Technology and the Future of Work

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I’ve been doing that lately in thinking about technology and the future of work. I ran an event a few weeks ago in San Francisco called The Next Economy Summit to bring together people to help understand how technology is changing the future of work and the future of business.

In a piece I wrote on Medium, I also referred to it as the WTF Economy. WTF! can be an expression of wonder or an expression of dismay, and in today’s economy, we see both.

Amazement: Self-driving cars; phones that we can talk to and that talk back, giving advice about the best restaurant nearby or the fastest route to work today; AIs that write news stories or even advise doctors; 3D printed replacement parts — for humans; new forms of corporate organization that use swarming marketplace algorithms to marshall thousands of on-demand workers so that consumers can summon services at the push of a button in an app.

Dismay: the fear that robots and AIs will take away jobs, reward their owners richly, and leave formerly middle class workers part of a new underclass; tens of millions of jobs here in the US that don’t pay people enough to live on; little-understood financial products and profit-seeking algorithms that nearly took down the entire world economy and drove millions of people from their homes.

Let’s start with that notion of robots and technological unemployment. There has been a lot of speculation about technological unemployment and the ability of robots and AI to take away human jobs, leading to what James Manyika calls “No Ordinary Disruption.” A recent Oxford University study argued that almost 50% of human jobs are susceptible to replacement by automation, including many formerly high-skilled jobs.

But I like to remind people that no one can see the future. What we can see is the present, notice things that other people haven’t, and use that to understand where the world is going.

So let’s consider a case from the present, and what it teaches us about the future.

The Uber or Lyft driver is a technology-enabled worker. A human job - the taxi dispatcher - has been automated, but this has resulted in more human workers, not fewer, because the automated routing technology that makes Uber and Lyft possible allows many more people to participate in providing a service that was formerly available only to a few.

The dispatcher has been replaced by software, but the driver is augmented by software. This augmentation happens in multiple ways: the Uber or Lyft app that allows anyone to summon a car from where-ever they are, and that allows a driver to find them, a needle in the haystack of the city; the use of Google Maps and Waze that allows someone to navigate without the kind of training that used to be required. (“The knowledge”, the London Black Cab exam, was one of the hardest exams in the world.) Estimates from some of the cities where Uber/Lyft are most widely deployed is that there are 3-5x as many people providing driving services as the entire previous taxi industry. Note however that many of the workers are not full time - the entire structure of the industry is changing.

The way that algorithms are changing the shape of the corporation is not limited to Next Economy companies like Uber and Lyft.

Geoff Colvin, writing at Fortune, discussed how companies like Tesla and Apple have become “dematerialized” by algorithm. Tesla’s recent “recall” required only an over-the-air software update rather than a physical network of repair shops. Apple builds hundreds of millions of devices without owning a single factory through miracles of modern coordination and data-enabled logistics. One of their vendors says “We replace inventory with information.” Business process outsourcing means that the people who serve you every day may not actually work for the people whose logos hang overhead. This is all turbocharged by technology. In companies like Uber and Lyft you can see the ultimate logic.

Every corporation is increasingly a network - consider franchising models, business process outsourcing and the like - but with technology, you can increasingly have “the franchise of one.”

This leads to a new kind of relationship between companies and the people providing the work.

In her excellent book, Peers Inc, Robin Chase, the founder of Zipcar, talks about the rise of platforms that leverage consumer self service, peer production, and unused capacity in the economy to deliver new kinds of services. Uber and Lyft are key examples, but they are not alone. The so-called peer economy requires both peers - who provide their own resources to do the work and the Inc, the platform that makes that capacity available to others.

Interestingly, Chase points out that the untapped capacity isn’t just personal cars and spare rooms, it is the untapped capability of cell phones and GPS, now in everyone’s pocket, that has made many of these services possible.

In another piece I wrote on Medium, I took a deeper dive into the labor issues raised by the on-demand economy. I was concerned by many of the arguments that on-demand economy jobs are not good jobs. Workers have no safety net, no guaranteed hours, and are not paid as well as glowing advertisements might promise.

What I realized was that most commentators were comparing these jobs to some idealized idea of a “good job” and not to the reality of today’s comparable jobs. It turns out that most low-wage jobs in America also have no safety net, no guaranteed hours, and don’t pay very well.

