Humanoid robots in factories: for when?
In our humanoid robotics market deck, you will find everything you need to understand the market
SUMMARY
Humanoid robots in factories: for when? They are already entering factories today, but broad factory-scale adoption is more likely a 2030s market than a 2026 or 2027 reality.
The strongest signal is that factory humanoids have moved from stage demos to measured industrial work. Figure’s BMW deployment, Agility’s GXO tote-moving operation, Apptronik’s Mercedes and Jabil pilots, and Sanctuary AI’s Magna partnership all point to the same thing: the category is becoming operational, not just theatrical.
The first adoption wave is not about replacing a full factory worker. It is about automating narrow, repetitive physical gaps that sit between existing automation systems, especially tote movement, parts loading, line-side delivery, kitting, inspection, and machine-adjacent handling.
ROI is beginning to appear only where the task is boring enough to measure. The important shift is that buyers can now compare humanoids against wage cost, injury risk, AMRs, conveyors, custom cells, overtime, turnover, and missed output.
The biggest technical issue is no longer whether humanoids can walk. The harder threshold is whether they can work across shifts with low intervention, predictable cycle times, acceptable safety behavior, and service costs that do not destroy the payback.
Factories are a better early market than homes because they are more structured and more economically urgent. Parts, routes, lighting, bins, workstations, and safety zones can be standardized, while labor shortages and output constraints make automation easier to justify.
Dexterity will decide how deep humanoids go into production lines. Basic manipulation gets robots onto the floor, but tight-tolerance assembly, cable routing, fastening, connector insertion, tool use, and flexible-material handling are still much harder.
Safety is manageable but not generic. Industrial robot safety frameworks already exist, but humanoids are mobile, tall, whole-body systems, so each workflow still needs validation around workers, forklifts, conveyors, tools, and unexpected human movement.
The humanoid form factor is useful because brownfield factories are already designed around human bodies. But walking through a human workspace is only the first integration step; the robot still has to connect to manufacturing systems, work orders, maintenance logs, safety records, quality systems, and supervisor dashboards.
Cost will remain use-case-specific because humanoids compete with strong alternatives. They win only where flexibility, human-compatible movement, and brownfield integration beat arms, cobots, AMRs, conveyors, gantries, fixtures, and custom cells.
The market forecasts are bullish, but their spread tells us the market is still uncertain. Goldman Sachs, Morgan Stanley, IFR data, Tesla’s capacity plans, and China’s industrial-robot ecosystem all suggest serious institutional momentum, but the proof still has to come from fleets that work reliably at many sites.
All things considered, humanoid robots are a 2024–2027 pilot reality, a 2027–2030 early commercial category, and probably a 2030s scale market. The real inflection comes when plant managers can buy and maintain them like standard industrial equipment rather than treat them as experimental robotics projects.
This market map, featured in our humanoid robotics market deck, highlights top companies and startups in the humanoid robotics market
What is missing today to get humanoid robots in every factory?
Humanoid robots are already entering factories, but they are not yet a normal layer of industrial automation.
Today, we have passed the “cool demo” phase. Figure has worked inside BMW’s Spartanburg plant. Agility’s Digit has moved totes in a live GXO logistics operation. Apptronik has pilots with Mercedes-Benz and Jabil. Sanctuary AI has a manufacturing partnership with Magna. Tesla is preparing Optimus manufacturing capacity. NVIDIA is building the software and compute stack around “physical AI.”
Now the question is when they become boring enough to buy, deploy, maintain, and scale like industrial equipment.
And here is what is missing for now.
