The answers about AI ethics often surprise founders.
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
Across 234 angel investments, including Anthropic, a few patterns in AI ethics come up again and again.
The founders who handle AI ethics well understand that most founders overthink this and underspend on execution.
The Counterintuitive Truth
In AI ethics, the best practitioners often do less, not more.
The lesson: you need to move fast and break things. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on AI ethics rests on assumptions that made sense five years ago. The world has moved on.
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.
Many teams spend months on the wrong path before realizing that you need to move fast and break things. Once they make the switch, everything changes.
The Numbers Don't Lie
The gap between companies that take AI ethics seriously and those that don't is usually clear.
Investing early in AI ethics can be 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
Best practice in AI ethics is changing 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.
If you're building in this space, the best time to get 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
Q: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.
Q: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.
Q: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.
Q: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.