The platform shift is moving from infrastructure to applications.
Mobile unlocked new consumer behaviors because the device was always with us, location-aware, and socially connected. AI-native applications create a similar behavioral shift: software can now reason, draft, operate, personalize, and complete work with the user instead of waiting for step-by-step input.
The investable layer is not a generic chat box. It is workflow replacement, proprietary context, distribution insight, and product behavior that becomes more useful as customers use it.
Tiny teams have more leverage than the market has priced in.
The old startup pattern assumed bigger teams were required to build, sell, support, and operate. AI changes the labor curve. A small team can prototype faster, research customers faster, produce content faster, serve customers faster, and iterate with less burn.
- High revenue per employee matters earlier.
- Product velocity is a funding signal.
- Social distribution and founder-led GTM are durable advantages.
- Capital efficiency is a strategy, not a consolation prize.
The founder filter should be explicit.
I am focused on U.S.-based pre-seed and seed founders building AI-native applications with evidence of velocity. The company should have a clear customer, a workflow being replaced or transformed, early traction, and a product that would be weaker or impossible without modern AI.
I am usually not the right fit for non-U.S. companies, Series A or later rounds, hardware, crypto/Web3, or teams that have no evidence of product speed.
The help is operator-heavy.
I am most useful on GTM strategy, customer intros, fundraising story, investor targeting, operating cadence, hiring choices, and pressure-testing whether the product is truly AI-native. The goal is to help founders turn early pull into repeatable motion without adding unnecessary complexity.