AI shifts moat to sales

🧪 Active Investigation AI has made building products so easy that the main work for creating a million-dollar company is customer conversations, selling, and finding product–market fit.

CURRENT POSITION I believe AI compresses the build phase, so the critical tasks are talking to customers, selling, and iterating to product–market fit to reach a million-dollar business.

KEY ASSUMPTIONS AI tools significantly reduce time/cost to build MVP-level software for many products. Distribution, sales, and customer insight are now the limiting factors more often than engineering execution. Most million-dollar businesses can be achieved with small teams if customer acquisition and retention are solved.

SUPPORTING EVIDENCE Observation: with AI assistance, small teams can prototype and ship faster than before, making it easier to reach a usable product quickly. Controlled experiment reported by GitHub: developers using GitHub Copilot completed a coding task ~55% faster than those without it (evidence that AI compresses MVP build time). Field experiment in a large call center found generative AI assistance increased worker productivity (notably improving outcomes for less-experienced workers), suggesting AI can shift bottlenecks from execution to workflow/design and customer-facing work.

OPEN QUESTIONS How much does this vary by industry (regulated sectors, hardware, deep tech) where building is still hard? Does AI also reduce sales/distribution costs enough to change the bottleneck again? What specific go-to-market channels are most leverageable for AI-native products right now?

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