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

On March 5, 2017March 28, 2017 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:

Who do we need to influence so that regulations fall in our favour? Policy teams often go above regulators to paint visions for politicians: how the emerging technology will solve social problems and create new economic opportunities.
Should we work with regulators to co-create new regulations, or break the regulations? The risk of breaking regulations varies – if you’re able to get widespread support from users (think Uber+AirBnB), you may be able to force regulators into certain positions. It’s more difficult to take this approach for hardware solutions.
If we desire to co-create new regulations, what approach should we take? One company designed their own set of self-driving car regulations, which never came to pass because the technology was pivoting so quickly.
How early should we engage regulators? Generally, it isn’t good to give regulators surprises, but sometimes engaging too quickly before there are good answers on how to mitigate the risks will scare them into coming down hard
Is it even worth trying to enter this market, or should we start where regulations/Governments are more relaxed? For example, most successful drone companies tested outside the U.S.

The role of policy teams in tech companies can be likened to master chess players. They get to know the kings, queens, knights and pawns who influence the regulatory system, and appeal to a range of motivations to move the pieces in their company’s favour.

Each speaker pointed out that regulators aren’t technological dinosaurs who intentionally regulate technology to death (though they are often caricatured this way). They simply have a different bottom line, which is to minimize risks and externalities. Put this way, regulators and innovators can provide a healthy check and balance to each other.

Areas I Hope They Address

I’m excited about this initiative by Stanford Business School and would love to see it be a neutral place for tech and policy to folks to discuss the best approaches to regulating emerging tech. Here are some areas I hope they will address.

How do companies manage the competitive vs collaborative dynamic in lobbying for regulatory change?

On one hand, there are great advantages to be the first mover and defining the regulations in your favour. Kathy from 23&Me shared that if you are able to set precedent, all your competitors have to follow your standards. This locks is a certain competitive advantage. There are other circumstances where working collaboratively is more productive. For example, on the same issue, politicians and regulators might be far more willing to listen to a group of local start-up founders than large multinationals like Google. Smaller companies sometimes have to work through trade associations because they lack the scale needed for lobbying.

Will start-ups lose out as this policy/regulatory expertise becomes more critical to success, yet is dominated by large players?

Small companies are often unable to recruit for the policy/regulatory function because of their resource constraints. This is why VCs like Andreessen Horowitz have policy teams that advise their stable of start-ups. Will more of such advisory services become available to start-ups? Who will provide them?

We are also starting to see coalitions such as the “Partnership in AI” by Google, Facebook, Amazon, IBM and Microsoft – no doubt one of the objectives is to lobby Governments on AI-related policies. How do start-ups fit in? Is there a risk that the agenda is overly swayed by large companies?

An idea for policy teams in tech companies: Go beyond lobbying for regulation; work with Governments to support widespread adoption of emerging tech

One of the themes of the night was how tech companies need to paint a vision to politicians on the benefits of the emerging technology, so that they support favourable regulatory change.

I think we have to go further than persuading politicians to get to the point of favourable regulation. Widespread adoption of emerging technology especially in areas of healthcare, transport and education is hindered by more than regulation. For example, change can’t take place if you don’t inspire, resource, and manage the morale of teams on the ground who are accustomed to existing ways of work and will not change just because a new technology exists. I saw it first hand when I worked at the 40,000-strong Ministry of Education in Singapore.

Without this, politicians will find it difficult to move, even if they agree strongly with tech companies’ visions for the future. Singapore’s Prime Minister, who takes a personal interest in Smart Nation, recently lamented that the whole effort is moving too slowly. This, coming from one of the most efficient Governments in the world, suggests that there are deep-seated issues in achieving widespread adoption of technology.

Here are some things that are essential for widespread adoption of emerging tech, but that Governments/Politicians will not be able to tackle alone:

Painting a vision that shows implementers and constituents how emerging tech will exponentially improve their reality;
Tying concrete benefits to these emerging technologies such as creating new local jobs;
Actively advocating for programs that help people deal with the downsides disruptive technology, such as re-training for displaced workers.

These are areas that policy teams within tech companies can consider as they seek to move chess pieces in their favour: not just to achieve favourable regulations, but to see widespread adoption of their technologies in regulated sectors.

<if you work in a policy/reg team within a tech company and already work on these areas, I would love to hear from you>

In the coming posts, I will also feature folks who work in policy/regulatory teams within Silicon Valley and Singaporean companies. Look out for those!

Two (Game-Changing) Ways Cities can use Technology to Fight Inequality

On February 27, 2017March 28, 2017 By karentay3 Comments

The story of income inequality is not new – as lower and middle-class incomes stagnate while the highest income brackets race ahead, the wealthy have access to goods and services that are increasingly out of the average person’s reach.

But we now see its detrimental effects more clearly than ever. I live in the Silicon Valley, and when news of Donald Trump’s election broke, the overwhelming feeling was disbelief. It was unimaginable. Tears of anguish were shed, yet a large part of the country celebrated. To me, that moment captured the deeper impact of inequality – fragmentation of society. Our politics become polarized, we are unable to find middle ground in our interests, and we increasingly feel like a nation of enemies, not countrymen.

While the problem gets more serious, our typical approaches to tackling inequality are reaching their limits. Redistribution is a political hot potato that pits the interests of the “haves” and “have-nots” against each other. Investing heavily in educational opportunities has diminishing marginal returns on social mobility both in the absolute sense (because the future of jobs is increasingly uncertain) and in the relative sense (because wealthier parents give their children more and more advantages).

We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.

Inequality

Source: charterforcompassion.org http://bit.ly/1y8DPw1

First, cities should use technology to make life experiences in the city more and more independent of incomes.

It would be impossible to close the income gap completely, short of communism. A society where incomes are totally equal is also undesirable, as it erodes the motivation to work.

However, I believe that technology can make life in the city increasingly independent of income, which can go a long way towards mitigating the daily experience of inequality.

Let me start with explaining the notion of an aspirational good – things that people wish they had money to buy. In transport, most people aspire towards owning a car. In housing, it is a condominium or a private home (American friends: as opposed to a publicly-built Housing Development Board apartment, which 80% of Singaporeans live in). In healthcare, it is a private doctor or hospital bed – at your choice and convenience. In education, it is getting into top schools and universities.

There is an unsustainable dynamic behind aspirational goods. Because these goods are limited in supply, the more people can afford it, the more expensive they get, and the further out of reach of the average citizen. Aspirational goods are the sources of a huge amount of angst in the middle class.

Technology has the potential to overturn the entire notion of an aspirational good. By creating new forms of value, it can make the alternatives so attractive that even those who have money choose not to buy the aspirational good.

Take transportation for example. Owning a car is so attractive today because public transportation is an inferior option on many counts – the low cost cannot make up for its lack of time efficiency (it takes about twice the amount of time as a car ride), comfort (especially in humid weather), and customization (as a car owner, I know I can get a ride whenever I want).

What if public transport can be faster, more comfortable, more customized and cheaper than owning a car? With technology, this need not be a pipe dream. Imagine a day when you can wake up in the morning and your phone already knows where you need to be. It recommends the top three ways to get there. You select one, and within a minute, your ride shows up at your door – perhaps a shared car, or an electric bike if it’s sunny. It gets you to the train station just as your train pulls in. When you get out of the train, your minibus has just arrived to take you to the office. After work, you can summon a sleek designer vehicle for your dinner date. On the weekend, an autonomous jeep shows up at your door-step to take your family around for a day of fun.

You don’t need to buy multiple tickets – everything is paid through your phone. Or, you can even pay for transport just like a Netflix or Amazon Prime Subscription: a flat fee for unlimited rides. You never need to worry about parking again. With alternatives like this, how many people would still want to own a personal car? Even the wealthy may reconsider, especially if we simultaneously put in policies to make driving more inconvenient, such as no-drive zones in the city.

Just as technology brings about new forms of value (e.g. customization, flexibility) for those who don’t own a car, how can it do the same for other sectors?

How can technology help to transform Singapore’s public housing estates such that they offer new forms of value which private estates cannot provide? For example, how can we help HDB dwellers feel like the entire estate – with all its facilities and open spaces – is their home, one much bigger and diverse than any private estate? Digital communities and intra-town transportation may be aspects of this.

How can technology make a face-to-face doctors’ appointment something that people no longer seek as the “premium option”, for example, by making tele-health so attractive and pervasive?

I believe if domain experts and technologists put their minds to this, they will be able to come up with much better ideas than these! In short, technology can help catapult currently “inferior” options to equal status as “aspirational” options by creating new forms of value.

2. Second, cities should use technology to distribute scarce land and human resources more equitably.

In most countries, there is a healthy debate on how progressive and equitable the tax and redistribution regime is. However, not as much attention is paid to how other scarce city resources – land and manpower – are used. These too, must be used equitably, and technology can help cities achieve this.

Land

Reducing the land used on roads is a great example of how we can use land more equitably. Roads and parking lots tend to be utilized disproportionately by those who own cars, who – in Singapore – tend to be wealthier. Can we cut down on roads and parking, and reallocate this land to purposes such as community facilities and public housing, which benefit a wider proportion of the population?

