For business owners already using AI, and still doing all the work themselves
A real job in your business, running on its own every day,
even if you’ve never written a line of code. That’s level one.
By the end you’re running a whole crew of them.
Built on the same system that shipped 19,561 changes to my software last year.
The ordinary way with AI ships 537.
Watch first · 14 minutes
Pays for itself in 90 days or your money back
Read this if…
You’re not a beginner. You’ve tried the things. And every one of them made you slightly faster at doing the work yourself:
None of it compounds. Monday looks like last Monday. The work still stops when you stop.
AI is going to displace an enormous number of jobs. That is not a prediction and it is not fear-selling. It is already happening, in my companies and in yours. The people who adopt it now will run circles around the ones who wait. The ones who wait do not get those years back.
I am not here to sell you the next shiny object. I have no interest in being a guru. I spend fourteen to sixteen hours a day obsessed with this because it runs my own businesses, and every week I write down exactly what I did so you can go and do it too.
You cannot learn this from theory. You learn it by building.
World Economic Forum, Future of Jobs Report 2025, surveying 1,000 employers across 22 industries and 55 economies, together employing over 14 million people.
Here’s the actual problem
Your competitor opens the same window, types into the same box, and gets roughly the same answer you do. There is no edge left in the tool. There hasn’t been for a while.
The gap between a good week and a great year isn’t which model you pay for. It’s whether there is a system underneath it: something that keeps working when you close the laptop, and something that catches the mistakes before your customers do.
Almost nobody has built one. Not because it’s hard. Because nobody told them it was the job.
2 hours to your first working agent
Everyone has access to the same models. What separates a good week from a great year is whether there is a system underneath, and whether the checks survived the speed.
Everything moves at the speed of one person typing. The bottleneck is never the thinking. It is the hours.
A chat window open in another tab. Real help, occasionally, but nothing compounds, because nothing was built to.
Agents with defined roles, running in parallel, every branch reviewed before it lands. Peak day: 410 contributions.
My last twelve monthsSpeed without gates is just a faster way to ship bugs. Every branch goes through the same checks, and the reviewer is never the same model that wrote the code.
And it isn’t the prompts either
If you have bought a prompt pack, you already know how this ends. The clever wording works once, in a demo, on a task that was chosen because the wording worked. Then you try it on your own business and it falls apart.
What you type. It lives for one conversation and dies with it.
Change any one of these and the same words produce completely different work. Change the words and almost nothing happens.
A brilliant prompt into an agent with none of that gives you an impressive-looking answer that nobody can trust. A blunt instruction into an agent that has all five gives you finished work. There are names for this now: some people call it context engineering, and the scaffolding it all sits in gets called a harness. You need neither term. You need the five things, and all five get built in the first level.
2 hours to the first job off your desk
Don’t take my word for it
Two of these show why AI on its own quietly fails. Two show what happens when it’s run properly. Read them in that order.
METR · randomised controlled trial · 2025
Experienced developers given AI tools took 19% longer to finish real tasks than developers without them, and afterwards still believed AI had made them 20% faster.
MIT Project NANDA · State of AI in Business · 2025
Of enterprise AI pilots, 95% produced no measurable return on $30–40 billion of spending. The report blames workflow integration and undefined outcomes, not the models.
Anthropic · on their own codebase · May 2026
More than 80% of the code merged into Anthropic’s own production codebase is now written by Claude. Their typical engineer merges 8× as much code per day as in 2024, and they describe the direction plainly: humans stop writing code and shift to only reviewing it.
Anthropic · ~400,000 sessions studied · 2026
Across ~400,000 sessions from ~235,000 people, every one of the ten largest occupations landed within seven points of software engineers on succeeding at what they set out to build. The fastest-growing groups weren’t engineers. They were management, sales and legal.
Sources, so you can check them: METR, July 2025: 16 experienced open-source developers on early-2025 tools, so it’s a small and specific population; I’m citing it for the perception gap, which is the part that generalises. MIT Project NANDA, 2025: 300+ deployments, 52 case studies, 153 leadership surveys. Anthropic, “When AI builds itself” and “How Claude Code is used in practice”. I have no relationship with any of them beyond paying for the tools.
