a way to contribute? In a short digital survey conducted at one of my Singaporean-in-tech events, data showed that over 70% wanted to contribute to Singapore even though they might not be ready to move back
In 2018, I want to reflect on what it means to be a country in this digital, globalized age. Perhaps countries will no longer define themselves by their borders but by their people. The Singaporean diaspora is spread all throughout the world, many in highly influential positions (we have CTOs all over the valley!). How can we involve them in our country’s future?
I am also interested to explore who is in this overseas Singaporean tech community and how to engage them in a way that is suitable to their needs and preferences. Case in point: in July this year, Jacqueline Poh, our Chief Executive of Govtech Singapore, spent six weeks in the Valley and wanted to host a dinner with Singaporeans in tech. By then I had met dozens of Singaporeans living in the Valley, but very few women. “How many Singaporean women-in-tech do you think there are in the Valley?” I asked some friends who had been around longer. “5? 10? At most 15 perhaps. Don’t get your hopes up”; most replied.
Within an hour of posting my invitation on Linkedin, we had reached full capacity of 60 Singaporean women. Data scientists, product managers, investors, software engineers, product and growth marketers showed up in full force. In past events, women formed at most 5% of the attendees – why?
Singaporeans living in the Valley: how would you like to engage with Singapore’s tech scene more? Singaporeans at home: how would you like to engage with fellow Singaporeans living in the Valley?
Exploring issues facing women and other workplace minorities
2017 was the year that gender-based harassment and discrimination exploded to the public eye in the Silicon Valley. Personally, I also came to identify with the experiences of minorities, upon entering the technology sector as a non-engineer and moving to America as a foreigner in the Trump era.
I’m curious about the experiences of women, and other minorities, in the workplace.
I had an amazing four days engaging with women who are doing ground-breaking work, in big tech and start-ups, legal advocacy for girls in war-torn countries, healthcare providers in underserved communities etc. My experience and reflections on womens’ and minority issues here.
November: Singaporeans-in-tech: Panel and Dinner
In November, I led a group of amazing volunteers to organize a panel and dinner with Singaporean women-in-tech, who shared their career journeys in the Bay Area. Our event was over-subscribed by both men and women, Singaporeans and non-Singaporeans. The panel was honest, thought-provoking and inspiring.
A memorable moment was when I asked the panellists which Singaporean mindsets which helped and hindered usin the Valley. Aihui Ong, founder of Edgilife, shared how her Singaporean comfort with multiculturalism subconsciously shapes her hiring decisions – Edgilife is one of the most diverse start-ups in town (represent!)
Yen Low’s dogged Singaporean attitude enabled her to acquire data science skills and become a respected member in a male-dominated field at Netflix. Aakriti and Joo Lee shared the challenges of breaking into a market where they had no prior networks, plus tips for putting yourself out there and developing a ‘personal brand’ – something many of us did not have to do in Singapore (in fact it can be frowned upon in Singapore).
I could almost hear a sigh of relief from the audience (and certainly myself) when these accomplished women shared their experiences so candidly.
Hearing from fellow Singaporeans helped many attendees normalize, rather than personalize the uncomfortable experience of being a foreigner who needs to ‘break into’ the prevailing culture. Dozens sent me private messages afterwards to ask if we could have more of such conversations in the Singaporean community, and volunteered to help put more events together.
December: Asia Society Women’s Leadership Breakfast
December rounded off with an Asia Society womens’ leadership breakfast, where I had the opportunity to discuss issues facing Asian women in the Silicon Valley with Shie Lundeberg (Google), Shirley Ma (McKinsey), Tina Lee (Mothercoders) and Katie Benner (New York Times). Another eye-opening conversation, another set of inspiring women.
The minority experience is one of constant reinvention, and defying – even overcompensating for – stereotypes. It is never quite feeling like an insider. I come out of 2017 much more aware of the responsibilities that majorities have in making workplaces and common spaces inclusive, and the responsibilities of minorities to support each other in a way that does not become exclusive or incendiary.
Pursuing my passion for coaching
Finally, I enrolled in an 8-month coaching certification program in 2017. Though I’ve done this informally for many years, a personal goal is to master the art of helping people become more effective in achieving their goals. A workplace relationship they want to improve? A difficult conversation with their boss? Managing a major transition healthily? Fixing communications breakdowns? Becoming the boss for the first time?
Coaching is about creating that safe space for someone else to explore different perspectives, widen their options, and stay accountable to committed actions. I don’t know about you, but I find that the busyness of adult life makes it difficult to break out of old patterns, even if they are inhibitive to our professional and personal goals. Developing a coaching relationship is one solution.
In 2018, I’ll be writing more on the topic of coaching and leadership, building on these three articles that I wrote in 2017.
As part of my course I’ll also be taking on coaching clients starting in February, so do get in touch if you are interested!
Challenges in 2017
2017 has been one of the most exciting and challenging years of my career to date. I’ve learned that I enjoy the ‘start-up’ life: experimenting, iterating, pivoting, and finding that elusive ‘product market fit’.
However, like any start-up, work is fraught with high highs (the market is responding; this is what is needed!) and low lows (what am I even doing?). I’ve learned to follow my convictions, amidst the many confusing signals about what I ‘should be’ or ‘should not be’ doing.
Fortunately, I have amazing, progressive bosses (Kok Yam and Chee Khern) who have given me so much latitude and trust. This is definitely NOT a traditional Government posting. I also have many, many brilliant, supportive co-workers across the Singapore public service who are just a call away (shout-out to Daniel Lim, Feng Yuan, Mark Lim, Jacqui, Pui San, Rebecca, Shi-Hua, Chor Pharn, Titus, Kai Jit, Yang Boon, Simon Phua, Victor Tan, Stanley Leong, Lynn Khoo, Kenneth Teo, Melanie Tan, Heng Jie, Brandon, Sidra Ahmed… the list goes on). My husband, who is doing a PhD in statistics at Stanford, has also been an incredible support at work.
Nevertheless, I’ve realized that the extrovert in me needs a team in the same geography to be collaborators, sounding boards, and ultimately to start scaling up. Thankfully, I’m expanding the team in 2018, and plan to work much closer with other teams in the Bay Area!
I also need to work on pacing myself better in 2018. In my impatience to adjust to a new job, new country, new baby, new home and new community, I ended off 2017 very tired.
Thankful for some much-needed down-time by the ocean, with family!
Bring it on, 2018.
I enter 2018 with a mood of curiosity.
How can I serve the world, and my local community, in 2018?
Governments and Social Media companies are in the midst of a heated debate on how to regulate social media platforms. This can often fall into finger-pointing and mutual suspicion. For example, many Governments believe that social media companies like Facebook, Twitter and YouTube cannot be trusted to act in the public interest because they will always prioritise business interests. In my previous article “Policy Issues Facing Social Media Companies: The Case Study of YouTube”, I argued that social media companies are often not trading-off public interests for business interests. They are more often trading-off competing public interests, which creates many dilemmas that Governments may not understand.
This article goes a step further and argues that Governments must fundamentally shift their paradigm towards regulating social media companies, recognizing that social media companies, like Governments, are representations of public interests. Here it goes:
Proposing a New Paradigm for Regulating Social Media Companies
By enabling anyone to produce and share content, social media platforms like Facebook and YouTube have decentralized how information and opinions are shared in society. This has brought tremendous public value, such as freedom of speech and enabling access to education. However, it has also enabled individuals to spread hate speech, terrorist agendas and fake content, which can threaten national security and social harmony.
Some argue that the social media space should be completely free and left to the discretion of users. Users will rise up to counter offensive or fake material, or judge for themselves that these should be ignored.
This anti-regulation approach is irresponsible towards public interests. Targeted defamations and incitements to racist violence can easily go viral on social media platforms. Without swift actions by authorities, consequences to personal wellbeing and national security could be irreparable.
Some regulation is necessary to strike the balance between advancing free speech and protecting public interests such as national security and social harmony – the question is how.
“Co-regulation”: A New Paradigm In Regulation
I propose a new paradigm for how Governments regulate social media companies, which I term ‘Co-regulation’.
In the media space, Governments have traditionally seen themselves as guardians of public interest, enacting regulation to prevent content which violates standards of public decency. Governments must recognize that unlike traditional media companies, where content is generated by small group of individuals, social media platforms represent a broad base of content producers and users. Social media platforms, like Governments, are avenues for public interests to be represented.
Hence, Governments cannot see themselves as enforcers of public interest against social media companies. Instead, Governments and social media companies are joint stewards of public interests on social media platforms. This is the paradigm which undergirds ‘Co-regulation’.
‘Co-regulation’ has three components:
First, content standards should be interpreted and operationalized on social media platforms through an inclusive mechanism. When it comes to interpreting content laws, the scale and speed of the digital world make court decisions impractical. While it would be expedient to assign responsibility to social media companies to interpret and operationalize content laws, this would be unrepresentative of public interests. One idea is for Governments and social media companies to co-develop a swift mechanism which allows a spectrum of public voices to influence the interpretation of content laws in grey cases.
Second, Governments and social media companies should establish a system of public accountability. A good example is the Code of Conduct on Countering Illegal Online Hate Speech, established by the European Commission and four major social media platforms in 2016. It sets public goals for how quickly illegal hate speech should be reviewed and removed. Results are published on a regular basis.
Third, Governments and social media companies should both make commitments, and be held jointly accountable, to public goals. For example, while social media companies invest in systems to detect and review potentially illegal content, Governments should engage the public on what constitutes ‘hate speech’ and ‘fake news’, so that user-flagging is more effective.
Why Not Legislate the Problem Away?
By implementing a law which enables hefty fines for social media companies which fail to take down ‘obviously illegal content’, Germany has argued that without legislation, social media companies will not take their responsibilities seriously.
In my view, the costs of legislation generally outweigh the benefits. The upside – better enforcement – is limited. Business incentives to remove objectionable content are already in play: advertisers are social media platforms’ main source of revenue, and none want their ads to be associated with objectionable content. An advertisers’ boycott on YouTube earlier this year suggests that market forces are alive and well.
On the other hand, legislation can have dangerous effects. Placing legal responsibility on social media companies to identify the lawfulness of content on their platforms creates an incentive to err on the side of greater caution, i.e. more censorship. Beyond undermining the right to free speech, companies may inadvertently censor important public feedback, for example, on Governmental corruption. Besides, enacting legislation sends a signal that social media companies cannot be trusted to act in the public interest, which is inimical to the principles of co-regulation.
Governments worldwide should recognise social media platforms as legitimate representations of public interests. As co-stewards of public interest, Governments and social media companies hold joint responsibility and accountability for regulating the social media space in a way that best represents public interests. It is about time Governments and Social Media Companies work collaboratively under this new paradigm of co-regulation.
I’ve never thought deeply about gender issues because I personally haven’t experienced gender-based discrimination. In high school/junior college I was a Science and Math student (back then, it that meant that I only took these subjects, no humanities). In my class, girls outnumbered boys, and did as well as them – there was no differentiation. When I went on to college, and work, I didn’t experience gender-based discrimination.
So when I was invited to a “Women’s Forum” in October, I hesitated. Would it be disingenuous to identify myself with “women’s issues” if I had never suffered on behalf of being a woman? I went ahead anyway, because I wanted to explore this question. I have also been more conscious about minority issues since moving back to America in the midst of the polarizing 2016 Presidential election.
Fast forward… last week, I was in Paris for the Women’s Forum for the Economy and Society. I participated in a panel on ‘How Technology Can Keep People in Work’ with Christele Genty (Google Europe), Elisabeth Moreno (President of Lenovo France) and Heather Cykoski (ABB), and was nominated for the Rising Talents Initiative for Women leaders under 40. As a result, I got to hang out with a bunch of really talented and driven women for three days straight, all sponsored by the Conference organizers.
Here are five takeaways from my experience:
Women’s issues are real
To many, this seems like a “duh” point to make. But I write this because for those who have not personally experienced discrimination, it is easy to downplay or distance ourselves from the issue. For example, at the back of your mind, you may ask, “is he/she overplaying this? Does he/she have a hidden agenda?” – that’s you distancing yourself, and I’m guilty of this too.
I took the opportunity to ask many women about their experience, and more than I expected had experienced some form of harassment or discrimination. Examples:
Harassment: anything from a boss saying “hey, I’m bored, send me an explicit video of yourself now” to inappropriate touching
Conscious and subconscious discrimination: the pay gap with their male counterparts, having one female toilets in manufacturing and chemical plants for the sizeable female workforce.
Listening to personal stories helped me realize, deep down, that this is real – the lived experience of thousands of women. I should – indeed I must – care, even though it is not my own lived experience.
Women’s issues are a small sub-set of minority issues. Let’s treat them as such.
Emphasizing women’s issues can lead us to unintentionally de-emphasize issues faced by other minorities, especially if you turn it into a “men vs women” debate.
Case in point: at my hotel in Paris, all the room cleaners were men. I found myself inherently suspicious of them, and more cautious about my valuables. Why? I had an unconscious bias against men, who are a minority in domestic and caring jobs. Why do we not wage war on disparaging attitudes against men in these roles, in the same way that we wage war against disparaging attitudes against women in executive roles? The situations are not completely parallel, I admit. But you get the idea. Men can be minorities too and let’s not understate their experiences.
I have not even talked about racial, religious, educational minorities. The point is – minorities exist in every micro-context. I see Women’s issues as a sub-set that points us to the broader set of minority issues. We need to be aware of the existence and experience of minorities, which brings me to my next point.
Accept it: If you are in the majority, you are blind to your blindness on the barriers faced by minorities.
I spoke to several women from big tech firms, who shared their experience of making complaints about harassment. “In the end, the only thing you can do is leave” – was the overwhelming sentiment. Some companies require you to give a written statement of the incident, without telling you how the statement will be used and the implications it might have. Others advise you that you can take it to senior management, or the courts, but then your own reputation would be dragged through the mud. Bosses tell you “oh, that guy is just a jerk. Just ignore him”. I’ve heard similar stories for racial minorities – one just happened to a college friend of mine in an investment firm.
Rules and processes for dealing with harassment and discrimination really have to take into account the experience of the minority – often one of disempowerment and sometimes shame. If you are in the majority in any particular context, you have to go out of your way to ensure that processes that enable reporting of harassment or discrimination make the minority feel safe.
Here’s one good example: the most inspiring woman I met at the conference was an international human rights lawyer who started a non-profit, “We are Not Weapons of War” to provide advocacy and legal services to victims of rape in conflict zones. She found that it was impossible to get these girls to go to the doctor or seek legal assistance because of their aversion to men and deep feeling of shame. Technology, she shared, enabled her organization to support the girls without requiring them to leave their comfort zone.
Women-specific events are helpful, but be careful to be inclusive
I see a place for women-specific events. In my experience, women tend to come out in greater force when the event is women-specific. I don’t quite know why, but this seems to be a common experience. Furthermore, if you are in a minority, solidarity and community helps with gaining new perspectives on your particular challenges.
However, we really shouldn’t forget that the nub of the issues is diversity and inclusiveness. We need to be careful about making sure these women-specific events do not turn exclusive by either disparaging men or subconsciously excluding other minority groups from the agenda.
Educating our next generation of female leaders to rise in a male-dominated professions without conforming to male stereotypes
It was a lot of fun participating in the Rising Talents Initiative, which recognized a dozen women leaders under 40. One very cool lady I met through this program wasEstelle Touzet, the Chief Sommelier at the Ritz. She is only 36, and oversees a team of 8 sommeliers. We visited her at work late one evening (she works 14 hour days). Witnessing her excel in such a male-dominated industry with grace and femininity was inspiring. It’s a beautiful thing when women rise to the top of their profession without altering themselves to fit the stereotypes of the dominant gender.
It made me reflect on my school experience. I went to an all-girls school which advocated breaking female stereotypes. Looking back, I appreciated the spirit of equality – women can achieve whatever men can. But I am more wary about the subtle messaging that we have to give up our femininity to do so. How can we avoid mixing up the two for the next generation of female leaders?
A further thought – it is not just women who are pressured to conform to male stereotypes in leadership. Many men also do not display such traits naturally and suffer for this, which brings me back to point 2: let’s realize that women’s issues are only a small subset of minority issues. They point us to something larger.
And now, for some personal anecdotes:
There’s a stereotype that women tend to prepare better for professional meetings and engagements. This was my first time on an all-women panel and this was absolutely true. Our incredible moderator May Busch arranged two pre-conference calls to establish the angles that each of us, based on our experiences. Everyone added their points to a Google document. May shared her facilitation plan. Result = dozens came up to us to say that we worked like magic together, that they had never seen such chemistry between panelists and blow after blow of impactful points. I’ve attended too many sessions where I’m like – wait – are these panellists discussing the same topic? Or worst – why does this Very Important Person seem so…unoriginal? Lesson learned when it comes to assembling panels, preparation >> raw genius. So is it true that women tend to prepare better, or just my one-off experience?
I learned some personal lessons from my co-panellists: May Busch (ex Morgan Stanley banker) and Elisabeth Moreno (President of Lenovo France). Before our session, they said: before you speak, think of what you want your audience to think, feel and do after you’re done. I’ve subconsciously thought of panels as ways to convey ideas. Connecting and inspiring? Perhaps incidentally, but never a main goal. These two ladies showed me that you can connect, inspire and share new ideas without coming across as cheesy and unprofessional. May was very deliberate about engaging the audience. At the start, she asked them to look out for that one point to take away, and at the end, she reminded them to share that point with one person. Elisabeth wanted to give the audience conviction that they could rise to the challenge of harnessing technology to improve their jobs, rather than fearing that technology would take their jobs. She was very deliberate about addressing the audience personally and used her body language to do so. These two women gave me food for thought on how I communicate.
I am not sure how I feel about this yet, but I’ll write it here for entertainment. In typical conferences, all the booths are related to ‘work matters’, but in this one… photo-taking studio (super long lines), free Philosophy (cosmetic) products. 😉
Overall, I’m really thankful for the opportunity to participate in the Women’s Forum for the Economy and Society. It was a really helpful step on my journey to greater empathy and advocacy for those who are marginalized by society. Hope this has been interesting for you and I would love to hear your thoughts 🙂
Here’s a quick video they did of me for the Rising Talents Initiative. They picked the part on my advice for young leaders. Check it out here.
I’m excited to profile Annalyn Ng, a self-taught data scientist and #womanintech, who is pushing for the adoption of data science in the public service. She currently works at the Ministry of Defence (Singapore), where she analyses data to identify predictors for personnel performance in military vocations. Originally a psychology and economics major, she first learnt about data science in a statistics class, and has been addicted ever since.
In this article, she outlines the challenges and solutions to enabling data science in the public service, and ideas about how to build these capabilities individually and in your own organization. All opinions here are her own.
Introducing Data Science in the Public Service: Challenges and Solutions
My plea for wider application of data science is a personal one. My mum passed away due to a misdiagnosis when doctors administered wrong medication while stalling the treatment she required. Then, I wondered—if we can teach machines to play games like Go and Starcraft, can we invest as much to teach machines how to save lives? While we’ve had breakthroughs, such as in automated interpretation of medical image scans, similar success for general diagnosis seems lacking.
Many people regard data science as a craft that is exclusive to tech companies. Let’s dispel this myth. The fact is, wherever there is data, there is potential for data science. If fashion retailers can use purchase history to recommend products and predict trends, we can easily apply the same methods on past medical data to recommend treatment and predict diagnosis.
Despite being a profit driver in the private sector, the use of data science is still relatively immature in public service. Healthcare analytics is one specialised domain with untapped potential, but data science can also be applied in mainstay departments like policy (e.g. analysing public feedback), finance (e.g. flagging fraudulent transactions), and human resource (e.g. personnel deployment).
So, what’s stopping us?
There are two parts to data science: 1) data collection, and 2) data analysis, each with its own unique set of challenges to overcome:
Getting data is often the hardest part of any data science effort. As public data is sensitive, infrastructure is needed to collect data systematically and securely. To reach deeper insights, data from different agencies and ministries need to be merged, and this process usually begs questions on confidentiality.
Hence, data collection requires collaboration across agencies. Mutual trust must be built to ensure that useful data is exchanged for insights to be uncovered. Ownership and maintenance of IT infrastructure should be established, and stress tests conducted regularly to ensure data security. We rely on senior management to set this stage, before public servants can take cue to play their part.
Once we have data, we need to analyse it. Skilled data scientists are required for this role, but talented ones might be enticed away by private companies while those committed to stay might not be given the support to learn, thereby resulting in a lack of expertise.
However, expertise can be developed. It is a misconception that data science is solely quantitative. Data literacy can be divided into two levels: 1) knowing how data analysis works, and 2) executing the actual analysis.
The first level is basic knowledge on how algorithms work and their assumptions. These do not involve much math, and thus should be made accessible to everyone.
Algorithms are increasingly being automated, lowering the bar to allow people with non-technical backgrounds to do basic data exploration through apps and dashboards. As data science research becomes more accessible, we need to improve data literacy among regular public servants, to ensure that conclusions made from such research are accurate.
Besides checking results for errors and assumptions, a broad understanding of data analytics can help managers to identify potential data sources, as well as to facilitate collection of data in a suitable format for analysis. In turn, analysts are likely to be appreciative of managers who provide conditions for work to be done effectively.
The second level is technical know-how of math and coding that data scientists, rather than managers, need to master. To nurture expertise, we need to build an ecosystem for experts to thrive. Many agencies have made the mistake of recruiting data scientists in isolation. Without peers who can provide feedback and healthy competition, data scientists may have fewer ideas to build on and less motivation to improve. Therefore, it is crucial to deploy data scientists in teams.
While data scientists can either be trained in school or self-taught, enlightened employers have since realised that the medium of learning is less important than the rigor and continuity in learning. Many companies, including Google and Facebook, have sought out programmers with no formal degree but nonetheless armed with a solid portfolio of coding projects.
Regardless of our current level of expertise, data science is an evolving field, so a data scientist’s learning journey never ceases as they seek to add new techniques to their toolbox through constant reading and practice.
So, how do we start learning?
Traditional classroom training is growing obsolete as they are costly, time-consuming, and possibly ineffective as participants are likely to forget technical details without constant review. Moreover, data science is a fast-moving field, and any one-off training is unlikely to suffice for public servants whom we wish to groom as experts.
As a data science convert myself (having majored in psychology and economics), I have a few alternatives to suggest:
Enrol into massive open online courses (MOOCs), which are video courses available freely or easily priced within $20. Examples of established course platforms include Coursera, Udacity and Udemy. Participants can choose courses based on reviews, and good instructors are also prompt in addressing Q&A on forums. With courses spanning a range of difficulty levels, both beginners and experts can find content suited for their needs. Moreover, as course videos are usually made available for a lifetime, participants can review them whenever they need to.
Learning is not just about sponging up knowledge, because knowledge is easily forgotten without practice. Therefore, to apply what I learn, I’d usually pair my learning with relevant projects. Managers can also encourage a proactive learning culture, such as allowing staff to reserve time for research and experimenting with new data science methods.
After mastering new techniques, I’d share what I learn with others because teaching reinforces learning. Writing blog articles is a convenient way to do this. To engage a non-technical audience, I’d leave out the math and jargon, and instead focus on intuitive explanations and visuals. I eventually compiled the tutorials into a book: Numsense! Data Science for the Layman, which, I’m ecstatic (!) to share, has since been chosen by top universities like Cambridge and Stanford as reference text. Nevertheless, simply keeping a blog can be gratifying, knowing that your tutorials can benefit a global audience.
As for colleagues just starting out in data science, I frequently encourage the recruitment of interns with statistics or computer science background to help with relevant projects. This is a win-win arrangement—supervisors get to learn more techniques, while interns get to appreciate data science applications in the public sector. To ensure accuracy of results, projects can be vetted by trained colleagues.
Finally, there are opportunities for everyone, regardless of expertise, to get together to share ideas. Data science meetup groups are common in major cities, often featuring a range of speakers from different industries, and attracting large audiences interested to learn and network.
So, where do we go from here?
Learning data science is just a means to an end. In public service, the end goal would be to use data science to improve lives.
A predictive algorithm to diagnose heart disease would be useless if we cannot pack it into a fast and intuitive interface that any doctor can use. To build products incorporating data science, we need to plug data scientists into interdisciplinary teams of engineers and designers. Here, good communication is essential to facilitate teamwork, as well as to convince end users of product benefits.
In implementing a data science product, we also need to validate it regularly, to ensure that it remains effective over time. This is not as straightforward as it sounds. Take, for example, an algorithm that predicts whether a person requires medical treatment for a latent disease. To conclude that the algorithm is more accurate than doctors’ judgement, we need to compare the health outcomes of two groups—one selected by the algorithm, and the other selected by doctors. This inevitably raises ethical questions of whether we’d be denying early medical treatment to the group judged by doctors, at the possible expense of their lives. There is no perfect solution to this problem, but awareness is a good start.
Apart from conducting data science within the government, we can also consider publishing non-sensitive data, to put public service into the hands of the public. Open satellite imagery, for example, has enabled community involvement in humanitarian search efforts for missing Malaysian Airlines flight MH370, as well as detection of illegal forest fires in Indonesia. Pollutants from forest fires can be a regional health hazard, and boycotting culpable companies has been a way for the public to fight back. Crowdsourcing has emerged as a check and balance to ensure that corporations and government maintain social responsibility.
With more data available and data literacy improving, the potential for data science to improve the lives of citizens has never been greater. Whether we can successfully introduce data science in the public service will depend on how ready we are to tackle its accompanying challenges.
Thanks, Annalyn! We can’t wait to see what you get up to next.
Here’s a 20-minute talk I did at the Singularity University Global Summit last month. It’s a crash-course (no pun intended) on the different types of autonomous vehicles and use cases, the challenges that stand in the way of city-scale deployments, and ideas for how autonomous vehicles will transform cities, not just transportation systems.
One of the goals of www.techandpublicgood.com is to bridge the worlds of Government, tech and business, which often hold a degree of suspicion towards each other. This article dives deep into controversial policy issues surrounding social media companies.
As a case study, it elucidates the challenges, considerations and dilemmas behind YouTube’s policies. This is me, a Government policy-maker, putting myself in the shoes of a YouTube policy-maker. I figure our considerations are similar despite our different contexts. If you know better than me on any of these issues, feedback is much, muchwelcomed.
The Unexpected Responsibilities of Social Media Companies
We live in an increasingly divided world. The forces driving these divisions, for example, rising income inequality, geopolitical, racial and religious tensions, were in play long before the advent of social media.
However, social media has provided a channel for divisions to widen. Lowering the barriers for individuals to share and ‘viral’ their knowledge and opinions has brought tremendous benefits, such as spreading education and freedom of speech. On the other hand, it has given greater voice and reach to malicious or ‘fake’ content. Algorithms designed to push us to what we will most likely click create an echo chamber, reinforcing our beliefs and biases.
When a flurry of social media companies took to the scene in the 2000s, their intention was to create platforms for people to find what they wanted – friends, funny videos, relevant information, roommates or hobbyist items. Very few would have imagined that their platforms would completely change how everyday folks conversed and debated, shared and consumed information.
Policy issues facing social media companies
Today, social media companies are adjusting to the new responsibilities that this influence entails. Here is an overview of the issues at stake.
Free speech and censorship
It is important to recognize the role of social media in democratizing how information is generated, shared and consumed. At the same time, not everything is appropriate to be shared online. Social media platforms recognize that they must have a moral view on harmful content that should be taken down, for example, content which aims to instigate violence or harm to others.
However, censorship cannot be overused. Social media platforms cannot become arbiters of morality because many issues are subjective, and it is not the platform’s role to make a judgment on who is right: The same LGBT content can be affirming for some, but offensive for others. When is it fake news, or merely a different interpretation? Here’s a real dilemma: let’s say someone reports an outbreak of disease on Facebook. The Government requests to take down the report until their investigations are completed because it will incite unnecessary fear in their population. Is Facebook best placed to assess who is right?
In general, a social media platform’s policy must identify and take down of content that is inherently harmful, while catering to subjectivity by providing choice – to users, on the content they receive, and to advertisers, on the content their brands are associated with. It is an intricate balance to strike, requiring nuanced, consistent policy backed up by a strong and coherent detection, enforcement and appeals regime.
