Two of my portfolio companies had opposite approaches to why most founders get ai research completely wrong. The one you'd expect to win didn't.
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
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with why most founders get ai research completely wrong.
The biggest misconception is that you need to customer feedback is the only metric that matters. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about why most founders get ai research completely wrong. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach why most founders get ai research completely wrong with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
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.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that you should focus on one thing and do it exceptionally well. Once we made the switch, everything changed.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to why most founders get ai research completely wrong are fundamentally flawed. Not slightly off. Fundamentally flawed.
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
I've tracked the performance of companies in my portfolio that take why most founders get ai research completely wrong seriously versus those that don't. The difference is stark.
Companies that invest early in why most founders get ai research completely wrong see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's 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
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about why most founders get ai research completely wrong: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat why most founders get ai research completely wrong 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 why most founders get ai research completely wrong, 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
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.
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.
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.