McKinsey: Cheaper AI models, bigger AI bills

Good morning.

Intelligence is getting radically cheaper, but enterprise AI bills keep climbing anyway.

That was the central paradox McKinsey senior partners Tanguy Catlin and Lari Hämäläinen tackled on Tuesday during a McKinsey Live virtual session, “Improving the Economics of Agentic AI,” which highlighted the firm’s State of AI in 2026 survey.

“Intelligence at a certain capability level is getting a lot more affordable,” said Hämäläinen, who is also a leader in McKinsey Digital.

For example, GPT-4 launched in early 2023 at $60 per million output tokens.

Today, models with roughly GPT-4-class performance on established benchmarks can be served at a fraction of that cost—in some cases hundreds of times cheaper, with prices falling from tens of dollars per million output tokens to well below a dollar, he explained.

But as the cost of producing a unit of intelligence collapses, the amount businesses consume is exploding.

Models are getting cheaper per unit of capability while enterprises ask them to perform vastly more reasoning and work, especially through autonomous agents.

AI vendors are also capturing some of those efficiency gains through higher margins, Hämäläinen said.

In software development, for example, AI agents can repeatedly inspect, modify and rewrite entire codebases, generating far more code than a human developer would typically touch, he said.

The economics of agentic AI Companies are only beginning to understand the economics of agentic AI, Hämäläinen said.

Unlike traditional software, where the cost of running a task is relatively predictable, agents can take different paths to the same result, making costs highly variable.

The same task can cost up to 30 times more from one run to another, he said.

Much of that cost comes from the reasoning and repeated refinement behind the final output.