We just completed a massive audit of 100+ production AI models, and the results on bias are staggering. I'm pulling back the curtain on the real numbers—not the sanitized corporate reports. This is what hidden bias actually looks like in the wild.
The Framework That Actually Works
Here is a simple framework for evaluating AI bias. It's not complicated, but it requires discipline.
Step 1: your team matters more than your technology
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI bias right treat it as an ongoing process, not a one-time project.
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 simplicity beats complexity every time. 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 data tells a different story than your gut. Once they make the switch, everything changes.
Real Talk: What Actually Matters
Here is what actually matters in AI bias.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI bias. Many fail because they moved too slowly.
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. It sounds obvious, but many founders make decisions about AI bias in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around responsible AI, AI safety, AI alignment 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: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.
Q: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.