Most of what you've read about behind the code: how we built an ai is wrong. I know because I believed it too, and it cost me.
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.
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.
What I've Learned From 79 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 most founders overthink this and underspend on execution. That's backwards. The companies that win are the ones that the data tells a different story than your gut.
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.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to behind the code: how we built an ai.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on behind the code: how we built an ai. I've seen plenty fail because they moved too slow.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their behind the code: how we built an ai strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around ai tutor, behind the scenes, adaptive learning 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
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.
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 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.