I've had this conversation about the future of deepfakes: what to expect next year with at least 50 founders. Here's the distilled version.
After analyzing 100+ Deepfakes incidents, I found a terrifying pattern. This is what you need to know before it's too late.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating the future of deepfakes: what to expect next year. It's not complicated, but it requires discipline.
Step 1: simplicity beats complexity every time This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap 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 in my portfolio that nail the future of deepfakes: what to expect next year are the ones that treat it as an ongoing process, not a one-time project.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the future of deepfakes: what to expect next year 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 75 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 future of deepfakes: what to expect next year.
The biggest misconception is that you need to the data tells a different story than your gut. 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 future of deepfakes: what to expect next year. Their approach was counterintuitive but brilliant.
What I Tell Founders
When a founder in my portfolio asks me about the future of deepfakes: what to expect next year, I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around zero-day AI, AI phishing, AI security tools, adversarial AI, AI threat detection that I've been thinking about a lot lately.
The Bottom Line
Look, the future of deepfakes: what to expect next year isn't 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 the future of deepfakes: what to expect next year aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take the future of deepfakes: what to expect next year seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
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.
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.