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Autonomous Vehicles Are a Dead End. Here's What We Should Be Building Instead.
I’m going to say something that might get me in trouble in Silicon Valley. The dream of a fully autonomous car in every garage is a fantasy. For over a decade, we’ve been promised a future where we can summon a car, take a nap, and wake up at our destination. We’ve poured billions of dollars into the problem. And what do we have to show for it? A handful of heavily restricted, geofenced services that still require a human to be ready to take over at a moment's notice.
I’ve seen the hype cycle from the inside. As an angel investor in over 200 companies, including some of the biggest names in AI like Anthropic and OpenAI, I get pitched on “the next big thing” every single day. And I can tell you, the pitch for autonomous vehicles has gotten stale. The promises are getting bigger, but the results are staying the same. It’s a classic case of a technology that is 90% solved, but that last 10% is a killer. The number of edge cases in real-world driving is practically infinite. A plastic bag floating in the wind, a child chasing a ball, a sudden downpour – these are things human drivers handle instinctively. For an AI, they are monumental challenges.
I remember sitting in a board meeting for a company in the autonomous space a few years back. We were looking at a chart of disengagements – the number of times a human safety driver had to take control. The numbers were going down, but they weren’t going to zero. And the scenarios causing the disengagements were getting weirder and weirder. It became clear to me then that we were chasing a mirage. We were trying to solve the hardest problem first, instead of focusing on where robotics and AI could make a real impact today.
The Real Robotics Revolution is Already Here
While everyone has been obsessed with self-driving cars, a quiet revolution has been happening in warehouses, factories, and hospitals. This is where physical AI is actually working, and it’s a much bigger deal than most people realize.
Take warehouse robots. I remember visiting an early Kiva Systems (now Amazon Robotics) facility. It was mind-blowing. An army of little orange robots zipping around, lifting shelves, and bringing them to human workers. It wasn’t about replacing the humans, but about making them superhuman. No more walking miles of aisles to find a single item. The robots did the grunt work, and the humans did the thinking and packing. This is a solved problem. It’s a multi-billion dollar industry that has completely transformed logistics. And yet, it gets a fraction of the attention of the latest self-driving car prototype.
Then there are surgical robots. The Da Vinci surgical system has been around for over two decades, and it has performed millions of procedures. These robots allow surgeons to operate with a level of precision and control that is simply not possible with human hands. We’re talking smaller incisions, less bleeding, and faster recovery times. This isn’t a futuristic dream; it’s happening in hospitals all over the world, right now. And the next generation of surgical robots will be even more incredible, incorporating AI to help guide the surgeon’s hand and identify cancerous tissue in real-time.
The Humanoid Bet
This brings me to the next frontier: humanoid robots. Companies like Figure AI and Tesla with its Optimus robot are taking a different approach. Instead of trying to build a robot that can do one very specific, very complex task like driving, they are building robots that can operate in human environments and perform a variety of tasks. This is a much smarter bet in my opinion.
Think about it. The world is designed for humans. We have doors, stairs, tools, and workflows that are all built around our bipedal, two-armed form. A humanoid robot that can learn to open a door, climb a ladder, or use a power drill is infinitely more useful than a car that can only drive on a pre-approved route in perfect weather.
I’ve invested in this space, and the progress I’m seeing is staggering. These robots are learning to walk, to manipulate objects, and to understand natural language commands. They are not yet ready for prime time, but the path to commercialization is much clearer than it is for autonomous vehicles. The first applications will be in structured environments like factories and warehouses, helping with tasks that are dangerous, repetitive, or physically demanding for humans. From there, the possibilities are endless.
My Investment Thesis: Augment, Don't Replace
As an investor, I look for companies that are solving real problems and have a clear path to profitability. And when it comes to robotics, my thesis is simple: augment, don’t replace. The most successful robotics companies are not trying to build a fully autonomous system that eliminates the human from the loop. They are building tools that make humans better, faster, and safer.
This is why I’m bearish on the current crop of autonomous vehicle companies. They are trying to solve a problem that is too hard, with a solution that is too complex and too expensive. The regulatory hurdles alone are a nightmare. And for what? To save a few minutes on our commute?
I’m much more excited about the companies that are building specialized robots for specific industries. The ones that are making our supply chains more efficient, our surgeries more precise, and our factories more productive. That’s where the real value is being created. And that’s where I’m putting my money.
The future of robotics isn’t about replacing us. It’s about giving us superpowers. It’s time to stop chasing the self-driving car fantasy and start building the tools that will help us solve the real challenges facing humanity. '''
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.
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.
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 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.