Another policy area surrounds copyright. Individuals sharing content online may inadvertently or intentionally infringe on others’ copyrights. On one level, better detection of copyright infringements is needed. YouTube invested $60m in a system called ContentID, which allows rights holders to give YouTube their content so that YouTube can identify where it is being used.
What to do about copyright infringements is another issue. Should they be taken down immediately, or should the platform provide choice to copyright owners? Paradigms have shifted over the years in recognition that copyright owners may have different preferences: to enforce a take down, seek royalties or take no action.
A third category of policy issues surrounds managing users’ privacy rights.
First, how can the platform generate advertising revenues and keep their user base engaged, while respecting different preferences for personal privacy? This typically pertains to the practice of combining personal information with search and click history to build up a profile of the user, which enables targeted advertising. Information is sometimes sold to third parties.
Second, what does it mean to give people true ‘choice’ when it comes to privacy? Many argue that long privacy agreements which do not give people a choice other than quit the app do not provide people a real choice in privacy.
Third, should individuals have the right to be forgotten online? The EU and Google have been in a lengthy court battle on the right of private citizens to make requests for search engines to delist incorrect, irrelevant or out of date information returned by an online search for their full name, not just in their country of residence but globally.
Children bring these policy issues into sharper focus based on notions of age-appropriateness, consent, manipulation and safety. Platforms like Facebook do not allow users below 13. YouTube introduced ‘Restricted Mode’ as well as YouTube Kids, which filter content more strictly than the regular platform.
Similarly, higher standards apply to children’s privacy. Should companies be allowed to build profiles on children, and potentially manipulate them at such a young age? Should people be allowed to remove posts they made or online information about them while they were children?
Safety for children is also a huge issue particularly on interactive platforms where children can be groomed by predators. Taking into account privacy considerations, how can we detect it before harm is inflicted, and what is the right course of action?
The YouTube Case Study
I have not scraped the bottom of the barrel on the range of policy issues that social media companies deal with, but the broad categories are in place. Now let’s get into specifics of how social media companies have answered these questions through policy, implementation and resource allocation.
To put some meat on this, here’s a quick case study of YouTube’s approach. There are at least four components:
Enhancing user choice within existing products
Closing the policy-implementation loop
Strategic communications and advocacy
1. Product differentiation
Product differentiation is one way to cater to different appetites for content and privacy. In 2015, YouTube has launched ‘YouTube Kids’ which excludes violence, nudity, and vulgar language. It also provides higher privacy by default through features such as blocking children from posting content and viewing targeted ads, and enabling them to view content without having to sign up for an account. ‘YouTube Red’ offers advertisement-free viewing.
However, product differentiation has its limits because significant resources are required for customization. There is also a slippery slope to avoid: if YouTube rolled out “YouTube China” with far stricter content censorship, imagine the influx of country requests that would ensue!
2. Enhancing user choices within existing products
Concerning privacy, users who do not want their personal data and search/click history to be linked can go to the activity controls section of their account page on Google, and untick the box marked “Include Chrome browsing history and activity from websites and apps that use Google services”. For particular searches, you can also use “incognito mode”, which ensures that Chrome will not save your browsing history, cookies and site data, or information entered in forms. These are ways to provide real choices in privacy.
3. Closing the Policy-Implementation Loop
A robust policy defines clear principles which determine when content should be taken down or excluded from monetization opportunities and Restricted Mode. Implementation policy then becomes critical. With the large volume of content coming online every minute, it is impossible for YouTube employees to monitor everything. YouTube has to rely on user flagging and machine learning to identify copyright infringements or offensive content.
However, algorithms cannot be 100% accurate and often cannot explain why decisions are made. A robust appeals and re-evaluation process with humans in the loop is needed to ensure the integrity of the policy. More importantly, the human touch is needed to positively engage content producers (who hate to be censored).
In my previous jobs, we often quipped: “policy is ops”. It is no point having a perfect policy if enforcement and implementation simply cannot support it. Policy teams need a constant feedback loop with implementation teams, to bridge the ideal with the possible.
4. Strategic communications and advocacy
Finally, robust policy is necessary, but insufficient for social media companies. Strategic communications and advocacy are an absolute must.
Public criticism of a company’s policies can negatively impact business. Boycotts and greater Government regulation are examples. YouTube is swimming against a common but simplistic narrative that tech companies are simply trading of public interests in privacy and security for business interests such as the growth of advertising revenue.
Misperceptions about policies can also have dangerous impacts. A few years ago, Israel’s Deputy Foreign Minister met with YouTube executives, raising the issue of Palestinians leveraging YouTube videos to incite violence against Israel. She later released a statement which inaccurately suggested that Google would collaborate with Israel to take down this content. Google refuted this, but the nuance could have already been lost with segments of the public. YouTube’s policy of neutrality must come across clearly, even as lobby groups try to drag it into their agendas.
The purpose of Strategic Communications is to create a wide circle of advocates around YouTube’s policy stance so that negative press and misperceptions are less likely to take off. Elements of Strategic Communications include:
Going beyond the ‘what’ of policy, to the ‘why’. It is important to illuminate the consistent principles behind YouTube’s policy stances, as well as the considerations and trade-offs entailed. Channels such as blog posts enable this, since mainstream media is unlikely to provide the level of nuance needed.
Building strategic relationships and advocates. This includes entering into conversations and debates with your most strident critics, and building alliances with third parties who advocate your views.
Strong internal communications. Since social media companies themselves are run by an aggregation of people with different beliefs, it is essential that employees do not feel disenfranchised by the company’s policy stance.
Providing an alternative narrative. In addition, an important point for YouTube to make is that more is at stake than taking down offensive video content. Ultimately, we are all fighting against greater divisiveness and polarization in society. Although some elements of YouTube exacerbate this, YouTube can also make a huge dent in bridging divides. Hence, I love what YouTube is doing with “Creators for Change”, a program that cultivates creators who aim to counter xenophobia, extremism and hate online. These creators are working on web series on controversial issues, as well as educational workshops for students. They are using the YouTube platform to close divides.
It is far too simplistic to say that companies only pursue business interests, leaving Governments to protect public interests. Every new product, including social media platforms, is a double-edged sword, with the potential to bring us closer to or further from where we want to be as a society.
Both Governments and Social Media companies are trying to push us towards the first scenario. However, Governments will tend to advocate for more conservative policies as their primary objective is to minimize downside on issues such as national security, privacy and Government legitimacy. On the other hand, private businesses are simultaneously managing downsides while pushing the boundaries on issues such as free speech and revenue generation models.
A natural tension between these two positions is healthy as we decide, as countries and global communities, where we collectively fall on issues. This is how democracy works, after all.
Last week, I was at the Singularity University Global Summit in downtown SF giving a presentation on Autonomous Vehicles. It was at the Hilton on Union Square. One lunch break, I hopped out for a bite. Turning the corner, I found a street lined with homeless people sleeping on cardboard. Waste filled streets and it smelled like a public toilet. It was jarring – the contrast between a high-energy, lavish tech conference solving “exponential problems”, and the poverty right at our doorstep.
Bringing it even closer to home, did you know that one-third of school children in East Palo Alto are homeless? They live in trailer parks and the back of cars with their families. This is happening just 10 minutes away from Palo Alto, Mountain View and Menlo Park, some of the richest districts in the world.
Even the highest paid tech workers are not spared, but the burden has disproportionately fallen on middle to low-income service workers – cooks, cleaners, security officers in tech companies – where minority races (African-American, Latino) are over-represented. Teachers, nurses and other service professionals are also affected because their salaries can’t keep pace with the housing prices.
“Inclusive” and “Growth”: Can we have both?
The problem I highlighted above isn’t particularly a “tech” or “Silicon Valley” problem, although it certainly is exaggerated here. Any region undergoing rapid growth experiences a surge in demand for services and infrastructure (such as homes, healthcare and roads). When infrastructure growth can’t catch up, people at the lower end of the income spectrum are priced out. In the case of the Silicon Valley, they move further out and commute to work, or live in trailer parks. Some leave the region altogether. In this way, the Silicon Valley has become an exclusive bubble of wealth.
Singapore’s earliest leaders understood the trade-off between growth and inclusiveness acutely. They knew that the problem I outlined above would be many orders worse in an island whose total land area is half the size of Los Angeles, with little room to expand: in large countries like America, people who are priced out of one region can move to a cheaper region. People make their money in one state and retire in another. A large land area is a natural buffer against economic upheaval.
This was not an option for Singapore: our little island needed rapid economic growth to stand a chance for survival. At the same time, we couldn’t afford to push people out when the cost of living increased. We had to remain a comfortable home for people at all life stages and all incomes across many cycles of economic change.
Four social policies served as bastions for inclusiveness as our economy grew:
First: our housing policy. 80% of all Singaporeans live in public housing built by the Government. Families earning below S$170K a year (about USD$115K) are eligible for public housing. Public housing in Singapore is very different from how Americans imagine: They are not gray, dingy rental facilities serving low-income neighborhoods. Apartment blocks are modern and undergo periodic regeneration. Urban planners design each public housing estate to include libraries, parks, common spaces, transportation networks and schools. Our public housing is highly subsidized, with lower-income families receiving higher subsidies. This policy keeps homes affordable for the large majority of the population.
Second, our healthcare policy. You can read about it comprehensively in this New York Times article, but I will point out one aspect: universal healthcare insurance. Instead of leaving insurance completely to free market providers – which potentially prices people out of this critical good – the Singapore Government provides a basic layer of healthcare insurance for all Singaporeans, called “Medishield”. Singaporeans are free to buy additional plans and riders from private insurers, but these are built on top of the basic, universal medical insurance.
Third, our education policy. We have a universal education system covering ages 7-16. Education is almost free. Schools are centrally resourced, not by the tax districts they are in.
Singapore’s social policies are not perfect. There are many issues we are reviewing, some of which I worked on prior to my current job. However, our approach demonstrates an active and systematic attempt to tackle the trade-off between economic growth and inclusiveness – I have not seen the equivalent in the United States.
How can the Valley achieve Inclusive Growth?
The United States has a very different context. The Government has not traditionally played a large role in social policy, and there is great political resistance to a change in this direction (for example, the attempt to repeal Obamacare).
Who will step in to fill this large gap in basic public services? I’ve always admired Americans’ ability to self-organize and provide for the needs of their community, which Daniel Saver is doing through his work in East Palo Alto. However, the magnitude of the problem – especially in the Silicon Valley – calls for someone to take more radical responsibility in ensuring basic services for the local community.
I believe technology companies can, and should take on greater responsibility to demonstrate that inclusive growth is possible. Much like how they might form a “Partnership in AI” to recommend rules and ethics in making socially-responsible AI, I believe they can come together to discuss how they may systematically contribute to inclusive development in their local backyard.
Could the technology companies provide subsidized housing in their own backyards? Facebook plans to build a new campus that will offer 1500 apartments at subsidized rent to the public. It’s a great step, but very small in the grand scheme. Perhaps they can commit to providing some subsidized housing for every X sqft of new development (the local governments should commit to opening new land for this too).
Can we help to invest in educational districts that are traditionally under-resourced?
Can we contribute to the thriving of the teaching, nursing, and social services community in the Bay Area?
The problems in the Valley are certainly not caused by the technology companies alone. Failing infrastructure, outdated policies and politics are a huge part of the problem. However, tech companies are becoming more powerful and rich than many states today, and it is worth asking what new responsibilities come with that.
Tech companies have made exceptional contributions to worldwide causes – from education to hunger and healthcare. I would like to see them applying their tremendous intellect and resources to problems in their own backyard. Perhaps we can make the Silicon Valley an example of inclusive growth, rather than a picture of super fast growth plus ugly inequality. Now, isn’t that something we want to scale throughout the world? It would give many countries and people a greater hope as they seek to emulate us.
The field of AI has progressed significantly in recent years. Breakthroughs in machine learning have enabled computers to mimic humans in areas such as image understanding and speech processing. However, many problems remain unsolved, such as teaching computers to read and understand anything. It will be decades before we have Artificial General Intelligence which surpasses human intelligence, including runaway bots that humans can no longer control.
For now, we must be realistic about what AI can achieve for several reasons.
The Risks of Unrealistic Expectations in AI
First, unrealistic expectations create incentives to deploy unsafe or unreliable systems. Take autonomous vehicles for example. AI systems enable a car to accurately perceive its environment, plan and execute its path. Ensuring that these systems work safely and reliably together is a mammoth task. There is a reason Waymo has not made commercial deployments despite having tested for years.
Unrealistic expectations from investors and customers on the timeline for commercial autonomous vehicles may pressure companies to deploy unsafe or unreliable vehicles prematurely. Uber’s deployment last December was one such example. In this case, regulators were able to clamp down quickly. However, cases will become less clear cut in the future, and will depend on self-regulation of technologists. Premature testing puts human life at risk, and when public opinion plunges due to an accident, the entire industry is delayed from delivering the enormous safety benefits of autonomous vehicles.
Second, hype about Artificial General Intelligence misfocuses our attention. We often forget that as technology has progressed, humans and organizations have historically evolved to master it. We should focus our attention on facilitating this evolution, for example, by enabling a cycle of re-skilling and job re-design so that people increasingly play roles which machines cannot. New value arises when people focus on what they do best. For example, pharmacists in Singapore provide better patient counselling now that intelligent robots manage medicine packing.
Finally, AI researchers need a reliable stream of funding to make long-term investments in basic research that yield step changes in the field. Unrealistic expectations make the field of AI susceptible to funding crashes, hindering progress – the history of AI winters demonstrates this. One side effect is that during these winters, only large players with deep pockets can continue to cement their advantages. If this privilege is not used responsibly, what does this mean for the distribution of benefits arising from AI? On a side note, this is why I think that Governments should commit long-term funding to basic AI research.
How Can We Better Distinguish Reality and Hype?
The hype surrounding AI can be detrimental, but how can we help people better distinguish reality and hype?
Personally, I feel that reality and hype are best distinguished in the context of problem-solving. The AI community needs to work closely with people who own problems such as improving preventive health, boosting educational equality and solving unemployment. What can, and cannot be solved through AI? What are the risks? How can AI systems be designed to supplement humans? Google set up People + AI Research (PAIR) to explore this. However, the onus is not on the AI community alone. Problem owners must become advocates and critics of AI in their own contexts. They must play a role in public education.
One challenge in distinguishing hype from reality in AI is the competitiveness in the community. The market incentive is for emerging companies to upsell what AI can deliver to raise investment.
On some issues, the AI community needs to lay down their guns and unite. Educating the public to differentiate hype and reality in AI is one of them, and big companies have to take a disproportionate share of responsibility because their existence is less dependent on their ability to upsell. The technical community must also work together to solve problems such as AI safety, which should not be a basis for competition. This is the intent of the Partnership in AI. I personally hope to see the Partnership build strong relationships not just within the commercial community, but with third parties such as Governments and Non-Profits who, in some contexts, are trusted as neutral arbiters on technology issues.
Ultimately, what is at stake is the tremendous value AI can bring to humanity if it progresses quickly, safely, and with the trust and collaboration of users. To this end, fostering realistic expectations about AI is instrumental.
This is a contributed post from Katarina Hasbani. Drawing from her deep experience in the energy sector, Katarina gives advice to start-ups looking to work in highly-regulated sectors, arguing that regulation is not always the enemy.
Katarina is a specialist in policy and regulation surrounding the energy sector. She has almost 15 years of experience from Europe, Middle East and South-East Asia. She moved to Singapore from Dubai, where she was directing Dubai Government’s efforts to reduce the Emirate’s energy consumption by 30% in the horizon of 2030. In the European Commission, she was involved with EU’s gas diversification efforts in its Easter and Southern Neighborhood along with policy work on electricity and gas market liberalization, increased use of renewables and improvements in energy efficiency.
For tech startups, government regulation can be more than just necessary evil to deal with. Regulation can be an opportunity for growth, partnerships and influencing future direction of regulatory regimes for new products and services. Take a wild leap of faith and read on.
Is marriage between regulation and start-ups set up for a crash landing? Let’s revisit the reasons why some might think so.
There are inherent conflicts in the operating speed and style of start-ups and Governments. Governments use regulation to protect the environment or consumers’ health, and to correct market failures. The process of government regulation is a long, formalized and yes, boring process.
On the other hand, startups are created with the purpose of challenging the status quo. Their new services and products are created where there is a need. Startups work fast and iterate often as they advance in their execution. Because of the need for speed, they have tended to take the approach of “going in first and dealing with regulations later”. As a result, many start-ups have experienced regulatory backlash:
Airbnb is facing tough restrictions to its operations in several cities, including Amsterdam where Airbnb guests cannot rent their premises for longer than 60 days a year.
Zenefits, a corporate benefits software startup, hit a significant regulatory snag in 2015 when media reports revealed that its insurance offerings were being sold by salespeople who were not licensed in the heavily regulated insurance industry.
In June 2016, new U.S. government regulations limited commercial drone service for deliveries and squashed Amazon’s drone delivery service plans in the country. Amazon took its plans to the U.K., which doesn’t have such strict regulations, and completed its first drone delivery in December 2016.
However, this is only one side of the story: Government regulation does provide opportunities for start-ups, and founders would be wise to pay attention to where this is happening.
Regulating competitive practices is central to the role of the European Commission (EC), which has been systematically assessing impacts of mergers on innovation. Interesting evidence is emerging to illustrate the impact of mergers and consequently dominant position in a market on innovation. The research states that mergers, both horizontal and vertical, may reduce innovation by, inter-alia, decreasing budges allocated to R&D. The EC has been eying tech industry and dominant position by some of its key players in an attempt to spur competition. Its ruling against Google for abusing dominance in its market comes with an astonishing fine of 2.42 billion EUR. Google’s case might incentivize other large tech players to open up their respective market segments, resulting in more competition, innovation and opportunities for start-ups.
Regulating on data availability is another way that governments can create opportunities for building businesses. UK land registry publishes data on house transactions allowing Zoopla to create a model valuing all UK properties. Transport for London makes its data freely available spurring a plethora of apps to help with journey planning. The Competition and Markets Authority in UK is reshaping the energy market: Energy supply data on customers will soon have to be made available so that customers can be approached with a lower price offer from one of the alternative suppliers.
How to capitalize on regulation: Lessons for startups
I’ve argued that Government regulation is not always detrimental to start-ups. But how can start-ups find opportunities in highly-regulated sectors? If you are a start-up looking to enter a highly-regulated industry, here are three tips from the experiences of the energy sector, which I have worked in for many years:
First, Government regulation can be a market opportunity but you need a transition plan in case regulation changes.
European governments decided few decades ago to give favourable treatment to electricity produced from renewable energy sources. The mechanism used was a feed-in-tariff, which provides renewable energy generators a remuneration above retail rates of electricity. What came about in Europe was a renewable energy boom and pressure to innovate among technology providers. The number of patents in renewable energy increased three times in Germany between 2007 and 2013. Three years in a row (2010 -2012) Germany added on annual basis more than 7 GW of solar installed capacity, which equals to half of Singapore’s total power generation capacity.
Until today, Several German companies stay technologically ahead of the game despite the competitive pressures from China and its cheaper products. Would the renewable energy revolution happen anyway without government regulatory support? It might but it would not happen as fast.
Second, not all the governments regulate the same way and there is always a window of opportunity that can be used to pioneer your product or service with a friendly government regulator.
The case of hydraulic fracking represents the contrast between regulatory approaches in the US and the EU. The US regulates only when any issues of public concern arise, the EU takes preventive measures to avoid any possible negative public impacts.
Fracking involves injection of highly pressurized liquid in the rock to extract natural gas and petroleum from previously unreachable locations. The technology was pioneered and actively pursued in the US with relatively limited regulation until some of the negative impacts started emerging. Accidents and exposure to harmful substances used at fractured wells were raised as main concerns, which resulted in ban on hydraulic fracking in some states (Vermont and New York). While controversial in its environmental and public health impacts, hydraulic fracking is widely credited for the comeback of US energy independence based on its domestic production of natural gas and reduced role of the Middle East in global energy supply.
This contrasted with the EU approach, which has taken preventive rather than reactive approach and has banned the fracking before larger application by the industry. France banned fracking in 2011 and other countries in Europe introduced measures limiting fracking in subsequent years.
Third, follow how government regulations are shaping their industry and/or market in country of their operation and worldwide. Regulatory changes might make incumbents more open to potential partnerships.
Finally, the German utilities sector offers an additional view on potential macro-consequences of government regulation. RWE (now Innogy) and EoN, two of Germany’s largest utilities are changing their business models completely as an unintended result of the government policies and regulations in German energy market. A mix of renewable energy targets and obligations to improve energy efficiency has forced both companies to restructure. The traditional energy assets remain with the legacy company while a new entity was created by both entities with a focus on services in decentralized energy and energy efficiency. The move puts incumbents on equal footing with number of young, agile startups, which are exploring the energy services space. Incumbents are keen to create partnerships to capture new market niche faster.
In summary, my advice for start-ups seeking to enter highly-regulated sectors is that Government regulation will have a profound impact on your startup, whether you want it or not: so make sure it works to your advantage.
Job displacement is not new, but the scale and speed will increase in the coming years – a 2013 study by Frey and Osborne suggested that 47% of workers in America held jobs with a high risk of potential automation.
A few months ago, I wrote “Tackling AI-Driven Job Displacement: A Primer”, which argued that current avenues for people to retrain and find new jobs appeal mainly to those who are already motivated. When changing jobs is a choice, we don’t have a problem. However, when changing jobs is a necessity – which it will be as the speed of job displacement increases – we need the whole population to be motivated and proactive about re-skilling and finding new jobs.
How can we achieve this? For countries, this will be one of the defining challenges of our generation. If we cannot get a large proportion of our population to continuously re-skill to fit emerging jobs, we will face economic slowdown, increasing unemployment and most probably political upheaval.
In this article, I give a big picture of my ideal future, some of the outstanding efforts by tech companies and Governments, and the two big challenges that require collaboration.
My Ideal Future: The Big Picture
The job market today is wrought with inefficiencies which create barriers for job-seekers and employers.
For individuals, the job search is intimidating because most of us do not have a good understanding of our skills, and how these map onto other jobs. There is also little incentive to obtain new skills if the link between these skills and future employment seems tenuous. It is a simple cost-benefit analysis.
Education providers can profit greatly from the lack of transparency, when people are willing to pay for credentials that have no bearing on employment outcomes. On the other hand, they may struggle to reach a wider audience because only motivated people are seeking out their services. Adult education providers suffer here.
Imagine a different future with me for a few seconds.
If you are a regular person who wants to stay employed
Imagine a day when making plans for your next job is as much a part of your life as making plans for your next purchase: the barriers to your job search are so low.
Just as you have a bank account, you have a skills account which elucidates all the skills you have accumulated through your education and previous jobs. These include self-reported skills, as well as skills that are verified by an authority that has studied the relationship between job descriptions and skills across sectors.
Just as Facebook and Google push you advertisements based on your click history, you are consistently notified about emerging jobs with a close skills match to your account. “Good morning, we noticed that these jobs, which match your skillset, are trending. Salaries in these jobs are 5% higher than your current salary”.
Suppose these jobs are sorted by degree of match to your existing skillset – 90%, 80%, 70%. You click on one and get an assessment of the best next steps – “Hi Karen, looks like you’re an 80% match to this job, and people with your profile have successfully entered this job by [taking this course] [doing a side project in this area] [taking a 1-month internship with X company]. Click to proceed”.
You are also notified if your job is at risk, and nudged to take action. “Based on trends, your ‘risk modelling’ skill is increasingly automated by companies. Job listings for this are are decreasing at a rate of 5% a year and this is expected to speed up. Here are some adjacent skills you should pick up ASAP.”
When it’s so easy, won’t more people start to inculcate this as part of their lifestyle?
If you are an employer
Now imagine you are an employer. You need the right talent to serve emerging needs and want to access this talent, fast. Imagine if you not only had access to candidates who applied for their jobs, but candidates who had a >80% match to needed skill-sets, and could be a perfect fit if they underwent prescribed training or certification. You could reach out to these individuals and nudge them towards this, or sponsor training where it makes economic sense.
Rather than firing staff when functions are automated, you may prefer to move them into emerging jobs within the company, as long as the cost is not prohibitive. What if you could assess the degree of match between a potential lay-off with all the emerging jobs in your organization? This could help you make a choice on whether to invest to train the potential lay-off for these roles, or to favour of a new hire.
The information and incentives are better aligned to help you invest in training, rather than to go through rapid cycles of hiring and firing, which could undermine the fabric of your company.
If you are an education or training provider
Now imagine you are an education or training provider. When your courses are not standalone units but part of a pathway that quantifiably increases someone’s chances at securing an emerging job, your user base will likely increase. With more employers investing in education and skills training, your revenue also increases.
If you are a country Government
Now imagine you are a country government who has realized that the traditional model of centralized labour force planning is far too slow for the rate of job displacement. You can breathe easy because there is no longer a need for this outdated method. Individuals are motivated to reskill by good information and clear outcomes. Employers are willing to invest in training that will bring them the skills they need. Training providers fill real gaps and less people are overspending on education that yields little outcomes. The market is finally working – not just for the motivated few, but for your whole population.
Existing Efforts by Tech Companies are Laudable, but Insufficient
What I have laid out is pretty ambitious. It requires:
A closed loop between individuals, training providers and employers (graph below). Having a common language to describe skills and jobs lies at the core – if we continue to describe the same skills and jobs in different words, employers do not know the full extent of their candidate pool, job-seekers do not know where they fit, and educational institutions do not know how their programs really help. It’s the wild, wild west.
User-centric design that caters for the full range of our population: from the highly-motivated time-rich youth, to time-strapped parents and people who have worked in the same industry for decades. Some may not even use the internet. Employers similarly have to come on board – from the large multinationals to our mom and pop shops.
Several companies have already been working on parts of this puzzle. For example:
In my opinion, no one is better placed than Google to do this because of their experience with relational models, which group relevant search terms and results. Relational models form the fundamental basis for how Google yields relevant results even when you are not sure how to search for it. I wrote about how Google uses relational models in health search here, which has strong parallels to the job search.
Linkedin is well-positioned to create a closed loop between job-seekers, training providers and employers because all these players are in their platform. Linkedin helps individuals develop their skills portfolios through self-reporting and endorsements. It is also starting to map individual skills and jobs. If you have a premium subscription, you can see how your skills compare to other candidates and the percentile of applicants you fall into based on your Linkedin profile (example below). With the acquisition of Lynda.com, Linkedin is increasingly able to recommend courses to help people up-skill towards their desired job outcomes. To provide greater transparency to job seekers and employers, Linkedin’s Economic Graph team is also mapping the demand and supply of various skills across the world. However, Linkedin’s current business model is expensive for users, which could exclude a large part of the population and limit the data it collects.
Start-ups are also playing important roles in this ecosystem. Just two examples:
Interviewed,an SF-based start-up, helps to close the loop between skills training and jobs. It started out by successfully developing a range of assessments to help employers assess the skills of candidates. It has since developed platforms such as Rightskill, where employers commit to hiring or at least interviewing people who obtain a certain score on hosted course. MooCs like Udacity have also made strides to closely link skills training to job placements – an essential move if they want to motivate a wider population.
JobKred, a Singapore-based start-up, uses predictive analytics and data science to connect job seekers to their best opportunities, recommend personalised learning plans, and help employers zero in on their top prospects. They provide a customised user experience in the job search, making the job search less frightening.
Two Big Challenges Ahead
The creativity and experience of these companies will go a long way to achieving my ideal future. However, there are two big challenges ahead that I hope companies, Governments and non-profits will tackle together.
First, aligning the way that everyone describes skills.
Google’s occupational ontology gives a top-down understanding of 1,100 job families, a task typically undertaken by Governments.
Google’s skills ontology maps out 50,000 hard and soft skills and the relationships between these skills.
The skills and jobs ontologies are then mapped onto each other to paint an objective picture of the hard and soft skills required in each job, regardless of how they are described.
