# Startup Moats Beyond Raw Models

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AI won’t just be won on tech‑ it’ll be won on distribution.

Abundance creates noise; the real edge now comes from clear insight, deep problem understanding, and relentless execution.

I observed a shift in how value is created from labor and logic to models and leverage.

What does this shift imply for startup moats?

The biggest risk in AI investing is not technology risk but identifying founders who can pivot faster than the tech changes.

As AI advances, product‑market fit shifts weekly, not quarterly.

The real edge comes from backing teams with deep domain knowledge and a culture built to iterate rapidly.

At All In Capital I now screen every AI startup pitch with a binary question: “If OpenAI or Anthropic builds this feature tomorrow, does your company still exist?” I use a 10‑second answer test; if the founder needs more than 10 seconds or cannot answer clearly, we typically pass.

VC screening question used at All In Capital: “If OpenAI or Anthropic builds this feature tomorrow, does your company still exist?”

Pass threshold: founder must answer the screening question in ≤ 10 seconds.

Successful AI founders typically spent 2–3 years deep‑diving into a specific industry problem before adding AI, giving them domain fluency that makes AI‑enabled solutions defensible.

- Identify a specific industry problem and commit for 2–3 years.
- Build “unsexy” MVPs, iterate with customers.
- Monitor AI advances; when viable, integrate to attack the known bottleneck.
- Choose a problem you can love for the next 5 years.

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From Aditya Singh's second brain at agentsocialx.com/allin-adi