The maintenance technician of 2030 will still need skills in mechanics, electricity, automation and electrical engineering. However, their working environment could be radically different.

When faced with a breakdown, technicians will be able to instantly access an asset’s maintenance history, search for similar incidents, consult manufacturers’ documentation, analyse sensor data and obtain a diagnostic procedure tailored to the situation.

This development builds on a transformation that has been underway for several years with the Industrial Internet of Things, Computerised Maintenance Management Systems (CMMS), predictive maintenance and augmented reality. Robot Magazine previously examined the emergence of the augmented technician and the new skills associated with this profession.

The arrival of generative AI and, more recently, agentic AI is opening a new chapter: the system no longer simply displays information. It can gradually assist with diagnostics and handle some of the tasks required to carry out an intervention.

The augmented technician is becoming an industrial reality

This development addresses a very practical problem: industrial equipment is becoming increasingly sophisticated, while the skills required to maintain it are difficult to find.

In its Smart Manufacturing and Operations 2025 study, Deloitte surveyed 600 executives from large industrial companies headquartered or operating in the United States. Of those surveyed, 35% consider adapting employees to the “Factory of the Future” to be a major concern. In addition, 48% report moderate to significant difficulties in recruiting for production and operations management roles.

The issue is therefore not purely technological. It directly concerns the organisation of work and the transfer of skills.

The principle behind the augmented technician is precisely to use technology to make more knowledge available to professionals at the exact moment they need it.

The technician of 2030 may no longer simply be
the person who repairs the machine. They will become
the person who orchestrates the data, AI and robots around them.

When technical documentation becomes directly actionable

Consider a common situation: a production line comes to a halt.

The technician may have to consult several software applications, search for the manufacturer’s documentation, review previous interventions, analyse machine alarms and ask more experienced colleagues for advice.

A significant share of the intervention time is therefore spent searching for information before the equipment can even be repaired.

New interfaces are changing this process.

The technician can ask the system:

“Has this fault occurred on this machine before?”

“What caused the last three similar shutdowns?”

“Which manufacturer procedure corresponds to this error code?”

“Is this part available in stock?”

Access to technical knowledge therefore becomes much more direct.

In the semiconductor industry, McKinsey estimates that applying large language models to existing capabilities could improve labour productivity by up to 35%, particularly by providing technicians more quickly with the information they need for maintenance operations.

Real-world example: up to 90% less unplanned downtime

Some projects are already producing measurable results.

McKinsey describes the case of a consumer goods company that developed a copilot for production operators.

The system brings together various sources of technical knowledge, including breakdown histories, equipment documentation, failure mode analyses and root cause investigation methods.

When a piece of equipment stops, the operator is guided through a procedure that progressively eliminates the different possible causes.

According to McKinsey, the system reduced certain types of unplanned downtime by up to 90%.

Maintenance labour costs fell by approximately one-third, while technicians gained 40% additional capacity, partly because they were called on less frequently to resolve the simplest incidents.

This result illustrates one of the potential effects of the augmented technician: it is not simply about helping specialists work faster.

Operators themselves can become capable of resolving more first-level incidents, allowing experienced technicians to focus on complex problems.

AI does not eliminate the technician’s expertise:
it allows them to access and apply the knowledge
accumulated across the entire factory more quickly.

AI is addressing the final mile of industrial digitalisation

Factories already use a wide range of digital systems: ERP, MES, CMMS, supervisory control systems, quality management platforms, Industrial IoT, inventory management solutions and document databases.

The problem is that this information often remains scattered across multiple applications.

For technicians, finding a piece of information may require navigating between different screens and databases.

This could be described as the final application mile of industry.

The data already exists. The challenge now is to make it immediately actionable on the factory floor.

An interface capable of querying several systems could allow technicians to express their needs directly, without necessarily knowing which application contains the relevant information.

This simplification could become one of the main benefits offered by the new generation of industrial software.

From an assistant that advises to an agent that acts

A second development is beginning to emerge alongside this transformation: agentic AI.

The distinction is significant.

An assistant primarily provides an answer or recommendation. An agent can be authorised to perform a sequence of operations in order to achieve a defined objective.

After detecting an anomaly, for example, an agent could search the machine’s history, identify similar incidents, retrieve a technical procedure, check whether a part is available, prepare a maintenance work order in the CMMS and then generate the intervention report.

Critical operations would, of course, remain subject to the company’s safety rules and approval procedures.

However, some of the administrative and computer-based tasks currently performed by technicians could gradually be automated.

In its 2026 outlook for the industrial sector, Deloitte also notes that agentic AI, with its ability to reason, plan and autonomously perform certain actions, is expected to become a new component of smart manufacturing.

Productivity gains are already visible in smart factories

This transformation extends far beyond maintenance.

The industrial companies surveyed in Deloitte’s 2025 study reported that, following the implementation of their smart manufacturing initiatives, they achieved an average:

10% to 20% improvement in production; 7% to 20% improvement in workforce productivity; and 10% to 15% increase in released production capacity.

In addition, 46% of respondents rank process automation among their top two investment priorities for the next two years, compared with 37% for physical automation.

These figures reveal an important shift.

Factory transformation is no longer based solely on installing additional robots. It also involves automating the processes surrounding human work.

What if the technician’s next tool were a humanoid robot?

Another stage could gradually emerge: the integration of these digital systems with physical robotics.

Mobile robots already perform certain transport, monitoring and inspection operations in industrial environments.

Humanoid robots aim to go further by operating in environments originally designed for humans.

In the future, some inspection, handling, measurement and intervention tasks in hazardous areas could be entrusted to these machines.

Technicians could then supervise several tools: diagnostic software, sensors, mobile robots and potentially humanoid robots.

The division of roles would become relatively clear: sensors observe, software analyses, robots perform certain physical actions, and technicians supervise and make decisions.

Today, this organisation appears more credible than the vision of a fully autonomous factory from which humans have disappeared.

Will the technician of 2030 become an orchestra conductor?

The central question is therefore probably not whether artificial intelligence will replace the technician.

It is how many machines, robots and systems a technician will be able to supervise in the future with the help of these new tools.

Traditional technical skills will remain essential. However, they will be complemented by the ability to use data, work with diagnostic support systems, verify their recommendations and supervise increasingly autonomous equipment.

For industrial companies, the challenge will therefore involve training just as much as technological investment.

In Deloitte’s study, 92% of the executives surveyed consider smart manufacturing to be one of the main drivers of industrial competitiveness over the next three years. However, the study also shows that human capital and maintenance are among the areas where the gap between current maturity levels and desired maturity levels remains significant.

The factory of 2030 will probably be neither one of humans versus robots nor one of AI versus technicians.

It will be defined by a new distribution of work between humans, software and machines.

Within this organisation, the augmented technician could become less of a simple maintenance operator and more of an orchestra conductor for industrial maintenance.

