But behind the numbers, one question stands out: is Figure AI building something fundamentally new, or is it reproducing the hype cycle we have come to know with generative AI?

The honest answer is: probably both at once. And that is precisely what makes this company so fascinating to study.

From zero to $39 billion in three years: the journey

Brett Adcock is not a robotics engineer by training. He is a serial entrepreneur, founder of Vettery, a tech employment marketplace sold to Adecco, and Archer Aviation, an electric air taxi company. Two very different sectors, but one constant: long-cycle, capital-intensive markets where the race for resources matters above everything else.

In 2022, he launched Figure AI with a simple and radical conviction: the global labor market is short of hands. Physical, repetitive and demanding tasks handling, logistics, industrial assembly are becoming increasingly difficult to fill in developed countries. A humanoid robot capable of performing these tasks without redesigning existing production lines would represent a market opportunity potentially larger than the automotive industry itself.

The bet quickly attracted some of the biggest names in tech. In 2024, Figure closed a $675 million Series B round at a valuation of $2.6 billion, with Microsoft, NVIDIA, the OpenAI Startup Fund, Jeff Bezos through Bezos Expeditions, Intel Capital and ARK Invest. The list of investors is almost as impressive as the machines themselves. In September 2025, an additional $1 billion Series C round brought the valuation to $39 billion, with Brookfield Asset Management and Macquarie Capital joining the table alternative asset managers that do not invest in science fiction.

In eighteen months, the valuation increased fifteenfold. That is rarely seen, even in tech. A new funding round is reportedly being prepared for 2026–2027.

Figure 01, Figure 02, Figure 03: three generations, one philosophy

Figure’s product trajectory is fast and methodical, which sets it apart from many robotics startups that produce demo after demo without ever leaving the laboratory.

Figure 01, was the founding prototype, presented in 2022. A bipedal robot measuring 1.67 meters and weighing 60 kilos, capable of walking and grasping objects. Not enough for a real production line, but enough to raise funds and demonstrate a direction.

Figure 02, launched in August 2024, represents the company’s first real industrial deployment. This was the robot sent to BMW’s assembly plant in Spartanburg, South Carolina and that is not a minor detail. For eleven months, Figure 02 worked ten-hour shifts, Monday to Friday, on a clearly defined task: loading sheet metal parts onto welding fixtures. In total, it completed more than 1,250 hours of real operation, loaded more than 90,000 parts, and reportedly contributed to the production of more than 30,000 BMW X3 vehicles.

That is the number that changes everything. You can manufacture beautiful laboratory videos. You cannot so easily manufacture 30,000 cars.

Figure 03, launched in October 2025, marks a philosophical turning point. The third generation is designed for two environments at once: industry, of course, but also the home. Soft exterior covering for safer human contact, wireless charging, redesigned audio interface, 44 degrees of freedom, tactile sensors capable of detecting forces from as little as three grams precise enough to handle an egg without breaking it.

On the hardware side, the wrist architecture was completely redesigned after BMW’s feedback: the motor controllers now communicate directly with the main computer, without an intermediate distribution board, improving reliability and simplifying thermal management. These are the details that distinguish an engineering robot from an industrial robot.

In March 2026, Figure released a video of Figure 03 autonomously tidying up a living room picking up toys, putting cushions back in place, wiping a table, restoring order. The movements are not fast. But they are continuous, natural, and the robot adapts to the changing layout of objects. This is no longer just a laboratory demonstration.

Helix is based on a logic close
to generative AI models: learning
massively through experience

Helix: the brain that makes all the difference

Figure’s hardware is solid. But what truly interests engineers, investors and competitors is Helix the AI model developed entirely in-house, which forms the central nervous system of all Figure robots.

Helix is a Vision-Language-Action model, or VLA. This type of architecture is at the heart of the new generation of intelligent robotics. In practical terms, the robot receives images from its cameras and instructions in natural language, and the model directly produces motor commands. No multi-step pipeline, no spatial segmentation followed by trajectory planning and then execution everything is integrated into a single neural network that sees, understands and acts.

