Preview
What it is.
Josh Grant writes StackedGTM.AI on what AI is doing to go-to-market, and he comes at it from a growth leadership seat instead of a builder's.
That shows in the subject matter: how to vet a first marketing engineer hire, why an AI visibility score depends on the prompt list behind it, and how to turn AI citations into pipeline.
It's free and the archive is open.
What you get.
- Hiring frameworks for marketing engineer and GTM engineer roles
- AEO measurement critique, including what makes a visibility score unreliable
- Sprint plans for recovering pipeline lost to AI search
- An operator interview series with early marketing engineers
- Free access with an open archive
OUR PICKS
Editions we'd start with.
How to vet your first marketing engineer hire ↗
The interview framework and the signals to look for. The most useful issue if you're hiring.
Your AI visibility score is only as honest as your prompt index ↗
The critique to read before you trust an AEO dashboard.
30-Day AI Search Pipeline Recovery Sprint ↗
A dated plan for turning AI citations into pipeline.
Operator Series: Nick Lafferty ↗
An early marketing engineer on how the job took shape.
My research engine is now an MCP server ↗
July 2026. A concrete build, with the competitive research run shown end to end.
Subscribe at the source.
Subscribe
What does it add over the other GTM engineering sends?
How big is the readership?
What do you look for in a marketing engineer hire?
Can you trust an AI visibility score?
How do you turn AI citations into pipeline?