For the last three years, enterprise AI has been told one story: pick the most powerful model and you win. Every launch brought a new benchmark, a bigger context window, and a sharper reasoning score. Boardrooms debated which model to bet on as if it were a once-in-a-decade infrastructure decision.

That story is running out of road. The gap between the leading models is narrowing every quarter, and most enterprises can now access near-frontier intelligence through a single API call. When everyone has access to the same brain, the brain stops being the advantage.

The next battle in enterprise AI will be won in context: the proprietary knowledge, history, signals and judgement that tell a model what your business actually needs. Models are becoming the engine. Context is becoming the fuel, the map and the destination.

Consider how quickly the landscape has shifted. A capability that felt magical 18 months ago, such as summarizing a 100-page contract or writing production-ready code, is now table stakes across multiple providers. Open-weight models have closed much of the gap with proprietary ones. Inference costs keep falling. Switching from one model to another, once a painful migration, is increasingly a configuration change.

This is the classic arc of any foundational technology. Electricity, cloud computing and bandwidth all followed it: a period of scarcity and differentiation, followed by abundance and standardization. Nobody today wins because they have a better power supply. They win because of what they build on top of it.

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