I hired top hackers to test our AI's defenses. It was scary but eye-opening. I’m sharing what went down during our first red-teaming exercise, the problems they uncovered, and how we're fixing them.
Why Most Approaches Fail
Many common approaches to AI red teaming are flawed at the root.
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.
The Counterintuitive Truth
In AI red teaming, the best practitioners often do less, not more.
The lesson: you need to move fast and break things. It sounds simple. It's incredibly hard to execute.
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: customer feedback is the only metric that matters
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the data tells a different story than your gut
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.
What I Tell Founders
Before deciding anything about AI red teaming, 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, EU AI Act, AI alignment, AI governance, AI safety 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: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.
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.