The difference is that if you work for Uber, Lyft, Postmates, DoorDash, Instacart, or any of the other on-demand companies, you set your own hours, you work as much or as little as you like. But if you work for Walmart, The Gap, McDonald’s, or even a progressive low-wage employer like Starbucks, you are at the beck and call of an impersonal scheduling algorithm that often has workers closing a store at midnight and reopening it at 6 am, gives them no flexibility for childcare emergencies, and - this was the shocker - is programmed to make sure that they never get more than part time work, to minimize the need to include them in expensive benefit plans. And because they can’t see their schedule more than a week in advance, and have no control over it, they can’t even work for other employers - except on-demand companies. I’ve heard that in some cities, more than half of Starbucks baristas also drive for Uber or Lyft because it’s the only way that they can get enough work.

There are enormous policy implications here.

Another key element to the success of companies like Uber is realizing the power of new technology to completely rethink real world processes - not just the act of summoning a cab, but also things like payment, which are radically simplified in the new model.

box.net founder Aaron Levie put it perfectly in a tweet. “Uber is a $3.5 billion lesson in building for how the world should work instead of optimizing for how the world does work.” I believe their latest valuation was north of $60 billion, but you get the idea.

This leads to great new experiences for consumers.

And with the advent of voice interfaces like Siri

and Google now, we are talking to our devices and getting real world services and information back.

One of the biggest changes in user expectation that technology will bring into our everyday lives in the next few years is going to be via “agents” like Siri and Google Now, which will bring predictive analytics to bear on routine tasks that we already depend on our computers for. Google Now routinely alerts me to leave early for work when traffic is bad,

Predictive analytics and all kinds of data-driven decision making will definitely change the jobs of many middle managers. This has already happened in fields like stock trading and advertising. Algorithmic agents have long ago replaced Don Draper as the dominant force in ad placement.

It is quite remarkable to be able to say to your phone “OK, Google Now, remind me to buy currants next time at Whole Foods” and have an alert show up the next time I am at the store! This is AI in the real world.

As this kind of capability comes to business AI, we will see a great reduction in the need for personal assistants. Or rather, the personal assistants will be Silicon assistants.

There’s a lesson from IBM Watson’s attempts to bring AI to bear on healthcare. They are leveraging one of the themes that we saw with Uber - the use of technology to augment workers rather than replace them — but they really aren’t thinking hard enough about the architecture of service delivery.

Imagine how AI, sensors, on-demand, and telemedicine could be used to completely reinvent healthcare. Community health workers making house calls, backed up by AI, remote sensing, on-call experts. There are a lot of startups working on “the uber of healthcare” and one of them is going to have a breakthrough - but the system as a whole needs to support that.

That point about the system as a whole needing to work to support the changes in healthcare is a good example of an important point. Our lives are increasingly ruled by systems that we no longer control and that we often don’t understand.

John Mattison of Kaiser put it well: How do we make sure our algorithms take humans into account?

And that brings me to my final topic - the choices that we have to make. Remember the Financial Crisis of 2008, where the world financial system was nearly brought down by financial firms creating derivatives that even they didn’t understand. I like to point out that this was our “Skynet moment” - and the market itself may be the “AI” that we no longer control and that is inimical to human values.

That’s why I found Joseph Stiglitz’ recent book so refreshing. He points out how many of the things we take for granted about our current market system are the product of rules that we wrote, and that it’s time to rewrite the rules.

One of the rules we need to rewrite leads to My own policy prescription: Think about work, not jobs.

Here are some of the grand challenges we face.

Rebuilding the infrastructure by which we deliver water, power, and goods.
Dealing with the “demographic inversion” — the lengthening lifespans of the old and the smaller number of young workers to pay into the social systems that support them.
Income inequality. “The people will rise up before the robots do.”
Climate change.
Displaced people. How could we use technology to create the infrastructure for whole new cities, factories, and farms, where they could be settlers, not refugees?

Nick Hanauer, who was one of the speakers at my event, put it best. he said: “Technology is the solution to human problems. We won’t run out of work till we run out of problems.”

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Technology and the Future of Work

Tim O'Reilly

Technology and the Future of Work