Everyone is talking about Deepfakes, but 99% of founders are doing it wrong. I learned the hard way so you don't have to.
The Reality Nobody Talks About
A lot of the advice on deepfakes rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the best solutions are often the simplest ones. 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 customer feedback is the only metric that matters. Once they make the switch, everything changes.
What I've Learned From 118 Companies
A few patterns in deepfakes come up again and again.
The founders who handle deepfakes well understand that the data tells a different story than your gut.
Real Talk: What Actually Matters
Here is what actually matters in deepfakes.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on deepfakes. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about deepfakes in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI cybersecurity, AI security tools, AI threat detection, zero-day AI, AI phishing that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in deepfakes, but there are smarter paths.
The strongest teams treat deepfakes 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 deepfakes, 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: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 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.