A working theory of AI-native go-to-market.
Research from building these systems in production. How we work, the evidence behind it, and what broke along the way.
We're working through three questions. Everything here is dated, and we revise as we learn more. Before a piece is final, we send it to the people it cites.
Why does AI stall in go-to-market?
5 voices shaped how we work. Where their thinking changed what we do, we say so.
By Sam GongFounder, Fresh Context
The Jagged GTM Frontier
Two adjacent GTM tasks sit on opposite sides of what AI can do, and the line between them is invisible until you cross it. That line is the jagged GTM frontier. Where it falls comes down to the context AI can reach and how clearly the task is defined. Both are fixable.
Pre-publication - out for commentRead the diagnosisSources, thinking & influences
This is the reading and thinking behind the pieces above. Search it, or click any node to see what it connects to.
What we're writing now
The next two questions. Notes for both are already in the graph above. Each one moves up the page when it publishes.
What does AI need to know, and where should that knowledge live?
Go-to-Market Context Is a System, Not a Document
Owned, refreshed, and curated, across three layers - conceptual, canonical, deterministic. One architecture, many systems.
In draft
How does a whole GTM org get good at this, not just a few people?
AI-Native GTM Teams
Redesign the roles, don't just add tools. The build/run split, and capability that spreads by design instead of luck.
In draft