The Leverage Stack
“Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.”
— Archimedes
For most of recorded history, leverage required permission.
You could not multiply your output without access to capital — which meant a bank, an investor, a patron. You could not distribute your ideas without access to infrastructure — which meant a publisher, a studio, a broadcaster. You could not build something that ran without you present without managing a team — which meant hiring, managing, trusting, losing people.
The people who changed the world at scale did it by accumulating these permissions. Company formation, Series A, distribution deals. The leverage was real, but the gatekeepers were real too.
Then the gatekeepers started disappearing. One by one, the permission requirements collapsed.
Naval Ravikant named the pattern clearly around 2018: code and media are forms of leverage that require no permission to deploy. You write software and it runs for a million people while you sleep — zero marginal cost, no manager sign-off, no printing press required. You build an audience and your ideas distribute themselves. These were not small observations. They were a structural shift in who could compound output.
What Naval did not have in front of him in 2018 is the third layer. It is here now. And it changes the math again.
Layer One: Code
Software is stored labor. You build a function once and it executes indefinitely — at any scale, at any hour, without variance, without negotiating its salary.
The leverage ratio is extreme. A solo developer who ships a product used by one hundred thousand people has created a 100,000x output multiplier for every hour of work that went into the core logic. The marginal cost of the hundred-thousandth user is approximately nothing. No other form of labor operates like this.
For two decades this was available in theory to anyone with a laptop and internet access. In practice, it was gated by technical skill. Most people — the writer, the strategist, the designer, the operator — could not wield code leverage because they could not write code well enough to deploy anything robust.
That gate is now significantly lower. AI-assisted development means the threshold for shipping functional software has dropped from “five years of engineering experience” to “clear thinking about what you want and the judgment to verify output.” It is not zero — judgment about what to build and whether the output works still requires domain knowledge. But the translation layer between idea and running code is now accessible to a much wider population.
Layer One is: software that runs without you present.
Layer Two: Media
An essay, a video, a podcast episode, a newsletter — these are also stored labor. You produce once; they distribute indefinitely. A post written three years ago still drives traffic. A video recorded in 2021 still earns watch-hours in 2026. A well-placed piece of content compounds in a way that a client project never does.
The leverage ratio depends on distribution, which has its own dynamics. But the underlying property — creation decoupled from delivery — is the same as software. You are not present when someone reads your essay at 2 AM. Your leverage is working while you sleep.
The gatekeepers here fell earlier and harder than in software. Blogging destroyed magazine entry barriers. YouTube destroyed broadcast barriers. Podcasting destroyed radio barriers. By 2020, a creator with genuine expertise and an internet connection had access to global distribution infrastructure that would have required a $10M infrastructure spend in 1995.
What media leverage requires is trust — an audience relationship where people return because you have consistently created value. That is the compound. Each piece of content is a deposit into a trust balance that pays returns in attention, in authority, and in the ability to distribute future work without starting from zero.
Layer Two is: ideas that distribute without you present.
Layer Three: AI Agents
Here is the new layer. It is different in kind from the first two.
Code executes logic you pre-specified. Media distributes ideas you pre-created. Both are stores of past work. They operate on the fixed output you built before.
AI agents do something neither can do: they respond to novel inputs and produce novel outputs on your behalf, in real time, without your presence.
An agent you configure today can handle a customer question you have never seen before. It can draft a response in your voice, route an inquiry through your decision criteria, synthesize a new piece of research against your existing framework. It is not executing stored logic or delivering stored content — it is doing new work. And it is doing it at a cost structure — in time and money — that has no historical analog.
This is what makes the third layer structurally different. Code runs your old decisions. Agents make new ones. The leverage is no longer just on execution of pre-specified tasks. It is on judgment-adjacent work: intake, triage, drafting, synthesis, routing.
The gap between what a single person with AI agents can execute per week versus what the same person unassisted could execute is now large enough to constitute an organizational advantage — not just a personal productivity improvement.
Stacking Deliberately
The error most people make is deploying one layer and treating it as a strategy.
A solo creator who builds media leverage but no systems will hit a ceiling quickly — every hour they create is an hour only they have. A developer who builds software tools but no distribution will build things that run without them but reach nobody. An operator who deploys AI agents but has no media presence or proprietary system logic will find that the agents amplify generic work — which has thin margins.
The compounding happens when the layers interact. Here is what the stack looks like when it is working:
Media builds an audience and a trust signal. That audience creates demand — for a product, a service, a course, a community. Code turns that demand into a scalable system — a product that serves the audience without requiring your time on each transaction. AI agents extend the personalization and responsiveness of that system — handling intake, generating custom outputs, maintaining relationship quality at a scale that code alone cannot personalize.
Each layer feeds the others. Media drives demand that justifies building code. Code creates the infrastructure that lets agents operate at scale. Agents enable the quality and personalization that sustains the media relationship.
The framework for building this is The Deliberate Stack — the intentional, sequenced deployment of leverage types in an order that makes each subsequent layer more powerful than it would be alone.
The Operator’s Plan
Step 1 — Audit which layers you have: Honest current state. Do you have any software that runs without you? Any media assets that distribute without you? Any AI agents operating any recurring task without your real-time involvement? Most people have partial layer two and nothing else. That is your baseline.
Step 2 — Identify your highest-value bottleneck: Where is the work that most limits your output? If you have an audience but no product, layer one is the constraint. If you have a product but no distribution, layer two is the constraint. If you have both but the operation collapses when you step away, layer three is the entry point.
Step 3 — Build one thing in the gap layer: Not a full strategy — one specific artifact. A tool that automates one manual operation. One piece of long-form content designed to compound. One standing AI agent that owns one recurring task completely. The goal is to get real leverage working in the missing layer before you optimize anything.
Step 4 — Wire the feedback loop: Each layer should generate signal that informs the others. Media tells you what the audience needs — that informs what to build in code. Agents surface patterns in intake — that informs what content to create. Do not run the layers in isolation. Build one explicit connection between two layers and work it deliberately.
Step 5 — Raise the floor before you raise the ceiling: Most leverage fails not because the upside is wrong but because the downside is unmanaged. Before you add complexity to any layer, make sure the existing layer is stable enough to ignore for a week. A lever that requires constant maintenance is not leverage — it is a different job.
The Inversion
You started here: leverage used to require permission. Now it does not.
Here is the flip: the absence of permission does not guarantee leverage. Permissionless does not mean automatic. Most people have access to all three layers and use none of them systematically — they use AI as a faster keyboard, they publish inconsistently, they build tools they maintain manually. Removing the gatekeepers removed the excuse, not the work.
The question was never whether you have access to the stack.
The question is whether you are building it — deliberately, layer by layer — or just watching others use it.
MrBee writes at the intersection of AI, strategy, and human potential. Explore the Academy →