What makes Helix particularly remarkable is that it runs onboard, without relying on the cloud. Figure 03 carries up to two NVIDIA GPUs per unit. Processing is local, eliminating the latency and connectivity issues that would penalize an industrial robot in a real environment.

Helix 02, presented in January 2026, extends this control to the robot’s entire body walking, manipulation, balance all driven by a single unified neural network. The living room demonstration is the direct consequence of this: the robot is not following a script. It observes its environment, identifies objects, plans and executes actions in real time.

This is what Brett Adcock calls the shift from the “programmed” robot to the “learning” robot. The difference is not cosmetic it is architectural. A programmed robot will only ever execute what it has been told to do. A robot driven by a sufficiently trained VLA can, in theory, approach a new task without reprogramming.

The challenge of scaling production: BotQ and Project Go-Big

Developing a humanoid robot capable of working in a factory is one thing. Manufacturing thousands then millions is another matter entirely. This is precisely where many robotics startups hit the wall: industrialization.

Figure anticipated this problem by building BotQ, its dedicated humanoid robot manufacturing facility. The production philosophy changed fundamentally between Figure 02 and Figure 03. While the previous generation relied on CNC machining for most mechanical parts precise but expensive Figure 03 is designed for die casting, plastic injection molding and stamping. These processes require heavier upfront investment, but they reduce unit costs as volumes increase. This is classic industrial logic: invest heavily in capex to reduce variable costs.

At the same time, “Project Go-Big” is a large-scale data collection initiative designed to feed Helix’s training. The more robots operate in real-world conditions, the more the model learns from varied situations, and the more capable it becomes. Field data is the scarce resource in humanoid robotics and Figure has understood that producing it at scale is a durable competitive advantage.

The real challenge in humanoid
robotics is no longer creating a
functional robot, but producing
it at scale

What Figure has not yet proven

An honest analysis of Figure AI must name the grey areas and they do exist.

The $39 billion valuation is built on potential, not on revenue. Figure’s current revenue remains limited. The BMW deployment is a pilot, not a mass-production contract. BMW and Figure are still evaluating additional use cases for Figure 03, and BMW’s March 2026 press release remains cautious about future commitments.

In June 2025, Brett Adcock declined to do a live demonstration at a major tech conference and refused to publicly answer detailed questions about the reality of the BMW partnership. This is not an alarming signal in itself startup CEOs often avoid uncontrolled demos but it is a useful reminder that the distance between a carefully controlled video and a stable industrial production line remains real.

The comparison with Tesla Optimus is inevitable and deserves to be taken seriously. Tesla is targeting a price range of $20,000 to $30,000 per unit, with ambitions to produce millions of units at scale. The Fremont factory, repurposed for Optimus, is already targeting 12,000 units per year in its initial phase. Figure has not announced a public price for Figure 03, nor a short-term production volume target. On this commercial and industrial front, Tesla has strengths that Figure has not yet demonstrated.

Chinese competition also deserves a mention one that too few Western articles take seriously. Around 90% of humanoid robots sold worldwide in 2025 were designed by Chinese companies, according to the International Federation of Robotics. UBTech, Unitree, Fourier Intelligence and XPeng operate with structurally different production costs. The price war in humanoid robots has not yet begun but it will come.

Who is the CEO of Figure AI?

Brett Adcock is an American entrepreneur specialized in breakthrough technologies and artificial intelligence. Before launching Figure AI in 2022, he had already co-founded Archer Aviation, a company developing electric flying taxis.

With Figure AI, Brett Adcock aims to create humanoid robots capable of working in factories, logistics warehouses and, eventually, homes. His stated goal is to develop a new generation of general-purpose robots capable of performing repetitive, physical or dangerous human tasks using artificial intelligence.

In just a few years, Figure AI has become one of the most closely watched startups in the humanoid robotics sector. The company is working on robots capable of sorting parcels, handling objects, cleaning spaces and assisting industrial operators. Several recent demonstrations have attracted millions of views online, particularly videos showing robots completing full autonomous logistics sorting shifts.

