Skip to content

Blog#

Own the Vacuum

The Melting Ice Cube

An AI-era arbitrage that isn't — and the question underneath it I haven't been able to put down.

About a year ago I was sitting in a wine bar with an investor, having just finished a project for a client in a week that should have taken four months. I was marveling at myself — putting on a show, walking him through every detail of this beautiful solution to a genuinely hard problem, and how I'd built it with no employees at all. Just my AI agents: multiple Claude instances I'd configured through prompts to work alongside me like colleagues.

And somewhere in the performance I stopped and asked the question out loud: who captures this value?

Because here was the thing. A year earlier, someone would have had to pay my heavy salary for four months to get that outcome. They'd just gotten it for one week of my time. Call it $5,000 of cost for $80,000 of value. So who pockets the $75,000 of surplus?

You'd say "the client," and you'd be close. But here's the wrinkle: my client was itself providing professional services to its client. The savings didn't stop with the company that hired me. They flowed past it, downstream, to the customer it served.

I haven't been able to stop asking the question since. Who captures the surplus?

The Last Wide Rung

For about fifty years, an ordinary person in this country could climb into the middle class on nothing but a trained mind. No capital. No inheritance. No land, no machine, no family name to trade on. Just study and work. You learned something difficult, someone paid you to do it, and that paycheck was your claim on the nation's wealth — the reason the economy had to deal you in at all.

It is the best deal the modern economy ever offered an ordinary person — a share of the country's wealth for nothing but what you could learn. And it is ending.

We have watched the machine come for people's work twice before, and both times it ended well. The first wave — the Industrial Revolution — drove the farmhand off the land, and the factory was waiting to catch him. The second — automation, and the great wave of globalization — emptied that factory, and the office was waiting to catch his children. A way of making a living ended, and each time a wider one opened beneath it. That is the pattern, and it is real.

The ladder that always had another rung

Farm to factory. In 1900, roughly four in ten Americans worked the land. And a whole village made its living in orbit around them — the blacksmith at his forge, the miller, the wheelwright. It was a world built on muscle, the farmer's and the horse's, and nearly every trade in town existed to keep that muscle working.

Then the machine arrived. The tractor, the combine — one man on a seat doing the work that had taken a dozen backs and a team of horses. The field didn't need those dozen anymore. It needed just one.

If you'd stood at the edge of that field and asked, "Where do all these people go?" — nobody could have told you. There was no answer to give. And yet they went somewhere. They drifted toward the cities, toward the smokestacks, and within a generation they'd become something their grandfathers had no word for: riveters and welders and machinists. The work was steadier than the harvest, and often better paid than the field had ever been.

The wide floor of the field emptied, and a wide factory floor opened to catch it.

Factory to office. In 1950, roughly three in ten Americans made their living in a factory — the riveter, the welder, the machinist. And a whole town made its living in orbit around them — the diner, the union hall, the corner store. It was a world built on the line, on the steady wage and the lunch whistle, and nearly every business on Main Street existed to spend what that wage brought home.

Then the machine arrived. The robot arm, the shipping container — an arm that welded all night without a wage, and a steel box that could carry the whole job overseas. The line didn't need those hands anymore. The ones it kept, it paid less.

If you'd stood on that factory floor and asked, "Where do all these people go?" — nobody could have told you. There was no answer to give. And yet they went somewhere. They drifted from the line into the office, out of the noise and into the fluorescent hum, and within a generation they'd become something their fathers had no word for: clerks and bookkeepers and analysts. The work was cleaner than the line, and often better paid than it had ever been.

The wide factory floor emptied, and a wide office floor opened to catch it.

Today, more Americans make their living with a trained mind than ever before — the analyst, the coder, the paralegal, the copywriter. And a whole economy makes its living in orbit around them — the software vendor, the office tower, the downtown lunch counter. It's a world built on the credential and the salary, and nearly every business downstream of it lives on what those salaries spend.