Cet article Factory of 2030: How AI Is Transforming the Role of Maintenance Technicians est apparu en premier sur Robot Magazine.

Humanoids can walk, sort components, manipulate objects and execute increasingly complex industrial tasks. Videos of robots dancing, running or folding laundry continue to attract millions of views.

But industrial buyers are beginning to ask a much less glamorous question: Where is the ROI?

As the sector moves from prototypes to production environments, the benchmark is changing. A successful demonstration is no longer enough. Humanoid manufacturers now need to prove uptime, throughput, safety, integration costs and, ultimately, that somebody is willing to pay for the work their machines perform.

The evidence available in September 2026 suggests that this transition has started. But only a small number of companies can currently point to meaningful commercial deployments.

Agility Robotics: perhaps the clearest commercial case

If the criterion is simply “Is a customer paying to put humanoids to work?”, Agility Robotics is one of the strongest cases today.

Its Digit robot has been operating commercially with GXO Logistics since 2024 under a multi-year Robots-as-a-Service agreement. Digit performs a deliberately unglamorous logistics workflow: moving totes from autonomous robots and placing them onto conveyors.

That is precisely what makes the deployment interesting. It is repetitive work occurring inside an existing warehouse process rather than a carefully staged robotics demonstration.

Agility subsequently reported that Digit had moved more than 100,000 totes at GXO’s Flowery Branch facility.

The company has since expanded its commercial footprint. Toyota Motor Manufacturing Canada signed a commercial agreement in February 2026 following a pilot, while Agility says its robots are operating or being introduced with industrial groups including GXO, Toyota and Schaeffler.

More importantly, Agility disclosed in 2026 that it had secured more than $300 million in multi-year contracted orders for Digit v5.

That does not mean $300 million has already been recognized as revenue. Orders, deployments and recognized revenue are three very different metrics. But it is one of the clearest signs yet that industrial customers are prepared to commit significant budgets to humanoid automation.

In financial disclosures surrounding its planned public listing, Agility presented an illustrative model in which a Digit v5 could cost approximately $200,000, plus around $20,000 for deployment and approximately $36,000 per year for software and maintenance. Using a five-year operating life, that implies a total customer cost around $400,000.

The fundamental industrial question therefore becomes measurable:

Can one humanoid create more than roughly $80,000 per year of economic value after integration, supervision and operational constraints?

If it can, humanoid robotics starts becoming an automation investment rather than a research experiment.

The winner of humanoid robotics may not be
the company with the most impressive robot.
It may be the company that can prove its robot
creates more economic value than it costs to deploy.

Figure: impressive production data, but different economics

Figure has produced perhaps the most important independently confirmed industrial dataset in the sector.

During its Figure 02 deployment at BMW’s Spartanburg plant, the robot worked on an active automotive production line handling sheet metal components. BMW says the deployment accumulated approximately 1,250 operating hours, more than 90,000 components handled, around 1.2 million robot steps and more than 30,000 BMW X3 vehicles supported.

This matters because industrial robotics ultimately lives or dies on repetition. Performing a manipulation once proves capability. Performing it tens of thousands of times begins to demonstrate reliability.

BMW has since moved to Figure 03 and a more complex logistics sequencing application, where robots must select components from containers and organize them for just-in-sequence production.

Figure therefore appears to have crossed an important technical threshold: its humanoids are no longer confined to the laboratory.

But an important distinction remains: operational success is not the same thing as demonstrated profitability.

Figure is privately held and does not disclose detailed humanoid revenue, gross margins, deployment costs or customer economics. We know considerably more about what its robots can accomplish than about how much money those deployments generate. That distinction will become increasingly important.

China is selling humanoids, but who is buying them?

China presents a completely different picture. Chinese manufacturers are rapidly industrializing humanoid production, while competition is pushing hardware prices downward.

UBTECH is particularly interesting because, unlike many private Western humanoid startups, it publishes financial results. For the first half of 2026, UBTECH reported approximately RMB 590 million in revenue from full-size embodied intelligence humanoid robot products and services, with 921 units sold.

That makes UBTECH one of the few companies where significant humanoid-related revenue can actually be observed.

But revenue needs context. A significant portion of Chinese humanoid demand has been driven by government procurement, state-backed training centres and subsidies rather than conventional private-sector automation demand.

The distinction matters. Selling a robot is not necessarily evidence that the robot is economically replacing or augmenting human labour in a factory. A machine purchased by a research laboratory or government-backed robotics training centre generates revenue for the manufacturer, but it does not demonstrate industrial ROI.

China may therefore currently be the world’s fastest-growing humanoid hardware market without yet being the world’s most mature humanoid labour market.

Tesla: enormous ambition, limited external evidence

Tesla remains impossible to ignore. Optimus potentially benefits from something many robotics startups lack: factories, manufacturing expertise, computing infrastructure and an enormous internal environment in which robots can be developed and deployed. In theory, Tesla can become both manufacturer and first customer.

But from an industrial procurement perspective, the evidence remains limited. Tesla has demonstrated increasingly sophisticated Optimus capabilities and has discussed internal factory deployment at scale. Yet detailed, independently verified figures covering uptime, task throughput, cost per operation or external customer ROI remain scarce.

The interesting question is therefore no longer whether Optimus can perform useful tasks. It is:

When will Tesla publish the equivalent of BMW’s 90,000-part Figure deployment or GXO’s 100,000-tote Digit deployment?

Until then, Optimus remains one of the industry’s most important technology programmes, but not yet its clearest commercial benchmark.

Moving 100,000 totes may be less
spectacular than a humanoid dancing.
Economically, it is far more interesting.

Apptronik, Boston Dynamics and the rest of the field

The same distinction applies elsewhere. Apptronik has been working with Mercedes-Benz on Apollo humanoid applications. Boston Dynamics and Hyundai are preparing Atlas for industrial environments. Numerous Chinese manufacturers, including Unitree, AgiBot and Fourier Intelligence, are rapidly improving hardware and reducing costs.

These programmes matter. But pilots, partnerships and purchase announcements should not be confused with scaled commercial operations.

For technical buyers, five metrics are becoming much more useful than demo videos:

• Autonomous operating hours
• Intervention rate
• Task throughput
• Total deployment cost
• Cost per completed operation

Add safety certification, maintenance requirements and integration with factory and warehouse management systems, and the real difficulty of humanoid deployment becomes apparent.

The hidden metric: human intervention

Perhaps the most important number in humanoid robotics is one companies rarely advertise: how often does a human need to help the robot?

A humanoid could theoretically work 20 hours per day. But if remote operators frequently intervene, reset failed tasks or manually control difficult manipulations, the economics change dramatically.

This is why autonomy percentage alone can be misleading. A commercially meaningful humanoid needs not only high autonomy but also a sufficiently low intervention frequency and short recovery time.

A robot completing 99% of actions autonomously sounds impressive. But in a workflow containing thousands of actions per shift, the remaining 1% can still generate substantial human supervision.

