I’m going to say something that might be unpopular in Silicon Valley right now: most AI companies are bullshit.
There, I said it. As an angel investor with over 200 investments in companies like Anthropic, OpenAI, and Scale AI, I’ve seen more AI pitches than I can count. And let me tell you, the hype is deafening. Everyone is slapping "AI" on their pitch deck and expecting a blank check. But here’s the hard truth: most of these companies are building features, not businesses. They’re building on top of someone else’s platform, with no real defensible moat, and they’re going to get wiped out the moment a big player decides to enter their space.
After two successful exits and a decade in the trenches, I’ve learned to ignore the noise and focus on what really matters. I’ve developed a set of frameworks that I use to vet every single AI investment I make. These aren’t some theoretical, academic exercises. These are battle-tested principles that have helped me separate the hype from the real, investment-worthy ventures. And I’m going to share them with you.
Framework 1: The "Moat" Framework
When I look at an AI company, the first thing I ask is, "Where’s the moat?" What is the sustainable, long-term competitive advantage that will prevent this company from being crushed by Google, Microsoft, or Amazon? In the world of AI, the traditional moats—network effects, brand, switching costs—are still important, but there are new, AI-specific moats that are even more critical.
One of the most powerful moats in AI is proprietary data. If a company has a unique, high-quality dataset that no one else can replicate, they have a massive advantage. This is why I invested in Scale AI. They understood early on that the bottleneck in AI development wasn’t the algorithms, it was the data. They built a platform for labeling and curating massive datasets, and in doing so, they created a data moat that is incredibly difficult for anyone else to cross.
Another powerful moat is what I call "systems of record." These are companies that are so deeply embedded in their customers’ workflows that it would be incredibly painful to switch to a competitor. Think about your accounting software or your CRM. You’re not going to rip that out and replace it overnight, even if a slightly better alternative comes along. In the AI world, companies that can become the system of record for a particular industry or function will have a huge advantage. This is one of the reasons I’m so bullish on companies that are building AI-powered tools for specific vertical markets, like healthcare or finance. They have the opportunity to become the indispensable platform for that industry.
Finally, there’s the "talent" moat. In the war for AI talent, the best engineers and researchers can go anywhere they want. The companies that can attract and retain the top 1% of talent will have a significant advantage in building cutting-edge products. This is why I look so closely at the founding team and the early hires. I want to see a team of A-players who are obsessed with solving a big problem and who have the technical chops to pull it off.
Framework 2: The "Problem-First" Framework
One of the biggest mistakes I see founders make is falling in love with a technology and then trying to find a problem to solve with it. They’ll build a cool new algorithm or a fancy new model, and then they’ll go around looking for a nail to hit with their hammer. This is a recipe for disaster.
The best companies start with a deep understanding of a real, painful problem that customers are facing. They obsess over the customer and their needs, and then they work backwards to figure out how to solve that problem with technology. This "problem-first" approach is at the heart of my investment philosophy.
I learned this lesson the hard way with my first company, MovieLaLa. We had a cool technology that could predict what movies you would like based on your social media activity. We thought it was a game-changer. But we made a critical mistake: we didn’t spend enough time talking to our users and understanding their real problems. We were so focused on the technology that we lost sight of the customer. The company was eventually acquired by Gfycat, but it was a humbling experience that taught me the importance of starting with the problem.
Now, when I’m vetting a new investment, I spend a lot of time digging into the problem they’re solving. I want to know: How big is the problem? How painful is it for customers? And how much are they willing to pay for a solution? I also want to see evidence that the founders have a deep, almost obsessive understanding of the customer and their needs. I want to see that they’ve spent countless hours talking to users, running experiments, and iterating on their product based on real-world feedback.
Framework 3: The "Team" Framework
At the end of the day, investing is about betting on people. A great idea with a mediocre team will go nowhere, but a great team with a mediocre idea can pivot and find success. This is why the "team" framework is the most important of the three.
When I’m evaluating a team, I’m looking for a few key things. First, I’m looking for a team of founders who are obsessed with the problem they’re solving. I want to see that they’re willing to run through walls to make their vision a reality. I’m also looking for a team with a unique combination of technical expertise and business acumen. I want to see a team that can not only build a great product, but also knows how to sell it, market it, and build a real business around it.
I’ve been incredibly fortunate to back some of the most talented founders in the world, from the team at OpenAI to the folks at Hugging Face. And what they all have in common is a relentless drive to solve a big problem and a deep-seated belief that they are the right people to do it. They are not just building companies, they are building the future.
My Final Word
So there you have it. My three frameworks for vetting every AI investment I make. These aren’t the only things I look at, of course, but they are the most important. If a company has a strong moat, is solving a real problem, and has a world-class team, they have a real shot at building a category-defining company.
For all the founders out there, my advice is this: don’t get caught up in the hype. Focus on building a real business that solves a real problem for real customers. And if you do that, the rest will take care of itself. The AI revolution is just getting started, and I, for one, can’t wait to see what you build next.
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.
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.
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.
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.