What a Robot-Hour Actually Costs: AgiBot’s 2,976 Hours, Leju’s 308,000-Yuan Instrument, and the Utilization Nobody Discloses
China’s robot-data factories may be selling below cost. AgiBot’s own disclosures put the labor in one trajectory-hour anywhere from 265 to 2,244 yuan, and the going rate is 500 to 1,000.
The Construction Closes
Four issues of this series have been assembling one number. What does it cost to manufacture one hour of robot trajectory data in China?
The equipment term came from Leju’s ChiNext filing: 577 Kuavo humanoids sold in 2025 at an average recognized price of 308,100 yuan, and 44.94 percent of that product line’s revenue came from data collection buyers. The labor term came from the wage ladder: 21 to 26 yuan per shift-hour of collector time, under a market where the resulting data changes hands at 500 to 1,000 yuan per hour. The compute term came from the card-hour market and the treasury voucher: 1.3 to 2.3 yuan per domestic card-hour after rebate, the cheapest of the three by a wide margin and the one export controls were supposed to make dear.
Three terms, all bounded. What was missing was the denominator. Every one of those costs is a cost per day or per unit, and converting them into a cost per usable trajectory-hour requires knowing how many trajectory-hours a robot and its operator actually produce in a day. That number is the hinge of the entire embodied-data investment case, and no filing in China states it.
It can now be bounded, and the bound is wide enough to change the conclusion. Using two disclosures from the same company, the operator of the largest such facility in the country by its own account, the labor cost of one usable trajectory-hour lands somewhere between 265 and 2,244 yuan. The 500-to-1,000-yuan selling band sits inside that range. Add the equipment amortization and most of the range sits above the band.
This piece is not the discovery of a hidden loss. It is the discovery that the industry’s own numbers cannot tell you whether the business is profitable at the gross level, and that the gap between its best case and its average case is a factor of eight, hiding in a variable nobody reports.
One correction to this series belongs at the top rather than buried. The labor piece bounded the labor term and said explicitly that yield was an unobservable multiplier it would not invent. That was a scoping decision and it was stated each time. This piece closes it. The same piece also said the equipment term amortizes to a small figure per hour at any plausible utilization. That was a conditional claim, the condition is now measurable, and at the utilization implied by the operator’s own cumulative disclosure the condition does not hold. The equipment term is not small. It is the largest of the three.
The Unit the Industry Reports In
Before any of this can be computed, the industry’s numbers have to be converted into a unit they are not published in.
Chinese embodied-data operators report output in trajectories, or clips. AgiBot’s data factory produces tens of thousands of clips a day per its own promotional material. Pasini’s Tianjin facility projects close to 200 million clips a year. Demand, meanwhile, is denominated in hours. The founder of the physical-AI data platform Mifeng, who is also the executive running AgiBot’s collection operation, estimates that reaching a GPT-3.5-like general capability in embodied models requires data on the order of 100 million hours, against a global effective supply he puts in the hundreds of thousands of hours, a gap of two to three orders of magnitude. That is an Estimated figure from an operator with a commercial interest in the scarcity he describes, and it is used here for its unit, not its magnitude.
Production is counted in clips. Demand is counted in hours. The conversion rate between them is published almost nowhere.
It is published in one place, and that place is a primary technical document. The AgiBot World Colosseo paper on arXiv states the dataset’s size in both units: 1,001,552 trajectories with a total duration of 2,976.4 hours. That is 10.7 seconds per trajectory, and roughly 336 trajectories to the hour. The number is corroborated by the only other disclosure this series has found that gives both units, the Jiangsu exchange listing examined earlier, at 25,000 clips of about ten seconds each. Two operators, two document types, the same answer.
Apply the conversion and the industry’s headline volumes deflate by a factor of three hundred. AgiBot World, described in its own paper as the largest trajectory dataset released to date, is 2,976 hours. Pasini’s projected 200 million clips a year, converted at AgiBot’s rate and flagged as a cross-source extrapolation because Pasini does not publish clip durations, is on the order of 590,000 hours a year. Against an operator’s own estimate of the requirement, the largest open dataset in the field is three parts in a hundred thousand.
A clip is not a small hour. It is ten seconds. The industry reports in clips because clips is where the numbers look like industrial output, and hours is where they look like a research project.
What One Robot Actually Produces
Now the denominator, from three disclosures by one company.
The staffing ratio first, because it decides everything downstream. Standing in the facility in May, AgiBot’s embodied business president told visiting press that the site runs 200 machines and that each machine is staffed by at least one collector, with some tasks adding a second person to reset the scene. Not one operator supervising a bank of robots. One operator per robot, sometimes two. A training ground in Shandong reported in January runs 33 collectors against 31 robots across 28 stations, a ratio of 1.06. Tesla’s collection seat is described the same way, one robot plus one motion-capture rig plus one operator. Three facilities on two continents converge, because teleoperation is a human driving a machine in real time and there is no version of that where one human drives five.