This is an amazing first step – let’s just take a moment to appreciate it. The fact that they make their relational models available through the Google Cloud Jobs API is even more amazing.
However, I believe these ontologies should not be kept on the back-end, used only when someone conducts an internet search for jobs. Many aspects of the job and education journey take place offline. Constraining the benefits to people who make proactive internet searches may miss out on a huge swathe of potential job-seekers.
Public and non-profit organizations should help with nudging individuals, employers and training institutions towards describing skills and jobs in the same language – perhaps Google can consider working with Linkedin, to combine Google’s job and skills ontologies with Linkedin’s data on self-reported skills, and make these a shared resource. However, the business model must make sense for the companies. Instead of users, Governments, charities or philanthropists should pay as this is a public good.
To reach the widest population, we also need to proactively help individuals populate and manage their skills accounts, just as they manage their bank accounts. UX designers need to work on interfaces that give people a bias to action. I covered some ideas in my “ideal future” section.
Second, bringing users on at scale to enable powerful Artificial Intelligence.
AI will play a huge role in enabling my ideal future: it underpins recommender systems and relational models, just to name a few areas. In turn, these AI-powered systems are enabled by data. Here we face a chicken-and-egg issue: unless we bring on the wider population to use the system, the software will not be able to serve the wider population. To collect data, we need to bring employers, individuals and educational institutions onto this system at scale.
This is an area where technology companies will benefit from the help of public and non-profit institutions. Singapore’s decision to give every citizen $500 for skills training and to create a unified skills and jobs database for individuals is a first step towards bringing a larger segment of the population on board. For segments of the population who are not digitally savvy, we need humans to come alongside and help them get onto the system.
My ideal future is one where everyone – not just the highly motivated, time-rich or digital savvy – is empowered to continually update their skills and move to emerging jobs, rather than get left behind in the wave of job displacement. Finding your next job must be made as easy as finding the next place you want to eat at.
In all likelihood, this vision will not come through 100%. New safety nets must be put in place for people who simply cannot find new jobs. However, I believe that keeping people in jobs must be our utmost priority: not only are jobs the best safety net, there is also something dignifying about work that I do not believe can be replaced by a handout.
The goal of motivating a population at scale and overcoming inefficiencies in the job market is an area ripe for solutions by technology companies. I am encouraged that many – from conglomerates to start-ups – are working hard to achieve this vision. At the same time, I want see more collaborations that will enable large segments of our population to benefit. I would love to hear your thoughts – what would you like to see in this space?
I was in Austin, Texas, in June to represent Singapore at Smart Cities Connect 2017. I participated in a panel on Data and Networks with the CIO/Chief Data Architects of San Jose, Orlando and Austin; served as a reviewer for eight Urban Mobility Start-up pitches, and had incredible side-meetings with CTO/CIOs across America. In an interview with Chelsea Collier, Smart City Connect’s Editor-at-Large, I shared some takeaways:
An Interview with Karen Tay, Smart Nation Director, Singapore
By: Chelsea Collier, Smart Cities Connect
Chelsea Collier [CC]: I’m so happy to have you here at Smart Cities Connect.
Karen Tay [KT]: Thank you, I’m glad to be in your city after you visited Singapore a few months ago.
CC: I was so blown away not only by what you all are doing, but how you’re doing it, and how intentional and collaborative everyone in the government proper is. I was very very inspired by what I saw there.
KT: Thank you, yes it’s not without challenges. I think one of my takeaways from this conference is that we all face the same challenges, and part of it is organizational: how we are set up in a way that gets all the different domains to collaborate on Smart City Projects. That is not something that comes naturally because we are so used to working in silos. By setting up a smart city team (typically within the CIO or CTO’s office), actually many cities in the U.S. are doing similar things to Singapore.
CC: Good, and I’m so excited that there’s so much progress being made just in the past year. This is the second time we’ve done this conference, the first time was in June here in Austin in 2016, and just in that span of one year I’ve seen so many cities go from intention, and more of an ethereal concept to really launching into strategic conversations and into pilots, and talking about scaling. So I’m impressed by how quickly it’s moving. It might not feel that way on the city side because you’re there day in and day out but from the outside world it’s really exciting.
KT: Definitely and I think it’s also driven by compelling use cases. One of the great people I met at this conference was Rosa Akhtarkhavari, the Orlando CIO. She talked about how the Orlando shootings really brought to the fore some of the technology needs that needed to be met and how they’re now going to build video analytics capabilities. I think it’s always driven by the use case. You cannot build too far ahead without the use case in mind. And I think that’s what driving the speed of progress.
[NB: during the Orlando nightclub shooting in 2016, Rosa’s ideal scenario was if she could pump the secondary shooter’s picture into the system, and analytics at the edge could flag out which locations the secondary shooter was seen. (Rather than to bring all the video feeds in and analyze in the cloud/data center, which would slow things down). However, she found that current capabilities did not allow for that.]
CC:Perfect. So any big lessons learned these past couple of days, or what you’ve learned that can benefit you back at work?
KT: I think one of the dominant themes of a smart city conference in the U.S. is how are you going to pay for this digital infrastructure.
In Singapore we are prudent with how we spend but I think we are fortunate that we have the resources to build some of these things. But what I really took away is that even if you have the resources, you have to have discipline of thinking about the economics of it.
I really appreciated the discussions like Chicago collaborating with the university, or San Jose working with a company that’s willing to sponsor a lot of these sensor deployments, or even companies like Civic Connect, which are saying “well, our funders are okay for us to pay for this free of charge to the city as long as we have a business model which will reap the benefits”. I think in Singapore we will benefit a lot from thinking about this economic discipline, just as the U.S. is forced to do. I think that was one of my main takeaways.
However I also think that the government cannot run away from paying for some of these services. It cannot completely be left to the private sector because of this idea of digital inclusion. Fundamentally, the government needs to be able to ensure that use cases which will not yield economic returns will still be accounted for. Who else could do that in society?
[Another key takeaway that I did not mention in the interview concerned privacy in smart cities. All cities – especially American – want to change the narrative that lumps “surveillance” with “data collection”. CIOs recognize that if there is the capacity to identify people for serious crimes such as terrorism, there is the capacity to identify anyone else. Hence the issue is not about limiting our capacity to do identify people through video footage, but ensuring predictability, accountability and transparency in how the data is used.
Cities need to give citizens utmost assurance in this regard. I believe Seattle has a good model to learn from: Seattle put in place a privacy “self review” process, where every department seeking to launch a technology solution has to undergo a “self review” according to the 6 privacy principles (which were established a few years ago). Any technology project that collects data also has to pass through the City Council’s approval. “At-risk” cases are flagged up by the city’s Chief Privacy Officer in the CTO’s office. She advises on precautions and typically pushes them to conduct a public consultation.]
CC: I think as the public sector and the private sector get more comfortable working together they can have strategic and very honest conversations about that and everybody can own their piece of it. And there’s just no time to waste, the problems aren’t getting smaller. They’re only escalating and technology has the potential to really help make some headway there.
KT: I think so. I think there are problems like homelessness, inequality, access to healthcare – all these are big problems waiting to be solved and technology can. It’s a matter of having those conversations.
CC: Perfect. So glad you’re here. Thanks for joining us.
Does your advice apply to team members who are unmotivated? If not, how should I go about managing them?
How do I balance being a coach, collaborator and challenger with urgent demands to deliver?
Truth is, Google, Linkedin and Facebook, as premium employers, can hire all their staff for strict criterion such as high motivation, capacity and adaptability. I have worked in some places like that, where it is easier to make the shifts I suggested. Everything seems to flow.
On the other hand, I have also worked in places where motivation, capacity and attitudes were varying. This is the more common experience of the two. What do you do when you are a new manager, your boss is breathing down your neck for a deadline that was yesterday, and there are team members who simply cannot do the job “up to standard” or simply do not want to do it?
These are extremely complex and painful questions that many managers face when working with a team of varying motivation, capabilities and attitudes. Besides what my previous article advocated – building a network of peers who can provide sounding boards and coaching – I would give three buckets of advice:
How to address “less motivated” team members
Very briefly on self- and boss-management: more later
On self-management: if you are a high performer, chances are verything seems “urgent” to you because your frame of reference is how fast it can be done. You need to stop and think about what is truly urgent, and whether the standards you are applying are truly necessarily (a silly example but some of you may identify: do I really need people to write in perfect English ALL the time, or is it just my preference??) Think hard, because cost is alienating your team by trying to deliver everything that 10 clones of yourself could do. More on self-management later, but I define this field as understanding your own triggers and tendencies that make your over-react, so that you can catch yourself before the damage is done. We all have these: I have many.
On boss management: If you are a high performer who recently became a new manager, perhaps you think your boss hired you to make his life easier.You want your boss to have the perception that everything is under control, so you try to settle as many things without knocking on his door. Pick your bosses carefully, and if you find a decent one, he/she will be more sympathetic than you think. Having honest conversations with your bosses about priorities and staff development should be inbuilt to your monthly routine. Sometimes, stepping out and having your boss work directly with your team members helps bring issues into focus. More on boss engagement here.
How to help “less-motivated” staff
Now for the hard part: how to help “less-motivated” staff, especially when your team is under pressure to perform. My friend Sandra Soon, who has had more than a decade of management experience, mentally sorts her team along two dimensions: capacity and alignment.
Capacity refers to the ability to do the job as the organization requires.
Alignment refers to the closeness between the individual’s motivations and the organization’s motivations.
The matrix below shapes her developmental strategies.
Try to persuade them briefly and if that fails I exit them
Work on exiting them immediately
My last article works better for the top half of the table. For the groups in the bottom half of the table, the challenging part is not making value judgments immediately; Sandra says: “I try to control my natural urge to be critical and make an effort to assume the best of people at the beginning — often lack of motivation stems from quite benign reasons eg they are distracted due to their many other interests, or they may not actually realise that they can do so much better.” In other words, don’t jump to the exit option too early.
My friend Pearlyn Chen further points out: “all of this is framed first by seeing each team member as individuals with their own potential and not just a management issue.”
Aaron Maniam, another respected leader with years of experience under his belt, provides elaboration on strategies you can take to help “less motivated” team members before considering exiting them. These are particularly pertinent in work contexts where exiting staff may be very difficult (another difference from Google/Linkedin/Facebook) – bureaucracies and family business are examples.
Unearth external and seasonal factors.
Aaron writes: “If people *seem* unmotivated, we need to talk to them and find out why. The answer could be something as simple as stuff going on at home, or a bad spell. Our job as leaders is then to help them through this bad patch or transient valley. This might involve some reallocation of work, having them work from home for a while so they can take care of kids, etc”.
Unearth reasons for disengagement.
“If the problem is not external, then perhaps they may not feel engaged by their work. Then our job is to help figure out what makes them tick – and not everyone understands themselves, sometimes, so having conversations about personality type, natural preferences, sources of energy and motivation, etc can be helpful. We might then be able to help them reframe their work to find stuff in it that is motivating. Eg not seeing writing minutes as a chore, but a way of learning the deep language of an organisation so that a person internalises its thinking process and hones his/her own analytics. Or helping a fresh graduate who enjoyed learning in uni, to see how there are also learning opporunities at work, even if those look like merely routine tasks.”
Fix the structure and infrastructure of work.
“I remember having introverts on my team who really found open offices difficult (this was an open office that wasn’t well designed and there weren’t enough quiet spaces for people to go to if they need to do extended thinking/writing). I just let them telecommute as much as they needed, and suddenly their productivity went through the roof. We had to do some norm setting in terms of how everyone on the team would stay connected if some were “off site”, and we did this collectively so everyone bought into the norms. Once the norms were set, they generally worked well.”
Accept, and figure out a strategy towards unambitious team members.
Aaron writes “I personally think it’s ok for people to make decisions that they don’t want to be particularly ambitious, and are content to work at a steady pace, go home at a regular hour on most days, etc. They of course need to be realistic and accept that this means they aren’t going to get promoted super soon or get an enormous performance bonus — but as long as they keep their expectations realistic, I think this is fine and a perfectly legitimate life decision. They might change it later on, or someone who was once a high performer might decide to reallocate their energies a bit after a few years, and good leaders/bosses need to know how to work around this. If the expectations are unrealistic, then of course a clear and firm conversation needs to be had.” Adding to this, Sandra points out that if you choose to retain an average performer, you will need to manage the perceptions of the rest of the team, who might feel they are shouldering an unfairly large share of the work.
What is a good exit?
Sandra and Aaron also address what it means to exit team members well. Exiting team members must be done with a sense of responsibility towards both the individual and the organization. It is not about passing them to other teams as quickly as possible, but helping them find a better job fit: “for example, some people who are motivated by short-term, quantifiable targets will do better in sales jobs than in policy jobs”, Sandra writes.
Aaron shares that “Leaders have a duty to the overall system/organisation, not just their immediate teams, and can help facilitate linkups or meetings with other managers, who might be able to take the person on. I’ve seen instances where a lateral transfer has resulted in total transformation for a person.”
I love their advice. Personally, in trying to balance the need for speed/quality versus the desire to nurture team members, I try to take a structured approach. I map out the mission-critical priorities and may assign high performers to those as a start. Especially as a new manager, it’s best not to fail on mission-critical priorities early on. However, it is easy to get into the cycle of only giving priority work to high performers. One must have a systematic way of reviewing work assignments, and ensuring that “stretch” projects go to team members who initially demonstrate less potential or motivation. (For sanity, I do this at a time when I have bandwidth to provide coaching).
I also want to speak to those of us who are managers of managers: we need to be particularly mindful of what new managers are going through, and assure them that they have our trust as they strive to gain the trust of their team. That can make all the difference, as new managers often feel torn between their bosses and teams. I personally think it is wise to give a new manager a 30-90 day period where they function as a peer to their team, rather than a boss – learning the ropes of the job before they are asked to supervise it.
If you are working with less-motivated team members, this article provides some exercises that you can undertake. If you do this, and there is no fruit, in good faith move towards the exit option. Make a journal of the steps you took, for personal accountability, and also because some organizations require robust evidence when exiting a person.
I also want to end off by saying that you cannot please everyone – learning to face opposition graciously is a huge part of the leadership journey. You also need to take care of yourself and actively find support: bring in your boss to the process; hire a coach or ask a trusted advisor to walk with you for this season. Acknowledge your shortcomings and forgive yourself for them; it’s a painful part of growing up but it will eventually set you free (of trying to be impossibly invincible).
Reach out and I’d be happy to chat more too: the advice above may be too blunt for your situation; or you may feel overwhelmed by the complex emotions that arise if you are a new manager, and perceive yourself to be failing at it. This is a topic close to my heart as I have been through this phase and am deeply empathetic to those of you going through it!
This article, “The Rise of the Thought Leader”, was circulating widely on my social media feeds of late. In short, it argues that:
The “super-rich” in America are supporting “thought leaders” who push narratives that are favourable to their business interests. In no other sector than technology is this more evident. Technological evangelism spurs investment, which perpetuates the cycle of success for technology companies. Intellectually, are we being captured by vested interests?
These “thought leaders” are attractive to mass readers because their messages tend to be simple and evangelical – they “develop their own singular lens to explain the world”. This is in comparison to public intellectuals like Noam Chomsky or Martha Nussbaum, as well as independent academic intellectuals, who traffic in “complexity and criticism”.
One of the key worldviews of these “thought leaders” is that “extreme wealth and the channels by which it was obtained are not only legitimate but heroic.” This supports a ‘Great Man’ theory of events, which traditional public and academic intellectuals tend to reject (I gather they put more weight on culture, institutions, luck).
The decline in public and philanthropic funding for think-tanks has allowed them to be increasingly captured by political interests. Their new sponsors are “less interested in supporting intellectually prestigious, nonpartisan work than they are in manufacturing political support for their preferred ideas.”
What does this mean for you and I?
This article picks one side of the story and chases it down with scathing arguments. It is deliberately unbalanced. It does not mention any benefits of this new generation of “thought leaders”. For example, there is much for traditional intellectual institutions to learn from new “thought leaders” on how to makes their ideas accessible to the average joe. Sheryl Sandberg is such an effective communicator because she knows how to speak to the heart of her audience, not just to their minds. She knows how to inject the right amount of vulnerability and confidence, while traditional intellectuals seem to speak from high horses.
Nevertheless, I understand the point of this article. When people visit the Valley, they typically want to talk to the “oracles” – successful venture capitalists, serial start-up founders, tech giants – hoping that they will catch an insight that will help them transform their perspective, or business. Even when I was in Singapore, events by technology superstars were oversubscribed. Institutions pay thousands of dollars to engage “thought leaders”.
I am not saying that what these folks have to offer is not valuable – all of us should be seeking to expand our perspectives by reading, listening, and networking. But often we put so much weight on what these successful people have to say that we fail to take our own ideas and perspectives seriously. If we cannot articulate our own perspectives, can we deeply interact with what these people are saying? Can we deliberately choose to reject some and accept others, or are we stuck at the level of repeating their quotes to each other, as if it is accepted wisdom?
The article suggests that the problem is thought leaders themselves (and the institutions that back them), but I think an associated problem lies within our personal control. As institutions and individuals, we need to be “thought leaders” in our own right – articulating clearly what we know, what we believe, our theory of change, our lens by which we view the world. Personally, writing this blog has made me a better learner; it helped me to separate the fluff from the substance and understand that I had a voice in this conversation – I was not a passive absorber of ideas. If more of us can articulate this, we can have better, deeper conversations, and we will not be captured by evangelical ideas that may have little relevance to what we are trying to achieve.
Ultimately, inequality has many shapes and forms. One of the characteristics of inequality is that it is self-perpetuating. The rich fortify their riches, the poor get left behind. Intellectual thought can have a similar dynamic. Those who talk a lot get affirmation and attention, which boosts their confidence. Those who underestimate their value lose confidence and become passive listeners. One of the ways to fight this is a democratization of ideas, where everyone feels their voice matters – because it does. This is why I encourage folks to write guest articles on my blog.
How do I start?
Many people have asked me how I started writing. The first step I took was admitting to myself that I knew something. No one knows everything, but everyone knows something. Think about your experiences at work, in school, in the home. Those are unique, and capture many valuable lessons for others. Those experiences have also shaped you and given you a lens by which you can approach a part of the world you want to understand more about.
For me, one of the lenses I view technology is through the problems I worked on for many years before I started in this field: equality in access to public services, sustainable financing, good governance, building communities. These were a jumping point for me to start learning and writing about technology. I also write about topics like leadership and change management, which I thought deeply about in the course of my work.
You may be thinking of writing your first article or making your first podcast, but you may not know where to start. If this is you, reach out to me via the “Contact” button. I’d love to help you think about how to get started, and walk with you on the way. The commitment you have to make is that within 2 weeks you will write your first article! I promise it is possible.
Leadership and coaching has been a one of my side interests for the longest time. I recently wrote 3 Tips for Middle Managers in “Day 2” Organizations. This article goes a little deeper – challenging us not just to take on tips, but to fundamentally reorient our (often sub-conscious) mindsets.
What makes a great leader? Is it inherent?
The Silicon Valley is known for some really great leaders (and some very terrible ones – but that is a story for another day). CEOs like Mark Zuckerberg and Jeff Weiner are famous for creating highly productive workplaces – where people feel empowered to solve problems in creative ways and teams are more than the sum of their parts.
How did they become great leaders? Is the ability for good leadership somehow inherent to these individuals, or inherent to a particular breed of (young) (engineering-minded) people?
I don’t think so. Rather, I believe leaders like Zuckerberg and Weiner simply grasp what it takes to lead successful teams in the new economy, which can be characterized as a rapid series of disruptions whose timing and nature are difficult to predict. The skills-sets needed to help a company be continually successful are evolving faster than before.
Hence, in the new economy, good leadership is less and less defined by subject-area expertise, and more and more defined by the ability to hire well and create the conditions for talented individuals to propel the business forward, such as trust and autonomy.
Unspoken assumptions about good leadership held me back
Changing a leadership culture in incumbent organizations is arguably more difficult than setting up new ones like Facebook or Linkedin. I believe that more than anything, it is the subversive assumptions about good leadership that hold us back from adapting.
In the Asian context (the roots may trace to patriarchy), much of the subconscious narrative around “good leaders” centers around three characteristics:
Teachers, who impart years of experience in subject matter or organizational navigation to team members
Protectors, who shield their teams from the vicissitudes of the workplace so that they can focus on their tasks
Lonely heroes, who personally soak up the stress and always present a calm front to superiors, peers and team members
The problem with this definition is that it assumes a certain hierarchy in knowledge and ability that is inconsistent with the dynamic and evolving needs of the new economy. It drives leaders to limit, rather than unlock their team’s potential.
My Turning Point: Three Big Mindset Shifts
For a good 2.5 years of my leadership journey, I was unaware of that I held these beliefs. It was only when I attended a 5-week leadership training programme in 2015 that these assumptions were unearthed. The training included a 360 degree feedback exercise which 15 staff, 15 peers and 2 superiors filled in anonymously, group and individual coaching, and leadership simulations.
Through the course, I realized that I needed understand my role as a leader differently in order to truly unlock the potential of my team. Here are the three mental shifts I had to make:
From Teacher to Coach
The first shift was to see myself as a coach, rather than a teacher. Most leaders feel safe when they know better than their teams. It is a natural way to garner respect and confidence from the team. When I started my first managerial position at 26 in a team that was older and more experienced, I constantly asked myself what areas I “knew better”, so that I could establish value by imparting some sort of wisdom. That was not a good move. This mindset made me unnecessarily (and subconsciously) controlling.
When leading a team, the right starting point is not I, but them. A leader who primarily sees himself as a coach believes in the potential for each member to bring some magic to the team which he cannot. This is more in line with the reality of the new economy, where what we need to know is rapidly evolving.
A coaching leader also understands that every member has both personal and work objectives when they arrive at the office. He gets to know these objectives and is committed to helping them achieve it. He acts as a mirror, a challenger and supporter, as the individuals pursue their objectives.
Grasping the shift from teacher to coach invigorated me. I started to see team development not just as “making the team better at their work (in the narrow way I defined ‘better’)” but unlocking the potential of every member. This opened up whole new spaces of interaction, especially with team members who had gained mastery at their work. We probed into issues such as helping a highly intelligent but quiet woman contribute more during debates, helping a team member change an overly confrontational communication style, and working through an unhealthy competitive dynamic between two members. I believe we grew individually and became a more productive team.
A coaching approach also set me free. Instead of trying to solve their problems, I saw clearly that my responsibility was to help them understand themselves and take steps towards their objectives, thereby maximizing the potential of my team without bearing unnecessary burdens.
From Protector to Challenger and Collaborator
The second shift is moving away from a “protector” mindset, which can really hold your team back.
At a management course, I was asked to draw a picture representing my relationship with my team. I drew a picture of a sheep pen and shepherd. Very noble, one would think. And why would it be the wrong solution? My team gave positive feedback about my protector role: I made things clean, structured and efficient so they could deliver the outcomes. Navigated the politics on their behalf. Reading the 360 degree feedback, I felt like I was doing a good job at leading.
I had not realized that delivering on ‘work outcomes’ and having a happy team did not mean I was succeeding in unlocking their potential. My role was to prepare each team member for the next bound of leadership, not to keep them happy within the current role.
I needed to be generous enough to allow them to experience frustration, ambiguity and conflict. To give them a safe environment to face some of this messiness down themselves; to decide what was the right thing to do, how firmly to stand, how much compromise they were willing to make; to stay confident when people disagreed and made things personal. This is what it means to move from the role of a “protector” to “challenger”.
It also struck me that leaders need to move from the role of “protectors” to “collaborators”. One leadership simluation drove the point hone. Each one in our cohort of fourty was assigned a role in an imaginary organization – either a “Top”, “Middle” or “Bottom”. We had to maximize some outcome in a tight timeline (I believe it was about accumulating shoes…). Instructions would be given to the “Tops”, who would then hold meetings with “Middles”, who would execute the tasks with their teams. Every 20 minutes, we would pause and give each other feedback.
One of my friends who was a “Middle” faced a huge uprising from his team. They fed back that they could not trust him because he was not being transparent with them; he seemed to be concealing some instructions. He said that the instructions were changing so quickly and he wanted to establish some clarity before communicating with him. It struck me that when we hire top talent, they want agency, they want to be collaborators – not sheep that are protected. “We want to discuss as a team how to address ever-changing instructions, rather than have you hide some instructions from us to protect us.”
From Lonely Hero to Highly Networked with Peers
My final shift involved understanding the value of being tightly networked with my peers. If you subconsciously see yourself as a protector and teacher, you will start to become isolated because you perceive your role as constantly “helping” and “giving”, absorbing difficulties for others, being a hero.
However, if you shift your perception of what good leadership entails: moving from teacher to coach; from protector to challenger and collaborator, you will start to see yourself as needing a community of people who can serve as your coaches, challengers and collaborators.
There is no better group to do this than your peers. Yet many of us neglect our peers as we spend time managing upwards and downwards. I remember a piece of feedback from several of my peers, which said, in gist: “Karen is super effective at bringing us together and helping to get cross-team projects done, but we wish she would tell us more about herself – what she thinks, what she likes, what she feels about issues.” I reflected on this feedback with my coach, and realized that I resisted being vulnerable with my peers because I believed I should not be a burden. It was an unhealthy belief not just for me, but evidently for my wider organization.
This insight, along with the intensive group coaching I went through with 5 peers during the course, made me understand that good leaders cannot be lonely heroes. We must be tightly networked with peers for accountability, challenge, support and perspective. As we seek to navigate a far more volatile and ambiguous economy, this has never been more important.
I’ve argued that the new economy demands a new style of leadership, but subconscious, subversive beliefs about what makes a good leader can severely hold us back.
Which of the three shifts: teacher to coach, protector to challenger and collaborator, lonely hero to networked – do you feel is holding you back?
Realizing this is a great first step to working through it and becoming a leader who can truly unlock the valuable human potential that is in your team, just as Facebook, Linkedin and Google do in theirs.
These principles do not apply equally to all jobs. For example, professional skills still require superiors to play a strong teaching role, although seniors need to be open to learning from younger doctors who might have a stronger grounding in technology. Another example is when the team is new and needs to be taught basic skills.
Furthermore, a focus on bringing out the potential of your team and being a good coach does not mean that you cannot be firm, especially with people who are not motivated, or whose goals are wildly disaligned with that of the organization. I have examples from my own experience, but that is a story for another day.
I have a strong interest in coaching and will be undergoing training in the next year. If you’d like to experience coaching, I can point you towards resources and possibly be your coach within the next few months once I’ve received my training.
On my trip to Singapore in May, I was invited to host a panel on “The Future of Intelligent Mobility” at Innovfest Unbound. I was particularly excited to host it for two reasons. First, I was interested in contrasting the Asian context on future mobility, after spending 7 months studying the U.S. context; Second, I was given autonomy to curate the panel’s members and direction. The panelists were people I admire deeply, having known or worked with them prior.
We covered topics ranging from the future of the mobility industry, who should run and regulate mobility marketplaces, the impact of autonomous vehicles, and the strategic advantage of working in Singapore.
An introduction to the panelists from left to right (I’m sad I didn’t get a better pic of the panellists looking at the camera! This one was taken by a friend in the audience).
Feng Yuan Liu, Director of Data Science at Govtech. Feng Yuan’s interest in transportation analytics was sparked when he worked in the Land Transport Authority. He has since built out an incredible team that serves the data science needs of the Singapore Government. Feng Yuan is interested in how digital technologies enable transportation systems to become evolutionary and ground-up – essentially an exercise in building community – compared to traditional centrally planned, static, fixed lines. Feng Yuan’s team launched a crowd-sourced mobility service, Beeline, a couple of years ago.
Doug Parker, Chief Operating Officer of Nutonomy, a self-driving car company started in Singapore, which is now also testing in Boston. Nutonomy’s mission is to drive the shared cars of tomorrow, and to make transportation as safe and efficient as possible.
Nick Jachowski, Chief Data Officer of SWAT, a start-up that offers on-demand, dynamically-routed bus services (think Uberpool/Grabshare, but for high occupancy vehicles). Its mission is to move the most amount of people with the least amount of vehicles for the greatest public good. Its underlying technologies are mobility optimization and dynamic routing.