2 hours to the first job off your desk
Who’s telling you this
That was the gap. For fifteen years it was the one thing standing between an idea and a product, and the only way round it was to pay somebody. Designers, then developers, then someone to keep the servers alive. Every product started with a quote and ended with an invoice: tens of thousands of dollars, sometimes hundreds, before a single customer ever saw the thing. And it always moved at the speed of whoever was slowest that month.
I could see exactly what I wanted. I could not build it, I could not check whether what came back was any good, and I could not fix it myself when it wasn’t. If you have ever run a business where the most important work happens in a room you cannot walk into, you know the feeling.
That bottleneck is gone, and I never did learn to code. Last year I shipped six products in twelve months. Construction management, B2B data, customer support, surveys, community, AI memory. Real products with real customers. No employees. No funding. Nobody to wait for.
The companies who lead those categories raised $1.92 billion between them. They were founded between 1999 and 2017. Some of them have four thousand employees.
I’m not smarter than those teams and I don’t work more hours than you do. I built something they never had to, because when they started it didn’t exist: a way to put a whole team’s worth of work through one person. The skill it needs is the one I already had from fifteen years of running companies, which is knowing how to brief people, insist on evidence, and tell good work from bad.
Mockups, revisions, a handover file, and a build that never quite matched it.
Hired, briefed, waited on. The whole product moved at the speed of the slowest one.
Someone to get it live and keep it alive, on a retainer, whether anything shipped or not.
The same three jobs, done by a crew you direct, reviewed and tested, and shipped without waiting on anyone’s calendar.
Fifteen years of invoices · then twelve months and six products
I never wrote the code. I run the team that writes it. That is a completely different job, and it happens to be the job I was already good at.
Skin in the game
This isn’t a course I assembled after the fact. It is the operating system I run my own companies on, today, and every one of these depends on it. If it stopped working on a Tuesday, my businesses would stop with it.
CommunityGo and look ↗
ResearchGo and look ↗All six are live, and you can click any of them. They run on the same machinery you’re being taught. The same roles, the same review gate, the same night shifts, the same handbook. Not a version of it simplified for students. The actual thing, because I only maintain one.
This program runs on it too. The lessons that go stale are re-drafted by the same agent team. The checks on your qualifying submissions are the same automated gates that guard my own work. Even this page was built by the crew, in an afternoon.
Which means I can’t quietly let it rot. The day I stop refining this system is the day my own six businesses start falling behind, so the incentive that keeps your curriculum current isn’t goodwill or a content calendar. It’s that I need it to be right before you do.
The method
Three moves. It’s the whole thing, and once you’ve seen it you can’t unsee why a chat window was never going to get you there.
Claude
Codex
GeminiThe enginesThe same ones
everyone has
Finished workNot drafts.
Not suggestions.
That middle box is the entire difference. Everyone starts on the left with the same engines, and almost everyone runs straight to the right and wonders why the output needs so much fixing. The five things in between are what turn a capable model into a business that produces work you can ship. Each one is a module, and you build them in that order.
Not one assistant doing everything. A crew, each with one job it owns. A Conductor that decides what happens next. A Planner that turns your sentence into tasks. A Designer, an Implementer, and a Reviewer that answers to a different company’s AI entirely. Each gets a brief, a definition of “done,” and the evidence it has to hand back. This is the part everybody skips.
You leave with the briefs already writtenThe whole grid goes at once, each in its own lane so they never overwrite each other, through the night. You stop being the thing the work waits for. You wake up and read what happened, the way a manager reads a morning report, not a dashboard full of cartoon robots. A log of finished work.
This is where the 19,561 comes fromNothing lands until a different engine has checked it, never the one that wrote it. Tests run for real, and a guard checks that they actually ran. Then one human decision at the end: mine. Speed without this isn’t leverage, it’s just a faster way to break your business.
