I've had this conversation about we analyzed 10,000 student interactions with an ai with at least 50 founders. Here's the distilled version.
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.
What I've Learned From 33 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 you should focus on one thing and do it exceptionally well. 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 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 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 timing is everything in this game. Once we made the switch, everything changed.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take we analyzed 10,000 student interactions with an ai seriously versus those that don't. The difference is stark.
Companies that invest early in we analyzed 10,000 student interactions with an ai 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 tutor, learning analytics, edtech research, student data 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.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
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.