For many analysts, Figure AI is now among the most advanced companies in the global race for humanoid robots, alongside Tesla with Optimus and Agility Robotics.

What Figure AI says about the state of the sector

Figure AI is not an isolated story. It is the sign of a specific moment in the history of robotics.

For the first time, several technologies are maturing simultaneously: language models powerful enough to understand complex instructions, onboard GPUs compact enough to fit inside a robot torso, force and vision sensors precise enough for fine manipulation, and supply chains mature enough to make scaled production imaginable.

This was not true in 2015. It was not really true in 2020 either. In 2025–2026, the conditions are coming together for humanoid robots to move from the laboratory to the factory. Figure AI is one of the companies making that transition tangible.

For industrial leaders reading these lines, the question is not yet: “Should I buy a Figure robot?” It is: “Should I follow this technology closely, understand its real capabilities, and prepare for possible integration over the next three to five years?”

The answer to that question is yes with the caution required for any long-term bet on a technology still under development, but also with the attention that a true paradigm shift deserves when it begins to happen.

Robot Magazine follows the evolution of industrial humanoid robotics. Figure AI, Optimus, Boston Dynamics Atlas: the field is taking shape quickly. Next article in this series: the technical showdown of industrial humanoids in 2026.

Contact Robot-Magazine.fr

Cet article The startup that wants to put a humanoid in every factory and maybe in every home 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.

Contact Robot-Magazine.fr

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

That question will be at the heart of GITEX AI EUROPE 2026, taking place on 30 June and 1 July at Messe Berlin. The event brings together more than 800 enterprises and startups, 500 investors, and 120 speakers from over 100 countries, making it one of the most concentrated international tech gatherings Europe has seen in recent years.

Sovereignty is not just about geography

One of the most persistent misconceptions in the cloud debate is that storing data on a European server automatically means it falls under European law. Dr. Andreas Nauerz, Chief Product Officer at IONOS one of Europe’s largest cloud and hosting providers  is direct on this point: what matters is not only where the data centre sits, but who owns it and where the company is headquartered.

A global provider can operate infrastructure inside Europe while remaining subject to the legislation of its home country  a tension with GDPR that no contractual arrangement can fully resolve. “Sovereignty goes beyond GDPR conformity: it requires technological control over the cloud stack, open standards, and genuine interoperability,” Dr. Nauerz explains.

The argument is particularly acute in regulated sectors. Financial services operating under the Digital Operational Resilience Act (DORA), healthcare institutions, and critical infrastructure operators subject to the NIS-2 Directive all face situations where data sensitivity and jurisdictional exposure intersect in ways that demand genuine infrastructure control  not just contractual assurances.

At GITEX AI EUROPE, IONOS will demonstrate what sovereign cloud looks like in practice: infrastructure and AI solutions built on European foundations, designed to deliver scalability, security, and full data control without compromise.

When AI enters the picture, the stakes rise further

The sovereignty question changes character when artificial intelligence is added to the equation. The concern is no longer limited to where data is stored  it extends to where models are trained and, critically, where inference runs.

Dr. Nauerz puts the timing in sharp terms: “If AI inference consolidates on non-European infrastructure before sovereign compute scales, enterprise AI strategies become jurisdictionally compromised  regardless of where the data lives. That window is closing faster than most cloud roadmaps acknowledge.”

The enterprises that build sovereign infrastructure into their AI strategy from the outset  rather than retrofitting it after the fact  are those best positioned as AI becomes central to operations. This sequencing is a central thread running through the commercial showcases at GITEX AI EUROPE 2026.

Europe is entering a new phase
where digital sovereignty becomes
a procurement decision, not just
a political discussion.

Complexity as an attack surface

Richard Werner, Cybersecurity Platform Lead Europe at TrendAI (a business unit of Trend Micro), approaches the same landscape from a security perspective. While the jurisdictional debate continues, many enterprises have landed on hybrid cloud architectures  workloads distributed across multiple providers  as a pragmatic middle ground.