The yield mechanism is documented too, and it is stricter than this series assumed. At the Shandong site, collectors work about 7.5 actual hours and must hit a daily quota of effective collection time, where effective is defined by the output passing standard. A take that runs thirty seconds and conforms counts thirty seconds. A take that fails is redone and the time is not counted at all. Collectors there repeat a single action sequence more than a thousand times in one scenario.
Now the output rates, and they do not agree with each other.
The station rate. A reporter visiting AgiBot’s facility in June 2025 watched a drinks-shop station collect about 200 clips a day, with the item positions changed every take and the cup and bag styles rotated every ten. At 10.7 seconds a clip, that station produced about 36 minutes of trajectory data in a day.
The facility rate. The same executive told the same reporter that the facility, running since September 2024, had accumulated over one million clips. Across roughly nine months that averages about 3,700 clips a day, which is 11 hours of trajectory data a day across the whole building. A separate visit in February 2025, when the site ran 100 robots, reported the facility completing just over a thousand clips a day, which is under three hours.
Put the two rates side by side and the discrepancy is the finding. The station rate multiplied by a hundred robots would be 20,000 clips a day. The facility’s own reported output is between 1,000 and 3,700. The working station is running five to twenty times faster than the average robot in the same building.
That difference is utilization, and it is the number the entire sector declines to publish. It is scenario rebuilds, equipment failures, staffing gaps, robots down for maintenance, stations between tasks, and the simple fact that a facility with a hundred robots does not have a hundred robots collecting on any given day. Every projection of a data factory’s annual output that this publication has seen is built by multiplying a station rate by a station count. The operator’s own cumulative disclosure says that multiplication overstates by most of an order of magnitude.
That completes the free layer: the conversion rate established from the primary document, the staffing ratio confirmed by the operator, and the utilization gap measured against the operator’s own two numbers. The paid layer assembles the three cost terms against that denominator and prices the robot-hour.
Assembling the Robot-Hour
The construction runs at two utilizations, because the operator published two, and this piece will not pick one for them.
At the station rate, 36 minutes of trajectory data per robot-day, one usable trajectory-hour consumes about 1.7 robot-days. With one collector per robot on a 7.5 to 9.5 hour shift, that is 13 to 16 collector-hours, and at 21 to 26 yuan per shift-hour the labor term is 265 to 416 yuan.
At the facility rate, 6.6 minutes per robot-day, one usable trajectory-hour consumes about 9.1 robot-days. That is 68 to 86 collector-hours, and the labor term is 1,431 to 2,244 yuan.
The equipment term moves with the same denominator and moves harder, because a robot depreciates by the calendar whether or not it collects. At Leju’s filed 308,100 yuan and a three-year life, the instrument costs 281 yuan a day. Spread over 36 minutes of output, that is 473 yuan per usable trajectory-hour. Spread over 6.6 minutes, it is 2,555. A five-year life, generous for a machine whose recorded data does not transfer across embodiments, gives 284 and 1,533. The three-year figures are used below and the five-year figures are the sensitivity.
The compute term remains what the previous piece found, tens of yuan per usable hour at plausible intensities, and it is now visibly the rounding error in the construction rather than its center.
Add them. At the station rate, one usable trajectory-hour costs roughly 740 to 890 yuan before overhead, annotation, premises, and quality inspection. At the facility rate, it costs roughly 3,990 to 4,800 yuan. The market pays 500 to 1,000.
The conclusion follows without needing to choose between the two rates, which is what makes it usable. At its best observed station utilization, a Chinese data factory produces a trajectory-hour for slightly less than the top of the price band and more than the bottom of it, before any cost that is not labor and the robot itself. At the utilization its own cumulative output implies, it produces one for four to nine times the price band. There is no version of these numbers in which the gross margin is comfortable, and the most defensible version, resting on a cumulative figure rather than a single good day at a single station, says the activity loses money at the going rate.
This inverts the reading this series published two issues ago. That piece described the gap between a 26-yuan wage and a 500-yuan price as a window rent being closed from beneath by wages and from above by supply. The rent was measured per input hour, which was the honest measurement available at the time and was labeled as resting on an unobserved yield. Measured per output hour, at the utilization the operator has since disclosed, the rent may never have existed. What looked like a spread was a conversion rate that had not been applied.
Who Is Actually Making Money
If collecting the data does not clear its cost, the economics of everything this series has traced have to be re-read, and they resolve cleanly.