The half nobody else teaches
ConductorClaude🏁
PlannerClaude🏁
DesignerClaude🏁
ImplementerCodex🏁
ReviewerGemini🏁
ClaudeReasoning · design
CodexImplementation
GeminiIndependent review
SupersetThe cockpit
1PasswordThe vault
LinearThe task board
GitHubVersion control
SentryMonitoring
TailscalePrivate network
PlaywrightAutomated QAThis is the actual stack, as of today, not a diagram of one. Three AI companies deliberately, so the one that reviews the work never shares a brain with the one that did it. And it will change: some of these will be replaced within a year, which is exactly why the current-stack lessons are small, swappable, and updated for you rather than filmed once and left to rot.
Velocity only counts if nothing slips. Anyone can go faster. Going faster without breaking things is the entire trick, and it is the reason this is a system and not a prompt pack.
Pays for itself in 90 days or your money back
Tools change every month. The infrastructure you run them on (the systems, the review steps, the processes that keep the work honest) is what actually compounds. That is what I write about, and it is the only reason any of this holds together.
Where the work runs, how it is monitored, what happens when a job dies at 3am. Boring, and the reason the rest is possible.
Not a chat window. Defined roles, written standards, and a review step that catches the thing you would have shipped by accident.
How work gets picked up, checked, and closed, so output does not depend on whether you felt sharp that day.
Nothing here is a prediction. It either shipped or it didn't, and I'll tell you which, including the weeks it didn't.
The difference
Almost everyone reading this is in the first column. The smart ones have clawed their way into the second and found out it doesn’t scale, because it still runs on them. The third column is what you build.
The middle column is the trap. It feels like progress (the notes app, the prompt document, the assistant) and every piece of it still needs you standing behind it. That’s not a smaller version of the third column. It’s a different thing that quietly caps out.
2 hours to your first working agent
An entire step, gone
Drawing a picture of software before building it made sense when building was the expensive part. It isn’t any more. So the whole mockup stage, the wireframe, the design file, the approval round, the handover, is just a delay you pay for twice.
How everyone still does it
How we do it
↻ That loop runs in minutes, as many times as it takes
You never argue about a drawing. You look at the actual thing, on your actual phone, and say “make that bit bigger.” Two minutes later it’s bigger. The design tools were never the point. They were a workaround for how slow building used to be.
You already know the look: the same centred hero, the same three cards, the same gradient, the same rounded everything. Everyone can spot it now, and it quietly tells your customer that nobody cared. Getting past that is a taught skill (taste, restraint, and knowing what to ask for) and it’s a track of its own inside Velocity. This page is an example of the output: built by the system, in a few hours, from nothing.
Around the clock
You’ve seen the videos: a glowing dashboard, forty-seven little avatars pulsing away, numbers ticking up. It looks incredible. It produces nothing you could put in front of a customer, because nobody planned the work and nothing checked the output.
You are looking at a Tuesday after two years of building this up. Nobody starts here, and nobody needs to understand any of it to begin. Day one is the single highlighted row at the top: one job, running on its own. Everything else you can see is just that same thing repeated, once I had decided it was worth repeating.
Agents drift. They follow your rules on Monday and quietly forget them by Friday. And an agent that has drifted at 3am, unsupervised, with permission to publish, is how you wake up to a problem instead of a report. So supervision isn’t constant, and it isn’t absent. It changes through the day, on purpose.
Anything that costs money above a limit you set waits for you, however obvious it looks at 2am.
Destructive work stops at the line. Nothing irreversible happens without a person on the other side of it.
Nothing reaches a customer, an inbox or a public page overnight. It gets built and it waits.
And when it hits something ambiguous, it parks it. It does not guess. That single rule is the difference between waking up to nine finished jobs and two sensible questions, and waking up to a mess someone confidently made while you were asleep.
You read what happened overnight and decide what matters today. Twenty minutes, with coffee.
Lanes running in parallel, everything reviewed by a second engine before it lands anywhere real.
Clear rules about what they may do alone. Anything ambiguous gets parked, not guessed, and waits for you.
Support backlog cleared. 41 replies drafted, checked and sent.
