Recently a colleague told me how he runs multiple instances of Claude Code in parallel. He was very excited about how this speeds up his product’s development. But I think this is a trap.

The trap is twofold.

The Technical Trap

The first level is of course the huge potential for bad architecture and hard-to-find bugs. Despite increased context windows, coding agents still do not analyze the entire codebase when adding new changes. They make assumptions and decisions based on incomplete information – unless their coding is preceded by meticulous planning with plans thoroughly reviewed by a human who knows what he is doing.

And I suspect very few of those carried away on the wings of AI coding agents do detailed planning or carefully review the generated code. Why would they? The whole appeal is speed and reduced effort. The moment you introduce rigorous review and planning, you lose much of that perceived benefit – and slow down.

When you run multiple agents in parallel, your mind quickly switching context and your attention spread over many windows, you’re multiplying this problem. Each agent makes its own assumptions and decisions. The result? A codebase that works – until it doesn’t. And when it breaks, good luck figuring out why, even with the help of those very AIs that built it.

The Business Trap

But the second level is even more insidious: building too much of the product too quickly, creating too many features before having validation from real users, on the market.

Here’s the uncomfortable truth: the primary risk in product development is not how to develop something but rather how to sell it, how to match client needs. Most products fail not because of poor technical execution but because they solve problems nobody actually has – or solve them in ways nobody wants to pay for. Or they do both, but lack marketing skills and advertising funds.

Yet you do not face those problems until you try to sell the product to first users. It is very easy to get pulled into fascination with our own creations, especially so if they appear quickly and with less effort. And we wake up one day with a technically impressive product that nobody wants to buy.

AI coding agents amplify this risk dramatically. They remove the natural brake that slow development provided. When coding was hard and time-consuming, you were forced to validate ideas before investing months of work. Now you can build entire features in days. That sounds like progress, but it also means you can go very far in the wrong direction very quickly.

The problem isn’t AI coding agents themselves. They’re tools, and like any tool, they can be used well or poorly. The problem is the mindset that more features, faster, equals better product.

It doesn’t. It never did.

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