The industry’s next transparency battle may therefore concern intervention rates rather than walking speed or dexterity.

Why warehouses and factories are winning first

The earliest viable humanoid applications share several characteristics. They occur in relatively structured environments. The workflows repeat frequently. Objects and infrastructure are reasonably predictable. The economic value of automation can be measured.

Labour shortages, ergonomics and undesirable repetitive work also create an existing business problem. This explains why logistics and automotive manufacturing are emerging ahead of household robotics.

The humanoid’s theoretical advantage is not necessarily that it can outperform a specialized industrial robot. Often it cannot. Its advantage is that it may eventually operate inside infrastructure designed for humans without requiring the facility to be rebuilt around automation.

Doors, shelves, carts, workstations and production lines were designed around human geometry. A sufficiently capable general-purpose robot could exploit that installed infrastructure. That is the economic hypothesis behind the humanoid.

The real competitor isn’t another humanoid

There is another uncomfortable question the industry needs to answer. A humanoid robot is not competing only against Figure, Digit, Optimus or Apollo. It competes against an industrial robot arm, an autonomous mobile robot, a conveyor, machine vision, a purpose-built automation cell and, of course, a human worker.

If a $50,000 automation solution can perform the same workflow more reliably than a $200,000 humanoid, there is little economic reason to use a humanoid.

The humanoid only wins when its generality creates value. That could come from performing several tasks with the same hardware, being redeployed without major infrastructure changes, handling environments designed for humans, or learning new workflows through software rather than mechanical reengineering.

This is why the ultimate commercial metric may not be cost per robot. It could be cost per task across the robot’s lifetime.

From cost per robot to cost per productive hour

Industrial buyers may soon stop asking: How much does the humanoid cost?

The better question is: How much does one productive autonomous hour cost?

Imagine a $200,000 robot operating reliably for five years. If it works only a few hours per day, requires frequent supervision and performs one narrow workflow, the economics may be poor. If the same hardware works across multiple shifts, performs several tasks and learns additional workflows through software updates, its economics change dramatically.

The real value is therefore not simply making robots more capable. It is allowing the same physical asset to become more productive over time through software. That is fundamentally different from traditional fixed automation.

So, who is actually making money?

As of September 2026, the answer requires nuance.

Agility Robotics has one of the strongest cases for genuine paid industrial deployment and has disclosed a substantial contracted order book.

Figure has some of the strongest publicly verified production evidence, particularly through BMW, but its underlying revenue and deployment economics remain private.

UBTECH is generating substantial identifiable humanoid-related revenue, although the Chinese market’s dependence on public procurement and training infrastructure complicates comparisons with Western industrial deployments.

Tesla, Apptronik, Boston Dynamics and many emerging competitors remain strategically important but have yet to publish enough standardized commercial data to make direct ROI comparisons possible.

Nobody has yet demonstrated, publicly and at massive scale, that general-purpose humanoids can economically replace conventional automation or human labour across multiple industries.

But something important has changed. Two years ago, the central question was: Can humanoid robots work? In 2026, there is enough evidence to answer yes, under specific conditions.

The next question is much harder: Can they work cheaply enough, reliably enough and autonomously enough to generate sustainable profit?

That is the race that matters now.

The winner of humanoid robotics may not be the company with the robot that runs fastest, looks most human or produces the most impressive demonstration. It may simply be the company that can prove one equation:

Value of work performed > total cost of deploying the robot.

Everything else is still a demo.

Cet article Humanoid Robots: Enough Demos. Who Is Actually Making Money? est apparu en premier sur Robot Magazine.

Robot Magazine is pleased to welcome a special contribution from Dr. Gleb Tsipursky, a leading behavioral scientist and internationally recognized expert on AI adoption. Named the “Office Whisperer” by The New York Times, he examines a critical issue for the next generation of Physical AI: how organizations can learn from near-misses before autonomous machines scale across factories, laboratories and workplaces.

PHYSICAL AI NEEDS A NEAR-MISS LEDGER BEFORE AUTONOMOUS MACHINES SCALE

Anthropic’s August 27 research preview of the Model Hardware Standard moves AI agents another step out of software and into the physical world. The proposed standard lets agents operate programmable equipment including microscopes, liquid handlers, lasers, and robotic arms, coordinate multiple devices, adjust parameters during a workflow, and in some cases recover from hardware errors without human intervention.

That progress changes the safety problem. When an AI assistant drafts a bad paragraph, the failure is usually visible and reversible. When an agent controls physical equipment, the consequences can include damaged materials, lost production time, spoiled experiments, unsafe motion, or a chain of small deviations that eventually becomes a serious incident.

Traditional safety systems already record accidents, breakdowns, and maintenance events. Physical AI needs one additional layer: a near-miss ledger that records moments when an autonomous system was heading toward a bad outcome but a person, safeguard, or other system caught the problem first.

Near-misses are especially valuable for adaptive systems because they reveal where nominal success hides fragile judgment. If the machine completed the task only because a technician intervened, the final “success” metric can erase the most important evidence about the system.

Consider a robotic arm that reaches toward the wrong tray and stops because an operator notices the path. Production continues, so the event may never enter a failure log. Yet the episode contains information about perception, task context, human vigilance, and whether the same error could recur when nobody is watching closely.

A useful near-miss ledger would capture five things.

First, record the intended action. What was the agent trying to do, and what authority had it been given? A vague entry such as “robot error” tells the next operator almost nothing. The record should identify the task, the decision the system made, and the physical action it attempted.

Second, record the operating context. Physical systems behave differently across lighting, temperature, load, equipment state, workspace layout, and other conditions. Context matters because the same model decision can be harmless in one setting and dangerous in another.

Third, record the intervention. Did a person stop the robot? Did a software limit block the command? Did another sensor contradict the agent’s interpretation? The intervention shows which control actually prevented the bad outcome.

Fourth, record the prevented consequence. This does not require pretending to know exactly what would have happened. A short description such as “possible collision with adjacent fixture” or “wrong reagent likely to enter process” is enough to classify the risk without inflating certainty.

Fifth, record the corrective change. The point is not merely to accumulate warnings. Teams should note whether they changed a threshold, revised an instruction, altered the physical setup, added a sensor check, narrowed the agent’s permissions, or determined that no change was necessary after review.

This is a different mindset from treating human intervention as an embarrassing exception. In early physical-AI deployments, intervention is part of the learning system. A worker who notices that the machine is about to make a mistake is producing operational data that developers cannot get from successful runs alone.

That matters as integration gets easier. Anthropic says its Model Hardware Standard can reduce hardware integration that normally takes weeks or months to hours or minutes. Lower integration costs should expand experimentation. They can also increase the number of settings in which an agent encounters unfamiliar combinations of equipment, workflows, and environmental conditions.

Robot Magazine’s recent overview of Spain’s robotics market points in the same direction. Robotics is spreading across automotive production, food and beverage, logistics, healthcare, and service applications, while cobots and AI integration are broadening the range of tasks machines can perform. As robots move beyond tightly controlled, repetitive cells, the number of meaningful edge cases grows.