| What is missing | Why it matters | Difficulty | Where we are now |
|---|---|---|---|
| Repeatable factory ROI | Industrial buyers need payback, uptime, throughput, and labor savings | Very hard | First measurable pilots exist |
| Reliable industrial autonomy | Robots must handle variation without constant engineering support | Very hard | Improving, but still task-limited |
| Dexterity for real manufacturing tasks | Kitting, loading, tool use, cable routing, and assembly require precise manipulation | Very hard | Narrow manipulation works; broad dexterity is early |
| Safety validation | Humanoids must operate near workers, forklifts, conveyors, tools, and machines | Hard | Standards are maturing, deployment evidence is young |
| Line integration | Factories cannot rebuild every workflow around a new robot | Hard | Humanoid form helps, software integration remains hard |
| Uptime and maintenance | A robot that needs constant expert support is not industrial equipment | Very hard | Stronger pilots, limited fleet proof |
| Cost versus alternatives | Humanoids compete with arms, cobots, AMRs, conveyors, and custom cells | Hard | Costs are falling, but ROI is use-case-specific |
| Manufacturing scale | Factories need hundreds or thousands of units, not a few prototypes | Hard | Big capacity plans, limited proven humanoid output |
| Workforce acceptance | Workers need robots to remove strain, not create fear or chaos | Medium-hard | Best framing is dull, dirty, repetitive work |
| Fleet software | Factories need monitoring, logs, integration, updates, safety records, and task libraries | Hard | Robotics stacks are maturing quickly |
| Task selection discipline | Chasing too many jobs too early can kill adoption | Medium | Best deployments focus on narrow workflows |
| Procurement confidence | Manufacturers buy validated systems, not narratives | Hard | BMW/GXO-style proof is changing the conversation |
Where are we on real humanoid robot deployment in factories?
Humanoid robots in factories are now real, but still concentrated in narrow, carefully selected workflows.
The most concrete signal is Figure at BMW. Figure 02 ran 10-hour shifts Monday through Friday, logged more than 1,250 runtime hours, loaded more than 90,000 sheet-metal parts, and contributed to the production of more than 30,000 BMW X3 vehicles. That matters because it gives us industrial units of proof: hours, parts, shifts, and production contribution.
GXO and Agility give the logistics version of the same story. Digit moved more than 100,000 totes in live commercial deployment after GXO signed a multi-year Robots-as-a-Service agreement with Agility. This is useful because warehouse operators do not care whether a robot looks impressive. They care whether it moves goods repeatedly without breaking flow.
Apptronik with Mercedes-Benz, Apptronik with Jabil, and Sanctuary AI with Magna show that automotive and electronics-adjacent manufacturers are testing humanoids across more than one vendor. One isolated pilot could be marketing. However, several industrial buyers testing similar systems suggests a category is forming.
As this chart shows, and as featured in our humanoid robotics market deck, search interest in where to buy robots has been rising steadily
Where are we on ROI for humanoid robots in factories?
Humanoid robot ROI in factories is beginning to show up, but only where the task is narrow, repetitive, and easy to measure.
The early ROI pattern is clear. Robots are being asked to load parts, move totes, deliver kits, inspect components, or bridge gaps between existing automation islands. Factory ROI starts with one workflow, not with a full job description.
The BMW signal is strong because it connects a humanoid to actual production volume. More than 90,000 loaded parts over 1,250+ hours means the robot was doing repeated work under operational pressure. So, humanoids can now be evaluated using normal factory metrics.
GXO’s tote-moving milestone points the same way. A tote workflow can be compared against wage cost, injury risk, turnover, AMRs, conveyors, and manual handling. That makes the buying decision more concrete than a general-purpose robot narrative.
All things considered, humanoid factory ROI will arrive first in workflows that are physically annoying, frequent, and trapped between existing automation systems. The first scalable value pool is not magic general labor but boring work that happens thousands of times.
If you want more recent data on this point, please see our latest humanoid robotics market report.
Where are we on labor pressure for factory humanoids?
Labor pressure makes humanoid robots more urgent in factories than in homes.
The US manufacturing signal is unusually clear. NAM and Deloitte estimate that manufacturers may need to fill up to 3.8 million jobs between 2024 and 2033, with 1.9 million potentially unfilled if workforce challenges are not addressed. Deloitte also points to strong manufacturing investment and nearly 13 million US manufacturing employees as of early 2024, which means the shortage sits inside a large, active industrial base.
That is important because factories buy automation differently from consumers. A household can delay buying a robot because chores are annoying but tolerable. A factory with open shifts, overtime, missed output, or ergonomic injury risk has a more immediate reason to test automation.
The pressure is also not only American. Aging workforces in Japan, Korea, Germany, China, and parts of Europe create similar automation incentives. Reshoring and industrial policy add another layer: countries want more domestic manufacturing, but they do not always have enough workers willing to do repetitive physical jobs.
So it looks like labor shortage will not guarantee humanoid adoption, but it will keep pilots funded.
