Intel Just Rewired the Chip and the Rules of Artificial Intelligence

Intel introduced PowerVia, a design shift the company calls nothing less than a revolution. Photo: Intel
Intel introduced PowerVia, a design shift the company calls nothing less than a revolution. Photo: Intel
TT

Intel Just Rewired the Chip and the Rules of Artificial Intelligence

Intel introduced PowerVia, a design shift the company calls nothing less than a revolution. Photo: Intel
Intel introduced PowerVia, a design shift the company calls nothing less than a revolution. Photo: Intel

In the blistering heat of the Arizona desert, Intel staged a quiet revolution. At the Intel Technology Tour 2025 in Phoenix, the company didn’t just unveil new processors. It revealed a plan to rebuild the foundations of computing itself.

This wasn’t a spec-sheet update. It was the kind of pivot that comes along once in a generation, one that could rewrite how artificial intelligence is powered, trained, and trusted.

At this invite-only event, where Asharq Al-Awsat was the sole Arabic media presence from the Middle East, Intel showed off technologies that don’t merely shrink transistors but re-imagine how electricity and intelligence flow through silicon.

The Day Power Flipped
“For the first time in semiconductor history, we’re moving power delivery to the backside of the chip,” said James Johnson, Intel’s senior vice president and head of client computing, as he introduced PowerVia, a design shift the company calls nothing less than a revolution.

He wasn’t exaggerating. Instead of channelling energy through the maze of wires on top of a processor, PowerVia feeds it directly from behind, shorter paths, less resistance, fewer losses. The result: chips that run 30 percent more efficiently and 10 percent denser than before.

Paired with Intel’s new 2-nanometer RibbonFET transistors, the technology anchors Intel’s audacious roadmap: “Five nodes in four years.” By 2026, the company wants to reclaim the lead it ceded to TSMC and Samsung in advanced manufacturing.

“What we’re seeing,” said Stephen Robinson, one of Intel’s senior fellows, “is an unprecedented convergence between architectural innovation and manufacturing maturity.”

In other words, it’s not just about how small the chip gets, it’s about how smart it becomes.

Beyond the Shrink
For decades, the semiconductor race was about scale: who could pack more transistors into less space. But Robinson insists the game has changed.

“It’s no longer about shrinking the transistor,” he told Asharq Al-Awsat. “It’s about rethinking how every element works together to reach efficiencies no one’s seen before.”

Intel calls this philosophy System Technology Co-Optimization, or STCO. It’s engineering meets orchestration: physics, logic, and AI co-designed in a single loop. Think of it as turning the chip into a living ecosystem, not a static piece of silicon.

Robinson calls this moment a “once-in-a-lifetime opportunity” for the industry, a rare alignment of physics, data, and human ingenuity.

The AI Inside Everything
If the chip is the body, then AI is the brain now wired into it.

According to Thomas Petersen, Intel’s senior fellow for architecture and graphics, the company’s next move is about making every processor think collectively—a symphony of CPU, GPU, and NPU working as one organism.

“We’re designing processors to think together, not separately,” Petersen said.

“The days of each chip doing one job are over.”

The star of this new generation is Panther Lake, Intel’s 2026 platform for the AI PC. By weaving neural processing directly into the CPU, your laptop becomes a stand-alone AI engine, running tasks locally, instantly, and privately without the cloud on constant call.

“The goal isn’t just to get an answer from a smart model,” Petersen said. “It’s to get it instantly, privately, and with minimal energy. That’s the philosophy of the next intelligent computer.”

The shift marks a turning point from “assisted intelligence” to “active intelligence.” The PC won’t just help, it will collaborate. Users will work side-by-side with autonomous AI agents that analyze, plan, and respond in real time.

“We’re building chips that understand the meaning of data,” Petersen said, “not just calculate it.”

When AI Becomes a Colleague
At a session titled Gemini Enterprise AI, Intel described the next stage of enterprise computing: Agentic AI, systems that don’t just support humans but work alongside them.

“AI is no longer a tool,” said one speaker. “It’s a co-worker.”

Intel’s idea of Agentic Work Environments envisions teams of human employees and AI agents collaborating, making decisions, and even negotiating outcomes within secure, governed frameworks. The glue that holds it all together? Trust—not as a software patch, but as hardware architecture.

“Autonomous agents can behave unpredictably,” said an Intel security engineer. “That’s why trust must live in the silicon itself.”

To enforce that trust, Intel upgraded its Trusted Execution Environment (TEE) and hardware isolation systems, ensuring that AI models run inside encrypted, quarantined zones. In an era where synthetic content and model-to-model interaction are exploding, Intel sees this as the first line of defence in the new AI frontier.