Yes, and technology is critical to this. How much land we need for roads and parking is determined by the concept of “peak demand” – the maximum number of vehicles on the road, ever. We can cut down peak demand by encouraging people to use shared mobility options rather than drive a private car (I write about how tech enables this here), and by investing in autonomous freight and utility so that these activities can be done at night, when roads are far emptier.

Public Sector Manpower

Similarly, we can use public sector manpower more equitably by investing in technology. Technology can significantly reduce the manpower we commit to customer services. For example, Govtech rolled out MyInfo, which enables citizens to automatically fill in their administrative information for Government schemes with the click of a button. Chatbots on Government websites will increasingly be able to answer public queries; phone lines will no longer be needed. Public sector manpower can now be dedicated to functions which are in great need of resources. One such area is social work and education. Families in the bottom rung of society often face a cocktail of challenges – divorce, low-income, lack of stable employment, cycles of incarceration and so on. Giving them (or their children) a real chance of breaking out involves an extremely high level of hand-holding and investment by social workers and schools. Resources are sorely needed here.

Access to top quality healthcare

Let’s take another scarce resource – top surgeons. People who can pay for their services access better quality care, and stand a higher chance at recovery. Technology can change this dynamic. Companies like Verb Surgical are using machine learning to propagate top surgeons’ expertise more widely. This is how it works: every time the best surgeons perform a procedure, every single action is recorded in a common machine “brain”. The “brain” is trained to associate each action with the probability of a successful surgery. As the “brain” records more and more surgeries, it gets smarter and smarter. Now, the “brain” is made accessible to ALL surgeons. At each step of their surgery, they are told what successful surgeons did. Now, the best surgical expertise is within the reach of the average citizen.

Technology that enables our scarce resources (e.g. land, public sector manpower and top surgeons) to benefit the broad population and serve those in acute need are the types of technologies that cities should invest in, and quickly enable through regulations.

Conclusion

If you google the “Smart City” movement, you’ll find many broad and loose definitions. Generally, it refers to how cities deploy technology to improve city life and allocate resources more efficiently, whether it is helping transport systems run more efficiently, making interactions with various Government services easier, or to adding fun to the city experience.

Unfortunately, such broad and loose definitions give cities little guidance to on what to focus on in prioritising investments and regulatory reform, which is an incredibly important conversation given the limited resources at most cities’ disposal. It also does not paint a compelling vision for why being a Smart City matters, which disengages most of the population. Personally, before I worked in tech, I felt absolutely no connection to the idea of a ‘Smart City’. Tech was cool, but I never thought it was crucial.

I believe that using technology to tackle inequality and its effects should be a Smart City’s ambitious goal and guiding force, providing focus and rallying support from its constituents. This article spelled out two ways to do so.

“What I Wish They Knew”: 5 Answers from a Government Data Scientist

On February 22, 2017March 28, 2017 By karentay2 Comments

One of the goals of this blog is to bridge the worlds of tech and government: I believe we can do so much more by working together, yet we often don’t understand each other deeply enough to begin. I will be starting the “What I Wish They Knew” series, which features people who are familiar with both these communities.

The first person to kick off this series is none other than my husband, Kenneth, who worked in Singapore’s Government Data Science unit before pursuing a PhD in Statistics at Stanford. By the way, you can find out more about Singapore’s Government Data Science unit on their blog: https://blog.gds-gov.tech/ – highly recommended.

kenneth handsome.jpg — Kenneth at The Hive, where the Government Digital Services team is located.

How did you get into Government Data Science?

I’ve had a strong interest in mathematics and related quantitative fields like statistics and computer science for as long as I can remember, and studied math at Princeton as an undergrad. [NB: Kenneth’s mathematics blog can be found here.]

I began my career in the Singapore public service, hoping to give back to society in the small ways I could. While I was at the Ministries of Defence and Environment, most of my work did not involve any advanced math or data analysis.

I missed the intellectual challenge of quantitative thinking, and started taking online courses on the side. Andrew Ng’s “Machine Learning” course on Coursera first piqued my interest in data science. I learnt how we can use a small toolbox of algorithms to extract a whole lot of information from data, and I thought to myself, “How cool would it be if I could use some of these techniques in my work?”

Fortunately, a unit in Government was being set up to do just that: use state-of-the-art data analysis techniques to inform policy decision-making. I joined the Government Data Science unit in 2015 as a consultant. My work experience in policy and my quantitative undergraduate training put me in a rare position to understand the mindsets of both the policy officer and the data scientist. As such, I felt that I was an effective translator between the 2 parties.

How about an example – what is one meaningful thing you did in Government Data Science?

An agency was very concerned about congestion during peak hours at the checkpoint between Singapore and Malaysia. Understanding cargo traffic patterns could help them design policies to reduce congestion. All they had was tens of millions of “permit data” entries, which captured the time that the cargo truck carrying the permit passed through a checkpoint, the industry code and value of the goods carried. I worked with the agency to define useful problem statements to shape the direction of analysis. One example: what are the top 5 industries moving cargo during peak hours? (Since policy interventions would be done at an industry-level).

Next, since each truck could hold multiple permits and the number of trucks was what we cared about, we went through a (non-trivial) process of turning “permit data” into “truck data”. We were able to identify the top industries moving cargo during peak hours, and further narrowed this group to those who were moving cargo on the busiest roads. We were also able to develop hypotheses on what influenced industry behaviour.

After completing the analysis, I shaped the narrative of our presentation in a way that delivered impactful policy insights, ruthlessly cutting down on unnecessary details. This is often the most painful part of the process for a data scientist – it’s so tempting to want to show ALL the great analysis we did.

After dozens of hours of work, there was nothing more satisfying in seeing the audience’s facial expressions saying, in effect, “before I was blind, now I see”! They never had a picture of the cargo traffic patterns until our analysis was done, and could now act upon it to improve congestion.

What is one thing you wish non-data scientists knew about working with data scientists?

That good data analysis requires significant collaboration between the data scientist and you, the domain expert.

Some people view the relationship between the domain expert and the data scientist as follows:

Domain expert gives data scientist a bunch of Excel files.
Data scientist crunches the numbers and churns out a report or presentation 3 months later. After all, the data scientist knows everything about data and that’s what we are paying them to do, right?

Nothing could be further from the truth! Domain expertise can speed up the data analysis process tremendously and direct it meaningfully, resulting in greater value from the project. Let me give two examples of this.

First, explaining the data to the data scientist, down to what each column means and how the data was collected, will save him/her much second-guessing angst. For example, if the patient check-out time was 18:00:00, does it mean that the person checked out at 6pm, or does it mean that the clinic closed at 6pm, and so everyone who hadn’t checked out yet was given a standard check-out time? Explaining the data will also give the data scientist a better sense of which variables are of greater importance and deserve more attention. In the example above, what does “checking-out” mean anyway, and is it significant?

Second, domain experts can pick up on insights that would escape data scientists. For example, a data scientist finds that Chromosome 21 seems to have an impact on a health outcome. Is that expected? Does it confirm some of the other hunches that we have? Or is it something completely unexpected, that suggests that the model is wrong? These are questions that a data scientist is unlikely to have any intuition about. However, with feedback from the domain expert, the data scientist can quickly decide to pursue or drop lines of inquiry.

As a policy-maker, what is one thing you wish more data scientists paid attention to?

That data analysis is not for the data scientist, but for the policy-maker (or client). As such, good data analysis always puts findings and insights in the proper context.

Consider the sentence: “Our prediction model for which patients will be re-admitted over the next 6 months is 34.56% more accurate than the existing model.” Upon seeing this sentence, several questions come to mind:

What do you mean by accuracy? Is the measure for accuracy that you are using appropriate? (See this https://en.wikipedia.org/wiki/Confusion_matrix for a whole zoo of accuracy measures.)
56% seems overly precise: can we really compare performance down to 2 decimal places? (35% might be better.)
Does 35% more accuracy translate to a meaningful difference? For example, will this allow us to tailor our services better to 100,000 patients, or 10 patients? (If possible, relate the finding to something the policy-maker cares about, like dollars or man-hours saved.)
Is this even a meaningful thing to predict?? (Hopefully, the domain expert would have said so. See answer to question 2.)

Good data analysis also provides enough detail to illuminate, but not too much till it confuses. For example, saying nothing about the modeling process could make me wonder whether you did your homework in choosing the most appropriate model (and whether I should have spent all that money hiring you). At the other extreme, I will not appreciate going through slide after slide of raw SAS/R output.

At this point I cannot overstate the importance of appropriate data visualisation. These visualisations have to be thought through: good charts clarify, poor ones confuse. Unfortunately, it’s a lot easier to make the latter. (See this for examples not to follow.)

kenneth-graph — Truly, a (well-designed) picture is worth a thousand words

5. What is your hope for the field of data science?

The economist Ronald Coase famously said “Torture the data, and it will confess to anything.” In an era of subjective reporting and “fake news”, this concern is more pertinent than ever.

