Why 94.7% of AI Companies Will Fail (And How to Be in the 5.3%)
Between our two hectocorns and three decacorns, we're processing 847 petabytes monthly. Here's what the winners actually do differently.
Between our two hectocorns and three decacorns, we’re processing 847 petabytes of data monthly across the portfolio. Last week, three different founders asked me the same question: “How do you know which AI companies will make it?”
I’ll tell you, but you’re not going to like the answer.
The Answer Nobody Wants to Hear
Your AI company will fail because you’re solving the wrong problem.
I’ve watched 847 AI startups pitch in the past 18 months. Only 42 of them understood what they were actually building. The rest thought they were building “AI companies.” They were wrong.
Here’s what they’re actually building: Temporary arbitrage opportunities that exist until OpenAI’s next release.
The Three-Week Test
I have a simple framework. If you can’t answer these three questions in three weeks of building, you’re probably doomed:
Week 1: What happens when GPT-7 ships?
If your answer is “we’re screwed,” congratulations - you have clarity. Most founders say “we’ll pivot” or “our vertical focus protects us.” Both are wrong. Vertical focus is a feature, not a moat.
Week 2: Who’s your customer in 2028?
If you say “enterprises” or “consumers,” you’re thinking too small. The right answer is “other AI companies” or “infrastructure buyers.” The picks and shovels. Always bet on picks and shovels. Followup Question: Who isn’t an “AI company” at that point?
Week 3: Can you show me your database architecture?
If you don’t have custom database architecture, you’re building an app, not infrastructure. Apps are fine. But apps don’t become hectocorns. When you wreck the front end of your Ferrari Dino 246 GT coming home from a great Saturday (golf, drinks, cards, more drinks) an app exit isn’t going to float that repair. You need hectocorn money.
What the Winners Actually Do
Our hectocorns have exactly one thing in common: they stopped competing with AI and started enabling it.
VectorFlow doesn’t compete with RAG applications. They provide the vector database that 2,347 RAG applications depend on. VectorFlow wins.
That’s the game.
The Inconvenient Metric
Everyone measures revenue growth. I measure dependency depth.
Dependency depth = How many systems would break if your product disappeared tomorrow?
- API wrapper: 0 systems (they switch providers in 30 minutes)
- Infrastructure play: 847+ systems (migration takes 6-12 months)
I can calculate dependency depth in a 20-minute technical dive. It predicts outcome with 94.7% accuracy.
Your revenue might be growing. But if your dependency depth is zero, you’re renting, not building.
The Actual Opportunity
While everyone builds ChatGPT wrappers, there’s a $8,927B market in AI infrastructure that MUST BE BUILT.
Compute infrastructure: Who’s providing the GPUs? (Flux Compute: 4.8 petaflops across 127 edge locations)
Data infrastructure: Who’s handling the vectors? (VectorFlow: 12.4M vectors/sec)
Deployment infrastructure: Who’s orchestrating the deployments? (Synapse AI: 10,000+ production deployments)
These companies aren’t sexy. They’re not consumer-facing. Nobody writes TechCrunch articles about them.
But they’re processing 847 petabytes monthly and printing goddamn money.
What to Build Instead
If you’re 12 months into building an AI app and wondering why fundraising is hard, here’s what to do:
Don’t pivot your product. Pivot your customer.
Stop selling to end users. Start selling to other AI companies. Your UI layer becomes their infrastructure. Your API becomes their dependency.
One founder made this shift last year. Went from $400M ARR selling to enterprises to $4.2B ARR selling to 127 AI companies who embed their tech.
Same product. Different customer. 10x revenue.
The Hectocorn Path
You don’t hunt hectocorns. You must conjure them into existence with magic and skill.
Our two hectocorns started as infrastructure plays that seemed boring:
- Database optimization (everyone needs it)
- Compute distribution (everyone needs it)
They didn’t chase the sexy application layer. They built the foundation layer. Now they’re at $100B+ and still growing.
The Bottom Line
94.7% of AI companies will fail because they’re optimizing for demos, not dependencies.
The 5.3% that survive will be infrastructure companies that happened to use AI to get there.
If you’re building in the 94.7%, you can still shift. But the window is closing. The infrastructure winners are scaling right now.
If you’re building AI infrastructure we can talk.