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
Here is a simple framework for evaluating 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 that get adversarial AI right treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
In adversarial AI, the best practitioners often do less, not more.
The lesson: timing is everything in this game. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Many common approaches to adversarial AI are flawed at the root.
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 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 people at adversarial AI are rarely the most technically gifted. They are 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 you don't need to get adversarial AI right on day one. You just need to get started and keep improving.
Founders who do well at adversarial AI tend to 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
Q: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.
Q: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.
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.