What Happened When We Let Hackers Attack Our AI

Published 2024-04-12 · Updated 2026-05-05 · 7 min read · AI Ethics and Regulation · By Sahin Boydas

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.

I spent $50,000 learning this lesson about what happened when we let hackers attack our ai the hard way. You can learn it in 10 minutes.

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

Let me be direct: about 70% of the approaches I see to what happened when we let hackers attack our ai 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 what happened when we let hackers attack our ai: 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 you need to move fast and break things. It sounds simple. It's incredibly hard to execute.

The Framework That Actually Works

I'm going to share the exact framework I use when evaluating what happened when we let hackers attack our ai. 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 in my portfolio that nail what happened when we let hackers attack our ai are the ones that treat it as an ongoing process, not a one-time project.

What I Tell Founders

When a founder in my portfolio asks me about what happened when we let hackers attack our ai, I usually start with three questions:

  1. What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
  2. What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
  3. 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

After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about what happened when we let hackers attack our ai: there are no shortcuts, but there are smarter paths.

The smartest founders I work with treat what happened when we let hackers attack our ai 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 what happened when we let hackers attack our ai, 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.

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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