Deep|Physical AI: From the Robot-Data Race to a Second Demand Curve for Compute

Executive Summary

This is the first report in our Physical AI research series, examining how robot-data investment translates into compute demand. Robotics may become the next focus of competition among large-model companies, and it is beginning to pull in a new wave of investment in advanced training compute. Our channel checks indicate that one LLM frontier lab is driving a robot-data procurement programme exceeding $1b, while other frontier labs are expanding their robotics teams. As new data and R&D resources flow into training pipelines, models for robotics and the physical world are likely to make visible progress over the coming year, and competition may intensify markedly.

Robot post-training can consume more GPU resources than text-only post-training at the same parameter count. According to our channel checks, at equal parameter count a robot post-training job that ingests video, joint and action signals consumes roughly three to four times the GPU resources of a text-only post-training job. Each sample carries far more input tokens than a text sample, increasing computation, alongside additional overhead from video decoding, stream synchronisation and data loading. The robot models covered by our channel checks are mainly in the 7–13B-parameter range, and a single training run is far smaller than a frontier LLM pre-training run. The incremental compute demand from robotics therefore comes mainly from the combination of data processing, simulation, evaluation and repeated iteration, together with more model companies entering training at the same time. As data investment grows and iteration accelerates, these workloads both raise aggregate compute demand and tilt procurement towards next-generation platforms with higher throughput and inference efficiency. This demand curve adds little in the near term, but we expect it to grow as more companies enter.

Figure’s compute agreement illustrates the trend. On 3 September 2026, Nscale announced a multi-year agreement with Figure: an initial compute commitment of $3.5b, with the intention to expand beyond $6b, covering up to 100,000 NVIDIA GPUs on the Vera Rubin platform, with first deployment planned for the second half of 2027; the agreement covers model training, simulation validation and on-robot deployment. Competition in robot models has thus begun to translate into large commitments to advanced compute, covering a combined workload of training, data processing and inference.

Astra shows progress as robotics R&D investment grows

General-purpose models have begun to enter robotic systems through visual understanding, spatial reasoning and task planning. Astra’s results suggest developers can build on these capabilities, then add data, training and systems engineering for real-world tasks.

RoboCurve’s pick-and-place experiment offers an intuitive illustration. On the task of placing a block into a bowl, Claude Fable 5, Fable 5.1 and GPT-6 Astra each ran twenty trials, succeeding once, eight times and nineteen times respectively; Astra averaged 2.5 minutes per full task at an estimated API cost of $0.94. The result shows that, given a suitable robot interface, a general-purpose model can already complete some tasks that require real action output.

Figure 1 | Success rate and API cost in the pick-and-place task. Across twenty trials each, Astra succeeded nineteen times at an estimated cost of about $0.94 per task; the chart reflects performance under this evaluation setting. Source: RoboCurve.

The experiment shows the potential for general-purpose models to take part in robotic tasks, but there is still a gap to stable operation in industrial settings. For model companies, an existing capability base combined with clear room for improvement is a reason to keep investing in data, training and systems R&D.

Model capability and investment in robotics R&D are advancing together. OpenAI’s robotics job postings cover data collection, evaluation and lab build-out; Anthropic has published research on robot control. Our checks suggest Nvidia, Tesla and others are buying human egocentric video.

Channel checks indicate that one LLM frontier lab is driving a robot-data procurement programme exceeding $1b, and that other frontier labs are expanding their robotics teams. Priced at market rates for processed data (see below), a programme of that size corresponds to about 5–6.7 million hours at $1b and $150–200 per hour; channel sources expect fulfilment to span more than one year. Taken together with the build-out of collection systems, robotics research activity and Figure’s compute agreement, robotics R&D is attracting more resources, and investment is extending from data and people into training infrastructure.

Frontier labs are entering robotics in different ways. According to a channel source’s estimate, non-text data makes up roughly 35–40% of the training data for that lab’s flagship multimodal model, with pure egocentric video estimated at no more than 5–10%; that share is expected to rise as the lab applies the model to more robotic settings. For this lab, physical-world data is best understood as training resources accumulated ahead of future embodied-AI applications. Another frontier lab is also spending heavily on robotics data, but for a separate physical-AI model; channel sources observe that most robotics companies likewise use standalone models rather than training physical and non-physical data into one model. The coexistence of the two routes means robotics-related training demand shows up both in frontier labs’ multimodal budgets and in a large number of independent, mid-sized training jobs.

According to a June report by 36Kr, ByteDance’s 2026 budget for world-model training data runs to tens of millions of yuan, covering vision-language-action (VLA), long-video and 3D data; according to a supplier quoted in that report, ByteDance’s spending in this area is roughly three to four times that of other vendors. An August report added that ByteDance has established a top-level AI Data and Safety department, and that the Seedance data-evaluation team exceeds a thousand people. Seedance provides the capability base for world-model development: Seedance 2.5 supports 30-second audio-video generation in a single pass, multi-round extension, and stronger motion reference and camera control. Bloomberg reported in September that ByteDance is developing a real-time spatial video model on top of Seedance, aimed at interactive virtual worlds, and is allocating compute to support it. Video-model companies could therefore become important participants in robotics and physical-world model development.

Over the coming year, we expect robot models to improve and competition to intensify. Newly procured data will enter R&D in batches as training and testing continue. Better results could attract more capital and teams. If more leading labs in China and abroad enter, robotics could become a wider focus of competition across the frontier-model industry.

Data expansion supports repeated training and iteration