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The Truth About Why Our First Warehouse Robots Failed Miserably

We spent $2 million on three warehouse robots. They were supposed to be the beginning of a revolution. Instead, they ended up as a very expensive lesson in humility.

I’m Sahin Boydas, and I’ve been building companies in Silicon Valley for over a decade. I’ve had a couple of successful exits – RemoteTeam, which was acquired by Gusto, and MovieLaLa, which was acquired by Gfycat. I’ve also been fortunate enough to be an early investor in some of the most transformative companies of our time, like Anthropic, OpenAI, and Scale AI. But today, I want to talk about a failure. A big, expensive, and very public failure.

It was 2018, and the hype around warehouse automation was at an all-time high. Amazon had acquired Kiva Systems for $775 million a few years earlier, and the race was on to build the next generation of warehouse robots. We were a young, ambitious startup with a team of brilliant engineers, and we were convinced we could build a better, cheaper, and more flexible solution than what was on the market. We raised a seed round from some of the top investors in the Valley, and we got to work.

Our vision was simple: a fleet of autonomous mobile robots (AMRs) that could navigate a warehouse, pick and pack orders, and work alongside humans. We weren’t just building hardware; we were building the AI to power it. We were building the future of logistics.

Or so we thought.

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What Went Wrong

So, what happened? Why did our multi-million dollar robots fail so spectacularly? It wasn’t one single thing, but a perfect storm of overconfidence, technical miscalculations, and a fundamental misunderstanding of the problem we were trying to solve.

Here’s a breakdown of our biggest mistakes:

• We underestimated the complexity of the real world. Our robots worked perfectly in our lab. They could navigate our pristine, controlled environment with flawless precision. But a real warehouse is a chaotic, unpredictable place. There are uneven floors, unexpected obstacles, and humans who don’t always follow the rules. Our robots, with their rigid, rule-based navigation systems, simply couldn’t cope.

• We tried to do too much, too soon. We were trying to build the hardware, the software, and the AI all at once. We were a small team trying to solve a problem that much larger, better-funded companies were struggling with. We should have focused on one piece of the puzzle – either the hardware or the software – and partnered with other companies for the rest.

• We ignored the human element. We were so focused on the technology that we forgot about the people who would be working alongside our robots. We didn’t involve the warehouse workers in the design process, and we didn’t do a good enough job of training them on how to use the new system. As a result, they saw the robots as a threat, not a tool.

It was a humbling experience, to say the least. We had to go back to our investors and tell them that we had failed. We had to lay off most of our team. And I had to face the fact that I had led my team down the wrong path.

Lessons Learned and the Future of Robotics

That failure taught me more than any of my successes. It taught me that humility is just as important as ambition. It taught me that you have to respect the complexity of the real world. And it taught me that technology is only as good as the people who use it.

I carry those lessons with me in everything I do now, especially in my angel investing. When I look at a robotics company today, I’m not just looking at the technology. I’m looking at the team. I’m looking at their understanding of the problem they’re trying to solve. And I’m looking at their plan for how they’re going to work with humans, not replace them.

Take Tesla Optimus, for example. It’s an incredibly ambitious project, and I have a huge amount of respect for what they’re trying to do. But the challenges they’re facing are the same ones we faced back in 2018, just on a much larger scale. The real world is messy, and building a general-purpose humanoid robot that can navigate it safely and reliably is an astronomical challenge. I’m cautiously optimistic, but I also know how many things can go wrong.

It's the same story with surgical robots. The da Vinci surgical system is an amazing piece of technology, but it’s not autonomous. It’s a tool that enhances the skills of a human surgeon. That’s the right way to think about robotics, in my opinion. It’s about augmentation, not automation.

And then you have drone AI. This is an area where we’ve seen incredible progress in a relatively short amount of time. But again, the most successful applications of drone AI are the ones that are focused on solving a specific problem, like inspecting infrastructure or delivering medical supplies. They’re not trying to be all things to all people.

The Takeaway

So, what’s the truth about why our first warehouse robots failed? The truth is, we were arrogant. We thought we could solve a problem with technology alone. We were wrong.

The future of robotics isn’t about building bigger, better, and more autonomous robots. It’s about building smarter tools that help humans do their jobs better. It’s about collaboration, not replacement. And it’s about having the humility to admit when you’re wrong, and the courage to learn from your mistakes.

That $2 million we lost on those robots? It was the best money I ever spent.

Frequently Asked Questions

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.

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.