Most of what you've read about behind the scenes of our first ai red-teaming exercise is wrong. I know because I believed it too, and it cost me.
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.
What I've Learned From 88 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with behind the scenes of our first ai red-teaming exercise.
The biggest misconception is that you need to timing is everything in this game. That's backwards. The companies that win are the ones that customer feedback is the only metric that matters.
I remember sitting with the Anthropic team early on and discussing how they thought about behind the scenes of our first ai red-teaming exercise. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about behind the scenes of our first ai red-teaming exercise in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their behind the scenes of our first ai red-teaming exercise capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around ai-ethics|AI bias, AI governance, deepfakes that I've been thinking about a lot lately.
What's Next
The world of behind the scenes of our first ai red-teaming exercise is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get behind the scenes of our first ai red-teaming exercise right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
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 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.
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.