My hope is that the general population will have enough statistical knowledge so that they can call a bluff when they see one, and demand quantitative evidence for decisions their leaders make. To this end, I hope to see reforms in statistics education at the high-school level so that it becomes a subject that people feel is relevant and interesting, rather than abstract and theoretical (which is often how it is taught today).

Thanks for reading! Know anyone who should be featured in this series? Do let me know at karentay@gmail.com.

Tackling AI-driven job displacement: A Primer

On February 13, 2017March 28, 2017 By karentay3 Comments

I’m starting a series of posts on solutions to AI-driven job displacement.

I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?

I use the word “hope” because it is the prospect of a better future within reach which will inspire people to continually re-invent themselves when they face setbacks like job loss, rather than to give up. It’s not enough to give them concessions.

Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:

Technology-driven job displacement is not new, but is happening faster than before. Tim O’Reilly has a helpful historical view of job displacement. Andrew Ng, Chief Scientist of Baidu, has pointed out that “increasingly smart machines will displace workers faster than how industrialization displaced agricultural workers or automation displaced factory workers”. This is his biggest worry about AI, not super-intelligent killer robots.

In the long run, there will be a net positive in jobs, but in the short run there will be pain: especially among the middle class. The White House published a useful overview on the impact of AI on society last December and Gary Bolles has great resources to understand the hollowing out of the middle class. One very concrete example of middle-class job loss: as shared mobility becomes an increasingly attractive option for city-dwellers, less people will feel the need to own a car. The GMs, Fords, Toyotas and Tier-1 suppliers of the world will scale down their manufacturing business and move into new lines such as mobility services. New jobs will most definitely be created – for example, operators who manage fleets of autonomous vehicles and software engineers whose algorithms will continually improve the cost and energy efficiency of fleets. It’s likely these jobs will hire new people, not those who were displaced.

Progress is at stake if we can’t give people the confidence that the new system will work in their favor. We see this in widespread support for anti-trade platforms, even though no economist doubts the overall benefits of trade. America has taken a few steps back, and while some of this may be attributed to the ignorance of one man, it is also the collective anxiety of people speaking through their votes – and this anxiety is not unfounded. This is not a particularly American problem. Brexit is the other most popularly cited example, but you see it everywhere, both in the developed and developing world: Egypt, the Philippines, Singapore: a push back among those who feel that the system no longer works in their favor.

Many of us empathize with insecurity more than we think. I hear it when peers confide that they wish they had studied computer science instead of finance or liberal arts in college, because that’s where opportunities are growing. Rumors swirl about how much a self-driving car engineer is paid, and how lawyers and top investment bankers will be replaced by automation (e.g. Goldman Sachs has already replaced 600 traders with 200 computer engineers over the years). Even hot jobs like software engineering will change significantly in the coming years – Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, predicts that traditional coding will decline in importance compared to the skill of creating the “scaffolding” within which machine learning can operate. This is a skill that just a couple of hundred people have today.

So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.

Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?

A Framework to Understand our Target Group

To start off, it’s useful to have a framework to understand the universe of people we need to reach. I offer two dimensions that differentiate people in our target group.

*these are just illustrative examples! Obviously, the reality is a lot more nuanced.

Proactive vs Reactive

On one end, we have people who are “proactive” about keeping themselves relevant to the job market. Geeks like my husband, who completed a dozen Coursera/Udacity courses, taught himself new coding languages, mastered Tableau and ran a successful math blog, all while managing his day job.

On the other end we have people who are “reactive” – they are hardly thinking about the possibility of losing their job; even if they are, they aren’t taking action. This could be the 45-year old who worked a 9-5 job for the past 20 years or a new parent who has no bandwidth to think about anything but the present.

Inherent personality traits are one determinant of where a person falls on this spectrum. Life stage is another – it affects how much time we have to think about alternative plans. The prevailing culture of family and community also shapes what we value such adventure or stability, ambition or work-life balance. Much of this is determined by geography.

Where someone falls on this spectrum is never static. People can shift left and right, and motivation is key.

New Entrant vs Old Timer

The “new entrant” vs “old timer” distinction is useful in framing solutions. To illustrate this, let’s imagine Uber can fully automate all its fleets by 2025, and will no longer need human drivers.

“Old timers” are existing Uber drivers. The challenge is to give them sufficient fore-warning of impending job loss, and opportunities to pivot into new industries. The difficulty is in encouraging them to take action before it’s too late, and to ensure there are good alternatives.

“New entrants” are those who will be entering the job market in 2025. The challenge is to equip them with skills that will be relevant to the job market in 2025, including skills required to operate Uber’s fleet of autonomous vehicles in localities throughout the world. The difficulty is that no one knows exactly what skills will be needed in 2025, and how many jobs will require these skills.

Here are some topics I’d like to address based on this framework.

First, most solutions today address those who are “proactive” – it’s the easiest group to address because it’s sufficient to make learning resources available; they’ll find them and take them up. How can we motivate or guide those who fall into the “reactive” half of the framework? What can tech companies do? What can Governments and employers do? How do these efforts tie together?

Second, how can tech work with higher education institutions to prepare new entrants (our children today) for the future job market? About five years ago, some predicted the death of higher education institutions as Massive Open Online Courses took off. Now, most of us acknowledge that this is not going to happen at a large scale any time soon. Most people aren’t as confident enough to ditch traditional paths, even if these don’t make sense anymore. How can tech companies help to reform higher education and make it far more responsive to the job market than today? I will draw on my experience working in higher education to address this question.

Third, what social security policies do we need in an era of accelerating job displacement? In the Silicon Valley, the idea of a Universal Basic Income – where everyone receives a basic monthly stipend from the state – has gained much traction as a policy solution to job-displacement. How will a Universal Basic Income impact the different groups in this framework? What are the alternatives or complements to a Universal Basic Income? I will draw on my experience working in and studying social policy to address this.

A summary here: Stay tuned!

The Dark Side of the Sharing Economy in Transport (and Three Solutions)

On February 7, 2017March 28, 2017 By karentay1 Comment

photosource: cnbc

The shared economy is a net positive for society…

In my previous posts, I’ve talked about how the shared economy – expedited by technology – can have tremendous benefits for society. For example, it can mitigate the feeling the inequality by closing gaps in the transportation experience. The benefits are even greater when private companies work with Governments to reach the elderly, poor and underserved.

I’m pretty sure that the shared economy in transport has a net positive effect on the economy too, though evidence is nascent. Last March, Lawrence Katz and Alan Krueger conducted the RAND-Princeton Contingent Worker Survey, which showed a substantial rise in the incidence of alternative work arrangements[1] for workers in the US, from 10.1% in 2005 to 15.8% in late 2015. Strikingly, this implies that all of the net employment growth in the US from 2005–15 appears to have occurred in alternative work arrangements.

One reason is that the shared economy provides part-time or intermittent work for people who otherwise cannot find a suitable job. All these Uber drivers I’ve met before – the young man who lost his job and needs time to search for new employment. The father fighting a costly custody battle, who needs a flexible job so he can show up in court. The lower-income mother who needs to supplement her day job to pay for her mortgage. Uber driving is a particularly attractive part-time job – typical part-timers get paid disproportionately less than full-timers. In contrast, Alan Krueger and Jonathan Hall found that no matter how many (or few) hours Uber drivers work, their hourly earnings were the same.

…BUT the distribution of benefits matters, and here’s how cities should think about it

Even though the shared economy creates tremendous new value, the distribution of value favours some groups over others. The tensions that arise can undermine companies operating in the shared economy, as we’ve seen in several cities worldwide. Cities need to consider how they may ensure a fair distribution of the shared economy’s benefits along three dimensions:

Workers vs companies
Incumbents vs new entrants
Now vs 10 years’ time, especially with the advent of autonomous vehicles

Companies would be wise to work collaboratively with cities to resolve these issues early on, rather than lock horns in costly legislative battles, or get blocked from new markets.

Fair Distribution of Benefits Between Drivers and Companies

The shared economy benefits both ride-sharing companies and their drivers, but arguably companies benefit a lot more. The business model of ride-sharing companies like Uber, Lyft and Grab is to provide a technology platform which enables matching of riders to drivers. These drivers are essentially self-employed contractors who log on to the platform whenever they wish. Many are able to find work and supplement their income through this platform.

However, these drivers are not employed by ride-sharing companies and hence do not receive certain benefits. In Singapore, employers are required to contribute up to 17% of their employee’s salary to a savings account for housing, retirement and healthcare. In the U.S., many receive healthcare insurance through their employers, who are often able to get better rates than individuals. In the UK, employees are protected by the minimum wage legislation. Drivers for Uber and Grab do not receive such benefits because they are not considered employees.

As companies rely more on these self-employed workers to fuel their business, risks are passed from companies to workers. As Singapore’s Deputy Prime Minister Tharman Shanmugaratnam has said “….it serves the interests of the company because they’re really pushing risk to the contract worker…who actually can’t take much risk – the risk of instability in wages and the risk of not being prepared for retirement because of a lack of social security contributions.”