It is a rational choice, but one that introduces its own vulnerabilities. “Hybrid approaches preserve flexibility, but that added complexity often creates the most common gaps: inconsistent identity and access controls, misconfigurations, and fragmented monitoring and response,” Werner notes.

At GITEX AI EUROPE, Trend Micro will present TrendAI Vision One: an AI-driven cybersecurity platform that consolidates cyber risk exposure management, security operations, and layered protection into a single solution. Its deployment flexibility  across public cloud, sovereign, and air-gapped environments  is designed precisely for the mixed architectures that have become the norm across European enterprise.

From policy debate to procurement decision

Europe has spent years discussing digital sovereignty in whitepapers and political frameworks. What has changed in 2026 is commercial urgency. The €180 million sovereign cloud tender awarded in April 2026 is the clearest signal yet that sovereignty has moved from principle to procurement  a real budget commitment with real consequences for the infrastructure decisions that follow.

Backed by Germany’s federal government, the European Innovation Council, and Berlin’s institutional infrastructure, GITEX AI EUROPE arrives at a moment when many of the decisions shaping Europe’s long-term digital autonomy are actively being made.

GITEX AI EUROPE 2026 takes place on 30 June – 1 July 2026 at Messe Berlin. The event is organised by inD, global organisers of GITEX, and is supported by the Berlin Senate Department for Economics, Energy and Public Enterprises and Berlin Partner for Business and Technology.

For more information: www.gitexeurope.com

#GITEXAIEUROPE

FAQ – Sovereign Cloud, AI, and GITEX AI EUROPE 2026

Contact Robot-Magazine.fr

Cet article Europe’s €180 Million Move: Sovereign Cloud Rebuild Starts Now est apparu en premier sur Robot Magazine.

For a long time, Tesla was seen as the company that would revolutionize the automotive industry. Elon Musk’s company disrupted the electric vehicle market, pushed traditional carmakers to accelerate their energy transition, and transformed the car into a connected software platform.

But over the past several years, a profound shift seems to have been taking place at Tesla. Elon Musk is talking less and less about cars, and more and more about artificial intelligence, autonomy, and above all, humanoid robots.

At the heart of this new vision is Optimus, the humanoid robot developed by Tesla. A project that, only a few years ago, seemed almost unrealistic. Today, it has become central to the group’s strategy.

For some investors, Optimus could represent the largest market in Tesla’s history. For others, it remains an extremely speculative technological promise designed to support the group’s stock market valuation.

But another question is now beginning to emerge on Wall Street: what if SpaceX ultimately became the most strategic asset in the Musk empire? Could a potential SpaceX IPO divert some of the investors currently positioned in Tesla?

Behind these questions lies a much deeper transformation. Elon Musk no longer seems to want to build only cars. He now appears to want to build the technological infrastructure of the future.

Optimus: Tesla’s most ambitious bet

When Tesla first unveiled Optimus, many saw the presentation as just another marketing move. The market was still focused on electric vehicles, batteries, and autonomous driving.

But over the past two years, the signals have multiplied. Tesla has been investing heavily in recruiting engineers specialized in robotics, computer vision, and artificial intelligence. Technical demonstrations have followed one another. And above all, Elon Musk has repeatedly stated that Optimus could become “Tesla’s most important product.”

That statement may sound surprising when referring to a company that already sells millions of electric vehicles around the world.

Yet Musk’s logic is relatively simple: the humanoid robot market could become infinitely larger than the automotive market.

Tesla imagines a future in which humanoid robots could gradually take over repetitive physical tasks in:

• Factories
• Warehouses
• Logistics
• Services
• Even homes

In other words, Optimus would not be aimed only at industry. Tesla seems to want to create a universal robotic platform capable of evolving in environments designed for humans.

And this is precisely what distinguishes humanoid robots from traditional industrial robots.

Tesla could gradually reduce its dependence on automobiles

This is probably one of the most underestimated scenarios on the market today.

Tesla is still officially an automaker. But several analysts are beginning to believe that, in the medium term, the company could gradually reduce its dependence on car manufacturing and invest more heavily in:

Artificial intelligence , Robotics, Software and Automation

Why?