Hyper-Connectivity: The Nervous System of AI
Fast intelligence is meaningless without fast connection.

At the “Wireless Innovations” session, Intel engineers previewed Wi-Fi 8, 5G Advanced, and early glimpses of 6G. It is a future where every connected device becomes a mini data center, processing information locally with near-zero latency.

“The edge,” said one network architect, “is the new frontier for AI. The next models won’t just live in the cloud; they’ll live in the world around us.”

That world includes the Middle East. From NEOM’s digital twins to autonomous transport grids across Saudi Arabia and the UAE, the region’s smart-city projects rely on the kind of ultra-low latency and reliability Intel is building into its chipsets and modems.

The New Metric: Sustainability
Even in a week obsessed with speed, sustainability was the quiet headline.
“Efficiency isn’t just performance per watt,” said Tim Wilson, Intel’s vice president of design engineering. “It’s responsibility per watt.”

Intel now recycles over 95 percent of its water, pursues zero-waste fabs, and designs chips that literally waste less power inside themselves. PowerVia doesn’t just make circuits cleaner, it makes computing greener.

“In the age of AI,” Wilson said, “sustainability isn’t optional. It’s a design constraint.”

That ethos mirrors the Middle East’s own goals: energy-efficient cities, renewable-powered data centers, and carbon-neutral digital growth under Saudi Vision 2030 and the UAE’s Net Zero agenda.

A New Connection with the Middle East
Though Phoenix was the stage, the conversation kept circling back to the Gulf.
Saudi Arabia is investing billions into AI, cloud infrastructure, and sovereign data centers laying the groundwork for a future semiconductor industry of its own. Intel, sensing the region’s momentum, has begun collaborating with Gulf universities and research labs on chip design and AI engineering.

A senior Intel official confirmed ongoing talks with sovereign wealth funds on potential partnerships for advanced packaging and local manufacturing projects.

The subtext: the Middle East isn’t a spectator in the AI race, it’s a stakeholder.

Making AI for Everyone
Perhaps the most radical idea at Phoenix wasn’t technical, it was social.

Intel wants to democratize AI. Through its Gaudi3 and Gaudi4 accelerators, the company is offering a low-cost alternative for training massive models up to 50 percent cheaper than rival platforms.

“AI shouldn’t be a luxury item,” an Intel executive said. “It should be like electricity, accessible, reliable, and sustainable.”

That principle could reshape emerging tech ecosystems, especially in places like Saudi Arabia, where national AI strategies hinge on local innovation. Affordable compute means universities and startups can train their own models, rather than rent power from global giants, a leap toward digital sovereignty.

The Hidden Infrastructure of Trust
As AI grows more autonomous, the question isn’t what it can do, it’s who decides what it should do.

Intel’s answer lies deep in the chip’s DNA.

“We used to protect data,” one Intel researcher told Asharq Al-Awsat. “Now we protect behavior. When models can make decisions, you need silicon that understands trust.”

The company is developing digital IDs for AI agents, encrypted model training, and physical data isolation layers, technologies increasingly vital for sectors like defence, energy, and finance.

In the Gulf, this vision echoes work by SDAIA, Saudi Arabia’s Data and AI Authority, which is crafting a national framework for AI governance and safety.

Both share the same core belief: trust isn’t a checkbox; it’s an engineering discipline.

A Legacy Reinvented
By the end of the Phoenix tour, one thing was clear: Intel isn’t just trying to win the AI race. It’s trying to redefine what leadership looks like in an era where machines think, learn, and act.

Intel sees itself as “the custodian of computing’s evolution” the thread connecting the first microprocessor to the age of autonomous intelligence.

“We stand at the intersection of physics, logic, and imagination,” Robinson said in his closing remarks. “That’s where the future of intelligence, human and artificial, truly lies.”

Petersen added a line that could have come straight from Wired’s own manifesto:

“The future of AI is too big to be locked behind closed walls. Our role is to empower everyone, from startups to governments to build on our technology.”



Humanitarians Look to Put the AI in Aid

The World Food Program is using autonomous trucks to deliver aid in South Sudan, Sudan and Uganda. Rian COPE / AFP
The World Food Program is using autonomous trucks to deliver aid in South Sudan, Sudan and Uganda. Rian COPE / AFP
TT

Humanitarians Look to Put the AI in Aid

The World Food Program is using autonomous trucks to deliver aid in South Sudan, Sudan and Uganda. Rian COPE / AFP
The World Food Program is using autonomous trucks to deliver aid in South Sudan, Sudan and Uganda. Rian COPE / AFP

From remote-controlled trucks delivering life-saving aid in dangerous settings to mobile phone data analysis flagging mass displacement, humanitarians are eyeing ways in which artificial intelligence can speed up and improve their operations.

