Abdul Qadeer

This really hits on something a lot of people miss when they hype AI as “able to build anything.” It can generate code, systems, even entire products—but that doesn’t mean it understands why those things should exist or how they behave outside the patterns it’s seen. In a way, it feels like we’ve optimized for output before truly solving understanding. What stood out to me is how similar this is to how modern AI is trained—more like “growing” something than engineering it step by step, which explains why it can be incredibly capable yet still unpredictable in deeper reasoning. I think the real edge right now isn’t just using AI to build faster, but pairing it with strong human judgment—especially in defining problems, not just solving them. Curious how others see this: do you think better tooling will close this “understanding gap,” or is it something fundamentally different from how current AI works?

Good take, but I think it’s both. Infra handles the load, but good architecture makes sure you don’t waste it. You can throw servers at a bad system and still crash. Real scalability is using resources efficiently and having enough of them.