For decades, the most sophisticated investment strategies were reserved for the wealthy elite. But AI is changing that. I’m exploring how AI is democratizing access to strategies like factor investing, risk parity, and alternative risk premia. The playing field is being leveled.
The Counterintuitive Truth
In AI investing, the best practitioners often do less, not more.
The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
Here is a simple framework for evaluating AI investing. It's not complicated, but it requires discipline.
Step 1: customer feedback is the only metric that matters
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap
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 investing right treat it as an ongoing process, not a one-time project.
What I've Learned From 125 Companies
A few patterns in AI investing come up again and again.
The founders who handle AI investing well understand that customer feedback is the only metric that matters.
The AI Angle
Any discussion of AI investing in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of AI investing. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around algorithmic trading, fintech AI, AI fraud detection, AI trading that I've been thinking about a lot lately.
The Bottom Line
None of this is 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 AI investing aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take AI investing seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
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: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.
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.