The first time I tried to implement stop doing ai cybersecurity like this (do this instead) at scale, everything broke. Not metaphorically. Actually broke.
I used to think AI Cybersecurity 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 stop doing ai cybersecurity like this (do this instead). It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: customer feedback is the only metric that matters 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 stop doing ai cybersecurity like this (do this instead) are the ones that treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
Most people approach stop doing ai cybersecurity like this (do this instead) with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that timing is everything in this game. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that the best solutions are often the simplest ones. Once we made the switch, everything changed.
The AI Angle
I can't talk about stop doing ai cybersecurity like this (do this instead) 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 stop doing ai cybersecurity like this (do this instead) 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 cybersecurity, adversarial AI, zero-day AI, AI phishing 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 stop doing ai cybersecurity like this (do this instead): 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 stop doing ai cybersecurity like this (do this instead) 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
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.
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.
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.