Xinwei Ngiam, Director of Strategy at Grab, Southeast Asia’s premier ride sharing company. Xinwei works on “zero to one” projects in Grab, such as GrabHitch and Grabshuttle, where there were no previous business models. Grab’s goal is to provide many different mobility options so that in dense urban cities like Singapore, people will no longer see the need to own a car.
Sharing the key insights with you here.
Part 1: Future of the Mobility Market
The mobility market is in a phase of “a thousand flowers blooming”, as new entrants vie for a piece of the pie, and incumbents seek to transform themselves. Some have estimated that it will be a $5 Trillion market over the next 5 years. What is the future of the market for mobility services? Will there be some sort of saturation and consolidation?
Xin Wei (Grab): The truth is that there are as many business models as there are use cases. We will see many more companies come up. For example, bike sharing is big in China will hit our shores. Some will succeed, many will fail. But overall this is very good for the consumer. We should allow as many different options and use cases to be tested out as possible.
The truth is that there are as many business models as there are use cases.
Doug (Nutonomy): At least for autonomous vehicles, there is lots of room for new players who can work on research and experimentation, as many issues have not been solved.
I would say that the future of the industry lies in partnerships, not consolidation. Nutonomy’s vision is to drive the shared cars of tomorrow. To do this, we are partnering with Peugot (who will supply the cars) and Grab, (which has expertise in car-sharing). There are two other car-sharing partners which Nutonomy has not revealed.
If we have lots of new mobility options, what happens to the commuter’s experience? Will it become increasingly fragmented?
Feng Yuan (Govtech): Seamlessness from the commuter’s perspective is key. We must remember that transportation is a “derived demand” – a commuter’s ultimate demand is to get to a place, not to ride a vehicle. So what people want is the seamless transport experience. If we do it well, people will be willing to move away from the notion of personal car ownership.
We must remember that transportation is a “derived demand” – a commuter’s ultimate demand is to get to a place, not to ride a vehicle. So what people want is the seamless transport experience.
The whole idea of integration and seamless transport is not new in Singapore. It has been baked into the psyche of Singapore’s transport planners. You currently use an EZ link card on both the trains and buses. Other cities have different tickets for different modes.
What is interesting now is the blurring of lines between private and public transport, and the need to integrate the experience between the two.
Traditionally, the Government has provided public transport infrastructure in the form of new rail lines and bus stops. Now, we have to think about the digital infrastructure that will allow the mobility ecosystem to thrive.
Traditionally, the Government has provided public transport infrastructure in the form of new rail lines and bus stops. Now, we have to think about the digital infrastructure that will allow the mobility ecosystem to thrive.
To achieve seamlessness between different modes, it is important that there is an open platform which allows different players to integrate. We also need to enable data and information sharing between players, to enable seamlessness. When we launched Beeline, we designed it such that all APIs in Beeline are open. This allows a coordination point for systems to integrate around.
Doug (Nutonomy): In the future, there will be many different providers of mobility services plugged into platforms like Grab or Googlemaps. Using a single platform, people can pick their options based on waiting and commute time, for example. Nutonomy’s question is how we can get a fair share of market – we believe that autonomous vehicles will allow us to scale up our services quickly, and that will give us an advantage.
Xinwei (Grab): Some people call this a “Mobility Marketplace”.
Feng Yuan, do you think Google or the Government should build this marketplace? Once you have a marketplace, new liability issues emerge. For example, if someone sells an illegal service on eBay, what are eBay’s liabilities? This surfaces new issues for the Government.
Feng Yuan (Govtech): The Government is happy to allow the private sector to innovate. But from our perspective, what is important is whether it will be a truly open platform, where open competition takes place. The idea of a marketplace is to allow people to plug into it easily. It cannot be closed off.
Issues around marketplaces will evolve. In the early stages of eBay, there were questions about liability too, but those are resolved. We willl tackle the issues as they come. The question is what is the value of marketplace and who will be the one to drive it. We are keen to see how we can encourage a mobility marketplace but are still exploring the options.
What is important is whether it will be a truly open platform, where open competition takes place.
Part 2: The Future and Impact of Autonomous Vehicles
Let’s turn our attention to the topic of autonomy. Doug, tell us about Nutonomy’s next steps, now that you are testing in Singapore and Boston.
Doug (Nutonomy): What many autonomous vehicle companies are working on does not look like true urban driving like we see in the One North test circuit, where cars have to queue, bypass a construction site, and deal with jay-walkers. Nutonomy’s technology is uniquely suited to urban driving. We believe the best use case is urban taxis which can drive anywhere in an urban environment.
Singapore is a unique first market because of the strong Government support, geography and people. Boston may have the most complicated driving scenario in America. For one, it has the highest insurance claims. In the future we want to test with more cultures and road conditions, to ensure that our software is global and takes into account cultural information. Then we will scale across the globe with partners such as Peugot and Grab.
You mentioned the importance of cultural information. Given different cultural contexts, is self driving technology scalable across borders?
Doug (Nutonomy): That is a great question. In Singapore, drivers are attentive, but margins between cars are slim. In the US, drivers are less precise, but margins are wide. We need to learn market by market. One of the hardest pieces is negotiating with the human. At One-North there is a junction where people jaywalk (cross illegally). Just the other day, a group of teenagers started playing with the car – stepping forward and backwards in front of the car to get it to stop and go. They did not stop until we gestured to them. It is not a scalable solution, but it is part of every day urban driving so it is important for us to tackle it.
Nick and Xinwei, your companies work on the commuter-facing portion of mobility services. Tell us how your business model will evolve with the advent of autonomous vehicles?
Nick (SWAT): We designed our company for the autonomous future. We started off by thinking: what is the transport landscape going to look like 10 years from now? What type of tech will support that future, but work right now?
If you think about it, all of our cars today are autonomous. Drivers are in the car independently and autonomously making decisions about where to go, and when. This is a non-optimizable system. The future of autonomous cars is that you can combine all the vehicles into a hive mind, and they can work together to solve the problems your city has. Autonomy at the vehicle level allows us to make transportation efficient for both the individual and the city.
The future of autonomous cars is that you can combine all the vehicles into a hive mind, and they can work together to solve the problems your city has.
While what we are doing now is providing dynamically routed buses, SWAT’s underlying technologies – mobility optimization and dynamic routing, will form the basis of optimizing autonomous vehicles at the city-level.
Xinwei: I would be lying if I said we set up the company with autonomy in mind. A few years ago when Grab was established, the idea of hailing a car with a mobile phone was strange, which shows how quickly technology evolves.
My guess is that in self driving, public acceptance is going to lag behind technology readiness. We are going to start by using it in smaller, more acceptable cases and then transitioning to widespread uses. The first few use cases will be for remote locations where drivers don’t go because they don’t get a return fare, or serving the transportation needs of rehabilitation or old folks’ homes. These niche use cases are not well-served by current business models, but the economics could work with an autonomous vehicle. We will start there, and slowly we can move towards a fully autonomous future where the efficiencies Nick talked about will come to fruition.
The first few use cases will be for remote locations where drivers don’t go because they don’t get a return fare, or serving the transportation needs of rehabilitation or old folks’ homes.
Part 3: Why Singapore? 1
1. We have five minutes left. To close us off, I want to ask everyone: “why Singapore?” – is this a strategic market for you? How does it fit into your international expansion plans?
Doug: Three-quarters of our team is in Singapore and most of our fleet is in Singapore. One of the reasons is great tech talent, for example, coming out of the Singapore-MIT alliance. Many of our robotics experts came from there. Computer science talent is also strong. Second, there is clear government support and no regulatory blocks. Third, it has a great marketplace for ride-hailing because of the high taxi penetration. Fourth, it has great roads to test out autonomous cars.
One of the reasons is great tech talent, for example, coming out of the Singapore-MIT alliance.
Nick: When we were setting up our company we studied many markets, but they all paled in comparison to Singapore. The first reason is that Singapore has the lowest car penetration in any major developed country. Less than 10% of individuals own cars, the vast majority take public transport or taxis. If we want to launch some sort of new mobility technology, there is no better place than Singapore to give it a try.
The first reason is that Singapore has the lowest car penetration in any major developed country. Less than 10% of individuals own cars, the vast majority take public transport or taxis.
Besides the high density of commuters, there is also a high cost differential between taxi and bus ($1.50 for a bus, and $20-$30 for a taxi depending on your journey). It suggests there is a massive gap that people aren’t filling, and a big canvas that’s open for companies to come in and provide more solutions for commuters.
Feng Yuan: Nick makes a great point about the economics of density. In transportation, the economics of density matter more than economics of scale. I would say that Singapore is a great place for two reasons. First, Singaporeans have high expectations and demand for quality. We once asked ourselves what is a reasonable punctuality score for buses? In some cities, 20 mins is acceptable. In Japan, 1 min late is too late. Singapore is more like Japan because of our high stress urban environment.
Second, there is a big commitment from the Government with Smart Nation Initiative. There is a strong call to action to use tech and data to improve mobility. We can move faster as a small city, and there alignment from Government on that.
Xinwei: Singapore is very different from Grab’s other markets, one prime example being cash versus cashless penetration. But it is important to us for several reasons.
First, the standard of public transport is so high that if you want to deliver something people use, you need to exceed that standard. It is an aspirational benchmark which we can then apply to other markets.
Second, there is a lot of regulatory clarity. For example, when I was working on Grabhitch, we had clarity on how many carpool rides each driver could give a day. We also introduced fixed-fare taxis, and without Government support we would not have been able to do that.
Finally, Singapore is probably one of the only cities in the world where the newest technologies can be tested out. The Government has an open stance towards these technologies. Singapore is a lab and incubator for things we can see the rest of the region and the world working towards.
Singapore is a lab and incubator for things we can see the rest of the region and the world working towards.
On my last work day of my trip to Singapore, I caught up with a boss and mentor of many years, the Head of Civil Service. One of the questions he asked me was where I thought AI has the greatest potential. With 3 seconds to think, I said: where we have the biggest problems, and where we have data (or the potential to collect it quickly, at scale).
I shared the areas I am passionate about:
1. Skills and Education. In an era of rapid job displacement, how can we constantly re-skill and place people in emerging jobs, at scale, without additional manpower resources? This is traditionally done through centralized planning, but the speed of change will render this approach ineffective. To achieve scale we must empower individuals, employers and training providers through better information, better matching, customized motivation and pathways – these can all be supported by AI, but there are other essential pieces to the puzzle. For example, we need a common Skills Framework that combines top-down skills trees with bottom up self-reporting of skills. We need to help everyone use the same language in describing skills: educators, students, workers, employers. Linkedin and Google are already working on parts of this story. It is an area ripe for public-private partnerships.
2. Transportation. How can we optimize the flow of people, goods and services at the country level, while helping individuals feel that they benefit? Autonomous vehicles solve many problems, but one important objective is enabling all our vehicles to be optimized as a system, rather than as individual units. AI will help us optimize, but ultimately we need to deal with a very human issue: individual commuters who feel that they are sacrificing something for system-level optimization. The crux is shifting user preferences – incentives and policy complements will be just as important as the technology.
3. Healthcare. One of the biggest problems in healthcare is enhancing quality while containing cost. Enabled by AI, how can clinical and policy interventions for a population be more upstream, targeted and outcomes-based? Imagine the benefits if we can prevent the onset of disease, manage disease before it reaches the extremely costly stage, and administer interventions based on personal – rather than general – outcomes.
But the Devil is really in the details
With the proliferation of data and advanced AI techniques, the use cases for prediction, optimization and customization are infinite.
However, if we want AI to truly deliver impact, the devil is in the details of implementation and organizational change. My current job gives me a wide view of all the different technology activities going on in Singapore. On my trip, I touched base with a wide range of folks working in technology domains – health, energy, transport, digital government and ports. I also had in-depth conversations with people who were building capabilities in the technology sector – creating a critical mass of industry capabilities, establishing a data sharing governance framework, enhancing talent development. We talked about the challenges of their work.
I was reminded that lofty goals inspire, but equally important are the small steps that will enable AI (and other technologies, for that matter) to be deployed for the maximum public good. This includes:
People and processes. Our workforce has to trust and use new technologies. This will not come easy, if technology is perceived to be threatening or complex. I saw first-hand how a pharmacy in one of our hospitals introduced robots to sort and pack medicines. Not a single pharmacist lost their job in the process – they were retrained to invest more time in customer-facing roles.
Governance and organizational changes. Decisions around technology investments need to be made by domain-area experts in a far more rapid and iterative way, rather than by traditional hierarchies in long sales-cycles – this is not how Governments are typically structured and we must change; There is a natural tension between delivery and experimentation in a Government’s technology agenda, since resources are finite. There is also a chicken-and-egg issue which lies in having ready use cases before collecting data, and collecting data
Societal changes. Huge behavioral changes are needed to achieve any vision. How can we make the technology-enabled option so attractive that people prefer it over the options they are used to? We often underestimate the power of inertia. Tech companies have shown that clever user design, incentives and achieving a network effect at scale can help. This is a gold standard. Governments have a lot to learn
On the need for strong partnership between the Tech and Gov communities
Back to the three areas where I believe we have the biggest problems (and hence AI can make the biggest impact). Health, education and skills, and transportation are areas where tech and government cannot afford to work without each other:
– We will get the best outcomes if we make these shifts at scale – if say, an entire city or country is on-board. Companies have the technological expertise, countries (at least some of us) have the mandate to bring different stakeholders together.
– Without the right policy complements, technology won’t achieve the desired impact: autonomous vehicles may lead to more congestion, better information on personal health may lead to over-consumption and cost inflation. I write more about it here.
My visit home strengthened my conviction that the government and technology communities need to work much closer together to deploy technology for public good: as we introduce new, tech-driven ways to solve big societal problems, tech companies and Governments should co-design the surrounding policy and regulatory environment and put in place incentives, nudges and public education. In addition to clever AI techniques, all these pieces have to be in place to achieve true impact.
I’m interested: if you were asked the same question (where does AI have the greatest potential), how would you respond?
We’ve heard it before: robots and AI are eating our jobs. New jobs will emerge, but we may not be equipped to do them. I’d written an overview of the issue here.
This week, Genevieve Ding gives an overview of how technology can help KEEP people in work, by mitigating job displacement. Gen speaks from a position of experience: over the past five years, she headed economic strategy in the Ministry of Finance, where her team introduced the SkillsFuture initiative in 2015. She currently works at the National Trade Union Congress (NTUC), where she is interacts with employers and workers to understand the employment landscape across the spectrum: old and young workers, blue collar and white collar jobs. She was previously in the Foreign Service for 4 years, and was posted to Beijing, reporting on all aspects of the Chinese economy.
Technology and Job Displacement: Not a Foregone Conclusion
Auntie Sally is a union member at an electronics factory in Singapore producing wire bonds, the tiny wires in your mobile phone, tablet or step tracker that connect the semiconductor chip to its housing. A toothy lady with boundless energy and a knack for making you feel like her daughter, she has been in this job for 15 years. With no more than a primary school education, she began by operating the machines which make aluminium wire bonds, and has risen through the ranks to become a supervisor in her department. Recently, she was transferred to a new department, where she has to use software she does not understand. Frustrated and under immense pressure, she feels lost and fears that she may soon be let go. Once fiercely proud, her sense of self tied up so closely to the value she used to provide at work is now deflating. Worse still, her company’s prospects are bleak because the process of wire bonding risks being entirely replaced by advances in materials engineering.
There have been many involvedgovernment policy discussions on how rapid technological disruptions and the progress of artificial intelligence will inevitably displace workers like Auntie Sally along the entire value chain, hollowing out the lower and middle classes. The pace of disruptive technologies makes it ever more difficult to train workers fast enough to transition to new jobs and sectors.
Underlying this is a deep, visceral and very real fear that the diligent, dutiful employee has of losing his job. The US elections and Brexit last year and the ongoing protests against Uber in part reflect this widespread, deep-rooted fear. The sheer potential and associated impact of disruptive technology could trigger the same fears that offshoring has, driving an even deeper divisive cleave between proponents who are able to extract benefit from it, and those who fear they will lose out.
There is plenty of literature on how we can partner technology to improve productivity thatmake jobs easier, but risk displacing workers, whether by hovercraft technology to reduce the need for painful manual work; collaborative robots to increase productivity, data mining programmes such as Verifi and Margin Matrix that perform time-consuming routine research and drafting so lawyers can focus on more complex and meaningful work; or smart manufacturing systems that use predictive data analytics to increase yield rates and optimise operations in manufacturing.
In a technologically-enabled nation, can technology be used to tackle some of the very problems arising from its development? So that workers are not mere spectators or recipients of help, but active players with a call to action, and who can see the reality of a better, more hopeful life? Workers will be more invested in the adoption of emerging technology if they can see themselves jointly sharing in progress.
How can technology can play a role in mitigating job displacements and making workers more valuable? I suggest three ways.
1. Helping companies identify their skills needs
As technology evolves, the skills required in the workplace to enable companies to keep ahead are evolving more quickly than ever. The biggest challenge for the labour movement or employment agencies is how keep up with the rapid change in the skills profile of their workforce.
What was somewhat surprising to me when speaking with companies, is that they too are often feeling around in the dark for skills they will need in the future, before suddenly realising that they’re behind — leaving too short a runway to train workers. Data science for instance was a skill which was suddenly hot, prompting companies to scramble to develop expertise. But for some time many didn’t really understand how data science would add real value and what specific skills were required, so job descriptions for data scientists had grandiose expectations looking for “unicorns” that were almost impossible to fill.
Technology has a role to play in helping both government and companies, especially smaller ones, better anticipate the changing skillsets needed to remain competitive. Data analysis has the potential to trawl through thousands of online job descriptions to fish out skills that are trending in particular sectors, or even identify emerging skills across sectors which may give rise to new niche areas of growth. Once identified, companies are better able to start training workers as soon as possible before their old skills become outmoded.
2. Matching skills, eliminating biases
Just as Tinder and Coffee Meets Bagel help people find their other halves based on users’ preferences and profiles for a better fit, finding good matches between skills and skills in demand is essential to helping workers.
Algorithms, not unlike those that help you find a compatible date, have the potential to match job seekers to jobs based on skills, interests, aspirations, and cultural fit. At the same time, algorithms can help workersidentify skills gapsin their resumes based on the skills most in demand or trending in job descriptions, helping them to identify training opportunities.
Digital labour platforms like LinkedIn or CareerBuilder also create more transparent job markets and disrupt previously closed labour markets by increasing workers’ access to a wider variety of jobs and employers’ access to a wider pool of job seekers, reducing the advantage of “old boys clubs”, often driven by wealth and connections.
The playing field is levelled even further by technology platforms that attenuate hiring biases such as paper qualifications and gender, by enabling testing for the specific aptitudes required on the job. Platforms like Codility and GitHub help employers seek out and test for quality of coding and development skills, not certifications. Catalyst DevWorks’Catalyst Talent Platform uses machine learning on thousands of variables from hundreds of thousands of individual engineer and developer candidates to identify innate capabilities and predict whether someone will be exceptional talent in the job, whether or not they have a degree or a good resume.
3. Real-time, real-world training
Lifelong learning ismuch easier said than done. Massive open online courses (MOOCs) have already democratised learning, providing easy access to countless new courses and possibilities. However, much of this learning remains theoretical and does not train or test “on-the-job”, so it is less useful for industries such as manufacturing.
Even more depressingly, upskilling won’t get you a job. I’ve had the unenviable position of speaking to a electronics engineer Mr. Lee who was retrenched. In tears, he related how he tried to take professional courses in the biomedical sector, with hopes of entering what was then one of Singapore’s growth sectors. Despite his burnished qualifications, all the companies he approached felt that he didn’t have the job experience commensurate with someone else his age in the industry.
Virtual and augmented reality (VR and AR) open up new possibilities of providing “on-site” , “hands on” training for workers and might provide a solution to learning that accelerates job transition and meaningful skills acquisition throughout one’s life.
In manufacturing for instance, AR smart glasses that overlay computer-generated graphics and real-time instructions canimprove productivity without prior training. This will shorten the time required for onboarding new workers and help close skills gaps.
Significantly, these upskilling technologies can also help companies “test” out potential employees during the hiring process in a simulated environment, assuring them that the job seeker – even if, like Mr Lee, did not have prior work experience – can perform to standard. Real-time, real-world training with AR will also workers help existing workers learn continuously and at an accelerated speed, increasing organisational learning agility.
Why is this so difficult? The challenge of scale
Using technology to help mitigate the impact of job displacements can only be really effective however if we can adopt them at scale. This can be challenging. For instance, identifying in-demand skills across sectors or on a national level, or skills matching through data analytics will be most robust if there is open access to large volumes of job offerings on the demand side. Markets are more transparent the larger the source data.
However, much of this information is fragmented across various platforms and job portals–with a significant proportion of hiring done through personal referrals or headhunters. National job portals where all employers are required to list job openings with job descriptions and skills needed–such as Singapore’s national online Jobs Bank–would go some way to address this. Google for Jobs, which was recently launched, will also contribute to this.
Technology will also affect various constituents to differentiated degrees. Eliminating biases through Codility or GitHub for example is limited to skills that are more quantifiable and thus demonstrable on a platform. Less quantifiable skills such as learning agility or strategic thinking may not be as easily evaluated through mediated platforms.
Last, technologies such as VR and AR for training are most impactful if they can both be customised and scaled up. Cost constraints and access to these technologies in the near-term will limit their scalability. Addressing these challenges in-depth is certainly worth a separate discussion.
Conclusion: Technology as a force for social resilience and collective progress
Challenges notwithstanding, by deliberately harnessing technology in these ways, we are negotiating a new narrative: one that empowers workers and shows them that they too have a stake in our collective progress. Technology no longer divides, but instead buttresses society’s resilience. It provides Auntie Sally a vision of progress that she can once again take pride in contributing to.
Indeed, the potential benefits are tremendous, but so are the risks: in the form of escalating medical bills, with unproven – or worse still – harmful treatments. In this article, I give Governments and Healthcare providers three areas to pay attention to when it comes to personalized medicine: Regulation, Healthcare Finance, and democratizing the benefits of personalized medicine.
New Pressures on Regulation
As new personalized treatment modalities emerge, regulators are facing increasing pressure to green-light interventions, even if clinical benefits are not clear – to provide patients with a chance to live.
A watershed case was the US Food and Drug Administration (FDA)’s ruling against a scientific advisory panel, in favor of patient advocacy groups to approve Exondys 51 marketed by Sarepta for treating Duchenne Muscular Dystrophy. Despite a majority (7 to 6) of the experts citing inadequate convincing clinical evidence, the FDA director greenlit the approval of Exondys 51 due to a lack of clinical alternatives. Many commentators felt that this case set a precedent for the approval of personalized medicine products based on surrogate endpoints without clinical benefits.
There are also many grey areas when it comes to regulating clinical trials. As with any emerging technology, the benefits come at a risk, which people desperate for a cure may be willing to take. Due to the exploratory nature of trials involving new treatment modalities, patient safety is often left in the hands of researchers: a simple search on ClinicalTrials.gov shows nearly 6000 clinical studies involving stem cells, some of which have not been approved.
Regulatory bodies such as the FDA must continually engage and balance the needs of the scientific, patients, and clinical communities in meeting these new regulatory challenges – unfortunately, there are no easy answers.
2. New Pressures on Healthcare Finance
With the flood of new interventions, another issue to consider is cost. If all interventions are fully reimbursed (i.e. paid for) by state and private payers, the healthcare system will soon become bankrupt. Yet, if no help is given, the cost to patients of living longer is bankruptcy. The American Society for Clinical Oncology (ASCO) wrote in a brief that a patient living with cancer is now three times more likely to file for bankruptcy than a healthy person.
Policymakers must strike a fine balance of curbing the rapid rise in healthcare spending without disincentivizing innovation and depriving patients of access to life saving treatments.
When weighing the clinical benefits of a new drug product with the cost, healthcare economists typically apply a measure called the incremental cost effectiveness ratio (ICER) which takes the difference in cost between the new drug and existing alternatives and divides it by the change in quality adjusted life years (QALY). The National Institute for Health and Clinical Excellence (NICE) of the U.K., for example, sets an ICER limit of £30,000 per QALY gained for new drugs including targeted therapies. Most policymakers in the U.S. generally apply an ICER limit of USD$50,000 per QALY gained.
Early evidence suggests that personalized medicine tests are generally cost effective, with 20% of them resulting in cost saving and more than half achieving ICER of less than $50,000 per QALY gained. However, measures of cost effectiveness apply a single threshold to a heterogeneous population. If reimbursement was based on this alone, some people would receive more healthcare than they would choose, and others less. As such, commentators have noted that “reimbursement mechanisms for targeted therapies are still very blunt in an era of personalized medicine”.
Policymakers must leverage data and work with other stakeholders to improve reimbursement policies, especially taking into consideration the underserved population. Yet, the onus does not belong to the policymakers alone. Drugmakers, payers and clinicians are very much involved in the determining how drugs are priced and reimbursed. Recently, there have been exhortations by clinicians for more value-based pricing whereby reimbursement is contingent upon patient outcomes. The focus on outcomes could ensure that personalized medicine realizes its full clinical value. To achieve that, drugmakers could enter risk-sharing agreements with payers for partial reimbursement prior to demonstrating clinical effectiveness.
Together, stakeholders can work to ensure that personalized medicine is conscionable and cost effective.
3. Democratizing the impacts of Personalised Medicine
Finally, perhaps personalized medicine should be about more than just the diagnosis and the cure. Personalized medicine could go a long way towards disease prevention and mitigation by engaging the laymen and teaching them to monitor and manage their own health. In a recent trip with my parents to their dental appointment at a polyclinic in Singapore, I could not help but notice that the Health Promotion Board set up a booth that encouraged senior citizens to get screened for colorectal cancer. Participants were instructed to fill out their information, collect samples of their stool at home in the kits provided, and send the kits back for analyses. Though seemingly mundane, campaigns like this are probably the most effective way of bringing personalized medicine to the masses.
We have already seen the good that personalized medicine can do. Yet, if we want the broader public to benefit from personalized medicine while minimizing both the financial and clinical risks to society and patients, there is still so much more that we must do. Stakeholders must continue working together to advance the personalization of medicine, not for fame or fortune, but for the greater good.
Thanks for reading this two-part series on personalized medicine and public good. When I started this blog, one objective was to use it as platform for issue-experts in technology fields to give us mini crash-courses, and to sketch out the implications for society. I am sure Johnathan will be more than happy to discuss these issues further. Let me know if you’d like to be connected!
This week, I have my dear friend and brilliant scientist, Dr Johnathan Ng, PhD in Biomedical Engineering at Columbia University, give us a crash course on personalized medicine – what is it? what does it mean for the field of medicine and society? Johnathan now works at a start up in NYC, which grows bone for facial reconstruction. This is a two-part series, with the first part focusing on an overview of personalized medicine, and the the second part focusing on implications for healthcare systems and Governments. A super educational read for anyone with interest in the healthcare space. I certainly learned a lot while editing. Enjoy!
It’s getting personal
Personalized medicine has been hailed as the future of healthcare. At the forefront of clinical and scientific debate lie questions that could transform our healthcare landscape. Can medicine truly be personalized? Will the “personalized medicine” of today simply be medicine in the future? How can we leverage the personalization of medicine for the betterment of humanity?
A Brief History of Personalized Medicine
First, what is personalized medicine? In contrast to conventional medicine, which applies statistical information taken of the general population to the individual, personalized medicine uses information about a person’s genes, proteins and environment to prevent, diagnose and treat diseases.
As the prescient Hippocrates once said, “It’s far more important to know what person the disease has than what disease the person has.” The roots of personalized medicine predate our understanding of the human genome. For example, blood type matching for transfusion between the donor and the recipient to prevent hemolysis due to incompatibility was first reported more than century ago.
However, it is only with recent advances in genome sequencing technology that we can map the human genome and study it at an unprecedented scale. This has ushered in a new era of personalized medicine. In this overview, I cover three aspects of personalized medicine: 1. Personalized disease modifying drugs; 2. Autologous cell therapies; and 3. Stem cell therapies.
