If you're a founder dealing with my approach to user research for an ai, stop what you're doing and read this. Seriously.
After years working with AI product teams at big companies, I want to share the practical playbook we used to build AI products that millions of people actually used.
What I've Learned From 27 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with my approach to user research for an ai.
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 you need to move fast and break things.
I remember sitting with the Anthropic team early on and discussing how they thought about my approach to user research for an ai. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach my approach to user research for an ai 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 you should focus on one thing and do it exceptionally well. 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 simplicity beats complexity every time. Once we made the switch, everything changed.
The AI Angle
I can't talk about my approach to user research for an ai in 2026 without mentioning 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.
I've seen companies use AI to 10x their my approach to user research for an ai capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around user research ai, innovation, product discovery that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about my approach to user research for an ai: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at my approach to user research for an ai share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
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.
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'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.