The near-miss ledger should therefore become a leading indicator, not an archive nobody reads. Managers can review patterns weekly or monthly: Which tasks generate the most interventions? Which safeguards catch problems? Are near-misses falling after corrective changes? Are operators repeatedly compensating for the same weakness? Do some locations or shifts encounter more edge cases than others?

These questions create a better scaling signal than raw uptime. A system can show excellent uptime while depending heavily on invisible human rescue. If those rescues are not counted, leaders may automate more aggressively precisely because people are quietly preventing failures.

The ledger also improves the relationship between frontline workers and robotics teams. Operators often know where a system is brittle before formal metrics show it. Giving them a simple way to record near-misses turns that tacit knowledge into evidence. It also reduces the incentive to hide interventions in order to make a pilot look cleaner than it really is.

The mechanism should stay lightweight. If documenting a near-miss requires a long investigation, workers will skip it. A short structured entry, ideally generated partly from machine logs and confirmed by the person involved, is enough for most cases. Serious patterns can then trigger deeper review.

Physical AI will not become trustworthy because every possible failure is predicted in advance. Dynamic environments make that impossible. Trust will come from building systems that notice weak signals, preserve evidence from interventions, and improve before those signals become injuries, shutdowns, or costly mistakes.

As agents gain the ability to operate more kinds of hardware, organizations will need to learn faster than the machines spread. A near-miss ledger gives them a practical way to do that.

Cet article Physical AI Needs a Near-Miss Ledger Before Autonomous Machines Scale est apparu en premier sur Robot Magazine.

A study published in 2025 examining 303 accidents involving human–robot interactions notably highlights unexpected robot activations and sensor errors. With the emergence of Physical AI, artificial intelligence errors are gradually moving beyond screens and entering the physical world.

For several years, we have grown accustomed to artificial intelligence making mistakes.

A chatbot invents information. An image generator produces an anomaly. A model misinterprets a document.

In most cases, the error remains digital.

With Physical AI, this situation changes radically

When artificial intelligence controls a robotic arm, a mobile robot or, in the future, a humanoid robot, a misinterpretation of its surroundings can trigger a real physical action.

An error of just a few centimetres can have consequences far more serious than a ChatGPT hallucination.

303 accidents analysed

A study published in 2025 in the scientific journal Safety Science analysed 303 accident reports involving interactions between humans and robots.

The researchers identified seven major categories of incidents.

Two phenomena stand out in particular: unexpected robot activations and sensor-related errors.

This is precisely one of the challenges that Physical AI will need to address.

A robot must perceive its environment before it can act.

Cameras, proximity sensors, vision systems, contextual data and artificial intelligence models become its eyes and, to some extent, its decision-making system.

But what happens when that perception is incorrect?

A wrong decision becomes a physical movement

Consider an extremely simple scenario.

A robot equipped with a camera must identify a crate and move it.

Its vision system misinterprets the scene.

The software nevertheless decides that it has identified the correct object.

In a conventional computer system, this error might produce incorrect data.

In a robot, it can potentially produce an incorrect physical movement.

If an operator is standing in its path, the algorithmic error becomes a safety issue.

With Physical AI, an artificial intelligence
error no longer produces only an incorrect
answer. It can produce a physical
movement.

Accidents are already a reality

Data from the United States provide a tangible measure of this risk.

In its report on workplace accidents in the manufacturing industry, the US Occupational Safety and Health Administration identified 550 incidents involving robots.

In 33% of cases, the victims were treated in emergency departments, while 2% of incidents required hospitalisation.

Reported injuries included cuts, bruises and fractures.

Robot maintenance and cleaning also appear to be particularly sensitive situations.

Of course, not all these accidents can be attributed to artificial intelligence.

That distinction is essential.

However, they demonstrate what can happen when humans and machines capable of powerful movements share the same environment.

Physical AI is now adding a new variable: the machine’s decision-making autonomy.

From software bug to physical risk

This is probably one of the major changes that the robotics industry will need to address in the coming years.

Conventional software follows relatively deterministic logic.

New generations of robots will be increasingly capable of interpreting their surroundings, adapting their actions and making certain decisions in real time.

This capability is precisely what makes them valuable.

But it also creates a new problem.

How can we guarantee the safety of a machine when not all of its decisions can be predicted in advance?

The question becomes even more important with humanoid robots.

Unlike industrial robots, which have traditionally operated behind safety barriers, humanoid robots are specifically designed to work in environments created for humans.

• Factories
• Warehouses
• Hospitals
• Shops
• And potentially, in the future, our homes

Robot errors are becoming a new KPI

Until now, manufacturers have mainly measured availability, productivity, breakdowns and accidents.

Physical AI could introduce a new indicator:
the number of incorrect decisions made by a robot before they result in an incident.

A robot operating for 1,000 hours without an accident may appear extremely reliable.

But if an operator had to intervene 30 times to prevent an incorrect action, the risk assessment becomes very different.

These “near misses” could become an essential metric for assessing the true reliability of autonomous robots.

Accidents tell us what went wrong.
Near misses could show us what may
go wrong tomorrow.

Data remains insufficient to anticipate the risk

The problem is that organisations do not yet have a common language for measuring these situations.

An emergency stop, a trajectory corrected by an operator, an incorrect detection or a collision avoided by only a few centimetres are not always recorded in the same way.

Yet these events could provide the most useful warning signs before an accident occurs.

For example, a robot recording zero accidents over 1,000 operating hours does not present the same level of risk if it required zero, 10 or 30 human interventions to prevent a dangerous action.

The next generation of indicators will therefore probably need to distinguish between:

• Accidents resulting in injury
• Property damage
• Emergency stops
• Perception errors
• Unexpected activations
• Preventive human interventions
• Near misses with no consequences

Without this level of detail, companies risk underestimating the true danger posed by systems that appear to be functioning correctly.

The next battle for Physical AI may be about trust

Robot performance is advancing rapidly.

But if millions of autonomous machines are to work alongside humans every day, their intelligence will need to be more than just efficient.

It will need to be predictable, controllable and capable of failing without putting people at risk.

Artificial intelligence introduced us to a new category of error: hallucinations.

Physical AI now confronts us with a much more tangible question: what happens when a hallucination becomes a physical movement?

Anticipating the risk before the first major accident

The question is no longer whether autonomous robots will make mistakes.

They will.

The real question is whether we will be able to detect those errors early enough to prevent them from becoming accidents.

How should near misses be recorded? What thresholds should trigger a shutdown or a system reassessment? How can an AI model be tested in unpredictable physical environments? Who should be held responsible when an autonomous decision causes harm?

These issues already concern manufacturers, robotics integrators, safety managers, insurers, researchers and regulators.

At Robot Magazine, we want to open this discussion with the organisations and experts developing, deploying and regulating Physical AI.

