For decades, the technological singularity belonged more to the realm of science fiction than engineering. Today, the concept has become a subject of discussion in artificial intelligence laboratories, major industrial corporations, investment funds, and robotics companies around the world.

The spectacular acceleration of generative artificial intelligence, autonomous robots, foundation models, and AI agents has revived a fundamental question: what will happen when machines become capable of improving their own intelligence faster than humans can?

No one can say with certainty whether this “singularity” will ever occur. One thing, however, is certain: current advances in AI are forcing industrial companies to consider this possibility no longer as a philosophical curiosity, but as a genuine strategic issue.

A Sixty-Year-Old Theory

The origins of the technological singularity date back to 1965, when British mathematician I. J. Good, a former colleague of Alan Turing, imagined that an “ultra-intelligent machine” capable of designing even more powerful machines would trigger a genuine intelligence explosion.

In the 1990s, mathematician and author Vernor Vinge further developed this idea, arguing that once artificial intelligence reached a certain level, technological development would simply become impossible for humans to predict.

The concept was later popularised by Ray Kurzweil, now a principal researcher at Google, who believes that artificial general intelligence, or AGI, could emerge within the next few decades before gradually leading to the continuous self-improvement of intelligent systems.

Unlike today’s AI systems, which remain specialised despite their impressive performance, an AGI would be capable of learning, reasoning, and solving problems across virtually every intellectual field, with versatility comparable or even superior to that of a human being.

The singularity therefore does not simply refer to the moment when AI surpasses humans. It primarily describes the point at which intelligent systems become capable of improving their own algorithms, hardware architectures, research methods, and capacity for innovation.

The true AI revolution may not lie in its ability
to answer our questions, but in its ability to learn
without being shown every step.

Why Has the Debate Become So Serious?

Just five years ago, most industrial robots operated primarily through deterministic programming.

Today, the situation is changing extremely rapidly.

Large language models, vision-language-action models, autonomous agents, and foundation models are gradually enabling robots to understand natural-language instructions, interpret their surroundings, plan complex actions, and learn new tasks without task-specific programming.

Companies such as NVIDIA, Google DeepMind, Tesla, Figure AI, Boston Dynamics, Agility Robotics, Sanctuary AI, and Physical Intelligence are now investing billions of dollars in developing this new generation of intelligent robots.

At the same time, the global robotics market continues to grow.

According to the International Federation of Robotics, more than 4.2 million industrial robots are currently operating in factories around the world, while more than 540,000 new robots are installed every year. Robot density has now reached record levels, particularly in South Korea, Singapore, China, Japan, and Germany.

Goldman Sachs and Morgan Stanley estimate that the humanoid robot market could be worth tens of billions of dollars annually by 2035, with potentially several million robots deployed in manufacturing, logistics, healthcare, and service industries.

These figures explain why the singularity is no longer merely an academic subject.

The Computing-Power Revolution

One of the main drivers of this acceleration is the spectacular increase in available computing power.

The most advanced AI models are now trained on infrastructures composed of tens of thousands of graphics processing units, or GPUs.

Supercomputers dedicated to artificial intelligence now achieve performance measured in exaFLOPS while consuming several hundred megawatts of electricity.

The cost of training the most advanced models, which amounted to only a few million dollars several years ago, has now reached several hundred million dollars. Some analysts even estimate that future generations of models will require investments exceeding one billion dollars.

At the same time, every new generation of AI chips developed by companies such as NVIDIA and AMD significantly improves performance while reducing energy consumption per operation.

This simultaneous progress in hardware, algorithms, and infrastructure creates a virtuous cycle that continuously accelerates the capabilities of artificial intelligence.

When Robots Begin to Learn

For a long time, every new robotic task required weeks of programming.

That era is beginning to disappear.

Foundation models designed for robotics now make it possible to transfer knowledge from one task to another.

Instead of programming every movement individually, engineers can provide a few demonstrations or simply describe a task in natural language.

Platforms such as NVIDIA Isaac and the RT models developed by Google DeepMind perfectly illustrate this evolution.

Eventually, each robot could learn from the experiences of thousands of other robots connected to the same artificial intelligence model.

An improvement made in one factory could therefore benefit every other machine deployed around the world almost instantly.

Although this does not yet represent the singularity, this form of collective intelligence already constitutes an early form of large-scale continuous improvement.

The question is no longer simply how to build
more intelligent machines, but how to ensure
that they remain aligned with human interests.

Peter Thiel’s Perspective

Among Silicon Valley investors, PayPal co-founder Peter Thiel has adopted a particularly interesting position.

From an early stage, he financially supported organisations working on advanced artificial intelligence and invested in several major companies in the sector.

Unlike the most optimistic visionaries, Peter Thiel considers the singularity to represent both the greatest economic opportunity in history and the greatest technological risk humanity may ever face.

He has sometimes compared the arrival of artificial superintelligence to a first encounter with an extraterrestrial civilisation: the real issue would not be its power, but its ability to remain compatible with human interests.

Sceptics Point to the Limitations

Not all researchers share this optimism.

Current systems continue to produce errors, struggle to reason over very long periods, consume enormous amounts of computing resources, and still possess only an imperfect understanding of the physical world.

Many specialists believe that simply increasing the size of models will probably not be enough, on its own, to produce genuine general intelligence.

Others point out that physical limitations remain very real, including global semiconductor production, energy consumption, data-centre cooling, the availability of critical metals, and infrastructure costs.

The development of AI could therefore be slowed as much by economic constraints as by scientific challenges.

Alignment: The Real Challenge

Ultimately, the greatest challenge may not be building a more powerful intelligence.

It may be ensuring that it remains aligned with human objectives.

This is the central purpose of AI alignment research, which seeks to make artificial intelligence systems transparent, interpretable, controllable, and safe.

In robotics, this issue is even more critical.