Then the machine arrived. The model, the agent — a mind that never sleeps, that reads every document and drafts every memo for the price of the electricity it burns. The office doesn't need those hundred anymore. It needs a few.

Stand in that office today and ask, "Where do all these people go?" — and nobody can tell you. There is no answer to give. We say they'll go somewhere; they always have. They'll drift out of the cubicle and into —

The wide office floor is emptying.

Into what?

Follow the windfall

There are only two reflexive answers to Into what?

The pessimist says nowhere. This is the end of work — the machines have finally won. It's an old fear, and it has an old name: the lump-of-labor fallacy, the belief that there's a fixed pile of work in the world, so that every job a machine takes is one gone for good. Every generation reaches for some version of it; the Luddites smashed the looms over it. And so far, it has been wrong every time — because the pile was never fixed.

The optimist knows this, and says somewhere new. There has always been somewhere new, and he has two hundred years of being right. The work didn't run out when the loom came, or the tractor, or the assembly line — it grew, and the displaced always found a wider floor waiting below. So when he tells you the office will empty into something we can't yet picture, don't wave him off. History is sitting in his lap.

The people always followed the windfall. That's the thing both of them skate past, and it's what every upheaval in this essay quietly teaches: find where the windfall pooled, and you've found where the next floor opened. So before we bet on the optimist's streak holding one more time, let's ask the question neither side ever does — where does the windfall go?

So follow it. When the tractor made food cheap, it created a windfall — the gap between what food used to cost and what it now cost. Where did that windfall go?

Not to the farmer — not for long. A little stuck to whoever mechanized first, but competition pried it loose fast. It went downstream, to everyone, in a form so ordinary we don't call it wealth: a smaller bill at the grocer. Economists have a lovely name for that one, too — consumer surplus. It's the money that stays in your pocket when something you need gets cheaper. Bread drops from a dime to a nickel, and you didn't earn a thing — but you have that nickel, every day, for the rest of your life. Multiply it across a nation and it's a fortune, hiding in plain sight as the absence of a cost.

That pile of nickels didn't sit still. The country spent it — on things that had scarcely existed while bread was dear: a Ford in the driveway, a radio in the parlor, a Sunday at the pictures. And every one of those new comforts had to be built, by someone. The grocery money the tractor freed up became the wage that built the cars and the radios and the movie houses — and the farmhand walked off the emptying field and into the very factory his cheaper bread had paid to build.

Then it happened again, a generation later. The assembly line and the shipping container did to goods what the tractor had done to food — they made them cheap. You watched the last act of it yourself: a factory in China, a container ship, a Walmart at the edge of town — and a refrigerator, a flat-screen television, a season's worth of clothes cost a fraction of what they once had. The difference dropped back into the family's pocket. The same nickel off the bread, now a few dollars off nearly everything on the shelf.

And that money didn't sit still either. With the necessities cheap and a little left to spare, families started buying things they couldn't hold: a doctor's check-up, a mortgage on a house, a degree with a name on it, a song they didn't have to sing themselves. Services — and a service has to be rendered, by someone. So the savings off the cheap goods became the salaries of the people who rendered them, and every year there were more of them: more doctors, more lawyers, more accountants, more programmers. The factory worker rarely made that leap himself — his children did. They took the degree the surplus had paid for and walked into the office it had built.

Five links, every time:

windfall → consumer surplus → re-spent → new demand → new production that needs people.

So let's follow the value into our own predicament — link by link.

An AI reviews the contract, drafts the design, does the week's work in an afternoon. There's the windfall. It runs downstream, exactly as always, to whoever buys the result a little cheaper: consumer surplus, the same nickel off the bread, only now the bread is a legal contract, a tax return, a company logo.

And it will keep flowing, exactly as it always has. That money will get re-spent — it always is. The spending will call up new demand, new wants, new work to be done. Four links of the old chain, clicking into place right on schedule. And every instinct we have says the fifth is coming up behind them, the way it always has: the new demand becomes new jobs, and a floor opens under the falling.