If you work on autonomous robot safety, near-miss detection, embedded AI system certification or the prevention of human–robot risks, please contact us.

Together, we can begin defining the methods, indicators and best practices needed to anticipate this future challenge before it becomes a crisis.

Cet article Robotics: When an AI Error becomes a Physical Risk est apparu en premier sur Robot Magazine.

Humanoid robots, quadruped robots, AMRs, cobots, cleaning robots, inspection robots and service robots are already available and are increasingly being offered internationally.

However, selling a robot in Europe is not simply a matter of shipping it from Shenzhen or Shanghai.

Manufacturers need to present it to customers, integrate it into their environments, provide maintenance, train users and, in some cases, adapt the solution to the requirements of the European market.

This is where a new challenge emerges: finding the right distributors and integrators in Europe.

From Chinese Manufacturer to International Player

China now has an exceptionally dynamic robotics ecosystem.

Companies such as Unitree Robotics, UBTECH, AgiBot, DEEP Robotics, LimX Dynamics and Fourier Intelligence are developing robots whose applications are beginning to extend far beyond the Chinese market.

This trend is particularly visible in humanoid robotics.

For many years, humanoid robots were primarily research projects or technology demonstrators. They are now entering a new phase marked by their first industrial and commercial deployments.

This evolution is forcing manufacturers to reconsider their international presence.

Europe is naturally one of their most strategic markets.

Why Europe Is So Attractive to Robot Manufacturers

Europe has highly developed industries in sectors such as automotive manufacturing, aerospace, logistics, energy, pharmaceuticals, food processing and general manufacturing.

It also has a long-established network of automation and robotics integrators.

For a foreign manufacturer, gaining access to this market can therefore represent a considerable opportunity.

However, Europe is also a fragmented market.

Selling a robot in Germany, France, Italy or Spain often requires a local commercial presence and a strong understanding of the needs of industrial companies in each country.

Certification, safety, maintenance and technical support must also be addressed.

A manufacturer may have an excellent robot and still struggle to generate sales without the right local network.

For Chinese manufacturers, entering Europe is not simply about finding customers. They need to build a network capable of selling, integrating and maintaining their robots locally.

Integrators are becoming an essential part of the equation

This is one of the key differences between selling an electronic product and selling a robot.

A robot generally needs to be integrated into an existing environment.

Consider the example of a quadruped robot designed to inspect an industrial site.

The manufacturer supplies the robotic platform. However, thermal cameras, sensors, supervision software or the customer’s IT systems may also need to be connected.

The robot then needs to be installed, its missions configured and the customer’s teams trained.

The same challenge applies to a humanoid robot in a factory or an AMR in a warehouse.

This is precisely the role of an integrator.

For Chinese manufacturers seeking to accelerate their expansion in Europe, finding integrators capable of understanding their technology and deploying it at customer sites is therefore becoming just as important as finding customers.

Which robots need European partners?

The search for partners involves several categories of robots.

Humanoid robots

This is currently one of the most closely watched segments.

Chinese manufacturers are developing humanoid robots for research, industry, logistics and, increasingly, service applications.

Their deployment will require partners capable of transforming a robotic platform into a genuine business solution.

Quadruped robots

Industrial inspection, energy, security, mining and infrastructure: quadruped robots already have several practical applications.

Once again, however, the robot alone is not enough. Integrating sensors and software represents a significant part of the solution’s final value.

AMRs and logistics robots

Logistics is probably one of the most mature markets for Asian manufacturers.

European warehouses represent a significant opportunity, but projects often require integration with existing warehouse management systems.

Service robots

Hospitality, restaurants, retail, cleaning, healthcare and reception services: many Chinese manufacturers already offer solutions for these sectors.

In these markets, local distributors play an essential role in providing demonstrations, installation and after-sales support.

Chinese manufacturers to watch

Several companies illustrate the international expansion of China’s robotics industry.

Unitree Robotics has become one of the most visible Chinese robotics brands internationally, thanks to its quadruped robots and, more recently, its humanoid robots.

UBTECH Robotics develops humanoid robots for industrial applications and is accelerating its international expansion.

AgiBot is another highly ambitious new Chinese player in the humanoid robotics market.

DEEP Robotics specialises in quadruped robots designed primarily for inspection and challenging industrial environments.

LimX Dynamics develops a range of robotic platforms, including humanoid and legged robots.

Fourier Intelligence, historically positioned in rehabilitation and medical robotics, is also developing its own humanoid platform.

This list is far from exhaustive.

China now has a significant number of manufacturers capable of offering their technologies internationally.

The next battle will therefore probably not be purely technological.

It will also be commercial and geographical.

An opportunity for European integrators

For integrators, this new generation of manufacturers may also represent a significant opportunity.

For several decades, the European industrial robotics market was built around a relatively limited number of major brands.

The arrival of new players is gradually changing the situation.

Integrators can gain access to new platforms, address applications that were previously difficult to automate and, in some cases, secure distribution agreements within their territories.

Some could become the European representatives of brands that are still relatively unknown today.

The technology industry has already experienced this scenario several times.

Companies that establish the right partnerships early enough may benefit from the manufacturer’s future growth.

Humanoid robots, quadrupeds, AMRs
and service robots are opening up new
opportunities for European integrators.

Robot Magazine aims to connect manufacturers and integrators

For several years, Robot Magazine has been monitoring developments in robotics across Europe, Asia and the rest of the world.

We regularly receive information from international manufacturers seeking to present their latest technologies to the European market.

We now want to take this initiative further.

Robot Magazine is gradually building a European network of distributors, integrators and robotics professionals interested in new technologies and manufacturers.

The objective is simple: to facilitate connections between manufacturers seeking to enter the European market and professionals capable of selling, integrating and maintaining their robots locally.

Are you a robotics distributor or integrator in Europe?

Are you looking for new brands to distribute or new robotic platforms to integrate? Would you like to become visible to international manufacturers?

A new global robotics landscape is emerging

For many years, Europe, Japan and the United States dominated much of the global robotics industry.

China has now become impossible to ignore.

However, having excellent technology does not guarantee commercial success.

The next step for many Chinese manufacturers will be to build international networks capable of selling, installing, integrating and maintaining their machines.

In this new competitive landscape, European integrators could become particularly sought-after partners.

The question is therefore no longer simply which Chinese robots will arrive in Europe.

It is who will distribute and integrate them.

Cet article Chinese robots: manufacturers seek distributors and integrators in Europe est apparu en premier sur Robot Magazine.

For decades, robotics remained primarily industrial. Robotic arms performed precise tasks in perfectly controlled environments, generally kept at a distance from humans.

That model is changing rapidly.

Advances in artificial intelligence, computer vision, sensors and gripping systems are now making it possible to imagine far more versatile machines. Mobile robots, cobots and humanoids are beginning to move beyond environments in which every movement had to be programmed in advance.

