Uber and Google appear to be parting ways according to a report by Bloomberg’s Brad Stone Monday. Google Ventures invested in Uber’s C and D rounds, and its chief legal officer and SVP of corporate development, David Drummond, has sat on its board since 2013. An unnamed source “close to the Uber board” reports that Drummond has given notice that Google is developing an automated taxi service on top of its driverless car technology. It is unclear if he will remain on the board.
Not to be outdone, Uber also announced Mondaythat it is partnering with Carnegie Mellon University to build a research facility to develop its own autonomous car technology. According to the company, “the Uber Advanced Technologies Center in Pittsburgh, near the CMU campus… will focus on the development of key long-term technologies that advance Uber’s mission of bringing safe, reliable transportation to everyone, everywhere.”
The prospect of a war between Google Cabs and Uber Bots makes for vivid headlines, but where does it leave the legions of Uber drivers and other task workers? This is one of the questions that Peter Reinhardt, CEO and co-founder of Segment, asks in a timely post, Replacing Middle Management with APIs. Services like Uber replace a layer of task management with software APIs. Customers use an app interface to enter their data into the system. Reinhardt represents this in pseudocode as: uber.drive(card, pointA, pointB);. The app sends a request that includes account data, pickup and dropoff locations via API to Uber’s servers that poll available drivers nearby and dispatches one to the customer to fulfill the request. The only two humans involved are the customer and the driver. Danny DeVito has been furloughed!
But what about Uber’s drivers and other workers whose tasks are directly managed by software. Reinhardt makes an original observation about this layer of software that divides the makers from the doers:
Drivers are opting into a dichotomous workforce: the worker bees below the software layer have no opportunity for on-the-job training that advances their career, and compassionate social connections don’t pierce the software layer either. The skills they develop in driving are not an investment in their future. Once you introduce the software layer between ‘management’ (Uber’s full-time employees building the app and computer systems) and the human workers below the software layer (Uber’s drivers, Instacart’s delivery people), there’s no obvious path upwards. In fact, there’s a massive gap and no systems in place to bridge it.
As he describes it, working “below the API” is a dead end. Uber drivers, Amazon Mechanical Turk workers, 99design contestants, TaskRabbit taskers and HomeJoy cleaners are all targets for further automation. Software is the business of building models of systems that can be executed, optimized and combined with other models. In terms of employment, this means that these APIs will be able to orchestrate more and more complex collections of tasks over time. Reinhardt concludes, “As the software layer gets thicker, the gap between Below the API jobs and Above the API jobs widens. And economic incentives will push Above the API engineers to automate the jobs Below the API: self-driving cars and drone delivery are certainly on the way.”
Yes, self-driving cars on the way, and it is likely that automated taxi fleets will be the first commercial application of this technology. Navigating roads and avoiding other vehicles is a complex task, but it is far more concise to model than cleaning a cluttered apartment. The smart money is on automating the labor that is both necessary and expeditious to model.
Reinhardt cites Elon Musk’s $10 million donation to the Future of Life Institute to research AI safety as a precaution that “does seem apropos.” Indeed, once a wide range of tasks are automated via APIs machine intelligence may develop the ability to recombine them in unforeseen ways. As an example, Reinhardt proposes a mashup of Redfin and Zirtual that could enable a long-distance real estate investor to flip a house “completely programmatically.”
Right now, the proportion of total employment mediated by APIs is still small, but it is rising. As Farhad Manjoo wrote in the New York Times last week, “Uber, and more broadly the app-driven labor market it represents, is at the center of what could be a sea change in work, and in how people think about their jobs. You may not be contemplating becoming an Uber driver any time soon, but the Uberization of work may soon be coming to your chosen profession.” Note that he said “professions.” The Uber effect does not just affect cab drivers and house cleaners, but extends to lawyers, doctors and even (some day) venture capitalists.
As much as task workers enjoy the control of their schedules and working conditions, the uncertainty of the gig economy has significant downsides. Manjoo quotes former secretary of labor Robert Reich:
I’m glad if people like working for Uber, but those subjective feelings have got to be understood in the context of there being very few alternatives. Can you imagine if this turns into a Mechanical Turk economy, where everyone is doing piecework at all odd hours, and no one knows when the next job will come, and how much it will pay? What kind of private lives can we possibly have, what kind of relationships, what kind of families?
And the pressure of automation will give these workers very little leverage—unless they are doing things that are hard to automate, or they possess skills that make their work qualitatively better than the automated product. Task modeling and management will continue to grow in importance, but so will technologies that match skills to needs in very immediate and fine-grained ways. For the 21st century economy to remain productive for humans and capital, it will need to become far more supple than the initial Uberization and Googleification of work allows.
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