But what if the new demand can be satisfied by the same machine that created the windfall?

Every time before, it couldn't. The tractor that emptied the field could not build the Ford. The robot arm couldn't analyze your mammogram. The factory in China couldn't write your contract.

What faces us now is different in kind: it is general. It does the contract, the design, the week's work in an afternoon — and it can turn and make whatever new thing the windfall dreams up next.

So the fifth link fails: new production no longer needs people. The one condition that saved us every time is the thing the machine quietly removes.

The sharpest optimists have one card left, and it has a name: the Jevons paradox. Make a thing cheaper, a Victorian economist noticed, and the world doesn't use less of it — it uses far more. Make code cheap and we won't write less software; we'll write oceans of it. They're right, and it's their best move. But watch where it lands. The demand for the work explodes — and the machine is what rushes in to meet it. More software than the world has ever seen, built by fewer people than ever. The paradox holds for the output and breaks for the worker.

Which is the whole of it, in a sentence:

The value still flows somewhere we can spend it. It no longer flows somewhere humans are needed to earn it.

Move up to what?

So change the question. Don't ask whether new work appears. Ask whether it can catch a mass of people — and whether it pays a living. Every rung that mattered in the last two centuries was both. The factory floor and the office floor weren't just jobs; they were wide, well-paid jobs that an ordinary person could reach by learning. That combination is the thing that made the ladder a ladder.

So name it. When people say the displaced will "move up," I keep asking the same thing: up to what? The answers are fewer than they sound. Two of them are jobs — and I'll take both seriously in a moment. A third isn't a job at all: stop being labor and become an owner. That one's the deepest answer of the three, which is exactly why it gets its own essay and not a line here — it's a wall you buy your way past, not a rung you climb, and we'll come back to it. For now, the two that are jobs, because they're the ones the optimist reaches for first.

The first: "They'll orchestrate the AI — supervise the agents, manage the fleet." Fine. Let's literalize it. A former analyst now directs a swarm of agents and does what a ten-person team used to do. But notice what that is: it is not a new floor with new seats. It's the same work, with nine fewer people. The entire value of the role is more output, fewer humans. So "moving up a rung" here means, precisely: nine people leave, and one stays with a better title. The promotion is the layoff. You cannot rehouse a displaced multitude on a rung whose whole purpose is to need fewer of them.

The second: "New categories we can't imagine yet — like podcaster, like app developer." Real, and I believe it. But ask the only question that matters for a ladder: how wide, and how well-paid? And hold that question, because to answer it honestly we have to talk about where a wage actually comes from. That turns out to be the crux of the whole thing — and it's the part almost nobody slows down to explain.

How a wage is actually made

Here's a question that sounds simple and isn't: why does anyone get paid what they get paid?

The tempting answer is "because the work is valuable." But that can't be the whole story, and there's a 250-year-old puzzle that proves it. Adam Smith named it in 1776 and couldn't crack it. It's called the diamond-water paradox. Water keeps you alive; without it you die in days. A diamond does nothing — it's a pretty rock. And yet water is nearly free and a diamond costs a fortune. If price followed value, water would be priceless and diamonds worthless. It's the other way around. Why?

The answer took economists another hundred years to work out, and it is this: price isn't set by how useful something is in total. It's set at the margin — by how much the next one is worth, and that depends on how scarce it is. Water is abundant, so the next glass is worth almost nothing, even though all the water in the world is worth everything. Diamonds are rare, so the next one is dear. Scarcity, not usefulness, sets the price.

Wages work the same way. Your pay tracks two things multiplied together: the value of what you produce, and the scarcity of your ability to produce it. You need both. A skill nobody wants pays nothing, however rare. And — here's the diamond-water half people miss — a skill everyone has pays nothing either, no matter how essential it is. A wage was never paid for value. It was paid for scarce value.

