A flex in corporate America, AI ‘tokenmaxxing’ fades as workplaces look
to cut tech spending
[July 28, 2026] By
MATT O'BRIEN
A corporate fad of “tokenmaxxing” on artificial intelligence technology
is hitting its limits as workplaces throwing AI at everything are seeing
the costs rise without a similar spike in productivity.
What started as tech industry-fueled springtime hype over squeezing as
much AI-generated work as possible out of products like OpenAI's ChatGPT
and Anthropic's Claude has shifted to a summertime backlash.
“It's very easy to create something you don't need with AI,” said
Vincent Gusdorf, head of AI analytics at Moody's Ratings and author of a
new report that recommends a more disciplined approach.
"Tokenmaxxing” refers to maximizing usage of tokens — the building
blocks of generative AI that correspond to small pieces of text that an
AI system reads or writes. Each token is about three quarters of a word.
And there's typically a limit to how many you can use, with pricier
versions of AI products offering higher caps.
“As bills started to pile in, people realized that those new tools are
quite expensive and you need to use them wisely,” Gusdorf said.
Tech executives cast high AI usage as a badge of honor
Just a few months ago, Silicon Valley executives were promoting high
token consumption as a signal of high-performing employees. The
stereotypical tokenmaxxer was staying up late — perhaps ignoring their
significant other — while orchestrating an army of 24-hour AI agents
performing work on their behalf.
OpenAI CEO Sam Altman said in May he was “excited to see what will
happen with tokenmaxxing startups, both for how they work internally and
the products they can build.”
Nvidia CEO Jensen Huang said “if your $500K engineer isn’t burning $250K
in tokens, something is wrong.” Facebook parent Meta had an internal
competition rewarding token usage.
The trend boosted revenue for leading AI large language model developers
like Anthropic and OpenAI, but it fizzled as it became apparent it
wasn't necessarily the best strategy for everyone else.
Microsoft CEO Satya Nadella has admitted that tokenmaxxing can be
addictive but warned in a recent blog post that customers of those
models are paying twice for AI, first in spending on tokens and second
by feeding all their proprietary data to them. While promoting
Microsoft's own approach, Nadella's comments were unusual in the way he
raised doubts about the data protection assurances of leading AI
providers.
Palantir CEO Alex Karp went further, telling CNBC earlier this month
that something had gone “completely wrong.” He said he was channeling
the voice of American businesses privately “livid” about paying so much
for tokens that create no value.
“The basic view among enterprises in this country is, 'I’m going to
chillax and waste my time with tokens. I'm going to get no value and
they're going to get my IP,” Karp said.
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 Workplaces look more for better
‘routing’ of their AI work
Bain & Company management consultant Jue Wang said many of the big
businesses her firm advises have been taking a closer look at
returns on their AI investments.
“The token cost for them has been doubling, almost every other
month,” she said. “Let’s say $200 per developer per month. Multiply
that by 20,000 developers, which is often what we’re dealing with at
these companies, and that quickly gets you to a number that is not a
line item that any general manager has planned for.”
Sometimes that just means not using the AI equivalent of a
sledgehammer to crack a nut.
“Not everything needs a Claude Opus 4.6,” she said of one of
Anthropic's more capable models suited to software engineering or
deep research. “And yet you see so many companies, so many users,
default to using Opus for everything, including generating emails.”
That's led to a search for tools that do AI “model routing” — in
which easier queries get automatically sent to cheaper and more
efficient AI systems and more complex tasks go to more powerful
models.
Open-source AI models built in China offer less costly
alternatives
Software developer Hassan El Mghari said companies' sticker shock
over the “ridiculous amount of money” spent on subscriptions to AI
products from leading U.S. companies has led many away from
rewarding high usage.
“It’s better to kind of just empower employees on how to use this
stuff and let them use AI when and however much they need to,” said
El Mghari, who leads developer experience at the startup Together
AI, which supplies developers with a variety of “open-source” AI
models.
At the same time, those who favor racking up as many tokens as
possible are having a field day with new open-source models from
Chinese startups like Moonshot's Kimi or Zhipu's GLM, which nearly
match the capabilities of top U.S. models at a fraction of the
price.
“There is some validity to the theory that this could push
tokenmaxxing a little bit further,” said Raffi Krikorian, the chief
technology officer at Mozilla. “But if we look at the industry
overall, I think it's realizing that tokenmaxxing is a dumb thing.”
It's similar, Krikorian said, to how software companies once
considered how many lines of code a programmer wrote to be a good
metric of productivity. That later fell out of favor.
“I think tokenmaxxing is moving through the exact same pattern,” he
said. “I think this is going to be an interesting blip that we’re
all going to look back to laugh at in a year.”
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