Why AI Can't Find Your Product-Market Fit (At Least Not Yet)

AI made building software almost free. It didn't make finding product-market fit any easier. If anything, it made the hard part easier to skip.

I spent last week at New York Tech Week, and the same conversation kept repeating. People introduced themselves as founders with several startups. When I asked what the startups were, the honest answer was a handful of apps and prototypes built with AI tools. No team. No revenue model. No go-to-market motion. When I asked who the customer was, the answer was "everyone." When I asked how they made money, the answer was "it's free, for now."

The most revealing version was a line I heard more than once: "I built it for myself because I needed it, so I'll put it on the App Store and others can use it too." Solving your own problem proves a product can exist. It proves nothing about whether a market will pay for it. Using yourself as a stand-in for the customer isn't customer discovery. It's the most comfortable way to avoid it, because you never have to hear anyone say no.

That's not a portfolio of startups. It's a portfolio of builds.

The barrier that used to filter founders is gone

Two years ago, turning an idea into working software required technical skill or a technical co-founder. That requirement did real work. It filtered. By the time you had a product, you'd cleared a bar that meant something. AI removed the bar, and that's a genuine gift. More people get to try.

But it created a quiet problem. Building used to feel like progress because building was hard. Now building is easy and still feels like progress, except it no longer proves anything on its own. "Everyone" is the absence of an ICP. "It's free" is the absence of a business model. These founders have the most visible part of a company and none of the parts that make it one.

This isn't just a beginner's problem

It would be comfortable to read this as a first-timer's mistake. It isn't. The same confusion shows up in companies with real revenue, real teams, and real logos, where it costs far more to fix.

The idea-stage builder confuses an app with a business. The post-traction founder confuses traction with product-market fit. It's the same error at a higher altitude. You have customers, but naming who you're for takes five segments. The sales motion works, but only when a founder is in the room. Revenue is up, but it blends committed contracts with pilots and "likely to convert." Strategy reopens after every board meeting because the decision underneath it was never firm enough to hold.

That's not product-market fit. That's traction that hasn't been forced into a decision yet. Product-market fit isn't discovered. It's decided. And growth doesn't stall because teams stop executing. It stalls because nobody decided what the execution was for.

What AI Changed About Product-Market Fit, and What It Didn't

Here's where many founders are getting the moment wrong. The assumption goes: if AI can build the product, write the outreach, and analyze the market, it can carry the go-to-market too. Replace the employees, replace the consultants, delegate the founder's own work to the model.

As of now, that's not how it plays out. AI is genuinely strong at the execution layer. It drafts, builds, analyzes, and iterates faster than any team in history. And to be fair, deciding who you're for is partly a data problem, and AI handles data well when you can hand the data over.

The catch is that the data that matters most can't be handed over, because it doesn't live in a spreadsheet. It's in the pause before a customer answers. The word a prospect chooses, and the one they avoid. The moment a conversation drifts and the real objection surfaces, and you go off script to chase it, asking a question you didn't know you needed until you were in the room. So far, a model can't feel its way through a conversation like that. It can guide you, summarize you, and analyze what you bring back from the lens you describe. It can't sit in the room for you to gather all the cues like a human.

And this work isn't only beyond AI. It's beyond delegation, period. You wouldn't hand early customer discovery to a junior salesperson either, because the point isn't completing the task. The point is what the founder learns firsthand about who the market is and why they buy. Founder-led discovery and founder-led sales aren't a phase to automate or delegate away. They're where the commitment decision gets its evidence. The most useful outside help at that stage isn't someone who does the work for you. It's someone unbiased who's seen the pattern across many companies and pressure-tests the decision you're about to make. Your own team can't fully play that role. They're inside the story, and people inside the story tend to tell the founder what's safe to say.

AI augments all of this. It doesn't replace it. The founders who get the distinction use AI to move faster on the work that was always theirs. The ones who don't use it to avoid that work, then wonder why the activity never compounds into growth.

The questions that separate a build from a business

If you've built something and you're not sure what you have, two questions cut through quickly. Who specifically is this for, named tightly enough that you could list the next ten you'd call? And why will they pay, in a way that survives them having a free alternative?

If you already have traction, add a third: is your growth compounding from a focus you committed to, or accumulating across everything you said yes to?

Building got you to the starting line faster than anyone in history. Traction got you partway down the track. Neither is the same as the decision that makes growth compound, and as of now, no tool makes that decision for you.

If you want to pressure-test where you actually stand, start with the PMF checklist. It's the same first read I run with founders.