I haven’t been sleeping well. For the past few months, I’ve been wrestling with a problem that I can’t get out of my head. It’s a threat so subtle and so powerful that it could destabilize markets, swing elections, and shatter personal relationships. I’m talking about deepfakes.
After my last company, RemoteTeam, was acquired by Gusto, I’ve had more time to dig into the next wave of AI. I’ve made over 200 angel investments in the space, including in companies you’ve probably heard of like Anthropic, OpenAI, and Scale AI. I’ve seen the good, the bad, and the ugly of artificial intelligence. But nothing has prepared me for the sheer disruptive potential of deepfakes.
I’ve analyzed over 100 recent deepfake incidents, from the fake Joe Rogan podcast to the AI-generated images of the Pope in a puffer jacket. And I’ve found a terrifying pattern. The technology is getting exponentially better, cheaper, and easier to use. What once required a team of PhDs and a supercomputer can now be done on a laptop in a few hours. This is not a distant, sci-fi threat. It’s here, now.
But here’s the thing. Where there’s chaos, there’s opportunity. And I believe that the solutions to the deepfake problem represent one of the biggest investment opportunities of the next decade. This is the next trillion-dollar market.
The Anatomy of a Deepfake Attack
To understand the opportunity, you first need to understand the threat. A deepfake attack isn’t just about creating a funny video. It’s a sophisticated, multi-stage process designed to manipulate and deceive. Here’s how it typically works:
- Data Collection: The attacker gathers images and videos of the target from social media, news articles, and public appearances. The more data they have, the more realistic the deepfake will be.
- Model Training: The attacker uses a type of machine learning model called a Generative Adversarial Network (GAN) to learn the target’s facial features, voice, and mannerisms. This is the most computationally intensive part of the process, but it’s getting faster every day.
- Content Generation: The attacker uses the trained model to create a new video or audio clip of the target saying or doing something they never did. This could be anything from a fake endorsement of a product to a fabricated confession of a crime.
- Distribution: The attacker releases the deepfake into the wild, often through social media or anonymous forums. The goal is to make it go viral before it can be debunked.
This is a classic example of what we in the cybersecurity world call a “zero-day” attack. By the time we realize what’s happening, the damage is already done.
The Trillion-Dollar Opportunity: AI Security
So, how do we fight back? The answer is not to ban deepfakes. That’s like trying to ban Photoshop. The technology is already out there, and it’s not going back in the bottle. The only way to combat deepfakes is with better technology. We need to build a new generation of AI security tools that can detect and neutralize deepfakes in real-time.
This is where the trillion-dollar opportunity lies. I’m not just talking about a few niche startups. I’m talking about a whole new industry dedicated to AI security. Here are just a few of the areas where I see massive potential:
- Real-time Deepfake Detection: We need tools that can analyze video and audio streams in real-time and flag suspicious content. This could be integrated into social media platforms, news organizations, and even video conferencing software.
- Digital Watermarking: We need a way to embed a secure, unforgeable watermark into all legitimate video and audio content. This would make it much harder for attackers to create convincing deepfakes.
- Provenance and Verification: We need a system for tracking the origin and history of all digital content. This would allow us to verify the authenticity of a video or audio clip and see if it has been tampered with.
- AI-powered Phishing Detection: Deepfakes are the next evolution of phishing. Instead of a fake email from your bank, imagine a fake video call from your CEO asking you to transfer money. We need AI-powered tools that can detect these sophisticated social engineering attacks.
My Personal Journey with Deepfakes
This isn’t just a theoretical exercise for me. I’ve seen the power of deepfakes firsthand. A few years ago, a video started circulating that appeared to show me endorsing a new cryptocurrency. It was a pretty convincing deepfake. My voice was a little off, but my face and mannerisms were spot on. The video got millions of views, and I was inundated with messages from people asking if it was real.
It took me and my team weeks to get the video taken down. And in that time, the damage was done. The price of the cryptocurrency skyrocketed, and a lot of people lost a lot of money. It was a painful lesson in the power of this technology.
But it also opened my eyes to the opportunity. I realized that the world was going to need a new set of tools to deal with this problem. And I decided that I was going to be one of the people to build them.
The Future is Now
I know what you’re thinking. This all sounds a bit like science fiction. But I’m here to tell you that it’s not. The technology is here. The threat is real. And the opportunity is massive.
I’m putting my money where my mouth is. I’ve already made several investments in the AI security space, and I’m actively looking for more. I’m convinced that the next Google, the next Amazon, the next Apple will be a company that solves the deepfake problem.
So, if you’re an entrepreneur, an engineer, or an investor, I urge you to take a serious look at this space. This is not just another trend. This is a fundamental shift in the way we interact with technology. And those who get in on the ground floor will be richly rewarded.
Don’t be afraid of the chaos. Embrace it. Because in the world of technology, chaos is just another word for opportunity.
Frequently Asked Questions
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.
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.