We recently hired a team of elite hackers to break our own AI, and it was terrifying and enlightening. I'm taking you inside our first-ever AI red-teaming exercise—the process, the shocking vulnerabilities they found, and how we're fixing them. This is a raw look at what it takes to build responsible AI.
The Counterintuitive Truth
In AI red teaming, the best practitioners often do less, not more.
The lesson: customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.
What I've Learned From 27 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI red teaming come up again and again.
The founders who handle AI red teaming well understand that simplicity beats complexity every time.
The Framework That Actually Works
Here is a simple framework for evaluating AI red teaming. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: you should focus on one thing and do it exceptionally well
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 red teaming right treat it as an ongoing process, not a one-time project.
Real Talk: What Actually Matters
Here is what actually matters in AI red teaming.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI red teaming. 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 red teaming in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI alignment, EU AI Act, AI regulation 2026, responsible AI, AI governance that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI red teaming, but there are smarter paths.
The strongest teams treat AI red teaming 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 AI red teaming, 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
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: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.
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.