When I first started working with my controversial take on adversarial ai, I thought I had it figured out. I was dead wrong.
I used to think Adversarial AI was just a buzzword. Then it almost destroyed my company. Here's the exact framework I use now to stay protected.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating my controversial take on adversarial ai. 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: the best solutions are often the simplest ones 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 my controversial take on adversarial ai are the ones that treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
Here's what surprised me most about my controversial take on adversarial 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 timing is everything in this game. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to my controversial take on adversarial 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.
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 my controversial take on adversarial ai 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 my controversial take on adversarial ai 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 security tools, zero-day AI, AI phishing, adversarial AI that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about my controversial take on adversarial ai: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at my controversial take on adversarial ai share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
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.
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.