Behind the Scenes of Our First AI Red-Teaming Exercise

Published 2025-01-07 · Updated 2026-05-23 · 7 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.

The best advice I ever got about behind the scenes of our first ai red-teaming exercise came from a founder who'd failed at it three times.

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 Counterintuitive Truth

Here's what surprised me most about behind the scenes of our first ai red-teaming exercise: the best practitioners do less, not more.

When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.

The lesson I took from that experience, and from watching hundreds of other companies, is that the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.

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 EU AI Act, responsible AI, AI governance, AI regulation 2026, deepfakes that I've been thinking about a lot lately.

The Bottom Line

Look, behind the scenes of our first ai red-teaming exercise isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.

If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at behind the scenes of our first ai red-teaming exercise aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.

I've been doing this for over a decade. The patterns are clear. The companies that take behind the scenes of our first ai red-teaming exercise seriously outperform the ones that don't. Every single time.

If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.

Frequently Asked Questions

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.

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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