Over time, as networks reach monopoly or near-monopoly status, they must wrestle with the issue of how to create more value than they capture—how much value to take out of the ecosystem, versus how much they must leave for other players in order for the marketplace to continue to thrive.

A traditional organization has high alignment but low autonomy, because managers tell people what to do and how to do it. In the kind of organization parodied by the comic strip Dilbert, neither the manager nor the workers know why they are doing what they are doing. This is a low-alignment/low-autonomy organization. A modern technology engineering organization (or an entire organization like Amazon or Spotify) seeks to have high alignment and high autonomy. Everyone knows what the goal is, but they are empowered to find their own way to do it.

[...] In the SRE approach, by contrast, the humans inside the machine who keep it going augment themselves by constantly teaching the machine how to duplicate what they do, at ever-increasing scale.

Each charges for its services. On a private platform like the App Store, developers have accepted that 30% is the tax they have to pay to Apple for all the services it provides to the economy it supports. People also take for granted that platforms like Uber and Lyft take a cut from their drivers, and Amazon a cut from its resellers. So too, in a democratic society, people tax themselves to pursue common goals, to finance the platform upon which society builds. In a closed society, those in power extract rents from those who use the platform. But one way or another, we must pay. The question is how much, and whether we think what we get for what we pay is worth it.

[...] A number of the startups spun up by Code for America fellows have been acquired; others have received significant venture funding. Remix, the app that was started as a way for citizens to reimagine transit routes in their city, developed into a powerful tool for urban planners and was funded by top VCs who gave it a valuation of $40 million.

When Google introduced its pay-per-click ad auction in 2002, what had started out as an idealistic quest for better search results became the basis of a hugely successful business. Fortunately, unlike other advertising business models, which can pit the interests of advertisers against the interests of users, pay-per-click aligns the interests of both.

Perhaps the most important question for machine learning, as for every new technology, though, is which problems we should choose to tackle in the first place. Jeremy Howard went on to cofound Enlitic, a company that is using machine learning to review diagnostic radiology images, as well as scanning many other kinds of clinical data to determine the likelihood and urgency of a problem that should be looked at more closely by a human doctor. Given that more than 300 million radiology images are taken each year in the United States alone, you can guess at the power of machine learning to bring down the cost and improve the quality of healthcare.

This notion of “the creep factor” should be central to the future of privacy regulation. When companies use our data for our benefit, we know it and we are grateful for it. We happily give up our location data to Google so they can give us directions, or to Yelp or Foursquare so they can help us find the best place to eat nearby. We don’t even mind when they keep that data if it helps them make better recommendations in the future. Sure, Google, I’d love it if you could do a better job predicting how long it will take me to get to work at rush hour. And yes, I don’t mind that you are using my search and browsing habits to give me better search results. In fact, I’d complain if someone took away that data and I suddenly found that my search results weren’t as good as they used to be.

But we also know when companies use our data against us, or sell it on to people who do not have our best interests in mind. [...]

These people are privacy bullies, who take advantage of a power imbalance to peer into details of our private lives that have no bearing on the services from which that data was originally collected. Government regulation of privacy should focus on the privacy bullies, not on the routine possession and use of data to serve customers.

Labor advocates point out that the new on-demand jobs have no guaranteed wages, and hold them in stark contrast to the steady jobs of the 1950s and 1960s manufacturing economy that we now look back to as a golden age of the middle class. Yet if we are going to get the future right, we have to start with an accurate picture of the present, and understand why those jobs are growing increasingly rare. Outsourcing is the new corporate norm. That goes way beyond offshoring to low-wage countries. Even for service jobs within the United States, companies use “outsourcing” to pay workers less and provide fewer benefits. Think your hotel housekeeper works for Hyatt or Westin? Chances are good they work for Hospitality Staffing Solutions. Think those Amazon warehouse workers who pack your holiday gifts work for Amazon? Think again. It’s likely Integrity Staffing Solutions. This allows companies to pay rich benefits and wages to a core of highly valued workers, while treating others as disposable components. Perhaps most perniciously, many of the low-wage jobs on offer today not only fail to pay a living wage, but they provide only part-time work.

Which of these scenarios sounds more labor friendly?

Our workers are employees. We used to hire them for eight-hour shifts. But we are now much smarter and are able to lower our labor costs by keeping a large pool of part-time workers, predicting peak demand, and scheduling workers in short shifts. Because demand fluctuates, we keep workers on call, and only pay them if they are actually needed. What’s more, our smart scheduling software makes it possible to make sure that no worker gets more than 29 hours, to avoid triggering the need for expensive full-time benefits.

or

Our workers are independent contractors. We provide them tools to understand when and where there is demand for their services, and when there aren’t enough of them to meet demand, we charge customers more, increasing worker earnings until supply and demand are in balance. We don’t pay them a salary, or by the hour. We take a cut of the money they earn. They can work as much or as little as they want until they meet their income goals. They are competing with other workers, but we do as much as possible to maximize the size of the market for their services.

That is, both traditional companies and “on demand” companies use apps and algorithms to manage workers. But there’s an important difference. Companies using the top-down scheduling approach adopted by traditional low-wage employers have used technology to amplify and enable all the worst features of the current system: shift assignment with minimal affordances for worker input, and limiting employees to part-time work to avoid triggering expensive health benefits. Cost optimization for the company, not benefit to the customer or the employee, is the guiding principle for the algorithm.

By contrast, Uber and Lyft expose data to the workers, not just the managers, letting them know about the timing and location of demand, and letting them choose when and how much they want to work. This gives the worker agency, and uses market mechanisms to get more workers available at periods of peak demand or at times or places where capacity is not normally available.