This chart, featured in our humanoid robotics market deck, illustrates yearly venture capital funding for humanoid robotics startups
Where are we on autonomy for humanoid robots in factories?
Factory autonomy for humanoid robots is advancing fast, but it is still bounded autonomy rather than general worker intelligence.
Factories are easier than homes because the world can be structured. Parts are known. Routes can be mapped. Lighting can be improved. Bins can be standardized. Safety zones can be marked. A task can be decomposed into steps, and exceptions can be escalated to a human operator.
The AI stack is also improving. NVIDIA’s Isaac GR00T includes open robot foundation models, simulation frameworks, data pipelines, middleware, and Jetson Thor for real-time robot inference and control. Figure’s Helix points toward vision-language-action control for humanoid manipulation. RT-X showed that cross-robot learning across 22 robots, 21 institutions, 527 skills, and more than 160,000 tasks can improve transfer. Physical Intelligence’s π0 is another signal that robotics is moving toward generalist policies rather than hand-coded behavior for every task.
The interpretation is that robot learning is becoming more scalable. A factory humanoid will increasingly be taught through data, simulation, demonstrations, and fleet learning rather than months of bespoke programming.
But factories still punish small failures. A shifted bin, a warped part, a blocked aisle, a misread label, or a person crossing unexpectedly can break the task.
So factory humanoid autonomy is likely to scale as “bounded generality”: flexible within a defined workflow, monitored at first, and gradually trusted with more exceptions.
If you want more recent data on this point, please see our latest humanoid robotics market report.
Where are we on dexterity for manufacturing tasks?
Humanoid robot dexterity in factories is good enough for simple handling, but not yet enough for the highest-value manufacturing work.
The current deployments tell us what is achievable now. Tote moving, part loading, kitting support, line-side delivery, and simple inspection are the first serious use cases. These tasks require mobility and manipulation, but they avoid the hardest part of manufacturing: tight-tolerance assembly.
The harder jobs come later. Fastening, cable routing, connector insertion, tool changing, flexible material handling, rework, and complex quality inspection require force control, alignment, tactile feedback, and consistent hand performance. That is closer to the real economic prize because those tasks sit deeper inside production lines.
As seen above, Figure’s BMW work is useful here because it shows the current edge of real deployment. Loading sheet-metal parts into a welding fixture is valuable, but it is still a constrained manipulation task. Apptronik’s Mercedes use cases around delivering assembly kits and inspecting components also sit in the same early zone: useful, adjacent to the line, and safer than asking the robot to perform complex assembly.
At the end of the day, dexterity will decide how deep humanoids go into factories. Basic manipulation gets them onto the floor. High-reliability manipulation gets them into the line.
This chart, featured in our humanoid robotics market deck, shows how Agility Robotics is capturing share in humanoid robotics
Where are we on safety for humanoid robots in factories?
Humanoid robot safety in factories is more mature than home safety, but it still has to be proven workflow by workflow.
Factories have an advantage: industrial robot safety already has a framework. ISO 10218:2025 is the first major overhaul of the industrial robot safety standard since 2011, and it tightens expectations around design, integration, validation, maintenance, and collaborative applications. ISO material also shows that collaborative robot guidance from ISO/TS 15066 has been incorporated into the updated series.
That matters because humanoids will not enter factories as completely unknown machines. Buyers already understand risk assessments, safety-rated stops, speed and separation monitoring, protective devices, training, restricted zones, and emergency procedures.
The challenge is that humanoids are mobile, tall, and whole-body systems. A robotic arm can be placed in a cell. A humanoid can walk into human space, carry objects, turn, stumble, reach, and interact with multiple pieces of equipment. The safety question is therefore about the entire body, not just the hand or arm.
All things considered, safety will slow adoption but not block it.
Where are we on integration into existing factories?
Humanoid robots have a strong integration argument because factories are already built for human bodies.
This is the main reason the form factor matters. A humanoid can use stairs, doors, carts, shelves, bins, tools, and workstations designed around people. In brownfield factories, that can be more attractive than rebuilding the layout around fixed automation.
But the body shape only solves the physical-entry problem. The robot still has to connect to the factory’s operating system: manufacturing execution systems, warehouse systems, line schedules, safety systems, maintenance logs, quality records, and supervisor dashboards.
