Wall Street is busy preparing for what could become the three biggest initial public offerings in history. SpaceX has already gone public, and Anthropic and OpenAI are expected to follow suit. Yet even as investors scramble to get in, doubts are growing over whether these frontier AI labs can ever generate enough value to justify their astronomical spending.
The sheer scale of it is just staggering. Anthropic lost more than $8 billion last year despite generating $4.6 billion of revenue, and has already committed more than $500 billion to the computing power and artificial intelligence infrastructure it will need to keep growing in the years ahead.
OpenAI, meanwhile, expects revenue to rise almost tenfold—from $36 billion this year to $350 billion by 2030—yet still projects a cumulative cash burn of nearly $280 billion over the same period.
On the face of it, the economics are ugly. Even so, Anthropic’s investors believe the company could float at a valuation of more than $2 trillion. But those backers are looking in the wrong place. Frontier AI labs may never be the biggest winners of the AI race they ignited.
Case studies
That’s because the companies that invent breakthrough technologies rarely capture the biggest chunk of the value the techologies generate. This value usually accrues to those who build the complementary assets, in this case the products, platforms, and services around them. And that means the frontier AI labs could end up creating enormous value without capturing most of it themselves.
History is not on the side of these labs. In the early twentieth century, electricity came to transform pretty much every industry. But it wasn’t the power generators who gained the most; the biggest productivity gains came from manufacturing companies like Ford that used it to revolutionize production. The risk is the AI labs end up like utility companies, providing a shared foundation that others profit from.
The internet was similar. Telecoms companies spent heavily fashioning the fiber networks and connectivity that made the web possible. But much of the long-term value went to the businesses that built services on top of it: search engines, e-commerce and social media platforms, plus cloud software.
In a similar vein, frontier AI is becoming infrastructure, not software; the leading labs are plowing staggering sums into AI chips, data centers, electricity, and cloud infrastructure. And the models themselves are becoming a bedrock that others capitalize on, much like electricity or the cloud.
On top of that, AI models are rapidly becoming commoditized. Performance is converging, with Chinese labs in particular closing what once looked like an unassailable lead held by their U.S. rivals. And that makes technological leadership far harder to defend.
There is one fly in the ointment. The companies that so far control today’s complementary assets are already enormous. The “hyperscalers”—Amazon, Alphabet, Meta, Microsoft and Oracle—own much of the cloud infrastructure, software, or customer relationships through which businesses actually use AI. Microsoft, for example, can plug different models into its Copilot AI assistant, and sell them through software already used by millions of businesses.
No more easy wins
One thing, however, looks certain: The low-hanging fruit has been plucked. The frontier labs have already scraped much of what is publicly available on the internet for their models to feast on. But that well has already been running dry. This moves the competitive advantage over to those who have proprietary data, and especially the data held by private companies and those with unique or niche expertise.
That knowledge becomes the moat that protects them. For business leaders, that is reassuringly good news. For years, many have fretted that tech giants would swallow every industry whole. That has not always happened. Specialist businesses that know their customers better than anyone else have continued to thrive.
Take Sweetwater, a U.S. music retailer that sells many of the same guitars and microphones as Amazon but still pulled in $1.86 billion in sales last year. Its edge comes partly from specialist sales engineers who know the gear and their customers inside out—something AI cannot just scrape from the internet.
AI is unlikely to change that. If anything, it raises the value of knowing your customers, your products, and your market better than anyone else. The companies that do that may have far more to gain from AI than the companies building the models.
Which brings us back to the question of where the value ultimately ends up. Some of it is already flowing to the companies selling the “picks and shovels” of the AI boom, such as the chipmaker Nvidia, whose meteoric rise to status of the world’s most valuable company shows just how lucrative it can be.
History suggests the richest fortunes may still lie ahead, and may be minted by companies we haven’t even identified yet. They are most likely to be those that figure out how to reinvent themselves around the technology, rather than simply build it.
The frontier AI race may be producing extraordinary technology. But history tells us inventing technology is rarely the same as making money from it.