A very long rant, riffing on the opportunity for bootstrapped startups who seek to create value using AI. I wrote it a while ago, thought it was too long, but in arguing with an AI today (everyone needs a hobby) I realized it was worth sharing:
Most AI success stories to date are about cost reduction or speed improvement. A startup offers businesses a way to get more done with fewer people, replacing customer service or programming teams with bots. The upside of cost reduction is that it’s a very easy sale—give the client a free sample, once it’s demonstrated to work, they have an instant benefit in switching.
The downsides: it’s difficult to win a race to the bottom, since someone can always promise more savings than you. And it’s finite—once the savings are made, there’s no incremental value left to create.
The opportunity lies in something generative. A use of AI that doesn’t reduce costs, it creates value. It opens new opportunities, leads to growth, connection, and utility.
Worth paying for: Most bootstrappers target price-sensitive customers and then wonder why growth is hard. But people and organizations with expensive problems and real resources don’t just put up with paying more for things they value—they prefer it. Premium pricing signals seriousness. Look for a market where the problem is real, the budget exists, and the solution creates something they couldn’t get otherwise.
What people actually pay for: At the foundation of almost every premium purchase are three drives:
status (I matter, people like me see me as significant),
affiliation (I belong, there are people like me and they accept me), and
freedom from fear (I am safe, the threat is not coming).
Freedom from fear may be the most primitive—you can’t pursue status or affiliation while in survival mode. And most premium purchases are a quest for freedom from fear pretending to be something else.
Built on those roots is a middle layer of things that offer one or more: legitimacy, transformation, belonging to a narrative, control, certainty, protection, trust, health and longevity, leverage.
And the outer layer that’s easier to measure—things people buy because they deliver the middle layer: access, capital, time, attention, convenience, efficiency, delight, new experiences, beauty.
Commodities—food, shelter, sex, addictive substances—are often outside this hierarchy. They don’t really build toward the three roots; they allow survival or temporarily suppress the anxiety that comes from not having them.
An AI business worth building delivers something from the middle layer, justified by the outer layer. Nobody goes shopping for transformation.
What businesses actually pay for: The hierarchy for individual consumers doesn’t translate directly to organizational purchases. In B2B, the customer is spending someone else’s money. That means that the dominant question they’re asking is, “what will I tell my boss?” Three desires sit at the foundation of almost every business buying decision:
Avoid blame — if this goes wrong, it won’t be my fault. The IBM principle: nobody ever got fired for buying the market leader. The champion inside the organization often needs a defensible story before they’ll act.
Claim credit — I brought something in that worked and people noticed. The flip side of blame avoidance, and the engine of the internal champion. If your solution lets someone look good, they’ll sell it to their peers, you won’t have to.
Reduce uncertainty — we can plan around this, the chaos goes down. Organizations pay significant premiums for the ability to forecast, commit, and stop worrying.
Built on those roots is a middle layer of things organizations reliably spend on: growth, efficiency, compliance, competitive advantage, talent, morale, resilience, optionality, speed, legitimacy, relationships.
And an outer layer that justifies the middle: cost savings, time savings, data, access, convenience, integration, reporting, support.
Mechanics without a story is the race to the bottom, and being the cheapest is not the best use of your time.
New vs. repeat purchases require different approaches: Repeat purchases are won by switching costs, relationships, and relentless incrementalism—you’re replacing someone, which means you need to be cheaper, easier, or have a better story and sales force. New purchases require someone inside the organization to become a champion, which means they need a story that serves their career, not just their company’s interests.
Not all problems are equally interesting: Some purchases—like gaining market share or entering a new category—are chaotic and interesting, with room for narrative and ambition. Others—like cheaper materials or faster processing—are grinding commodities where the only story is price. Commodity buyers fear paying too much. Buyers in chaotic spaces fear making a wrong choice.
The forcing function: Businesses rarely lead the way on new purchases without a crisis compelling them. Without a forcing function, even a perfect solution sits in the pipeline forever—committees form, pilots stall, and champions get reassigned.
Three kinds of crises create forcing functions:
Competitive crisis — a rival did something and now there’s urgency. “They have it and we don’t” is a sentence that ends a discussion and starts the buying process.
Technology crisis — the old way stopped working, or a new capability made the old way look reckless. AI itself is currently creating this for many industries simultaneously. This time, the forcing function and the solution are the same thing.
Public/market upheaval — regulatory change, cultural shift, a collapse in input costs, a pandemic. These are the most powerful and least predictable. They create entirely new categories of buyer.
The opportunity for a bootstrapper: sell into a forcing function that already exists, don’t try to create one. Organizations already feeling the crisis don’t need convincing—they need a solution that lets their champion say “I found it.”
NOTES:
Naked AI is a trap. If all you’re doing is building a gateway to Anthropic or ChatGPT, your token costs eat a significant portion of your revenue—and you have no defensible position.
Hidden prompts are insufficient. Breakthrough prompting can create real value, but there’s no protectable, reliable way to sell it as a business. If one of the frontier companies made it a business model, the mechanics would work in the bootstrapper’s favor, but I haven’t seen this.
