Fail fast, fix faster: Why faster AI models beat smarter ones

The smartest model doesn't always win.

In agentic coding loops, a model that is 10x faster but only marginally competent can often fail its way to success before a frontier model finishes reasoning.

AJ Fisher breaks down the maths behind this counterintuitive result using diffusion models like Inception Labs' Mercury 2. Unlike autoregressive models that generate tokens sequentially, diffusion models refine outputs in parallel, removing a serial bottleneck that slows iterative agent loops.

If each attempt improves a solution by even 20%, dozens of iterations per minute quickly compound into faster convergence than slow, high-quality reasoning.

With live code examples and a bit of napkin maths, this talk shows why loop velocity is becoming the dominant factor in AI-assisted engineering, and why verification, not model intelligence, will become the real bottleneck.

The key question isn't "how smart is your model?" It's "how fast is your loop?"