Behind the Scenes of Our First AI Red-Teaming Exercise

Published 2024-06-10 · Updated 2026-05-23 · 5 min read · AI Ethics and Regulation · By Sahin Boydas

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.

Two of my portfolio companies had opposite approaches to behind the scenes of our first ai red-teaming exercise. The one you'd expect to win didn't.

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 Framework That Actually Works

I'm going to share the exact framework I use when evaluating behind the scenes of our first ai red-teaming exercise. 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: timing is everything in this game 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 in my portfolio that nail behind the scenes of our first ai red-teaming exercise are the ones that treat it as an ongoing process, not a one-time project.

Why Most Approaches Fail

Let me be direct: about 70% of the approaches I see to behind the scenes of our first ai red-teaming exercise 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.

Lessons From the Trenches

I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.

Lesson 1: The best time to start thinking about behind the scenes of our first ai red-teaming exercise was yesterday. The second best time is now. Don't wait until you have the perfect plan.

Lesson 2: Hire for attitude, train for skill. The best behind the scenes of our first ai red-teaming exercise practitioners I've met weren't the most technically gifted. They were the most curious and persistent.

Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.

This connects to broader themes around AI regulation 2026, AI governance, AI safety 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 behind the scenes of our first ai red-teaming exercise: there are no shortcuts, but there are smarter paths.

The smartest founders I work with treat behind the scenes of our first ai red-teaming exercise 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 behind the scenes of our first ai red-teaming exercise, 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

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.

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.

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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.

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