This is where pilots often become harder than they look. A robot can perform a task in isolation and still fail operationally if it cannot report exceptions, receive work orders, sync with line timing, charge at the right moment, or hand off cleanly to a human worker.
This chart, featured in our humanoid robotics market deck, illustrates yearly funding for humanoid robotics startups
Where are we on uptime and maintenance for factory humanoids?
Uptime is probably the biggest gap between a working humanoid robot and a purchasable factory product.
Factories need equipment that runs predictably. A robot that performs well when engineers are watching can still be commercially weak if it needs constant resets, expert support, expensive repairs, or frequent software intervention. Industrial buyers care about mean time between failures, service time, spare parts, intervention rate, and whether a technician can fix the system without calling the robotics company every time.
The measured pilots are encouraging because they have started to produce uptime-style evidence. Figure’s 1,250+ runtime hours at BMW, Agility’s 100,000+ tote milestone at GXO, and multi-year RaaS agreements all move the market toward operational metrics. That is the right direction.
But fleet maturity is a different bar. Running a robot in one site is not the same as running hundreds across multiple plants with different shifts, workers, layouts, and maintenance teams. Software updates also become risky in industrial settings because one model change can affect validated behavior.
Currently, the industry has proof that humanoids can work. It still needs proof that they can be maintained like industrial assets. That is why the next signals to watch are repeat orders, multi-site rollouts, intervention rates, and service costs.
If you want more recent data on this point, please see our latest humanoid robotics market report.
Where are we on cost versus existing factory automation?
Humanoid robots in factories must beat the best alternative, not just beat human labor.
This is where the market can be overhyped. Factories already have industrial arms, cobots, AMRs, conveyors, gantries, fixtures, automated storage, machine vision, and custom cells. For many narrow tasks, those tools are cheaper, faster, safer, and more reliable than a humanoid.
Humanoids become interesting where existing automation struggles: brownfield sites, variable workflows, labor gaps between automated islands, tasks that require moving through human-designed spaces, and operations where custom automation is too expensive for the volume.
The cost curve is moving in the right direction. Unitree’s G1 brought humanoid hardware into a much lower price band around $16,000 for a research-oriented platform. Tesla’s Optimus manufacturing plans point to a much more aggressive future scale strategy. RaaS models, like GXO’s agreement with Agility, reduce adoption friction because the buyer can pay for work rather than buy a complex machine outright.
This chart, featured in our humanoid robotics market deck, compares the main business model options for humanoid robot manufacturers
Where are we on manufacturing scale for humanoid robots?
Humanoid robot manufacturing scale is being planned aggressively, but proven output is still small.
Tesla is the biggest signal here. Its Q1 2026 update says preparations for the first large-scale Optimus factory begin in Q2, with a Fremont line designed for 1 million robots per year and a Texas line designed for 10 million robots per year in long-term annual capacity. That is design capacity, not current production, but it changes the ambition level of the category.
The broader robotics industry also gives useful context. IFR counted 542,000 industrial robot installations in 2024 and 4.664 million industrial robots operating worldwide. Annual installations have topped 500,000 for four straight years. This means factories already know how to absorb robots at large scale, even if humanoids are a new form factor.
China matters here too. IFR says China represented 54% of global industrial robot installations in 2024, and Chinese suppliers sold more than foreign suppliers in their domestic market for the first time. That suggests humanoid hardware cost-down may come not only from US AI companies but also from Chinese manufacturing ecosystems.
So, currently, the scale story is plausible but not proven. The first company that ships thousands of reliable humanoids, supports them in the field, and improves them through fleet data will separate the market from the hype.
Where are we on the first factory tasks humanoids will actually do?
The first factory tasks for humanoid robots are boring, repetitive, and close to material handling.
The most likely early tasks are tote moving, parts loading, line-side delivery, kitting support, machine tending, simple inspection, fixture placement, and moving items between human-designed stations. These tasks are attractive because they are frequent, physically tiring, and easier to measure than broad “worker replacement.”
This pattern is already visible. BMW/Figure is about loading parts. GXO/Agility is about moving totes. Mercedes/Apptronik is about bringing assembly kits to workers and inspecting components. Jabil/Apptronik is about simple repetitive work such as inspection, sorting, line-side delivery, and fixture placement.
