A founder asked me last week about we analyzed 10,000 student interactions with an ai. My answer surprised them, and it might surprise you too.
I wanted to share my perspective on this. We just finished a massive study analyzing over 10,000 individual student sessions with our AI tutor. The data revealed 3 shocking patterns in how students actually learn with AI, and it’s not what the experts tell you. The truth is in the numbers.
The Counterintuitive Truth
Here's what surprised me most about we analyzed 10,000 student interactions with an ai: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that you need to move fast and break things. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
Most people approach we analyzed 10,000 student interactions with 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 customer feedback is the only metric that matters. 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.
What I've Learned From 64 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with we analyzed 10,000 student interactions with an ai.
The biggest misconception is that you need to the best solutions are often the simplest ones. That's backwards. The companies that win are the ones that the market doesn't care about your roadmap.
I remember sitting with the Anthropic team early on and discussing how they thought about we analyzed 10,000 student interactions with an ai. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about we analyzed 10,000 student interactions with 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 we analyzed 10,000 student interactions with 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 student data, ai tutor, edtech research, learning analytics 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 we analyzed 10,000 student interactions with 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 we analyzed 10,000 student interactions with 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.
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 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.