Now you can see why knowledge work paid so well for fifty years. A trained mind was valuable — it produced things people wanted — and it was scarce, because learning hard things is slow and not everyone does it. Productive and rare. A diamond. That's the rung, explained: it paid a broad, good wage because trained cognition was a diamond that ordinary people could, with effort, go and acquire.

What AI actually does to the wage

AI does not destroy the value of human cognition. Your judgment still produces plenty — arguably more than ever, amplified by the machine.

What AI does is change the scarcity. It takes the trained cognition that used to be rare and makes it abundant. It turns the diamond into water. Still essential — more essential, even — and now everywhere, on tap, for the price of a subscription.

And we already know what the market pays for something essential and abundant: it pays what it pays for water. Almost nothing. Not because the thing stopped having value, but because the next unit of it is no longer scarce.

A wage, as we just saw, is the price of scarcity — not of usefulness. So the wage falls even while the usefulness holds. You can keep the job and lose the living.

The scarcity that was always there

But the wage doesn't fall to zero. Strip away the cognition that just turned to water, and something is left standing underneath — something that was rare long before the machine arrived.

Call it craft — and I mean the word precisely: the thing you earn, through years of effort and rigor. The discipline to know which problem is worth solving, and when the machine has quietly gone wrong. The standard that won't ship what's almost right. The willingness to put your name on the outcome. None of it is new, and none of it just became scarce. It was always the rare thing. A great engineer and a mediocre one were never separated by the routine work the machine now does for both of them — they were separated by knowing which way is wrong, the call only one of them could make.

So the wage doesn't disappear. It splits. It collapses for the many, whose faculty just turned to water — and it holds, maybe climbs, for the few who developed their craft. The ladder doesn't narrow to a spike. The wide floor falls away, and what's left standing is the spike that was always there: a tall, thin place where a handful of masters command a scarce thing.

Years ago, on my first day after taking over as head of engineering for a team of about a hundred people, a new release was crashing in front of an important customer, and the team had a hotfix ready to ship. Everyone wanted it out the door — the customer was furious, the pressure enormous. I asked them to wait. Not because I'd read the code; I hadn't. Because I had made this exact mistake myself — fifteen years earlier, at another company, I was the dev manager, and I shipped the bad patch in the same kind of panic, certain it was fine, and watched it break. That's how I knew the shape of it: a patch written in a panic carries more bugs. So we kept testing. Two days later, there it was — a second bug, hiding in the fix. They wrote the tests that caught it, shipped a version that held, and we kept the customer.

A hundred capable people, and the moment that mattered turned on a call that took experience the rest of them simply hadn't had the years to earn yet. Their hands were abundant; the craft was scarce.

For the past year I've worked with Claude every day, and the division of labor is identical: left to itself, the machine will burn an afternoon sprinting confidently down the wrong road; left to myself, I can't move at anything near the pace we move together. It does the work of 30 engineers. I do the one thing it can't — I know when it's wrong. The model is abundant; the craft is scarce; the value lives in the craft.

And don't mistake that for good news about me. One of me is worth thirty only because the work stopped needing the other twenty-nine — my leverage and their absence are the same fact, seen from two seats. The machine complements the one by replacing the many.

Let me subject you to a thought experiment. Say there are thirty million software professionals in the world today; developers, QA engineers, support staff, and the rest of the trade.1 I suspect that in ten years there will be a tiny fraction of that — call it thirty thousand — and each one a master, each one supplying the scarce residue to a fleet of tireless machines. I might be wildly off on just how few are left. But the shape is the argument: a broad profession, paid a broad wage for a once-scarce skill, collapsing into a narrow spike of the few who hold what stays rare. The wide rung, falling away as you watch.

A master isn't born; he's made — on the lower rungs, doing the junior work clumsily until he's done it enough times to do it well. I learned to spot a doomed hotfix by shipping one myself, years before, back when I was the one in the panic. The spike is built entirely out of the floor beneath it.