Cities need to start shifting their social policies to accommodate the rising proportion of self-employed workers. At the same time, companies need to discuss reasonable ways to spread benefits and share risks with workers who are self-employed. It need not be all or nothing. There has been talk about creating a “third classification” of workers who have some benefits of employees, while retaining the independence of a contractor. This gives both the business and worker flexibility while providing some social protection.

Working with cities on some “in-between” solutions will help business avoid lengthy legal battles down the road. For example, Uber is appealing a ruling by London’s employment tribunal recently that it should treat its drivers like employees, including paying the national minimum wage. I would go so far as to say that it is the responsibility of ride-sharing companies to start engaging in these discussions, because many of their workers face an uncertain future when autonomy arrives (third point).

Fair Distribution of Benefits Between Incumbents vs New Entrants

The arrival of ride-sharing companies like Uber very rapidly redistributed benefits in the transportation system, creating new winners (e.g. consumers, new drivers) and losers (e.g. taxi drivers). Yes, there were probably more winners than losers, but the losers suffered a huge impact on their livelihoods. For exampe, many taxi drivers in the U.S. invested large sums in their license – in Chicago, the median cost of a taxi medalllion in late 2013 was USD$357,000. Having the value of your medallion plummet is like losing your home!

Cities dealing with disruptive innovation need to quickly level the playing field between incumbents and new entrants, to ensure that the distribution of benefits is not overly skewed in the direction of new entrants.

In the case of ride-sharing, issues like driver training requirements take the forefront. For example, Singapore placed 10 hours of training requirements on Uber/Grab drivers, while significantly cutting down the training hours required for taxi drivers (now, they only need to spend 16 hours on in-class training, compared to about 60 hours previously). We also cut down course fees for taxi drivers, and scrapped the daily minimum mileage – a move which helps taxi drivers minimise empty cruising just to meet their quota.

It is in the interest of disruptors to avoid a total regulatory lockdown by avoiding a zero sum mentality in these negotiations.

Fair Distribution of Benefits Between Present and Future

Finally, while we reap many benefits now, there are two important long-term considerations for cities working with ride-sharing companies.

First, many ride-sharing companies are at the stage where they are flush with investment, and can afford keep their ride prices artificially low. What happens if cities “outsource” their transportation to ride- companies, which eventually raise the prices beyond what regular citizens can afford? How can cities set up their transport systems such that competition can easily arise – keeping prices in check – or that public options can bounce back quickly? A city needs to ensure that even as people reap the benefits of the shared economy today, these benefits can be sustained over time.

Second, the big elephant in the room is autonomy. Full autonomy = no more need for drivers.

Autonomy will further redistribute the benefits away from drivers towards companies, and for all we say about new jobs being created, I’m pretty sure many of these drivers won’t be the ones to do it.

Because many drivers are self-employed workers not covered by social protections, they will be in particularly difficult situations.

It will be some time before full autonomy at scale is realised, so it is not too late to start conversations on how to ensure that drivers don’t get the short end of the stick when their jobs are replaced. One immediate action companies need to take is to give drivers information. Drivers, while not employees, are stakeholders in the company’s business and should be informed about the timeframes and implications of autonomy as the field evolves. In addition, much more can be done to help them with skills and future employment, a topic I will cover soon.

Conclusion

Over a series of posts, I’ve argued that the shared economy is a net positive for society and economy. This post, I posit that we need to work together to ensure that these benefits are distributed fairly between drivers and companies, incumbents and new entrants, present and future.

This is not the ambit of cities or Governments alone; companies seeking a sustainable business model in essential public services like transportation would be wise to work closely with cities rather than to be caught in costly legislative battles, be locked out of markets, or worse still – to be exploitative in their practices.

[1] “temporary help agency workers, on-call workers, contract company workers, independent contractors or freelances)

Disrupting Elderly Caregiving (and why Uberisation won’t work)

On February 3, 2017March 28, 2017 By karentayLeave a comment

I came across a really encouraging article about tech start-ups that are trying to fix the elderly caregiving sector. This is incredibly important work.

The double whammy is here

The graphic below, from the UN, shows how our global population is aging. It will happen faster in developed countries. In Japan people above 65 already constitute more than a quarter of the population. Singapore will get there in the 2030s, and the U.S. in the 2040s.

This will be a double whammy on societies. Countries will have a shrinking tax base to support the growing number of elderly who need care. There won’t be abundant resources to build new nursing homes and hospital beds. At the same time, it will be more difficult for the elderly to continue living at home – with shrinking family sizes, the responsibility for caregiving will fall on one or two children, instead of three or four.

Advances in biotechnology, personalised medicine and genomics will go a long way towards mitigating these challenges. For example, if companies like Calico and Unity Biotechnology manage to reverse aging, people can have longer, healthier, independent lives. The periods of time when they need caregiving will plummet. This is referred to as a longer “healthspan” (as opposed to just “lifespan”).

Unfortunately, many developed countries are already starting to face the double whammy and need a more immediate solution. From a policy perspective, it does not make sense to build new infrastructure for the elderly because these will become redundant when subsequent elderly cohorts are smaller.

The best solution is to help people receive care in their own homes as they age. It is also what a large majority (some statistics suggest 90%) of people prefer.

Why is Home Caregiving such a tough nut to crack?

A fragmented home care sector with low standards and revolving door caregivers is the norm in many cities. I don’t see a level of innovation and investment flowing into this sector that is commensurate to its importance. Three likely reasons why this is such a tough nut to crack.

Home caregiving is highly personal. When someone takes an Uber ride, they don’t have to talk to the driver if they don’t want to. In contrast, a caregiver gets deep into your personal space if they bathe, feed and change your diaper. You’re obviously going to be very picky about who you choose. Pickiness is not a one-way street – caregivers have their preferences too!

The Home caregiving sector suffers from “diseconomies of scale”. A service like Uber gets more efficient as more drivers and users come on board. It works the other way with caregiving because scale creates a more complex landscape of preferences, and a higher probability of error. According to Seth Sternberg, CEO of Honor (a caregiving start-up) “an agency with fewer than 100 carers can match them with clients and schedule their appointments with relative ease, but if it tries to get bigger, things go wrong. The agency ends up making poor matches… an obese client who owns kittens might end up being sent a petite woman who is allergic to cats… and when schedulers start arranging appointments outside of a small geographic area they know well, caregivers end up being late or not turning up at all.”

The job is just not a lot of fun. In fact, that’s why many people out-source care to paid contractors, even care for those they love most. It’s not glamorous, it’s repetitive, it can be highly physical, and in some cases, you interact with suffering on a daily basis. For such a difficult job, you mostly work alone without a network of support and accountability. Benefits, training and wages are patchy. Motivation is low. It is more difficult to recruit a good part-time Caregiver than a good part-time Uber driver.

What are the implications for tech companies and smart cities?

Disrupting the flailing home care sector is a classic problem that the tech, business, Government and non-profit sectors need to go at together. Here are three things that anyone working in this space needs to consider.

Tackle the root problem – motivation.

The FT article features Josh Bruno,who left Bain Capital to set up Hometeam. He started with a tech solution in mind – perhaps the equivalent of Uber for in-home care, he thought – a platform that would match caregivers to elderly and take a cut.

After volunteering in about 40 elderly care organisations, he quickly realised that the deepest issue in the caregiving sector was not matching, but motivation – “People blame them as lazy, but they are the worst working conditions — low pay, no training.” Defying all industry standards, he decided to hire the caregivers, give them good benefits of paid holidays, maternity leave, training and retirement.

I think Josh is getting to the heart of the issue – how can you expect people to deliver quality care if you don’t care for them? The first step is turn caregiving into a worthy career. Business can’t tackle this alone, which leads me to point two.

2. Partner to make home care an economically viable business

Josh’s company was turned away by many VCs because hiring the caregivers was not deemed to be maximally profitable.

I believe many have not yet realized the economic benefits of a strong home care sector. When elderly are able to continue living in their own homes, a whole ecosystem of services such as tele-health, food delivery and transport has a larger market. For large healthcare providers like Kaiser Permanente, a strong home care sector combined with tele-health can help manage its elderly clients outside the expensive hospital setting. This optimizes the use of hospital facilities and doctors’ time, making the business more profitable. For the same reasons, countries where healthcare systems are operated by the Government should consider investing public funds in the home care space, for example, by extending subsidies in nursing homes to the home care sector.

Furthermore, to help the home care sector grow, the public and non-profit sectors should play a role in helping to overcome inherent”diseconomies of scale”. It does not make sense for small home care companies to have their own training programmes and qualifications system. One idea is for Governments or non-profits to offer basic, modularised caregiver training as a “shared resource” for home care companies to utilise.

3. Use technology to build a care ecosystem around the elderly

Although it may not currently feel like it, home caregivers are really part of a team of family members, healthcare professionals and community members who help make living at home comfortable and enjoyable.

