AI Has an Uber Problem

AI Has an Uber Problem

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“The economic problem of society…is a problem of the utilization of knowledge which is not given to anyone in its totality.”

—Friedrich A. Hayek, “The Use of Knowledge in Society

Silicon Valley venture capitalists and many entrepreneurs espouse libertarian values. In practice, they subscribe to central planning: Rather than competing to win in the marketplace, entrepreneurs compete for funding from the Silicon Valley equivalent of the Central Committee. The race to the top is no longer driven by who has the best product or the best business model, but by who has the blessing of the venture capitalists with the deepest pockets—a blessing that will allow them to acquire the most customers the most quickly, often by providing services below cost. Reid Hoffman called this pattern “blitzscaling,” claiming in the subtitle of his book with that name that it is “The Lightning-Fast Path to Building Massively Valuable Companies.”

I disagree. It is a dark pattern, a map to suboptimal outcomes rather than the true path to competition, innovation and the creation of robust companies and markets. As Bill Janeway noted in his critique of the capital-fueled bubbles that resulted from the ultra-low interest rates of the decade following the 2007–2009 financial crisis, “capital is not a strategy.”

Venture capitalists don’t have a crystal ball. To the extent that entrepreneurial funding is more concentrated in the hands of a few, private finance can drive markets independent of consumer preferences and supply dynamics. Market discipline is significantly delayed—until the initial public offering or later. And of course, today IPOs are delayed, often precisely because companies can get all the capital they need from a small number of deep-pocketed investors. Founders and employees are even able to cash out some of their shares without having to face the scrutiny of public markets, much as if bettors on a horse race could take their money off the table as the horses round the first turn. Thus, far from finance being an extension of the market (with lots of independent signals aggregated to ensure competition and consumer choice), capital can ignore the will of the market. 

The ride-hailing business offers a classic example of the distortive over-reliance on capital rather than consumer choice. It began with bold prophecies of ride-hailing replacing not just taxis but all private vehicles, and ended with a national duopoly of on-demand taxis at prices no better and often worse than those of the previous over-regulated local taxi market. In a well-functioning market, many startups would have explored a technology innovation like on-demand transportation over a much longer period. In that alternate history, entrepreneurs would have competed with different pricing strategies, different rate structures for drivers and perhaps even completely different business models. Eventually, those that survived would have done so because they were delivering the service chosen by the most customers and the most drivers. That is true product-market fit. 

But in the Central Committee version of Silicon Valley, Uber and Lyft, backed by billions of dollars of venture capital, drove out the competition rather than defeating it, subsidizing customer acquisition and an unsustainable business model—and in the case of Uber, continuing to attract new capital with promises of speculative future cost savings via self-driving cars. Instead, once the market had consolidated, Uber and Lyft only reached profitability through massive price increases. What might have happened if there had been true competition in this market? We will never know. 

By contrast, during the dot-com bubble, most companies consumed tiny amounts of capital by today’s standards. The funding was spread across thousands of companies, and it took a decade or more of relentless innovation and competition for the industry to become dangerously concentrated. This is a classic example of what Janeway calls a “productive bubble.” Remarkably, most of the winning companies were profitable in only a few years, and eventually they became hugely profitable. Google raised only $36 million in venture capital on its way to dominance. Facebook raised billions, but it did so only to fund faster growth for a business model that insiders have told me was very close to profitable the entire time. They weren’t buying users with subsidized prices; they were building data centers. Even Amazon, long unprofitable, took in very little investment capital, instead funding itself with debt supported by a business model that produced previously unprecedented levels of free cash flow.

To be sure, sometimes companies do require a lot of capital to lay the groundwork for a possible future. Tesla and SpaceX are good examples. They used their funding to do serious research and development, to build factories, cars, batteries, rockets and satellites. This is using capital properly: to fund the hard costs associated with creating something new until the projected unit economics lead to a self-sustaining business. It’s also worth noting that in those cases private funding was powerfully augmented by state support: carbon credits and electric vehicle incentives for Tesla, and NASA progress payments for SpaceX.

That kind of investment was unnecessary in the case of ride-hailing. The startups simply used the money to amass market power by subsidizing blitzscaled growth. Others had already deployed the capital to build much of the infrastructure for ride-hailing—GPS satellites and GPS-enabled smartphones. Even the innovation of using GPS to match passengers and drivers was not developed by the VC-backed market leaders, but by the true market pioneer, Sidecar, which was quickly sidelined when it failed to raise enough capital to gain a leading share in the market it had first envisioned. 

In the case of artificial intelligence, training large models is indeed expensive, requiring large capital investments. But those investments demand commensurately large returns. The investors who pile billions of dollars into a huge bet are expecting not just to be paid back, but paid back a hundredfold. The capital-fueled race to build the largest models has already led to bad behavior. OpenAI, for example, has trained not just on publicly available data but reportedly on copyrighted content retrieved from pirate sites. This has led to lawsuits and settlements. But even those settlements are likely to be bad for the development of a healthy entrepreneurial ecosystem. As Mike Loukides points out, “Smaller startups…will be priced out, along with every open-source effort. By settling, OpenAI will eliminate much of their competition.”

Meanwhile, the largest models’ absorption of all content into “the Borg” of AI data will eliminate opportunities for the owners of specialized content repositories to profit from their own work. Innovators are already finding that much can be done at lower cost with smaller, more targeted open-source models. They can fine-tune these smaller models for specific problem domains, allowing trusted content providers (like my own company’s O’Reilly Answers and related AI-generated services) to profit from our own expertise.

OpenAI is making an effort to create a platform on which entrepreneurs can build vertical applications, but only if they pay tribute to the centralized business model in the form of API fees. OpenAI is also skimming the cream, quickly dominating some of the most profitable categories—image generation, video generation, speech synthesis, computer programming—that in a well-functioning market would be explored by dozens or hundreds of competing efforts, until one or two find the winning combination of product and business model. If entrepreneurs discover other profitable categories, giants such as OpenAI will move quickly to dominate these as well. 

The capital-fueled AI land grab is of course only one axis of premature market concentration. As Max von Thun points out in “Monopoly Power Is the Elephant in the Room in the AI Debate,” much of the investment to train models is coming in the form of strategic partnerships (including both cloud computing credits and potential revenue deals) with existing industry giants Microsoft, Amazon and Google (and in the case of open-source models, Meta Platforms). As von Thun notes, “These partnerships appear to be serving the same purpose as ‘killer acquisitions’ in the past—think of Facebook’s acquisition of WhatsApp or Google’s purchase of YouTube—raising serious concerns about fair competition in the fledgling AI market.” The risk of these deals is, again, that a few centrally chosen winners will quickly emerge, meaning there’s a shorter and less robust period of experimentation.

And, at least based on recent reporting by The Information about Anthropic’s operating margins, it may be that, like Uber and Lyft, the overfunded AI market leaders may only be able to deliver on investors’ heated expectations by crushing all competition. That’s not betting on the wisdom of the market and what Hayek called “the utilization of knowledge which is not given to anyone in its totality.” That’s betting on premature consolidation and the wisdom of a few large investors to choose a future everyone else will be forced to live in.

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