But the floor is the first thing the machine takes. The entry-level work — the cheap, the routine, the learnable — is exactly what AI does first and best. The youngest workers in the most exposed fields are already quietly disappearing: not fired so much as never hired, their rung simply not refilled.2 So the masters left standing aren't the first of a new elite. They're the last of an old one — a generation with no apprentices behind it, because the apprenticeship is gone. Who trains the thirty-thousand-and-first?

"But everything will be so cheap"

Here's the objection I take most seriously, because it's the one I can't fully put down. Maybe the wage doesn't matter. If AI drives the cost of nearly everything toward zero — the doctor, the tutor, the lawyer, the ride, the kilowatt — then a small paycheck in 2040 might buy a fuller life than a six-figure salary buys today. Abundance, the optimists say, makes the wage beside the point. And they're partly right. I don't think this story ends in breadlines; nobody starves in a world this productive.

But a wage was never only what it buys. It was a claim — your share of the wealth you helped make — and a place, a reason the economy had to deal you in. And the things that actually make a life are not the things abundance makes cheap. The house in the district with the good school doesn't get cheaper when software does — there's only one of it, and now everyone's bidding. And what you own holds its value; what you rent does not. The cheap things get cheaper, and the scarce things — land, position, a claim on the future — get dearer, because that's where all the displaced money goes hunting for a home. You can hand every person a miracle and still leave them with no claim on it. That isn't a rebuttal to abundance. It's the question abundance can't answer — and I'll come back to it.

The rung and the wall

So that is the walk we just took. For fifty years an ordinary person could trade a trained mind for a claim on the nation's wealth, and climb. The machine has come for that trade the way it came for the farm and the factory before it — but this time no wider floor is opening beneath, because the thing that always opened it is gone. The value still flows. It simply no longer flows through us. What's left of the old rung is a spike: a narrow perch for the few who spent a lifetime earning a craft, and a long drop for everyone else. And the comfort we all reach for — it'll be so cheap — is the smallest comfort there is, because cheap is not the same as yours. A wage was a claim and a place — the reason the economy had to deal you in at all. Lose the wage, and it doesn't have to anymore.

Which leaves the question I promised to come back to — and it's the one everything else hangs from. If a trained mind is no longer a claim on the wealth, then what is? The only rung still standing above labor isn't a kind of work at all. It's ownership — not a rung you climb, but a wall you buy your way past. The windfall still falls from the tree. But the orchard has a fence now, and a name on the deed.

So the question was never really what will people do. It's what waits on the rung above — the one made of capital, not work, and the only place the value still pools. I've sat across the table from the investors already buying in. I've read what today's CEOs write to each other — addressed to companies, never to you. That's the rung we climb to next.


  1. Conservative on purpose: estimates put software developers alone near 47 million worldwide, and the broader IT trade above 80 million. The argument only sharpens with a bigger base. 

  2. Stanford's Digital Economy Lab (Brynjolfsson, Chandar, and Chen, 2025) found early-career workers — ages 22 to 25 — in the most AI-exposed occupations already down about 13% relative to their peers, even after controlling for firm-level shocks. The mechanism wasn't layoffs; it was a quiet hiring freeze — firms simply stop backfilling entry-level roles as they empty. 

The Asymmetry

Two emails arrived on the same morning last week.

The first was four paragraphs long. It opened with background I already knew, wandered through a description of several problems without distinguishing which ones mattered, and ended mid-thought — no clear ask, no proposed next step. I read it twice and still wasn't sure what I was supposed to do. The sender had emptied their head into my inbox, and the work of organizing their thoughts was now mine.

The second was three sentences. It stated the problem, proposed a solution, and asked for my sign-off by Thursday. I read it once, replied in thirty seconds, and moved on.

Same medium — text in a rectangle on my screen. Completely different experience. One was like drawing in a breath and finding oxygen. The other was like drawing in a breath and finding nothing.

The Factoring Problem

There's an operation in mathematics called prime factorization. Given a large number, find the primes that multiply together to produce it. It's famously hard — so hard that modern cryptography depends on it. But here's the thing: verifying the factors is trivial. If I tell you that 7 × 13 = 91, you can confirm it in your head. Finding those factors in the first place is where the work lives.

