Uber's engineers burned through the company's entire 2026 AI budget for coding tools in about four months. The overspending starts with a question few AI leaders ask directly: What does it cost to finish a single task once the people checking the model's work and the hardware running it are on the bill?
Joe Markwith is AI Transformation Strategist at CDW, the IT solutions provider serving business and public-sector customers. He has helped organizations adopt emerging technology since 1986, with work spanning healthcare and government relations. At CDW, he steers MOAT, short for Mastering Operational AI Transformation, a methodology the company built in early 2023 after ChatGPT reached the market. Decades of watching technology waves arrive have convinced him the business design has to come before the build.
"People are chasing use cases without thinking about the total cost of ownership. What AI really is in the business landscape is an operating model, a new operating model for your business," Markwith says. "You need to handle the business acumen first, before you get into the use case." He puts AI strategy on the CEO's desk from day one. "Finance, legal, and security have to be part of that solution and have their hands in the batter, because they're going to protect the company on all those fronts." Markwith affectionately calls those functions the departments of no.
The Ferrari rarely needs to leave the garage
Markwith's issue is the habit of sending every prompt to the priciest model on the menu, including requests that amount to a Google search. "Everyone wants to drive the Ferrari, but it costs more money to drive the Ferrari. I would argue that 80% of what's going into that model doesn't require the Ferrari engine," he says. "You can do a lot with open-source models on local hardware, and people aren't planning ahead for how to properly architect their solution."
Buyers have started testing that argument with real traffic. The CEO of startup Lindy told CNBC that moving his workloads to cheaper open-weight alternatives sent costs crashing, and Fortune has tracked U.S. enterprises fine-tuning open-source models for industry-specific work while saving frontier models for the jobs that demand them.
The best roster beats the best player
I've watched more than one leadership team greenlight the flashiest demo in the room and then spend the next year defending it in budget reviews. Markwith leans on Moneyball to explain his fix. In the film, a general manager and a data analyst tune out the veteran scouts and build a team from affordable players who reliably get on base.
His approach scores every candidate use case the same way. "You do a very cold, analytical suite of metadata on each use case and start weighting and scoring each of those," Markwith explains. "At the end of those 10, 20, 100, 500 use cases, you get a roster of your top five or 10 players. Low cost, high return on value. They get on base, you can afford them, and those are the ones you test first."
Scoring also drains the politics out of prioritization. A department sitting at number 60 on the list gets a data-backed explanation, and its ranking moves only when the attribute data does. A ranked roster gives every experiment a financial case from day one, which is exactly what's missing when most AI pilots stall before they ever touch the P&L. Those early, affordable wins double as training for the organization itself. "A year is like many lifetimes in AI," Markwith says. "Get the operating model down first with quick wins. Then, when you run the differentiating and transformational use cases, their chances for success are exponentially higher."
The human on the roster changes the model's price
Cost per task gets far more interesting once a model stops short of full autonomy. In one scenario raised during the conversation, two models work through 100,000 support tickets, one cheap and resolving about 80% on its own, the other triple the price and closing 95%. The bargain looks obvious until every ticket the cheaper model can't close lands on a person, and that person's time becomes part of the model's price. "The question is, which human? What's the cost of that human?" Markwith says. "Is it still the expert-level resource we had before, or is it someone with a totally different temperament who's really good at managing agents, an agent whisperer?"
Who handles the leftovers can flip which model comes out cheaper. Routing escalations to a specialist built for supervising agents could free the veteran who used to do the job for work only an expert can handle, changing the economics of the cheaper model's missing 20%. That role barely exists on most org charts today, and companies that create it could land on a different model than their first analysis picked.
The API side carries its own exposure. Pricing models are changing, and GitHub's move to usage-based Copilot billing this June showed how quickly a predictable line item can turn into a meter. "The API cost they're charging us isn't what it really costs to deliver that intelligence. The prices are artificially low," he says. "Rate limits are being lowered, and tasks are getting shuffled off to lower-quality models. Shrinkflation is going to happen in token economics, because the economics demand it." A roster scored at today's token prices can look very different after the next pricing change,, so the use cases that make the cut this quarter may not make it next quarter.
Companies capping AI tool spend risk throttling the very engineers whose output justified the investment. "Once you give a team of engineers a tool and they find themselves more efficient with it, and you take it away from them, you're going to have demoralization and frustration, and you're going to lose people. Think about the knowledge drain the company is going to have," Markwith says. "People want to be two, three, four, five times more productive. It's hard to take that away from them and get them to stay."
That's the catch with treating AI economics like a Moneyball roster: cost alone can't pick the team. The calculation has to account for for what the model accomplishes, what humans have to do around it, what infrastructure it requires and what happens when the economics change. Cost per task gives executives a way to make those tradeoffs explicit before a use case becomes too expensive to unwind.
The views and opinions expressed are those of Joe Markwith and do not represent the official policy or position of any organization.
