Every so often the tech world appropriates a perfectly pedestrian word and imbues it with meaning well beyond its settled definition. For centuries “computer” was an obscure term referencing an obscure craft: people who performed mathematical calculations. And prior to the mid 1990s, anyone talking about a “web” was probably discussing spiders.
“Token” has broken out as technology’s latest etymological adaptation. The word has a long history in the tech world, and even richer origins in early English and Germanic languages. But as AI has ascended to primacy in tech, “token” has become a way for executives, financiers, and journalists to grasp the maddeningly opaque workings of an industry that seems to be swallowing our economy whole.
Until recently, anyone in the tech industry obsessing over “tokens” would have been talking about crypto, that much-maligned domain of hustlers, crooks, and con men. While crypto is a punchline today, just five years ago blockchains, “NFTs,” and “initial coin offerings” were all the rage. Venture capitalists like a16 were raising billion-dollar “Web 3” funds. Tokens lay at the center of this resurgent crypto world: these “programmable digital assets” offered a new approach to financing, governance, ownership, and more. The world was going to change, big time, and we had tokenization to thank for it.
Crypto never lived up to its own hype, and for the most part the sector has been marginalized by the Valley’s Next Big Thing – generative AI. I’ve always thought there was a natural symbiosis between what crypto is good at and what AI needs. Regardless of whether the two merge, it’s clear that right now, we really, really need a way to price the value of AI in society. And to get that work done, we’ve once again turned to the word token as a container for that work.
What’s the Business (Model)?
I’ll begin by stating the obvious: For better or worse, investments in AI related sectors has ballooned to bubble-like proportions. Major tech platforms now carry $1.65 trillion in AI-related debt. Just this year, nearly $1 trillion will be invested in data centers alone. In the first quarter of 2026, 75 percent of all VC dollars went into just five AI companies – OpenAI, Anthropic, x.AI, Waymo and Databricks. AI-related firms hold a market capitalization of more than $27 trillion – more than a third of the entire US stock market. Dozens of market analysts, lead economists at major banks, hedge fund managers, and even AI cheerleader-in-chief Sam Altman have warned that we’ve over-invested in AI.
With trillions at stake, a question naturally arises around return on investment. When will AI companies (and their clients) start producing profits that justify those enormous outlays? And most importantly, how will they do it?
I’ve been posing exactly that question to dozens of leaders in tech over the past few months, and so far I’ve found credible answers elusive. Almost everyone agrees that whatever models we come up with now, things are will change dramatically over the next few years. Some believe companies like Anthropic and OpenAI will end up becoming trillion-dollar cashflow machines, mostly through conquest of massive markets like software, legal and health services, and retail. Others believe those same companies will become commodity providers to a more distributed economic revolution that drives unprecedented growth across all market sectors. And more than a few have predicted a crash: a necessary correction jolting us into whatever new structures we’ll need to create to accommodate “intelligence as a service” in our society.
That last answer feels the most likely to me – it rhymes with society’s adaptation to the Internet in the years following the dot.com bust. However all that future growth ends up happening, “tokens” have become the consensus mechanism for how we’ll count the money along the way. You’ve probably heard the term quite a bit in the past few months – breathless stories of “tokenomics” and “tokenmaxxing,” which has cost organizations from Uber to the US Army hundreds of millions of dollars.
So what are tokens? According to my pal Google, in the context of AI, they are “the fundamental units of text that an AI model reads and generates. In English, 1 token is roughly equivalent to 4 characters or about 1/4th of a word. AI providers charge for usage on a per-token basis, categorized into distinct, billable stages.”
Put another way, tokens stand in for “delivery of value” by an AI service provider. You prompt AI, it burns a certain number of tokens to deliver you a response, and you get charged on a per token basis. This simple usage model is driven by the very real costs discussed above: Data centers, Nvidia chips, electricity, and billion-dollar engineers.
For the past year or so, corporate America drank from the token hose like there was no tomorrow – and the AI labs were happy to subsidize their true costs so as to capture new customers. But in the last few months, the bill has come due, and no one is sure how to split the check. All these economic realities are forcing the nascent market into necessary and predictable rationalization: What is the true value of AI? Might there be more efficient ways to acquire it than simply giving everyone a Claude Code account, then holding your nose when the invoice comes?
Again, History
Cast your mind back 75 years, when a remarkable new technology emerged offering “intelligence as a service.” The newfangled mainframe computer could run payrolls, calculate missile trajectories, and tabulate massive datasets like the US Census. But mainframes were unwieldy and expensive machines that took months to set up. It made little economic sense for corporations to have one of their own. Instead, early tech firms like IBM created “service bureaus” which allowed companies to “time share” mainframe computing resources. The bureaus metered each client’s usage based on how much time and CPU processing cycles they consumed.
Over the next few decades computing migrated from the centralized mainframe to the distributed personal computer, and the concept of a centralized compute resource fell dormant as corporations took their computing budget in house. But then came the Web, with its insatiable demand for domain hosting, bandwidth, and storage. Once again it made sense to “rent” your compute from a centralized “cloud” based on a usage model (a model which, by the way, was orders of magnitude cheaper and far easier to use than its predecessor). And it’s that very model – a centralized, metered service – that dominates AI’s current economic playbook. Only this time, the element being metered isn’t processing time or storage capacity, it’s token usage.
Will that model last, or will the AI industry see an evolution similar to what happened with computers? It’s hard to say, but one thing is certain: Intelligence as a service has to get far less expensive if it’s going to become ubiquitous. And plenty of trends seem to point in that direction. Far cheaper Chinese AI models now account for 41% of open-source downloads on Hugging Face, according to one report. The market is speaking: Paying for AI is fine, but overpaying is not.
As Enrique Dans, a keen observer of AI’s impact on business, puts it: “a growing portion of what was previously accounted for as human labor hours is now expressed in terms of computational consumption, iterations, context, agents, and tool calls.” The accounting method? Tokens. “The company that understands this transition will learn to manage tokens as a scarce resource,” Dans advises.
Dans is right, and in that insight lies another: When tokens become currency, everyone can compete on the cost of that currency. Just as it did with the rise of the cloud, the architecture of the internet will once again shift, with potentially tectonic implications. Only this time, the internet isn’t just an interesting sandbox where (mostly American) businesses are trying to figure out how to have a dot.com presence. This time, the whole shooting match is in play.
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