Unlike conversational models, robots interact directly with the physical world.

A logistics robot, surgical robot, or autonomous construction robot will have to make decisions continuously in unpredictable environments, sometimes while working in direct contact with humans.

Safety could therefore become a competitive advantage just as important as the performance of the artificial intelligence itself.

A Transition Rather Than a Sudden Breakthrough

Will the technological singularity occur in twenty years? In fifty years? Or never?

No one can answer that question today.

One reality, however, is already clear: artificial intelligence is transforming robotics at an unprecedented speed.

Robots are becoming adaptive rather than programmed.

Factories are becoming software-driven.

Humanoid robots are gradually leaving laboratories and entering their first industrial sites.

Engineers now work every day with AI assistants capable of generating code, designing mechanical components, optimising trajectories, and analysing thousands of simulation scenarios.

Perhaps history will ultimately remember the singularity not as a sudden event, but as a long transition during which machines gradually stopped being simple tools and became genuine engineering partners.

From this perspective, the real question is no longer whether artificial intelligence will one day surpass human capabilities, but how industrial companies will learn to collaborate with systems capable of innovating alongside them.

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With Robostral Navigate, Mistral is introducing an approach that could transform how mobile robots are designed and deployed. The objective is ambitious: to enable a robot to understand an instruction expressed in natural language, interpret its surroundings using a simple RGB camera, and plan a route to its destination without relying on a complex sensor infrastructure.

A Vision-Centred Approach

Autonomous navigation traditionally relies on several combined technologies: LiDAR, depth cameras, inertial measurement units (IMUs), GPS where available, and simultaneous localisation and mapping algorithms, commonly known as SLAM.

This architecture delivers excellent performance, but it has two major disadvantages: cost and integration complexity.

Robostral Navigate adopts a different philosophy. The model uses only images from a standard colour camera together with natural-language instructions. The AI continuously analyses the video feed, identifies the structural elements of the scene, assesses possible routes, and generates the commands required for navigation.

This Vision-Language-Action, or VLA, approach brings robotics closer to the advances made in recent years by multimodal models, which can reason simultaneously across visual and textual data.

For robot manufacturers, this simplification represents a considerable economic advantage. An RGB camera costs only a few dozen euros, compared with several hundred or even several thousand euros for an industrial LiDAR sensor.

A Compact 8-Billion-Parameter Model

Robostral Navigate is based on an 8-billion-parameter model, offering a balance between computing power and operational efficiency.

Unlike very large general-purpose models that may contain several hundred billion parameters, this smaller size enables faster execution while remaining compatible with embedded platforms that have limited resources.

In robotics, this optimisation is essential. Navigation decisions must be made within a few dozen milliseconds to ensure smooth and safe movement.

The model must therefore:

• interpret camera images in real time
• understand the user’s instruction
• locate the target within its environment
• plan an optimal route
• avoid dynamic and static obstacles
• continuously correct its trajectory

All these processes are handled within an integrated system, without requiring several independent software modules.

With Robostral Navigate, Mistral AI demonstrates that autonomous navigation can rely more heavily on artificial intelligence than on the multiplication of sensors.

Large-Scale Training in Simulation

One of Robostral Navigate’s most remarkable features is its training process.

According to Mistral AI, the model was trained on nearly 400,000 trajectories across more than 6,000 simulated environments.

Simulation-based training has become standard practice in modern robotics. It makes it possible to rapidly generate millions of scenarios that would be impossible to reproduce physically at a reasonable cost.

Virtual robots can therefore learn to navigate buildings, offices, warehouses, factories, and homes with an extremely wide variety of layouts.

Simulated environments can also introduce numerous disruptions, including:

• lighting variations
• unexpected obstacles
• displaced objects
• open or closed doors
• changes in furniture placement
• people moving through the environment

This diversity improves the model’s ability to generalise when faced with unfamiliar situations.

Benchmark-Level Performance

Mistral AI evaluates Robostral Navigate using the Room-to-Room Continuous Environment benchmark, known as R2R-CE, an international reference used to measure the performance of robotic navigation algorithms.

The model achieves:

• a 79.4% success rate in environments already encountered during training;
• a 76.6% success rate in completely new environments.

These results outperform several competing approaches that rely solely on an RGB camera and rival certain architectures that incorporate depth sensors.

This ability to generalise is probably one of the model’s most interesting features. In practice, a robot will need to operate inside buildings it has never encountered before.

Understanding Natural Language

One of the major innovations is the direct use of natural language.

Instead of programming GPS coordinates or selecting a destination on a map, an operator can provide instructions such as:

“Go to the meeting room.”

“Proceed to the shipping area.”

“Go through the corridor, then turn left after the red door.”

The model interprets these instructions, associates them with visual information, and gradually builds a navigation plan.

This development brings robotics closer to everyday use and reduces the technical expertise required to operate autonomous robots.

Reducing dependence on LiDAR could make
mobile robots more accessible and accelerate
their large-scale deployment.

Immediate Industrial Applications

The potential applications are numerous.

In logistics, Robostral Navigate could make it easier for robots to move autonomously between warehouse shelves without requiring extremely precise mapping.

In manufacturing, mobile robots could transport components between different production stations while adapting to changes in their surroundings.

Inspection drones also represent a promising use case. By understanding an instruction such as “inspect the third conveyor line,” they could carry out their mission with greater autonomy.

Quadruped robots designed for industrial inspections or interventions at sensitive sites could also benefit from this technology.

Finally, service robots operating in hotels, hospitals, or office buildings could provide more natural navigation while requiring less specialised infrastructure.

Mistral’s Physical AI Strategy

This announcement confirms Mistral AI’s ambition to become a major player in Physical AI.

After developing language models capable of competing with those created by major American technology companies, the French company is now extending its expertise into autonomous systems.

