When competition intensifies, innovation accelerates. This pattern repeats across every technological frontier.

Geographic clustering of research creates feedback loops: talent attracts capital, capital funds infrastructure, infrastructure enables breakthroughs. The distribution of model releases maps directly onto these concentrations of capability.

Dominance duration shrinks as competition intensifies. The faster things move, the harder any lead is to maintain.

This compression of tenure at the top follows a power law: early advantages compound, but so do the efforts to overcome them. When multiple well-funded teams pursue the same objective, breakthroughs arrive from unexpected directions.

Open systems eventually catch closed ones. The lag shrinks as knowledge diffuses and techniques become reproducible.

Proprietary advantages erode when the underlying science is understood. Once a capability is proven possible, independent teams can reverse-engineer the approach. The question shifts from 'if' to 'how soon.'

Capabilities stack. Once a modality is solved, it becomes a baseline expectation rather than a differentiator.

Each new input type—vision, audio, video—follows the same adoption curve: initially rare, then common, then required. Systems that can reason across modalities unlock compound capabilities unavailable to single-mode approaches.

Cost deflation in compute-intensive industries follows exponential curves, not linear ones.

When infrastructure scales and competition increases, prices don't just fall—they collapse. Each order-of-magnitude reduction unlocks new use cases that were previously economically impossible, expanding the total addressable market.

Every measurement instrument has a ceiling. When systems exceed it, we learn about the test, not the system.

Benchmark saturation is a signal of progress, but also a limitation. A test that everyone passes reveals nothing about relative capability. The search for harder evaluations is itself a form of scientific inquiry.

Objective performance and subjective preference measure different things. Both matter.

Test scores capture what a system can do in controlled conditions. Human preference captures how it feels to use. The gap between them reveals the difference between capability and experience—a distinction that matters for real-world deployment.