Rolling out AI bias at scale is where most plans break.
We just audited 100+ production AI models. The results on bias are not good. I'm showing the real numbers, not the sanitized corporate fluff. This is what hidden bias looks like in the wild.
The Reality Nobody Talks About
A lot of the advice on AI bias rests on assumptions that made sense five years ago. The world has moved on.
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.
Many teams spend months on the wrong path before realizing that the best solutions are often the simplest ones. Once they make the switch, everything changes.
What I've Learned From 28 Companies
A few patterns in AI bias come up again and again.
The founders who handle AI bias well understand that the data tells a different story than your gut.
The Numbers Don't Lie
The gap between companies that take AI bias seriously and those that don't is usually clear.
Investing early in AI bias 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, EU AI Act, AI safety, AI regulation 2026, deepfakes that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get AI bias right on day one. You just need to get started and keep improving.
Founders who do well at AI bias tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q: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.
Q: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.