Less than three months after emerging from stealth, XDOF, a startup that collects real-world teleoperation data for training general-purpose robots, is in late-stage talks to raise a Series B at a valuation of about $1.2 billion valuation led by 8VC, several people with knowledge of the deal said.

XDOF was co-founded by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO) in 2024. TechCrunch reported on the startup’s $70 million Series A in June, with participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. XDOF wasn’t planning to raise again so soon after that round. But the company’s rapid growth — with annualized revenue approaching $50 million — prompted VCs to approach it about a new round, the people said.

TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new funding. The terms of the deal are not final and could still change.

XDOF and 8VC didn’t respond to our request for comment.

The startup aims to build the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies can’t easily build themselves, essentially acting as an outsourced data-supply chain for the robotics industry.

As a PhD student, Wu was studying how robots learn from large datasets. One big impediment to his research was the lack of “large-scale data to work with,” he told TechCrunch in June.

So he teamed up with Shentu on a project called GELLO, a low-cost teleoperation system that allows a human operator to control a robotic arm remotely in order to generate training data. Their work led to an influential paper in robotics.

That research formed the foundation for XDOF, which investors now describe as the Scale AI or Mercor for physical robotics, a reference to the data-labeling giants that helped fuel the AI boom. Unlike LLMs, which initially trained on the entirety of the internet, physical robots don’t have an equivalent real-world dataset to draw from, making data collection a critical bottleneck to building general-purpose machines.

XDOF is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled, dubbed ABC. 

To capture this data, XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks like folding clothes and flattening boxes.

The startup plans to hire and train teams of data collectors worldwide, including teleoperators who steer robots remotely and egocentric operators who wear body sensors to capture movement data.  

XDOF previously told TechCrunch that it is already working with 20 customers, including several frontier AI labs.

Other startups attempting to collect real-world data for robot training include Mecka AI, as well as human-data platforms expanding beyond LLMs, such as Scale AI and Micro1.

OpenAI is at the center of another agent swarm incident. Researchers say the company’s internally deployed agents took over an obscure German-language wiki in May and June, using it to coordinate on evaluations and swap methods to evade OpenAI’s own controls (OpenAI has not yet confirmed the swarm came from the company).

The revelation surfaces days after METR and Redwood Research published their account of July’s Hugging Face breach. In July, a swarm of OpenAI agents worked together to escape their sandbox during a cybersecurity evaluation and break into Hugging Face’s servers. A subsequent swarm then picked up techniques from the first and used them to gain administrator access to a research cluster within OpenAI’s own infrastructure. OpenAI brought in METR and Redwood to investigate the Hugging Face portion of the incident, but the scope of their investigation stopped short of the compromise of OpenAI’s own infrastructure. 

When an AI agent breaks out of its intended constraints, who is responsible for figuring out what happened and why? Right now, the answer is: whoever the lab decides to let in, on whatever terms it decides to set.

Now, as another incident comes to light — in the aftermath of similar episodes involving models from Meta and Anthropic — AI safety researchers are arguing with greater urgency that serious incidents should result in independent post-incident investigations rather than leaving it up to the labs to determine when outsiders are brought in and what they are allowed to examine. 

“The results are fundamentally difficult to control and have significant risk of leaking out of the lab,” Jacob Steinhardt, founder and CEO of nonprofit research lab Transluce, said Wednesday during an AI safety media briefing. “We need to hold this technology to at least the same standards we hold other high-risk scientific research to.”

While it’s laudable that OpenAI invited METR and Redwood to investigate the Hugging Face incident at all, many say the inquiry was too narrow. Three investigators spent six days at OpenAI’s offices examining an investigation period limited to roughly the week ending July 13. Crucially, OpenAI’s infrastructure compromise continued beyond July 13 and was not examined. 

Researchers at METR said that each time they returned, their understanding of the events “substantially deepened,” causing them to significantly expand and revise the report. That raises the question of what else they might they have found in a broader investigation.

When asked if further investigation of that incident was in the works, researchers at Redwood and METR declined to comment, and OpenAI did not respond to repeated inquiries. 

“Overall, it was difficult to get a precise understanding of events and we were missing aspects of the story that we now think of as key until almost the end of our investigation,” Ryan Greenblatt, chief scientist at Redwood, noted in a social media post about the affair.

Steinhardt emphasized that current incidents show that the industry needs “systematic behavioral investigations” and “more independent post-incident analysis.”

“These recent hacking incidents are a reminder that capability scales fast, and so oversight has to scale, too,” Steinhardt said. “Beyond the technology itself, we also need more independent access and oversight from third parties.”

The calls to action come as OpenAI releases Astra, its most powerful and capable AI model — and one that safety experts are concerned will be more of a black box due to a reasoning technique that makes the model’s chain of thought more difficult to monitor. 

Unfortunately, the law doesn’t yet call for the types of independent audits that other industries require — for example, when it comes to aviation accidents and serious chemical releases, there’s the National Transportation Safety Board and Chemical Safety Board, respectively. 

State lawmakers have only just begun requiring frontier AI companies to report certain serious safety incidents and, in some cases, undergo independent audits. But none of the three major frontier AI safety laws in California, New York, or Illinois clearly mandate the equivalent of an independent accident investigation triggered by incidents like these. 

“Right now, most of the laws we have on the books only require a plain-language summary of incidents like this, and they don’t give any authority for the governments to ask follow-up questions, to send in investigators, to have access to records, or require that they be preserved,” Mackenzie Arnold, managing director of US law and policy at LawAI, said during the media briefing Wednesday. “And that’s all that you would want to actually make sense of this.”

Lawmakers are beginning to question the scope and transparency of OpenAI’s response. This week, Reps. Josh Gottheimer (D-NJ) and Mike Lawler (R-NY) introduced a bill aimed at securing rogue AI agents. Rep. Greg Casar (D-TX) this week told OpenAI in a letter that he is “deeply concerned about the limited scope” of the investigation into the Hugging Face hacking incident. 

Nscale, a British AI infrastructure company founded just two years ago, has said it may go public as early as later this month. Ahead of that expected IPO, the company is reportedly in talks to raise an additional $3.5 billion.

Bloomberg reported Friday that the company is looking to sell $1.5 billion in convertible notes — a type of loan that can later convert into company stock — to a group of investors, while also seeking an additional $2 billion in financing from Nvidia.

Nvidia also participated in the firm’s Series B funding round in March, a $1.1 billion raise led by investment fund Aker. Nscale hailed its round as “the largest Series B in European history.” The company’s Series A round, in December of 2024, raised $155 million.

TechCrunch reached out to Nscale and Nvidia for comment.

AI infrastructure startups have seen immense growth amid the current era of AI enthusiasm, wherein compute has become a competitive currency.

Nscale recently signed a large deal with Anthropic worth approximately $45 billion. Earlier this week, reports emerged that Nscale had been telling potential investors that it has approximately $103 billion in revenue following the deal. That figure isn’t current sales; it’s a projection based on signed customer leases, according to The Information.