02:14 · reviewed by a second engineWants to change the pricing page wording. Parked. Publishing needs a human.
03:52 · waiting 3h 49mRefund above the limit you set. $340.
05:07 · waiting 2h 34mYou do not need to be at a desk to run this. The overnight report arrives on your phone, the two things that need a human are sitting there as questions, and answering them takes about as long as reading a text message.
That is the part that changes your week, not your workload. You can be at a school pickup, on a flight, or three days into a holiday, glance at it, tap approve twice, and put the phone back in your pocket, and the business carries on without you having opened a laptop.
You can also go the other way when you want to: open a live session from your phone and direct the work, or watch any of your machines from anywhere. Most days you won’t need to. The point is that you can.
2 hours to the first job off your desk
What’s inside
No quizzes. No “watch 80% of the video to unlock.” Each level opens when you show proof that the last one actually worked in your business. It’s called qualifying, and you can attempt any of it on day one.
Level 01
Hands on the machine. You install your first agent, give it a real job, put it on a schedule, and prove it did the work while you weren’t watching. This is the two hours.
Level 02
One becomes a team. Roles, parallel work, handoffs, and the review layer that stops them marking their own homework. You stop doing the work and start directing it.
Level 03
The business runs without you in the seat. Night shifts, monitoring, cost control, and the rhythm that keeps it all honest while you’re not looking.
Piece by piece
Twenty-seven modules. Every one ends with something installed and running in your business, not a video you’ve watched.
You go from using AI to operating it. At the end, at least one agent is doing real work in your business every day: measured, remembered, and cost-controlled.
The first thing you unlearn: chasing every new model is why you have nothing running. You end this module on one system you’ll actually refine, and from then on I test the new stuff so you don’t have to.
The workspace where you can see every agent working at once, instead of one chat window and a lot of guessing. Installed click-by-click, with a skip-ahead checklist if you already know the ground.
One home for everything, identical on every machine you own. Because an agent that can’t find the file will confidently invent the answer. And so will you, at 11pm, looking for the thing you wrote last Tuesday.
Your agents use your passwords and API keys without ever seeing them, and go get their own when they need a new one. You never paste a credential into a chat box again.
The single page that turns “can you look at this” into finished work: the job, what “done” means, and the evidence it has to hand back. You write your first one against a real task from your own business.
How to catch the confident lie. AI is at its most dangerous when it’s wrong and cheerful, and this is the habit that stops that reaching your customers.
Every report comes back in three sentences you can act on (what you asked, what happened, what it needs from you) with the technical detail underneath and decisions as buttons you click. Enforced automatically, not politely requested.
Why agents follow your rules on Monday and forget them by Friday, and the three fixes. Written once, vendor-neutral. Every AI you ever hire reads the same handbook, whoever made it.
Your business gets a memory, and it belongs to you rather than to any AI company. Switch models, add a second vendor, replace one entirely, and everything they know about your business follows you across.
Cheap by default, expensive only on purpose. Most people burn money running a heavyweight model on work a cheap one does perfectly. You also produce your baseline spend number here: the “before” the guarantee is measured against.
The moment it stops waiting to be asked. A real job in your business runs every day, on schedule, whether you open the laptop or not. This is the two hours.
One becomes a team. Directed, running in parallel, quality-gated, and they ship a real, working piece of software you keep.
Why three cheap specialists beat one expensive generalist, and who does what: the one that plans, the one that assigns, the ones that do. You hire the same way you’d hire people: for a role, not for everything.
One place where you and your agents see the same reality. Every job is a ticket with acceptance criteria; agents claim tickets so two never do the same work; status lives on the board instead of buried in a chat you’ll never scroll back through.
Ten agents working at once without overwriting each other’s work. This is the difference between a team and a pile-up, and it is almost entirely about giving each one its own space.
Nothing ships unreviewed, and the reviewer is never the one who did the work. A different AI, from a different company, checks it. This single rule is why the speed doesn’t cost you your reputation.
