AI prices dropped 97% since 2023. So why are AI bills 3x higher?

Something I keep running into with engineering and GTM teams: the per-token price of AI has fallen roughly 97% since early 2023. Models that cost hundreds of dollars per million tokens now run under $1. And yet enterprise AI bills are about three times higher than two years ago. Both things are true at once, and the "AI saves money" pitch never accounted for it.
Token spend per company is up 13x since January 2025. Goldman Sachs projects agentic AI could push token consumption up 24x by 2030. The price keeps falling. The bill keeps climbing.

Why falling prices don't mean falling bills

It's cloud storage in the early 2010s all over again. Cost per gigabyte cratered year after year, and AWS revenue still grew every quarter, because the amount people stored exploded. Price per unit dropped, total spend soared. AI billing is following the same arc, just faster. Once vendors moved from seat licenses to usage pricing, your bill stopped tracking headcount and started tracking consumption. And consumption is outrunning price cuts by a wide margin. (This seat-to-usage shift is reshaping SaaS pricing well beyond AI vendors, we dug into that here: B2B SaaS Pricing in 2026.)

Agentic workflows are the real multiplier

A single chat interaction is cheap, a few hundred tokens. Agentic workflows are a different beast. Reasoning models generate long thinking chains, agents call themselves in loops, context windows grow with each iteration. One agentic coding task can trigger 5 to 20 model calls and burn 10 to 50x the tokens of a chat exchange. Gartner pegs agentic workloads at 5 to 30x the compute of a standard chatbot call.

The uncomfortable part: the more autonomous and "efficient" your AI usage looks on the surface, the more expensive it quietly becomes underneath. Uber reportedly burned its entire 2026 AI coding budget in four months and had to cap spend at $1,500 per employee per month. If they didn't see it coming, most mid-market teams are flying blind.

The costs that never show up on the invoice

Model licensing feels like the main expense. It's typically around 20% of true total cost of ownership. Data preparation absorbs up to 45% of project effort in many organizations. Integration work, prompt drift, shadow AI usage, uncontrolled agentic overruns: none of it appears on a per-token invoice. It shows up months later as engineering hours and budget conversations nobody wants to have. (It's also why the build-vs-buy math surprises so many startups: agency vs. building in-house.)

The fix is operational, not strategic

Freezing budgets or rolling back AI is the wrong response to the wrong diagnosis. Three levers actually work:

Routing: not every task needs a frontier model. Sending simple tasks to smaller models cuts costs 30 to 85% without degrading quality on those tasks.

Caching: semantic caching serves stored results when a new prompt is functionally identical to a previous one. On high-repetition workflows it saves 30 to 90%, and almost nobody does it.

Visibility: if you can't trace which team, workflow, or product line drives spend, you're reacting to bills instead of shaping usage. This is what makes the other two levers possible.

The teams solving this aren't the ones spending less on AI. They're the ones spending smarter.

Curious what this community is seeing: is anyone actually tracking token spend per workflow? And if you've implemented routing or caching, what savings did you actually get versus the claimed ranges?