Personalized Disease-Modifying Drugs
Some of the earliest breakthroughs in personalized medicine came in the form of personalized disease-modifying drugs, including:
Breast cancer. In 1998, researchers found that a particular type of protein, HER2, was overexpressed in aggressive breast cancer cases. Consequently, Herceptin, an antibody therapy which suppresses HER2 activity, and a companion diagnostic test for HER2 expression in breast cancer cells were approved. These have become standard treatments today.
Cystic Fibrosis. In 2012, the U.S. Food and Drug Administration (FDA) approved Kalydeco, a drug for treating cystic fibrosis by restoring the function of a protein misfolded due to mutation of the G551D gene. Restoring this protein’s function abolishes mucus buildup that leads to life-threatening respiratory and digestive problems. With that approval, Kalydeco also became the first drug that treats the underlying cause of the disease and not the symptoms.
Immunotherapy. Over the last two years, a new class of antibody called checkpoint inhibitors was approved for treating some cancers. Opdivo and Keytruda are antibodies that disable checkpoints in immune cells by neutralizing the programmed death receptor (PD-1). Thus, immune cells bypassing these checkpoints are able to kill cancer cells more effectively. In a recent pivotal study, Merck showed that Keytruda reduced the risk of death by 40% among patients expressing PD-L1 levels greater than 50%.
Besides harnessing information encoded in our genes to improve treatment response, personalized medicine is also about helping to unleash the immense capacity of our body to repair and mend itself.
Our cells contain information, latent or potent, that can be manifested into cure. Autologous cell therapy involves harvesting cells from a patient’s body, enriching the cell population outside of the body, and re-infusing the cells into the body.
The New York Times documented the miraculous journey of Celine Ryan who enrolled in a revolutionary clinical trial for her advanced colon cancer. Inherent in our immune system is the ability of lymphocytes to locate, infiltrate and kill tumors. However, some tumors grow to counteract our immune response by damping or evading it. To help Ms Ryan overcome her tumors, the doctors mined her lymphocytes from the tumors, enriched and re-infused them into her body. These enriched lymphocytes intensified their attack on the tumors and after 9 months, she gradually recovered and entered full remission.
The success of Ms Ryan’s clinical trial provided scientists with a new strategy: engineering and enhancing patients’ T-cells to target and destroy tumor cells with distinct markers. These engineered cells are known as chimeric antigen receptor (CAR) T-cells, and they have both the ability to locate and destroy their targets. Emma Whitehead, 6, suffered from acute lymphoblastic leukemia (ALL) and twice relapsed from chemotherapy treatment. Without any other resort, her parents turned to an experimental treatment which used CAR T-cells to target CD-19, a marker expressed by both her healthy and malignant B-cells. The doctors rescued Emma from the brink of death and she is now cancer free.
Some key limitations remain in stem cell therapy as adult stem cells have a limited range of differentiation. Although embryonic stem cells are pluripotent (meaning that they can differentiate into any cell type), there are ethical limitations to using them as they require the sacrifice of embryos.
To overcome these limitations, Dr. Shinya Yamanaka and colleagues discovered a method to induce adult somatic cells into a pluripotent state. These cells, termed induced pluripotent stem cells (iPSCs), have ignited the imagination of scientists and clinicians as they could enable the treatment of diseases caused by the failure of specialized cells such Parkinson’s disease and heart failure. In a recent interview, Dr. Yamanaka (now a Nobel laureate) confirmed that clinical trials for iPSCs therapy will be underway over the next decade. However, he also cautioned against overstating the benefits of targeted stem cell therapies as they can only address a small subset of all human diseases.
What does personalized medicine mean for society?
Personalized medicine is improving the precision and efficacy of treatments by enabling the clinicians to make more well-informed decisions. Advances in pharmacogenomics have helped to reduce wastage of drugs and their incurred cost due to non-responders, and tailor the dosage according to the patient’s metabolism.
However, these efforts are not without cost. The cost of developing targeted therapies in an era of precision medicine is almost $2.6 billion. These treatments also bring about new regulatory risks for hospitals and Governments, who are facing increasing pressure to green light advances that give people unprecedented (but perhaps unproven) hope. My next article elaborates on three areas that Governments and healthcare systems need to pay attention to when it comes to personalized medicine, to maximise its benefit to public good.
Everyone wants it to be “Day 1”
Jeff Bezos’s 2017 letter to Amazon Shareholders had some piercing insights about running a “Day 1” organization. He doesn’t exactly define Day 1, but the concept is clear when he describes a “Day 2” organization: “Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”
There are many gems on running an innovative organization, but I’ll highlight two:
First, a Day 1 organization never lets itself be owned by process. Processes exist to serve an outcome – following the process should never be the outcome. “The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.”
Second, a Day 1 organization masters the cycle of rapid decision-making, prototyping, failing and trying again. Jeff urges his people to take action based on 70% of information they would ideally have, and to be willing to disagree and commit (rather than disagree and grudgingly assent): it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?”
I recently shared a part of Jeff’s letter on Linkedin, and the responses suggested two things:
Many look upon tech companies like Amazon with envy, because their organizations seem so “Day 2” in comparison
Most (if not all) of us want our organizations to be “Day 1”
What if my organization is already Day 2?
What do I do if my organization is already Day 2? This question is important to the area of “tech and public good”, the subject of my blog (www.techandpublicgood.com).
Why? Because the main reason organizations (Governments, civil society and private sector alike) fail to harness technology – or for that matter any type of innovative practice – that will clearly improve their customer experience and operations is internal resistance. In other words, it is because they are in “Day 2”.
It is often not deliberate resistance. It is a slow, painful death, precipitated by adherence to process and status-quo practices, and a lack of clear ownership for the outcome.
People matter, and in my experience, the level of innovation in an organization depends on the behaviours of middle managers. Middle managers are some of the most powerful influencers in an organization – they set the tone for their teams’ culture, and can almost singlehandedly dampen or stimulate innovative behaviour among a large majority of your workforce.
Forces that make middle managers susceptible to “Day 2” behaviours
Unfortunately, I have noticed that when people transition from “team member” to “middle manager”, gravity seems to pull them towards being “upholders” rather than “innovators”.
This could be the result of coping mechanisms to deal with increased responsibility. When promoted from member to manager, one takes on a multitude of new objectives. In addition to the original objective they were hired to achieve, they have to help their team navigate relationships with other departments, obtain resources and gain the confidence of their bosses, manage HR issues, and prioritize their team’s bandwidth. Email load triples; more time is spent managing upwards, downwards and sideways; the appetite or bandwidth for innovations gets lost.
I worked in 5 teams as a member and middle manager over the years, and have seen the following traps among leaders:
Falling into a “process-orientation” in their leadership style to cope with volume. Simply ensuring that process is being followed can give some comfort that the team is on the right track, without having to commit too much mental bandwidth. There’s limited upside, but also limited downside.
Defining success by whether everyone (bosses, team members, fellow managers) is happy, and hence failing to challenge the status quo.
Feeling de-motivated. I have had friends lament that when they became middle managers, they felt removed from the real groundwork, but not high enough to influence decisions. They lost their motivation to challenge the status quo.
Three Important Questions for Middle Managers (and their bosses)
It would be a natural point for me to go into how an organisation should select, train and reward middle managers. In the past, such articles have left me feeling validated but disempowered, as it leaves the action to the organisation – and who knows how long it will take.
As middle managers, we have agency. I’d hence like to address middle managers who are aware that they are in a Day 2 organization and are possibly perpetuating Day 2 practices. What questions can you ask yourself, and have conversations with your boss and team about? Three tips:
First, where exactly am I creating new value for my organisation?
This seems like an obvious question, but we need to be very ruthless in holding ourselves to an answer. “Keeping the peace”, or “making my boss and team happy” are bad answers that busy people can easily default to.
What are your objectives and how are you adding to them? As a middle manager, you have a wider scope than a team member, and your value could come in a variety of forms, including:
Taking ownership of an issue that falls between departmental lines. Take on the “start-up cost” until everyone is on-board. For example, put together a short think-piece that outlines the issue you want to tackle, and why it would be beneficial all around if different teams to got involved. Use it as a starting point to rally people across departments, and eventually bring it to upper management for endorsement and resourcing. When I became a middle manager, I realized I was uniquely positioned to take the lead on such issues, as I had greater ability to cross inter-departmental lines.
Examining a process that does not work, and proposing an alternative. I read this lovely article on Linkedin about the prevalence of bad systems, and why they are allowed to persist. “The problem is that bad systems often end up in a kind of corporate Bermuda Triangle — no one really monitors them; worse, one is empowered to change them when the need arises.” When you see a stupid process, don’t let it pass you by. I once had a boss who had this mentality of fixing bad systems. I admired her deeply. Our department had to manage hundreds of event invitations to our Ministers. For a long time, one person oversaw all these events because it was “the only way to not drop the ball”. It clearly drained her. My boss, our “czar of bad systems”, developed a tracking list which could be rotated among 3-4 people on a weekly basis, spreading the task around and allowing us to monitor follow-through far better than before.
Work on a development plan for your team members. HR is not a “good to do”, but an essential objective that should take up a middle manager’s work time. Everyone does it differently, but I had monthly check-ins with each team member, where we discussed where they needed to be challenged, or supported. I kept a file on their work record, training and future job aspirations. It certainly came in handy when I had to argue for their promotions and opportunities.
“But I don’t have the time!” So true. Something has to give. Most of your bosses will have a vague idea of what is important in your job (they’re too busy thinking about their own jobs), so you have to tell him/her. Proactively engage your boss on the 2-3 pieces of value you want to deliver, and why you think it is important. Use it as an opportunity to talk about what you will not be dedicating much bandwidth to i.e. I will not be responding as fast on x, y, z issues, or for x, y, z issues, my team members will report directly to you. If you are working for a boss worth his/her salt, you are guaranteed a good conversation and probably a green light. You will definitely feel more motivated.
Second, am I creating a team in my own image, or does each individual feel empowered?
Usually, people are promoted to management positions because they did well as a team member. The result can be a narcissistic impulse to make your team members perform in the same way that you do. It is also a key source of overwork – vetting and thinking about everything each team member does. I encourage middle managers, especially new ones, to break down their management job into at least two roles, and define their value differently for each.
Roles where there is a legitimate need to uphold standards. In my experience, issues that fall into this category have included managing external relationships (such as a foreign university pulling out of a partnership with a Singaporean university, with implications on our students), sensitive complaints (such as allegations of sexual harassment by a Professor or teacher), or a reply to the Minister on a key policy issue (to ensure we make the best use of the Minister’s time). These are areas where a middle manager’s experience must be applied to help the team member make the right proposal.
Roles where you provide one perspective, but let the team member fly. However, there are also issues where the team member should be allowed deep ownership. Proposing an overhaul of the preschool sector? Revamping communications materials for Principals following a policy announcement? Please proceed. I’ll give you my perspective, but it is up to you what you propose to our boss. In one of my favorite jobs, my bosses were very conscious about giving us autonomy. Ministers and even the Deputy Prime Minister would call the “lowest level” staff directly, and we never felt hesitant to make our arguments.
Again, be clear to your own boss how you see your role in the different projects under you. It helps to be transparent with your team members about this too, so that they can hold you accountable if you become a micro-manager or if you’re not providing enough guidance. I’ve treasured the times my team members have said “hey Karen, we need to talk about your involvement in this project.”
If you get this right, you will also be better able to manage your time. Type 1 issues will command more of your bandwidth than Type 2. Ultimately, you want to train your team to the point where your role is largely Type 2. The more your team can fly, the more bandwidth you have. Both you and them will feel more motivated when you have that autonomy.
Third, how often do I disagree and commit, compared to having my team disagree and commit?
In his article, Jeff Bezos gave an example about how he strongly disagreed with a team’s proposal, and though their discussions did not change his mind, he committed strongly to their proposal. I was impressed – in typical organizations, the bulk of “disagreeing and committing” is by team members, not team leaders.
As middle managers or leaders of any form, we should ask ourselves how often we disagree and commit to our team members, compared to the other way around. It is a reflection of how empowered our teams are (and hence how well we are using the precious human resources we hire). It is also a proxy for how innovative our team’s culture is.
If you feel like you work in a “Day 2” organization, I believe you have the agency to bring about change, and I hope that by reflecting on these three questions, you will have some idea of how to push your team towards “Day 1”. I write about this as a fellow learner in the journey towards good leadership. Many of these lessons were the result of making painful mistakes, observing others’ strengths and weaknesses, getting feedback from my bosses and team and reflecting deeply on my own practice of leadership. I would love to hear your experiences as well.
When I first met Bert, we had already heard of each other, and immediately hit it off. In addition to being extremely kind and generous, Bert is a killer combination of a big-picture, systems thinker – from his days in Washington – and an embodiment of the generous, action-oriented, and creative start-up culture in the Valley, where he currently works.
In this interview, I ask him about his transition from Washington to Silicon Valley, issues surrounding self-driving, what he wishes Government folks knew, and how else he thinks technology should be harnessed for public good.
This is my third profile piece on folks who work at the intersection of tech and public good, following Xinwei Ngiam and Kenneth Tay. Enjoy!
Tell us a little more about yourself. Why did you move to the Valley after almost 8 years in Washington?
I spent most of my time growing up on the East Coast and down South, so from a cultural standpoint, I always thought Washington was a great mix of north and south—“The Northern most Southern city.” From a professional standpoint, I am a lawyer who loves policy issues, so I gravitated towards Washington after law school. But what I discovered about myself over the past decade is that I really love building organizations and organizing initiatives around good ideas. And there is no better place in the world to build these things than the capital of innovation and entrepreneurship. My move out to the Bay Area was prompted by my fiancée who was in graduate school at Stanford, and because my role as an appointee in the Obama Administration was winding down.
What did you enjoy most about your job in Washington and how did it prepare you for your current role at an autonomous vehicle startup?
Before joining the Obama Administration in 2013, I spent five years growing an organization called Business Forward. We started Business Forward to help business leaders from across the country do a better job of advising Washington policymakers and, conversely, to make it more efficient and transparent for policymakers to listen to business leaders. Through that experience, I faced the challenges of building an organization from scratch and learned the importance of taking a long-term view. Our funding came from about 60 large companies—our members—and I traveled around the country, engaged with thousands of people, and learned about issues that businesses of all shapes, sizes and ages faced as the country emerged out of the 2008-09 financial crisis.
That experience prepared me for the chance to join Penny Pritzker’s team at the Commerce Department. Not only did I get to work for an incredibly brilliant, demanding, and hard-working leader in Secretary Pritzker, but I also had the opportunity to help build and manage an initiative we created called the Presidential Ambassadors for Global Entrepreneurship. This initiative worked across The White House, Commerce and State Departments, USAID, the Small Business Administration, NASA, and with some of America’s most successful entrepreneurs to mentor, motivate, and in some cases fund aspiring entrepreneurs from across the U.S. and around the world. I also worked on policy issues related to the digital economy on areas like data privacy and cybersecurity.
In my role now, as an in-house lawyer working on policy in a sea of engineers and computer scientists, it’s important to communicate clearly and to understand the policy implications of the technology that we’re developing. This is important both within my organization and externally. Transportation is a highly-regulated space, for many important reasons. As a society, we want people to move around freely, but we also want to ensure that they can do so safely. The advent of autonomous vehicles will lead to innovation in road safety. What we are doing is so new that we have the opportunity to create best practices that can set the bar for future policy.
Policy is really important to any technology business intersecting with regulated markets. Technology startups that fail to consider policy or regulatory implications do so at their own peril. Conversely, regulators need to understand that the regulations should be nimble, flexible, and fair and not cumbersome. These principles will allow technology to advance on a level playing field.
What is one thing you’ve learned or experienced that you wish your colleagues in Washington had a chance to?
Meaningful innovation is hard and takes time, so it is important to take a long view. Government can and should catalyze and support innovation through funding basic and applied research and challenge grants. Government should set ambitious policy goals while at the same time leaving innovation to the private sector.
For example, between 2004 and 2007, DARPA (the Defense Advanced Research Projects Agency) set out some “Moonshot-like” challenges and put forth a modest amount of prize money for autonomous vehicle-related technology. Today, the payoffs are huge. The teams that competed in those challenges are the fathers and mothers of all the autonomous driving R&D now taking place across the entire automotive industry. In other words, a series of small government challenges have generated an enormous amount of private sector investment and job creation. Two lessons here: the first is that a little can go a very long way; the second is that government set a goal, got out of the way, and let academia and the private sector drive the evolution of the space.
Self Driving Car technology is one of the hottest areas in the Valley. What are a few things the international community should know about Self Driving Cars?
Three points here:
First, autonomous technology will usher in a paradigm shift as large as when we transitioned from the age of the horse and carriage to the age of the automobile. Getting around will allow for increased productivity, and for people who live in areas with poor access to public transit, it could make it easier to access jobs and opportunities. We will also think about real estate differently. For example, much of real estate today is built for and around the automobile. Think parking lots and parking garages. In a world of shared autonomous vehicles, demand for parking decreases.
Second, the first rollouts will happen in cities in a ridesharing model, not in vehicles sold to end customers. Cities can benefit from shared electric, autonomous transportation because it will ease congestion and decrease pollution. As more people move into cities, the idea of individual car ownership becomes less tenable. In this model, liability shifts away form individuals towards fleet managers and manufacturers.
Finally, and most importantly, safety is paramount. In the U.S., more than 35,000 people are killed every year in automobile collisions. Most of those fatalities are caused by human choice or error. Autonomous vehicle systems will be designed to interact safely on the roads with other road users like human drivers, pedestrians, and cyclists.
Moving away from Self Driving Cars, what is one problem in society today – perhaps one you encountered at the Department of Commerce – that you think we can solve more aggressively using technology?
I think that technology, correctly harnessed and understood, has the potential to improve the lives of many. Technology underpins most of our economy today, and it’s only going to compound over time, so we need to use technology to do a better job of training and educating people for the jobs of the future. Governments can identify important priorities and strategies and incentivize education and training so that people are prepared and trained for an evolving economy.
Finally, as I said earlier, if the private sector’s job is to drive innovation, government should work to ensure that there is an adequate social safety net in place for all people as the economy changes.
This week, I hit my sixth month in the Valley. In October last year, we packed up our lives into suitcases, took our 5-month old on a 20 hour plane ride, and landed at Stanford University, where we both started new gigs: my husband, a PhD in statistics at Stanford, and I, setting up the Smart Nation and Govtech office in the U.S., working on partnerships, strategy & research, engagement & communications of Singapore’s tech agenda.
It’s been exciting but exhausting to be a start-up parent, start-up at work, and start-up socially, leaving all the comforts of knowing and being known back in Singapore. This is a more personal post, but I wanted to share five things I’ve learned about starting life in the Valley, since people often ask. I don’t think I’ve got it right all the time, but I thought I’d pen down some thoughts my six-month checkpoint, and see how it evolves over time.
Regardless of the reason that brings you to the Valley, I hope you find this useful.
When I arrived in the Valley, I hardly knew anyone, save for a few college friends (most of them stayed on the East Coast). I was nervous about building up a network for my job. Again and again, I was surprised by the generosity of people I met. I told them about my objectives, and they generously made introductions and shared their insights.
Generosity is the ethos of the Valley. How is this different from other places I’ve lived in? I won’t deny that the natural human instinct is to ask “if I help this person, what’s in it for me?”. Some people are risk averse when it comes to this question, not helping unless they are sure they will get something out of it. In the Valley, people are more willing to take a risk that they will gain nothing in the short-term from that specific interaction, but that their generosity will come around one day.
Always pay forward generosity. As I go about my day, I keep in mind the people I’ve met and the professional and personal interests they’ve shared. I keep a look out not just for my own interests, but theirs as well. Nothing makes me happier than to help people to connect: to see minds meet, interests aligned, new opportunities explored. I’ve been lucky to facilitate many of these in the past six months, both within the Valley, and across the U.S. and Asia. I’ve experienced “what goes around comes around” first-hand.
2. Be Clear and Concrete About What You Bring
While generosity is pervasive, I urge people not to take this for granted. Silicon Valley is a prime destination for “innovation tourism”. I have had many people approach me to link them up for “learning trips”. In general, people in the Silicon Valley are game to meet new people and share what they know. But people are also very busy. When I make an introduction, I like to be sure that the person on both ends will benefit – whether it is a new insight, a new partnership or investment opportunity, or access to new networks.
Hence, my second learning is to be very concrete about what you bring to the table when you reach out to someone. It need not be a fanciful effort. Share about the idea you are exploring professionally, or personally. Share about your (our your organisation’s) experience, and how it might align with the person’s interests. If you have details about collaborations that the conversation could result in, share that as well. Be upfront about the opportunities and uncertainties.
3. Find your Voice
The third thing I learned was the importance of finding your voice. People in the Valley are often genuinely interested in you as a person – beyond your professional capacity. What was your journey? Why do you do what you do? I boil it down to a natural curiosity; a bent towards learning from others’ experiences.
I’m not one to naturally write, or get in front of an audience. But I started writing (www.techandpublicgood.com) and speaking at conferences, inspired by the many conversations I had with people who asked me deeper questions, after we had finished talking about work. Some pointed out that my eyes lit up when I talked about how technology could be used for public good, and how my experiences working on education, poverty and housing issues shaped my views. When I communicate on public platforms, none of it feels forced because these are issues that resonate with me.
Finding your voice has a snowball effect for building relationships. As I wrote and spoke, people with common interests contacted me from around world – all over the U.S., Australia, China, Singapore, the UK, Rwanda, Korea, Germany. My blog has had over 10,000 readers over a few months, which surprised me because I the content is not exactly “light”. People who both agreed and disagreed with my views reached out to debate. One of my favourite things: having start-up founders share their passion for working on social issues (such as elderly caregiving, pre-school, workforce development), and, based on my experience in these areas – being able to give good advice on the challenges and opportunities.
By finding my voice, I’ve also been able to play the role of a bridge, facilitating conversations and link-ups between governments, civil society and tech founders on issues at the intersection of tech and public good. It drives contacts towards my work as well.
4. Find Time to Think and Invest Deeply
My husband recently commented that being back in school for his PhD has been far more mentally tiring than working. Here’s what we concluded: when we work, we exercise many different capabilities, some of which require less intellectual effort. Especially when starting up, there are a million things to do, people to meet, process kinks to iron out. It’s easy to be swept away by the operations.
To make space for deep thinking, I’ve identified several areas where I have no ready answers, but which I think would be super impactful for Singapore if we get right. Every week, I set aside a few hours to work on these issues. I’ve developed hypotheses, and have a small circle of people I meet regularly to exchange ideas with. One such area is the policy, technology and data needed to tackle job displacement at scale.
Setting aside time for these things takes a little faith because you aren’t entirely sure how it will contribute to the bottom-line, or what is expected of you. But I’ve applied this principle in every job since I started my career, and I can assure you it does. In my first job, I felt strongly about equalising opportunities in pre-school education, and worked on a small side project reviewing all academic literature on the outcomes of preschool education. Because it was the right time, it became one of the key impetus for major preschool reforms, which I had the chance to work on subsequently.
5. Keep Your Perspective
Finally, find ways to keep a broader perspective of the world. One of my best friends from college left the Valley shortly before I arrived. He expressed relief to be moving out of a place that was so “uniformed” – all about tech, largely upwardly-mobile and highly-educated. Another recently told me she plans to move permanently to Rwanda, because you can’t avoid the realities of human suffering the same way you can in a developed country – even more so the Silicon Valley.
Especially with the 2016 Presidential Election behind us, we all realise we live in a bubble. Even after moments of self-reflection, it’s tough to keep a broader perspective: My news and social media feeds, meetings and calls are overwhelmingly tech-related.
In the next quarter, our family wants to get more involved in community service (though admittedly, with a one-year old, it’s harder to get out!). In the mean time, one of the things I do is to make sure I read a book in a completely different field every few weeks. My two favourites this year: “Hillbilly Elegy” – a compelling narrative on the social issues facing middle America – and “My Promised Land: The Triumph and Tragedy of Israel”, which helped me reflect on geopolitics, race, and foreign policy.
Back at You
As I write this, I’ve realised that these need not apply just to starting life in the Valley. They could be applied to starting out in any new place, or even to building a new network in your existing home. Many of you have experiences that far exceed mine. Would love to hear your thoughts!
Also, I am back in Singapore for the month of May and would be happy to catch any of you there.
I loved this article by David Gilford on how we should think about Smart City technology. He captures simply what matters, and why – from the technology perspective. Enjoy!
The phrase “smart city” conjures up images of gleaming new infrastructure, from intelligent street lights to NASA-style command centers. In reality, however, technology’s biggest impact on urban life is much less flashy.
Rather than betting on VR headsets or other currently popular interfaces, communities that invest in six underlying capabilities are best positioned for the longer term:
Allowing personalized recognition between people and systems
In the popular imagination, small towns are where “everybody knows your name,” sharing goods and spaces without a second thought. The urban bike rack may seem like the polar opposite, yet programs like Citi Bike entrust strangers to pick up objects worth hundreds of dollars and drop them off across town. Such systems recognize individuals and extend privileges accordingly, using technology from biometrics to token-based authentication to enable the sharing economy. If sharing one’s home is an expression of faith in fellow citizens, Airbnb is an early example of how technology expands circles of trust, bringing aspects of small communities to even the largest cities.
Providing context-aware, location-based information for efficient and engaged movement
Mobile phones with ubiquitous GPS have nearly rendered the feeling of being lost obsolete, yet the built environment is only beginning to understand where people want to go. While autonomous vehicles dominate headlines, simply understanding people’s locations and desires offers both a big economic and social payoff. Ridesharing services like Via blur the distinction between private and public transportation, effectively creating “on-demand buses.” Similarly, by analyzing millions of data points from Amsterdam to Singapore, the MIT Senseable City Lab estimates that intelligently matching riders could reduce the total number of taxi trips by as much as 40 percent.
Observing, understanding and anticipating the world around us, from the movement of people to the quality of our environment
Sensors are hidden but ubiquitous components of the urban landscape. Beyond piecemeal installations, communities are recognizing the benefits of a holistic approach. The Array of Things project is deploying environmental sensors across Chicago, aiming to be a “fitness tracker” that captures and analyzes data impacting quality of life, including air quality, climate and noise pollution. New York City’s recently announced Neighborhood Innovation Labs take this a step further, partnering with communities, government, technologists and educators to solve locally-identified challenges, starting in Brownsville, Brooklyn.
Creating secure, convenient methods to pay for goods and services
Blockchain, best known as the technology behind bitcoin, is finding unexpected applications in the built environment. The Brooklyn Microgrid shows how solar power can be shared across a neighborhood, improving sustainability and resilience to disruptions. By facilitating decentralized, low-cost and secure transactions, blockchain empowers citizens to participate in what had previously been the exclusive purview of large utilities. Such peer-to-peer approaches offer the potential to transform other urban markets, from ridesharing to real estate.
Linking people to services, resources, amenities and each other
In a world where internet-connected devices outnumber humans, connectivity is essential to competitiveness. Through LinkNYC, my company, Intersection, is replacing old payphone infrastructure and bringing free gigabit wireless to New York City, where nearly 20 percent of residents lack broadband at home. Though the kiosks themselves are becoming a fixture of the streetscape, LinkNYC’s biggest impact may be invisible, with each unit supporting hundreds of simultaneous users. Since launching last year, over 1.3 million individuals have registered to use the WiFi, with over 5 million sessions occurring each week. As access to high-speed broadband is democratized, more citizens will be able to fully participate in their community’s growth.
Enabling different systems, information sources and data types to work together
As the examples above demonstrate, no technology operates in isolation. Just as smartphone apps connect with each other, physical systems need interoperability. Unlike consumer applications, however, systems in the built environment are harder to interconnect, from elevators to energy systems. Cities and real estate developers that overcome traditional biases towards closed and proprietary systems can provide a platform for others to build upon and improve.
To improve people’s lives, technology needs to serve people, not vice versa. Questions of accessibility and equity must remain at the forefront as communities envision responsive neighborhoods. While no one can predict what technologies will dominate next year’s headlines, places that embrace these foundational capabilities will be ready for whatever comes next. Beyond simply being “smart” today, such communities enable all of us to collaborate in building better environments tomorrow.
