Is finding the right idea becoming the real bottleneck for coding today?
A few years ago, the biggest challenge for building a software product was usually technical skills.
Today, with AI coding tools, someone with limited coding experience can create a functional MVP in days or even hours.
But I’m starting to wonder if the bottleneck has simply moved somewhere else.
Building is becoming easier, but:
How do you know if your idea actually solves a painful problem?
How do you validate demand before spending weeks improving it?
How do you turn an AI-generated prototype into something people will actually pay for?
I’ve been exploring AI coding workflows recently, and it feels like the future is not just about having a better AI model. The real advantage might come from combining AI tools with better product thinking, user feedback loops, and execution.
Curious to hear from people who have actually shipped and monetized products with AI coding tools:
What was the hardest part for you — building the product, finding users, or getting people to pay?
Replies
I think the biggest challenge is still finding users and building something people are actually willing to pay for. AI has made building an MVP much faster.
One thing I've also heard from many developers is that token usage becomes a real problem as projects grow. Tools like Claude Code need more context, so token costs can increase quickly.
That's why I think AI coding memory agents will become more important. They can remember project context, reduce repeated token usage, and make AI coding more efficient.
AI is making development easier, but understanding users and solving a real problem is still what matters most.
@hitesh_bhakal That’s a good point. I’ve noticed the same thing — once a project grows beyond a simple MVP, context management becomes a real bottleneck.
The first version is usually easy to generate, but every new feature depends on previous decisions, database structure, edge cases, and product logic. If the AI doesn’t retain that context well, you end up spending more time re-explaining things or fixing regressions.
@qiwap yeah and this is reality. That's why ai agent memory tools are launching.
Totally agree. AI coding tools make it much easier to build the first version, but they don’t automatically solve the “is this worth building?” problem.
I’ve been using @AutoCoder.cc to turn ideas into quick prototypes, and it really helps reduce the time from idea to MVP. But the hardest part is still validating demand, getting real users, and figuring out whether people would actually pay for it.