Behind the Scenes of Our First AI Red-Teaming Exercise

Published 2024-08-03 · Updated 2026-05-05 · 6 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.

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.

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: 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: most founders overthink this and underspend on execution 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.

The Numbers Don't Lie

I've tracked the performance of companies in my portfolio that take behind the scenes of our first ai red-teaming exercise seriously versus those that don't. The difference is stark.

Companies that invest early in behind the scenes of our first ai red-teaming exercise see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.

One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.

This connects to broader themes around deepfakes, AI alignment, EU AI Act 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

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.

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.

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