Two of my portfolio companies had opposite approaches to behind the code: how we built an ai. The one you'd expect to win didn't.
People ask me how our AI tutor feels so... human. It wasn't easy. This is the behind-the-scenes story of the algorithms, the data, and the breakthroughs that allow our platform to adapt to each student's emotional and cognitive state in real-time.
What I've Learned From 21 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with behind the code: how we built 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 timing is everything in this game.
I remember sitting with the Anthropic team early on and discussing how they thought about behind the code: how we built an ai. Their approach was counterintuitive but brilliant.
The Counterintuitive Truth
Here's what surprised me most about behind the code: how we built 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 the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to behind the code: how we built an ai 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 behind the code: how we built an ai seriously versus those that don't. The difference is stark.
Companies that invest early in behind the code: how we built 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 edtech development, adaptive learning, ai tutor, behind the scenes that I've been thinking about a lot lately.
The Bottom Line
Look, behind the code: how we built an ai isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at behind the code: how we built an ai aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take behind the code: how we built an ai seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
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.
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 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.