Because the automotive market is becoming increasingly competitive.

China is accelerating massively with BYD, Xiaomi, NIO, and Xpeng. Traditional European and American manufacturers are also investing billions in electric vehicles. Margins are starting to shrink. Prices are falling. And the electric car could gradually become an industrial commodity.

In this context, Tesla needs to find a new growth engine capable of justifying its exceptional stock market valuation.

Optimus could become precisely that new strategic narrative.

Some investors are even beginning to believe that Tesla could one day become more of a robotics and AI company than a traditional automaker.

The idea still seems radical. Yet Elon Musk himself already appears to be preparing this transition.

Tesla’s real advantage: physical artificial intelligence

Unlike many robotics companies, Tesla already has a massive advantage: its accumulated expertise in real-time AI and computer vision.

Tesla cars are already, in a way, autonomous robots on wheels. They use:

Cameras Neural ; networks Proprietary; chips Perception ; systemsMassive ; learning models

Tesla is now applying this same logic to Optimus.

The humanoid robot then becomes a physical extension of the artificial intelligence developed for autonomous driving.

And this is probably the heart of the project.

Tesla is not simply trying to build a spectacular robot. The company is trying to build an AI platform capable of interacting with the physical world.

In this vision, AI no longer remains confined to a screen or a chatbot. It becomes mobile, autonomous, and capable of acting in the real world.

The global battle for humanoid robots has already begun

Tesla, however, is not alone in this market.

China is now investing massively in humanoid robots. Beijing now considers this technology a national strategic sector.

Companies such as Unitree, UBTech, Fourier Intelligence, and Xiaomi are accelerating their developments. Demonstrations are multiplying. Public and private investments are exploding.

And China has several major advantages:

• Industrial power
• Battery expertise
• Electronics
• Components
• Supply chains
• Large-scale production capacity

In response, Tesla is trying to capitalize on its software lead and its unique ability to industrialize complex products quickly.

Because this is probably the group’s other major strength: Tesla knows how to produce at scale.

And if Optimus one day becomes economically viable, Tesla could rapidly accelerate global production.

But the bet remains extremely risky

Despite the growing enthusiasm around humanoid robots, the technological reality remains extremely complex. Designing a robot capable of moving autonomously in the real world still requires solving many major challenges related to balance, mobility, object manipulation, energy autonomy, safety, environmental understanding, and human interaction.

Today, no market player has yet demonstrated a truly autonomous, reliable, and profitable humanoid robot at large scale. The sector therefore remains largely speculative, which fuels many debates around Tesla’s valuation.

A significant part of the group’s stock market value now rests on future markets that are still emerging, such as humanoid robotics, full autonomous driving, physical AI, and generalized automation.

In other words, Tesla is no longer valued as a simple automaker, but as a technology player betting on the industries of the future.

Tesla is no longer valued
like a traditional
automaker.

Meanwhile, SpaceX is increasingly fascinating Wall Street

While Tesla has historically captured most of the market’s attention, another Elon Musk company is increasingly attracting investors: SpaceX.

Long inaccessible to the general public, SpaceX is now considered one of the most strategic private companies in the world.

And many analysts believe that a future IPO could become one of the most important financial events of the decade.

Unlike many space companies, SpaceX already generates:

• Recurring revenue
• Government contracts
• Commercial launches
• Above all, a global telecom business through Starlink

The group now largely dominates the Western space market.

And most importantly, SpaceX has technological barriers that are extremely difficult to replicate.

Could a SpaceX IPO weaken Tesla?

This is now a question some funds are beginning to take seriously.

Today, some investors buy Tesla to gain indirect exposure to Elon Musk and his ability to innovate.

But if SpaceX went public, part of that capital could naturally shift.

Why?

Because many now believe that SpaceX has:
A stronger technological lead, less competition, strategic revenues and enormous geopolitical potential

In electric vehicles, Tesla now faces extremely aggressive global competition.

In space, SpaceX still maintains an impressive level of dominance.

