This honestly explains one of the biggest misconceptions around AI in engineering right now. A lot of teams think AI magically fixes weak engineering culture, but usually it just scales the existing problems faster. The Stripe vs Amazon comparison was especially interesting because it shows that the real moat now is probably: -systems -testing -feedback loops -and engineering discipline not just “who adopted AI first.” Also fully agree with the point that most startups are chasing velocity metrics without thinking enough about maintainability or reliability until production breaks. Feels like this is exactly why a lot of early-stage founders now care more about technical direction and architecture thinking early on instead of just shipping features fast. Seen platforms like foundersbar helping founders think more long term about MVP foundations and scalable systems instead of pure velocity.

The biggest mistake I keep seeing is founders choosing platforms based on trends instead of what the product actually needs. A lot of products honestly do not need heavy architecture early on. People spend weeks debating stacks before even validating the idea. But at the same time, I agree with the point that bad early decisions around data flow and UI structure become painful later. Especially once dashboards, permissions, workflows, and larger datasets start growing. The hard part is balancing: speed early on without creating a complete mess later That’s probably why more founders are looking for guidance around architecture and MVP planning now instead of just hiring random developers. Seen platforms like foundersbar leaning into that space a bit by helping founders think through product direction and technical decisions earlier.