The pipeline that turns a sentence you said in the shower into small, assigned, finished tasks. Most people fail here: they hand over the idea instead of the plan, and get back something impressive and wrong.
A guided week where your team builds a real tool your business actually needs, with you directing. Not a demo project. Something you keep using afterwards.
Automated testing that clicks through your product like a customer and reports what it saw. “It works” from an AI means nothing; a screenshot and a log mean something.
Live on the internet with a real address, a pre-launch checklist, and a list of what to watch in week one. The part where most self-built things quietly die.
The business runs on shifts around the clock, including while you sleep, with monitoring, guardrails, and a playbook that gets better on its own.
Morning planning, evening handover, night crew, each with a different rule about what wakes you and what waits. This is the piece nobody else teaches, because almost nobody else is running one.
Overnight autonomy with rails: exactly what they may do alone, and what they may never do without you: spending, deleting, publishing. When they hit something ambiguous they park it, they don’t guess. You wake to a report.
You find out before your customers do. Error tracking and dashboards wired up so that when something breaks, your agents read the evidence instead of speculating, plus alerts that prove the background work is still alive. Includes running the whole thing from your phone.
Where the money actually goes, on a schedule. Long sessions cost roughly six times fresh ones, and the spend is never where you assume. Measure it, don’t guess. You produce your “after” number here, which closes the guarantee.
Automatic enforcement that blocks the dangerous move rather than reminding you about it, including how to catch an agent claiming a check passed when it never ran. That exact failure has bitten me, twice, on the same day.
Every mistake becomes a written rule, and every agent reads the manual at the start of every shift. It is the mechanism by which month six is genuinely better than month one instead of just busier.
The same machinery pointed at everything else: research, customer support, marketing, operations. Software is where it’s easiest to prove. It isn’t where it stops.
The final week. Mornings planned, nights autonomous, incidents parked and handled, and the logs to prove all three. Pass it and the business genuinely runs without you in the seat.
Specialty tracks you can take whenever you want them. They keep the main path tight without leaving anything out.
Build things that don’t look like AI made them, and skip the mockup stage entirely, because you can build the real thing faster than you could draw a picture of it.
Get it live, keep it live, and roll it back in one move when it isn’t. Confidence to ship on a Friday.
Run several AI vendors as one system, so the reviewer never shares a brain with the author, and one company’s outage or price rise never stops your business.
Turn your documents, transcripts and history into something your agents can actually answer from. The business brain.
Prove it works before your customer discovers it doesn’t. Automated, repeatable, and honest about what it did and didn’t check.
27 modules · 3 levels · 5 endorsements
The obvious worry
This is the question I would ask, and most people selling AI systems skip straight past it. Four answers, and none of them is “trust me”.
Every credential lives in a vault. Agents use them at the moment they need them and never read them, never print them, and never store them in a conversation you might paste somewhere. You will not hand a password to an agent at any point in this program.
Each agent gets access to the one thing it needs and nothing else, in its own workspace. When something goes wrong, and it will, the damage is bounded by design rather than by luck. That is the difference between a bad afternoon and a bad quarter.
Spending, deleting and publishing all stop at a line you set. Work gets built and prepared overnight, then waits. And a copy is taken before anything is changed, so undoing a decision is a minute of work rather than a rescue operation.
A different engine, from a different company, reviews everything before it lands. Tests run for real and a guard confirms they actually ran, because an agent claiming a check passed when it never did is a real failure I have had, twice, in one day.
You are already trusting AI with your business. It is in a chat window right now, with your customer information pasted into it, no record of what it was told, no second opinion on anything it hands back, and no limit on what you might act on. That is the version without any rails.
This is the same thing with rails: written rules it re-reads, a review it cannot skip, a boundary it cannot cross, and a log of everything it did while you were asleep. The risk was never adding the agents. The risk is running them the way almost everybody currently does.
Ownership
Worth asking about whatever you’ve built with AI so far: if your provider tripled the price on Monday, or shut down, or got overtaken by something better. What exactly would you still have?
Everything that makes it work is inside somebody else’s product, and you are renting the arrangement month to month.
