There is an algorithm
And it stacks the odds behind your next move
Next Move Theory is a methodology with a step-by-step algorithm for every product decision. It lays out every tactical and strategic move open to you and helps you choose the best, with the odds on your side. The foundations are open and free. AI skills run it on your product.
Runs in Claude Code and Codex. Free and open-source.
Recognize yourself?
Founder, pre-PMF
You've shipped the thing nobody cared about and spent months on features no one asked for. Building is comforting, but validating is scary. The next bet is another three months, and it feels like a coin flip.
Indie hacker / vibe-coder
You can ship anything now, but the last few got zero buyers. You don't want to get one more product nobody pays for.
Product manager
The last three things you shipped moved no metric. You've become a backlog manager, not a PM, shipping features without knowing why.
Product leader, VP/CPO
Every team decides a different way, none of it built on strong foundations — so you can't be sure any call is the right one, and you're moving blind.
Product marketer
You're launching into a crowded category where everything sounds like “yet another X,” and your channel tactics just aren't working.
You can't see all the moves in front of you, so you're betting on a guess. Here's the methodology that fixes that.
The essence of Next Move Theory
The operating system for product decisions
Next Move Theory is the algorithm behind every product call: how to find product-market fit, scale, position, grow conversion rates, improve retention. It works at every level, from this sprint's tactics to the company's strategy. Eight years of work, taught to 13,000+ founders and product leaders. The core of it is free in the open canon. The rest of the methodology lives in the courses and products here.
It lays out every move open to you
Most decisions feel like a coin flip because you only see the one option you'd already fixed on. Next Move Theory lays out every tactical and strategic move actually open to you, and most importantly the ones you'd have missed.
It helps you choose the best
Scores give you comfort, not confidence that these are the right moves. Next Move Theory weighs each strategy by how much it moves your goal and points you to the best, with strong logic behind it.
With the odds on your side
The whole point: more of your bets land. Hundreds of companies run on Next Move Theory, and across dozens of documented cases the metrics moved significantly: conversion, retention, revenue, market share.
See the casesBuilt for the makers
Same algorithm. Here's the win it lands for each segment
Decide what to build with the odds on your side
See every tactical and strategic move open to you, find the Job customers will actually pay for, and pick the strategy that wins before you bet the next three months on a hunch.
Pick a niche that actually pays
Writing code was never your problem. Choosing what to ship is. So let a methodology pick the Job people pay for and make the next build the first one with real buyers.
A roadmap that moves the metric — not theater
Run the logic from the foundations up, find where the metric actually breaks, ship the few moves most likely to shift it, and dramatically raise the odds of growth.
The operating system your product org runs on.
When every team reasons the same strong foundations, tactic to strategy, you can stand behind every call, and more of your bets pay off.
Positioning that isn't “yet another X.”
Find the angle in the customer's real Job, not channel hacks, and the odds it converts climb before you spend a dollar.
Why Next Move Theory skills beat other skills
Ask ChatGPT or Claude to run product research or apply a methodology and you get confident, generic output. Models barely know real methodologies, so without detailed methodology prompting the thinking stays shallow. The Next Move Theory skills carry the methodology foundations: thousands of theses and the exact algorithm, so the model reasons with it instead of guessing.
9/10 approved
alpha testers said the Next Move Theory skills produced better work than the other skills they'd tried.
Read & explore
Built on Advanced Jobs To Be Done
Eight years ago I discovered Jobs To Be Done, saw enormous potential in it, and made an unreasonable decision: rebuild it from scratch so it would finally yield an algorithm. It came together only when I found the science that explains what value actually is to a brain managing an energy budget, and how a person changes behavior. On that foundation I rebuilt JTBD into thousands of theses — Advanced Jobs To Be Done.
AJTBD alone still wasn't enough. To get a real algorithm, a few more disciplines turned out to be fundamental. Together they became Next Move Theory:
Advanced Jobs To Be Done
What value actually is and how to create it. How to segment customers and make strategic moves. How to communicate value. JTBD rebuilt from scratch into thousands of theses.
Riskiest Assumption Test
Every idea is a stack of assumptions, any of which can kill it. Buy the cheapest evidence against the deadliest one, before you build.
Unit Economics
Compete only for the Jobs of segments where the margin lets you grow and fund the next bet. And find the highest leverage metric from your current unit economics.
ABCDX Segmentation
Split the paying base by margin × satisfaction: double down on A/B, fire C/D, read X as the signal of where to grow next.
Theory of Constraints
Find the single bottleneck that actually limits growth of your product and fix that, instead of improving everything at once.
Today hundreds of companies run on this work, and dozens of cases are documented in the Cases section. The canon states the methodology as theses. The book tells the story of how it was discovered. The skills turn the algorithm into tools: feed in a product idea and get back a decision, not a description.
Ivan Zamesin, the author
Author of Advanced Jobs To Be Done and Next Move Theory. Ranked the #1 product expert in his home market by three independent industry studies.
Led image search at his country's largest tech company
30M MAU. His team took 25% of the market from Google Images, growing share from 55% to 72%.
Trained 13,000+ founders and product managers
Through the most popular product course in his home market, running since 2018.
Advised the largest tech companies in his country
Product-strategy consulting for market leaders, built on Jobs, proper segmentation and unit economics.
Founded and sold a startup
A therapist-matching service. Built it, grew it, and exited to a larger marketplace.
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