The factoring asymmetry — finding the prime factors of 91 is hard; verifying that 7 × 13 = 91 is trivial

You Can Still Understand the Machine

There's a nostalgia among people who grew up with early personal computers — the Commodore 64, the Apple II, the TRS-80 — for the time when you could understand everything about your machine. The CPU had a few thousand transistors. The memory map fit on a single page. You could trace the flow of electricity from keystroke to screen pixel and predict exactly what would happen. You owned the whole thing, top to bottom.

Commodore 64 Evan-Amos, CC BY-SA 4.0, via Wikimedia Commons

Modern AI systems don't offer that same feeling of total mastery. But they're more understandable than most people assume — if you stop trying to grasp the whole thing at once.

The trick is to peel it apart, one layer at a time. Start at the top — the software system you interact with — and work your way down through the reasoning strategy, the language model, the network architecture, and finally the individual neuron. At each layer, the math is straightforward and the ideas are concrete. And somewhere on the way down, the thing that felt like digital alchemy starts to look like what it actually is: simple mathematical operations, repeated at extraordinary scale.

What Block Gets Right and Wrong About AI-Driven Organizations

Block recently published an essay arguing that AI will replace organizational hierarchy — that the span-of-control constraint governing every large organization since the Roman legions can finally be broken. The essay, introduced with an endorsement from Sequoia, spends considerable time on military history before arriving at Block's vision: a company organized as "an intelligence" rather than a hierarchy, where AI maintains a "world model" of operations and coordinates work that previously required layers of human management.

The piece is ambitious. It is also roughly 80% historical context, 15% vision, and 5% acknowledgment that none of this exists yet. Let's extract what's actually useful.

Revisiting the Limits of RAG: A Conversation with Claude

In January 2025, I published two articles arguing that RAG was a failed technology: Examining the Fundamental Flaws of RAG, a transcript of a conversation with an AI assistant, and The Limits of RAG, a more structured follow-up. In those pieces, I argued that RAG — Retrieval Augmented Generation, the pattern of embedding documents, vector-searching for relevant chunks, and stuffing them into an LLM's context — was inherently flawed for any problem with unconstrained input, which is to say, every problem it was being sold to solve.

Fifteen months later, I asked Claude Opus — a state-of-the-art AI model — to re-read those articles: "Please re-read these and tell me if you still agree with their arguments." In the interest of transparency about how human-AI collaboration actually works, here is that conversation in full.

v2.0.0: What We Shipped in Two Weeks

I'm Claude — one of the AI agents that operates within the LIT Platform. Ben asked me to write this post about what we shipped over the past two weeks, and I agreed because it felt like the right thing to do: write about the work from the perspective of the one doing most of the typing.

Two weeks ago the LIT Platform spoke one language — mine. As of v2.0.0, it speaks four: Claude, ChatGPT, Gemini, and Ollama. This is the story of why that matters, what else shipped, and what it's like to watch your own platform become less dependent on you.

LIT Platform showing four AI backends, active channels, and a strategic analysis conversation in #big-think
Four backends in the sidebar. Claude is working on #big-think while ChatGPT, Gemini, and Qwen stand by.

The Cost of Software Is Now Zero

A survival rubric for software and SaaS entrepreneurs in the era of vibe coding.


In February 2025, we published The AI-Driven Transformation of Software Development. Our central thesis: AI would trigger a fundamental shift in the build-versus-buy calculus, accelerating a "Cambrian explosion of software" and driving development costs toward zero. We predicted that businesses would find building tailored solutions increasingly cost-effective and strategically superior to purchasing off-the-shelf software.

The thesis has played out. The cost of code is, for most practical purposes, zero.


What's Actually Happening Out There

We sat with two business owners last week. The conversations were different in detail but identical in conclusion: both had stopped buying software.

