I’ve been lucky enough to have a front-row seat to the AI revolution, both as an entrepreneur and as an investor. I’ve seen companies go from a crazy idea to a billion-dollar valuation. I’ve backed over 200 startups, including some of the biggest names in AI like Anthropic, OpenAI, and Scale AI. And I’ve learned a thing or two about what separates the hype from the real deal.
Right now, all the buzz is about Large Language Models. And don’t get me wrong, what’s happening in generative AI is incredible. But I’m going to make a bold statement: the next big thing in AI isn’t what you think. It’s not about making chatbots that can write poetry or generate prettier pictures. The real revolution is happening in the physical world.
The LLM Gold Rush
Everyone is chasing the LLM dragon. It feels like every day there’s a new model that’s slightly better than the last. The FOMO is real. But here’s the thing about gold rushes: most people who rush in don’t strike it rich. The ones who really make money are the ones selling the shovels.
I see a lot of companies building thin wrappers around OpenAI’s API. They’re basically just putting a new user interface on the same underlying technology. That’s not a sustainable business model. As the big players continue to improve their models, these wrapper companies will get squeezed out.
I’m not saying that there are no opportunities in the LLM space. There are. But you have to be smart about it. You need to be building something that’s truly differentiated, something that has a unique dataset or a proprietary algorithm. Otherwise, you’re just building on rented land.
The Quiet Revolution in the Warehouse
While everyone is looking at their screens, a quiet revolution has been happening in the background. It’s happening in warehouses, factories, and fulfillment centers. It’s the revolution of physical AI.
I remember visiting a warehouse a few years ago. It was chaotic. People were running around with carts, picking items from shelves, and packing them into boxes. It was a very manual and error-prone process. I recently visited that same warehouse again. The difference was night and day.
Now, the warehouse is run by a fleet of autonomous robots. These robots navigate the aisles, pick items with incredible precision, and transport them to the packing station. The humans are still there, but their roles have changed. They’re now managing the robots, overseeing the process, and handling the exceptions. The result? The warehouse is more efficient, more accurate, and safer than ever before.
This is not a far-off future. This is happening right now. And it’s a massive opportunity. The global warehouse automation market is expected to reach over $50 billion by 2026. That’s a huge number, and it’s only going to grow.
Drones That Do More Than Take Pictures
When most people think of drones, they think of hobbyists flying them in the park or real estate agents taking aerial photos of houses. But the real potential of drones is so much bigger than that.
I’m talking about autonomous drones that can perform complex tasks in the real world. Drones that can inspect power lines, survey construction sites, and even deliver packages. This is not science fiction. This is happening today.
I recently invested in a company that’s using drones to revolutionize the agriculture industry. Their drones are equipped with advanced sensors and AI algorithms that can analyze crop health, detect pests and diseases, and even apply fertilizer with surgical precision. This is a game-changer for farmers. It’s helping them increase yields, reduce costs, and be more sustainable.
The Humanoid Robot is Coming
This brings me to the most exciting development in physical AI: the humanoid robot. For decades, humanoid robots have been the stuff of science fiction. But now, thanks to advances in AI, they are becoming a reality.
I’m an investor in a company called Figure AI. They are building a general-purpose humanoid robot that can perform a wide range of tasks in the real world. This is the holy grail of robotics. A robot that can walk, talk, and interact with the world just like a human.
The challenges are immense. Building a humanoid robot that can navigate a cluttered environment, manipulate objects of different shapes and sizes, and interact safely with humans is incredibly difficult. But the team at Figure is one of the best in the world. And I’m confident that they will be the ones to crack the code.
The potential applications for a humanoid robot are almost limitless. They could work in factories, warehouses, and hospitals. They could assist the elderly and people with disabilities. They could even go to space.
It’s All About the System
The next big thing in AI is not just about the hardware. It’s not just about the software. It’s about the system. It’s about the seamless integration of perception, navigation, and manipulation. It’s about creating AI that can not only think but also do.
This is a much harder problem to solve than just building a better LLM. It requires expertise in a wide range of disciplines, from mechanical engineering to computer vision to reinforcement learning. But the rewards are also much greater.
The companies that can solve this problem will be the ones that define the next chapter of the AI revolution. They will be the ones that create the most value and have the biggest impact on the world.
So, while everyone else is chasing the LLM dragon, I’m placing my bets on the quiet revolution. The revolution of physical AI. Because I believe that the future of AI is not just about bits and bytes. It’s about atoms and actions.
Frequently Asked Questions
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.
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.