I had a founder pitch me an idea for a fully autonomous surgery machine last week. He was brilliant. A PhD from a top university, papers published, the whole nine yards. He showed me simulations of a robot arm performing a delicate procedure flawlessly. He told me, "In five years, surgeons will be obsolete."
I passed on the investment.
It’s not because the tech wasn't impressive. It was. But his core assumption was wrong. After two exits and over 200 angel investments in companies like Anthropic and Scale AI, you learn that pattern recognition is everything. You see the same beautiful, flawed ideas again and again. The hype around surgical robots is a classic. It’s a story I’ve seen play out in a dozen other industries.
Everyone is mesmerized by the idea of a machine that can heal us. It’s a powerful, almost mythic, image. But the reality on the ground, in the hospitals, in the operating rooms? It’s a lot more complicated. And the stories we're being told are often just that—stories. Let's cut through the noise. Here are the three biggest lies you've been told about surgical robots.
Lie #1: Robots Will Replace Surgeons
This is the big one. The sci-fi fantasy. The idea that a machine, with its steady hand and tireless precision, will simply take over the job of a human surgeon. It’s a compelling narrative. It’s also completely false.
Surgical robots aren't replacements. They are incredibly sophisticated tools. Think of it this way: giving a master painter a better brush doesn't make the painter obsolete. It allows them to create in ways they couldn't before. The Intuitive Da Vinci system, the most common surgical robot, isn't performing surgery on its own. A human surgeon is sitting at a console just a few feet away, controlling every single movement.
The robot is a force multiplier for the surgeon's skill. It translates their hand movements into smaller, more precise actions. It eliminates tremors. It provides a magnified, 3D view of the surgical site. But the judgment, the critical thinking, the ability to adapt to the unexpected—that all comes from the human in the chair. A surgeon friend of mine who specializes in complex cancer operations told me a story that stuck with me. He was removing a tumor, and found that it was wrapped around a major blood vessel in a way that no pre-operative scan had shown. The textbook anatomy was just a suggestion. He had to, in his words, "dance with the tissue," carefully dissecting it millimeter by millimeter, feeling the tension, seeing how it responded. A robot running a program would have just plowed through it. That's the difference. The human body isn’t a clean, predictable circuit board. It’s a messy, unique, and often surprising environment. No two surgeries are ever exactly the same. A human surgeon can improvise. A robot cannot.
I saw this firsthand when building RemoteTeam, which was later acquired by Gusto. The goal was never to automate the manager out of a job. It was to give them better tools to manage a global team. We automated payroll and compliance, but we didn't automate the human connection and decision-making that makes a great manager. The same principle applies here. The value is in augmenting the expert, not trying to replace them. My first startup, MovieLaLa, which Gfycat acquired, taught me a similar lesson. We used data to predict what movies would be hits, but we could never have written the script for a blockbuster. That requires a creative spark, a human touch that data can inform but never replace.
Lie #2: Surgical Robots Are Fully Autonomous
This lie is a direct consequence of the first. People hear “robot” and they think “autonomy.” They picture a machine thinking for itself, like a Tesla on Autopilot but for your insides. The reality is much more like a very advanced, very expensive video game controller.
As I mentioned, the vast majority of surgical robots in use today are based on a master-slave model. The surgeon (the master) manipulates controls, and the robot (the slave) mimics those movements at the patient’s side. There is no AI making decisions about where to cut or what to suture. The surgeon is the one in complete control. We are decades away from anything approaching true autonomy in the operating room.
My investments in companies working on autonomous vehicles have shown me just how hard this problem is. Driving a car on a road, with its clear rules and relatively predictable environment, is an immense challenge for AI. I’ve seen brilliant teams at my portfolio companies spend months trying to solve a single edge case, like how a car should react to a plastic bag blowing across the highway. Now imagine trying to create an AI that can navigate the infinitely more complex and delicate landscape of the human body, where a millimeter’s error can be the difference between success and disaster. The stakes are just too high.
