Asimov is paying people to record themselves cooking, cleaning, organizing, working at desks, running errands and doing other ordinary tasks. The company wants the resulting data for something much larger than surveillance footage or consumer AI: training humanoid robots.
The San Francisco startup, founded in 2026 and part of Y Combinator's Winter 2026 batch, is building what it describes as a supply chain for real-world human movement data. Its premise is straightforward. Humanoid robots are supposed to operate in the environments humans already inhabit, but much of the data used to train robots comes from controlled settings, teleoperation or narrow task demonstrations. (ycombinator.com)
So Asimov wants to collect what laboratories cannot easily reproduce: thousands of people moving through thousands of different homes, workplaces and everyday environments.
That idea has put the company squarely inside one of robotics' increasingly valuable infrastructure businesses. Investors are not simply betting on which company will build the best humanoid robot. They are also betting on the companies that can provide the data those robots need to learn.
The robot-data problem is more interesting than another humanoid robot company
A large language model can be trained on enormous quantities of text. Vision models can learn from massive collections of images and video. Robotics is harder because understanding the physical world requires more than recognizing what an object looks like.
A robot needs to understand movement, objects, spatial relationships and what happens when an action is performed in a real environment.
That sounds obvious until the task becomes something mundane.
A person opening a refrigerator in a clean demonstration kitchen is relatively easy to describe. Opening one in a cramped apartment while holding something in the other hand is different. So is doing it in a restaurant kitchen, an office pantry or a home where objects are not placed neatly where a training engineer expects them to be.
Asimov's argument is that this variation matters.
Its website says the company collects human activity data from real environments across multiple continents and turns it into annotated datasets for robotics. It contrasts that approach with robot training data generated through teleoperation and controlled collection environments. (tryasimov.ai)
The company describes its data as a way for robots to learn how humans move, interact with objects and perform everyday tasks. Its job listings specifically mention egocentric video of activities including cooking, cleaning, desk work, errands, gardening and repairs. (ycombinator.com)
The larger bet is that humanoid robotics may eventually face a problem familiar to artificial intelligence companies: the architecture exists, the hardware is improving, but useful training data becomes the limiting resource.
Asimov is trying to turn everyday life into robotics training data
The company's collection model is unusual because it treats ordinary human activity as a potential source of valuable industrial data.
Asimov says it works with households and businesses and allows contributors to record their daily routines. Y Combinator says the company is collecting real-world human movement data from households and businesses and delivering it to robotics labs. (ycombinator.com)
According to Asimov's YC launch materials, the company has built collection hardware, annotation tooling and a vertically integrated data pipeline. It also says individuals can earn money by recording activities they already perform. (ycombinator.com)
Its data-collection job listing offers a more concrete picture of what that means. Contributors may record cooking, cleaning, organizing, office tasks, errands, gardening and repairs using the company's mobile app. The listing says no technical experience is required and that the company does not collect audio; it also says faces and personally identifiable information are automatically blurred. (ycombinator.com)
That is a very different proposition from building a humanoid robot.
Asimov does not need to manufacture thousands of robots before it can begin collecting useful information. Its challenge is operational: recruiting contributors, getting consistent recordings, handling equipment and logistics, protecting privacy, annotating data and producing datasets that robotics companies actually consider useful.
One Asimov operations job listing explicitly describes coordinating contractors across multiple countries and time zones, managing equipment shipments and monitoring contributor activity. (ycombinator.com)
In other words, the startup's real product may be less like a robotics laboratory and more like a specialized data operation for the physical world.
The company's biggest claims should still be treated carefully
Asimov says it can generate thousands of hours of human movement data each day and that leading robotics labs are already customers. Y Combinator's company page repeats those claims, and YC's promotional material says Asimov has a global network of contributors and is providing data to major robotics companies. (ycombinator.com)
Those claims are significant. They are also difficult to independently verify from public information because the company has not publicly identified the robotics labs buying its data.
That distinction matters.
A startup saying it has customers is not the same as publicly documented commercial relationships. As of the available public information, Asimov's customer names, contract sizes and revenue have not been broadly disclosed.