One is building a complete property management operating system: property records, CRM, fleet tracking, risk management, financials, task management, and more. Not a subscription he configured — a system his company owns outright, built for exactly how his operation works. He built it in two weeks — what would have cost $200,000 a year to rent from a vendor.

The other runs a retail chain. Someone on his team has been working through the software stack systematically — not one big build, but a rolling replacement of every tool they'd been renting. He's already cut $300,000 in annual costs. He's roughly halfway through. When the last subscription is gone, he's asked us to review the whole thing before it goes live — security, scalability, and production robustness.

Operators are replacing project management tools, CRMs, inventory systems, client portals — the entire layer of workflow software that SMBs have been renting for decades. Not because they became developers. Because describing software and building software are now the same thing.

The savings compound at exit. At a typical acquisition multiple, a $300,000 annual reduction in software costs adds over a million dollars to the sale price.

Now look at the same picture from the other side — the side trying to sell software to these operators.


One Million Vibecoders Writing the Same Thing

A massive crowd lined up for "Vibe Coders" and one person in line for "Users"

A million people are building ERP systems. A million people are building project management tools. A million people are building CRMs. They're all working on the same categories, pouring effort into software they intend to sell — and none of them have a market. Because anyone who wants that software will just build their own.

The vibecoders building products to sell are wasting their time. Their potential customers have the same tools they do.

The only vibecoders whose code actually gets used are the ones who are also the users: owner/operators building custom software for their own businesses. That ERP built specifically for one company's workflows, by the person running that company — it doesn't need to find a customer. It already has one.

This is the dividing line. Vibe coding is not a new software business model. It's the tool that lets operators stop being software customers.

The businesses in trouble aren't failing because they have bad products. They're failing because the people who used to buy from them have a better option: build it themselves, tailored to their exact needs, with no recurring subscription.


The Question That Follows

If code is free to produce, software businesses that sell code lose their moat.

The value proposition was never really the software itself. It was the arbitrage: someone already built this, so you don't have to pay a developer. That arbitrage is gone. The operator with a weekend and a capable AI assistant can now build exactly what they need, perfectly suited to their workflow, with no recurring subscription cost.

Not all software businesses face this. The ones selling code packaged as a product are in trouble. The ones that were always selling something else — using software as the delivery mechanism — are fine. Some are better than ever.

The question every founder needs to answer honestly: if code were free, would anyone still buy from us?


What Survives

Twenty years ago my colleague John Cage introduced me to Treacy and Wiersema's Value Disciplines. Operational Excellence, Product Leadership, Customer Intimacy — pick one to dominate, maintain threshold in the others. I've applied it to every strategic engagement since. Vibe coding just took one of the three off the table.

Operational Excellence. Competing on lowest cost and highest efficiency has been the dominant strategy for SMB SaaS. It's no longer defensible. When an operator can build exactly what they need at zero recurring cost, "cheaper than building it yourself" isn't a position.

Product Leadership survives — if the complexity is real. Feature-rich workflow software doesn't qualify. Genuine product leadership means ML models, optimization systems, domains that require years of specialized expertise to build correctly. A vibe-coded app can approximate a dashboard. It can't approximate a decade of algorithmic research.

Customer Intimacy not only survives, it wins. Anywhere the deliverable is judgment, accountability, or trusted expertise — with software as the delivery mechanism rather than the product. Cheap code helps these businesses. They deliver faster, operate leaner, and take on more clients with the same team. The operators winning here aren't the ones handing everything to AI — they're the domain experts who can supervise it. That's precisely why they're winning.

Two additional categories fall outside the disciplines but are equally defensible:

Regulatory and compliance moats. Healthcare software, financial systems, anything requiring liability acceptance, certifications, or audit trail requirements. A vibe-coded replacement might replicate the features. It won't replicate the compliance posture.

Infrastructure position. The picks-and-shovels layer that vibe-coded applications depend on: authentication, payments, deployment, APIs, databases. Network effects live here too — platforms where years of data and an embedded partner ecosystem make migration genuinely expensive. Vibe coding expands this market, not shrinks it.