Three things belong to you and never leave, whatever happens to any AI company.
ClaudeReasoning
CodexBuilding
GeminiReviewingSomething better comes along and it’s an upgrade, not a migration.
That is the difference between learning a tool and building an asset. Tools get replaced and you start again. This gets better when the tools get replaced, because everything that made it yours (the memory, the rules, the way the work moves) stays exactly where it is.
2 hours to the first job off your desk
The obvious objection
It’s the right thing to worry about, and it’s true of almost every AI course ever sold, because almost every one of them teaches a tool. When the tool changes, the course is a museum. So here is exactly how this is built, and you can judge it for yourself.
Recorded once, and almost never touched, because none of it is about a model. It’s about running a team, and that hasn’t changed since people first had to run teams.
A new model shipping does not make any of this wrong. If anything it makes it worth more: a better engine in a business with no system is just a faster way to produce work nobody checked.
The specific tools, settings and models. Deliberately kept as short standalone lessons rather than baked into forty-minute videos, so when one changes, that one lesson gets re-recorded instead of the course rotting around it.
Claude
Codex
Gemini
Superset
Linear
Sentry
Playwright
→
whatever this becomes next year
These updates are the job, not an afterthought. When I change something in my own system, the lesson that teaches it gets redone, because I’m running the thing I’m teaching, every day, and a stale lesson would be teaching you something I no longer do.
People chase every launch, rebuild from scratch each time, and end up with a pile of tools and no system, so of course everything they learned expires. A system does the opposite: a better model drops into it as an upgrade, not a rebuild. That’s the whole of module one, and it’s the first thing you unlearn.
Which is why “AI moves fast” is the reason The Inside Line exists, rather than a reason to avoid this. You’re not buying a library that ages. You’re buying a system, plus somebody whose actual job is testing what’s new so you don’t have to.
“Hold on. Why are you working fourteen-hour days if AI is doing the work?”
Fair question, and here is the honest answer. AI did not give me my evenings back. Last year it did $414,600 of work at list price, across 290 billion tokens, and I paid a fraction of that in subscriptions. See the breakdown
Fourteen hours used to get one product limping along. The same fourteen hours now run six. The hours did not change. What comes out of them did.
I could run one company in four hours a day. I would rather run six.
That is a preference, not a requirement. What you do with the leverage is entirely your call. I just will not pretend it makes the work disappear. Anyone telling you it does is selling you something.
What changes
Fit
Why this, why now
Every so often one skill quietly separates the people who get ahead from the people working just as hard beside them. It’s never the people who worked harder. It’s the ones who learned the new thing while it was still new.
One person could suddenly model a whole business. The people who learned it ran finance departments. The ones who waited got managed by them.
“We don’t need a website” was a perfectly respectable opinion for about four years. Then it wasn’t.
Ads, audiences, funnels. You no longer needed permission from a gatekeeper to find your customers.
Not using AI. Everyone does that. Operating it. One person producing what used to take a funded company, around the clock, without anything breaking.
It multiplies whatever you already are. That’s what makes it different from every tool you’ve ever bought.
A good operator with this becomes a company. A good writer becomes a publisher. A good builder becomes six products. Nothing else you could learn this year has that shape. Everything else adds. This one multiplies.
And it compounds. The person who builds the system this year isn’t slightly ahead next year. They’re operating at a scale the others can’t reach by working harder, because the gap was never effort.
2 hours to the first job off your desk
The offer
Pricing not set (placeholder)
One payment · lifetime access · 90-day guarantee
Do the work, and if within 90 days your documented savings don’t exceed what you paid for this, you get a full refund.
Measured against the baseline report you build in Level 1 and the after report you build in Level 3. Both are things the program has you make anyway, so there’s nothing extra to prove and nothing for me to argue about.
Before you ask
No. Half of this is about delegation, evidence and running a team: skills you already have from managing people. The technical half is click-by-click, and there’s a checklist version if you want to move faster than the video.
It’s close to the opposite. Prompt engineering is work on the sentence you type, which lives for one conversation and dies with it. Almost none of the quality of the result comes from there.
