Building software used to be slow and expensive. The bottleneck was always engineering — not enough developers, not enough time, not enough budget.
AI changed that. Today, anyone can generate thousands of lines of working code in an afternoon. The cost of building is approaching zero.
But this doesn't directly translate to building better products.
Code was never the hard part. The hard part was always figuring out what to build, for whom, and why it matters.
Before AI, product managers existed to protect limited engineering resources. Two developers, six months — you had to be ruthlessly intentional. That constraint forced clarity.
Now the constraint is gone. You can build everything. And so people do. The result: sprawling codebases, features nobody uses, products that technically work but don't solve the problem.
AI is remarkably capable. But without someone holding the reins, it will happily over-engineer, over-build, and take you down paths that feel productive but lead nowhere.
The more powerful the tool, the more you need someone directing it with intention. Someone who:
- Understands the actual problem before touching code
- Decides what NOT to build — harder than ever when building is free
- Keeps AI focused — power without direction is just chaos
- Makes sure real people actually use the thing
The companies that get this right will ship products that feel like they were made by a team of 20. The ones that don't will ship code that looks impressive on GitHub but collects dust in production.
The bottleneck has moved. The question isn't "can we build it?" anymore. It's "should we?"
Have a product that needs building? Let's talk about what's worth building — and what isn't.
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