New Delhi, Jan. 28 -- When we talk about moving the needle on AI, the discussions still gravitate toward models: bigger models, better accuracy, higher benchmark scores - not because they matter most anymore, but because they are the easiest progress to measure.

And when systems fall short, the instinctive response is to assume the model isn't good enough yet. That assumption persists because, for a long time, it was true. Early AI systems failed because they lacked capability. Then, as stronger models emerged, they unlocked visibly better results, and progress followed a clear, almost linear logic.

But that logic now breaks down in real deployments. Many organisations and governments already have access to capable models - often the la...