This strategy comes at a time when the convergence between artificial intelligence and robotics is accelerating. Major technology companies are investing heavily in models capable of connecting perception, reasoning, and action.

The objective is no longer simply to create conversational assistants, but to provide robots with a sufficiently rich understanding of the world to act autonomously within complex environments.

What Challenges Remain?

Despite the promising results, several technical challenges remain.

The first concerns the transition from simulation to the real world, commonly known as Sim-to-Real. Performance achieved in virtual environments must be confirmed on robots operating under real industrial conditions, where lighting, reflections, dust, and unexpected obstacles can interfere with perception.

Robustness in exceptional situations represents a second major challenge. Robots will need to respond safely when they encounter a significantly altered environment or an ambiguous situation.

Finally, the energy consumption and computing resources required to run the model on embedded hardware will be decisive factors in its large-scale adoption.

Robot Magazine’s Analysis

With Robostral Navigate, Mistral AI is not simply seeking to improve autonomous navigation. The company is proposing a new way of designing mobile robots.

By reducing dependence on specialised sensors and placing vision and natural language at the centre of autonomy, the French company is opening the way for platforms that could be simpler, less expensive, and easier to deploy.

Should the performance observed in simulation be confirmed in real-world conditions, this approach could accelerate the democratisation of mobile robots across logistics, industry, critical infrastructure, and service sectors.

More broadly, it reflects a fundamental trend: the future of robotics will increasingly depend on general-purpose AI models capable of perceiving, reasoning, and acting within the physical world.

Robostral Navigate therefore represents an important milestone in the convergence between artificial intelligence and autonomous robotics.

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For years, industrial robots remained confined to production cells behind safety barriers. Today, another category of machines is attracting the attention of manufacturers: AMRs (Autonomous Mobile Robots).

Capable of transporting components, pallets or bins without requiring dedicated infrastructure, they are becoming one of the pillars of Industry 4.0. Their adoption is accelerating in logistics, e-commerce, the automotive industry and even hospitals, driven by labour shortages, the pursuit of productivity gains and advances in artificial intelligence.

Analysts estimate that the global AMR market will exceed $5 billion as early as 2026, with annual growth ranging from 14% to 20%, depending on the research firm. This makes it one of the fastest-growing segments of industrial robotics.

From AGVs to Truly Autonomous Robots

For many years, factories relied on AGVs, or Automated Guided Vehicles vehicles that appeared autonomous, but only to a limited extent.

In reality, these machines follow predefined routes using magnetic strips, QR codes or reflectors installed on the floor. As soon as an obstacle appears, they stop.

AMRs follow an entirely different approach.

Equipped with LiDAR sensors, cameras, depth sensors and real-time mapping algorithms such as SLAM, an AMR builds its own representation of its environment, calculates its route and instantly adjusts it whenever a person, forklift or pallet blocks its path.

This autonomy significantly reduces installation costs and allows industrial sites to be reconfigured quickly without modifying their existing infrastructure.

AMRs no longer simply follow a route.
They understand their environment and
make their own navigation decisions.

Why Are Manufacturers Accelerating Adoption?

The rapid growth of e-commerce and increasing pressure on delivery times have fundamentally changed warehouse requirements.

Companies are now looking for systems capable of absorbing peaks in activity without having to recruit large numbers of additional workers.

AMRs address this challenge directly.

They handle repetitive movements between storage areas, production lines and order-picking stations, allowing operators to focus on higher-value tasks.

This trend is being reinforced by recruitment difficulties in logistics and material-handling roles across most industrialised economies.

Case Study: Decathlon Doubles Its Order-Picking Capacity

One of the most striking examples is Decathlon, which has deployed Skypod robots, developed by Exotec, across several European logistics centres.

In Portugal, order-picking capacity increased from 57,000 to 114,000 orders, while the number of stores supplied rose from 41 to 73.

In the United Kingdom, order pickers now walk less than one kilometre per day, compared with more than 10 kilometres previously, reducing fatigue and improving working conditions.

Another revealing indicator is that the order-picking error rate was cut in half.

Amazon Prepares the Next Generation

At Amazon, autonomous mobile robots are already being used in several dozen logistics centres.

In 2026, the group introduced a new version of its Proteus robot, capable not only of navigating autonomously but also of interpreting instructions expressed in natural language through artificial intelligence.

The objective is no longer simply to move loads, but to enable robots to plan their own missions and routes according to operational priorities.

Benefits Observed in Real-World Operations

Initial feedback shows several recurring benefits:

• Reduced unnecessary movement for operators
• Improved safety through obstacle detection
• Faster deployment than a traditional AGV system
• Greater flexibility when production requirements change
• Continuous operation, including during night shifts and off-peak hours

Some market studies indicate that more than 60% of companies investing in robotics achieve productivity gains exceeding 25%, while investment in warehouse automation continues to rise sharply.

AMRs do not replace operators: they optimise
their movements and improve their working
conditions.

Significant Challenges Remain

However, AMRs are not a universal solution.

Integration with information systems such as ERP, MES and WMS platforms, cybersecurity, multi-robot fleet management and safe interaction with human operators remain major challenges.

Research is now focusing on robots capable of understanding the behaviour of people around them so they can adjust their trajectories in an even smoother and safer way.

Towards Fully Autonomous Logistics?

The future of AMRs is no longer limited to transporting pallets.

Future generations are expected to cooperate with robotic arms, inventory drones and, eventually, humanoid robots capable of carrying out complete order-picking operations.

In this vision, the AMR becomes the mobile platform of the smart factory, connecting every workstation through a continuous flow controlled by artificial intelligence.

Far from being a passing trend, autonomous mobile robots are now establishing themselves as one of the most cost-effective and rapidly deployable technologies in industrial robotics. For many companies, the question is no longer whether they will adopt AMRs, but how quickly they will be able to integrate them into their operations.