Technology can enable better communication and support between different members of this team. The home caregiver should be able to ask the healthcare professional whether a situation requires medical attention, and share information that would help the healthcare professional with diagnosis and prescription. Community members should be able to volunteer in bite-sized chunks of time, a topic I wrote about in my article “Three ways to build a transportation system that services the vulnerable”. Ultimately, when members of the team communicate, they can collectively give better care and receive support from each other. This can help to tackle the motivation problem for caregivers.

Technology can be part of the care team too! It can enable an elderly person to live independently as long as possible, only activating their caregiving team when necessary. One of the greatest fears of an elderly person is that something happens (a fall, a stroke) when they are alone. The solution, 24-hour care, is expensive and intrusive.

The Singapore Management University has been piloting sensor-enabled homes in Singapore through their SHINESeniors initiative. These sensors observe and analyze the elderly’s living patterns and immediately activate his care team when a change in pattern suggests a deterioration. Besides enabling independent living, this also allows companies and family members to allocate their time and resources efficiently.

Conclusion

Disrupting the flailing home care sector is essential to the quality of life of older folk, and the sustainability of healthcare systems when populations age. It’s a tough nut to crack, but I can’t think of a more important issue to work on right now, because our parents – and then us – are going to be the beneficiaries of positive disruption!

An Autonomous Vehicle Strategy for Smart Cities

On January 30, 2017March 28, 2017 By karentay2 Comments

I live in the Valley now, and it’s impossible to go a day without someone mentioning Autonomous Vehicles. It is an incredibly rich discussion space. Some of the topics of debate include:

When will it be technically possible for cars to be fully autonomous, not requiring a human driver to fulfill safety-critical functions? (This is typically referred to as “Level 4” or “Level 5” autonomy).[1] Most auto-OEMs are aiming for 2020-2022, Tesla’s most recent assessment seems to be 2018.

What is the best business model to introduce autonomous vehicles? We have Tesla, which sells autonomous cars to individuals. We have the OEMs and tech companies (Uber, Ford, GM-Lyft partnership), which plan to, or have already, introduced autonomous vehicles as fleets for ride-sharing. I recently spoke to Rahul Sonnad from Tesloop, whose autonomous vehicles will only provide long-haul car journeys that compete with short-haul flights (think the LA to Las Vegas journey).[2]

When the market settles, will we see vehicles owned and operated by a few large coalitions of companies (each having the holy trinity of fleet management – manufacturing – design and software), or will it continue to be as diverse as it is today? I have met autonomous vehicle start-ups that plan to manufacture their own autonomous vehicles and disrupt major OEMs. On the other hand, even the richest companies (like Waymo, previous under GoogleX) have announced that they won’t get into the business of manufacturing cars. It’s just too costly to set it all up, and is best left in the hands of the OEMs.

Comparatively, I don’t hear as much debate about how cities should think about their autonomous vehicle strategy. I think this is incredibly important. Cities need to plan ahead to make the technology work for their people, instead of reacting to technology and business models as they arise.

I discussed this topic at the Worlds Fair Nano with my good friend Elliot Katz (who is the co-chair of DLA Piper’s Connected and Self Driving Car Practice).

worldsfair

Summarizing four points here

1. Introducing autonomous vehicles shouldn’t be an end in itself. Smart Cities should aim for shared mobility and deploy autonomous vehicles towards this goal.

Shared mobility is essential for cities that struggle with land constraints and intensifying populations. To solve the problem of congested roads and unhappy commuters, we need less vehicles on the road. This requires a strong push towards shared mobility (and away from vehicle ownership). I write more about it here.

Fully autonomous vehicles will contribute significantly to the objective of shared mobility especially if they are deployed in fleets (think Uber, but with autonomous vehicles instead of drivers). When autonomous vehicles are deployed in fleets, operators can dynamically size the fleet – injecting new cars when demand surges, and deploying them to less-crowded outskirts when demand falls. Theoretically, you would get rides faster and more reliability, while cutting down on “surge pricing”. These cars will be constantly on the move, never taking up precious real estate by parking for lunch or waiting at the curbside for a next job. This will free up land for other purposes, especially in the crowded downtown areas.

Cities should carefully consider the allocation of private versus shared autonomous vehicles. If the objective is shared mobility, the optimal scenario is for all autonomous vehicles to be shared: either deployed in fleets, or for privately-owned vehicles to be shared among a smaller group of family members and friends. While this does not necessitate excluding private autonomous vehicles, how cities allocate road-space to private vs shared vehicles will determine the extent to which they will achieve shared mobility.

There are many policy levers to consider: from quotas for privately-owned vehicles (which Singapore employs), to incentives for private car owners to share their vehicles in limited capacities. When cars are connected, it is easy to design these incentives based on real-time information. For example, a car owner can receive an offer to avoid a road toll if they pick-up someone else along the route.

2. Introduce autonomous vehicles in a way that builds broad-based public acceptance – don’t just appeal to the 20% of early adopters.

Many have pointed out that one of the biggest challenges to autonomous vehicle deployment is broad-based societal acceptance. There will be maximum efficiency and safety gains when autonomous vehicles are deployed at scale, and this can only be achieved if a large majority of city dwellers is comfortable riding autonomous vehicles.

Cities need to introduce autonomous vehicles in a way that builds broad societal acceptance.

Unlike the US, where autonomous vehicles are tested on public roads, Singapore has introduced them in designated trial areas. For example, autonomous taxis by Nutonomy ply 12km of public road space in the One-North neighbourhood in Singapore. Autonomous electric buses ply public roads in the Jurong district. Soon, on-demand autonomous shuttles provide rides on Sentosa, an island dedicated to tourism and recreation.
The Government works closely with AV companies to ensure that the testing routes provide sufficient challenge, but are not too far out of the vehicles capabilities such that it creates dangerous scenarios. We also work closely on the requirements for autonomous vehicle testing, and on after-action reviews when accidents occur.

While this seems like an arduous process for both company and Government, it is a long-term investment towards shared mobility.

It is better for accidents to happen within limited contexts. In this early stage of testing, accidents provide valuable lessons that will lead to improvements in the technology. In late 2016, Nutonomy had its first accident with a lorry in the One-North trial area. Fortunately – and to some extent by design – the impact was limited and no one was injured. It is incredibly important to public perception that these accidents happen in a limited context with no fatalities, unlike what happened with Tesla earlier last year.
The Government’s commitment to working with AV companies gives assurance that there is an added layer of accountability for the safety of these vehicles.
These trials have arguably piqued Singaporeans’ interest in riding autonomous vehicles. While Nutonomy limits the pool of people who can bid on their autonomous taxi service, it is still more accessible than having to purchase a private autonomous car to experience riding in one.

3. Work with AV companies on your regulatory approach to autonomous vehicles

One additional benefit of a city working closely with AV companies on autonomous vehicle trials is that it creates a space for a meeting of minds. Unlike many US states, Singapore has not yet introduced regulations towards autonomous vehicles. Instead, we work with companies on trials that shape our thinking on the appropriate regulatory approach. At the same time, companies have given feedback that it is useful to understand the Government’s concerns so that they can address these concerns at the design stage.

4. Finally, invest in autonomous vehicles other than cars!

While autonomous cars receive the most attention, other forms of autonomous vehicles hold incredible promise for the objective of shared mobility. For example, if a city has autonomous freight and autonomous utility vehicles, these activities can be done in the dead of the night, when commuters aren’t trying to move around. This frees up road space in the day, and makes for a better commuting experience for everyone.

Singapore is investing in both autonomous freight and autonomous utility concepts.

In conclusion, Smart Cities should never deploy technology for its own sake. They should define their objectives and target their time, money and policy interventions in a way that achieves these objectives. Transport is just one area – this applies to healthcare, education, housing, and any issue that a Smart City deals with!

[1] http://www.techrepublic.com/article/autonomous-driving-levels-0-to-5-understanding-the-differences/

[2] He believes that the market for short-haul car journeys within cities will be commoditized – people won’t care what type of autonomous vehicle they get as long as it’s cheap and fast. It will be difficult for smaller companies to compete. On the other hand, there will be room for significant differentiation in services for long-haul car journeys.

Three ways to build a transportation system that serves the most vulnerable

On January 25, 2017March 28, 2017 By karentay2 Comments

So far, I’ve talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city.

But transportation systems, even the very best, will never serve everyone equally. Where the transportation system is not inclusive, the cost is borne by some of the most vulnerable in society.

stock-disabled-01

People who do not just need first and last mile transport – they need first and last meter transport. This includes the growing number of elderly (>65 years old), whose population will triple by 2030 in Singapore. It also includes those who have physical disabilities through accidents, illness or congenital conditions. Where the transportation system fails to provide first and last meter support, their caregivers bear the cost. When someone, (especially in a low-income household) leaves the workforce to be a full-time caregiver, there is a huge impact on the financial wellbeing of the family. In a survey by AgingCare.com 62% of caregivers said the cost of caring for a parent had impacted their ability to plan for their own financial future.