There have been plenty of warnings about the dangers of AI for aid agencies, who face growing challenges of securing often extremely sensitive data and swelling misinformation about their operations and beneficiaries, said AFP.

But at the AI for Good summit in Geneva this week, a handful of humanitarian-focused displays emphasized the technology's positive potential.

Parked in one corner of a vast hall at the Palexpo conference center was a giant white SHERP vehicle, resembling a hulking Martian rover, decked out with cameras and sensors and a drone landing-pad on the roof.

Made in Ukraine, SHERPs are amphibious vehicles that can float on water, drive through swamps and flooded rivers with their giant wheels, and climb over obstacles up to one meter (3.3 feet) high.

The UN's World Food Program is preparing to begin field-testing a version of the AI-enabled truck that can be steered remotely through the most dangerous and difficult terrain to reach people in need.

"I think this could be a game-changer," Bernhard Kowatsch, head of WFP's global accelerator and ventures innovation division, told AFP.

The technology, he said, "should allow us essentially to reach people that otherwise never would have been reachable".

Not possible without AI

WFP already has drivers using SHERPs to deliver aid in Sudan, South Sudan and Uganda.

But after numerous heartbreaking losses of drivers, it tasked the German Aerospace Center (DLR) to help equip the vehicles with AI and other technologies, making it possible to control them remotely through particularly dangerous terrain.

The idea is to set up a shipping container control room in a safe area, where a human can remotely control the vehicle on the last, most treacherous leg of its journey.

Tests have been conducted in Germany, and will be tried out in the field in Uganda in 2028, said Armin Wedler, who is coordinating DLR's Autonomous Humanitarian Emergency Aid Devices (AHEAD) project.

Standing next to the 2.8-meter high vehicle, he told AFP that the team had used "remote-control technologies which are based on mathematics and old-school... research", but stressed: "We would not be able to process everything without using also AI".

It would be possible to make the vehicle fully autonomous, Wedler said, but stressed that in complex humanitarian settings "we have to have a human in the loop".

"We're not talking about driving on clear streets with clear lanes. There are no streets," he said, also describing scenes where aid trucks are suddenly swarmed by desperately hungry people.

"There's no AI autonomous algorithms ever capable to handle that safely."

'Life-saving'

Among more than 200 exhibitors at the summit -- showing off everything from humanoid robots to bionic prosthetics and emotional companions -- the other humanitarian displays were more discreet, with pamphlets detailing how AI tools are boosting and streamlining operations.

Among them, the UN refugee agency detailed a new Legal Virtual AI Assistant for lawyers and legal officers representing refugees, enabling them to swiftly determine the rights available within country-specific legal frameworks.

Rebeca Moreno Jimenez, the lead data scientist at UNHCR's Innovation Service, told AFP that building cases faster and more efficiently can be "life-saving for many refugees".

Another UN initiative called Data Insights for Social and Humanitarian Action, or DISHA, relies on partnerships with private actors such as Google and McKinsey to provide humanitarian organizations with data and AI models to speed up and improve disaster responses.

One project uses AI analysis of anonymized mobile phone data to spot mass-population movements during disasters, determining where people are fleeing, to help humanitarians better tailor their response.

Another uses AI for rapid analysis of satellite images taken before and after disasters like last month's earthquakes in Venezuela to determine building damage.

The aim is to give humanitarians "accurate information early enough to make better decisions (and) avoid going to the wrong place when there are people who need you somewhere else", DISHA product lead Andreas Kortis told AFP.


How AI Supports Real-Time Decision-Making in Saudi Arabia's Airports and Ports

Airports and ports require an intelligent operational layer that connects data, processes, and resources to support better decision-making during disruptions. (Adobe)
Airports and ports require an intelligent operational layer that connects data, processes, and resources to support better decision-making during disruptions. (Adobe)
TT

How AI Supports Real-Time Decision-Making in Saudi Arabia's Airports and Ports

Airports and ports require an intelligent operational layer that connects data, processes, and resources to support better decision-making during disruptions. (Adobe)
Airports and ports require an intelligent operational layer that connects data, processes, and resources to support better decision-making during disruptions. (Adobe)

Infrastructure investment in Saudi Arabia is entering a new phase, one measured not only by the airports, ports, logistics corridors, energy systems, and digital infrastructure that have been built, but also by the ability of these assets to operate as a single integrated system.