That tells us the first wave is not trying to automate the whole factory worker. It is trying to automate the awkward physical gaps that sit between existing systems. A conveyor can move something along a line. A robotic arm can handle a precise cell. An AMR can transport a cart. A humanoid is interesting when the task sits between those tools.
So it looks like the first scaled factory humanoids will be mobile helpers, not universal assemblers. That is less dramatic, but much more likely to work.
This chart, featured in our humanoid robotics market deck, shows the revenue mix across customer segments in the humanoid robotics market
Where are we on worker acceptance in factories?
Worker acceptance for factory humanoids will depend on whether robots remove strain or feel like a blunt replacement plan.
The early use cases help because many of them are ergonomically bad jobs: repetitive lifting, walking, tote handling, parts loading, and material movement. If a humanoid reduces injury risk, overtime pressure, and unfilled shifts, workers may see it as support rather than threat.
The labor-shortage context matters here. When manufacturers face unfilled roles, robots can be framed as capacity expansion. That is a very different adoption story from replacing stable, desirable jobs. The more companies deploy humanoids in jobs people do not want or cannot staff, the easier the internal politics become.
Still, factories need honest communication. If the deployment is framed as “robots are coming for everyone,” resistance will rise. If it is framed as “robots take repetitive physical work while humans supervise, maintain, troubleshoot, and improve the system,” the adoption path becomes more credible.
At the end of the day, acceptance will be earned on the floor. Workers will judge whether the robot makes the shift easier or creates another fragile system they have to babysit.
If you want more recent data on this point, please see our latest humanoid robotics market report.
Where are we on factory software and fleet management?
Factory humanoid robots will scale only if the software layer becomes industrial-grade.
A manufacturer does not only need a walking robot. It also needs task assignment, fleet monitoring, uptime analytics, safety logs, maintenance alerts, remote diagnostics, software version control, system integration, and clear escalation when something goes wrong.
This is why NVIDIA’s robotics stack matters. GR00T, Isaac simulation, Cosmos-style synthetic data, and Jetson Thor point toward a world where humanoid developers can train, deploy, and run models more systematically. It does not solve factory deployment by itself, but it gives the ecosystem a stronger base layer.
The harder part is operational software.
A plant manager needs to know which robot is blocked, why a task failed, whether a safety stop occurred, how many interventions happened per shift, and whether yesterday’s software update changed behavior. That is industrial software, not demo software.
This chart, featured in our humanoid robotics market deck, shows how factory humanoid robot technology has evolved over time
Where are we on market forecasts for factory humanoids?
Humanoid robot forecasts are bullish, but the range of numbers shows the market is still uncertain.
Goldman Sachs raised its humanoid robot market forecast to $38 billion by 2035, up more than sixfold from its earlier $6 billion estimate. Morgan Stanley has modeled 8 million working humanoid robots in the US by 2040, with a $357 billion wage impact, and 63 million by 2050 in a much larger long-term scenario.
Those numbers should not be read as precise predictions. The spread is too wide because the market depends on several curves moving together: autonomy, cost, uptime, safety, labor shortages, manufacturing scale, and buyer trust.
But the forecasts are still useful because they show a change in seriousness. Humanoid robots are now being modeled as labor infrastructure, not just as robotics curiosities. That affects capital, supplier commitments, and customer willingness to run pilots.
So we should treat the forecasts as directional evidence. The exact 2035 number may be wrong, but the category has clearly moved into institutional planning.
Where are we on the hardest blocker for humanoid robots in factories?
Well, the hardest blocker for humanoid robots in factories is repeatable production reliability.
Walking is no longer the magical threshold. Humanoids can walk, lift, carry, pick, place, and follow instructions in controlled contexts. The harder question is whether they can do useful work across shifts with low intervention, predictable cycle times, acceptable safety behavior, and manageable service cost.
A 95% success rate can still be weak in a factory if the remaining 5% stops a line, damages a part, creates a safety review, or requires an engineer. A slower robot can still be useful if it fills an unstaffed shift. A faster robot can still fail commercially if maintenance costs destroy the payback.
That is why the next evidence should be boring: intervention rate, uptime, cycle time, payback period, safety incidents, service frequency, number of sites, and repeat orders. Demo complexity matters less than operational repeatability.
So the real threshold is when humanoids become boring enough to procure. Once plant managers can treat them like standard equipment rather than experimental projects, the market changes.
If you want more recent data on this point, please see our latest humanoid robotics market report.