People who cannot afford rides. In my college years, I volunteered with Homefront, an organization that serves the homeless in New Jersey. I vividly remember talking to a mother whose children only ever ate canned food and hot dogs because they stayed in a Motel on Route 1, and it was too expensive to get to soup kitchens for a proper meal. A once-a-week supermarket trip was all they could afford. In this case, the cost is borne by the children, in the form of health and wellness.

People who live or work in inaccessible areas, where it does not make economic sense to deploy a public bus or even a ride-sharing car because there is so little demand. Where the transportation system does not provide, the cost is borne by the individuals or companies who have to cater private transport.

None of these groups are mutually exclusive. In fact, I hazard a guess that the number of families who fulfill at least two of these three conditions is not small, and will grow with the forces of aging and inequality.

For cities to provide a truly inclusive transportation experience, we need to explore three ideas:

Closing the first-and-last meter transport gap through community participation

Currently, caregivers are responsible for the first-and-last meter transport gap. If 85-year old Jim needs to go to the clinic, his caregiver helps him onto the wheelchair at home, takes the elevator to the ground floor, and helps him board either the bus or the taxi. When he arrives at the clinic, his caregiver helps him out of the bus, and into the clinic for registration.

Can volunteers fill the first-and-last meter transport gap instead? For example, when Jim orders his ride to the clinic, can a request be blasted to volunteers who are in the 200-yard radius of his home or destination? It can be a simple five to ten minute volunteering stint – helping Jim out of his home and onto the bus, or out of the bus and into the clinic for registration.

With one click of a button, Jim should be able to pick and pay for his transport menu to the clinic, and get first-and-last meter support from the community. Volunteers who choose to be in the network can do good in bite-sized chunks. They don’t need to go out of their way – they receive alerts as they go about their daily lives. Perhaps we can lighten the load of caregivers.

This idea has taken off with apps like GoodGym, where runners can sign up to visit an elderly person or help with one-off tasks while on their running route. It would be important to integrate these efforts with our transportation networks so that people like Jim can enjoy seamless transportation experiences and live independently in the community, as many elderly desire.

Closing the affordability and accessibility gap through public-private partnerships

Take Zara, the mother of four living in a Motel on Route 1 in New Jersey. Stranded because there are no public transportation options along Route 1, while ride sharing and taxis are too expensive.

It may not make sense for the Government to provide a public bus that passes by her Motel, simply because there is too little demand. I’ve personally experienced this. I used to live in a relatively inaccessible area in Singapore. Our municipality constantly lobbied the Government to provide a new bus line to serve us. We finally got it after 2 years, but every time I boarded that bus I counted no more than 5 people on it. Great for me, but it just wasn’t a great use of public funds to deploy a $100,000 bus way below capacity. Not to mention the additional congestion we created.

Here’s one idea for Governments: instead of buying a new bus to provide a bus line in inaccessible areas, use the money to subsidize rides by private providers such that it matches the cost of public transport. Furthermore, if a family like Zara’s is eligible for subsidies on public transportation, these should be applicable when they take rides by private providers.

This will require close collaboration between the Government and private providers (yes, operational issues will not be easy!), but is the most cost-effective way of closing the accessibility and affordability gap.

Some cities in the U.S. are working on this concept. For example, The Southeastern Philadelphia Transportation Authority (SEPTA) had insufficient parking lots at their train stations to accommodate commuters who drove to the station and dropped off their cars for the day. It did not make sense to make a huge investment in building new parking lots. Last year, they partnered with uber to provide a 40% discount on Uber rides to and from rail stations, encouraging people to share rides instead of drive.

Deploying autonomous vehicles

Autonomous vehicles hold tremendous promise for our objectives of inclusive transport because they will likely reduce the cost of rides. First, a bulk of a ride’s cost today is the salary of the driver. Second, companies are moving towards deploying autonomous vehicles in fleets. When vehicles are constantly utilized, companies can afford to charge less per ride. Finally, with technological advances, we can expect the hardware of autonomous vehicles, such as Lidars, to decrease in cost.

When this occurs, it will make more economic sense for companies to deploy vehicles to inaccessible areas, even if there is no promise of a return trip. Reduced prices also means that transport will be more affordable to families like Zara.

A city that plans ahead will ensure that autonomous vehicles are deployed in a way that benefits the broader population. For example, road space should not be dominated by privately-owned autonomous vehicles; Autonomous vehicle fleets should be embraced. Helping city-dwellers accept autonomous vehicles as part of their daily transportation experience is also an important part of the equation.

Conclusion

In my first post, I talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city. In my second post, I explored the ways Governments must work with private transport providers to ensure a truly seamless commute in the sharing economy – one that mimics the comfort of car ownership.

This third post covered three ways to ensure that our transportation system caters to some of the most vulnerable members of our society: community participation, public-private partnerships, and embracing autonomous vehicles. Inclusivity is an objective that is particularly close to my heart.

My final post in this series will be about the darker side of the shared economy, and how cities and business must work together to manage disruptions to our transportation system, including the rise of ride-sharing technology companies, as well as the advent of autonomy.

Solving social isolation and unaffordable housing through the sharing economy

On January 21, 2017January 22, 2017 By karentay1 Comment

Taking a brief interlude from my posts on transportation to share an article I read this morning on BBC.

It’s about Ensemble2Generations, a French company that helps elderly home owners open up their homes at below-market rates to students who can’t afford rent otherwise.

It’s a wonderful example of how the sharing economy helps to solve several social and economic problems:

Unaffordable rents inhibit the younger generation from moving to cities, or stacks on debts for those who do. This has knock-on effects on the innovation and vibrancy of the economy
Many elderly today are “asset-rich” and “cash-poor”. They own properties that the young today cannot afford because of rapid property price appreciation over the past 20-30 years. At the same time, longer life expectancies mean that retirement cash savings have to be stretched out. Why don’t they move to smaller properties and liquidate the cash? A perfectly rational argument, but Policymakers are finding that many elderly do not want to move out of a place that holds memories – often of spouses that have passed or good days gone by. Financial incentives can’t change that.
As nuclear families become increasingly spread out, more and more elderly are living alone and suffering from “social isolation”, with its associated physical and mental health risks.
Social media and busy modern lifestyles have exacerbated echo chambers – we mix with people who share our life stage, views, interest and start to lose empathy for those who are different. Does this make us less able to find middle-ground, to trade-off our interests for someone else’s, to be truly accepting of diversity in our workplaces and neighbourhoods? I think so. In addition, losing connection with our older generation means losing history, perspective and collective wisdom.

Helping students find affordable rent in older folks’ homes doesn’t just solve an important economic problem. It also hits the heart of what busy, fragmented societies struggle with – empathy gaps and social isolation.

However, this will not be a profitable business in the same way as Airbnb or other home sharing/ rental platforms. Each match is a relatively long term arrangement, which means the company can’t cream off a steady stream of “taxes”. In any case, a high “tax” doesn’t sit well with the objective of affordable housing. Furthermore, I suspect the company has to spend more time and cash on background checks and assessing compatibility issues, given that the objective is building a long-term relationship, not just making a transaction. Finally, the total addressable market may not be large. For these reasons, I personally think a company working in this space would have difficulty attracting VC investment.

Here is a case where there is clear public good, but where it is unlikely for the concept to hit the scale, efficiency and funding required to reap maximum benefit.

Do you agree with this assessment? Are ideas such as these only feasible in a non-profit/ government-subsidised setting?
What is the potential of technology and business models in helping this idea take off in a bigger way?
What are the limitations of technology and business models in helping this idea take off in a bigger way?

Would love to hear from you!

Partnering towards truly seamless travel in the sharing economy

On January 19, 2017February 9, 2017 By karentayLeave a comment

In my last post, I said that the sharing economy can help tackle inequality in cities: by creating great travel experiences without the cost of car ownership, and helping cities use land more progressively.

We can only do this with good partnerships between the public and private sectors. Partnerships are valuable for at least three objectives

Truly seamless travel experiences
Reaching the under-served
Managing societal tensions: by looking after both the welfare of incumbents, and employees of the shared economy

Truly seamless travel experiences

We go out of our way to avoid walking and waiting. Generally, commuters prefer to take a single mode of transportation – even if it takes significantly longer – than to transit between modes. In Singapore, this applies even within the train system. Commuters would rather take a roundabout direct train, than to make transitions between train lines (a couple minutes’ walk).

To be truly attractive, the experience of commuting in the shared economy must mimic the seamlessness of owning a car: minimal walking, waiting and pesky payments.

I must be able to hop on an electric bike close to my apartment block, get to the train station just as the train pulls in, and have my Uber pull up at the curb just when I step out of my train.
Ideally, I can order this menu of transport in just one app, and have it all paid through one click on my iPhone. I can even pay a flat monthly fee for unlimited rides in the shared economy.
This platform should be smart – for example, it should rank my options based on my preferences and the weather conditions.

Here’s the problem in getting there – every transport operator has its own interests, payment system and data sharing policies.

For example, if Uber ran this platform, would Lyft or Chariot trust it to neutrally present the transport options to commuters?
Data on commuter preferences, pick-ups and destinations is treasure for tech companies because it is the basis on which they improve their services and beat their competitors. Who would they trust to protect the data collected from the platform, and to fairly distribute it to the operators?

