During the MovieLaLa days, we learned something about what i found about ai bias in our 2026 audit that I still apply to every investment I make.
We audited over 100 AI models in use today and found some eye-opening bias issues. I'm showing you the real data, not the usual polished reports, so you can see what bias looks like in practice.
What I've Learned From 51 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 about ai bias in our 2026 audit.
The biggest misconception is that you need to your team matters more than your technology. That's backwards. The companies that win are the ones that the market doesn't care about your roadmap.
I remember sitting with the Anthropic team early on and discussing how they thought about what i found about ai bias in our 2026 audit. Their approach was counterintuitive but brilliant.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to what i found about ai bias in our 2026 audit are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
What I Tell Founders
When a founder in my portfolio asks me about what i found about ai bias in our 2026 audit, I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around AI regulation 2026, ai-ethics|AI bias, deepfakes, AI governance that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about what i found about ai bias in our 2026 audit: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat what i found about ai bias in our 2026 audit as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with what i found about ai bias in our 2026 audit, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
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.
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.
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.