Crédito de imagen: Image via TechCrunch — Robotics. Used under fair use for news commentary. · source
I'm going to say something that might get me yelled at: the most important robotics company of the next decade might not build a single robot.
Now before you fire off an angry email (and please do, my address is on the about page), let me explain why Config, a Korean startup that just landed backing from Hyundai, Samsung, and LG, has me thinking about semiconductor history instead of servo motors.
Config doesn't make robots. They make robot data. And if that sounds like a boring business model, well, you probably thought the same thing about a Taiwanese chip foundry in 1987.
The company's own pitch is that they want to be "the TSMC of robot data," which is either the most audacious comparison I've heard this year or the most accurate one. I genuinely can't decide which.
Here's the thing that keeps nagging at me: TSMC didn't become the most important company in semiconductors by designing chips. They became essential by being the best at manufacturing chips that other people designed. The insight was that fabrication itself could be the product, not just a step in someone else's supply chain.
Config is making the same bet about data. The theory goes like this: robots need massive amounts of training data to learn how to operate in the real world, data about how objects feel when grasped, how surfaces respond to pressure, how to navigate factory floors without killing anyone. Collecting this data is expensive, tedious, and requires expertise that most robotics companies don't have and don't want to develop.
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So why not outsource it?
I've been covering tech long enough to know that "why not outsource it" is the question that built Silicon Valley. And also the question that occasionally destroys companies when they outsource the wrong thing. The jury's still out on which category robot data falls into.
Let me be honest about the limitations here. Config hasn't disclosed how much they've raised, what their data actually looks like, or how many customers they have beyond the strategic investors. The TechCrunch report that broke this story is thin on specifics, and the company's own materials are heavy on vision and light on metrics.
What we do know: Korea's three biggest manufacturing conglomerates are backing them. Hyundai has been pouring money into Boston Dynamics. Samsung has been quietly building out robotics capabilities for years. LG has consumer robot ambitions that keep getting delayed. All three of them apparently looked at the problem of robot training data and decided they'd rather pay someone else to solve it.
That's interesting! That's genuinely interesting. Because these aren't venture capitalists pattern-matching to the last successful pitch deck. These are industrial companies with actual robot programs who presumably understand what they need.
Or maybe they're just hedging their bets. Call me old-fashioned, but I've learned to be skeptical when corporate venture arms start writing checks.
Here's what's changed in the last eighteen months: AI coding agents got good. Really good.
I spent a weekend recently reading about WIRED's experiment giving an AI agent a physical robot body, and the implications are sort of staggering. The argument is that AI models have gotten so capable at writing and debugging code that the software side of robotics, which used to be the hard part, is becoming almost trivially easy for certain applications.
Which means the bottleneck is shifting. If you can spin up robot control software in an afternoon, what's left? Hardware (still hard, still expensive) and data (increasingly the thing everyone needs and nobody has enough of).
This is the self-driving car hype cycle all over again, except maybe this time the companies have learned something. The autonomous vehicle industry spent a decade and roughly $100 billion learning that the last 1% of driving scenarios requires 99% of the data collection effort. Waymo won (sort of, in limited geographies) not because they had better algorithms but because they had more miles of real-world data than anyone else.
Config seems to be betting that robot manipulation will follow the same pattern. The first 80% of tasks will be easy. The last 20% will require data that's incredibly expensive to collect. And whoever has that data wins.
I should be clear that I don't actually know if this is going to work.
The TSMC comparison is flattering, but it glosses over some important differences. Semiconductor fabrication has massive economies of scale, you need billion-dollar fabs, so consolidation makes sense. Robot training data might not work the same way. Maybe every factory needs slightly different data. Maybe the data doesn't transfer well between robot platforms. Maybe the whole premise is wrong and robots will learn better from simulation than from real-world data collection.
There's also the question of defensibility. TSMC's moat is physical, you can't just copy a cutting-edge fab. Data moats are trickier. If Config collects a million hours of manipulation data, what stops a well-funded competitor from collecting two million hours? What stops the robot companies themselves from eventually bringing this capability in-house once they understand what they actually need?
I don't have answers to these questions. Neither does anyone else, as far as I can tell. The Config team believes that data collection requires specialized expertise that will be hard to replicate, and that being first will create network effects as more robots train on their data and more edge cases get captured. Maybe! But I've heard similar arguments from companies that no longer exist.
Here's why I think this matters even if Config specifically doesn't pan out.
The robotics industry is starting to disaggregate. For decades, robot companies were vertically integrated by necessity, you built the hardware, wrote the software, collected your own training data, and sold a complete system. That model is breaking apart. You've got companies specializing in actuators, companies specializing in manipulation software, companies specializing in simulation, and now apparently companies specializing in data.
This is what happened to computers. It's what happened to smartphones. It's arguably what's happening to AI models right now, with the training compute layer separating from the inference layer separating from the application layer.
When an industry disaggregates, the companies that control the bottleneck layers tend to capture most of the value. Intel captured the PC era. TSMC captured the mobile era. Nvidia is capturing whatever era we're in now.
The bet Config is making, and the bet that Hyundai and Samsung and LG are apparently making alongside them, is that data will be the bottleneck layer for robotics. Not hardware, not software, but the messy, expensive, tedious work of teaching robots how the physical world actually behaves.
I've been wrong before about these things. I was skeptical about cloud computing in 2008 (why would anyone trust their data to someone else's servers?) and I was skeptical about smartphones replacing computers in 2010 (the screens are too small!). The kids building robots today will probably look back at this column and laugh at whatever I'm getting wrong.
But I've also been right about a few things, and my pattern-matching brain keeps telling me that data infrastructure companies tend to matter more than anyone expects. The boring picks-and-shovels plays often outlast the flashy gold miners.
Config might be the TSMC of robot data. Or they might be one of the hundred companies that tried to be the TSMC of something and ended up as a footnote. We genuinely don't know yet, and anyone who tells you they're certain is selling something.
What I do know is that the biggest manufacturers in Korea are taking this seriously enough to write checks. And when industrial companies start betting on infrastructure plays, I pay attention.
But what do I know. I'm just a guy who still prefers email to Slack, watching another tech cycle unfold and wondering if this time will be different.