I sit down with Elie Steinbock to unpack loop engineering and how to run a business on loops. We start with the roots of the idea in the lean startup and Toyota's manufacturing, then move into practical, copy-ready workflows for SEO, Facebook ads, and product feedback. Elie walks through a live Google Search Console example on Draft Fantasy and shows how to set up an SEO loop that runs once a month for years. The core promise for listeners: hand repeatable business work to an AI agent that measures an objective metric and improves over time. By the end, you know how loops work and how to launch your first one today.
Timestamps
00:00 – Intro and episode promise
02:54 – What is Loop Engineering
06:51 – Loops with AI agents: build and verify
11:17 – Example of Loop: SEO as an objective-metric loop
15:29 – Setting up the SEO loop and tools
25:27 – Cost and token economics
29:05 – The Paid ads loop
33:10 – The product feedback loop
36:25 – A minimal viable loop for every channel
39:21 – Closing Thoughts
Key Points
Loop engineering means giving an agent a task, an objective metric, and a stop condition so it improves on a schedule.
The lean startup and Toyota's build-measure-learn cycle map directly onto AI agents.
An SEO loop connects to Google Search Console and Data for SEO, then pushes rankings up month over month.
These loops run cheaply — often a few dollars per monthly run — which beats the cost of an agency.
The same pattern extends to Facebook ads, and a product feedback loop stands as the ultimate version.
Start small with a minimal viable loop tied to a clear metric like impressions or ten likes.
Numbered Section Summaries
The Promise of Running a Business on Loops I open by asking Elie what listeners will walk away with, and he frames the whole episode: use loops to automate SEO, ads, and more. We agree the aim is clear, copyable workflows people can launch today.
Where Loop Engineering Comes From Elie traces the recent buzz to Boris from Claude Code and Peter Steinberger, plus a joking tweet from his friend Dimitro about software that builds itself. He grounds it in the lean startup's build-measure-learn cycle, which itself grew from Toyota's lean manufacturing.
Loops With AI Agents: Build and Verify Elie explains the agent version: a build step paired with a verify step and a clear stop condition. He uses Inbox Zero's evals as an example, where the agent keeps adjusting the prompt or model until accuracy passes 90%.
The SEO Loop We dig into SEO as the flagship example, where Google ranking serves as a clean, objective metric. Elie describes a loop that runs once a month, learns from the last run via a markdown memory file, and steadily climbs the rankings.
Setting It Up on Real Data Elie shows his Draft Fantasy Search Console, connects the agent to Google Search Console and Data for SEO, and runs the loop live in Codex. He shares the Atom Eve prompt as a deeper template people can copy.
Cost and Token Economics I raise Ross Mike's skepticism about loop buzz and token spend, and Elie makes the case that an SEO loop stays cheap — often under five dollars per monthly run. He adds that Max-plan users have plenty of headroom, while tight budgets suit cheaper open models like GLM 5.2.
Ads, Product Feedback, and the Ultimate Loop We move to a Facebook ads loop that tests copy and creative variants, favoring a mix of human hooks and AI optimization. Then Elie describes the product feedback loop — reading customer feedback, analytics, and logs to prioritize and ship — as the closest thing to a business that builds itself.
Starting Small We close on the minimal viable loop: begin with one channel and a modest, verifiable metric like impressions or ten likes, then let it compound. Elie and I agree that every part of a business could sit on a loop, and starting one today makes for a low-risk experiment.
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