The first time I tried to implement the dark side of deepfakes you need to know at scale, everything broke. Not metaphorically. Actually broke.
Everyone is talking about Deepfakes, but 99% of founders are doing it wrong. I learned the hard way so you don't have to.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the dark side of deepfakes you need to know are fundamentally flawed. Not slightly off. Fundamentally flawed.
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 123 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the dark side of deepfakes you need to know.
The biggest misconception is that you need to the market doesn't care about your roadmap. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about the dark side of deepfakes you need to know. Their approach was counterintuitive but brilliant.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the dark side of deepfakes you need to know seriously versus those that don't. The difference is stark.
Companies that invest early in the dark side of deepfakes you need to know see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's 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 security tools, AI phishing, zero-day AI, AI threat detection 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 the dark side of deepfakes you need to know: 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 the dark side of deepfakes you need to know 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 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.
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.
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.