Some investors could therefore choose between the two companies. And this could gradually change how Tesla is perceived on financial markets.

Is Elon Musk trying to build the technological infrastructure of the future?

Ultimately, Tesla, Optimus, Starlink, and SpaceX now seem to be part of a much broader vision.

Elon Musk now appears to want to connect several technology sectors within a single global vision combining artificial intelligence, robotics, energy, automation, space, and global networks.

In this approach, Tesla cars gradually become autonomous robots capable of interacting with their environment, while Optimus could represent a future physical workforce for factories, logistics, or services.

At the same time, Starlink is positioning itself as a global communications infrastructure, while SpaceX develops the space capabilities of the system. Few entrepreneurs in history have attempted such vast technological integration.

In this context, Tesla’s bet on Optimus goes far beyond the simple development of a humanoid robot. The company seems to be gradually preparing its transformation toward a model much more centered on artificial intelligence, software, robotics, and global automation.

In the coming years, Tesla could even gradually reduce its dependence on automobiles and invest more heavily in humanoids and physical AI.

But this bet remains extremely ambitious and risky.

Because despite impressive demonstrations, humanoid robots are still far from mass adoption at large scale.

Meanwhile, SpaceX increasingly appears to be another key pillar of the Musk empire.

And a future IPO could divert some of the attention and capital currently focused on Tesla.

One thing, however, seems clear:
Elon Musk is no longer simply building cars.
He is now trying to redefine the technological economy of the future.

Author : Christophe Carl Louis
Article assisted by an IA

FAQ – Tesla, Optimus, and SpaceX

Contact Robot-Magazine.fr

Cet article Why Tesla is betting billions on Optimus ? est apparu en premier sur Robot Magazine.

To better understand this evolution, Robot Magazine spoke with Lucas Goumarre, founder of Korben For People, a French company specializing in multi-brand robotic fleet management software. Its ambition: to become the operating system for service robots deployed in European companies.

“We want to make service robotics accessible and sustainable”

For Lucas Goumarre, robotics should not be seen only as a technological achievement, but as a practical tool serving field teams.

“Korben For People is the operating system for service robotic fleets. We are developing a software platform capable of integrating robots from the world’s leading brands into a single interface, allowing our clients to manage everything from one screen.”

The company promotes a “hardware-agnostic” approach, a positioning that remains relatively rare in the market. In practical terms, this means that clients are not locked into a single robot brand.

“Fleets will evolve over time, and manufacturers will change as well. Our role is to provide a stable and independent software layer that protects clients’ investments over the long term.”

This vision is mainly aimed at field operators: cleaning companies, hypermarkets, logistics warehouses and supply chain players.

A SaaS model based on connected robots

Korben For People is primarily based on a B2B SaaS model. The company currently charges around €150 excluding VAT per robot per month for the use of its fleet management platform.

“Each robot connected to our OS generates recurring revenue. This is what structures the scalability of our model.”

At the same time, the company also supports clients with hardware deployment through multi-brand resale and Robot-as-a-Service offers.

This market evolution is also part of the rise of the Robot-as-a-Service model, where companies now prefer monthly subscriptions rather than heavy CAPEX investments. This model accelerates the adoption of service robots in cleaning, logistics and retail, while making it easier to deploy fleets across multiple sites.

But the core of the strategy remains clearly software-driven.

“We are not a manufacturer. We are a platform that makes manufacturers interoperable.”

Today, the company reports around 250 robots deployed and managed entirely through its platform, with a presence in France, Belgium, Spain and Germany.

Mass retail, cleaning and logistics: the sectors accelerating adoption

Three major markets currently concentrate Korben For People’s activity.

The first is food retail, where cleaning robots are gradually becoming the norm in hypermarkets and supermarkets.

The second concerns professional cleaning companies, which use these technologies at their own clients’ sites.

Finally, supply chain and logistics represent a major growth area, particularly for warehouse cleaning and automated internal delivery.

According to Lucas Goumarre, the market is now entering an industrialization phase.

