My First Surgical Robot Prototype Was a Disaster, But It Taught Me Everything
Sparks flew. Not the good kind. The kind that signals a catastrophic failure. I remember the smell of burning plastic and the dead silence that followed. My first surgical robot, a machine I had poured months of my life and a significant chunk of my savings into, was a smoking wreck. It was supposed to be a marvel of precision engineering, a glimpse into the future of medicine. Instead, it looked like a failed high school science project.
I wanted to build a machine that could perform delicate surgeries with superhuman accuracy. The idea was born from a mix of fascination with robotics and a deep-seated belief that technology could solve some of the world's most pressing problems. I spent countless nights in my garage, surrounded by circuit boards, actuators, and a mountain of empty coffee cups. I was convinced I was on the verge of a breakthrough. I was wrong. So, so wrong.
That spectacular failure, however, turned out to be the most valuable lesson of my career. It taught me more than any success ever could. It taught me about the brutal reality of building things in the real world, the importance of humility, and the power of perseverance. It was a painful lesson, but it was a necessary one. And it’s a story I want to share with you, because in Silicon Valley, we have a tendency to celebrate the wins, but we often forget to talk about the messy, frustrating, and ultimately beautiful process of getting there.
The Dream: A Robot with a Surgeon's Touch
My vision was a machine that could mimic the delicate movements of a surgeon's hands, but with the precision of a computer. I imagined a robot that could perform minimally invasive surgeries, reducing recovery times and scarring for patients. This wasn't just about building a cool gadget; it was about revolutionizing healthcare. I wanted to make surgery safer, more accessible, and more effective.
The robot, which I had ambitiously named 'Chiron' after the wise centaur from Greek mythology, was supposed to have seven degrees of freedom, just like a human arm. It would be equipped with high-resolution cameras and a suite of sensors to provide real-time feedback to the surgeon. The surgeon would sit at a console, controlling the robot's movements with a pair of haptic controllers. The idea was to create a seamless connection between the surgeon and the robot, to the point where the robot would feel like an extension of the surgeon's own body.
I spent months sourcing the best components I could afford. I bought motors from a Swiss company that usually supplied the aerospace industry. I found a German optics company that made lenses for professional cameras. I even managed to get my hands on a prototype of a new haptic feedback system from a startup in Japan. I was obsessed with perfection. Every detail had to be just right. I was convinced that if I used the best parts, I would get the best results. That was my first mistake.
The Nightmare of Integration
Getting the best components is one thing. Making them work together is a whole different beast. The Swiss motors had their own proprietary control language. The German cameras used a different data protocol. The Japanese haptic system was a black box with minimal documentation. I spent weeks, then months, trying to get these a-list components to talk to each other. It was a classic case of the whole being less than the sum of its parts. A lot less.
I wrote thousands of lines of code, trying to create a software layer that would translate between the different systems. I built custom circuit boards to handle the signal conversions. I even had to machine my own brackets and mounts to physically connect everything. My garage started to look less like a lab and more like a scrapyard. I was so focused on the individual components that I had completely underestimated the complexity of the system as a whole. That was my second mistake.
The first signs of trouble appeared during the initial calibration tests. The robot's movements were jerky and unpredictable. The haptic feedback was either non-existent or so strong that it felt like I was wrestling with a wild animal. The camera feed would flicker in and out. I told myself these were just minor glitches, teething problems that I could fix with a few software tweaks. I was in denial. I was so invested in the project, both financially and emotionally, that I couldn't bring myself to admit that I was in over my head.
The Day It All Went Up in Smoke
The day of the big demonstration was a mix of excitement and sheer terror. I had invited a few potential investors and a couple of surgeon friends to see Chiron in action. I had a gelatin mold set up, simulating human tissue, and the plan was for the robot to make a series of precise incisions. It was supposed to be a triumphant moment, the culmination of all my hard work. It was anything but.
The demo started well enough. The robot powered on. The cameras came online. The haptic controllers felt responsive. I took a deep breath and guided the robot's arm towards the gelatin mold. The first incision was a little shaky, but it was a start. I made a few adjustments and tried again. This time, the robot's arm twitched violently, then shot forward, plunging the scalpel deep into the gelatin. I tried to pull it back, but the controls were unresponsive. The motors started to whine, a high-pitched scream that sent a shiver down my spine.
Then came the sparks. A shower of them, erupting from the main control unit. The smell of burning plastic filled the air. The robot's arm went limp, and the whole machine went dead. The silence in the room was deafening. My investors looked at me with a mixture of pity and disappointment. My surgeon friends tried to be supportive, but I could see the skepticism in their eyes. I had failed. Publicly and spectacularly.
What a Pile of Smoking Metal Taught Me
In the days that followed, I did a lot of soul-searching. I replayed the events of the demo over and over in my head. I went through my code, my schematics, my notes. I was looking for a single point of failure, a single mistake that I could blame for the whole disaster. But there wasn't one. The failure was systemic. It was a result of a flawed approach, a series of bad decisions that had compounded over time.
Here are the big lessons that expensive pile of junk taught me:
Systems, Not Parts: I was so obsessed with getting the best individual components that I lost sight of the bigger picture. A successful robot isn't just a collection of fancy parts; it's a tightly integrated system where every component works in harmony with every other. I learned that integration is not an afterthought; it's the main event. You have to design for it from day one.
Embrace Simplicity (At First): My ambition outran my ability. I tried to build the most advanced surgical robot in the world, all at once, in my garage. I should have started with something much simpler. A single-axis robot, maybe. Or a robot that could just hold a camera steady. The key is to build, test, and iterate. Get something working, then make it better. Don't try to build the Millennium Falcon on your first go.
Fail Fast, Fail Cheap: My failure was expensive, both in terms of money and time. I spent months building a machine that was destined to fail. A better approach would have been to build a series of cheap, disposable prototypes. I could have tested my ideas, identified the flaws, and learned a lot more, a lot faster, and for a fraction of the cost. In the startup world, we call this the Minimum Viable Product (MVP). It's a lesson I've carried with me ever since.
From Surgical Arms to Humanoid Workers
That disastrous prototype was the end of my surgical robotics career, but it was the beginning of my journey as an entrepreneur and investor. The lessons I learned in that garage have been the foundation of everything I’ve done since. They’ve informed my investments in over 200 companies, including some of the most exciting names in AI and robotics today, like Figure AI, which is building humanoid robots for the workforce.
The challenges I faced with Chiron are the same challenges companies are facing today, whether they are building surgical robots, autonomous vehicles, or general-purpose humanoids. It’s all about systems integration, iterative development, and learning from failure. The technology has gotten exponentially better, of course. The sensors are more sensitive, the actuators are more precise, and the AI is smarter. But the fundamental principles of building complex, real-world systems remain the same.
Looking back, I’m grateful for that smoking wreck of a robot. It was a humbling experience, but it was also a clarifying one. It stripped away the hype and forced me to confront the brutal realities of engineering. It taught me that innovation isn’t about a single moment of genius; it’s about the relentless pursuit of a vision, the willingness to get your hands dirty, and the resilience to get back up after you’ve been knocked down. And that’s a lesson that’s worth more than any successful prototype.
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.