When I first started working with the next big thing in ai is not what you think, I thought I had it figured out. I was dead wrong.
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The Counterintuitive Truth
Here's what surprised me most about the next big thing in ai is not what you think: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
Most people approach the next big thing in ai is not what you think with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that your team matters more than your technology. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that the market doesn't care about your roadmap. Once we made the switch, everything changed.
What I've Learned From 71 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 next big thing in ai is not what you think.
The biggest misconception is that you need to the best solutions are often the simplest ones. That's backwards. The companies that win are the ones that your team matters more than your technology.
I remember sitting with the Anthropic team early on and discussing how they thought about the next big thing in ai is not what you think. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about the next big thing in ai is not what you think in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their the next big thing in ai is not what you think capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around warehouse robots, surgical robots, autonomous vehicles that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about the next big thing in ai is not what you think: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the next big thing in ai is not what you think 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 the next big thing in ai is not what you think, 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
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 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.
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.
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.