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For several years, industrial robotics has been experiencing unprecedented acceleration. Artificial intelligence is no longer limited to analyzing data or generating text: it now controls machines capable of perceiving their environment, making real-time decisions, and interacting directly with human beings.

This evolution, often described as “physical AI,” is paving the way for a new generation of industrial robots, autonomous mobile robots, and humanoid robots.

But this transformation also comes with a major challenge. The more autonomous robots become, the greater the safety requirements. Unlike traditional robots enclosed behind protective barriers, these new machines will operate directly alongside workers, share the same workspaces, and collaborate with them on daily tasks.

In this context, safety is becoming a strategic issue just as important as computing power or algorithmic performance.

It is precisely to address this challenge that NVIDIA unveiled Halos for Robotics, presented as the first complete safety system designed for robots powered by physical artificial intelligence.

As robots become increasingly autonomous,
safety is becoming the true technological
frontier of physical artificial intelligence.

The announcement, made during Automate 2026 in Chicago, confirms the American tech giant’s ambition to become not only the global leader in AI platforms, but also one of the key players in robotic safety. This initiative could profoundly change the way manufacturers design future generations of robots and accelerate their deployment across industry.

When robots leave their protective cages

For several decades, industrial robot safety was based on a simple principle: keeping humans away from machines. Robots were installed in closed cells, surrounded by fences, optical barriers, and emergency stop systems. This approach was suited to robotics mainly dedicated to repetitive tasks such as welding, painting, or heavy material handling.

Today, the situation is changing rapidly. Manufacturers are looking for robots capable of working directly with operators in order to improve production line flexibility. Mobile robots transport components between different workshops, cobots assist technicians in their daily tasks, and humanoid robots are beginning to perform order preparation, handling, and quality control operations.

However, this proximity creates new risks. Robots must be able to detect an operator crossing their path, anticipate their movements, recognize unexpected objects, and adapt their behavior within milliseconds. Traditional safety methods are therefore reaching their limits. Manufacturers no longer simply want robots to stop when danger appears; they want them to be capable of avoiding dangerous situations before they even occur.

According to several analyst firms, the global industrial robotics market is expected to exceed $80 billion by the end of the decade, driven in particular by the rise of smart factories, automated warehouses, and humanoid robotics. However, this growth can only continue if trust in these systems progresses at the same pace as their performance.

The rise of humanoid robots is profoundly
transforming the certification requirements
for robotic systems.

Halos: a safety platform designed from the ground up

With Halos for Robotics, NVIDIA is proposing a radically different approach. Rather than treating safety as an additional layer added at the end of development, the company integrates it directly into the entire robotic architecture.

The platform covers the full technological chain. It takes into account computing hardware, specialized processors, sensors, safety software, artificial intelligence models, simulation tools, and the validation processes required for the certification of robotic systems.

This “full-stack” vision is one of the project’s main innovations. The different components communicate with each other to provide continuous monitoring of the robot’s operation and its environment. If an anomaly is detected, the system can immediately take the appropriate measures while maintaining a high level of operational availability.

NVIDIA also explains that Halos builds on development methods used for several years in the field of autonomous vehicles. Validation procedures, risk analyses, and software tools developed for self-driving cars are now being adapted to the specific needs of industrial robotics. The company says this expertise represents more than 18,600 cumulative years of engineering work dedicated to functional safety.

This reuse of already proven technologies makes it possible to accelerate the market launch of robots that meet future regulatory requirements, while limiting development costs for manufacturers.

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Behind this concept lies a simple but powerful idea: creating an extremely accurate virtual replica of a physical asset in order to understand, test, predict and optimize its behavior before taking action in the real world. What once belonged to science fiction is now becoming an operational reality in factories, energy infrastructures, logistics centers and the most advanced robotics programs.

The global digital twin market is also experiencing spectacular growth. According to several specialized research firms, it could exceed 100 billion dollars by the end of the decade. Manufacturers see it as a way to reduce costs, improve productivity and accelerate innovation in a context where international competition is intensifying.

From 3D Model to Living Digital Mirror

For a long time, manufacturers have used 3D models to design their products or facilities. The digital twin goes much further. It is no longer simply a graphical representation, but a living system continuously fed by real data.

Every sensor installed on a machine, every operating robot and every piece of information collected from the field can enrich the digital model. The model then evolves in real time and faithfully reproduces the behavior of its physical counterpart.

Engineers therefore gain unprecedented visibility over their operations. They can observe the condition of a machine, monitor its performance, detect anomalies and anticipate potential failures before they occur.

This predictive capability is now one of the main drivers behind the adoption of the technology. In many industries, just a few hours of production downtime can cost several hundred thousand euros. Being able to anticipate a breakdown before it happens therefore represents a major economic advantage.

The Virtual Laboratory of Modern Industry

Beyond maintenance, the digital twin offers manufacturers a unique testing environment.

Before modifying a production line, integrating a new robot or increasing manufacturing rates, engineers can virtually test dozens of scenarios. Each hypothesis is simulated in the digital environment before being applied on the factory floor.

This approach significantly reduces the risks associated with industrial projects. Design errors are detected earlier, investments are optimized and commissioning times are shortened.

In some sectors such as aerospace and automotive, digital twins are now used from the earliest design phases. Companies can build and operate an entire factory virtually before the first shovel even hits the construction site.

The result is spectacular: fewer unexpected problems, better cost control and a faster innovation cycle.

Robots attract attention.
Digital twins are truly transforming
industry.

When the Digital Twin Becomes the Training Ground for Robots

One of the most fascinating applications of the digital twin today concerns advanced robotics.

Training a robot in the real world is a complex and costly process. Every error can cause material damage, slow down production or require human intervention. Manufacturers are therefore looking to move part of this learning process into virtual environments.