In our humanoid robotics market deck, we identify pain points entrepreneurs should prioritize
So when will factories actually use humanoid robots at scale?
Factories are already using humanoid robots in pilots and first commercial deployments. The real scale-up starts later.
We think that humanoid robots in factories are a 2024–2027 pilot reality, a 2027–2030 early commercial category, and probably a 2030s scale market.
Factories come before homes because the environment is more structured, the tasks are easier to measure, labor pressure is stronger, operators are trained, and buyers already understand robotics. The industrial robot market is already installing more than half a million units a year, so the buyer base exists.
But humanoids still have to earn their place against strong alternatives. Industrial arms, cobots, AMRs, conveyors, and custom automation already solve many factory problems. Humanoids win only where flexibility, human-compatible movement, and brownfield integration create a better economic answer.
Here is a summary table with the timelines.
| Timeline | What likely happens | Probability |
|---|---|---|
| 2024–2026 | First real factory and warehouse pilots: BMW/Figure, GXO/Agility, Mercedes/Apptronik, Magna/Sanctuary-style deployments | Already happening |
| 2026–2027 | More paid pilots, narrow workflows, stronger autonomy stacks, and first repeat deployments by large industrial buyers | High |
| 2027–2030 | Early commercial scale in logistics, automotive, electronics, and material handling | Medium-high |
| 2030–2033 | Humanoids become a recognized automation category for brownfield factories and labor-constrained workflows | Medium |
| 2033–2035 | Multi-site fleet deployments become more common if uptime and service economics improve | Medium |
| 2035–2040 | Broader industrial adoption becomes plausible across ordinary factories, especially in high-wage and aging-workforce markets | Medium-high |
| Before 2027 | Fully general humanoid factory workers at scale | Very low |
| Before 2030 | Hundreds of thousands of humanoids operating across factories globally | Low |
| After 2040 | Humanoids become standard industrial labor infrastructure if cost and reliability curves continue | Medium-high |
OUR METHODOLOGY
This analysis tests when humanoid robots can become normal factory equipment based on the evidence available today. We compare the headline question with real deployment signals, measurable pilot data, labor-market pressure, autonomy progress, safety standards, manufacturing-scale plans, installed-base robotics data, and market forecasts.
The question of when humanoid robots become normal factory equipment is still unclear if treated as one broad yes-or-no issue. So we broke it into the dimensions that actually decide industrial adoption: ROI, autonomy, dexterity, safety, integration, uptime, cost, manufacturing scale, workforce acceptance, software, procurement confidence, and timing.
For each dimension, we looked at recent signals rather than relying on intuition, hype, or general robotics narratives. We prioritized evidence that showed operational reality more directly: runtime hours, parts handled, live deployments, paid pilots, named industrial customers, labor-market pressure, safety standards, manufacturing-capacity plans, robotics installed-base data, and institutional forecasts.
We then aggregated those signals point by point to judge where the evidence is already strong, where it is still early, and where the remaining blockers matter most. That is why the conclusion focuses less on whether humanoids can produce impressive demos and more on whether they can become reliable, maintainable, economically useful industrial equipment.
The final answer comes from comparing the evidence across dimensions, not from any single company, deployment, forecast, or technical breakthrough.
Key sources used for this analysis include: Figure on its BMW deployment metrics, Assembly Magazine on the BMW/Figure deployment, GXO on its multi-year Agility Robotics agreement, Agility Robotics on the GXO agreement, Apptronik on its Mercedes-Benz commercial agreement, PRNewswire on the Apptronik/Mercedes-Benz pilot, Sanctuary AI on its Magna partnership, NVIDIA Isaac GR00T, Figure Helix, the RT-X / Open X-Embodiment paper, Physical Intelligence π0, the π0 technical paper, ISO 10218:2025 material, The Manufacturing Institute and Deloitte on US manufacturing labor needs, Deloitte on manufacturing workforce context, IFR World Robotics 2025 industrial robot data, Tesla’s Q1 2026 update on Optimus capacity plans, Unitree G1, Goldman Sachs on the humanoid robot market forecast, and Morgan Stanley on the humanoid robot market outlook.
This chart, featured in our humanoid robotics market deck, shows the regional revenue mix across Europe, Asia, North America, Africa, and South America in the humanoid robotics market
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