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Nscale inks $3.5 billion deal with robotics firm Figure. By PYMNTS | September 3, 2026 Artificial intelligence cloud company Nscale has signed a major, multi-year agreement with humanoid robotics developer Figure. This deal will see Nscale provide computing resources for Figure and commit at least $3.5 billion - and up to $6 billion - to power the development of Figure's AI models and robots, Nscale announced Thursday (Sept. 3). "We're excited to partner with Figure as physical intelligence becomes AI's next frontier," Josh Payne, Nscale's founder and CEO, said in a news release. "We've seen incredible growth with inference and agentic AI and Figure is pushing the boundaries of AI even further. We're proud to be enabling the future of AI robotics together." Under the terms of the deal, Nscale will become a shareholder in Figure and serve as its preferred compute provider, directly supporting the development of Figure's next-generation Helix models and physical humanoid systems Please add us to your preferred sources list so our news, data and interviews show up in your feed. Thanks! The initial graphics processing units (GPUs) are scheduled for deployment beginning in the second half of next year in Barstow, Texas, the release added. To meet Figure's computational needs, Nscale intends to potentially deploy up to 100,000 Nvidia chips. Beyond raw processing power, the two companies also plan to explore opportunities to scale Nscale's supply chain using Figure's humanoid robots. "Humanoid robots extend physical AI into the world designed for people - opening a major new industry," said Jensen Huang, founder and CEO of Nvidia. "Nscale and Figure have activated the robotics flywheel," Huang said, as Figure's models train on Nvidia through Nscale's AI cloud, are validated in Nvidia and deployed in Figure's robots. "This is the physical AI flywheel that will accelerate the path from models to robots in the world." The announcement follows a report from last week that Anthropic has agreed to pay $45 billion to rent cloud computing power from Nscale's West Virginia data center. The Nscale/Figure partnership comes as American businesses are increasing their spending on robots, which has been good news for the company building them. As covered here last week, North American companies ordered 8,940 robots worth $622 million during the second quarter, respective increases over the same period in 2025 of 4.3% and 21.3%, per the Association for Advancing Automation. "Order value is growing nearly five times faster than order volume, a sign that companies are not just buying more robots but paying more per robot and choosing more capable machines than they did a year ago," that report said.

Nscale has agreed to supply at least US$3.5 billion worth of AI cloud computing capacity to robotics startup Figure, whilst making an undisclosed investment in the company. The London-based cloud provider will use Nvidia chips at a Texas site to fulfil the contract from the second half of 2027, with potential to scale beyond US$6 billion. The deal mirrors Nvidia's strategy of investing in customers and partners. Figure, founded in 2022, develops humanoid robots and raised over US$1 billion last year at a US$39 billion valuation. Nvidia is an investor in both Nscale and Figure. Nscale is reportedly seeking to raise up to US$3 billion in a US IPO, potentially as soon as September.

Figure launches Index, paying people to film everyday chores. Figure's Index app has paid $15M to 264,000 users filming household and workplace tasks, building training data for its Helix robotics stack. August 26, 2026 by HowAIWorks Team Introduction. On August 25, 2026, humanoid robotics company Figure came out of stealth with Index, a smartphone app that pays ordinary people to film themselves doing everyday chores. The company says it has already paid $15 million to users after four months of quiet operation, and that it intends to spend more than $1 billion on data and compute over the next 12 months. The pitch is a straightforward bet on scale: the physical data a general-purpose robot needs is not on the internet, so Figure is buying it directly from the people who generate it. What Figure has collected. The numbers Figure published for its four-month stealth period: * 264,000 app downloads across 108 countries * 44,000+ weekly active users, whom Figure calls Creators * 16 million videos uploaded * 30 minutes of video ingested every second - what Figure describes as 4.9 years of human work uploaded per day * $15 million paid out to Creators to date Figure also reports a diversity measure rather than a raw volume one: per 1,000 hours collected, the dataset contains 373 unique tasks, 1,146 unique manipulated objects and 116 unique environments. That framing matters more than total hours, because a million clips of the same kitchen teaches a robot very little. The tasks range from household work - cooking, cleaning, laundry - to commercial settings including logistics centers, restaurants, factories and offices. Figure cites cleaning kitty litter, changing oil and busing restaurant tables among the more obscure submissions. Creators can record in their own home or workplace, and the app also lets users book a Creator to come to their home or business and do chores while filming. Why buy data this way. Figure says it tried the conventional route first: "Prior to this we tried buying data. Vendors couldn't hit the throughput, diversity, or quality bar Helix requires, so we built the pipeline ourselves." The underlying claim is that generalization in robotics is a data problem before it is an architecture problem - the same scaling laws story that played out in language models, applied to physical tasks. Crowdsourced human video is one answer to the embodied AI data shortage; synthetic data from simulation is the other, and most labs use both. The long tail is the argument for crowdsourcing specifically. As Figure puts it, every new Creator brings "an unseen environment, unfamiliar objects, and their own idiosyncratic way of completing a task" - variation that is nearly impossible to specify in advance in a lab or a simulator. Ingesting consumer video at this rate forced Figure to rebuild its data infrastructure around consumer-app constraints. The processing pipeline has five stages: automated filtering for technical, visual and semantic quality; human fraud review auditing users for deliberate evasion; embedding-based deduplication that discards clips too similar to accepted data; rebalancing against task quotas and embedding clusters; and finally hierarchical text captioning of every episode. What is not in the announcement. Figure says the generalization results from training Helix on this data are "already validating this thesis" and that it will share more soon. That is the load-bearing claim of the whole post, and it arrives with no benchmarks, no evaluation protocol and no comparison against models trained without Index data. Until those appear, the verifiable part of the announcement is the collection operation, not its effect on robot performance. Two other gaps are worth noting. First, human egocentric video is not robot demonstration data: it lacks the joint states, forces and action labels a policy ultimately needs, and bridging that embodiment gap is an open research problem rather than a solved preprocessing step. Second, a global network of paid contributors filming inside homes and workplaces raises consent and privacy questions - for bystanders and employers as much as for Creators - that the announcement does not address. Figure frames Index as "laying the groundwork for ordering robots as a service," with today's human Creators as a placeholder for tomorrow's machines. That is a long way from 16 million phone videos, but the funding commitment behind it is specific: 100x the current collection rate, and over $1 billion in the coming year. Conclusion. Index is the largest public attempt yet to solve robot training data by paying consumers for it, and the operational numbers - $15 million out the door, 30 minutes of video per second - are real and substantial. Whether the data translates into robots that generalize is the question Figure has promised to answer and has not yet answered. The next release, with actual Helix results attached, is the one that will settle it. Sources.