This is precisely the strategy adopted by Dassault Systèmes. With its 3DEXPERIENCE platform, the French group is developing digital twins capable of reproducing the complete behavior of a robot, its working environment and its interactions with human operators.

In this approach, the robot is first tested in its digital double. Engineers can analyze its movements, identify its limitations and optimize its performance before physical deployment.

This logic becomes even more impressive with the arrival of humanoid robots. In 2026, the German manufacturer NEURA Robotics announced a strategic partnership with Dassault Systèmes to integrate its 4NE1 humanoid into an advanced digital twin environment.

The objective is to allow the robot to learn virtually. Every gesture, every movement and every interaction with its environment can be simulated thousands of times. Errors are corrected in the digital world before the robot ever faces reality.

This method considerably reduces development costs while accelerating machine learning. It could become a standard for the next generation of industrial and humanoid robots.

Schneider Electric Is Preparing the Smart Factories of Tomorrow

The digital twin is not only about robots. It is also transforming the way manufacturers design and manage their infrastructures.

Among the most advanced players is Schneider Electric. The French group is investing heavily in technologies that make it possible to virtually reproduce complete industrial installations.

The objective is to create a global view of the factory in order to optimize energy performance, production flows and equipment maintenance.

Humanoid robots will first learn
in virtual worlds before
evolving in the real world.

In collaboration with NVIDIA and ETAP, Schneider Electric recently presented a particularly ambitious project: a digital twin capable of modeling the full energy needs of infrastructures dedicated to artificial intelligence.

Thanks to this technology, manufacturers can visualize in real time the interactions between electrical networks, IT systems and production equipment. They are then able to identify weak points, reduce unnecessary energy consumption and improve the overall efficiency of the site.

This approach perfectly illustrates the evolution of the digital twin. It is no longer just a simulation tool, but a true control center capable of continuously optimizing industrial operations.

Europe Has a Major Card to Play

Faced with the rise of American and Chinese players in artificial intelligence, Europe has a strategic advantage that is often underestimated.

The continent is home to several global leaders in industrial software, automation and digital simulation. Dassault Systèmes, Schneider Electric, Siemens and ABB all possess unique expertise in modeling complex systems.

The future of robotics may depend as much on digital platforms as on the machines themselves.

Tomorrow’s robots will need to be trained, tested and optimized in increasingly sophisticated virtual environments. Companies capable of building these digital worlds will have a considerable advantage in global competition.

As robots become more autonomous and artificial intelligence becomes integrated into industrial processes, the digital twin is emerging as the invisible layer that connects the physical world to the virtual world.

The Hidden Brain of Industry 5.0

The industry of the future will not only be made up of robots, sensors and artificial intelligence. It will also rely on a permanent digital representation of its operations.

Every important machine could soon have its own digital twin. Every robot could be trained in a virtual universe before deployment. Every factory could be continuously simulated in order to optimize its performance and reduce its energy footprint.

This revolution is already underway.

While humanoid robots represent the visible part of industrial transformation, digital twins are probably its invisible brain. They allow companies to predict rather than endure, to test rather than risk, and to innovate faster than ever before.

In the coming years, the most efficient factories will not necessarily be those with the largest number of robots. They will probably be those with the best digital twins.

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Yet the market reality is clear: the digital twin is no longer a future concept. It is a market. A market worth around $34 billion in 2026, according to analysts, and expected to reach $180 billion by 2030. Behind these figures, three players dominate industrial conversations: Dassault Systèmes, Siemens and PTC.

Three companies. Three software architectures. Three visions of what a digital twin should be. And above all, three very different ways of answering the same fundamental question: how can the physical world and the digital world communicate in a useful way, in real time, at industrial scale?

First, let’s be honest about what a digital twin really is

Before comparing platforms, we need to clear up a confusion that costs many industrial companies dearly.

A digital twin is not a 3D model. Nor is it simply a nice simulation launched occasionally to validate a design. A true digital twin is a living system: a digital replica of a physical object or process, continuously fed with real data, capable of simulating, predicting and, ideally, prescribing actions.

Experts generally distinguish three levels of maturity: the descriptive twin, which shows what is happening; the predictive twin, which anticipates what is going to happen; and the prescriptive twin, which recommends what should be done. The vast majority of current industrial deployments stop at the first level. Moving beyond that stage requires quality data, serious IT/OT integration and rare internal skills.

It is in this context a market experiencing explosive growth, but where the promise often exceeds the reality that Dassault Systèmes, Siemens and PTC must be understood.

The digital twin is becoming
the core of modern industrial
control.

Industry 4.0 is built on the
ability to connect machines
and data.

The deeper shift: AI changes everything

What is profoundly transforming this market is the integration of artificial intelligence into digital twins themselves. And the three players are responding to it differently.

Siemens, with the acquisition of Altair and its program to develop an “Industrial Foundation Model” an AI model trained on 150 petabytes of verified engineering data is betting on the autonomous twin. A twin that does not merely simulate, but optimizes, recommends and, eventually, decides.

Dassault Systèmes is gradually integrating AI into 3DEXPERIENCE to accelerate simulations, generate design variants and automate certain validation steps. Its approach is more cautious, more focused on assisting the engineer than on giving autonomy to the system.

PTC, for its part, relies on cloud partnerships, particularly Microsoft Azure, to enrich its operational data with AI models. The challenge for PTC is to move from the descriptive twin, which shows what is happening, to the predictive twin, which anticipates failures and optimizes flows.

At the same time, players such as NVIDIA with Omniverse, and Microsoft with Azure Digital Twins, are reshaping the boundaries of the market. The digital twin no longer belongs exclusively to traditional PLM software vendors. Companies such as KION Group, Accenture and NVIDIA are building AI-driven warehouse twins at a speed that traditional players sometimes struggle to match.

How should you choose?

