AI-native marketing operator
EITAN
GORODETSKY
I run an AI-native marketing operation — and I write about what it actually takes to build one.
15 years inside iGaming, tech and digital — €100M+ budgets, 20+ markets, teams of 120. Now building in the open.
Annual Budgets
Largest Team
Regulated Markets
Organic Growth
What I Do
Three things I do well.
Build
Build
AI-native systems that run the business: persistent memory, live integration, codified workflows, and gated autonomy.
Operate
Operate
A multi-pillar operation — agency work, owned ventures, products — run largely solo, on the AI layer.
Find
Find
The hidden costs and quiet inefficiencies the dashboards miss. Still the through-line.
Latest Writing
Essays on running an AI-native operation.
Frameworks & teardowns · The source-type map
What AI actually cites — a 3,050-answer audit
I ran the same commercial questions through ChatGPT, Claude and Google AI Overviews and counted every source they cited. The engines don't agree with each other, and what they cite depends on your category. The data, and what it changes about AI-visibility strategy.
Build-in-public · The Compounding Memory Loop
The memory layer behind the AI operation I run
Almost every AI setup forgets everything the moment you close the tab — so you become its memory. Here's the architecture I built so mine remembers, and briefs me instead.
Transformation & operator economics · The architecture-over-headcount shift
Architecture over headcount: why AI-native leverage stops scaling with people
For codified work, AI drives the marginal cost toward zero. When that happens, output stops scaling with how many people you hire and starts scaling with how good your operating layer is. That's a different economics — and most marketing functions are still budgeting for the old one.
Experience across
I'm building an AI-native operation in the open — and writing about what it takes. New essays, roughly monthly.
Read the writing →