Companies take opposite approaches to data poisoning, and the one you'd expect to win often doesn't.
Forget everything you know about Data Poisoning. The rules have changed, and this is the new playbook for surviving the AI era.
The Counterintuitive Truth
In data poisoning, the best practitioners often do less, not more.
The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Many common approaches to data poisoning 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.
What I've Learned From 108 Companies
A few patterns in data poisoning come up again and again.
The founders who handle data poisoning well understand that the market doesn't care about your roadmap.
The Numbers Don't Lie
The gap between companies that take data poisoning seriously and those that don't is usually clear.
Investing early in data poisoning 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 threat detection, AI cybersecurity, AI security tools, AI phishing, adversarial AI that I've been thinking about a lot lately.
The Bottom Line
None of this is rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at data poisoning aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take data poisoning seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
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.
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: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.