David Gilford leads Intersection’s Connected Communities practice, helping municipalities, real estate developers and public-private partnerships create connected, responsive communities. Prior to joining Intersection, Gilford held multiple leadership positions with the City of New York, most recently as Vice President for Urban Innovation & Sustainability at the New York City Economic Development Corporation.
Uber recently made headlines with this feature by the New York Times, on how it uses behavioural insights to get drivers to work longer hours, and go to areas where there is high passenger demand. As Uber drivers are not employees, Uber has very little formal influence over their behavior – they can’t mandate how much drivers drive, or what area they cover. Behavioural nudges are a relatively costless way of getting drivers to do what Uber wants, instead of using monetary incentives. But is Uber particularly guilty?
A Brief Overview of Behavioural Nudges
The use of behavioural nudges to shape customer, constituent and employee behavior is certainly not unique. Examples abound, including:
Governments and constituents. The UK Government has its own Behavioral Insights team, which has helped sign up an extra 100,000 organ donors a year and doubled the number of army applicants by simply changing default options and how emails were written.
Employers and employees. One of the reason Google will fix your car, take care of your health and food needs all in one place is because they know it will get you to stay longer and work harder.
Now that you’ve seen some examples, what exactly are “behavioral nudges”? Definitions vary, but I’ll boil it down to two things:
Applying an insight about a person’s decision-making calculus (that the person might not even know about himself!) to get him to make the decision you want.
The person is likely unaware that this tool is being used (unlike a law or a policy, which he actively shapes his behavior to comply with).
An in-depth article on leading thinkers in the field of behavioural science (Kahneman, Tversky, Thaler, Lewis), can be found here.
The use of behavioural nudges is not new, but data has made it an increasingly powerful tool.
The potential for behavioural nudges is increasing with the proliferation of data about individuals. The more you understand how people make decisions – to work longer hours, to buy your product, to pay their taxes, to brush their teeth, to play a game, the more effectively you can nudge them towards your desired behaviour. Facebook knows more about me than I do. Uber knows more about their drivers than drivers themselves.
As a result, these companies can push buttons I didn’t even know existed. They have the potential to hack my operating system and change my behaviour.
Hence the ethical question of when a “nudge” becomes outright manipulation is more pertinent than ever.
Here are several ways to think about whether a “nudge” is being used ethically. <By the way, some people argue that it’s never OK to curb someone’s “moral freedom” through nudges, but I find that too idealistic – nudges have been used for time immemorial. It has to be a matter of degree.>
First, what is the inherent goodness of the outcome for the target population?
On the positive extreme, behaviours such as showing up at a doctors’ appointment, attending school or paying bills on time can be seen as actions that are positive for the individual. On the negative extreme, you could have outcomes such as an alcoholic purchasing more alcohol, or a suicidal person being nudged off the ledge.
There is huge scope for debate in between the extremes. Uber could argue that getting drivers to work longer hours during peak period is good for their earnings. Facebook would argue that repeatedly pushing advertisements that users are more likely to click helps them find what they need and like faster.
But here are two sub-questions to consider, in Uber’s case:
What is the distribution of benefits accruing to Uber vs the driver if the driver changes his behaviour? In this case, there seems to be a direct trade-off between Uber and drivers’ interests. As more drivers come onto the platform as a result of the nudges, drivers don’t benefit from surge pricing. On the other hand, Uber gets the benefit of more rides and hence more earnings.
Is there an intention to deceive? The author suggests that some of Uber’s methods nudged drivers towards geographical areas on the pretext of a surge, but when drivers got there, they found there was none. Even if this was not the intention, the asymmetry of information is unfair to drivers. More transparency is needed, perhaps by providing drivers a live feed of surge rates in various areas, including when surge is dropping.
Second, how easy is it to “opt-out”?
The ‘opt-out’ technique is one of the most commonly used “nudges”: always set your preferred option as the default, and count on human inertia (or ignorance) to keep people there. If you are a Netflix user, you’ve experienced this: once your episode ends, the next one comes on automatically in ten seconds. It is a nudge to keep you watching, but you can turn off this feature permanently. Google and Facebook will send you personalized ads, but you can opt-out and get those replaced by randomised advertisements instead.
If you are an Uber driver, you can also temporarily turn off the forward-dispatch feature, which dispatches a new ride to you before the current one ends (keeping you constantly driving, just as Netflix keeps you constantly watching). However, there is no permanent way to turn it off. It will keep popping back on when you take a new ride: you have to be constantly proactive about stopping it if you don’t want to overwork. Does the lack of a permanent opt-out feature make Uber more guilty? Perhaps. But I would like to find out more about the design considerations of both Uber and Lyft before giving a definitive view (hit me up if you have further insight!).
Generally, how proactively institutions educate their users/employees about the opt-out function matters, as does how easy it is to opt-out.
A More Important Question
So is Uber particularly guilty? On the surface it seems to. But want to hear my real answer? I have no idea, simply because much of the nudging that institutions do today is invisible, making it impossible to compare. We – as users, employees, constituents – do not even know that it is happening, and there is no legal obligation to tell us.
Hence, rather than ask whether Uber is guiltier than other institutions which deploy “nudges”, I believe the more important question should be: is self-regulation by these institutions sufficient? If not, does anyone have the moral high-ground to arbitrate? Should there be a system where institutions report their use of “nudges” and hold each other accountable? Would love to hear your thoughts.
This week, I feel lucky to have a veteran in healthcare technology and data science from the Silicon Valley, Alam Kasenally, give us an overview on how technology has already transformed healthcare, and the gaping hole which has yet to be filled: patient experience.
Alam recently moved to Mauritius with his wife, Min Xuan (one of Singapore’s brightest entrepreneurs), where they manage a hospital. They’re also busy inspiring youth toward entrepreneurship, and building an innovation hub in Mauritius. Prior to his relocation, Alam worked in Cancer Commons in the Bay Area, which provides patients and their physicians with the knowledge needed to select the best available therapies and trials, and to continuously update that knowledge based on each patient’s response. He also worked in Oracle, Yahoo and Crowdcast prior.
Alam and Min are two people who will inspire you with their commitment to using tech and innovation for public good, how deeply their invest in others, and their entrepreneurial experience. For our entrepreneur readers: If you are a founder trying to gain quick access to real users and customers to pilot quickly, their hospital in Mauritius provides an immediate incubator for medical technologies, while they also have trusted partners especially in the agriculture and tourism verticals that can move quickly. Let me know if you’d like to be in touch!
What’s the opportunity for Tech in Health?
Early Sunday morning, and that ridiculously healthy neighbor of mine is already lunging and squatting on my, I mean our lawn, ready for her half-marathon practice. My overwhelming instinct is to grab my fitbit and try and compete, but really, I should be (from an economist’s point of view) happy that I have an additional neighbor in my community that is healthy. For a start, the workforce is larger by one (and perhaps more than one: serious illnesses affect entire families of people who care for the patient). I enjoy a larger share of tax dollars deployed to Leslie Knope rather than, uh Gregory House. Finally, my neighbor is probably not lunging with a dripping nose. My neighborhood is safer.
So, now we’ve established that Health is a Public Good (as well as being “in the Public Good”), is there therefore a role for technology in Health? Well, there already is, and let’s take a tour of the landscape.
Technology, and I’ll focus on tech as the Valley knows it, minus traditional medical technology (prosthetics, diagnostic and treatment infrastructure, etc) has made a serious impact in the last 10 years. Smart entrepreneurs everywhere have caught on that tech can:
Lower overall costs through automation and efficiency (Epic, the Goliath of the industry, now faces an impressive challenge led by lean startups)
Lower overall costs through the finding of patterns in hospital big data
Avoid adverse selection and moral hazard through finding of patterns in insurance data and monitoring patient behavior (though I have to yet to see these cost-savings trickle down to patients)
Provide a variety of “quantified self” (steps, sleep, calorie, breath) in an effort to influence behavior change and lower their healthcare system’s cost
Lower overall costs through remote monitoring of patients (FBS, SPO2, EKG) and we’re even seeing these devices cross into the consumer space.
So is there any scope for the use of technology left?
There’s scope for a combination of technology, process, regulation and people. Healthcare is not only an expensive good, but a remarkably complex one. Sleep, Dieting, Breathing are just fine (though they have attracted the most VC dollars, as they are the easiest to do). The patient experience, on the other hand, is broken, in a million pieces. The complexity of choosing the institution and doctor that will lead to the best (and most consistent) outcome is daunting. The complexity of referrals is mind-boggling and reimbursement is ludicrous. The simple knowledge of viable treatment options and associated outcomes is not available to patients, their families and even doctors.
Is this the limit of the use of technology for Health? No, it’s only the beginning. It’s time for a true Uber of Healthcare to emerge.
“Huber” re-invents the patient experience just like Uber successfully re-invented the taxi experience. This company (and maybe government) will successfully join different partners and datasets, to create an experience that is to the patient’s (and her family’s) satisfaction, safety and in her interests. Datasets that only get smarter, as Healthcare outcomes, treatment models and patient preference filter back into the system. From my experience, certain countries remain crippled in regulation that thwart such efforts, often with the reasonable but ironic pretext of patient privacy. But others (Singapore comes to mind) have an honest broker, trustworthy IT custodian of the data and could write regulation and create necessary conditions that could well be in the patient’s interest.
Soon, participants will begin to realize that it isn’t just Health that’s the Public Good. But Data. Now, excuse me while I grab my fitbit.
I’m really excited to share this interview with Xinwei, Director of Strategy at Grab (formerly GrabTaxi), a ridesharing platform in Southeast Asia. She is also Regional Head of Grab’s social ridesharing service, GrabHitch, which beta-launched in Singapore in late 2015 and has since expanded to Kuala Lumpur, Jakarta and Bangkok. Prior to joining Grab, XW worked at the Boston Consulting Group and the Singapore Ministry of Finance.
In this wide-ranging interview, she shares her biggest lessons in her journey from policy-maker to consultant to start-up director, where she wants to see technology applied more aggressively, advice for companies looking to expand into Southeast Asia, and insights for both policy-makers and technologists from both sides of the fence. Besides being a good friend, Xinwei is someone I admire deeply for her work ethic, depth of thought and calm under pressure. Definitely someone to watch 🙂
1. How did you make the transition from Government to Tech? What’s it like working in a start up vs in a more traditional industry?
After I left Government, I joined consulting for about 2.5 years, and thereafter joined Grab, where I’ve been working now for almost 2 years.
I would recommend consulting for any generalist who is looking to learn at hyper-speed about the business world and about the region we live in. While at BCG, I spent at least half of my time in Indonesia (if not more), and it’s benefited me greatly now that I work in and manage teams in our Jakarta office.
Joining Grab opened my eyes to start-up life and culture. I’ve loved this way of working from the beginning – the juxtaposition between the casual team culture but incredibly intense pace of work; the tension between wanting to reach for the stars but having to ruthlessly prioritize based on your current resources and capabilities; the ever-present low-level existential crisis of not quite knowing whether you’re flying or falling. It’s a thrilling place to work, but with that thrill also comes stress and increasingly blurred lines between work and life (my husband will not hesitate to confirm this last point).
For those who are seeking to move from more traditional industries to start-ups, you have to be prepared to let go of some of what you know; but also have confidence that you’re bringing an expertise and knowledge base about how companies work that is very valuable to
start-ups. Some tips:
(a) Learn to embrace uncertainty.
Uncertainty will exist in all aspects of start-up life. The type that seems to affect people most is professional uncertainty. In a startup, it’s not uncommon to experience frequent reorganizations, to see the team you joined dismantled, or to undergo several title or portfolio changes in a few months. Then there’s business uncertainty – how do you know whether to invest in a new vertical/market/business or not? When choosing between two ideas that could 10X the business (or send it into a downward spiral) how do you choose? There is no playbook for what startups typically do, and that can cause a lot of anxiety.
There is no perfect remedy for this, but it helps to take a philosophical view that no matter what happens you’ll live to die another day. Channel all your nervous energy into obsessing about your business and outserving your customers, put aside your personal anxieties and just enjoy the ride.
(b) Execution is what makes good ideas great
There are two common pitfalls (that I have personally experienced many times now). The first is to overestimate your ability to execute, which results in jam-packed workplans where items are checked off the list, but not done in a truly excellent way. The second is to underestimate the need for excellent execution; this usually comes hot on the heels of a great idea where one is seduced into thinking that the awesomeness of the idea will carry the day.
The truth is that good ideas are everywhere, especially in fast-growing startups where everyone is obsessing over big questions such as how to win market share, how to serve customers better, or how to leapfrog the competition. What makes an idea truly great is elegant, flawless execution that delivers outsized results.
I don’t have any big secrets to share on how to execute well – I’m still very much a student in this journey – but I think a big part of it is about disavowing silver bullets and instead being very deliberate about tracking and measuring any intervention you make in your market. You want to get to a point where you know how best to deploy every dollar based on what channels you have at your disposal and what your objectives are. The tradeoff of course is that learning takes time (not to mention failure), and in a startup, time is often the one thing we don’t have. But our job is to walk that tightrope.
2. What is one problem in society today that you think we can solve more aggressively using technology?
I would really like to see how we can use technology to facilitate elderly lifestyles and caregiving. I think the amount of thinking and consumer research done in the field is simply not commensurate to the tremendous need and opportunity. In fact, elderly care has many similar themes with infant care (ranging from personal hygiene products to food to mobility solutions), but the two sectors are worlds apart in terms of customer-centricity, product variety and innovation. One reason is that elderly people aren’t as tech savvy as younger cohorts, nor are they constantly connected to the internet via smartphones – but that is changing very quickly. I think there is another deeper reason, which is that elderly care fundamentally faces a brand image problem – we associate it with the end-of-life, the loss of dignity, and diminished versions of ourselves, rather than simply a challenging stage in life where we have different needs and require more support and help than we used to.
I would love to see innovations in areas that facilitate independent living (mobility solutions, health monitoring and remote caregiving of some sort, seamless chronic care), reduce the burden on caregivers, and that use the internet to create active communities or learning opportunities for the elderly.
3. What’s one thing you wish your friends in Government knew about the tech sector, and one thing you wish your friends in the tech sector knew about Government?
That no one is really in this only for the money. There’s a common misconception that everyone in the private sector (and especially in tech companies) is out to make a quick buck. Of course, there are always going to be companies that fit that stereotype. But in my experience, the most impressive and successful entrepreneurs never quite set out to make big bucks. Rather they became obsessed with some crazy idea that they thought could deliver huge impact, executed on it and managed to bring the world along with them.Making money is a necessity for businesses (at least once the growth capital runs out) and so it’s unrealistic to expect companies to behave like charities. But just like the humans who found and build them, companies have their own personalities, culture and DNA. Of course, there’s a limit to how nuanced our regulations and economic policies can be, but if governments see that many businesses come from the same starting point of wanting to make a positive impact on society, then it paves the way for more open and productive engagement.
Another misconception – which, like the first, isn’t restricted to people in Government – is that what makes a tech company great is solely dependent on how good their tech is, and nothing else. The companies that we consider great “tech companies” – Apple, Amazon, Netflix, Facebook, Google – certainly had and continue to build superior technology; but what sets them apart is clarity of focus, a winning business model, and the willingness to fail and pivot.
I recall a conversation with a friend who was trying to understand how Didi beat Uber in China, and a sticking point was whether Didi had any original tech or whether they simply copied ideas; or whether Didi had superior tech which allowed them to win. There are many versions of this story, but what’s fairly clear to me is that technology was merely table-stakes in the Didi-Uber fight; these were two giants at the top of their game and a more finely-tuned surge algorithm was not going to be decisive. What Didi had was incredibly efficient and locally rooted ground operations (back to execution and the ability to deploy every dollar more efficiently than the competition), excellent and often viral marketing, and deep integration with China’s all-pervasive mobile payments network.
In terms of what I wish the private sector understands about Government – I think it’s that the current system of rules and regulations was constructed for a reason and changing it does require time and deep consideration. There’s a general impatience among the private sector with governments, and especially so in the tech sector given that so much of what we do challenges status quo norms and systems. But just as we wish governments understood that we are just trying to serve our customers the best we can, they too need to do the required diligence to make sure that this is the right thing for society as a whole. So the approach shouldn’t be to try and disassociate ourselves from government or brazenly disregard regulations, but to build bridges and try to align our interests. If you’re in it for the long haul, then engagement and trust is the only sustainable way forward.
4. You work extensively in Indonesia and Kuala Lumpur. What are they key differences in how you operate in these contexts? What advice to you have for companies looking to move into these regions?
One gradually exploding myth about Southeast Asia is that it is a coherent region; in fact, Southeast Asia is extremely fragmented with clusters of countries sharing some common cultural history while others are relatively unrelated. I’ve found that Singapore and KL feel culturally very similar, for obvious reasons. Indonesia, on the other hand, feels quite different, more so the further you travel from Jakarta. As my CEO likes to say, Indonesia is a continent, not a country. The energy and vibe is quite different from what you’ll feel in Singapore or KL. The war for talent is far more intense there. We’ve seen some really impressive tech companies come out of Indonesia in the past few years.
If you’re looking to expand to or start something in Indonesia (or really anywhere outside home ground), I think the most important thing to do is to spend time on the ground and learn the language. There’s only so much management you can do from afar, and most of these markets are intensely competitive. There is no substitute for being on the ground and experiencing your product and services in the local context. You’ll learn things that no management report could adequately describe.
5. Some of our readers are interested in entering the field of tech. What is your advice for them?
First, if you are currently in a non-technical role but would like to become a technical Product Manager, a software engineer or data scientist, then some formal training is required and there are tons of great options out there to acquire those skills. That aside, I believe that in every company will be a tech company in the future, in some shape or form. It will become increasingly meaningless to think about entering the “tech industry” because every company will have to adopt relevant technology to stay ahead, including how to use the internet to distribute services, understand their customers and facilitate payments and other transactions.
So I would encourage anyone keen on “tech” to first ask themselves what real-world problem they are trying to solve, or what business vertical they feel best fits their interest. Once you’ve figured that out, then go in search of a company that you think is harnessing tech in the right way to solve that problem. Otherwise you put yourself at risk of becoming an unknowing participant in “innovation theatre” in a company that’s just using tech as a marketing tool.
This is a guest post by Anita Ngai, who has extensive experience in technology, retail and urban development. She worked in McKinsey for 4 years before transitioning to Real Estate in Hong Kong, and online travel. She was trained as a structural engineer. She is currently exploring a start-up idea focused on helping developers become more data-driven in their planning and leasing processes. You can contact her here.
I love that domain experts – in this case – a structural engineer cum real estate professional, are thinking about how technology can transform the way their industry works. Hope you enjoy her article as much as I did!
WHY IS THE MALL DYING?
“The Death of the American Mall”, “Ghost Malls in China”, “Are Malls Over?”, “Is the Physical Shopping Mall Dead?”, “China’s Ghost Towns and Phantom Malls” – if you google search the term “shopping malls”, these headlines pop up. What’s interesting is that these headlines cover places as diverse as the Midwest US to the large metropolitans of China. Some reasons for this trend include:
Urbanization – higher concentration of population and/or wealth means less retail space needed in suburbs
Changing demographics – deceleration of population growth, aging core group
Slowing income growth/increasing inequality – weaker GDP growth; wealth more concentrated in hands of a smaller number of people
Change in consumer preferences – trend that millennials prefer to live and occupy less space
Overbuilding catch up – we have been overbuilding for some time, and it’s finally catching up (as New York Times quoted a real estate executive: “The mall genie was out of the bottle, and it was never going to come back.”)
Poor management – bifurcation of malls into great versus terrible ones that don’t survive
The death of the malls poses serious challenges to developers and planners. Their previous paradigm, “build a mall and people will come”, no longer holds today. Instead of building new malls, developers need to focus on conversions and repurposing of existing malls and spaces.
THE PROBLEM OF UNDER-UTILIZED ASSETS IN A CITY: WHY DOES THIS MATTER?
Underutilized mall spaces are not just a problem for developers – they are a waste of a city’s precious land resources. For example, in dense cities like Hong Kong, where I worked in real estate in different roles for four years, the competing demands on land are very real – retail is very much in demand by the upper-middle class and mainland Chinese tourists. On the other hand, the housing crisis is getting more and more acute because of the lack of space for new housing developments.
THE PROMISE OF TECHNOLOGY IN BOOSTING MALL UTILISATION
In the age of Airbnb and Uber, one would think we could do better in optimizing the underutilized assets in malls. Indeed, technology holds tremendous potential in helping developers do this – both at the planning and the post-completion stages.
Collecting and analyzing data can help developers customize their projects to their potential users. In the past, developers only had blunt demographic data (population size, income levels, age composition) on which to base their plans. Now, sensors and mobile phones can capture large volumes of finer data e.g. what types of shops women between 30-40 in the geographical vicinity dwell longer and spend more money at.
Combining all this data, developers can use sophisticated statistical simulations and machine learning to predict the foot traffic, occupancy levels, and likely visitor profile (e.g. income-level) of the project if they vary the proportion of space dedicated to retail vs entertainment vs hospitality/accommodations.
Testing hundreds of scenarios of the project mix and layout would only take seconds, but is close to impossible for humans to do – both from data collection and computational analysis perspectives.
After the project is built, there are decisions that developers and their leasing teams have to make continually – who should we lease each space to? How should we price each space? How long should the lease period be for each space ( the default now is 3-5 years depending on the market which works for some, but not for others).
Each of these decisions has tremendous ramifications for the mall’s utilization. For example, putting a fast-food restaurant at a certain entrance to a mall would draw a lot more footfall through that door, versus a beauty supply store or the front lobby of a three-star hotel. For each space in a mall – whether a back corner on the ground floor or center core on the third floor, a fast-fashion tenant, quick service restaurant or three to four food court stalls will each have a different footfall impact, chance of success, and likelihood of sustaining their business over the long-run.
Developers also need to be more flexible with the use of space – pop-up stores, for example, have helped ease some of the long-term vacancies or low footfall issues that landlords are seeing in their retail properties. But this is not done in a data-driven or widespread way: pop-up stores are often under the purview of marketing teams, and theleasing teams may only take a support role.
If developers collect and analyze data effectively, they will also be able to lease their spaces and re-configure their malls based on real-time data. All this boosts utilization and uses space most efficiently.
SO WHY IS IT NOT HAPPENING? AN INSIDER’S PERSPECTIVE
Having worked in real estate for a number of years, here are the factors that hold back these obvious innovations from taking off.
The first reason lies in how developers think about innovation. The only teams within those organizations thinking about innovation and technology – some form of a “digital” department and an incubator/VC – are not usually tasked with looking at the design process. They focus on “downstream” issues like improving customer experiences in a shopping mall or on having a bet in a start-up who will “hit it big” one day.
Second, even if a developer/owner is motivated to take a data-driven approach to design, a single company’s portfolio of property may not be large enough to yield data that is representative of the market view. Certain Asian developers come closest to controlling the ownership of an entire neighborhood or district, but worried about competition, they would not be motivated to share this data with the industry, competitors or brokerage firms.
A third reason is similar to what we have seen in many other industries: existing players will only make incremental changes, until someone new comes in to disrupt traditional practices. Tech start-ups have been active in the real-estate sector, but mainly in three areas:
Real estate transactions
IoT and smart homes/buildings/cities (the fridge that will order for you when you’re out of milk, the trash can that sends a signal when it’s full and needs to be serviced) and
Visualization (VR for potential buyers to walk through their unbuilt/faraway home; 3D rendering and VR experience of construction blueprints).
Unfortunately, I have not seen many start-ups work on applications that will help with the design and planning of malls. There are a few providing heat maps of where footfall is in a mall; or analyzing the type of store a given neighborhood needs, e.g. apparel, doctor’s office. Mapping start-ups are currently focused on other areas of applications, such as self-driving cars.
MY HOPES FOR THIS SECTOR
Retail makes up a significant portion of a city’s built space inventory: San Francisco has about 76.3 million square feet of office space versus 80.5 million square feet of retail space. It will remain a useful and desired part of city life for time to come. However, it will be a costly waste of precious city space if the trend of underutilization continues. Developers will be able to buck this trend if they use a far more data-driven approach to planning and leasing.
I sketched out the challenges above, and I believe they can be overcome if developers can take a longer-term view to invest in evolving their planning and design processes and to incorporate new data and technologies available. The benefits from using new approaches are not easily quantifiable without having tested them, so sticking strictly to ROI figures will not lead decision makers down this path.
Also, more startups and public agency collaborations such as Uber Movement and World Bank’s Open Transport Partnership would allow the immense amount of data being accumulated to become transparent for public use. Having a public agency host data from different private sources may help overcome more data privacy concerns floating around, though these agencies would likely need tech companies to help them improve on data security. Governments can play a more proactive role in facilitating progress, through regulations and test projects, and I believe the municipal level – because of smaller size and relatively less partisan impasse – will be the best testing grounds.
 (Of course, there are still places where there is a real growth in the population or local economy, and so new retail space is indeed needed.)
A number of studies actually show that higher densities can lead to higher public expenditure per capita, though there is evidence that this is due to government management practices, e.g. higher government employee compensation. In addition, lower densities do not necessarily increase public expenditure because the costs for sewage, electricity and other infrastructure are actually priced into the new houses, i.e. bore by the residents themselves. Benefits from higher density developments are more obvious if we include quality of life metrics (e.g. traffic congestion, air pollution).
Have you worried that your headaches are the result of a brain tumour, or that your child’s leg pain is caused by cancer? You’re not alone. You may well be a cyberchondriac: “a person who compulsively searches the Internet for information on real or imagined symptoms of illness.” If this sound familiar, you are in good company.
If you search “child leg pain”, google will auto-complete your search with “leukemia” – not because it is the most likely cause of your child’s leg pain, but because people who have searched “child leg pain” in the past were most likely to have clicked on links correlating this phrase with leukemia (probably because they wanted to understand the worst-case scenario). That’s how machine learning works – it pushes up the article that was most popular among other readers.
It makes sense to push up an article that most previous users clicked on – this is one of the best proxies for relevance to new users. However, the engineers behind search engines realise this isn’t necessarily beneficial for google users:
It’s scary– the average reader may assume cancer is the most common cause of child leg pain, or brain tumours are a common reason for headaches. Cyberchondriacs get even more paranoid.
It can encourage harmful behaviour– imagine if you search “best way to kill myself” and the top hits documented in detail the most painless way to die. Will the information push you over the edge in your decision?
Engineers behind search engines have to make a choice on what information to present to users – what people want (the traditional way) versus what they may need.
The Making of “Dr Google”
It was my pleasure to have Evgeniy Gabrilovich, Senior Staff Research Scientist working on health-related searches at Google, shed light on how Google thinks about it’s responsibilities to users. Evgeniy is addressing a sizeable group of Google’s customers. 5% of all google searches are health-related, 20% of which are people who type in a symptom hoping to find a cause.
Evgeniy’s team works on The Health Knowledge Graph, which aims to give users the best facts when they enter their symptoms. The Health Knowledge Graph does not replace traditional web search, it complements it. Try it out: Type in “chest pain”, “depressed” or “child leg pain” and you will get a side bar on the right which covers the ranked list of likely conditions, how common or critical the condition is, incidence by age group, etc. The center section still presents traditional web-search results.
When you type in a symptom you’re experiencing “child leg pain“, Evegeniy’s team aims to give you the most accurate diagnosis while minimising cyberchondria “Growing pains”.
Google realised that they didn’t have the expertise to do this on their own. It’s a huge technical challenge because of the large number of conditions and symptoms, and the overlaps between them. Furthermore, people use colloquial language to describe their symptoms, which the machine needs to decipher. Finally, user intent is often unclear. For example, if someone types in “weight loss” – are they trying to lose weight? Are they describing a side effect of medication?
Together with doctors from Harvard Medical School and the Mayo Clinic, they used machine learning to establish correlations between symptoms, conditions and treatments such that when you type in your symptom, you will get information that closely mirrors what a doctor might tell you (although it doesn’t go so far as to diagnose you… yet). Just to make sure, every result is evaluated by real doctors, who are asked “would you be comfortable with google showing these results”?
