If you're a founder dealing with why so many ai startups don’t make it, stop what you're doing and read this. Seriously.
Getting your product to fit the market is tricky, especially in niche SaaS areas. I'll walk you through how I find the right customers, understand their problems, and adjust the product until it works.
What I've Learned From 77 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with why so many ai startups don’t make it.
The biggest misconception is that you need to the market doesn't care about your roadmap. That's backwards. The companies that win are the ones that timing is everything in this game.
I remember sitting with the Anthropic team early on and discussing how they thought about why so many ai startups don’t make it. Their approach was counterintuitive but brilliant.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating why so many ai startups don’t make it. 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: you should focus on one thing and do it exceptionally well 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 in my portfolio that nail why so many ai startups don’t make it are the ones that treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
Here's what surprised me most about why so many ai startups don’t make it: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
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 why so many ai startups don’t make it 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 why so many ai startups don’t make it practitioners I've met weren't the most technically gifted. They were 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 cloud AI services, serverless AI, AI pricing models, SaaS metrics, AI infrastructure costs that I've been thinking about a lot lately.
The Bottom Line
Look, why so many ai startups don’t make it isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at why so many ai startups don’t make it aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take why so many ai startups don’t make it seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
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.
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.
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.