What we are seeing are baby steps toward supervised autonomy. Think of features that can automatically hold a camera steady on a certain point, or software that can highlight the edges of a tumor for the surgeon. These are helpful assists, like lane-keeping in a car. They aren't self-driving. The surgeon is still the driver, and they need to have their hands on the wheel at all times. The regulatory pathway for a truly autonomous medical device is also a nightmare. The FDA is, rightly, incredibly cautious. Getting a new tool approved is hard enough. Getting a new decision-maker approved is a challenge of a different order of magnitude. Any founder in this space who isn't spending half their time thinking about the regulatory strategy is naive.
Lie #3: The Biggest Challenge is the Technology
Founders, especially technical ones, always fall in love with the tech. They think the hardest part is building the thing. In the world of surgical robotics, that's just not true. The technology is the solved problem, for the most part. The real mountains to climb are cost, training, and integration.
Let's talk numbers. A single Da Vinci surgical system costs around $2 million. On top of that, you have annual service contracts that can run six figures, plus the cost of proprietary disposable instruments for every single procedure. A single surgery can use up thousands of dollars in disposables. For a hospital, this is a massive capital investment. It's not like buying a new MRI machine that can be used by many departments. This is a tool for a specific set of surgeons, and the hospital needs to be sure it will generate enough revenue to justify the cost. The business model is built on high volume.
It’s a business model I understand well from the enterprise software world. It’s not enough to have a great product. You have to navigate the customer’s budget cycles, their existing infrastructure, and their internal politics. Selling to a hospital is notoriously difficult. You have to convince not just the surgeons, but the CFO, the department heads, and the IT team. The sales cycle is brutal. I've seen startups with amazing tech die on the vine because they couldn't figure out how to sell into a hospital.
And even if a hospital buys the robot, who is going to use it? It takes a significant amount of time for a surgeon to become proficient. It's a steep learning curve. This isn't something you learn in a weekend workshop. It can take dozens of procedures to get comfortable, and hundreds to become an expert. This means taking your most valuable doctors out of the operating room for training, which costs the hospital money. You have to build a whole new workflow around the machine. You need specialized technicians to set it up and troubleshoot. It's a whole ecosystem, not just a box.
Then there's integration. Or the lack of it. Many of these robotic systems are closed platforms. The data they generate stays inside their own silo. They don't talk to the hospital's electronic health record system. This is a huge missed opportunity. The data from these procedures could be incredibly valuable for training, for research, for improving patient outcomes. But it's locked away. It's a problem we solved in the software world with APIs and open standards, but it's still a major hurdle in medical hardware.
The Real Future
So, what is the future of surgery? It's not a future without surgeons. It's a future where surgeons are empowered by incredible new tools. The focus needs to shift from the fantasy of replacement to the reality of collaboration. The surgeon and the robot are a team.
I'm excited about the companies that get this. I'm not looking for the company that wants to build a $5 million, do-everything robot. I'm looking for the startup that's building a $150,000 robotic scope for colonoscopies that uses AI to flag potential polyps in the surgeon's field of view. I'm looking for the team that's creating a haptic feedback system that lets a surgeon "feel" the tissue through the robotic instruments, restoring some of that lost tactile sense. These are the kinds of practical, focused innovations that will actually change medicine.
I’m also betting on data. The companies that can successfully aggregate and analyze the data from thousands of robotic procedures will be in a powerful position. They can identify best practices, create personalized training simulations, and even predict potential complications before they happen. This is where the AI revolution will truly impact surgery—not by holding the scalpel, but by providing a layer of intelligence and insight that humans alone can't achieve.
Stop waiting for the robot uprising in the operating room. It’s a distraction. The narrative of man versus machine is tired and wrong. The real story is man with machine. It’s about building tools that make our best people even better. That’s where the real revolution is happening. That’s where I’m putting my money. And that’s the future I’m excited to be a part of building.
Frequently Asked Questions
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 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.
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.