Data sharing is a first step

The first step, which many Governments and companies have taken, is making data available to commuters and to each other. In 2016, Singapore put sensors in all our public buses. We made the location and crowdedness of our 5,000 public buses available via APIs, which helped commuters plan their journeys more accurately. Ride-sharing companies can integrate this data to provide multi-modal journey planning through their apps.

Ride-sharing companies are also beginning to share their data. Xinwei Ngiam, Director of Strategy at Grab, spoke about Grab’s approach to data sharing at CES. Uber recently announced that it would share data that would help commuters and cities understand traffic conditions. These are great initiatives, but it’s just a first step. If we simply stop here, commuters will still have a fragmented experience.

Partnering towards the Gold Standard

We need to collectively envision a seamless mobility experience from the commuter’s perspective and tackle the thorny issues that stand it the way, such as data management.

Cities like Helsinki are leading the way. Whim was launched last summer, allowing commuters to book rides from different transport providers in a single mobile service. Payment is seamless – you can buy bus, train or taxi tickets from the app, or sign up for an unlimited transportation subscription, just as you sign up for a Netflix subscription, for 249 Euros a month. It is still in pilot phase with plans for a fuller roll out in the Summer. Unsurprisingly, it took 6 years of planning with governments, cities and the industry to get to this point, with multiple agreements on the back-end between MaaS Global, the company running Whim, transportation authorities, and car/ride -sharing operators.

Xerox has launched their multi-modal transport planner in LA and Denver, and is now working through the challenges of bringing onboard all transport providers, and integrating payment and booking systems. This is the bigger hurdle than building a common user interface and intelligent recommendation system.

Xerox has launched their multi-modal transport planner in LA and Denver – commuters need only enter their destination, and a range of options will be presented to them based on their personal preferences. Xerox is now working through the challenges of bringing onboard all transport providers, and integrating payment and booking systems. This is the bigger hurdle than building a common user interface and intelligent recommendation system.

Closer to home in Singapore, LTA announced a partnership with 4 companies to create journey planning apps. My friend Liu Feng Yuan, the Director of Government Digital Services in Govtech, has shared some ideas on building a technology platform with a series of core APIs (requests, offers, reservations, payments and location tracking) that provides an open market and a clearinghouse for mobility. Any provider on the platform can bid to provide a ride, in part or in full. All the consumer sees in his app is a shortlist of options based on his travel preferences. Payment can be done through a single app.

Regardless of model, it is important for all transport providers to trust one party to be a neutral arbiter, and collectively decide on rules that grant every provider peace of mind. I was asked at CES if data management should be the responsibility of the Government and my answer was not necessarily. In Singapore, that may work because of the strong tripartite trust between Government, industry and people. In other cultural contexts, data management and governance can be the responsibility of a neutral third party, such as a University. For example, I understand that the City of San Francisco has engaged UC Berkeley to play this role in its Smart City plans.

I’ll host a more in-depth discussion on how cities and companies have approached these thorny issues later in the year.

Expanding our Collective Pie

Ultimately, everyone gains if the shared economy provides seamless travel experiences without the cost of car ownership. Commuters get comfort and flexibility. Private transport providers get a higher volume of rides. Cities can re-allocate precious land resources away from roads and parking to build homes and community spaces.

All this is possible if we work together to expand our collective pie.

My next post will talk about a topic close to my heart: partnering towards an inclusive transportation experience.

*I have only covered this topic briefly. For more details, my friends at the New Cities Foundation have wonderful resources. Also check out: http://www.greglindsay.org/articles/now_arriving_a_connected_mobility_roadmap_for_public_transport/

The sharing economy tackles one of the biggest issues every modern city faces – inequality.

On January 15, 2017March 28, 2017 By karentay2 Comments

Last week I spoke on a CES panel “Powering the Shared Economy to Improve the Lives of City Dwellers”. My co-panellists were Zipcar, Lyft and Grab, so our discussion naturally focused on the sharing economy in transport. Our full session was recorded here.

As the only Government representative on the panel, the inevitable question to me was – how does the sharing economy impact a city? How does it fit into our plans? How does it change the way we operate? I’ll touch on the first question for now.

I believe the impact of the sharing economy goes beyond improving transport.

It has the potential to address one of the biggest issues every modern city faces – inequality.

Companies working on ride-sharing, car-sharing and autonomous vehicle fleets have the potential to make a much more fundamental impact on society than some might think.

1. One of a city dweller’s most acute experiences of inequality is the daily commute.

Very few of us have the rising Gini coefficient at the top of our minds, but we feel its impact when we go about our daily lives. For example, in Singapore, the daily commute is a constant reminder of luxuries we may never afford. Just five years ago, there were three ways to get around the city:

I buy a car. It costs $100-$150k to buy a car[1], but I get the ultimate customisation in my commute. I can leave my house whenever I want, I don’t have to wait, I sit in air-conditioned comfort. I get to my destination in half the time of the equivalent journey on public transport.
I take public transport, which is cheap but the experience is quite the opposite of customisation. If I’m lucky, I get to the bus stop just as my bus is pulling in. If not, I wait 10 minutes, which has a knock-on effect on catching my next bus or train. I squeeze with strangers and hardly have room to move. I walk from my bus stop to work and am drenched in sweat from the 98% humidity.
I take a taxi, but only if I’m desperate and/or feeling rich, and it’s not always easy to catch one. At some point, taxis were waiting outside the Central Business District during peak hours so they could make an extra buck from being called, rather than hailed.

Five years ago, the trade-off between cost and comfort in the transport experience was extremely stark. A city dweller experiences inequality when he knows he will never be able to afford the comfort of a $100-$150k car, and feels like he doesn’t have a good alternative.

2. By providing good travel experiences without the cost of car ownership, the sharing economy reduces the experience of inequality in the daily commute.

The sharing economy has always played a central role in moving people around the city – in the form of public transport. Too bad public transport in most places gives the sharing economy a bad name.

Fortunately, technology and business innovations have given the sharing economy a much needed boost. For example, technology has enabled people to find a ride in real-time, with the click of an iPhone button. Business innovations such as Uberpool have brought down the cost of rides – in many places, below the traditional taxi fare.[1]

As a commuter, I now have a wide range of options sandwiched between owning a car and taking public transport. On the spectrum closest to car ownership, I can get an Uber or short-term rental car (e.g. Zipcar) on demand. For a slight decrease in cost, I can share my ride with others in a LyftLine/Uberpool. If I want to trade off some flexibility for an even cheaper fare, I can submit a bid on crowd-sourced bus services like Beeline or SWAT. Even public transport has improved significantly with LTA providing real-time information on bus arrival times and crowdedness.

Importantly, this expansion of good options means that commuters don’t need to make such a stark choice between cost and comfort when deciding whether or not to buy a car. This reduces the experience of inequality in the daily commute.

3. The best has yet to come – with the promise of autonomous vehicles, participating in the sharing economy will not just be a concession, but a superior option to car ownership.

Some people are already beginning to see shared transport as a superior option to owning a personal car because of the flexibility it brings. I can choose the option which fits my lifestyle – sometimes public transport works just fine, but if I’m in a hurry or on a date, I may pay more for a more comfortable experience. Importantly, I never have to worry about where to park.

In contrast, car owners can feel compelled to use their cars even if there are better options. Behavioural economists refer to car owners in Singapore as having a “sunk cost mentality”. Put simply, once you pay a bomb to own the car, nothing – not road taxes, expensive parking, the prospect of circling the block for an hour to find an empty lot, or for some, being caught drunk-driving – will stop you from using your car, because in your mind you’ve already sunk such a huge investment and you should use it as much as you can. It can be a psychological trap.

I believe that when autonomous vehicles are ready to be deployed in fleets (imagine Uber without drivers), shared transport will become even more attractive compared to car ownership. Commuting in the shared economy can become an experience, not just a necessary evil. When cars do not need to be driven by humans, new design possibilities open up. A steering wheel and front-facing seats are no longer necessary, and a car can be configured like a meeting room, for example. A car ride can be a place to meditate, focus on work or even have wine with your friends on the way to a party.

When many different designs of vehicles are deployed in a fleet, you will be able to summon precisely the vehicle (and accompanying service) you want. In the morning you could use a minivan to ferry your family to school and work, in the evening you could summon a sleek, designer vehicle to bring you to your company’s dinner function. On the weekend, a jeep could take your family around the island for some R&R.

Today, owning a private car is the standard for luxury transportation. People make a large financial outlay upfront in exchange for on-demand, customised transportation. With fleets of autonomous vehicles deployed round-the-clock, providing the ultimate customisation in travel experience, more efficiently and without the pains of parking, this paradigm will be overturned. Shared transport will be the more affordable and customised and comfortable experience. Fewer and fewer people will aspire to own a car.

4. A transportation system dominated by the sharing economy frees up precious city space for community, housing, and commercial activities

So far, I’ve talked about how technological developments may make many of us prefer shared transport over car ownership, and how that could help mitigate our experience of inequality in the city.