The network effect matters. Selling benefits one person at a time is brutally expensive. The breakthroughs come with projects that have the network built in—where interactions work better when your colleagues are using them too.
Asymmetric information is worth seeking out. Some of the most durable advantages come not from network effects but from knowing something others don’t, or from helping a cohort work together to pool what they know against a party that currently has structural information advantage over them. Let all of Walmart’s vendors see information that they currently hoard, for example.
So, a theory of profit—a framework for the kind of project that becomes a business:
Bonus:
A note on data stack reality. A network built on user data is only as good as the willingness of users to populate it. And willingness requires two things: it has to be frictionless enough that people don’t have to think about it, and it has to feel safe enough that people don’t have to worry about it. These two conditions are almost always in tension. The more automatic the data collection, the more it feels like surveillance. The more control you give people, the more friction you add.
The most promising data stacks are ones where people are already generating the data, are already comfortable with it existing somewhere, and the innovation is simply giving them better access to what’s already theirs. The forcing function for consumer data sharing may be the simplest one of all: I already feel watched. I might as well get something back.
The cautionary version of this is the email surveillance tool—a business reads all internal email and gets a report on who’s helpful, who’s toxic, who’s looking for a job. The value is real and obvious. The fear is also real and obvious. And in most organizations, the fear wins. Any data stack business has to answer the question: who controls this, and what happens if it goes wrong? If the answer isn’t immediately reassuring, the business doesn’t get built.
The sponsored model. Not every valuable AI business needs the end user to pay. When the problem is real but the affected population lacks resources, a foundation, brand, or institution with aligned interests can fund the miracle instead. The economics flip entirely: instead of acquiring thousands of customers one at a time, you close one relationship with one institution that already has the distribution, the mission, and the budget. The user gets the miracle for free. The sponsor gets impact, data, or loyalty.
This model works when three things are true: the population being served is large and underserved, the value created is legible to an institution that cares about it, and the data generated serves both the individual user and the sponsor’s mission.
For example, a foundation pays $2,000,000 and 40,000 families of the incarcerated have access to a tool that generates a ten-page legal document instead of a bushel of random papers—and the shared data starts identifying patterns in the system (bad actors, defective paperwork) that no single case could surface alone.
A bank funds a personal finance tool for its own customers. A health brand funds a fitness coach for an underserved population. The viral problem is much easier to solve: once it’s free, you don’t need to work hard to persuade users to recruit each other, you need one institution with existing distribution to say yes.
The cheap inference model. Not every AI application needs a frontier model. The problems worth looking for here aren’t the ones that require reasoning or nuance—instead, look for structure, pattern recognition, aggregation, and organization at scale. Form filling. Document organization. Transcription plus summarization. Matching similar records across large datasets. These problems are unglamorous but enormous in volume and largely underserved.
Moore’s Law is on your side. The models that feel too limited today will evolve to become adequate in eighteen months. Building on cheap open-source inference means your margins improve as the technology does, without changing your product (which is the data stack and the network). And “huge” doesn’t mean huge—a single business school graduating class is enough to populate a meaningful census of what jobs actually lead where. The data stack doesn’t need to be large. It needs to be specific, trusted, and ahead of what anyone else has assembled.
This was a particularly long rant, thanks for hanging in. I started writing it for a friend six months ago (with many inputs from others), but it’s more true now than then.
Someone who has seen a lot of movies but has never made one has a certain kind of knowledge. The same is true for clients, patients and students. They haven’t solved a problem, healed a patient or taught a class, but they’ve seen it done.
They might have something helpful to add. Or they might not.
PS thanks to James Hunt for creating and maintaining THIS IS BROKEN, a directory of the 491 books I’ve recommended on this blog over the years.
The site inspired my podcast page as well.
Imagine if your job required you to maintain an updated handoff doc, something the boss could give to a new hire that would let them immediately get caught up on the work. All the file names, locations, assumptions, choices, dilemmas and contacts, organized and ready to go.
Creating something like this would be annoying and time consuming, and eventually we’d get very little done. In addition, we like leaving our intuition a bit unexamined and our choices somewhat undocumented.
But…
This is something your AI should be very good at. Clarity, documentation, commented analysis, all there, every day, an ongoing roadmap that makes it easier to unwind activities or transfer them to a different platform. Here’s a prompt you can cut and paste into the AI you use the most:
Maintain a handoff doc for this work. Its reader is a smart stranger
who has to take over tomorrow with no access to me or to our chats.
At the end of any session where something changed, update the doc before
you finish, without being asked. If nothing changed, say so in one line.
The doc has these sections, always in this order:
1. WHAT THIS IS — two or three sentences: the project, who it's for,
what "done" looks like.
2. CURRENT STATE — what works, what's half-built, what's broken. Dated.
3. WHERE THINGS LIVE — every file, folder, account, URL, and tool, with
the exact name and location. No "the spreadsheet." Name it.
4. DECISIONS — each choice we made, the alternatives we rejected, and why.
Never delete a decision; if we reverse one, mark it superseded and
note the date and reason.
