After burning out trying to do everything myself, I went all-in on AI. Some experiments were a disaster, but others were life-changing. I'm opening up my playbook to show you what worked, what didn't, and how to build your own AI-powered personal OS.
What I've Learned From 82 Companies
A few patterns in AI research come up again and again.
The founders who handle AI research well understand that most founders overthink this and underspend on execution.
The Reality Nobody Talks About
A lot of the advice on AI research rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the market doesn't care about your roadmap. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that you should focus on one thing and do it exceptionally well. Once they make the switch, everything changes.
Why Most Approaches Fail
Many common approaches to AI research are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Numbers Don't Lie
Investing early in AI research can be the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around AI scheduling, AI personal assistant, AI tools for founders, AI automation that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI research, but there are smarter paths.
The strongest teams treat AI research 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 research, 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: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: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: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.