Unitree, China’s leading robot manufacturer, lost nearly half its market value this week after falling from a $66 billion valuation following its initial public offering on the local equivalent of the NASDAQ.
Analysts say the drop highlights a core problem: while robotic hardware is improving, the software still lacks the know-how to perform value-creating work reliably.
At last week’s Actuate conference, a gathering of developers building AI brains for robots, the mood was one of high optimism. The event has tripled in size since it began in 2023. The organizer, Foxglove, reported 1,500 attendees. A sign on a booth for Avala, another infrastructure player, promised to solve “the robotics data crisis.”
That crisis is the lack of high-quality training data for AI models. Attempts to build generalised robots capable of any task remain far off. Using end-to-end learning for specific tasks has yet to deliver products with reliable, commercial performance. Developers must now mimic the advances of frontier AI labs by finding or creating more diverse data sets, experimenting with different training regimes, and refining reinforcement learning scenarios.
Harry Mellsop, a founder of Antioch, a startup building simulation tools, suggests physical AI is in its “GPT 2 era,” referring to the OpenAI model that pre-dated ChatGPT. More data and compute are needed to get over the hump, particularly GPUs optimised for ray tracing, which are used to create high-fidelity simulations.
Autonomous vehicles are currently the furthest ahead. This is partly due to the ability to collect relevant data from cars driven by people and partly because the main task is to avoid contact rather than manipulate the physical environment. Much of the model-building tooling comes from autonomous vehicle companies. Foxglove, for example, was founded by former employees at Cruise, General Motors’ erstwhile self-driving effort.
Car companies are now increasingly betting that their investments in machine learning tooling will allow them to compete with dedicated humanoid makers. Tesla is already trying this with its Optimus robot. Both AV-focused Wayve and ride-share giant Uber have now launched robotics labs focused on humanoid form factors as R&D efforts.
“I think you need to start in vehicles,” Alex Kendall, the CEO of Wayve, told TechCrunch. “Manipulation robotics is like self-driving five years ago. The data infrastructure, the simulation, ML ops infrastructure, will probably be shared, but the specific world model for the simulator will be a different post-training. There’s going to be a lot of more more commonality than not, but then there’s going to need to be some some differences for different embodiments.”
Kendall argues it is too early to commit to any one hardware platform. Advances in sensors and other components are coming quickly, and a truly general model should be more agnostic.
Théophile Gervet, the CEO of Genesis AI, a vertically-integrated humanoid robotics company that raised a $105 million seed round this year, disagreed. He told TechCrunch “we’re too early in this wave for a brain strategy to work; our take us there’s lots of opportunities to co-design hardware and AI.”
Gervet also touched on another hot topic in the sector: how specifically to focus your physical AI business. Robotics companies targeting specific tasks are getting their robots out in the field. Gritt is building solar farms, Agility is deploying robots in industrial settings, and Bedrock is operating excavators autonomously. Meanwhile, general-purpose humanoids are not getting out of the labs.
“No customer cares about the general purpose robot that works at 80% success rate,” Gervet said of the dilemma. “We see a lot of other players go general, but there is no value provided because there’s no vertical focus. .. but then, if you’re building [for a narrow] vertical on top of GPT 2, you’re going to get crushed by the company building on GPT 4.”
The temptation to invest in a specific vertical is tempting because it provides not just revenue but also real-world deployment data. While task-specific data might not have enough diversity to push general purpose models forward, it is an important for making a robot that adds value. Bedrock CTO Kevin Peterson noted that his company was just starting with excavation as a way to understand the challenges of “manipulation in the wild,” but plans to develop an intelligence layer that stretches across a series of construction machines.
Managing all that data is a challenge, especially because of the density of visual and lidar data. Foxglove announced a new product this week, built on top of an Nvidia’s Cosmos open weight world model, that allows engineers to search that data with sophisticated natural language queries to build out evaluations and simulations. The goal is faster triage and debugging so model builders can iterate faster.
So what will be the fabled ChatGPT moment for physical AI that Sam Altman recently said is just a few years away? Kendall points out that the largest robot deployment in the world are still consumer vacuum bots. For him, a ChatGPT moment would be something that excites consumers, not investors, who seem to be plenty excited already.
“One example of that would be when you get eyes-off autonomy for less than $1000 [worth of hardware] in a car,” Kendall says; not coincidentally, his company is licensing models to car makers in an effort to produce just that. And that business, which he sees as a multi-billion dollar opportunity, will allow them to build a truly general embodied AI model.
For Gervet, the moment when physical AI becomes real is “manipulation that just works out of the box. You can talk to a robot in natural language and have it do any basic task for manipulation, like say pushing, pulling, closing a laptop, cleaning up a table, whatever you want to do, and it works to some level of reliability, let’s say 80% plus out of the box— that’s roughly your ChatGPT experience.”
Adrian Macneil, Foxglove’s CEO, looks at the question a bit differently.
What it means
“There will not be a ChatGPT moment for robotics,” he told TechCrunch. “The thing that made ChatGPT a moment in time was the distribution—they went from zero to like a million active users in like a week…distribution in the real world is way harder than that, right? I would be very excited for the Apple II moment in robotics or the IBM PC moment in robotics. When can I buy like a home robot that is gonna start doing some useful and fun stuff?”
