The 1% no one is compounding
No one is compounding their AI interactions. And right now, that’s the single highest-leverage thing you can compound.
Most people use AI like a slot machine.
Pull the lever. Get a result. Move on. Pull the lever again tomorrow. Get a slightly different result. Move on again. Six months later, they’re pulling the same lever the same way. And they wonder why the people around them seem to be accelerating while they’re standing still.
Compounding has always been the most powerful force in the universe. Einstein supposedly said it. Every finance bro on LinkedIn reminds you daily. We all know this.
But almost no one is compounding their AI interactions. And right now, that’s the single highest-leverage thing you can compound.
What compounding actually means
Let me be specific, because “compound your AI usage” sounds like the kind of empty advice you’d scroll past on X.
Compounding means that every single interaction with an AI should make the next one better. Not by accident. Not by osmosis. By design.
The habit is dead simple. After every AI interaction, every single one, ask yourself (and your AI!) three questions:
What failed?
Why did it fail?
How do I make sure it never fails that way again?
That’s it. Three questions. But the discipline to ask them every time is what separates people who are 10x-ing from people who are treading water.
Most people skip this. They get a bad output, sigh, fix it manually, and move on. That’s the intellectual equivalent of burning money. You just paid for a lesson and refused to learn it.
Your AI’s long-term memory
At Neople, every project has a CLAUDE.md file. It’s a markdown file that sits in your repo and tells Claude how to behave in that context. Git workflow, coding conventions, architectural patterns, testing rules. All persisted across every single session.
Our main one is over 300 lines. It didn’t start that way.
It started with maybe 10 lines. “Use worktrees for isolation. Never modify the main repo directly. Run ruff and pyright before you’re done.” Basic stuff.
Then Claude started implementing fixes without creating tickets first. So we added a rule: always create the Notion ticket before writing code.
Then it kept skipping PR creation after finishing the code. Another rule.
Then it started over-engineering solutions when we wanted simple hardcoded values. Added “prefer simple solutions over dynamic ones unless explicitly asked.”
Then it kept jumping into code before understanding the problem. Added “never start implementing until the problem is fully understood and the approach is confirmed.”
Every single line in that file is a scar from a real failure. And every scar prevents the same failure from happening again.
This is a two-way street, though. If Claude keeps making the same mistake over and over, the answer isn’t always another rule. Sometimes your code is just a mess. If an AI can’t figure out what your function does, your coworkers probably can’t either. The best compounders don’t just teach the AI to work around bad code. They fix the code. Your human teammates will thank you too.
It’s like managing a team
I ran /insights recently. It’s a Claude Code feature that analyzes your usage patterns across sessions. Mine covered 1,524 messages across 145 sessions.
The number one friction pattern? “Wrong approach.” 24 instances. Claude jumping into code changes before fully understanding the problem. Over-engineering solutions. Misidentifying root causes.
Sound familiar? That’s what junior developers do.
I needed simple hardcoded alert thresholds for database I/O. Claude built a dynamic calculation system based on documentation it misread. I needed a one-line fix using a library’s built-in hook. Claude wrote a custom post-processing function. I asked for a bug fix. Claude started refactoring the type hierarchy before understanding what was actually broken.
Every single one of these is a management failure. My management failure.
When a junior dev over-engineers a solution, you don’t blame the junior dev. You blame yourself for not setting clear expectations. You write better tickets. You define “done” more precisely. You create guardrails. You make the right thing easy and the wrong thing hard.
AI agents are the same, except for one crucial difference: they’re not stubborn. A human will nod, say “got it,” and then do it their way anyway because they think they know better. An AI agent will actually follow the instructions you give it. Every time. Perfectly.
Which means if it’s doing something wrong, that’s on you. You wrote bad instructions. You left ambiguity where there should have been clarity. You didn’t encode the lesson from last time.
The moment you start blaming yourself for your AI agent’s mistakes, you start compounding.
When it becomes a system
At Neople, we’ve built workflows that encode entire development processes into repeatable systems. Not “a prompt I copy-paste.” Actual end-to-end workflows.
I have a /fix command. When I type /fix, Claude doesn’t just write code. It creates a Notion ticket, creates a branch in an isolated git worktree, implements the fix (tests first for backend), runs the full quality suite (ruff, pyright, tsc, eslint), cleans up its own AI-generated artifacts, creates a PR, and updates the Notion ticket. One command. Every step compounded from a previous failure.
We used to skip ticket creation. We used to forget to run type checks. We used to leave behind excessive comments and defensive code the AI generated. We used to create PRs without linking them back to the ticket. Every one of those problems happened, got identified, and got encoded into the workflow. Now they can’t happen.
Same for code review. /review-pr 123 checks out the PR in a temporary worktree, reviews the code, copies a formatted review to my clipboard, and cleans up the worktree. Same for ticket creation, project scaffolding, cleanup passes.