5. ASSUMPTIONS — things we're treating as true but haven't verified.
Flag which ones would hurt most if wrong.
6. OPEN QUESTIONS & DILEMMAS — unresolved tensions, stated plainly,
with the leading options.
7. PEOPLE — who's involved, their role, how to reach them, what they're
waiting on.
8. NEXT STEPS — the first three things a newcomer should do.
9. HOW TO UNWIND — what it would take to stop, hand off, or move this
to another platform.
Rules:
- Distinguish what I told you from what you inferred. Mark inferences.
- Record the reasoning, not just the outcome. The "why" is the valuable part.
- Be specific enough that someone could act without asking a question.
- If something I say contradicts the doc, point it out instead of
silently changing it.
- Keep it tight. Cut stale detail into a dated one-line summary rather
than letting the doc bloat.
- If you're unsure whether something belongs, include it under
Open Questions.The prompt needs a permanent home: put it in stored instructions (along with the doc itself), so it loads every time instead of depending on you to paste it.
Over time, the document will get larger, but that’s okay, it’s better than not having it.
I’d heard a lot about critical theory but didn’t really understand what was being talked about. The Frankfurt School of the 1930s wasn’t really a school. It was philosophers and academics who were arguing with each other about how the world actually works — and about why people keep accepting arrangements that hurt them. And like many things called ‘theories’, it’s easy to be confused about what’s actually being said.
Their core insight: most of what we call “normal” was designed by someone, for a reason, and that reason might not be your reason.
Traditional science tries to describe the world as it is. Critical theory asks: “who benefits from the world being described that way?”
When an economist says “the market determines wages,” that sounds neutral. But it isn’t. It’s a choice about what to measure and what to ignore. It’s a story. And stories can be told differently.
Pioneers like Horkheimer, Adorno and Marcuse (followed and reworked by Habermas)— looked at modern industrial society and noticed that Reason, with a capital R, which was supposed to liberate us, had turned into a tool of control. The Enlightenment promised freedom. What we got instead was efficiency.
They called it instrumental reason: the habit of thinking about how to do things without ever asking whether we should.
Reification: when the made-up becomes the inevitable
The word that unlocks the concept is reification. Human social relations and systems appear as natural, thing-like and outside our control because we end up treating them as if they were rocks. Permanent. Objective. Not up for discussion.
“That’s just how business works.”
“That’s just how things are.”
“You can’t fight human nature.”
Each of those sentences is a reification. Someone built a thing. Now they’re insisting it can’t be unbuilt.
Critical theory’s job is to thaw what has been frozen. To remind us that the rules we’re following were written by someone, and can be rewritten.
Systems are often invisible, and they hide behind normal. When in doubt, consider the system that we’ve assumed is the only way.
The Frankfurt School wasn’t just academic. They were trying to explain how otherwise reasonable people went along with war, consumerism and conformity.
Their answer: the system shapes what feels obvious. What feels like common sense. What feels like “just being realistic.”
Which means the most important thing a marketer, a leader, a maker can do is ask: am I solving a real problem, or am I reinforcing a convenient story about what people are supposed to want?
When someone points at a system and describes the interests of those who supported it, two things can happen.
The first: curiosity. Who built it? Why? What would a different version look like?
The second: discomfort.
If you’ve organized your identity around the map being true — if your status, your choices, your self-image all depend on the current arrangement being natural and inevitable — then someone questioning the map isn’t being philosophical.
They’re threatening you.
The fact that your status feels threatened is a self-reflective tell. Of course it feels threatened. That’s what the system wants you to feel.
“The market rewards hard work” isn’t just an economic claim for a lot of people. It’s a story about why they deserve what they have. Poke that story and you’re not critiquing capitalism. You’re telling them their success was partly luck, partly structural, partly the result of systems that excluded others.
Most of the fury isn’t intellectual. It’s tribal.
Critical theory became associated with universities and with certain political movements. So defending against it became a marker of identity for the other tribe. You’re not critiquing the argument — you’re signaling which team you’re on.
That’s ironic, right? Because “this belief is really about status and belonging, not truth” is exactly the kind of thing critical theory would say about the people attacking critical theory.
It’s tempting to announce that some questions aren’t allowed.
Those are usually the most important questions.
Consistency costs extra.
If you want to buy machine screws or widgets that are exactly the same to five decimal points, you’ll pay a premium for that. In exchange, you’ll get parts that are precisely as expected, making assembly more reliable.
Mechanization’s productivity and our fear of fear have driven us to do this with just about everything. Bananas, fast food and student performance are all pushed toward consistency, often at the expense of the possibility of extraordinary performance.
We do this to humans at our own peril.
Do we really want the artist to produce a carbon copy each time? For every Dead show to be the same? For customer service to be measured with a stopwatch, not our hearts?
Uniformity pays when the best definition of “excellent” is that it “meets spec.” This includes day-to-day freelance work, business hotel rooms and the way our phones work.
For everything else, perhaps we ought to pay a bit more for awe, insight and surprise.
[You're getting this note because you subscribed to Seth Godin's blog.]
Don't want to get this email anymore? Click the link below to unsubscribe.