Simulating, Testing, Iterating

We have talked a lot about AI making products faster to build. Vibe coding, AI-assisted development and increasingly capable design tools have dramatically compressed the distance between an idea and a functioning product. But I think there is another shift happening that may be just as important: AI is also changing how quickly we can learn what is wrong with a product.

Traditionally, zero-to-one development involves a slow loop. You research, run discovery, build an MVP, put it in front of a small group of users, wait for feedback, work out what is signal and what is noise, then rebuild and repeat. Early feedback is often limited, biased or incomplete, and some problems only emerge after enough people have used the product in enough different ways. We all know the first version is rarely the real product; the real product gets built through iteration.

AI gives us a way to collapse part of that journey. We can now generate realistic data, run workflows against different assumptions, simulate edge cases, test decision logic and expose inconsistencies before a real customer ever encounters them. AI sandboxes can help identify broken flows, missing states, strange combinations of actions and gaps in product logic far faster than a small beta group ever could. Combined with rapid AI-assisted build, that means we can test, learn, change and redeploy in a much tighter loop.

This does not remove the need for discovery. It changes what discovery is for. Real users are still essential for questions around trust, behaviour, comprehension, willingness to adopt, whether the interface feels intuitive and whether the product actually fits into a real working environment. But we should not be using scarce customer time to discover problems that could have been surfaced through simulation and structured testing first.

The real advantage, then, is not simply that founders can build faster. It is that we can arrive at the market with a product that has already been through far more cycles of challenge and refinement. We can use synthetic data and AI workflows to front-load iteration, reserve human discovery for genuinely human questions, and shorten the uneven path between first build and something much closer to product-market fit. For founders who learn how to use that well, the gain is not just speed. It is a better learning system around the product itself, which can get you to PM fit that much quicker and save months in your runway.