Companies take opposite approaches to AI cybersecurity, and the one you'd expect to win often doesn't.
After analyzing 100+ AI Cybersecurity incidents, I found a terrifying pattern. This is what you need to know before it's too late.
The Reality Nobody Talks About
A lot of the advice on AI cybersecurity rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that your team matters more than your technology. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that you should focus on one thing and do it exceptionally well. Once they make the switch, everything changes.
The Counterintuitive Truth
In AI cybersecurity, the best practitioners often do less, not more.
The lesson: the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
Investing early in AI cybersecurity can be 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 AI phishing, AI threat detection, adversarial AI, zero-day AI that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI cybersecurity, but there are smarter paths.
The strongest teams treat AI cybersecurity 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 AI cybersecurity, 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
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.
Q: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.