Compute abundance is the pillar of the Age of Abundance in which trained intelligence — inference, reasoning, and perception — becomes as cheap and ubiquitous as electricity. Where Energy Abundance collapses the price of joules, compute abundance collapses the price of cognitive work: translation, tutoring, diagnosis, design, and decision support. Its defining feature is that marginal intelligence approaches the marginal cost of the electrons that run it.
The cost collapse of inference
Between 2022 and 2026, the price of serving a token from a frontier-class model fell by roughly two orders of magnitude, driven by algorithmic efficiency, model distillation, speculative decoding, and purpose-built silicon. The implication is not that one company owns a cheap oracle but that inference becomes a commodity input, much like bandwidth after the fiber build-out of the early 2000s. Once a capability is cheap enough to embed everywhere, the locus of value moves from the model to the application, the data, and the coordination layer around it.
Open-weight models as public infrastructure
Open-weight models — distributed under licenses that permit inspection, fine-tuning, and local deployment — are to compute abundance what standardized protocols are to the internet. They enable audit, resist single-vendor capture, and allow high-assurance sectors such as medicine, law, and defense to run inference inside their own trust boundaries. Critics note that "open weights" is not the same as "open training data" or "open governance"; the distributional question of who benefits from open-weight ecosystems is unsettled.
Personal AI and the agentic substrate
Personal AI denotes models that act on behalf of a specific person, with durable memory, loyal defaults, and cryptographically verifiable identity. In the abundance framing, personal AI is the user-side counterpart to institutional AI: it negotiates, filters, and represents. Whether personal AI becomes genuinely personal — or is rebranded surveillance owned by platforms — depends on Governance Protocols for identity, consent, and data portability.
Critiques and open questions
Skeptics argue that "cheap inference" masks an expensive training regime concentrated in a handful of hyperscalers, and that energy and water footprints are externalized to vulnerable communities. Others question whether marginal-cost language has any purchase at all when fixed costs dominate and training frontiers keep moving. The wiki treats these as live, unresolved questions — the pillar is load-bearing only if its benefits are widely distributed.