In recent years we have seen how artificial intelligence advanced on a relatively abundant raw material: text, images, videos and code published on the web. With robots, the terrain changes completely. We are not just talking about answering a question well or generating a convincing image, but about acting in the physical world, moving pieces, grabbing objects and doing so without everything being perfectly prepared. That difference explains why part of the next AI race may play out away from the usual focus.
The investment. Settings It has not attracted the attention of just any investor, but of some of the large business groups in South Korea. According to Foley Hoagwhich legally advised Config on the operation, the startup, based in Seoul and San José, has closed a seed round of $27 million led by Samsung Venture Investment. ZER01NE Ventures, the investment arm of Hyundai Motor, LG Technology Ventures and SKT America have also participated. The operation values the company at more than 200 million dollars and brings its total financing to 35 million.
The “TSMC” of robots. The simile is not about chips, but about position in the value chain. Config aspires to position itself at a point similar to that of TSMC in semiconductors: not competing with its end customers, but rather supplying a part that others need to create their own products. In their case, that piece is not wafers or processors, but rather data for foundational robotics models. That approach is gaining traction as large manufacturers look to develop their own robotic AI without relying entirely on third-party vendors.
Key difference. In a language model, the big cost is processing enormous amounts of digital information; in robotics, as Config CEO Minjoon Seo explained to TechCruncheach piece of data must be collected physically. That means having robots, spaces where they can be tested, and human teams that make them work. As companies look for more capable machines, data collection and labeling can quickly become more expensive, because we are no longer talking about information that waits on the web, but rather actions that occur in the real physical world.
The key is in the conversion. The signature is based on an idea that is somewhat less obvious than the simple accumulation of data. Many robotics teams train their models with human motion data and then try to adapt them to machine behavior. The startup advocates another path: transforming that data before training begins so that it better fits the way robots move and interact with the environment.
They have already started. Config has almost 300 people working on producing that data. The startup claims to have gathered more than 100,000 hours of human movement data, compared to roughly 3,000 hours for AgiBot Worldwhich the source presents as the largest comparable open set. The difference, more than 30 times, helps explain why the company is so insistent on the scale of its data operation.
What’s coming. The next step will be to expand this machinery even more. Config wants to scale its operations in Vietnam and Seoul to reach one million hours of data collected, a goal that fits with its idea of becoming an infrastructure provider for third parties. The company also aims to take its enterprise platform to $10 million in annual recurring revenue by the end of 2027. The third front is to launch a Robot-as-a-Service product in the cloud, designed so that companies can use the foundational Config model without depending on hardware integrated into the robot itself.
Looking to the future. What this movement leaves is a fairly clear snapshot of where part of robotics may go in the coming years. Not everything will depend on the robot that we see in a factory, in a warehouse or in the field, but on all the previous work that allows human actions to be converted into useful learning for a machine. Config is still a young startup and its great promise has yet to be demonstrated at scale, but interest from Samsung, Hyundai, LG and other big names points to an idea with potential.
Images | Config | Igor Omilaev
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