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Earlier this year, OpenAI CEO Sam Altman declared, “We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter.”
Many other AI companies seem to be betting on a similar future. If that vision actually comes to be, then “AI tokens” may break out of the world of nerdy tech and econ conversations and become a much more familiar part of our lives. The number of AI tokens businesses and consumers use could even become one of the defining measures of a new industrial age — the AI equivalent of the kilowatt-hour for electricity: the standard way we measure and pay for AI usage.
Think of tokens as the meter running in the background every time you use AI. Every time an AI model reads your prompt, writes an answer, or does a task, that work is measured in tokens. They are the tiny chunks of text and other data that AI models read and generate. In general, the more work a model does, the more tokens it typically processes.
While AI companies still tend to offer flat-rate subscriptions to average consumers, they’re increasingly charging businesses and developers based on the number of tokens they use. At the same time, companies, particularly in the tech sector, have been using AI in more and more of their work. As their AI usage has soared, many businesses have discovered just how expensive token-based pricing can become.
After a period when tech workers were engaged in a kind of AI free-for-all — which some dubbed “tokenmaxxing” — companies like Uber and Amazon have been putting guardrails around AI use and reducing their soaring token bills (which one clever writer at The Information recently dubbed “tokenminimizing“).
But tokens aren’t just the way AI companies measure usage and charge many of their business customers. They also leave behind a kind of digital paper trail that a growing number of economists and other researchers are using to track AI usage and study its economic impact.
In a new working paper, Nicola Borri, Aleh Tsyvinski, and Yukun Liu do basically that. Using data from 380 trillion AI tokens, these economists try to understand how the growth of AI usage is reshaping financial markets. They ask a simple question: as overall AI consumption changes over time, which companies’ stock prices tend to rise with it — and which tend to fall?
To be clear, the economists aren’t tracking how many AI tokens individual companies are using — which, let’s be honest, would be way cooler. Instead, they’re looking at the rising tide of overall AI usage and asking which stocks rise and fall with it.
Part of their analysis is classic finance theory. The idea is that stock prices tend to reflect investors’ expectations about the future, so they can offer a window into which companies Wall Street sees as most positively or negatively impacted by the rise of AI.
Of course, investors can be spectacularly wrong. Hello, the long history of financial bubbles. That said, if you want to know what markets expect AI to do to the economy, stock prices are a compelling place to look.
Not surprisingly, the economists find that as AI usage has grown, financial markets have treated some companies very differently than others. The companies seen as the biggest AI beneficiaries have enjoyed higher stock returns — a pattern the researchers call an “AI premium.” More interestingly, they find it’s not just tech stocks that appear to earn that premium. Their findings suggest investors expect AI to benefit a wide range of companies and industries across the economy.
“The story of AI is no longer just a Silicon Valley story,” Tsyvinski says about their paper’s findings. “Financial markets already see Main Street being impacted.”
What’s most exciting about this study is probably less its findings and more that it offers a glimpse into future research possibilities. Instead of relying on surveys, earnings calls, or company announcements, economists may be able to study the spread of AI by following the digital paper trail created by AI tokens. AI tokens could become a powerful new source of data, allowing researchers to track AI usage in almost real time and study its economic effects, with a kind of precision not possible in past technological revolutions.
An exciting way to measure the AI economy
Borri, Tsyvinski, and Liu’s analysis relies on a relatively new source of data: OpenRouter.
OpenRouter is a kind of one-stop shop for AI models. Instead of contracting separately with OpenAI, Anthropic, Google, and dozens of other AI companies, developers can access hundreds of models through a single interface. As businesses and workers have become more conscious of their mounting AI token bills, they’ve turned to platforms like OpenRouter to compare prices, switch to cheaper models for less complex tasks, and manage their token spending. In the process, OpenRouter has amassed rich data on usage of AI tokens (and this data is anonymized to protect users’ identities).
The economists analyze the use of 380 trillion AI tokens between January 2024 and April 2026. That represents around 2 percent of monthly global AI usage. They then combine weekly growth in tokens, spending, and active users into a broad measure of AI consumption, which they call the “AI Factor.” Next, they estimate which companies’ stock returns move most strongly with changes in that factor.
The economists find that companies whose stock prices were most sensitive to increases in overall AI consumption subsequently earned significantly higher returns. The companies that Wall Street appears to view as the biggest beneficiaries of AI outperformed those viewed as the least likely beneficiaries by about 0.64 percentage points per week — the “AI Premium” described in the paper.
Of course, markets aren’t always right. These AI-related stock movements could reflect the wisdom of crowds — or simply a herd of excited investors hoping to cash in on a technological revolution that ultimately disappoints. In other words, take these findings with a grain of salt.
Probably the most interesting of their findings is that the AI premium can be found well beyond the tech world. Markets seem to believe that companies in industries ranging from airlines and cruise lines to utilities, industrial manufacturers, retailers, banks, and even waste management companies could all benefit as AI reshapes the economy. They also find that this “AI premium” is strongest for companies in the United States and Europe, and is much weaker in China and other emerging markets. Perhaps relatedly, they find that stocks are the most sensitive to increased use of the most technologically advanced, “frontier” AI models.
The economists even provide a list of S&P 500 companies with high AI premiums in their paper. The top five are: AppLovin, Carvana, Lumentum, Expand Energy, and Baker Hughes.
They also identify companies Wall Street appears to view as AI’s biggest losers. The bottom five are: Moderna, Estée Lauder Companies, ON Semiconductor, Skyworks Solutions, and Aptiv.
There are a bunch of caveats to their analysis. First, this is a working paper that has yet to be peer reviewed. Second, OpenRouter’s token data likely provides a skewed picture of AI usage. People who use OpenRouter are likely sophisticated, heavy users of AI who are trying to save money as they shop between competing models to get the best bang for their buck. Their data doesn’t represent the sort of average consumer, who may have a ChatGPT or Claude or Gemini monthly subscription and sticks with using those.
Perhaps most importantly, even if the researchers have correctly identified the companies investors expect to benefit from AI, that doesn’t mean those expectations will prove right. And if markets are reasonably efficient, much of that optimism may already be reflected in today’s stock prices. So buying the stocks they identify as potential AI-related winners and selling the ones that could be AI-related losers is by no means a good financial strategy. This is not financial advice!
Still, the paper’s biggest contribution may not be what it says about today’s stock market. It may be that it points toward a new way for economists to measure the spread of AI through the economy. We welcome this era of AI measurement-maxxing. There are still many big economic questions about AI, and better data is rarely a bad place to start.