The same caution applies to contributor scale. Asimov and YC have described a global network and thousands of hours of data collection, but independent public evidence about the exact size of that operation is limited.
That does not mean the claims are false. It means they should be attributed to the company and its accelerator rather than presented as independently established facts.
For an early-stage startup, that is not unusual. Much of the work happens before the market sees the customer list.
But Asimov's eventual value will depend heavily on whether its data is genuinely useful enough for robotics companies to keep buying it.
Investors are paying attention to the infrastructure around robots
Asimov is part of a broader shift in robotics investing.
The obvious companies in the humanoid boom are the ones building the machines: companies developing hands, actuators, cameras, batteries and AI systems capable of controlling physical bodies.
But another layer of the industry is emerging around the machines themselves.
Who provides the training data?
Who collects demonstrations of difficult tasks?
Who annotates physical interactions?
Who captures data from environments too diverse and unpredictable for a single laboratory?
That infrastructure layer is attracting attention because the industry is increasingly treating data as a major bottleneck.
Bouken Capital, which published an investment thesis explaining why it invested in Asimov, argued that the scarcity of diverse real-world human movement data is a central constraint on humanoid robotics. That is the investor's interpretation rather than an independently established market fact, but it captures the thesis behind the company. (boukencapital.substack.com)
Asimov also has the backing that comes with being a Y Combinator company. Dealroom lists Y Combinator as an investor and reports a $125,000 January 2026 seed investment, although early-stage funding databases can contain incomplete or delayed information and Asimov has not publicly announced a broader funding round in the sources reviewed here. (app.dealroom.co)
That is why describing the company as heavily funded would go beyond the public evidence. The more defensible observation is that Asimov has become a visible YC W26 robotics startup because it is addressing a problem investors increasingly consider important.
The real question is whether watching humans teaches robots enough
There is a technical complication in Asimov's thesis.
Watching a human perform a task is not automatically equivalent to teaching a robot how to perform it.
Humans have bodies shaped very differently from humanoid robots. We have different hands, different ranges of motion and an extraordinary amount of instinctive physical intelligence. A robot cannot simply replay a video of someone washing dishes and suddenly understand the correct forces, grip positions and movement trajectories.
The value of Asimov's data therefore depends on how robotics companies use it.
It could help train perception systems. It could help models understand task sequences. It could provide examples of how humans interact with messy environments. Combined with teleoperation, simulation and robot-specific demonstrations, it may help models generalize better across real-world situations.
But Asimov still has to prove that its particular kind of data produces a measurable improvement.
That is the commercial test ahead.
Robotics companies do not need the world's largest collection of human activity simply because it exists. They need datasets that improve robot performance.
Asimov's founders are building the supply side, not the robot
Y Combinator identifies Asimov's founders as Anshul Verma and Lyem Ningthou. The company's YC launch materials say Verma brings data infrastructure experience and Ningthou previously built data pipelines for the United States Air Force. (ycombinator.com)
The founders' public story is important mainly because Asimov is a data company operating inside a robotics market. The company is not attempting to outbuild every humanoid manufacturer at once. It is trying to become useful to whoever wins.
That strategy has precedent elsewhere in technology.
During earlier AI booms, many companies chased the model itself. Others built infrastructure around the models: chips, cloud computing, labeling operations and data pipelines.
Some of those infrastructure businesses became enormously valuable because every model builder needed them.
Asimov is betting humanoid robotics will develop in a similar way.
If dozens of companies eventually build capable humanoid machines, they may all need more examples of humans operating in the physical world.
That creates an attractive possibility for a data supplier.
It also creates intense competition. Large AI data companies, robotics labs and new specialized startups are all pursuing versions of the same opportunity.
The winner may not be the company with the most recordings. It may be the one that can consistently produce the data a robot actually learns from.
And that is the point investors are really paying attention to.
Humanoid robots have become spectacular demonstrations of hardware. The harder question is what they should learn before they are trusted to work in the real world.
Asimov is betting that part of the answer is already happening around us, every time a human opens a door, picks up a glass, cleans a kitchen or reaches for something without thinking twice.