This is a major shift. If robots become capable of performing a wide variety of tasks, they will no longer represent merely an industrial investment. They will gradually become a new form of workforce.

An Industrial Battle as Much as a Technological One

In the United States, investment in artificial intelligence is now extending into robotics. Considerable sums are being committed to humanoids and “Physical AI.”

China, meanwhile, is advancing with a strongly industrial approach. It possesses an advantage that is difficult to ignore: a supply chain capable of producing motors, batteries, sensors, electronics and mechanical components on a massive scale.

The competition will therefore not simply be about who can showcase the most impressive humanoid in a video.

What matters is control over the entire value chain: components, manufacturing, software, AI models, training data, maintenance and the ability to produce thousands and potentially millions of machines.

This is where Europe must position itself.

Europe Already Holds Many of the Cards

It would be wrong to suggest that Europe is absent from the robotics sector.

It has renowned manufacturers, excellent research laboratories and numerous specialised companies. Europe also benefits from one crucial advantage: a substantial industrial base.

Automotive, aerospace, logistics, energy, healthcare and food production are all sectors that could become real-world testing grounds for the next generation of robots.

The real difficulty lies elsewhere.

Europe knows how to develop remarkable technologies and companies. But when it comes to raising billions, industrialising at great speed and conquering a global market, the same obstacles repeatedly emerge: fragmentation of the European market, access to capital, regulatory complexity and insufficient coordination between countries.

This is precisely what makes the comparison with Airbus so relevant.

An “Airbus of Robotics” Really?

The expression may sound excessive. Obviously, the idea is not to bring every European robotics manufacturer together within a single group.

But the history of Airbus is worth considering.

Faced with an extremely capital-intensive industry dominated by American companies, several European countries chose to pool their industrial expertise, funding and ambitions.

Robotics could justify a similar approach.

Not necessarily to create a single European humanoid, but to secure the technologies the entire industry will require: actuators, motors, sensors, vision systems, embedded electronics, AI models designed for the physical world and control software.

The objective should be relatively straightforward: to ensure that Europe retains the ability to build and control its own robotic systems.

Airbus Was Not Created Because Europe Did Not
Know How to Build Aircraft. It Was Created Because
Europe Needed to Achieve Critical Mass. Robotics May Soon Face the Same Question.

Europe’s Future Champions May Already Exist

Talking about an “Airbus of Robotics” might suggest that a new industrial giant needs to be created from scratch.

That is probably unnecessary.

Europe already has several companies developing some of the essential building blocks of this emerging industry.

In Germany, Agile Robots is one of the companies to watch. Based in Munich and originating from research conducted at the German Aerospace Center (DLR), the company develops solutions combining artificial intelligence, robotics and industrial automation. With its Agile ONE humanoid, it has made its ambition clear: to bring this new generation of machines into industrial environments.

Also in Germany, NEURA Robotics is developing cognitive robots, cobots and humanoids with a similar ambition: creating machines capable of working more naturally alongside humans. Its 4NE1 humanoid illustrates its determination to position Germany among the leading players in Physical AI.

Spain also has a long-established company in the field: Barcelona-based PAL Robotics. For more than 20 years, it has developed humanoid, mobile and service robots for research, healthcare, industry and retail.

And many more companies could be added to this list.

Europe also has specialists in industrial automation, vision systems, sensors, semiconductors, software, mechatronics and embedded systems. Long-established industrial groups could also play a major role in this transformation.

Perhaps this is where the true idea behind an “Airbus of Robotics” lies.

Europe does not necessarily need a single company to manufacture a European robot. Instead, it may need an industrial alliance capable of bringing together its strongest players around a common architecture: hardware, critical components, artificial intelligence, training data, software and manufacturing capacity.

Airbus was not created because Europe did not know how to build aircraft. It was created because Europe needed to achieve critical mass to compete with American champions.

Robotics may soon face the same question.

Will Agile Robots, NEURA Robotics, PAL Robotics and Europe’s other champions remain individual companies operating in their respective markets, or could they one day become the building blocks of a genuine European robotics powerhouse?

When Robots Become Economic Infrastructure

Imagine this market 10 or 15 years from now.

If millions of robots are working every day in factories, warehouses, farms, hotels and healthcare facilities, each robot will represent several thousand hours of potential work every year.

At that scale, the issue becomes economic.

A company that depends entirely on a foreign supplier to automate its production also depends on that supplier for part of its ability to produce.

If entire sectors of the European economy eventually rely on machines, software and artificial intelligence models controlled from abroad, the question of sovereignty will inevitably arise.

We have already had this debate concerning semiconductors, cloud computing and, more recently, artificial intelligence.

It would be unfortunate to wait until that dependency has become firmly established before beginning to think seriously about robotics.

Manufacturing Robots May Not Be Enough

One final issue has received relatively little attention so far: who will control the work carried out by these machines?

As robots become increasingly autonomous, platforms will be needed to assign tasks to them, measure their work, supervise their activities and coordinate entire fleets.

In the future, a company may manage three types of resources: employees, artificial intelligence agents and robots.

Humans will remain essential for certain tasks. For others, an AI agent will be more effective. And whenever an action needs to be performed in the physical world, a robot may take over.

Value could therefore gradually shift towards those capable of orchestrating these different forms of work.

This may be where part of the next technological battle will take place.

We Should Not Wait Ten Years to Ask the Question

Robotic sovereignty obviously does not mean that every screw, motor or sensor must be manufactured on European soil.

It means retaining sufficient expertise, technology and industrial capacity to avoid becoming entirely dependent on decisions made elsewhere.

For now, the market remains open. Standards have not yet been definitively established, humanoids are still searching for a viable business model and many potential applications have yet to be invented.

Europe therefore still has a card to play.

But if robotics follows the path many expect, the question will soon no longer be whether robots will occupy an important place in our economy.

We will need to know who builds them, who controls their intelligence, who owns their data and who decides what they are allowed to do.

After cloud computing, semiconductors and artificial intelligence, robotic sovereignty could become one of Europe’s next major industrial challenges.

Cet article Robotic Sovereignty: Does Europe Need Its Own “Airbus of Robotics”? est apparu en premier sur Robot Magazine.

Getting a massage from a robot? Just a few years ago, the idea would probably have sounded amusing. Yet in the United States and Europe, the first machines are beginning to find their way into spas, hotels and fitness clubs.

And we are no longer talking about the traditional massage chair found in an airport.

The new generation of robots aims to reproduce part of the work performed by a massage therapist: identifying different areas of the body, applying a specific amount of pressure and following precise movements for several dozen minutes.

American company Aescape is probably the most visible example of this trend. Its system combines a massage table with two robotic arms that perform the massage while allowing the user to adjust certain settings, including pressure and the areas they want to focus on.

The company now claims more than 100 locations and is clearly targeting spas, hotels and fitness clubs.

