A founder asked me last week about what i learned after 5 years struggling with ai ethics. My answer surprised them, and it might surprise you too.
I spent years chasing complicated AI ethics ideas and missed the mark. I'm opening up about my tough experience moving from theory to actually creating responsible AI that works in the real world.
What I've Learned From 132 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with what i learned after 5 years struggling with ai ethics.
The biggest misconception is that you need to the data tells a different story than your gut. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about what i learned after 5 years struggling with ai ethics. Their approach was counterintuitive but brilliant.
The Counterintuitive Truth
Here's what surprised me most about what i learned after 5 years struggling with ai ethics: 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 what i learned after 5 years struggling with ai ethics 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 your team matters more than your technology. 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 need to move fast and break things. Once we made the switch, everything changed.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take what i learned after 5 years struggling with ai ethics seriously versus those that don't. The difference is stark.
Companies that invest early in what i learned after 5 years struggling with ai ethics 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 alignment, AI governance, AI safety, ai-ethics|AI bias, EU AI Act that I've been thinking about a lot lately.
What's Next
The world of what i learned after 5 years struggling with ai ethics is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get what i learned after 5 years struggling with ai ethics right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
What would you do differently looking back?
I'd move faster on the things that were working and cut the things that weren't sooner. Most founders, myself included, hold onto failing strategies too long because of sunk cost. Speed of learning is everything.
What was the biggest challenge in this case?
Almost always, the biggest challenge is people and alignment, not technology or strategy. Getting the right team focused on the right problem is harder than any technical challenge I've encountered.
Can these results be replicated?
The specific numbers will vary, but the underlying patterns and principles are transferable. The key is understanding the context behind the results, not just copying the tactics. Every company has unique constraints that shape what works.
How long did it take to see results?
Most meaningful business results take 3-6 months to materialize. Anyone promising overnight success is selling something. The companies in my portfolio that grew fastest were the ones that stayed patient and consistent.