![\"Humanoid](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/1780580230_701_0x0.jpg\")
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Humanoid robot Neo by 1X, which is launching the 1X World Model Lab to accelerate “the path to fully autonomous humanoids.”<\/p>\n
John Koetsier<\/p>\n
You can\u2019t fine-tune your way to AGI, 1X CEO Bernt B\u00f8rnich told me yesterday while announcing the launch of the 1X World Model Lab. The goal is to accelerate the path to fully autonomous humanoid robots, and 1X has hired Sam Sinha, a founding researcher at video-generation startup Luma AI, as its Head of World Models to run the new lab.<\/p>\n
This is a progression on the existing 1X World Model, which the company launched in January of this year. That AI foundation model was built on video data that let Neo, 1X\u2019s humanoid robot, turn a prompt into an action, even on objects it hadn\u2019t seen. But now the company is scaling actual production of robots and getting closer to shipping at scale. That means much more data is now becoming available, and the goal of the lab is to turn that increasing information into smarter and smarter robots.<\/p>\n
Those robots will eventually be fully autonomous, capable of working together in teams, and maybe even truly smart in ways not dissimilar to us.<\/p>\n
“You can’t fine-tune your way to AGI”<\/p>\n
That\u2019s B\u00f8rnich\u2019s line, and it\u2019s kind of the whole pitch in seven words.<\/p>\n
B\u00f8rnich says that every AI leap of the last five years has come from feeding models richer kinds of data, in the right order. Text first, because there\u2019s an inexhaustible lake of it online. Then text and images, then text and video. The mistake, he says, is treating robot data as an afterthought: a thin fine-tuning layer bolted onto a model that was pretrained on everything else.<\/p>\n
“You can\u2019t fine-tune your way to AGI,” says B\u00f8rnich. “You need to actually train the model properly.”<\/p>\n
I also chatted with Sam Sinha, who spent the last four years scaling multimodal models at Luma. For too long, Sinha says, robotics has been treated as \u201ca second-class citizen.\u201d Most humanoid robotics companies train on web-scale data, then fine-tune on a hundred hours of robot demonstrations. “That principle is so fundamentally broken,” he said. “You need to see your most important tokens from step zero.”<\/p>\n
His summary of the job: “Good tokens in, good tokens out.”<\/p>\n
The key is that when you have actual robots in the wild doing things every day, your data stream for training your AI foundation models can get extremely diverse and extremely rich. It\u2019s not just video data anymore, or text data, or images. Now there\u2019s the live visual stream. There\u2019s proprioceptive information: where Neo\u2019s joints are and what forces are acting on them. There\u2019s the data coming off 1X\u2019s very impressive hands themselves<\/a>, including pressure and force data. And there\u2019s everything else available outside on-policy robot rollouts, including web-scale human video for diversity and volume, simulation and other human-centric collection. <\/p>\n As Sinha put it, the difference between gripping a bottle just hard enough to lift it and not hard enough is \u201ctremendous,\u201d and a camera alone can never fully capture it.<\/p>\n Data \u2026 and why Neo is so close to human<\/p>\n The bet here is that robotics is the same as any other AI problem: it gets solved by scale. If that\u2019s true, you want to train on all the data. And guess what: in order to use all the human video on the internet, your robot has to be human enough that the data transfers \u2026 that\u2019s why it\u2019s relevant to what your robot can do.<\/p>\n “You build your embodiment so close, as small an embodiment gap as possible,” B\u00f8rnich said. “So now you can just pretrain all of the human video out there, and that actually transfers to your robot.”<\/p>\n Of course, there\u2019s always a gap, but this is why Neo looks and moves the way it does. Why it\u2019s tendon-driven rather than geared, and why it has a hand with a massive 22 actuated degrees of freedom. <\/p>\n