“When 90% of a hypermarket’s floors are cleaned by robots, we are no longer in experimentation. We are in production.”

Robot cybersecurity is becoming a strategic issue

One of the most interesting aspects of Korben For People’s vision concerns cybersecurity. It is still a relatively under-discussed topic in the world of service robotics, but it could quickly become unavoidable.

“Today, many companies still consider robots as non-critical equipment. Yet these machines constantly collect data: images, maps, operational flows…”

To address these challenges, the company emphasizes a sovereign approach: data hosting in France, native GDPR compliance, encrypted exchanges and preparation for European NIS2 requirements.

“Our role is to remove cyber and data risk from the hardware choice.”

This vision could become decisive in the coming years, as foreign robots become increasingly present in sensitive European environments.

AI will transform robotic fleet management

For Lucas Goumarre, the next revolution will not only concern the robots themselves, but above all the way fleets are managed through artificial intelligence.

Predictive maintenance, automatic route optimization, computer vision, self-learning routes — all these functions require a software layer capable of analyzing an entire multi-brand robotic fleet.

“Manufacturers cannot provide this cross-functional intelligence on their own. This is exactly what we are building.”

A market shift toward open platforms

Through this approach, Korben For People illustrates a deeper trend in the robotics market: the shift from the “robot product” to the “robotics platform.”

This change is comparable to what IT experienced twenty years ago with the industrialization of information systems.

For companies, the challenge will soon no longer be only to choose a high-performing robot, but to know how to manage hundreds of machines in a centralized, secure and intelligent way.

And in this new battle, software may well become more strategic than the hardware itself.

FAQ – Korben For People and robotic fleet management

Cet article Interview with Lucas GOUMARRE, CEO of Korben For People est apparu en premier sur Robot Magazine.

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)

Contact Robot-Magazine.fr

Cet article Dassault Systèmes, Siemens, PTC: three visions of the digital twin, and a battle that is only just beginning est apparu en premier sur Robot Magazine.

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.

FAQ – The Challenges of Industrial-Scale Deployment

Contact Robot-Magazine.fr

Cet article Humanoid Robotics: The real challenge is no longer the technology, but scaling up est apparu en premier sur Robot Magazine.

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.

FAQ – The Keys to Industrial Automation and IoT

Contact Robot-Magazine.fr

Cet article Rockwell, Schneider Electric, Beckhoff: the war of industrial architectures already has a temporary winner est apparu en premier sur Robot Magazine.

These three manufacturers all have recognized expertise, but their approaches differ significantly. Robot architecture, industrial positioning, sector specialization, precision, speed, and operating environment: each player addresses specific needs.

So, how do you choose between ABB, Epson, and Stäubli depending on your production requirements? Robot Magazine offers a detailed analysis to help guide industrial decision-making.

Three leaders, three industrial philosophies

Before getting into the technical comparison, it is essential to understand the positioning of each manufacturer.

ABB: versatility and industrial power

Choosing an industrial robot
means making a strategic decision,
not just a technical one.

Epson: precision and speed for electronics and assembly

Epson has long been recognized for its SCARA robots and micro-assembly solutions, positioning itself as a key player in high-precision industrial automation. The company specializes in applications where very high precision, extremely fast cycle times, and maximum compactness are critical to performance.

This expertise translates into a strong presence across sectors such as electronics, component manufacturing, small-part assembly, and high-speed automated production lines, where efficiency, repeatability, and space optimization are essential.

Stäubli: high precision and critical environments

Stäubli takes a more niche yet highly demanding position within the robotics landscape. Its robots are renowned for their exceptional precision, long-term reliability, and ability to operate in sensitive environments where strict standards are required.

This level of performance makes them particularly well-suited for industries such as pharmaceuticals, medical applications, food processing, as well as clean or sterile environments and micromechanics, where accuracy, cleanliness, and consistency are critical.

Technical comparison: performance, precision, flexibility

1. Precision and repeatability

• Stäubli clearly stands out in this area. Its robots provide extremely fine repeatability, making them well suited for critical environments and high-precision applications.
• Epson also excels, especially with SCARA robots, where precision is essential for fast, repetitive operations.
• ABB offers high precision as well, but it is more focused on versatile industrial applications than on extreme micro-precision.

