You don't need a six-figure consulting report to validate your AI market size. I'll show you how to run a series of simple, low-cost experiments to prove that a real, valuable market exists for your product.
The Reality Nobody Talks About
A lot of the advice on AI market sizing rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that you need to move fast and break things. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that customer feedback is the only metric that matters. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating AI market sizing. It's not complicated, but it requires discipline.
Step 1: the market doesn't care about your roadmap
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: customer feedback is the only metric that matters
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI market sizing right treat it as an ongoing process, not a one-time project.
What I Tell Founders
Before deciding anything about AI market sizing, start with three questions:
What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around AI due diligence, AI startup pivots, AI talent wars that I've been thinking about a lot lately.
What's Next
Best practice in AI market sizing is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
If you're building in this space, the best time to get AI market sizing right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q:How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.