TL;DR Most decision frameworks answer one of two questions: should I commit to this, or how should I think about it under uncertainty? Confusing the two is the most common failure I see. 37signals answers the first. Annie Duke answers the second. Pick the question first. The right framework follows.

A founder messages me: "I need a decision framework." I ask what the decision is. They cannot tell me. They want the framework first.

The framework is comfort. The decision is the work.

TL;DR Gokul Rajaram's 8 Moats framework, articulated in full on 20VC in March 2026, is the cleanest update to Hamilton Helmer's 7 Powers for the AI era. It is also dangerous if applied to a pre-seed or seed company without staging. By Rajaram's own admission, only three of the eight (Data, Workflow, Regulatory) are meaningfully assessable before Series A. The other five are post-scale phenomena that founders systematically claim and investors systematically credit. The Critical Path Layers framework supplies the missing discipline: it tells you at which CPL layer each moat earns its evidence, and what counts as evidence rather than intent. Without that overlay, the 8 Moats become moat theatre.

“The law of floatation was not discovered by contemplating
the sinking of things, but by contemplating the floating of things which floated naturally, and then intelligently asking why they did so.” (Thomas Troward)

A colleague recently shared notes from a roundtable in Paris bringing together VCs, Operating Partners, and key players from the French startup ecosystem. The conversation, by all accounts, was sophisticated. Concrete ROI metrics. Honest acknowledgments that "capital alone isn't enough." Thoughtful discussion of AI's impact on productivity and talent.

Reading through the summary, I was struck not by what was discussed, but by what wasn't.

One question was conspicuously absent:

If I had to start a VC or Operating Partner function from scratch today, knowing what I know and accounting for the three-year trajectory, what would I fundamentally do differently?

Instead, the discussion centred on incremental improvements to existing models. Adding GPT wrappers to knowledge bases. Thinking about AI for network matching. Debating batch formats versus continuous intake.

This isn't a criticism of that particular conversation. It's representative of where most of the ecosystem stands: mature enough for sophisticated execution discussions, not yet ready for uncomfortable structural questions.

This article is an attempt to ask those questions. Consider it food for thought.

The AI-Native Paradox presents significant challenges for startup founders and corporate innovators in today's rapidly evolving technological landscape. However, I find that Wardley Mapping offers a powerful strategic framework to navigate these challenges by providing situational awareness and enabling more informed decision-making (it is a kind of spatial "Where to play? How to win?" imho).