[...] Economists have long recognized this phenomenon. They call wages higher than the lowest that the market would otherwise offer “efficiency wages.” That is, they represent the wage premium that an employer pays for reduced turnover, higher employee quality, lower training costs, and many other significant benefits.

Algorithmic, market-based solutions to wages in on-demand labor markets provide a potentially interesting alternative to minimum-wage mandates as a way to increase worker incomes. Rather than cracking down on the new online gig economy businesses to make them more like twentieth-century businesses, regulators should be asking traditional low-wage employers to provide greater marketplace liquidity via data sharing. The skills required to work at McDonald’s and Burger King are not that dissimilar; ditto Starbucks and Peet’s, Walmart and Target, or the AT&T and Verizon stores. Letting workers swap shifts or work on demand at competing employers would obviously require some changes to management infrastructure, training, and data sharing between employers. But given that most scheduling is handled by standard software platforms, and that payroll is also handled by large outsourcers, many of whom provide services to the same competing employers, this seems like an intriguingly solvable problem.

Since the mid-1980s, Lazonick observes, “the resource-allocation regime at many, if not most, major U.S. business corporations has transitioned from ‘retain-and-reinvest’ to ‘downsize-and-distribute.’ Under retain-and-reinvest, the corporation retains earnings and reinvests them in the productive capabilities embodied in its labor force. Under downsize-and-distribute, the corporation lays off experienced, and often more expensive, workers, and distributes corporate cash to shareholders.

Meanwhile, there is an incentive to cut income for ordinary workers. Cutting wages drives up net income and thus the price of the stock in which executives are increasingly paid. Those executives who are not motivated by cupidity are held hostage. Any CEO who doesn’t keep growing the share price or who considers other interests than those of the shareholders is liable to lose his or her job or be subject to lawsuits. Even Silicon Valley firms whose founders retain controlling positions in their companies are not immune from pressure. Because so much of the compensation of their employees is now in stock, they can only continue to hire the best talent as long as the stock price continues to rise.

Algorithmically derived knowledge is a new source of asymmetric market power. Hal Varian noted this problem in 1995, writing in a paper called “Economic Mechanism Design for Computerized Agents” that “to function effectively, a computerized agent has to know a lot about its owner’s preferences: e.g., his maximum willingness-to-pay for a good. But if the seller of a good can learn the buyer’s willingness-to-pay, he can make the buyer a take-it-or-leave it offer that will extract all of his surplus.” If the growing complaints of Uber drivers about lower fares, too many competing drivers, and longer wait times between pickups are any indication, Uber is optimizing for passengers and for its own profitability by extracting surplus from drivers.

[...] It is easy to blame the ability of technology to replace human labor for declining wages and increased wealth inequality. But technology is being used for cost reduction rather than to empower people and to reach for the stars not because that is what technology wants, but because it is what the legal and financial system we have built demands.

For all its talk of disruption, Silicon Valley too is often in thrall to that system. The ultimate fitness function for too many entrepreneurs is not the change they want to make in the world, but “the exit,” the sale or IPO that will make them and the venture capitalists who funded them a giant pile of money. It’s easy to point fingers at “Wall Street” without realizing our own complicity in the problem or in finding a way to bring it under control.

Once companies take money from venture capitalists, they are committed to aiming for an exit. A typical venture fund is a partnership with a ten-year time horizon. Most of the investments are made within the first two to three years, with some money reserved for additional investment in the companies that are most promising. Once an entrepreneur takes money from a venture capitalist, he or she is promising to sell or go public within the lifetime of the fund. Yet VCs know that the vast majority of their deals will fail. Jon Oringer, the founder and CEO of Shutterstock, put it well in his advice to entrepreneurs: “What venture capital firms do is spread some number of millions of dollars to some number of companies. They’re not really rooting for every single one. All they need is for a few of them to succeed. It’s the way the model works. They have a totally different risk profile than you do. This is your only game in town. For the venture capital firm, it’s one of a hundred games in town.”

Every year, Google chief economist Hal Varian and his team publish an economic impact report. In their 2016 report, they estimated that during the prior year, Google increased US economic activity for their customers by $165 billion. They base this figure primarily on a conservative estimate of the expected impact of Google advertising on the increased revenues of their advertisers. [...]

We’ve seen how technology platforms are creating new mechanisms that make it easier to connect people and organizations to work that needs doing—a more efficient marketplace for work. You can argue that that is one of the key drivers at the heart of the on-demand revolution that includes companies like Uber and Lyft, DoorDash and Instacart, Upwork, Handy, TaskRabbit, and Thumbtack. The drawbacks of these platforms in providing consistent income and a social safety net shouldn’t blind us to what does work about them. We need to improve these platforms so that they truly serve the people who find work through them, not try to turn back the clock to the guaranteed employment structure of jobs in the 1950s.

One key to understanding the future is to realize that as prior knowledge is embedded into tools, a different kind of knowledge is required to use it, and yet another to take it further. Learning is an essential next step with each leap forward in augmentation.