Not long ago, a young company named Retool configured an agent to schedule out-of-office notifications for employees automatically. That’s exactly the kind of tedious, low-value task agents are made to automate and would have been a nice little AI success story if not for the fact that the agent in question was consuming $10,000 worth of tokens a day without anyone noticing.
Retool’s not the only recipient of an unexpectedly enormous token bill this year, if it’s any consolation to them. Uber famously blew its entire 2026 AI budget in four months while another company understandably reluctant to identify itself spent half a billion dollars on AI in a single month earlier this year.
That was back in the “tokenmaxxing” era though, when at least some businesses briefly decided that burning more tokens faster is always, inherently better. These days, the tide has shifted toward tracking, controlling, and optimizing token consumption. The memory of those earlier headlines lingers nonetheless.
“Those stories created anxiety,” says Shane Cronin (pictured), head of FinOps & ITAM services at tech solutions giant SHI. “Then that anxiety accelerated the need for a lot of people to come together and try and find a way to solve for this more broadly.”
Come together they did too last month, when 44 organizations ranging from Accenture and SHI to ServiceNow and SAP formed the Tokenomics Foundation, an offshoot of the non-profit Linux Foundation dedicated to “establishing open industry standards, benchmarks and best practices for the economics of AI.”
And not a moment too soon either as organizations find themselves using AI more but struggling to measure, let alone manage, the spending associated with that usage. Indeed, nearly 47% of businesses surveyed by Futurum came in over budget on AI in the first half of this year even as per-token prices are plummeting. Just 5.6% came in below.
If the Tokenomics Foundation’s mission was simply helping decision-makers get a grip on all that spending, it would be doing good work. What it’s really up to, though, is even harder, more interesting, and more needed. Businesses, the foundation believes, can’t right-size their investment in AI without first understanding their return on that investment, and they can’t determine that return without first determining both what they’re spending on AI and how much value that spending is getting them. That last part in particular is a real problem for most companies, Cronin observes.
“They’re trying to calculate this and they’re trying to find a defensible position to the CFO, but almost nobody is really meeting that quality threshold,” he says.
The foundation, whose work on the topic is still very much in progress, aims to help close that gap by developing more rigorous techniques for measuring AI’s cost and value. On the cost side, that involves looking past token spend alone to what the foundation calls TCA, as in total cost of AI.
“We have to think a bit more broadly about the real cost of AI as being everything that actually goes into energy, capital, silicon, data centers, software licensing, and the labor that’s required to actually build and review and supervise AI,” Cronin says.
The value side of the equation is even more thorough, encompassing ten categories across three groups. “Direct dollar wins” assesses things like revenue added or costs reduced. Labor weighs capacity gains and workflows either augmented or fully automated. And “reported outcomes” quantifies product quality, speed to market, risk reduction (think IT outages or compliance issues avoided), and new capabilities, as in the newly acquired ability to accomplish things you couldn’t do before AI made them possible.
It’s a complicated formula, but an immensely valuable one if it holds up for MSPs evaluating value-based AI pricing schemes that businesses will embrace only if the value they’re paying for is precisely defined and demonstrated.
Learning how to achieve that precision will take time, but MSPs who complete the journey will have not only a powerful tool for selling high-margin AI services but a new service offering potentially as well. Businesses desperately want help justifying all of their AI bills, not just the one their IT provider sends them every month.
“If we can master that, that’s a huge opportunity for MSPs,” Cronin says.