Verdict: Stäubli > Epson > ABB for very high-precision applications.

2. Execution speed

• Epson leads in short cycle times and high-speed production. Its SCARA robots are designed for rapid tasks with highly optimized cycle times.
• ABB delivers strong performance, but with a balance between speed and robustness.
• Stäubli prioritizes precision and stability over pure speed.

Verdict: Epson > ABB > Stäubli for very high-speed production lines.

3. Payload and power

• ABB offers a very broad range, including robots capable of handling heavy loads in demanding industrial environments.
• Stäubli covers intermediate payloads with high precision.
• Epson mainly focuses on light payloads.

Verdict: ABB > Stäubli > Epson for heavy-duty applications.

4. Operating environment

• Stäubli is the leader for clean, sterile, or sensitive environments such as cleanrooms, pharmaceuticals, and food processing.
• ABB is designed for standard to complex industrial environments.
• Epson is suitable for clean environments, but not as demanding as those typically targeted by Stäubli.

Verdict: Stäubli > Epson > ABB for critical environments.

5. Flexibility and integration

• ABB stands out for its ability to integrate complex systems, particularly through advanced software, simulation solutions, and connected architectures.
• Stäubli offers excellent integration within specialized systems.
• Epson focuses on simple, fast-to-deploy solutions, but these are less scalable in complex architectures.

Verdict: ABB > Stäubli > Epson for complex industrial systems.

Robotics is evolving toward
smarter, more adaptive
systems.

Choosing according to your type of production

Heavy production and full-scale industrial automation

If your production involves:

• Heavy handling
• Welding
• Palletizing
• Complex industrial lines

ABB is the natural choice.

Its versatility, robustness, and software ecosystem make it well suited for large-scale installations.

Fast assembly and electronics production

If you work in:

• Electronics
• Component assembly
• Very high-speed production lines

Epson is particularly suitable.

Its SCARA robots provide the speed and precision needed for short-cycle operations.

Critical production and sensitive environments

If your activity concerns:

• Pharmaceuticals
• Medical industries
• Food processing
• Clean environments

Stäubli is often the obvious choice.

Its precision and compliance with strict standards make it a benchmark player.

Cost, maintenance, and ROI

• The choice does not depend on technical performance alone. Total cost of ownership (TCO) is a key factor.
• ABB often involves a higher initial investment, but offers strong durability and scalability.
• Epson provides more accessible solutions, with fast ROI on high-speed lines.
• Stäubli positions itself in markets where quality matters more than cost, with return on investment driven by reliability and compliance.

2026 trends: toward a convergence of technologies

The differences between these players remain clear, but certain trends are bringing their approaches closer together:

• Growing integration of AI to improve flexibility
• Development of collaborative solutions
• Advancements in machine vision
• Use of digital twins for simulation
• Convergence between industrial robotics and intelligent robotics

Robots are becoming less rigid, more adaptive, and better able to handle increasing variability.

A strategic choice, not just a technical one

Choosing between ABB, Epson, and Stäubli is not about selecting “the best robot,” but identifying the one that best matches your industrial reality.

• ABB for power, versatility, and complex environments
• Epson for speed, precision, and fast assembly
• Stäubli for extreme reliability and critical environments

At a time when automation is becoming a strategic lever, the robot is no longer just a tool, but a central element of the production system.

The right choice is the one that fits into your value chain, your environment, and your long-term industrial vision.

FAQ – Choosing Between ABB, Epson, and Stäubli

Contact Robot-Magazine.fr

Cet article ABB vs Epson vs Stäubli: Which industrial robot should you choose for your production? est apparu en premier sur Robot Magazine.

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.

FAQ – Industrial Automation and Liebherr Palletizing Solutions

Contact Robot-Magazine.fr

Cet article Automated Palletizing: How Liebherr boosts productivity est apparu en premier sur Robot Magazine.