“The Rise of the Thought Leader” – a few thoughts (and an offer)

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.

An offer


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.



Three harmful ideas about leadership (and shifts you can make)

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 these guys great leaders? Source: Source: http://www.zimbio.com/photos/Mark+Zuckerberg/Jeff+Weiner a caption

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:

  1. 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.

  1. 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.”

  1. 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.



Afternote 1:

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.

Afternote 2:

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.




The Future of Mobility (A Panel at Innovfest Unbound)

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).


  1. 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.
  2. 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.
  3. 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.
  4. 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

  1. 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.

  1. 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

  1. 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.

  1. 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.

  1. 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.




Where does AI have the greatest potential? “Where we have the biggest problems.”

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?


ai pic


ai pic

3 Ways Technology Can Help Keep People in Work (Genevieve Ding)

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.



Photo from ntuc.org.sg 

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 involved government 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 that make 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 workers identify skills gaps in 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 is much 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 can improve 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.