I spent months researching Deepfakes and what I found shocked me. Here are the counterintuitive strategies that actually work.
Why Most Approaches Fail
Many common approaches to deepfakes 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.
The Framework That Actually Works
Here is a simple framework for evaluating deepfakes. 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: you should focus on one thing and do it exceptionally well
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 deepfakes right treat it as an ongoing process, not a one-time project.
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 zero-day AI, AI threat detection, 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: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'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 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.