The honest answer is that choosing a digital twin platform is first and foremost a strategic choice, not a technological one.

Choose Dassault Systèmes 3DEXPERIENCE if your challenge is the design of complex products, the simulation of advanced physical behavior and collaboration between engineering teams over long cycles. Aerospace, automotive, life sciences and energy are the areas where the platform delivers its full value. Expect a significant investment in integration and skills development.

Choose Siemens Xcelerator if you are looking for broad coverage, from design to manufacturing, if you already use Siemens equipment on your lines and if you are ready to invest in deep transformation rather than rapid deployment. The acquisition of Altair further strengthens this platform in simulation and AI, but the project remains ambitious to implement.

Choose PTC ThingWorx/Vuforia if your priority is operational: connecting existing machines, giving your teams real-time visibility, and deploying maintenance and operator assistance tools. The value is quick to obtain, visible and measurable, even if simulation depth remains limited.

The real challenge: moving beyond the proof of concept

The real digital twin battle is not being fought between these three software vendors. It is being fought inside factories, where dozens of pilot projects pile up without ever making the leap to industrialization.

Deploying a digital twin at scale, with data governance, serious IT/OT integration and hybrid profiles capable of keeping it alive, remains a challenge that few companies have truly solved in 2026. This is where the real gap lies between the market promise and factory reality.

Dassault Systèmes, Siemens and PTC each have their own answer to this challenge. None is universal. None is simple. But all three have understood something that industry is still taking time to fully grasp: a digital twin is not a product you buy. It is a capability you build.

Robot Magazine follows the evolution of the digital twin market and its key players. Are you deploying a digital twin in production? Share your experience. Field feedback is the rarest and most valuable content in this sector.

Author: Christophe Carl Louis
Content enriched with artificial intelligence tools.

FAQ – Digital Twin (Dassault Systèmes vs Siemens vs PTC)

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According to several industry estimates, the global humanoid robot market could reach between $30 billion and $60 billion by 2035, with annual growth exceeding 35%. Yet today, less than 1% of industrial companies use humanoids in real production environments. The gap between potential and adoption is telling.

From Technical Demonstration to Industrial Reality

Recent advances in robotics, driven by artificial intelligence and imitation learning, have made it possible to cross a major threshold. In 2025, several prototypes capable of performing picking or assembly tasks were unveiled. In 2026, some manufacturers are announcing tests involving fleets of 10 to 100 robots in controlled environments.

But those figures remain marginal. For comparison, a single automotive plant can deploy more than 1,000 traditional industrial robots.

The transition toward mass adoption involves challenges of a completely different nature:

• System standardization
• Large-scale maintenance
• Software interoperability
• Total cost of ownership (TCO)

Today, the cost of a humanoid robot still ranges between $50,000 and $150,000, which significantly limits large-scale deployment.

The real challenge is no longer
making a robot walk, but making
it work at scale.

We are no longer selling machines,
but ecosystems where hardware is
only the visible part of the data layer.

A Redefinition of the Robotics Model

The traditional robotics framework is evolving toward a systemic approach. In 2026, the most advanced players are no longer selling robots alone, but complete solutions integrating:

• Hardware
• Software
• Infrastructure
• Services
• Data

There is a clear convergence with SaaS models, where value shifts toward operations and data management.

Humanoid robotics is entering a new phase. After the era of technological innovation (2015–2025), the decade 2025–2035 will be defined by large-scale deployment.

The bottleneck has shifted toward infrastructure, regulation, and market integration. The companies that will dominate this market will not simply be those designing the most advanced robots, but those capable of deploying them at scale in real-world environments.

The next revolution will not be technological. It will be operational.

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In 2026, this silent war has taken a turn that the industrial press has not yet clearly named. Beckhoff closed 2025 with €1.24 billion in revenue, up 6% in a contracting market. At the same time, Rockwell Automation was cutting 900 jobs, lowering its forecasts, and promising that the “tailwinds from AI” would become visible “quarter by quarter.” Schneider Electric, meanwhile, was quietly reinventing its control architecture with an announcement that went under many people’s radar: the industry’s first software-defined DCS, Foxboro SDA, launched in February 2026.

This is not a story about brands fighting each other. It is a story about architectures, and about a fundamental question that industry is putting back on the table: a dedicated controller on a proprietary backplane, or an open industrial PC that runs everything at once?

The proprietary architecture: why it worked so well for so long

To understand what is happening today, we need to understand what worked yesterday.

Rockwell Automation and its Allen-Bradley systems have built one of the strongest installed bases in industrial history, particularly in North America. ControlLogix is robust, reliable, well documented, and mastered by thousands of integrators and maintenance technicians. In large chemical, pharmaceutical and automotive plants, a Rockwell controller is often a non-negotiable requirement from the project owner, not a choice made by the integrator.

The strength of this installed base is real and lasting. Nobody replaces a healthy ControlLogix network just for the pleasure of adopting a more elegant architecture. The cost of migration, in time, training and operational risk, is prohibitive. Rockwell knows this, and this is precisely the rent on which it has built its profitability for the past two decades.

Schneider Electric has followed a similar logic, with one specific difference: a dual foothold in discrete and process industries, in Europe and emerging markets. Its Modicon catalogue for discrete automation, and Foxboro and Triconex for process and safety, give it a presence in sectors that Rockwell has never truly penetrated: petrochemicals, water treatment and energy infrastructure. It also has a distinctive position in energy efficiency, which has been part of its DNA long before it became a regulatory obligation.

Automation is no longer a
hardware choice, it is a
20-year strategy.

In Industry 4.0, software is
the new master of the
forge.

The real question: who specifies new lines in 2026?

The heart of the debate is not technical. It is demographic and economic.