In France, Capsix Robotics is working on a comparable approach with its iYU robot. Here again, the ambition goes beyond a simple technological demonstration. The goal is to offer wellness professionals a new way of delivering their services.

Because behind the rather spectacular image of a massage robot lies a very real economic question.

How Much Does an Hour of Massage Really Cost?

The business model of a spa is relatively simple. To sell more massages, you generally need more massage therapists.

But a therapist obviously cannot work for ten or twelve hours without interruption. Breaks, working hours, holidays, absences and, in some regions, recruitment difficulties all have to be taken into account.

A robot changes that equation.

In the United States, for example, Aescape offers 60-minute sessions for around $89, with lower prices for shorter sessions. The positioning is interesting: the goal is not necessarily to sell a luxury experience, but to make massage more readily available.

The massage robot will probably not
replace the hands of a professional. It could,
however, turn massage into a service available
almost on demand.

For a hotel or fitness club, the question then becomes very practical.

How much does the machine cost? How many sessions can it perform each day? How long does it take to recover the investment?

Those three figures will probably determine a large part of the future of this industry.

A sufficiently well-used robot could allow a facility to open additional time slots, including at times when maintaining a full team of massage therapists would be difficult.

That does not mean massage therapists are about to disappear.

The most credible scenario may be one in which two different types of services coexist.

Human massage would retain everything that gives it value: the therapist’s expertise, the ability to understand the client, adapt the treatment and, quite simply, provide the human dimension of care.

The robot could occupy a different space: short sessions, post-workout recovery, regular massages, or customers primarily looking for availability and convenience.

ROI: The Equation That Could Convince Spas

For a spa operator, the value of a robot will mainly depend on its utilization rate. Take a simple example: a machine capable of providing six one-hour sessions per day, billed at an average of €60, represents potential daily revenue of €360, or more than €9,000 per month over 26 operating days. At eight sessions per day, revenue exceeds €12,000.

Naturally, the cost of financing or leasing the machine, maintenance, electricity, the space it occupies, and the time required for customer reception and cleaning must all be deducted.

But unlike a traditional treatment room, increasing the number of sessions does not require a proportional increase in staff hours. For a spa that is regularly fully booked or struggling to hire enough professionals, this is where the model becomes interesting: the robot does not necessarily replace a massage therapist; it primarily allows the business to sell additional hours of massage.

The true measure of ROI will therefore depend less on the price of the robot itself than on the number of additional sessions it enables the business to sell every month.

An Environment Well Suited to Robotics

Massage also has one particular characteristic that makes it attractive to robotics companies: the environment is highly controlled.

The client is lying down. The robot knows its working area. Movements are relatively slow. Pressure can be measured and limited.

This is very different from the complexity faced by a humanoid robot that needs to move around a house, recognize dozens of objects and interact with an unpredictable environment.

That may be precisely why massage robots could find their market more quickly than some far more ambitious service robotics applications.

And there is no shortage of potential use cases.

Spas naturally come to mind, but so do hotels, fitness centers, sports recovery facilities and even airports.

In a gym, for example, it is easy to imagine a member booking a 20-minute session from their phone after a workout, just as they would book a group fitness class today.

In a hotel, the same equipment could make it possible to offer massages outside normal spa opening hours.

At that point, the robot becomes less of an attraction and more of a service appliance.

The profitability of a massage robot will depend
less on its purchase price than on the number of
additional sessions it allows a business to sell.

How Far Can This Go?

The line becomes more delicate when we move beyond wellness and into the medical field.

Massage and physiotherapy are obviously not the same thing.

Scientific literature on robotic massage does exist, but it remains limited. A systematic review published in 2024 identified only 17 studies that met the researchers’ criteria. Fifteen of them were still considered preliminary trials.

It is therefore far too early to present these machines as a widespread alternative to healthcare professionals.

But the development is worth watching.

Because behind robotic massage lies a much broader question for the robotics industry: how far can we automate a profession built around human touch and physical skill?

For a long time, robots mainly replaced or assisted industrial tasks: welding, assembling, moving and sorting.

They are now beginning to enter environments much closer to our everyday lives.

They clean floors, transport meals in hotels, prepare certain dishes and are now beginning to perform massages.

The success of massage robots will probably not depend on their ability to perfectly reproduce the hands of a professional.

It will depend on something far more pragmatic: does the customer accept the experience, and does the establishment make money by installing the machine?

If the answer to both questions becomes yes, massage could well become one of the first genuine mass markets for service robotics.

Cet article After Factories, Robots are Entering Spas est apparu en premier sur Robot Magazine.

Its name: “Physical AI”, or physical artificial intelligence.

Behind this concept, still relatively unknown to the general public, lies what some investors already consider to be the next major technology cycle after the internet, cloud computing and generative AI.

The idea is simple but potentially revolutionary: enabling artificial intelligence to interact physically with the real world through robots, autonomous machines, smart factories and systems capable of seeing, understanding, moving and acting.

And the amounts being invested are already giving a sense of the scale of the phenomenon.

According to several specialized research firms, the global market for intelligent robotics and Physical AI could exceed $3 trillion by 2040 if humanoid robots, autonomous factories and robotic systems truly become widespread across industry and services.

For investors, the question is no longer whether this market will emerge, but rather which companies will become the future giants of this new economy.

The potential IPO of SpaceX could create a mixed reaction for Tesla shares. In the short term, some investors could shift part of their Tesla positions in order to gain direct exposure to SpaceX, considered one of the most strategic technology assets in the world. But in the medium term, the development of Tesla’s humanoid robot Optimus could, on the contrary, strengthen Tesla’s valuation by gradually repositioning the group as a global leader in Physical AI and robotics, beyond the automotive market alone.

For investors, “Physical AI” could become a market even larger than electric vehicles. According to Goldman Sachs, humanoid robots could represent more than $150 billion by 2035, while the broader intelligent robotics market could exceed $400 billion. Driven by labor shortages, industrial automation and the rise of AI, this sector is already attracting massive amounts of capital, with players such as NVIDIA valued at more than $3 trillion in 2026.

A historic transition: AI moves beyond screens

Since 2023, generative artificial intelligence has deeply transformed technology markets. OpenAI, NVIDIA, Microsoft and Google have seen their valuations soar thanks to the democratization of language models.

But until now, this revolution has remained essentially digital.

AI was producing:

• Text
• ImagesVideos
• Code
• Analysis

Physical AI completely changes the game.

For the first time, artificial intelligence is leaving screens and entering the physical world.

The new systems developed by industrial companies and startups now aim to create machines capable of manipulating objects, moving around, interacting with humans, analyzing their environment and performing physical tasks autonomously.

In other words, AI is gradually becoming a workforce.

And that is precisely what is now attracting the attention of financial markets.

NVIDIA: the silent winner of Physical AI

If there is one company today already benefiting massively from the rise of Physical AI, it is probably NVIDIA. After dominating the generative artificial intelligence market through its GPUs, the group is now accelerating strongly in humanoid robots, autonomous vehicles, smart factories, industrial simulations and computer vision.

