The product gets the attention. The economics decide the value.
This feature first ran as my guest post for James Presbitero’s Unpromptable. I’m publishing an adapted version here because the AI opportunity I keep seeing across Millennial Masters interviews isn’t only in building something new. A lot of it is in keeping more of what the business already earns.
The best AI business may already exist
Wrapping AI around an existing workflow and charging a subscription is one opportunity. It’s visible, and in some cases it can work. It’s also often less durable than it looks. The model providers can add the feature themselves. Bigger products can fold it in. What looked like a business can get squeezed fast.
The more interesting opportunity is what happens when founders use AI to change the economics of work they already understand. That came through when I went back through 66 Millennial Masters interviews with founders already using AI in one way or another.
The money is often in keeping more revenue and making parts of the business viable that were too expensive to touch before. Smaller teams are part of that story, but not the whole thing. 👇🏻
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Margin comes first
The clearest gains often show up in margin before they show up in fresh revenue.
A founder who already knows how to write, sell, analyse, research, or build can now cut a surprising amount of junior work out of the cost base.
The output still needs direction, judgement, and standards. The middle layer around it can get much thinner.
That’s one of the clearest ways founders are making money with AI right now: by keeping more of the revenue they were already capable of generating.
A business can hold the same top line and still get much stronger if delivery cost drops, outside suppliers matter less, and the same work gets done with less payroll around it.
That changes how much cash the business keeps, how flexibly it can price, and how long it can grow before it needs more people.
Anjeanette Carter described becoming “an army of copywriters” and deciding to stop working with writers, which cut a six-figure payroll.
That’s a story about protecting the revenue from a business she already understood by changing how the delivery gets done.
Hiring gets pushed back
Once margin improves, the next shift is headcount.
Some teams are still growing in the old way. A lot of others are holding the line on hiring because AI has changed what one capable operator can carry.
The business can do more before it needs another hire. Some roles do not disappear, but they arrive later.
If one strong operator can now carry work that once needed two or three people around them, the company can stay leaner for longer, delay hiring, and grow with a different fixed-cost base.
That changes when hiring makes sense and how much revenue the business needs before it can carry the next person.
Nick Holzherr said firms are no longer hiring marketing juniors in the same way. They’re keeping more senior people who can use AI well, which means they simply need fewer staff.
Carly Meyers believes huge companies will be built with teams of one, two, or three, with AI carrying a lot of the extra load those teams used to hire for.
Judgement still decides
This only works when the founder or operator already knows what good looks like.
AI can extend expertise. It does very little for people who did not know what good looked like before they touched it. Faster average work is still average work.
Anjeanette Carter was very clear that “if you don’t have expertise in a specific area, AI isn’t going to make it better. It’s only going to amplify your limited knowledge.”
That’s the point more founders need to sit with. AI can help when you already know what good looks like. It does far less when you don’t.
That matters commercially because it’s where the savings can turn false.
If founders mistake output speed for decision quality, they can ship more bad work, make more bad calls, and damage the business faster.
AI lowers the cost of production, but being wrong is still expensive.
Josh Payne said “human plus AI is currently still better than AI only in most cases,” and Ben Tasker described how an organisation replaced a call centre with AI, fired the staff, then hired them back a few weeks later because the system was awful.
The founders getting real value from AI are keeping human judgement on the parts that still need it. They’re using AI to absorb more of the repetitive work and the junior-heavy execution.
The human still decides whether the work is commercially safe and worth shipping.
Product is easier, winning isn’t
Once building gets cheaper, other parts of the business start to matter more.
Technical founders can now get further with less capital in less time. That opens useful opportunities. It also weakens the product as a moat when the product is the only thing the business has.
If more people can build something usable with AI tools, functionality alone becomes a thinner defence. Thibault Louis-Lucas asked where the value sits if you no longer need developers in the old way to create products. His answer is distribution and storytelling.
Simon Jenner explained that when build costs fall hard enough, niches that were once too small to matter start becoming viable. A business that made no sense at one cost structure can look very healthy at another.
When building gets cheaper, more founders can get to a working product. What becomes scarce is the ability to earn attention, build trust, and turn a functional product into something people actually choose.
The moat moves away from pure build difficulty and towards distribution.
The graveyard has money in it
Some of the strongest AI opportunities are sitting inside neglected assets that humans stopped touching because the economics were too weak before.
AI lowers the cost of revisiting things that were already there but no longer worth a person’s time.
Ryan Carruthers’ example was sales. He said AI is the best place to start there because you can test it on dead leads. If the process fails, the leads were dead anyway.
In his case, those systems had hundreds of conversations, booked sales calls, and did it in a place where the brand risk was low because the asset had already been abandoned.
It’s an operator looking at wasted value and realising the cost of revisiting it has changed. Yehong Zhu showed the same commercial instinct in a different market through licensing. She’s building a business around the way AI changes the value of media rights and data access.
The tool creates a new buyer. The founder changes what can be sold.
The premium moves elsewhere
The smartest founders are using AI to change the economics of work they already understand. They’re cutting waste from familiar work and reopening parts of the business that were too expensive to touch before.
If a product gets cheaper to build, the premium shifts toward trust, distribution, and judgement. Trust matters more when polished slop is cheap. Distribution matters more when a product is easier to copy.
Judgement still decides what is worth shipping, and domain understanding still decides what anyone will pay for.
Gary Das pointed to collapsing trust. Freddie Pullen pointed to the value of long-form human formats that still feel unmistakably real. Those are clues about where price and loyalty can still hold when AI has made the underlying production cheaper.
AI improves the economics around strong businesses.
Founders who already know how to create value can keep more of it, do more with fewer people, and make businesses work that would not have made sense under the old cost structure.
The lasting opportunity sits inside work that already has a business underneath it. That’s where more of the value stays.
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Great insights Daniel. Interesting how yesterday’s unviable products can now become viable due to margin expansion. Also distribution and branding (trust) being the moat. I would add the another one is the ability to quickly adapt through feedback loops and quick iteration.
It's the infrastructure