A seasoned industrial analyst put it bluntly in a specialist newsletter that circulated in May 2026: the engineers who specified Allen-Bradley in 2002 and 2010 are retiring. Those specifying new lines in 2026 are in their thirties. Their first contact with PLC programming was not a classroom course in a panel shop, but an open-source simulator on a laptop. They have a natural affinity for open environments, modern IDEs and architectures that feel like software development.

No marketing budget can compensate for a generational shift. Rockwell has an extremely loyal installed base, but its share of new greenfield lines has already been moving unfavorably for several years.

This does not mean Rockwell will collapse in the short term. Maintenance of the installed base, renewals, brownfield projects in plants with 20 years of ControlLogix behind them all of this represents a solid foundation for another 10 to 15 years. But the trajectory on new installations is concerning, and Rockwell’s product response FactoryTalk Optix as a visualization and connectivity layer does not change the underlying architecture. It is a surface adaptation on a proprietary foundation that has not fundamentally evolved.

Where each player still wins, and where each is losing ground

Rockwell remains essential in North American brownfield environments. Large plants with 20 years of ControlLogix will not change architecture over the next decade, whatever the competitive pressure. Sectors with strong regulatory inertia, such as chemicals, high-risk process industries and functional safety, also remain favorable ground. The depth of the Allen-Bradley integrator network in North America is an advantage Beckhoff has not yet reproduced at that scale.

Schneider Electric holds its ground in global process industries. Foxboro and Triconex are references in petrochemicals and functional safety. The Foxboro SDA announcement strengthens this position by adding a modernization layer that did not previously exist. In terms of energy efficiency, an increasingly decisive criterion in industrial specifications, Schneider has a positioning that its competitors have not managed to replicate as completely.

Beckhoff is winning in greenfield discrete automation, motion-intensive systems and vision-integrated machines. Machine builders selling in Europe and Asia on the same specifications one runtime, rationalized SKUs, a coherent architecture have a clear economic argument for TwinCAT and EtherCAT. And the fact that AI can be integrated natively into the runtime, without third-party infrastructure, is starting to weigh in comparisons.

What this battle says about automation in 2026

The “war of architectures” is not a trade-show headline. It is an industrial reality playing out today in every new greenfield project, every machine-builder specification and every runtime choice inside an engineering office.

What is being decided is whether the controller of the future looks like a specialized electronic device, proprietary by nature, or like an open industrial computer capable of hosting any function the machine requires, including AI models directly in the control loop.

The history of computing has already answered this question once, in the 1990s, when proprietary servers gave way to x86 architecture servers. It would be reckless to claim that automation will follow exactly the same path. Real-time constraints, functional safety and industrial reliability are different. But the direction of travel is readable.

Rockwell, Schneider and Beckhoff are each responding in their own way. None of the three will disappear over the next 10 years. But in 10 years’ time, the order of the podium for new installations will probably not look the same as it does today.

Robot Magazine follows developments in the industrial automation market. Are you an integrator, machine builder or technical manager? Your field experience is the most valuable source. Share it in the comments.

Author: Christophe Carl Louis
Content enriched with artificial intelligence tools.

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At a time when optimizing production lines is becoming a strategic priority for manufacturers, Liebherr-Verzahntechnik GmbH has unveiled, through a recent press release, a range of palletizing solutions designed to meet increasingly diverse industrial needs. From small production runs to complex manufacturing environments, the German industrial group highlights a practical and scalable approach to automation.

A legacy of innovation

At Liebherr, automation is not just a product, but the result of a deeply rooted engineering culture. The legacy of Hans Liebherr, founder of the group, is reflected in the company’s ability to design in-house solutions where the market falls short. It was precisely this philosophy that led to the development of the PHS Pro pallet handling system in 2008.

At the time, faced with a lack of suitable solutions for its own industrial needs, the company chose to develop its own system. The result was a complete automation solution capable of adapting to complex industrial environments while remaining scalable.

Automation is becoming a
strategic lever for optimizing
production lines.

From custom-made to modular

Building on its success, the PHS Pro evolved into the PHS Allround, a modular version designed to make automation more accessible. This system is particularly suited to companies with limited space or those wishing to invest progressively.

The PHS Allround stands out through its standardized modular design, allowing step-by-step expansion. Available in several configurations, it offers options such as front machine access, double-fork systems, and extended travel distances for loading.

This modularity is a major advantage for industrial SMEs, which can integrate automation solutions without compromising their ability to adapt in the future.

Significant performance gains

Beyond flexibility, Liebherr’s palletizing solutions deliver notable performance gains. According to the press release, cost per part can be reduced by up to 40%, while machine utilization rates can reach 90%. One of the most striking benefits is the ability to operate without personnel, particularly in automated production environments.

These systems are especially well suited to one-off production and small batch manufacturing, a segment that is growing rapidly in industry, particularly with the rise of product customization.

Real-World use cases

Several examples illustrate the diversity of possible applications.

In the field of metrology, a company in southern Germany uses a PHS Allround to automate a measuring machine. A specific software adaptation was developed to integrate the machine into the system, demonstrating Liebherr’s ability to meet unconventional needs.

Another emblematic case is the installation of a PHS Pro in 2021 at a manufacturer of mechanical components. This system integrates three robotic cells equipped with machine vision, capable of recognizing up to 20 part variants. The entire process from sorting raw parts to machining, cleaning, and discharge is fully automated.

Finally, at Renault’s Cléon site, Liebherr deployed a solution combining a rotary loading system (RLS) and bin-picking technology. This setup optimizes the production flow of crankshafts while integrating intelligent management of incoming parts at the start of the line.

Palletizing solutions can be
adapted to a wide range of
industrial sectors.

Automation serving multiple industries

Liebherr’s palletizing solutions are now used across many sectors: construction, agriculture, aerospace, machine tool manufacturing, and mold production.

This cross-sector relevance confirms a broader trend: automation is no longer reserved for large standardized production lines, but is now expanding into varied environments, often characterized by lower volumes and greater complexity.