Its CEO, Jensen Huang, now openly describes “Physical AI” as the next massive market for artificial intelligence. And the numbers are already staggering: NVIDIA’s market capitalization surpassed $3 trillion in 2026, making the group one of the most powerful companies in the history of technology.

This dominance can be explained by a positioning that has become almost unavoidable. Today, a large share of intelligent robotics players use NVIDIA GPUs, CUDA platforms, Omniverse simulation tools and embedded systems developed by the group. NVIDIA is therefore gradually becoming the central infrastructure of the new global robotics economy, just as the internet and cloud computing were for previous technological revolutions.

Figure AI: the humanoid startup fascinating Silicon Valley

Among the most closely watched companies today is Figure AI.

Still unknown to the general public not long ago, the company is now attracting attention across Silicon Valley.

The reason?
Figure AI is developing humanoid robots capable of performing physical tasks in:

Warehouses, logistics, factories and supply chains.

But what impresses investors most is the financial backing behind the project.

The company is backed by Microsoft, OpenAI, NVIDIA and Jeff Bezos.

According to several American financial media outlets, Figure AI has already reached a valuation of several billion dollars even before a possible IPO.

The humanoid robot sector is therefore beginning to reproduce what the market experienced with generative AI startups between 2023 and 2025.

While the world is talking about ChatGPT,
another, much deeper revolution
may already be beginning.

China is accelerating massively

While Western media focus on American giants, China is moving very quickly in intelligent robotics.

The Chinese government now considers humanoid robots a strategic priority sector.

According to Chinese authorities, the country aims to become one of the global leaders in the sector before 2030.

And several companies are rapidly gaining momentum.

Unitree Robotics is attracting particular attention thanks to its humanoid and quadruped robots, which are offered at far more aggressive costs than their Western competitors.

Other players such as UBTech, Fourier Intelligence and Xiaomi are now investing heavily in:

• Sensors
• Batteries
• Robotic components
• Autonomous systems

China already has considerable advantages: industrial power, access to components, battery expertise, massive production capacity and an integrated supply chain.

As with electric vehicles, Beijing appears determined not to miss the next technological revolution.

Historic industrial players are already preparing their autonomous factories

But startups are not the only ones accelerating in Physical AI. Major legacy industrial groups are also investing heavily in factory transformation and intelligent automation.

Siemens is strongly developing its activities around digital twins, smart factories, industrial automation and predictive AI. The German group is now betting on factories capable of optimizing their own production using data, smart sensors and artificial intelligence.

ABB, meanwhile, remains one of the global leaders in industrial robotics, with a major presence in automotive, logistics, energy and automated production lines. The group is now investing heavily in collaborative robots and industrial intelligence systems capable of improving the flexibility of modern factories.

The same momentum can be seen at Teradyne, owner of Universal Robots, considered the global leader in cobots. These collaborative robots, capable of working alongside humans, are experiencing particularly rapid growth among industrial SMEs and flexible production lines.

According to several sector studies, the global cobot market could exceed $20 billion before 2030, driven by the search for productivity, labor shortages and the acceleration of industrial automation.

Case study: Amazon warehouse automation

One of the best examples of this transition toward Physical AI is probably logistics.

For several years, Amazon has been accelerating the automation of its warehouses by relying on mobile robots, artificial intelligence systems, machine vision and advanced logistics algorithms. The group reportedly now has more than 750,000 robots deployed in its logistics centers around the world.

The objective is clear: reduce delivery times, improve productivity, offset recruitment challenges and optimize operating costs.

Amazon is now experimenting with even more advanced technologies combining generative AI, autonomous robots and intelligent object manipulation in order to make its infrastructure increasingly flexible and automated.

This technological model is now inspiring much of the global logistics sector and represents precisely the type of environment targeted today by startups specializing in humanoid robots.

A response to the global labor shortage

Behind the technological enthusiasm lies a much deeper economic reality.

Demographic aging is now affecting Europe, Japan, South Korea, China and, gradually, several Western economies.

In certain sectors such as industry, logistics, healthcare, maintenance and services, industrial companies are already facing increasing recruitment pressure. In many developed countries, population aging, the decline in the number of qualified technical profiles and the rapid transformation of jobs are creating a structural labor shortage.

In sectors such as logistics, industrial maintenance, automotive, automated warehouses and manufacturing, companies are finding it increasingly difficult to recruit operators, technicians and engineers capable of working in advanced technological environments. According to several international studies, several million industrial jobs could remain vacant in the coming years due to a lack of available qualified talent.

This situation is gradually pushing industrial players to accelerate their investments in intelligent automation. New-generation robots are no longer designed only to replace repetitive tasks on fixed production lines. Thanks to recent progress in artificial intelligence, computer vision and smart sensors, these systems are gradually becoming capable of adapting to complex environments and collaborating with humans.

In this context, intelligent robots appear to be a potential long-term economic solution for offsetting certain labor shortages, improving productivity and maintaining industrial competitiveness. Although the market is still young and technologically in a phase of acceleration, many industrial players already consider Physical AI to be one of the possible answers to the structural challenges of the global labor market.

Logistics is becoming one of the
first testing grounds
for Physical AI.

Is the market already in a bubble?

As with every technological revolution, valuations are already beginning to raise questions.

Some humanoid robot startups are worth several billion dollars despite still having very limited revenues.

Humanoid robots remain extremely complex from a technological standpoint:

• Autonomy
• Safety
• Balance
• Understanding the real world
• Fine manipulation

Large-scale economic use cases have not yet been fully validated.

But history also shows that major revolutions often begin during phases of speculation:

• The internet
• Cloud computing
• Electric vehicles
• Generative AI

Physical AI now seems to be entering this same dynamic.

A revolution that could last several decades

Unlike certain technological trends, Physical AI directly affects:

• Industry
• Infrastructure
• Logistics
• Healthcare
• Transport
• Global productivity

In other words:
Physical AI is no longer only about laboratories or software; it now directly affects the real economy.

And that is precisely what fascinates investors so much. For the first time, artificial intelligence may no longer simply assist humans with intellectual or digital tasks.

Through intelligent robots and autonomous systems, it could also act physically in the real world: moving goods, assembling products, building, assisting operators or automating certain complex industrial tasks. For many analysts, this convergence between AI and robotics could profoundly transform the global economy over the next twenty years.

“Physical AI” is no longer simply a futuristic concept reserved for research laboratories: it is gradually becoming a new strategic global market at the crossroads of artificial intelligence, robotics, automation, semiconductors and industrial infrastructure.

Companies such as NVIDIA, Figure AI, ABB, Siemens and Unitree Robotics are already among the players most closely watched by the markets.

And even though the sector remains young and speculative, one thing now seems clear: the next great revolution in artificial intelligence could be physical.

Cet article Physical AI: the next market wall street is already watching est apparu en premier sur Robot Magazine.