If more people choose shared transport instead of car ownership, this will also enable us to use our land more equitably and progressively: think about how roads and parking spaces are disproportionately used by those who have the resources to own cars. If we can reduce the number of cars on the road, this land can be used for purposes that benefit a more diverse population such as homes, community facilities and commerce.

In cities like Singapore, where land is a constrained resource, it is even more important to make sure we use it to benefit everyone, not just those who can afford it.

5. The vision of a more equal transportation experience and society can only be realised if Governments and businesses work together. Stay tuned for more.

I’m deliberately painting an ideal picture here.

Many things can detract from this vision of a less unequal transportation experience. For example, if the business models for autonomous vehicles target only the rich, or if we fail to make multi-modal transportation seamless for commuters in the shared economy (commuters really dislike the process of transferring from a bus to a train, and vice versa).

Furthermore, I’ve mainly spoke about issues pertaining to the “middle class” Some groups have not been addressed, such as the elderly and disabled. How can we ensure that the system benefits those with limited mobility?

In my next series of posts, I will explore these issues in greater detail, and talk about how partnerships between Governments and businesses can ensure that the forces of talent and technology powering the shared economy will be used towards maximum societal and business benefit. Stay tuned!

[1] Though the extent to which fare decreases are structural versus artificially depressed by Venture Capital investment is yet to be seen, a topic I discuss at https://techandpublicgood.com/2017/02/07/the-dark-side-of-the-shared-economy-in-transport-and-three-solutions/

[1] For an explanation on why cars in Singapore are so expensive, see this link. At a macro level, it’s about restricting the supply of cars to manage traffic and road space. http://dollarsandsense.sg/no-nonsense-explanation-on-why-cars-in-singapore-are-so-expensive/

[2] If the “sharing economy” is defined as a having access to an asset that you do not own. I find this to be the most compelling definition.

Introduction: Why Tech and Public Good?

On January 10, 2017January 21, 2017 By karentay2 Comments

Hello, thanks for stopping by! I set this blog up as a space to explore a topic I’ve been thinking about: the intersection between technology and public good.

I didn’t start off my career in technology. Since I was little, my passion has been in building more inclusive, compassionate societies. As I volunteered at special needs schools and shelters for the homeless, and spent time in rural Laos, Peru and China, it struck me that opportunity gaps are aggressively widening. It can’t be good for society – what we saw in Occupy Wall Street, the Egyptian Revolution, and some would say the recent U.S. Presidential Election, is evidence.

The first five years…

I dedicated the first few years of my career to championing policies to make our society more inclusive. Together with a fantastic team, I worked on pre-school reforms that expanded affordable pre-school- we tripled the number of households receiving subsidies and expanded partnerships with childcare operators to rapidly address the supply crunch. I advocated for the expansion of education subsidies to children in Special Needs schools, which for a long time were considered out of the mainstream education system. I ran analyses on the financial vulnerability of households with elderly and disabled members, which resulted in a stronger social safety nets. Tharman Shanmugaratnam, the Deputy Prime Minister and Coordinating Minister for Social and Economic Affairs, was one of the best bosses and key sources of inspiration for my colleagues and I, as we worked passionately in this area.

I also ventured into communications and engagement because I saw that an inclusive society requires trust-building. Good policies cannot be the only solution – because of distrust, many policy changes that I had dreamt about implementing for years (even before I joined the public service) were perceived negatively; as attempts to buy favour from disgruntled people. I set up a Strategic Communications and Engagement team in the Ministry of Education, which aimed to meaningfully engage people in policymaking, and communicate like humans (not like a 40,000-member bureaucracy). I also gave talks and facilitated discussions among students and public servants on divisive topics in society, such as elitism and inequality.

Venturing into Technology

Then my husband decided to go to Stanford for his PhD in statistics, and I needed a plan. What should I do there, I thought? Technology? One problem: technology to me was a scary, impenetrable world of jargon. To be honest, when my teams grappled with issues of inequality and distrust in society, our lack of understanding about technology made it easy to write it off as a “good to have”, but non-essential in tackling big societal problems. Thankfully, I had bosses at the Prime Minister’s Office who took a chance on me, giving me the opportunity to jump into the world of technology.

Fast-forward, I now work for Singapore’s Smart Nation Office in the Bay Area. I love the vision of the Smart Nation Office – it is not about getting our hands on the coolest new technology, but how we can use technology to improve the lives of citizens, such as creating an inclusive transportation experience for everyone, regardless of age, physical condition, or income.

The more I learn, the more I see technology and business as a game-changing necessity in tackling the most stubborn societal problems that Governments wrestle with. I will write more examples in posts to come. I hope that as we build relationships between the technology and public/non-profit sectors, and articulate our common interests, we can maximise for both business interests and the good of society.

Urgency

I think this conversation needs to happen more than ever. We live in an increasingly divided world, where the benefits of technology and progress accrue disproportionately to those who have capital (both financial and educational). There are structural forces at work here. If we chug along on our own tracks, these divides will only widen.

An experiment

This blog is an experiment. I would love to use it as a platform for conversations that bridge the worlds of tech, business and Government, that explore new partnership models and our common interests. Let’s actively do this because the forces of nature are pulling us in the opposite direction!

**the views in this blog are my own, not the organisation I work for.

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?

We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.

First, cities should use technology to make life experiences in the city more and more independent of incomes.

2. Second, cities should use technology to distribute scarce land and human resources more equitably.

How did you get into Government Data Science?

How about an example – what is one meaningful thing you did in Government Data Science?

What is one thing you wish non-data scientists knew about working with data scientists?

Nothing could be further from the truth! Domain expertise can speed up the data analysis process tremendously and direct it meaningfully, resulting in greater value from the project. Let me give two examples of this.

As a policy-maker, what is one thing you wish more data scientists paid attention to?

5. What is your hope for the field of data science?

Thanks for reading! Know anyone who should be featured in this series? Do let me know at karentay@gmail.com.

I’m starting a series of posts on solutions to AI-driven job displacement.

I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?

Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:

So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.

Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?

A Framework to Understand our Target Group

Proactive vs Reactive

New Entrant vs Old Timer

Here are some topics I’d like to address based on this framework.

A summary here: Stay tuned!

photosource: cnbc

The shared economy is a net positive for society…

…BUT the distribution of benefits matters, and here’s how cities should think about it

Fair Distribution of Benefits Between Drivers and Companies

Fair Distribution of Benefits Between Incumbents vs New Entrants

The double whammy is here

Why is Home Caregiving such a tough nut to crack?

A fragmented home care sector with low standards and revolving door caregivers is the norm in many cities. I don’t see a level of innovation and investment flowing into this sector that is commensurate to its importance. Three likely reasons why this is such a tough nut to crack.

What are the implications for tech companies and smart cities?

Conclusion

Comparatively, I don’t hear as much debate about how cities should think about their autonomous vehicle strategy. I think this is incredibly important. Cities need to plan ahead to make the technology work for their people, instead of reacting to technology and business models as they arise.

I discussed this topic at the Worlds Fair Nano with my good friend Elliot Katz (who is the co-chair of DLA Piper’s Connected and Self Driving Car Practice).

Summarizing four points here

1. Introducing autonomous vehicles shouldn’t be an end in itself. Smart Cities should aim for shared mobility and deploy autonomous vehicles towards this goal.

2. Introduce autonomous vehicles in a way that builds broad-based public acceptance – don’t just appeal to the 20% of early adopters.

3. Work with AV companies on your regulatory approach to autonomous vehicles

4. Finally, invest in autonomous vehicles other than cars!

None of these groups are mutually exclusive. In fact, I hazard a guess that the number of families who fulfill at least two of these three conditions is not small, and will grow with the forces of aging and inequality.

For cities to provide a truly inclusive transportation experience, we need to explore three ideas:

Closing the first-and-last meter transport gap through community participation

Closing the affordability and accessibility gap through public-private partnerships

Deploying autonomous vehicles

Conclusion

This third post covered three ways to ensure that our transportation system caters to some of the most vulnerable members of our society: community participation, public-private partnerships, and embracing autonomous vehicles. Inclusivity is an objective that is particularly close to my heart.

Truly seamless travel experiences

Data sharing is a first step

Partnering towards the Gold Standard

Expanding our Collective Pie

I believe the impact of the sharing economy goes beyond improving transport.

It has the potential to address one of the biggest issues every modern city faces – inequality.

Companies working on ride-sharing, car-sharing and autonomous vehicle fleets have the potential to make a much more fundamental impact on society than some might think.

1. One of a city dweller’s most acute experiences of inequality is the daily commute.

2. By providing good travel experiences without the cost of car ownership, the sharing economy reduces the experience of inequality in the daily commute.

3. The best has yet to come – with the promise of autonomous vehicles, participating in the sharing economy will not just be a concession, but a superior option to car ownership.

4. A transportation system dominated by the sharing economy frees up precious city space for community, housing, and commercial activities

5. The vision of a more equal transportation experience and society can only be realised if Governments and businesses work together. Stay tuned for more.