Caterpillar says AI-assisted data labeling is helping move field data into simulation or training in hours rather than the months or weeks its process once took. The claim comes from Caterpillar vice president Brandon Hootman in a SiliconANGLE report on a Fully Connected 2026 interview; it is an attributed account, not an independently measured performance result.
Why physical AI needs a faster learning loop
Physical AI refers here to AI systems intended to interpret the real world and support or control machines. Such systems need data that captures more than what a camera sees: they must also account for machine movement, controls, and performance. Training and validating those systems depends on connecting real-world observations to simulation or model training quickly enough to use what the machines encounter.
That is harder on construction sites than in many mines. Hootman said Caterpillar has experience with autonomous equipment at mine sites, where conditions can change but do not change frequently. Construction jobsites are more variable, so a model intended to work across them has to cope with a wider range of changing environments.
“Once that mine site gets instantiated, it does change, but it doesn’t change frequently.”
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The quotation is attributed to Hootman by SiliconANGLE. His point is that construction autonomy faces a more demanding adaptation problem than a comparatively stable, established mine deployment.
What data has to be labeled
The reported workflow brings together several kinds of information from machines: LiDAR, camera data, multi-second machine-control data, and performance data. These streams describe different aspects of an event. Sensors capture surroundings, while control and performance information helps show what the machine was doing and how it responded.
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The scale is substantial. Hootman said Caterpillar’s digital ecosystem contains about 18 petabytes of federated data gathered from machines, dealers, and customers. He also described one machine as generating terabytes of this combined data in a day. Those figures are Hootman’s reported estimates, not independently audited measurements.
“But you take [Light Detection and Ranging] data, camera data, that multi-second control data and performance data coming back in, you’re talking about terabytes of data within a given day for just one machine.”
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Hootman characterized the 18 petabytes as only a small part of the data needed to train physical AI for a construction-site environment. The figures help explain why manual review alone is difficult to scale, but the reporting does not specify data formats, labeling accuracy, or how much of the collected information is suitable for a particular training task.
How the reported labeling workflow works
- Collect field data. Caterpillar draws on machine data spanning LiDAR, cameras, controls, and performance, with information coming through its broader digital ecosystem.
- Use AI models to annotate and label it. The reported process applies AI models to incoming field data so that it can be organized and prepared for later use.
- Move usable data into simulation or training. The goal is to shorten the wait between observing something in the field and incorporating it into a simulation or training environment.
- Use the feedback in the workday. Hootman said that the process can make feedback available within the same workday, allowing teams to act on it sooner.
In Hootman’s account, a process that previously took “months to maybe weeks” is now getting down to hours. He also said the interval between the relevant field activity and use in simulation or training can fit within a workday. These are reported timelines for the described workflow, not a guaranteed turnaround for every dataset or project.
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“Now you’re taking things that were months to maybe weeks,”
“And now you’re getting it down to hours and your feedback loop between that happening and being able to use it in your simulation environment, or your training environment, happens within the given workday.”
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Both statements are attributed to Hootman in SiliconANGLE’s interview coverage. Faster labeling can bring field information into the engineering cycle sooner; the account does not establish that it automatically improves model quality, safety, or machine productivity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What CoreWeave contributes—and what is established
CoreWeave’s broader industrial-AI context is its Physical AI Field Engineering service, announced on Sept. 10, 2026. CoreWeave says it works with customer teams using data they already own, such as test results, simulation output, production sensors, and telemetry. Its description spans strategy, simulation infrastructure, data preparation, model building, validation, and deployment.
CoreWeave also describes example physical-AI workloads including world-model training, vision-language-action fine-tuning, sensor simulation, and closed-loop evaluation. These are the company’s descriptions of its capabilities, not independently verified results for the Caterpillar work.
The service announcement provides context for CoreWeave’s industrial AI offering, but it does not establish the precise scope, responsibilities, or contractual terms of its work with Caterpillar. SiliconANGLE’s interview report is the basis for the specific account of Caterpillar’s labeling and feedback timelines. The report does not provide a matched vendor comparison, independently measured baseline, or safety and outcome evaluation.
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What the report does—and does not—show
- It shows the operational problem: construction sites change often, and physical-AI development must handle sensor, control, and performance data at large scale.
- It describes a workflow: AI-assisted annotation and labeling is intended to help teams get field data into simulation or training sooner.
- It reports a timeline: Hootman says a process once measured in months or weeks is now measured in hours, with feedback usable within the workday.
- It does not establish broad outcomes: the coverage supplies no independent evidence that this particular work has improved safety, productivity, or model performance.
Caterpillar separately announced a collaboration with FieldAI on physical AI, autonomy, and robotics on Sept. 2, 2026. That announcement references digital twins and NVIDIA technology, but it is a separate collaboration and should not be treated as evidence about the Caterpillar–CoreWeave workflow.
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