What does this mean for the medical profession?
Fifteen years ago, very few would have trusted medical advice that wasn’t from a doctor. Ten years ago, people started turning to the search engines for advice it wasn’t ready to give. Now, search engines are training themselves to give professional medical advice. They will only get better.
What’s next? I recently met a start-up, Mendel Health, which automates matching cancer patients to clinical trials through personal medical history and genetic analysis. Founder Karim Galil was previously a medical doctor. He was motivated by the fact that a single doctor’s brain cannot capture all information about diseases, possible treatments and clinical trials. He had patients die because he, as their doctor, was not aware of a clinical trial that could have saved their life.
Let’s take Karim’s idea a step further – suppose all my genetic, medical information and daily physical conditions (heart rate, glucose levels…) are constantly updated in a database that is linked to all potential interventions, treatments and medications.
While I am healthy, I can be alerted to risk factors and preventative actions (for example – you have a 50% chance of becoming diabetic in the next year. If you do X, Y and Z, the probability drops to 20%).
When I am ill, I can understand all my treatment options and the probability of success.
When a machine can diagnose me and recommend potential treatments, what will be the role of my doctor?
Much of what a primary care doctor does – assessing my condition, referring me to other specialists or recommending basic medications – can be encoded in software and search engines. Will they simply be a ‘stamp of approval’ – a safety blanket of sorts – before I take my next steps to get treatment?
Perhaps new roles for doctors will open up – for example, in training and verifying Dr Google as more and more people rely on it.
Complex surgical procedures will likely still require human attention. However, with robotic technologies like Verb Surgical, which enable top surgical expertise to be propagated across many doctors, will the average level of surgical skill required by each doctor be lower than before?
Why does this matter?
I honestly can’t envision a world with no doctors. Health is so close to our hearts that it requires a personal and emotional touch. However, it is important to understand how technology will change the role of the doctor:
This this will have large impacts on how countries train doctors (e.g. how long? what skills?), allocate resources (e.g. primary care vs specialists), and design incentives in their healthcare system (e.g. if patients have access to so much information, will there be a trend towards over-consumption of medical services? Do co-payments have to change?).
I am certainly not an expert in the field of medicine or medical technology, but would like to continue exploring this topic – especially from the perspective of what countries need to know, and how they should respond. Ping me if you are a doctor / work in healthcare and medical technology – I would love to hear your thoughts.
This week I read three parallel articles: one on healthcare, two on transport, all with the same theme: how the introduction of disruptive technology in traditional ‘public services’ led to a flood of new demand, calling sustainability into question.
I’ve thus far painted a positive picture of how new technologies can democratize access to services: Riding in the comfort of a private vehicle is no longer restricted to those who have money to own a car. Tele-health, where patients can consult their doctors online rather than face-to-face, is cheaper and more accessible than a traditional doctor’s visit, cutting down unnecessary waiting and travelling time (issues that disproportionately affect the poor and elderly!).
But improving access often leads to a surge in demand, creating new problems for society. These articles point towards an important trade-off between consumer access and system-level health that I haven’t quite addressed. [Spoiler alert: we should care about both because they are ultimately about the consumer!]
“The Downside of Ride-Hailing: New York City Gridlock” empirically shows how ride sharing has worsened congestion in NYC because many have replaced their subway rides with an Uber or Lyft. “Average travel speeds in the heart of Manhattan dropped to about 8.1 miles per hour last year, down about 12 percent from 2010”. New Yorkers have famously pushed back against their Mayor’s attempts to restrict the number of Uber cars.
One of the promises of autonomy is that the car can be re-imagined. IDEO imagined how cars might become work-spaces in the picture below. Once the car becomes a comfortable place to work or relax, many of us might not mind spending more time on the roads. I might opt for an Uberpool even if takes twice the time of a train journey because it’s such a comfortable, productive ride.
If these autonomous vehicles are privately owned, people might send their cars on trips they would normally take. For example, sending their car to the McDonald’s drive-through, or far out of the city center to find cheap parking.
We will also take some time to get to roads where vehicles are 100% autonomous. In the interim, human drivers are likely to “bully” autonomous vehicles because they know that these autonomous vehicles are programmed to be risk-averse (an autonomous vehicle killing a person is perceived as a greater travesty than a distracted driver killing a person). In such a scenario, we will see autonomous vehicles driving at slower-than optimal speeds, creating more congestion.
This is a bigger problem if the new users actually didn’t need to see a doctor and a smaller one if they would have deteriorated if not for the medical treatment. The answer is likely somewhere in between – I believe closer to the former – 88% is huge (But a more in-depth study correlating the new use of medical services with health outcomes is needed). There is potential for tremendous waste in our already-stretched healthcare systems if we massively lower the cost of healthcare services without creating disincentives for unnecessary usage.
How can we get the best of both worlds: access and sustainability?
Technologies have amazing potential to help us use scarce resources like doctors’ time and road space more efficiently, creating greater supply. By lowering cost, they also ensure that this greater supply is spread out more evenly across the population, regardless of income.
However, doctors’ time and road space are ultimately still scarce resources that need to be rationed somehow. Capitalist countries are happy to ration these services by income. Countries on the socialist end of the spectrum (think the UK National Health System) tend to ration by waiting time. Neither fully takes into account the most important consideration: need and urgency.
How can we incentivize people to only use these new, accessible services only when they really need it? Here are some ideas.
In transportation, cities need to make mass people-mover systems (trains, buses) the core service used by most commuters: ride-sharing must complement, not replace trains and buses. The bulk of commuters should spend most of their journey in trains and buses where the road space per commuter is significantly lower. Ride-sharing can be a first-mile and last-mile solution (e.g. home to train station), but certainly not the default for the whole journey.
To achieve this, cities need to up their game in public transportation. It has to at least be reliable and predictable (which many, many aren’t). Examples of how Singapore has done this here and here. Taking a step further, payments and arrival/departure times should be integrated with ride-sharing platforms so that people can minimize waiting and inconvenience when transiting between ride-sharing and public transportation. Work-friendly design in public transportation (think flip-out work tables in public buses) will also help make these options less unattractive compared to IDEO’s self-driving pods.
When it comes to autonomy, cities also need to think about moving to 100% autonomous vehicles as quickly as possible, since the dynamics between human drivers and autonomous cars will likely increase congestion. A 100% autonomous vehicle scenario also creates the most gains in efficiency and safety – vehicles can travel bumper to bumper (more efficient use of roads) and provide information to each other about road and traffic conditions (safety and efficiency are both enhanced). I cover some strategies in this article though this is a topic worth exploring in greater depth.
Finally, slightly more “interventionist” policies may be needed, such as limiting private-use autonomous vehicles and rationing the total number of cars dedicated to ride sharing so that people are prodded towards mass people-mover systems like trains and buses.
Tech companies sometimes paint these suggestions as the Government acting against the consumer interest. I disagree: it is in the commuter and patients’ interest if we can manage the demands on our roads and doctors such that those who need it most can get the services in an affordable and timely manner.
In healthcare, raising co-payments is a commonly-used tool which helps people think twice before using a service. “Triaging” patients is another way – for example, having them first speak to a nurse practitioner and only passing them to the doctors if it is needed.
But let’s take the patient’s perspective for a minute. What’s motivating them to use a service they may not need? Anxiety that their condition may be more serious than they think, and lack of a place to clarify (short of calling up a doctor). Any new parent empathizes with this. I probably went to the doctor every week in the first month of my daughter’s birth for no good reason at all.
We need solutions that assuage a patients’ anxiety. I believe equipping home caregivers is going to be a big part of this. Home caregiving is currently an informal sector with minimal training, which is an incredible waste. Imagine if home caregivers could be the first line of defence – giving the patient assurance when they do not need a doctor, and quickly helping them access a doctors’ time when it is urgent.
If healthcare systems and healthcare insurance providers want to use tele-health to optimise their use of resources, the technology has to be complemented by human-centred solutions that assuage patients’ anxiety. If not, the technology won’t save them any money at all!
I hope that with the addition of this article, I’ve now painted a fuller picture of the impact of disruptive technologies on public services like transportation and healthcare. Indeed, they will make resources more abundant and accessible to people with lower-incomes. However, complementary policies and services are absolutely necessary to ensure that the system is not over-used – ultimately, so that those who really need the services can get it in both a timely and affordable manner.
The Stanford Business School just launched a new Policy Innovation Initiative. Earlier this week, I attended their launch event featuring Sarah Hunter, Policy Director of X (previously Google X), Kathy Hibbs, Chief Regulatory Officer, 23&Me, and Ted Ullyot, Partner of Policy and Regulatory Affairs, Andreessen Horowitz.
Why the need for policy and regulatory thinking within the tech world?
The motivation is simple. In the past decade or so, software innovations have dominated. We’ve seen how great software platforms – sometimes built by tiny crack teams – can scale rapidly in way that completely changes markets. Think Amazon and Ebay for commerce, Facebook, Snapchat, Instagram for social networking, Box, Salesforce, Workday and Slack for enterprise solutions (decode: software that helps us manage our HR, customer relations and intra-office discussions with much less pain). The market is increasingly saturated with software solutions for almost every area of life. Hence, while will continue to see gains in productivity and efficiency in these systems because of Artificial Intelligence, pure software will no longer be the area of rapid technological innovation.
Instead, technological innovation in the next decade will be dominated by technologies spanning hardware (things that are in our physical world) and software (the virtual world). Examples include self-driving cars and surgical robots, which are performing physical functions but controlled by algorithms in the virtual world. A term often used to describe this general area is “cyber-physical systems”.
Here comes the challenge: objects in the physical world are more directly risky to human life than software systems. Furthermore, these objects can harm people who don’t choose to use them – I choose to download Facebook if I can stomach the risks to my personal privacy. On the other hand, even if I never buy my own Self Driving Car, my life could be at risk if someone else owns a faulty one. There is a more acute need to manage risks to the general public.
Hence, the regulatory landscape is stacked against emerging tech. First, Legacy regulations abound to protect consumers from death or physical harm, such as long Food and Drug Administration (FDA) and vehicle/driver-licensing processes. Second, Because of potential harm to human life, regulators are likely to approach the emerging technology from the perspective of ‘mitigating every risk’ (read: adding even more new conditions and clauses). Third, regulations and legislation are typically based on precedent, and are hence biased towards incremental (as opposed to disruptive) improvements in incumbent tech and business models.
Regulatory risk will be the major Go-to-Market hindrance for most emerging tech companies in the next decade; if they fail to address regulations, a company could be dead in the water before they even begin. Policy teams within tech companies exist to minimize this regulatory risk. They advise companies on questions such as:
Who do we need to influence so that regulations fall in our favour? Policy teams often go above regulators to paint visions for politicians: how the emerging technology will solve social problems and create new economic opportunities.
Should we work with regulators to co-create new regulations, or break the regulations? The risk of breaking regulations varies – if you’re able to get widespread support from users (think Uber+AirBnB), you may be able to force regulators into certain positions. It’s more difficult to take this approach for hardware solutions.
If we desire to co-create new regulations, what approach should we take? One company designed their own set of self-driving car regulations, which never came to pass because the technology was pivoting so quickly.
How early should we engage regulators? Generally, it isn’t good to give regulators surprises, but sometimes engaging too quickly before there are good answers on how to mitigate the risks will scare them into coming down hard
Is it even worth trying to enter this market, or should we start where regulations/Governments are more relaxed? For example, most successful drone companies tested outside the U.S.
The role of policy teams in tech companies can be likened to master chess players. They get to know the kings, queens, knights and pawns who influence the regulatory system, and appeal to a range of motivations to move the pieces in their company’s favour.
Each speaker pointed out that regulators aren’t technological dinosaurs who intentionally regulate technology to death (though they are often caricatured this way). They simply have a different bottom line, which is to minimize risks and externalities. Put this way, regulators and innovators can provide a healthy check and balance to each other.
Areas I Hope They Address
I’m excited about this initiative by Stanford Business School and would love to see it be a neutral place for tech and policy to folks to discuss the best approaches to regulating emerging tech. Here are some areas I hope they will address.
How do companies manage the competitive vs collaborative dynamic in lobbying for regulatory change?
On one hand, there are great advantages to be the first mover and defining the regulations in your favour. Kathy from 23&Me shared that if you are able to set precedent, all your competitors have to follow your standards. This locks is a certain competitive advantage. There are other circumstances where working collaboratively is more productive. For example, on the same issue, politicians and regulators might be far more willing to listen to a group of local start-up founders than large multinationals like Google. Smaller companies sometimes have to work through trade associations because they lack the scale needed for lobbying.
Will start-ups lose out as this policy/regulatory expertise becomes more critical to success, yet is dominated by large players?
Small companies are often unable to recruit for the policy/regulatory function because of their resource constraints. This is why VCs like Andreessen Horowitz have policy teams that advise their stable of start-ups. Will more of such advisory services become available to start-ups? Who will provide them?
We are also starting to see coalitions such as the “Partnership in AI” by Google, Facebook, Amazon, IBM and Microsoft – no doubt one of the objectives is to lobby Governments on AI-related policies. How do start-ups fit in? Is there a risk that the agenda is overly swayed by large companies?
An idea for policy teams in tech companies: Go beyond lobbying for regulation; work with Governments to support widespread adoption of emerging tech
One of the themes of the night was how tech companies need to paint a vision to politicians on the benefits of the emerging technology, so that they support favourable regulatory change.
I think we have to go further than persuading politicians to get to the point of favourable regulation. Widespread adoption of emerging technology especially in areas of healthcare, transport and education is hindered by more than regulation. For example, change can’t take place if you don’t inspire, resource, and manage the morale of teams on the ground who are accustomed to existing ways of work and will not change just because a new technology exists. I saw it first hand when I worked at the 40,000-strong Ministry of Education in Singapore.
Without this, politicians will find it difficult to move, even if they agree strongly with tech companies’ visions for the future. Singapore’s Prime Minister, who takes a personal interest in Smart Nation, recently lamented that the whole effort is moving too slowly. This, coming from one of the most efficient Governments in the world, suggests that there are deep-seated issues in achieving widespread adoption of technology.
Here are some things that are essential for widespread adoption of emerging tech, but that Governments/Politicians will not be able to tackle alone:
Painting a vision that shows implementers and constituents how emerging tech will exponentially improve their reality;
Tying concrete benefits to these emerging technologies such as creating new local jobs;
Actively advocating for programs that help people deal with the downsides disruptive technology, such as re-training for displaced workers.
These are areas that policy teams within tech companies can consider as they seek to move chess pieces in their favour: not just to achieve favourable regulations, but to see widespread adoption of their technologies in regulated sectors.
<if you work in a policy/reg team within a tech company and already work on these areas, I would love to hear from you>
In the coming posts, I will also feature folks who work in policy/regulatory teams within Silicon Valley and Singaporean companies. Look out for those!
The story of income inequality is not new – as lower and middle-class incomes stagnate while the highest income brackets race ahead, the wealthy have access to goods and services that are increasingly out of the average person’s reach.
But we now see its detrimental effects more clearly than ever. I live in the Silicon Valley, and when news of Donald Trump’s election broke, the overwhelming feeling was disbelief. It was unimaginable. Tears of anguish were shed, yet a large part of the country celebrated. To me, that moment captured the deeper impact of inequality – fragmentation of society. Our politics become polarized, we are unable to find middle ground in our interests, and we increasingly feel like a nation of enemies, not countrymen.
While the problem gets more serious, our typical approaches to tackling inequality are reaching their limits. Redistribution is a political hot potato that pits the interests of the “haves” and “have-nots” against each other. Investing heavily in educational opportunities has diminishing marginal returns on social mobility both in the absolute sense (because the future of jobs is increasingly uncertain) and in the relative sense (because wealthier parents give their children more and more advantages).
We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.
First, cities should use technology to make life experiences in the city more and more independent of incomes.
It would be impossible to close the income gap completely, short of communism. A society where incomes are totally equal is also undesirable, as it erodes the motivation to work.
However, I believe that technology can make life in the city increasingly independent of income, which can go a long way towards mitigating the daily experience of inequality.
Let me start with explaining the notion of an aspirational good – things that people wish they had money to buy. In transport, most people aspire towards owning a car. In housing, it is a condominium or a private home (American friends: as opposed to a publicly-built Housing Development Board apartment, which 80% of Singaporeans live in). In healthcare, it is a private doctor or hospital bed – at your choice and convenience. In education, it is getting into top schools and universities.
There is an unsustainable dynamic behind aspirational goods. Because these goods are limited in supply, the more people can afford it, the more expensive they get, and the further out of reach of the average citizen. Aspirational goods are the sources of a huge amount of angst in the middle class.
Technology has the potential to overturn the entire notion of an aspirational good. By creating new forms of value, it can make the alternatives so attractive that even those who have money choose not to buy the aspirational good.
Take transportation for example. Owning a car is so attractive today because public transportation is an inferior option on many counts – the low cost cannot make up for its lack of time efficiency (it takes about twice the amount of time as a car ride), comfort (especially in humid weather), and customization (as a car owner, I know I can get a ride whenever I want).
What if public transport can be faster, more comfortable, more customized and cheaper than owning a car? With technology, this need not be a pipe dream. Imagine a day when you can wake up in the morning and your phone already knows where you need to be. It recommends the top three ways to get there. You select one, and within a minute, your ride shows up at your door – perhaps a shared car, or an electric bike if it’s sunny. It gets you to the train station just as your train pulls in. When you get out of the train, your minibus has just arrived to take you to the office. After work, you can summon a sleek designer vehicle for your dinner date. On the weekend, an autonomous jeep shows up at your door-step to take your family around for a day of fun.
You don’t need to buy multiple tickets – everything is paid through your phone. Or, you can even pay for transport just like a Netflix or Amazon Prime Subscription: a flat fee for unlimited rides. You never need to worry about parking again. With alternatives like this, how many people would still want to own a personal car? Even the wealthy may reconsider, especially if we simultaneously put in policies to make driving more inconvenient, such as no-drive zones in the city.
Just as technology brings about new forms of value (e.g. customization, flexibility) for those who don’t own a car, how can it do the same for other sectors?
How can technology help to transform Singapore’s public housing estates such that they offer new forms of value which private estates cannot provide? For example, how can we help HDB dwellers feel like the entire estate – with all its facilities and open spaces – is their home, one much bigger and diverse than any private estate? Digital communities and intra-town transportation may be aspects of this.
How can technology make a face-to-face doctors’ appointment something that people no longer seek as the “premium option”, for example, by making tele-health so attractive and pervasive?
I believe if domain experts and technologists put their minds to this, they will be able to come up with much better ideas than these! In short, technology can help catapult currently “inferior” options to equal status as “aspirational” options by creating new forms of value.
2. Second, cities should use technology to distribute scarce land and human resources more equitably.
In most countries, there is a healthy debate on how progressive and equitable the tax and redistribution regime is. However, not as much attention is paid to how other scarce city resources – land and manpower – are used. These too, must be used equitably, and technology can help cities achieve this.
Reducing the land used on roads is a great example of how we can use land more equitably. Roads and parking lots tend to be utilized disproportionately by those who own cars, who – in Singapore – tend to be wealthier. Can we cut down on roads and parking, and reallocate this land to purposes such as community facilities and public housing, which benefit a wider proportion of the population?
Yes, and technology is critical to this. How much land we need for roads and parking is determined by the concept of “peak demand” – the maximum number of vehicles on the road, ever. We can cut down peak demand by encouraging people to use shared mobility options rather than drive a private car (I write about how tech enables this here), and by investing in autonomous freight and utility so that these activities can be done at night, when roads are far emptier.
Public Sector Manpower
Similarly, we can use public sector manpower more equitably by investing in technology. Technology can significantly reduce the manpower we commit to customer services. For example, Govtech rolled out MyInfo, which enables citizens to automatically fill in their administrative information for Government schemes with the click of a button. Chatbots on Government websites will increasingly be able to answer public queries; phone lines will no longer be needed. Public sector manpower can now be dedicated to functions which are in great need of resources. One such area is social work and education. Families in the bottom rung of society often face a cocktail of challenges – divorce, low-income, lack of stable employment, cycles of incarceration and so on. Giving them (or their children) a real chance of breaking out involves an extremely high level of hand-holding and investment by social workers and schools. Resources are sorely needed here.
Access to top quality healthcare
Let’s take another scarce resource – top surgeons. People who can pay for their services access better quality care, and stand a higher chance at recovery. Technology can change this dynamic. Companies like Verb Surgical are using machine learning to propagate top surgeons’ expertise more widely. This is how it works: every time the best surgeons perform a procedure, every single action is recorded in a common machine “brain”. The “brain” is trained to associate each action with the probability of a successful surgery. As the “brain” records more and more surgeries, it gets smarter and smarter. Now, the “brain” is made accessible to ALL surgeons. At each step of their surgery, they are told what successful surgeons did. Now, the best surgical expertise is within the reach of the average citizen.
Technology that enables our scarce resources (e.g. land, public sector manpower and top surgeons) to benefit the broad population and serve those in acute need are the types of technologies that cities should invest in, and quickly enable through regulations.
Unfortunately, such broad and loose definitions give cities little guidance to on what to focus on in prioritising investments and regulatory reform, which is an incredibly important conversation given the limited resources at most cities’ disposal. It also does not paint a compelling vision for why being a Smart City matters, which disengages most of the population. Personally, before I worked in tech, I felt absolutely no connection to the idea of a ‘Smart City’. Tech was cool, but I never thought it was crucial.
I believe that using technology to tackle inequality and its effects should be a Smart City’s ambitious goal and guiding force, providing focus and rallying support from its constituents. This article spelled out two ways to do so.
One of the goals of this blog is to bridge the worlds of tech and government: I believe we can do so much more by working together, yet we often don’t understand each other deeply enough to begin. I will be starting the “What I Wish They Knew” series, which features people who are familiar with both these communities.
The first person to kick off this series is none other than my husband, Kenneth, who worked in Singapore’s Government Data Science unit before pursuing a PhD in Statistics at Stanford. By the way, you can find out more about Singapore’s Government Data Science unit on their blog: https://blog.gds-gov.tech/ – highly recommended.
How did you get into Government Data Science?
I’ve had a strong interest in mathematics and related quantitative fields like statistics and computer science for as long as I can remember, and studied math at Princeton as an undergrad. [NB: Kenneth’s mathematics blog can be found here.]
I began my career in the Singapore public service, hoping to give back to society in the small ways I could. While I was at the Ministries of Defence and Environment, most of my work did not involve any advanced math or data analysis.
I missed the intellectual challenge of quantitative thinking, and started taking online courses on the side. Andrew Ng’s “Machine Learning” course on Coursera first piqued my interest in data science. I learnt how we can use a small toolbox of algorithms to extract a whole lot of information from data, and I thought to myself, “How cool would it be if I could use some of these techniques in my work?”
Fortunately, a unit in Government was being set up to do just that: use state-of-the-art data analysis techniques to inform policy decision-making. I joined the Government Data Science unit in 2015 as a consultant. My work experience in policy and my quantitative undergraduate training put me in a rare position to understand the mindsets of both the policy officer and the data scientist. As such, I felt that I was an effective translator between the 2 parties.
How about an example – what is one meaningful thing you did in Government Data Science?
An agency was very concerned about congestion during peak hours at the checkpoint between Singapore and Malaysia. Understanding cargo traffic patterns could help them design policies to reduce congestion. All they had was tens of millions of “permit data” entries, which captured the time that the cargo truck carrying the permit passed through a checkpoint, the industry code and value of the goods carried. I worked with the agency to define useful problem statements to shape the direction of analysis. One example: what are the top 5 industries moving cargo during peak hours? (Since policy interventions would be done at an industry-level).
Next, since each truck could hold multiple permits and the number of trucks was what we cared about, we went through a (non-trivial) process of turning “permit data” into “truck data”. We were able to identify the top industries moving cargo during peak hours, and further narrowed this group to those who were moving cargo on the busiest roads. We were also able to develop hypotheses on what influenced industry behaviour.
After completing the analysis, I shaped the narrative of our presentation in a way that delivered impactful policy insights, ruthlessly cutting down on unnecessary details. This is often the most painful part of the process for a data scientist – it’s so tempting to want to show ALL the great analysis we did.
After dozens of hours of work, there was nothing more satisfying in seeing the audience’s facial expressions saying, in effect, “before I was blind, now I see”! They never had a picture of the cargo traffic patterns until our analysis was done, and could now act upon it to improve congestion.
What is one thing you wish non-data scientists knew about working with data scientists?
That good data analysis requires significant collaboration between the data scientist and you, the domain expert.
Some people view the relationship between the domain expert and the data scientist as follows:
Domain expert gives data scientist a bunch of Excel files.
Data scientist crunches the numbers and churns out a report or presentation 3 months later. After all, the data scientist knows everything about data and that’s what we are paying them to do, right?
Nothing could be further from the truth! Domain expertise can speed up the data analysis process tremendously and direct it meaningfully, resulting in greater value from the project. Let me give two examples of this.
First, explaining the data to the data scientist, down to what each column means and how the data was collected, will save him/her much second-guessing angst. For example, if the patient check-out time was 18:00:00, does it mean that the person checked out at 6pm, or does it mean that the clinic closed at 6pm, and so everyone who hadn’t checked out yet was given a standard check-out time? Explaining the data will also give the data scientist a better sense of which variables are of greater importance and deserve more attention. In the example above, what does “checking-out” mean anyway, and is it significant?
Second, domain experts can pick up on insights that would escape data scientists. For example, a data scientist finds that Chromosome 21 seems to have an impact on a health outcome. Is that expected? Does it confirm some of the other hunches that we have? Or is it something completely unexpected, that suggests that the model is wrong? These are questions that a data scientist is unlikely to have any intuition about. However, with feedback from the domain expert, the data scientist can quickly decide to pursue or drop lines of inquiry.
As a policy-maker, what is one thing you wish more data scientists paid attention to?
That data analysis is not for the data scientist, but for the policy-maker (or client). As such, good data analysis always puts findings and insights in the proper context.
Consider the sentence: “Our prediction model for which patients will be re-admitted over the next 6 months is 34.56% more accurate than the existing model.” Upon seeing this sentence, several questions come to mind:
56% seems overly precise: can we really compare performance down to 2 decimal places? (35% might be better.)
Does 35% more accuracy translate to a meaningful difference? For example, will this allow us to tailor our services better to 100,000 patients, or 10 patients? (If possible, relate the finding to something the policy-maker cares about, like dollars or man-hours saved.)
Is this even a meaningful thing to predict?? (Hopefully, the domain expert would have said so. See answer to question 2.)
Good data analysis also provides enough detail to illuminate, but not too much till it confuses. For example, saying nothing about the modeling process could make me wonder whether you did your homework in choosing the most appropriate model (and whether I should have spent all that money hiring you). At the other extreme, I will not appreciate going through slide after slide of raw SAS/R output.
At this point I cannot overstate the importance of appropriate data visualisation. These visualisations have to be thought through: good charts clarify, poor ones confuse. Unfortunately, it’s a lot easier to make the latter. (See this for examples notto follow.)
5. What is your hope for the field of data science?
The economist Ronald Coase famously said “Torture the data, and it will confess to anything.” In an era of subjective reporting and “fake news”, this concern is more pertinent than ever.
My hope is that the general population will have enough statistical knowledge so that they can call a bluff when they see one, and demand quantitative evidence for decisions their leaders make. To this end, I hope to see reforms in statistics education at the high-school level so that it becomes a subject that people feel is relevant and interesting, rather than abstract and theoretical (which is often how it is taught today).
Thanks for reading! Know anyone who should be featured in this series? Do let me know at email@example.com.
I’m starting a series of posts on solutions to AI-driven job displacement.
I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?
I use the word “hope” because it is the prospect of a better future within reach which will inspire people to continually re-invent themselves when they face setbacks like job loss, rather than to give up. It’s not enough to give them concessions.
Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:
In the long run, there will be a net positive in jobs, but in the short run there will be pain:especially among the middle class. The White House published a useful overview on the impact of AI on society last December and Gary Bolles has great resources to understand the hollowing out of the middle class. One very concrete example of middle-class job loss: as shared mobility becomes an increasingly attractive option for city-dwellers, less people will feel the need to own a car. The GMs, Fords, Toyotas and Tier-1 suppliers of the world will scale down their manufacturing business and move into new lines such as mobility services. New jobs will most definitely be created – for example, operators who manage fleets of autonomous vehicles and software engineers whose algorithms will continually improve the cost and energy efficiency of fleets. It’s likely these jobs will hire new people, not those who were displaced.
Progress is at stake if we can’t give people the confidence that the new system will work in their favor. We see this in widespread support for anti-trade platforms, even though no economist doubts the overall benefits of trade. America has taken a few steps back, and while some of this may be attributed to the ignorance of one man, it is also the collective anxiety of people speaking through their votes – and this anxiety is not unfounded. This is not a particularly American problem. Brexit is the other most popularly cited example, but you see it everywhere, both in the developed and developing world: Egypt, the Philippines, Singapore: a push back among those who feel that the system no longer works in their favor.
So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.
Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?
A Framework to Understand our Target Group
To start off, it’s useful to have a framework to understand the universe of people we need to reach. I offer two dimensions that differentiate people in our target group.
*these are just illustrative examples! Obviously, the reality is a lot more nuanced.
Proactive vs Reactive
On one end, we have people who are “proactive” about keeping themselves relevant to the job market. Geeks like my husband, who completed a dozen Coursera/Udacity courses, taught himself new coding languages, mastered Tableau and ran a successful math blog, all while managing his day job.
On the other end we have people who are “reactive” – they are hardly thinking about the possibility of losing their job; even if they are, they aren’t taking action. This could be the 45-year old who worked a 9-5 job for the past 20 years or a new parent who has no bandwidth to think about anything but the present.
Inherent personality traits are one determinant of where a person falls on this spectrum. Life stage is another – it affects how much time we have to think about alternative plans. The prevailing culture of family and community also shapes what we value such adventure or stability, ambition or work-life balance. Much of this is determined by geography.
Where someone falls on this spectrum is never static. People can shift left and right, and motivation is key.
New Entrant vs Old Timer
The “new entrant” vs “old timer” distinction is useful in framing solutions. To illustrate this, let’s imagine Uber can fully automate all its fleets by 2025, and will no longer need human drivers.
“Old timers” are existing Uber drivers. The challenge is to give them sufficient fore-warning of impending job loss, and opportunities to pivot into new industries. The difficulty is in encouraging them to take action before it’s too late, and to ensure there are good alternatives.
“New entrants” are those who will be entering the job market in 2025. The challenge is to equip them with skills that will be relevant to the job market in 2025, including skills required to operate Uber’s fleet of autonomous vehicles in localities throughout the world. The difficulty is that no one knows exactly what skills will be needed in 2025, and how many jobs will require these skills.
Here are some topics I’d like to address based on this framework.
First, most solutions today address those who are “proactive” – it’s the easiest group to address because it’s sufficient to make learning resources available; they’ll find them and take them up. How can we motivate or guide those who fall into the “reactive” half of the framework? What can tech companies do? What can Governments and employers do? How do these efforts tie together?
Second, how can tech work with higher education institutions to prepare new entrants (our children today) for the future job market? About five years ago, some predicted the death of higher education institutions as Massive Open Online Courses took off. Now, most of us acknowledge that this is not going to happen at a large scale any time soon. Most people aren’t as confident enough to ditch traditional paths, even if these don’t make sense anymore. How can tech companies help to reform higher education and make it far more responsive to the job market than today? I will draw on my experience working in higher education to address this question.
Third, what social security policies do we need in an era of accelerating job displacement? In the Silicon Valley, the idea of a Universal Basic Income – where everyone receives a basic monthly stipend from the state – has gained much traction as a policy solution to job-displacement. How will a Universal Basic Income impact the different groups in this framework? What are the alternatives or complements to a Universal Basic Income? I will draw on my experience working in and studying social policy to address this.
In my previous posts, I’ve talked about how the shared economy – expedited by technology – can have tremendous benefits for society. For example, it can mitigate the feeling the inequality by closing gaps in the transportation experience. The benefits are even greater when private companies work with Governments to reach the elderly, poor and underserved.
I’m pretty sure that the shared economy in transport has a net positive effect on the economy too, though evidence is nascent. Last March, Lawrence Katz and Alan Krueger conducted the RAND-Princeton Contingent Worker Survey, which showed a substantial rise in the incidence of alternative work arrangements for workers in the US, from 10.1% in 2005 to 15.8% in late 2015. Strikingly, this implies that all of the net employment growth in the US from 2005–15 appears to have occurred in alternative work arrangements.
One reason is that the shared economy provides part-time or intermittent work for people who otherwise cannot find a suitable job. All these Uber drivers I’ve met before – the young man who lost his job and needs time to search for new employment. The father fighting a costly custody battle, who needs a flexible job so he can show up in court. The lower-income mother who needs to supplement her day job to pay for her mortgage. Uber driving is a particularly attractive part-time job – typical part-timers get paid disproportionately less than full-timers. In contrast, Alan Krueger and Jonathan Hall found that no matter how many (or few) hours Uber drivers work, their hourly earnings were the same.
…BUT the distribution of benefits matters, and here’s how cities should think about it
Even though the shared economy creates tremendous new value, the distribution of value favours some groups over others. The tensions that arise can undermine companies operating in the shared economy, as we’ve seen in several cities worldwide. Cities need to consider how they may ensure a fair distribution of the shared economy’s benefits along three dimensions:
Workers vs companies
Incumbents vs new entrants
Now vs 10 years’ time, especially with the advent of autonomous vehicles
Companies would be wise to work collaboratively with cities to resolve these issues early on, rather than lock horns in costly legislative battles, or get blocked from new markets.
Fair Distribution of Benefits Between Drivers and Companies
The shared economy benefits both ride-sharing companies and their drivers, but arguably companies benefit a lot more. The business model of ride-sharing companies like Uber, Lyft and Grab is to provide a technology platform which enables matching of riders to drivers. These drivers are essentially self-employed contractors who log on to the platform whenever they wish. Many are able to find work and supplement their income through this platform.
However, these drivers are not employed by ride-sharing companies and hence do not receive certain benefits. In Singapore, employers are required to contribute up to 17% of their employee’s salary to a savings account for housing, retirement and healthcare. In the U.S., many receive healthcare insurance through their employers, who are often able to get better rates than individuals. In the UK, employees are protected by the minimum wage legislation. Drivers for Uber and Grab do not receive such benefits because they are not considered employees.
As companies rely more on these self-employed workers to fuel their business, risks are passed from companies to workers. As Singapore’s Deputy Prime Minister Tharman Shanmugaratnam has said “….it serves the interests of the company because they’re really pushing risk to the contract worker…who actually can’t take much risk – the risk of instability in wages and the risk of not being prepared for retirement because of a lack of social security contributions.”
Cities need to start shifting their social policies to accommodate the rising proportion of self-employed workers. At the same time, companies need to discuss reasonable ways to spread benefits and share risks with workers who are self-employed. It need not be all or nothing. There has been talk about creating a “third classification” of workers who have some benefits of employees, while retaining the independence of a contractor. This gives both the business and worker flexibility while providing some social protection.
Working with cities on some “in-between” solutions will help business avoid lengthy legal battles down the road. For example, Uber is appealing a ruling by London’s employment tribunal recently that it should treat its drivers like employees, including paying the national minimum wage. I would go so far as to say that it is the responsibility of ride-sharing companies to start engaging in these discussions, because many of their workers face an uncertain future when autonomy arrives (third point).
Fair Distribution of Benefits Between Incumbents vs New Entrants
The arrival of ride-sharing companies like Uber very rapidly redistributed benefits in the transportation system, creating new winners (e.g. consumers, new drivers) and losers (e.g. taxi drivers). Yes, there were probably more winners than losers, but the losers suffered a huge impact on their livelihoods. For exampe, many taxi drivers in the U.S. invested large sums in their license – in Chicago, the median cost of a taxi medalllion in late 2013 was USD$357,000. Having the value of your medallion plummet is like losing your home!
Cities dealing with disruptive innovation need to quickly level the playing field between incumbents and new entrants, to ensure that the distribution of benefits is not overly skewed in the direction of new entrants.
In the case of ride-sharing, issues like driver training requirements take the forefront. For example, Singapore placed 10 hours of training requirements on Uber/Grab drivers, while significantly cutting down the training hours required for taxi drivers (now, they only need to spend 16 hours on in-class training, compared to about 60 hours previously). We also cut down course fees for taxi drivers, and scrapped the daily minimum mileage – a move which helps taxi drivers minimise empty cruising just to meet their quota.
It is in the interest of disruptors to avoid a total regulatory lockdown by avoiding a zero sum mentality in these negotiations.
Fair Distribution of Benefits Between Present and Future
Finally, while we reap many benefits now, there are two important long-term considerations for cities working with ride-sharing companies.
First, many ride-sharing companies are at the stage where they are flush with investment, and can afford keep their ride prices artificially low. What happens if cities “outsource” their transportation to ride- companies, which eventually raise the prices beyond what regular citizens can afford? How can cities set up their transport systems such that competition can easily arise – keeping prices in check – or that public options can bounce back quickly? A city needs to ensure that even as people reap the benefits of the shared economy today, these benefits can be sustained over time.
Second, the big elephant in the room is autonomy. Full autonomy = no more need for drivers.
Autonomy will further redistribute the benefits away from drivers towards companies, and for all we say about new jobs being created, I’m pretty sure many of these drivers won’t be the ones to do it.
Because many drivers are self-employed workers not covered by social protections, they will be in particularly difficult situations.
It will be some time before full autonomy at scale is realised, so it is not too late to start conversations on how to ensure that drivers don’t get the short end of the stick when their jobs are replaced. One immediate action companies need to take is to give drivers information. Drivers, while not employees, are stakeholders in the company’s business and should be informed about the timeframes and implications of autonomy as the field evolves. In addition, much more can be done to help them with skills and future employment, a topic I will cover soon.
Over a series of posts, I’ve argued that the shared economy is a net positive for society and economy. This post, I posit that we need to work together to ensure that these benefits are distributed fairly between drivers and companies, incumbents and new entrants, present and future.
This is not the ambit of cities or Governments alone; companies seeking a sustainable business model in essential public services like transportation would be wise to work closely with cities rather than to be caught in costly legislative battles, be locked out of markets, or worse still – to be exploitative in their practices.
 “temporary help agency workers, on-call workers, contract company workers, independent contractors or freelances)
I came across a really encouraging article about tech start-ups that are trying to fix the elderly caregiving sector. This is incredibly important work.
The double whammy is here
The graphic below, from the UN, shows how our global population is aging. It will happen faster in developed countries. In Japan people above 65 already constitute more than a quarter of the population. Singapore will get there in the 2030s, and the U.S. in the 2040s.
This will be a double whammy on societies. Countries will have a shrinking tax base to support the growing number of elderly who need care. There won’t be abundant resources to build new nursing homes and hospital beds. At the same time, it will be more difficult for the elderly to continue living at home – with shrinking family sizes, the responsibility for caregiving will fall on one or two children, instead of three or four.
Advances in biotechnology, personalised medicine and genomics will go a long way towards mitigating these challenges. For example, if companies like Calico and Unity Biotechnology manage to reverse aging, people can have longer, healthier, independent lives. The periods of time when they need caregiving will plummet. This is referred to as a longer “healthspan” (as opposed to just “lifespan”).
Unfortunately, many developed countries are already starting to face the double whammy and need a more immediate solution. From a policy perspective, it does not make sense to build new infrastructure for the elderly because these will become redundant when subsequent elderly cohorts are smaller.
The best solution is to help people receive care in their own homes as they age. It is also what a large majority (some statistics suggest 90%) of people prefer.
Why is Home Caregiving such a tough nut to crack?
A fragmented home care sector with low standards and revolving door caregivers is the norm in many cities. I don’t see a level of innovation and investment flowing into this sector that is commensurate to its importance. Three likely reasons why this is such a tough nut to crack.
Home caregiving is highly personal. When someone takes an Uber ride, they don’t have to talk to the driver if they don’t want to. In contrast, a caregiver gets deep into your personal space if they bathe, feed and change your diaper. You’re obviously going to be very picky about who you choose. Pickiness is not a one-way street – caregivers have their preferences too!
The job is just not a lot of fun. In fact, that’s why many people out-source care to paid contractors, even care for those they love most. It’s not glamorous, it’s repetitive, it can be highly physical, and in some cases, you interact with suffering on a daily basis. For such a difficult job, you mostly work alone without a network of support and accountability. Benefits, training and wages are patchy. Motivation is low. It is more difficult to recruit a good part-time Caregiver than a good part-time Uber driver.
What are the implications for tech companies and smart cities?
Disrupting the flailing home care sector is a classic problem that the tech, business, Government and non-profit sectors need to go at together. Here are three things that anyone working in this space needs to consider.
Tackle the root problem – motivation.
The FT article features Josh Bruno,who left Bain Capital to set up Hometeam. He started with a tech solution in mind – perhaps the equivalent of Uber for in-home care, he thought – a platform that would match caregivers to elderly and take a cut.
After volunteering in about 40 elderly care organisations, he quickly realised that the deepest issue in the caregiving sector was not matching, but motivation – “People blame them as lazy, but they are the worst working conditions — low pay, no training.” Defying all industry standards, he decided to hire the caregivers, give them good benefits of paid holidays, maternity leave, training and retirement.
I think Josh is getting to the heart of the issue – how can you expect people to deliver quality care if you don’t care for them? The first step is turn caregiving into a worthy career. Business can’t tackle this alone, which leads me to point two.
2. Partner to make home care an economically viable business
Josh’s company was turned away by many VCs because hiring the caregivers was not deemed to be maximally profitable.
I believe many have not yet realized the economic benefits of a strong home care sector. When elderly are able to continue living in their own homes, a whole ecosystem of services such as tele-health, food delivery and transport has a larger market. For large healthcare providers like Kaiser Permanente, a strong home care sector combined with tele-health can help manage its elderly clients outside the expensive hospital setting. This optimizes the use of hospital facilities and doctors’ time, making the business more profitable. For the same reasons, countries where healthcare systems are operated by the Government should consider investing public funds in the home care space, for example, by extending subsidies in nursing homes to the home care sector.
Furthermore, to help the home care sector grow, the public and non-profit sectors should play a role in helping to overcome inherent”diseconomies of scale”. It does not make sense for small home care companies to have their own training programmes and qualifications system. One idea is for Governments or non-profits to offer basic, modularised caregiver training as a “shared resource” for home care companies to utilise.
3. Use technology to build a care ecosystem around the elderly
Although it may not currently feel like it, home caregivers are really part of a team of family members, healthcare professionals and community members who help make living at home comfortable and enjoyable.
Technology can enable better communication and support between different members of this team. The home caregiver should be able to ask the healthcare professional whether a situation requires medical attention, and share information that would help the healthcare professional with diagnosis and prescription. Community members should be able to volunteer in bite-sized chunks of time, a topic I wrote about in my article “Three ways to build a transportation system that services the vulnerable”. Ultimately, when members of the team communicate, they can collectively give better care and receive support from each other. This can help to tackle the motivation problem for caregivers.
Technology can be part of the care team too! It can enable an elderly person to live independently as long as possible, only activating their caregiving team when necessary. One of the greatest fears of an elderly person is that something happens (a fall, a stroke) when they are alone. The solution, 24-hour care, is expensive and intrusive.
The Singapore Management University has been piloting sensor-enabled homes in Singapore through their SHINESeniors initiative. These sensors observe and analyze the elderly’s living patterns and immediately activate his care team when a change in pattern suggests a deterioration. Besides enabling independent living, this also allows companies and family members to allocate their time and resources efficiently.
Disrupting the flailing home care sector is essential to the quality of life of older folk, and the sustainability of healthcare systems when populations age. It’s a tough nut to crack, but I can’t think of a more important issue to work on right now, because our parents – and then us – are going to be the beneficiaries of positive disruption!
I live in the Valley now, and it’s impossible to go a day without someone mentioning Autonomous Vehicles. It is an incredibly rich discussion space. Some of the topics of debate include:
When will it be technically possible for cars to be fully autonomous, not requiring a human driver to fulfill safety-critical functions? (This is typically referred to as “Level 4” or “Level 5” autonomy). Most auto-OEMs are aiming for 2020-2022, Tesla’s most recent assessment seems to be 2018.
What is the best business model to introduce autonomous vehicles? We have Tesla, which sells autonomous cars to individuals. We have the OEMs and tech companies (Uber, Ford, GM-Lyft partnership), which plan to, or have already, introduced autonomous vehicles as fleets for ride-sharing. I recently spoke to Rahul Sonnad from Tesloop, whose autonomous vehicles will only provide long-haul car journeys that compete with short-haul flights (think the LA to Las Vegas journey).
When the market settles, will we see vehicles owned and operated by a few large coalitions of companies (each having the holy trinity of fleet management – manufacturing – design and software), or will it continue to be as diverse as it is today? I have met autonomous vehicle start-ups that plan to manufacture their own autonomous vehicles and disrupt major OEMs. On the other hand, even the richest companies (like Waymo, previous under GoogleX) have announced that they won’t get into the business of manufacturing cars. It’s just too costly to set it all up, and is best left in the hands of the OEMs.
Comparatively, I don’t hear as much debate about how cities should think about their autonomous vehicle strategy. I think this is incredibly important. Cities need to plan ahead to make the technology work for their people, instead of reacting to technology and business models as they arise.
I discussed this topic at the Worlds Fair Nano with my good friend Elliot Katz (who is the co-chair of DLA Piper’s Connected and Self Driving Car Practice).
Summarizing four points here
1. Introducing autonomous vehicles shouldn’t be an end in itself. Smart Cities should aim for shared mobility and deploy autonomous vehicles towards this goal.
Shared mobility is essential for cities that struggle with land constraints and intensifying populations. To solve the problem of congested roads and unhappy commuters, we need less vehicles on the road. This requires a strong push towards shared mobility (and away from vehicle ownership). I write more about it here.
Fully autonomous vehicles will contribute significantly to the objective of shared mobility especially if they are deployed in fleets (think Uber, but with autonomous vehicles instead of drivers). When autonomous vehicles are deployed in fleets, operators can dynamically size the fleet – injecting new cars when demand surges, and deploying them to less-crowded outskirts when demand falls. Theoretically, you would get rides faster and more reliability, while cutting down on “surge pricing”. These cars will be constantly on the move, never taking up precious real estate by parking for lunch or waiting at the curbside for a next job. This will free up land for other purposes, especially in the crowded downtown areas.
Cities should carefully consider the allocation of private versus shared autonomous vehicles. If the objective is shared mobility, the optimal scenario is for all autonomous vehicles to be shared: either deployed in fleets, or for privately-owned vehicles to be shared among a smaller group of family members and friends. While this does not necessitate excluding private autonomous vehicles, how cities allocate road-space to private vs shared vehicles will determine the extent to which they will achieve shared mobility.
There are many policy levers to consider: from quotas for privately-owned vehicles (which Singapore employs), to incentives for private car owners to share their vehicles in limited capacities. When cars are connected, it is easy to design these incentives based on real-time information. For example, a car owner can receive an offer to avoid a road toll if they pick-up someone else along the route.
2. Introduce autonomous vehicles in a way that builds broad-based public acceptance – don’t just appeal to the 20% of early adopters.
Many have pointed out that one of the biggest challenges to autonomous vehicle deployment is broad-based societal acceptance. There will be maximum efficiency and safety gains when autonomous vehicles are deployed at scale, and this can only be achieved if a large majority of city dwellers is comfortable riding autonomous vehicles.
Cities need to introduce autonomous vehicles in a way that builds broad societal acceptance.
Unlike the US, where autonomous vehicles are tested on public roads, Singapore has introduced them in designated trial areas. For example, autonomous taxis by Nutonomy ply 12km of public road space in the One-North neighbourhood in Singapore. Autonomous electric buses ply public roads in the Jurong district. Soon, on-demand autonomous shuttles provide rides on Sentosa, an island dedicated to tourism and recreation.
The Government works closely with AV companies to ensure that the testing routes provide sufficient challenge, but are not too far out of the vehicles capabilities such that it creates dangerous scenarios. We also work closely on the requirements for autonomous vehicle testing, and on after-action reviews when accidents occur.
While this seems like an arduous process for both company and Government, it is a long-term investment towards shared mobility.
It is better for accidents to happen within limited contexts. In this early stage of testing, accidents provide valuable lessons that will lead to improvements in the technology. In late 2016, Nutonomy had its first accident with a lorry in the One-North trial area. Fortunately – and to some extent by design – the impact was limited and no one was injured. It is incredibly important to public perception that these accidents happen in a limited context with no fatalities, unlike what happened with Tesla earlier last year.
The Government’s commitment to working with AV companies gives assurance that there is an added layer of accountability for the safety of these vehicles.
These trials have arguably piqued Singaporeans’ interest in riding autonomous vehicles. While Nutonomy limits the pool of people who can bid on their autonomous taxi service, it is still more accessible than having to purchase a private autonomous car to experience riding in one.
3. Work with AV companies on your regulatory approach to autonomous vehicles
One additional benefit of a city working closely with AV companies on autonomous vehicle trials is that it creates a space for a meeting of minds. Unlike many US states, Singapore has not yet introduced regulations towards autonomous vehicles. Instead, we work with companies on trials that shape our thinking on the appropriate regulatory approach. At the same time, companies have given feedback that it is useful to understand the Government’s concerns so that they can address these concerns at the design stage.
4. Finally, invest in autonomous vehicles other than cars!
While autonomous cars receive the most attention, other forms of autonomous vehicles hold incredible promise for the objective of shared mobility. For example, if a city has autonomous freight and autonomous utility vehicles, these activities can be done in the dead of the night, when commuters aren’t trying to move around. This frees up road space in the day, and makes for a better commuting experience for everyone.
Singapore is investing in both autonomous freight and autonomous utility concepts.
In conclusion, Smart Cities should never deploy technology for its own sake. They should define their objectives and target their time, money and policy interventions in a way that achieves these objectives. Transport is just one area – this applies to healthcare, education, housing, and any issue that a Smart City deals with!
 He believes that the market for short-haul car journeys within cities will be commoditized – people won’t care what type of autonomous vehicle they get as long as it’s cheap and fast. It will be difficult for smaller companies to compete. On the other hand, there will be room for significant differentiation in services for long-haul car journeys.
So far, I’ve talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city.
But transportation systems, even the very best, will never serve everyone equally. Where the transportation system is not inclusive, the cost is borne by some of the most vulnerable in society.
People who do not just need first and last mile transport – they need first and last meter transport.This includes the growing number of elderly (>65 years old), whose population will triple by 2030 in Singapore. It also includes those who have physical disabilities through accidents, illness or congenital conditions. Where the transportation system fails to provide first and last meter support, their caregivers bear the cost. When someone, (especially in a low-income household) leaves the workforce to be a full-time caregiver, there is a huge impact on the financial wellbeing of the family. In a survey by AgingCare.com 62% of caregivers said the cost of caring for a parent had impacted their ability to plan for their own financial future.
People who cannot afford rides. In my college years, I volunteered with Homefront, an organization that serves the homeless in New Jersey. I vividly remember talking to a mother whose children only ever ate canned food and hot dogs because they stayed in a Motel on Route 1, and it was too expensive to get to soup kitchens for a proper meal. A once-a-week supermarket trip was all they could afford. In this case, the cost is borne by the children, in the form of health and wellness.
People who live or work in inaccessible areas, where it does not make economic sense to deploy a public bus or even a ride-sharing carbecause there is so little demand. Where the transportation system does not provide, the cost is borne by the individuals or companies who have to cater private transport.
None of these groups are mutually exclusive. In fact, I hazard a guess that the number of families who fulfill at least two of these three conditions is not small, and will grow with the forces of aging and inequality.
For cities to provide a truly inclusive transportation experience, we need to explore three ideas:
Closing the first-and-last meter transport gap through community participation
Currently, caregivers are responsible for the first-and-last meter transport gap. If 85-year old Jim needs to go to the clinic, his caregiver helps him onto the wheelchair at home, takes the elevator to the ground floor, and helps him board either the bus or the taxi. When he arrives at the clinic, his caregiver helps him out of the bus, and into the clinic for registration.
Can volunteers fill the first-and-last meter transport gap instead? For example, when Jim orders his ride to the clinic, can a request be blasted to volunteers who are in the 200-yard radius of his home or destination? It can be a simple five to ten minute volunteering stint – helping Jim out of his home and onto the bus, or out of the bus and into the clinic for registration.
With one click of a button, Jim should be able to pick and pay for his transport menu to the clinic, and get first-and-last meter support from the community. Volunteers who choose to be in the network can do good in bite-sized chunks. They don’t need to go out of their way – they receive alerts as they go about their daily lives. Perhaps we can lighten the load of caregivers.
This idea has taken off with apps like GoodGym, where runners can sign up to visit an elderly person or help with one-off tasks while on their running route. It would be important to integrate these efforts with our transportation networks so that people like Jim can enjoy seamless transportation experiences and live independently in the community, as many elderly desire.
Closing the affordability and accessibility gap through public-private partnerships
Take Zara, the mother of four living in a Motel on Route 1 in New Jersey. Stranded because there are no public transportation options along Route 1, while ride sharing and taxis are too expensive.
It may not make sense for the Government to provide a public bus that passes by her Motel, simply because there is too little demand. I’ve personally experienced this. I used to live in a relatively inaccessible area in Singapore. Our municipality constantly lobbied the Government to provide a new bus line to serve us. We finally got it after 2 years, but every time I boarded that bus I counted no more than 5 people on it. Great for me, but it just wasn’t a great use of public funds to deploy a $100,000 bus way below capacity. Not to mention the additional congestion we created.
Here’s one idea for Governments: instead of buying a new bus to provide a bus line in inaccessible areas, use the money to subsidize rides by private providers such that it matches the cost of public transport. Furthermore, if a family like Zara’s is eligible for subsidies on public transportation, these should be applicable when they take rides by private providers.
This will require close collaboration between the Government and private providers (yes, operational issues will not be easy!), but is the most cost-effective way of closing the accessibility and affordability gap.
Some cities in the U.S. are working on this concept. For example, The Southeastern Philadelphia Transportation Authority (SEPTA) had insufficient parking lots at their train stations to accommodate commuters who drove to the station and dropped off their cars for the day. It did not make sense to make a huge investment in building new parking lots. Last year, they partnered with uber to provide a 40% discount on Uber rides to and from rail stations, encouraging people to share rides instead of drive.
Deploying autonomous vehicles
Autonomous vehicles hold tremendous promise for our objectives of inclusive transport because they will likely reduce the cost of rides. First, a bulk of a ride’s cost today is the salary of the driver. Second, companies are moving towards deploying autonomous vehicles in fleets. When vehicles are constantly utilized, companies can afford to charge less per ride. Finally, with technological advances, we can expect the hardware of autonomous vehicles, such as Lidars, to decrease in cost.
When this occurs, it will make more economic sense for companies to deploy vehicles to inaccessible areas, even if there is no promise of a return trip. Reduced prices also means that transport will be more affordable to families like Zara.
A city that plans ahead will ensure that autonomous vehicles are deployed in a way that benefits the broader population. For example, road space should not be dominated by privately-owned autonomous vehicles; Autonomous vehicle fleets should be embraced. Helping city-dwellers accept autonomous vehicles as part of their daily transportation experience is also an important part of the equation.
In my first post, I talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city. In my second post, I explored the ways Governments must work with private transport providers to ensure a truly seamless commute in the sharing economy – one that mimics the comfort of car ownership.
This third post covered three ways to ensure that our transportation system caters to some of the most vulnerable members of our society: community participation, public-private partnerships, and embracing autonomous vehicles. Inclusivity is an objective that is particularly close to my heart.
My final post in this series will be about the darker side of the shared economy, and how cities and business must work together to manage disruptions to our transportation system, including the rise of ride-sharing technology companies, as well as the advent of autonomy.