Everyone inflates their AI market size, and VCs know it. I'll show you the intellectually honest way to calculate your TAM, SAM, and SOM that builds trust and gets you taken seriously, even if the number is smaller.
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: simplicity beats complexity every time
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.
The Counterintuitive Truth
In AI market sizing, the best practitioners often do less, not more.
The lesson: your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
What I've Learned From 62 Companies
A few patterns in AI market sizing come up again and again.
The founders who handle AI market sizing well understand that the market doesn't care about your roadmap.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about AI market sizing was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best people at AI market sizing are rarely the most technically gifted. They are the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around AI competitive moats, AI market sizing, AI exit strategies, AI startup pivots, AI due diligence that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI market sizing, but there are smarter paths.
The strongest teams treat AI market sizing as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with AI market sizing, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
Q:What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
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.