I've been wrong about 9 years of building ai culture more times than I'd like to admit. But the last mistake taught me something I can't unlearn.
After years of trial and error, I finally cracked the code on AI culture building. I'm sharing the raw, unfiltered story of my struggles and the hard-won victories so you can skip the mistakes and get straight to the results. It wasn't easy
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to 9 years of building ai culture 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 144 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with 9 years of building ai culture.
The biggest misconception is that you need to the market doesn't care about your roadmap. 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 9 years of building ai culture. Their approach was counterintuitive but brilliant.
The Counterintuitive Truth
Here's what surprised me most about 9 years of building ai culture: 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 timing is everything in this game. It sounds simple. It's incredibly hard to execute.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to 9 years of building ai culture.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on 9 years of building ai culture. 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 9 years of building ai culture strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI performance reviews, digital nomad AI tools, async communication AI that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about 9 years of building ai culture: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat 9 years of building ai culture as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with 9 years of building ai culture, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.
How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 200+ startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
Which item on this list has the highest impact?
It depends on your stage and context, but in my experience, the items near the top of the list tend to have the broadest applicability. That said, sometimes the less obvious items create the biggest breakthroughs for specific situations.