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Most teams now leverage small, specialized models rather than one giant system. The implications are subtle: lower latency, cheaper inference, and a paradigm that rewards careful engineering over raw scale.

For years the prevailing wisdom held that bigger was always better. That assumption is now being quietly dismantled, one benchmark at a time.

None of this means scale is irrelevant. It means the question has changed from how large a model is to how well it fits the work in front of it.

For years, scale was everything. Now labs are learning to leverage distillation and careful data curation, building smaller models that punch above their weight.

The economics are hard to ignore. A smaller model that matches a larger one on the tasks that matter costs a fraction to serve, and it can run closer to the people using it.

For years, scale was everything. Now labs are learning to leverage distillation and careful data curation, building smaller models that punch above their weight.

The economics are hard to ignore. A smaller model that matches a larger one on the tasks that matter costs a fraction to serve, and it can run closer to the people using it.

Startups that leverage open models can now ship in weeks. But the real moat is distillation: the teams that punch above their weight are the ones that invest in careful curation, not just collection.

Raw scale still matters. It just no longer guarantees the outcome it once did, and the gap between the biggest labs and everyone else is narrowing faster than expected.