Toward accessible and intelligent automation

Beyond technical performance, Liebherr’s positioning is based on comprehensive support for manufacturers. Choosing the right system, integrating it into existing operations, and ensuring proper follow-up all appear to be key success factors.

As product manager Agnès Schauppel points out in the press release, the added value lies in the ability to combine experience, expertise, and creativity. This approach aims to make automation more accessible while meeting growing demands for flexibility and competitiveness.

A strategic challenge for european industry

In a context of accelerated industrial transformation, marked by cost pressure, labor shortages, and the need to increase productivity, automated palletizing solutions appear to be a strategic lever.

The modular and scalable approach developed by Liebherr fits perfectly within this dynamic. It allows manufacturers to adopt automation progressively, without overly heavy initial investments, while maintaining room for future development.

At a time when European industry is seeking to strengthen its competitiveness against international players, this type of solution could well play a key role in modernizing production tools.

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As a media partner, Robot Magazine will closely follow this new edition and invites its readers engineers, industrial leaders, integrators, and robotics enthusiasts to explore an event that embodies the ongoing transformation in factories and production lines.

A Comprehensive Overview of Modern Industry

Global Industrie stands out by bringing together the entire industrial value chain. While some events focus on a specific technology, Global Industrie takes a holistic approach: it connects players in robotics, production, subcontracting, and industrial digitalization.

For several days, thousands of professionals gather to discuss major technological advances that are redefining production models. The event typically hosts more than 2,000 exhibitors and tens of thousands of visitors from across Europe.

In the exhibition halls, visitors can discover:

• Next-generation industrial robots and cobots
• Advanced automation systems
• Additive manufacturing solutions
• Predictive maintenance technologies
• Innovations related to industrial artificial intelligence
• Automated logistics platforms

This diversity makes it a true barometer of European industrial innovation.

Robotics does not replace
humans; it multiplies their
potential.

Robotics at the Heart of Industrial Transformation

For Robot Magazine readers, robotics remains one of the main points of interest at the show. The stands dedicated to robotics and automation are among the most dynamic of the event.

International companies such as ABB Robotics, KUKA, Fanuc, and Universal Robots regularly showcase their latest innovations here.

Visitors can observe robots in action:

• Assembling mechanical parts
• Handling electronic components
• Collaborating with human operators
• Automating production lines

These demonstrations provide a concrete understanding of how robotic technologies are now integrated across all industrial sectors: automotive, aerospace, electronics, logistics, and food production.

Collaborative robotics, in particular, is growing in importance. Cobots allow companies of all sizes, including SMEs, to automate certain tasks while maintaining close interaction with human operators.

Industry 4.0: The Convergence of Robots, Data, and AI

Beyond robotics, Global Industrie highlights the major trends of Industry 4.0.

Today, factories are no longer just automated; they are becoming connected, intelligent, and data-driven. Industrial sensors, vision systems, AI, and software platforms play an increasingly central role in process optimization.

The show offers a concrete glimpse into this technological convergence.

Visitors can discover:

• Predictive maintenance solutions based on data analysis
• Digital twins for simulating production lines
• Industrial control platforms integrating AI
• Autonomous logistics systems using mobile robots and AGVs

This digital transformation is now a strategic challenge for European industry, which faces competitiveness, energy transition, and technological sovereignty issues.

A Hub for Industry Stakeholders

Global Industrie is not just an exhibition. It is also a space for networking and sharing experiences.

Numerous conferences and roundtables address major current industrial issues:

• Industrial reshoring
• Energy transition
• SME automation
• Technical skills shortages
• AI’s impact on industrial professions

These discussions are valuable for decision-makers seeking to anticipate sector developments and identify technologies that can enhance organizational performance.

The event also welcomes deep-tech startups developing tomorrow’s industrial technologies, offering visitors the chance to discover promising innovations that are still little known.

A Must-Attend Event for Professionals

For engineers, industrial leaders, and tech entrepreneurs, Global Industrie offers a unique opportunity to take the pulse of industrial innovation.

In just a few days, attendees can:

• Observe the latest robotic advancements
• Meet suppliers and technology partners
• Network with industry leaders
• Identify new collaboration opportunities

In a context where automation, AI, and robotics are transforming industrial models, events like this play a key role in connecting ecosystem players.

Robot Magazine explores the
technologies shaping the industry
of tomorrow.

Robot Magazine at the Heart of the Event

In a context marked by accelerating automation, the rise of industrial AI, and technological sovereignty challenges, Robot Magazine will focus on advanced robotics, factory digitalization, and the transition to a more sustainable industry. Visitors will discover demonstrations of next-generation industrial robots, collaborative cobots, predictive maintenance solutions, and AI-based industrial control platforms.

Numerous conferences and roundtables will also bring together industrial leaders, researchers, and startups to explore deep transformations in the manufacturing sector. For professionals in robotics and automation, this 2026 edition is a unique opportunity to discover the technologies that will shape the factories of tomorrow and to meet key players in the European industrial ecosystem.

As a media partner of Global Industrie, Robot Magazine will cover the show’s standout innovations.

Our teams will meet with industrialists, startups, and experts to share with our readers:

• Interviews with executives and engineers
• Analyses of robotics trends
• Technology demonstrations
• Reports from the heart of the exhibition halls

Our goal is simple: to highlight the technologies shaping tomorrow’s industry and give a voice to the actors driving this transformation.

See You at Global Industrie

Whether you are an engineer, entrepreneur, integrator, or simply passionate about industrial technologies, Global Industrie offers a unique chance to discover innovations reshaping the industrial landscape.

Robotics, AI, and automation are no longer futuristic concepts; they are at the core of industrial strategies.

And this is exactly what you will be able to observe, understand, and experience at this unmissable European industrial event.

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