The money in China’s embodied-data economy is in selling the instrument, not in operating it. Leju’s own filing is the demonstration. Its flagship humanoid sold 577 units in 2025 at 308,100 yuan each, revenue on that line grew roughly twelvefold, and the single largest application category, at 44.94 percent, was data collection, sold into buyers named in the filing as municipal industrial development companies and a state research institute. Leju is not exposed to the price of a trajectory-hour. It is exposed to the budget cycles of the entities that buy the machines that produce trajectory-hours, and this series has already shown that those budgets carry the shape of a fiscal year rather than a demand curve.
The buyers are the ones holding the utilization risk, and most of them are public money. That is the capital judgment this construction produces. A state-funded data collection center underwritten on a station-rate projection has an asset whose output, at the facility rates the sector’s most advanced operator has disclosed, is worth a fraction of what the business case assumed. The gap is not a margin compression. It is a factor of five to twenty on the volume side, before the price question is even reached.
Three of the sector’s IPOs route 41 to 51 percent of their combined raise toward model development, and the previous piece established what that money must buy: not card-hours, which are cheap and discounted, but trajectory-hours, which are expensive to produce at realistic utilization and cannot be bought at scale because the supply this piece just measured is a few thousand hours at the frontier. The DeepMind comparison makes the physical scale concrete. Thirteen robots ran for seventeen months to produce roughly 130,000 trajectories, which at this conversion is about 390 hours. The bottleneck is not capital and it is not silicon. It is that a trajectory-hour requires a human hour, in real time, at a ratio of one to one, and no amount of capital compresses a ratio of one to one.
The position this implies is not a short on the sector. It is a preference along the chain. Exposure to the instrument, sold at a filed price into budget-backed buyers, is exposure to a disclosed number. Exposure to the operation of a collection center is exposure to a utilization rate that its own industry does not publish and that the one operator who has published enough to compute it appears to be running at a fraction of its own station-level claims.
The Disclosure That Settles It
Three documents would resolve the denominator, in the order this publication expects them.
First, Leju’s inquiry response at the Shenzhen exchange, still unpublished. The exchange has every reason to force disaggregation of the data-center contracts behind 44.94 percent of the flagship line, and any decomposition that separates hardware from data-service performance obligations reveals whether the buyers are contracting for machines or for hours. If they are contracting for hours, the contract prices those hours, and the first filed trajectory-hour price in China arrives inside a robot company’s inquiry response rather than at a data exchange.
Second, a procurement award from any of the state-funded collection centers that prices data collection per hour or per trajectory. The same document class this series has named twice would settle both the price and, if it specifies volume commitments against a facility size, the utilization.
Third, an operator publishing hours alongside clips. AgiBot has done it once, in a research paper, and that single act of dual-unit disclosure is what made this entire construction possible. Any facility that reports annual output in hours rather than clips is either confident in its utilization or has been made to state it.
Whichever arrives first, the test is the same. This piece says the cost of a Chinese robot-hour is set by a human hour at one-to-one and an instrument depreciating on a calendar, that the resulting cost straddles and probably exceeds the price the data sells for, and that the margin in the sector therefore sits with whoever sells the machine rather than whoever runs it. The three terms are assembled and the denominator is bounded. The first disclosure that narrows it will say which end of a factor-of-eight range this industry actually operates at, and that single number decides whether China’s data factories are an industry or an expenditure.
Inside China’s Machine is research, not investment advice. The trajectory-to-hour conversion is Confirmed from the AgiBot World Colosseo paper on arXiv, which states 1,001,552 trajectories and 2,976.4 hours; note that the project’s repository and successive paper versions give slightly different trajectory counts, and the paper’s figure is used throughout. The staffing ratio, the station rate, the facility’s cumulative output, and the promotional daily rate are all Reported from Chinese press coverage of site visits and interviews with AgiBot’s embodied business president, secondary and not independently verified, and they conflict with one another by design of this argument rather than by resolution of it. The inference that the open dataset approximates the facility’s total collection to that date is this publication’s, not the company’s, and if the internal store is materially larger the facility rate rises and the labor and equipment terms fall proportionally. Shandong training-ground staffing, shift length, and the effective-time definition are Reported from a January 2026 provincial media account. Leju’s unit price, volume, and 44.94 percent application share are Confirmed from its ChiNext prospectus as carried in this series. Wage figures, the 500 to 1,000 yuan selling band, and the compute term are carried from earlier pieces, where their verification status is stated. Depreciation lives are this publication’s assumptions and are run at three and five years. Demand-side hour estimates are Estimated and sourced to an operator with an interest in the scarcity described. Current as of July 24, 2026.