Each workflow was born from a sequence of “this didn’t work, let’s fix it.” Each one gets better every time it runs and something new goes wrong. That’s compounding at the system level.
Learning what you don’t know you’re doing
You can’t compound what you can’t see.
That /insights report I mentioned? It didn’t just show me friction patterns. It showed me that I delegate ambitious end-to-end workflows with clear goals but minimal upfront specs, then course-correct when Claude takes a wrong turn. I treat iterative redirection as my primary steering mechanism.
I didn’t know that about myself. I thought I was giving clear instructions. Turns out my interaction style is “give a vague goal, let Claude explore, then interrupt decisively when it goes off track.” That works, but it’s expensive. A lot of those 24 “wrong approach” corrections could have been prevented with one more sentence of context upfront.
/insights also showed me that my most successful sessions involve multi-file changes and debugging. Not writing new code, coordinating changes across 8+ files. That told me to invest more in architecture documentation, so Claude understands how the pieces connect before touching anything.
Most of us have blind spots about how we interact with AI. We repeat the same vague prompts. We consistently under-specify in the same ways. We have habits that silently cost us quality without realizing it. Tools like /insights make those blind spots visible. And visible problems are fixable problems.
The divergence
This matters right now more than it ever has.
Compounding has always existed. People who read books and applied the lessons compounded knowledge. People who reflected on their mistakes compounded wisdom. This isn’t new.
What’s new is the speed of the feedback loop.
Old compounding: read a book, apply a lesson, see results in weeks or months. AI compounding: have an interaction, learn what failed, fix it, see results in the next interaction. The feedback cycle went from months to minutes.
When feedback loops shrink like that, small differences in compounding discipline become enormous differences in outcome. Fast. The math on 1% daily improvement is 37x in a year. That’s not a typo.
And it’s not theoretical. I feel it. The gap between how I use AI today versus six months ago is staggering. Not because the models got that much better (they did, but that’s not the point). Because I compounded every single interaction into better instructions, better workflows, better mental models.
And I watch people around me, smart people, talented people, who are still pulling the slot machine lever.
The 30-second habit
Let me make this practical.
After every non-trivial AI interaction, do a 30-second post-mortem. You don’t need a journal. You don’t need a spreadsheet. Just the three questions.
What failed? Be specific. Not “the output was bad.” But: “It used camelCase when our codebase uses snake_case.” Or: “It over-engineered a dynamic system when I wanted hardcoded values.” Or: “It started coding before understanding the problem.”
Why did it fail? Almost always one of four things: you didn’t give it enough context, the context exists but isn’t somewhere the AI can access it, your instruction was ambiguous, or the AI made an assumption you didn’t catch.
How do I prevent this next time? Externalize the fix. Update your CLAUDE.md. Add it to your project’s memory files. Build it into a workflow. Add it as a rule.
Don’t keep the lesson in your head. Put it somewhere the AI can read it.
Your brain learning “always specify the approach before coding starts” is good. Your CLAUDE.md containing that rule is 10x better, because now every AI, every session, every team member benefits automatically. You learned the lesson once. The system remembers it forever.
The layers
A fully compounded AI setup has layers, and each one feeds the others.
Project instructions like CLAUDE.md. Your codebase conventions, architectural patterns, and “never do this” rules. All learned from real failures. Ours is 300+ lines and every line earned its place.
Memory files. Lessons learned across sessions, patterns that work, context that’s expensive to re-explain. Things like “always use self.session() context managers for database safety” or “frontend hooks must match backend paths exactly, including trailing slashes.” Your AI stops being a stranger every time you open a new conversation.
Workflows. Repeatable processes encoded as systems. /fix, /review-pr, /resolve. Each one born from a sequence of “this didn’t work, let’s fix it.” Getting better every time they run.
Meta-learning through tools like /insights. Patterns in your own usage, blind spots you didn’t know you had, continuous improvement of your improvement process.
Your insights improve your instructions. Your instructions improve your workflows. Your workflows surface new insights. It’s compounding all the way down.
The race you’re already in
This isn’t optional.
If you’re in any knowledge work, you’re already in a race where AI leverage determines your output. And the people who compound that leverage are pulling away from the people who don’t. Not linearly. Exponentially.
This is not about being an “AI power user.” It’s not about knowing the latest prompt tricks or having access to the newest model. It’s about the boring, unsexy discipline of learning from every single interaction and encoding that learning into something persistent.
The person who’s been compounding for six months doesn’t just have better prompts. They have project files that encode hundreds of lessons. Workflows that automate entire processes. Memory files that preserve context across sessions. Mental models refined by thousands of feedback loops. An AI that effectively knows how they think.
You’re not competing against that person’s raw talent. You’re competing against their compound interest. And compound interest is unbeatable given enough time.
The good news? It’s still early. The feedback loops are still fast. And the best time to start compounding was six months ago.
The second best time is your next AI interaction.
Start compounding at neople.io.
 | A guest post by
|