Productivity Is No Longer Free
Per-token prices are falling, yet enterprise AI bills are exploding. Both are true, and the reason rewrites who controls productivity.
In May, the president and COO of Uber sat down for a podcast and said the thing most executives spend energy not saying.
Andrew Macdonald was asked whether Uber’s surging use of AI coding tools was translating into anything customers could feel. Uber had pushed adoption hard — an internal leaderboard ranked teams by how much AI they used — and the engineers responded. Roughly 70% of committed code was now AI-generated. By every internal dashboard, the program was a triumph. Macdonald’s answer: “That link is not there yet.” He added that the company would now have to weigh token consumption against headcount. The CTO, separately, described the team as back to the drawing board.
This is a company that told its engineers to use AI, watched them do exactly that, and discovered four months into the year that it had spent its entire 2026 budget for AI coding tools.
The easy read is that AI was overhyped and the productivity never showed up. The more interesting part is the nature of the AI bill, and how that bill affects the org chart.
The cheaper it gets, the more it costs
The per-unit price of AI is falling, fast. Gartner projects that running a sophisticated model will cost roughly 90% less in 2030 than it does today. Every reassurance you’ve heard that AI is getting cheaper is true.
And it is entirely consistent with your bill tripling. Agentic workflows — the kind where a tool reads an entire codebase to plan a multi-step change — consume tokens at a scale that swamps any per-unit discount. The cost didn’t rise despite the tool getting better. It rose because the tool got better. Good enough that everyone used it, constantly, for everything. Uber’s engineers weren’t misusing the tool when they vaporized the budget. They were using it exactly as intended.
This is what the productivity pitch never priced in. The whole case for AI adoption was that it makes everyone more productive. Under metered pricing, the most productive employee is now the most expensive one. The better the tool works for them, the more they reach for it, the larger their line on the invoice. Productivity and cost, which a salaried headcount model holds apart, are now on the same curve.
Who owns a cost that behaves like this
For two decades, enterprise software ran on a premise so stable nobody examined it: pay per seat. A license cost the same whether the employee used it one hour or eight. The cost was a flat line you could draw a year in advance, and finance signed off as a formality.
Metering breaks the flat cost predictability. It also does something subtler in the process: it makes consumption visible.
A flat-rate license hid usage inside a fixed number. Nobody in finance knew or cared whether a team ran a Jira instance or Adobe Analytics tool hard or let it sit. Token billing turns that invisible usage into a reported figure — a line item on the income statement, a number that shows up in the quarterly review, something someone has to explain and defend.
Visibility changes the game. Companies won’t cap productivity — no executive issues a productivity halt — yet because spend now correlates directly with how much the tool gets used, capping the spend means rationing the productivity. Determining the output the company is willing to pay for, task by task, in a way it never had to when the cost was flat.
For thirty years, the person who chose the engineering team’s tools was the engineering leader. The CTO evaluated the tool and the cost — a predictable per-seat line — was finance’s rubber stamp. The decision lived with technology.
When the cost of a tool grows faster than the value it creates, the binding question shifts from “is this good” to “what will this cost, and can we model it.” That question belongs to the person who answers for the number on the statement. The clearest signal came in April, when Deloitte published a guide to AI token economics written for chief financial officers — a category of document that didn’t exist eighteen months ago. Advisory firms don’t build CFO playbooks for problems that belong to someone else. The playbook appeared because the decision moved.
You can watch the move happen even at companies that own the alternative. Microsoft wound down its internal Claude Code pilot this spring, redirecting engineers to Copilot. Microsoft escaped variable billing by using its homebuilt solution. Most enterprises can’t.
We have run this play before
The pitch — convert a fixed cost into a variable one, scale it with demand, shed the burden of carrying it — is the exact pitch of offshoring. Replace the salaried call center with a contract that flexes: pay for what you use, nothing when you don’t. For a decade that was the most fashionable line on the income statement.
The savings were real, specifically, where the work was genuinely commoditized. Where the task carried invisible value — institutional memory, judgment, the quality that doesn’t show up until it’s gone — the savings curdled, and the work came back, reshored at a premium once the true cost surfaced. The pattern wasn’t that offshoring failed. It worked exactly to the degree the work was a commodity. The failure was that companies were bad at telling the two apart until the quality dropped or the bill arrived.
AI-on-a-meter operates in a similar way. Token-priced automation will permanently take over the cognitive work that was truly a commodity. It will fail — expensively — on the work that only looked that way. The CFO now holds the lever on that bet, and the offshoring record is not encouraging about how well that bet gets made.
So the layoffs continue, for now. Cut headcount, add an AI line, present a leaner company to shareholders this quarter. But the swap trades a fixed cost for a variable one, and calls the difference savings. The last time the org chart believed that trade was permanent, it spent the following decade quietly hiring back the parts it had misjudged. This time, the bill arrives faster — and itemized by the token.