Month: March 2017

Featuring Xinwei Ngiam: Government Policymaker turned Start-up Business Strategist

On By karentay1 Comment

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 🙂

xwngiam

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.

ngiam and tay
XW and I at CES2017, speaking about the potential and challenges of the sharing economy in transport

The Death of the Mall, Why It Matters, And How Technology Can Help (by Anita Ngai)

On By karentayLeave a comment

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!

Anita

 

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:

  1. Online shopping
  2. Urbanization – higher concentration of population and/or wealth means less retail space needed in suburbs
  3. Changing demographics – deceleration of population growth, aging core group
  4. Slowing income growth/increasing inequality – weaker GDP growth; wealth more concentrated in hands of a smaller number of people
  5. Change in consumer preferences – trend that millennials prefer to live and occupy less space
  6. 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.”)
  7. 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.[1]

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.

Instead of allowing new retail spaces to be built nearby, or even tearing down retail spaces, it makes more sense to convert and enhance existing retail spaces. Maintaining density levels in urban and suburban areas can bring socioeconomic benefits. Furthermore, the carbon footprint of retrofitting has been shown to generally be much lower than demolition and rebuild.  All this means that potential public and private investments into our built environment can be better directed, to projects with higher value to society.

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.

Planning Stage:

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.

Post-completion Stage:

 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.

[1] (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.)

[2]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).

 

You’re Probably a Cyberchondriac, But Google will Help You.

On By karentay1 Comment

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.

cyberchondriacs-image-2

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.

Screen Shot 2017-03-15 at 12.41.02 pm.png

 

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.

 

Better Consumer Access AND System-level Sustainability: Can Cities Have Both?

On By karentay2 Comments

 

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!]

Transport

“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.

“Autonomous Vehicles: Hype and Potential” shows how autonomous vehicles can also exacerbate traffic congestion, slowing down the movement of people and goods around the city.

  • 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.

Source: IDEO

Autonomous work spaces

Healthcare

The parallel in the healthcare system is a study by RAND Corporation, showing how only 12% of tele-health visits have replaced visits to the doctor, while 88% represented new use of medical services. Unsurprisingly, this finding suggests that doctors’ visits are highly price-elastic – by halving the cost, we see a surge in new demand. Net annual spending on healthcare among patients with respiratory illnesses increased by US$45 per tele-health user.

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

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

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!

Conclusion

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.

 

Why Do Tech Companies Need Policy Teams More Than Ever? (An Evening with X, Andressen Horowitz & 23AndMe)

On By karentay1 Comment

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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
  5. 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!