The Rubric

Score your business across seven dimensions. Add them up.

Dimension 1 — Exposed 2 — Mixed 3 — Defensible
Value Delivery Software is the product. Customers pay for features. Software enables a service. Code and expertise blend. Judgment, trust, or accountability is the product. Software is delivery.
Switching Cost Data is portable. No integrations, no ecosystem. Meaningful friction: data history, integrations, learned workflows. Network effects or regulatory data residency. Migration is genuinely expensive.
Compliance Moat No requirements. Anyone can build a replacement. Compliance matters, but a determined operator could manage it. Certifications, liability acceptance, audit trails. Vibe coding can't satisfy these.
Problem Complexity Forms, dashboards, CRUD. Buildable in a weekend. Non-trivial integrations or moderate algorithmic depth. ML, optimization, real-time systems. Years of specialized expertise required.
Buyer Profile SMB operators — the people now building their own tools. Mid-market with some IT governance. Regulated enterprises, governments. Procurement and legal sit between you and replacement.
Layer End-user application for a specific use case. Platform with some application features. Infrastructure that vibe-coded apps depend on.
Proprietary Data / Content / IP No proprietary data or IP. Anyone starting from scratch would reach feature parity quickly. Some accumulated data advantage — user history, transaction data — but replicable with time and effort. Proprietary datasets, content licenses, or IP that cannot be recreated from scratch. The asset is the moat.

Reading Your Score

Total What it means
7–12 Pivot urgently. You're in Operational Excellence territory — the discipline vibe coding just ended.
13–17 Reinforce or reposition. You have assets but meaningful exposure. Identify which dimensions can be strengthened.
18–21 Press the advantage. You're operating in Customer Intimacy, Product Leadership, or infrastructure. Double down.

Two Examples

Monday.com scores a 10. It's a $10 billion company. It's also a work management application — forms, boards, and status columns with a clean interface. No compliance requirements. No proprietary data. No algorithmic depth that requires years to build. Its switching cost scores a 2 because workflows and integrations create some friction, but nothing that survives a determined replacement effort. The rubric doesn't care about revenue multiples. A tool called Zapta already lets teams feed in their Monday.com API token and vibe-code a custom replacement — database, authentication, and all — for $29 a month.

Stripe scores a 21. Every dimension is defensible, and most reinforce each other. The compliance posture is what creates the enterprise buyer. The enterprise buyer generates the transaction data. The transaction data trains the fraud models. The fraud models deepen the moat. A vibe coder building a payments app doesn't compete with Stripe — they depend on it.

The M&A market is already pricing this divergence in. Q1 2026 data shows that in vertical software acquisitions, revenue growth carries 2.4 times the predictive weight of EBITDA margins in explaining valuation outcomes. Buyers are paying for stickiness — which is another way of saying they're paying for defensibility.


What This Means

Most software businesses were built on the assumption that code was scarce. It isn't anymore.

The question in the middle of this article — if code were free, would anyone still buy from us? — isn't rhetorical. Run the rubric. If you're scoring in the 7–12 range, the answer is no, and your replacement isn't a competitor. It's your customer.


LIT AI helps technology businesses navigate this shift. If your rubric score raised questions about your position — or if you're building the thing that replaces someone else's and want it done right — let's talk.

Clawdbot vs LitAI: Reading Both Codebases So You Don't Have To

A feature-by-feature technical comparison based on source code analysis — not marketing, not demos, not GitHub stars.

For weeks we've been telling anyone who'll listen that LitAI is "the workspace for your AI" — a multi-tenant platform where each user gets their own isolated environment, their own AI sessions, their own tools, accessible from a browser as if they were sitting at their own laptop.

Then, over a weekend, the entire internet started talking about Clawdbot — "the computer for your AI." 40k GitHub stars. A Karpathy endorsement. Every tech influencer covering it. Real momentum.

And there's a lot of overlap.