What decides it is what the agent already knows about your business before you type anything, the rules it re-reads at the start of every session, what it is allowed to touch, what counts as done, and who checks it. Change any one of those and the same words produce completely different work. Some people call that context engineering, and the scaffolding around the model gets called a harness. The names matter far less than the fact that all five are things you build once and then stop thinking about. This program calls the whole of it The Grid System, which is the only name you will need here.
Less than you’re spending now, in most cases. You build a baseline cost report in Level 1 precisely so you can answer this with your own numbers rather than my promises. That report is also what the guarantee is measured against.
Most of it can’t go out of date, because most of it isn’t about AI. Delegating so work comes back finished, demanding evidence instead of trusting output, running several workers without collisions, never letting the author review its own work, the economics of what to run where, shifts and handovers. A new model launching doesn’t make any of that wrong. It makes it worth more.
The part that genuinely does age (specific tools, settings, models) is kept as short standalone lessons on purpose, so one can be re-recorded without touching anything around it. Those updates ship through The Inside Line, and they happen because I’m running this system in my own businesses every day: when I change something, the lesson teaching it gets redone, or I’d be teaching you something I no longer do.
And the honest version of this objection: what actually goes stale is a tool collection, not a system. People chase every launch, rebuild from scratch each time, and wonder why nothing they learned lasted. The first module in this program is about breaking exactly that habit. Build the system once and a better model arrives as an upgrade, not a rebuild.
Some of it, yes. All of the individual pieces exist somewhere. What doesn’t exist for free is the order they go in, which ones matter, and which of the confident advice out there is wrong.
The thirty-seven Incident Reports in this program are each a real failure of mine, with the date and what it cost: an agent that claimed a check had passed when it never ran, a night that burned money on nothing, a rule that looked sensible and quietly broke something a week later. You can absolutely discover all thirty-seven yourself. It took me about two years and a lot more than the price of this.
Yes, and the evidence isn’t mine. Anthropic studied roughly 400,000 sessions and found every one of the ten largest occupations succeeded within seven points of software engineers, with management, sales and legal growing fastest.
Software is where it’s easiest to prove, because a passing test is not a matter of opinion. The same machinery runs research, customer replies, proposals, reporting and operations. An agency uses it for client deliverables, a trades business for quoting and scheduling and follow-up, an ecommerce business for listings, support and supplier chasing. The roles, the rules and the review gate do not change.
I know, and being too busy to build this is the exact condition it treats. That is why the first level is two hours to one job running on its own, rather than a month of setup before anything pays off. You get time back before you have spent much of it.
There are no cohort dates and nothing expires, so the rest goes at whatever pace your week allows. Plenty of people do level one on a Sunday and the next level over a month of evenings.
You’re in The Inside Line for three months as part of this, and that is where the unsticking happens: other people running the same system, the monthly call where I take real setups apart, and the updated files when something in the stack changes underneath you.
Nothing in the program depends on me answering you personally, because that would break the moment more than a few people bought it. It depends on the checklists being right and the community being active, which is a far more reliable thing to lean on.
Because “I bought it” and “it works in my business” are different things, and only one of them is worth your money. Every gate is submittable on day one. If you can already prove it, you skip ahead immediately. Nobody skilled is held back.
Two hours to your first working agent. A few evenings to finish Level 1. The rest goes at whatever pace your business allows. There are no cohort dates and nothing expires.
Do the work and show the two reports. If your documented savings don’t beat the price inside 90 days, you get all of it back.
Last thing
Or you could open the same chat window tomorrow and do the same day again. I did that for a long time too. The difference between those two versions of your business isn’t the AI. You already have the AI.
Pays for itself in 90 days or your money back
Works whether you are building your own company or bringing this into somebody else’s. Two ways in.
Every Thursday: one thing I built, learned or improved, broken down so you can replicate it. No theory. No shiny objects.
Get Thursday’s issue A few a quarter A B C Have it builtA small number of engagements a quarter. I come in and build it inside your business, rather than handing you a course about it.
See how it works