Last year, I made a bet that changed how I think about what i found in our 2026 ai bias audit. Here's what happened.
We just finished auditing over 100 AI models in real-world use. The bias we uncovered isn’t what companies usually show. I’m sharing the real story behind those numbers.
What I've Learned From 120 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 found in our 2026 ai bias audit.
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 the best solutions are often the simplest ones.
I remember sitting with the Anthropic team early on and discussing how they thought about what i found in our 2026 ai bias audit. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach what i found in our 2026 ai bias audit 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 customer feedback is the only metric that matters. 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 should focus on one thing and do it exceptionally well. Once we made the switch, everything changed.
The Counterintuitive Truth
Here's what surprised me most about what i found in our 2026 ai bias audit: 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.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about what i found in our 2026 ai bias audit was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best what i found in our 2026 ai bias audit practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around AI regulation 2026, AI alignment, AI governance that I've been thinking about a lot lately.
The Bottom Line
Look, what i found in our 2026 ai bias audit 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 what i found in our 2026 ai bias audit 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 what i found in our 2026 ai bias audit 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 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.
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.
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.