The next source of value will not come solely from expanding capacity, but from improving the decisions that determine how aircraft, ships, cargo, energy, and data move from one moment to the next. At this stage, artificial intelligence becomes part of the operational equation itself: how can major infrastructure assets be transformed into operations that are more efficient, reliable, and resilient in the face of disruptions?

Bilal Abu-Ghazaleh, Founder and CEO of 1001. (Company)

In an exclusive interview with Asharq Al-Awsat, Bilal Abu-Ghazaleh, founder and CEO of 1001, a startup developing sovereign artificial intelligence that recently raised $30 million in a Series A funding round, said the difference between building assets and operating them efficiently is the difference between capacity and performance.

"Building an airport, a port, or a logistics corridor gives you capacity, but it does not automatically ensure the best use of it," he said. "A larger asset does not manage itself more efficiently. Instead, it creates a greater number of decisions that must be made correctly."

Intelligence Built into the Design

This idea lies at the heart of understanding the next phase of Saudi Arabia's transformation. Every new asset adds not only physical space or operational capacity, but also an entirely new network of relationships and interdependencies. A new airport terminal, an additional port berth, or a new logistics corridor does not function in isolation from the rest of the system. A delay involving a single aircraft or vessel can alter gate or berth assignments, triggering a chain reaction that affects truck movements, customs operations, warehouses, delivery schedules, and workforce allocation.

Abu-Ghazaleh explained that adding a new terminal, berth, or corridor also means "adding thousands of new connections between things that influence one another." Given the speed and scale of Saudi Arabia's development, he said, no human team, regardless of its experience, can keep track of all these relationships and consistently make the best decisions in real time.

Yet this challenge also presents an opportunity. Countries with aging infrastructure are often forced to introduce artificial intelligence after decades-old systems are already in place. Saudi Arabia, by contrast, can embed an intelligent operational layer into new projects such as King Salman International Airport and newly developed ports and rail networks from the design stage rather than years after operations begin.

Abu-Ghazaleh argued that "most countries around the world are stuck trying to bolt artificial intelligence onto legacy systems," whereas Saudi Arabia has the opportunity to design intelligence directly into its infrastructure assets from the outset.

Solving Complex Problems

At airports and ports, the most difficult challenges are not always a lack of capacity but a lack of coordination. That is why building more facilities, hiring more staff, or introducing another conventional software system is not enough. When a vessel is delayed at a major port such as Jeddah Islamic Port, or when a disruption occurs at a large airport, it sets off a chain of decisions: Which berth should be assigned? How should cranes be rescheduled? What happens to trucks and rail operations? How should yards, warehouses, and resources be reorganized?

Abu-Ghazaleh said, "The hardest problems in airports and ports are not capacity problems. They are coordination problems." He stressed that these cannot be solved simply by pouring more concrete or increasing the workforce. Adding more people can actually increase the coordination burden without necessarily providing a unified view of all the interconnected variables.

Conventional software also cannot fully bridge the gap because the core issue lies in fragmented data spread across multiple systems: one for transportation, another for warehouses, a third for enterprise resource planning, and others for customs or maintenance. Each system performs a specific function within its own domain, but none has visibility across the entire operation. The challenge, therefore, is to build a living operational model that unifies data, relationships, and business rules, enabling decisions to be made based on a single, comprehensive view of the system.

Deploying artificial intelligence in critical infrastructure requires human oversight, explainability, auditability, and a record of every decision. (Shutterstock)

The Role of Operational Intelligence

Abu-Ghazaleh emphasized that there is no single starting point for every industry. The greatest benefit from artificial intelligence may lie in improving capacity utilization at an airline, managing disruptions at a port, optimizing cargo flows across a logistics company, or reducing energy consumption at another infrastructure asset. "We don't start by guessing," he said. "We start by understanding the operation from the inside."

According to Abu-Ghazaleh, 1001's methodology places engineers within clients' teams to understand how an organization actually operates, rather than how it appears in diagrams or presentations. These engineers map workflows, data flows, and the highest-value operational challenges before working with operations teams to identify the first use case capable of delivering a measurable impact. They then build what he describes as a "living operational model," essentially a dynamic digital map that captures assets, processes, business rules, and the relationships between them, while updating continuously in real time.

The significance of this approach is that the value extends far beyond a single use case. Once this foundation has been established, subsequent applications can be developed much more quickly. Abu-Ghazaleh noted that while the first use case typically takes the longest to complete, the second and third benefit from the same underlying model, reducing implementation time from around 16 weeks to roughly four weeks. He added that the returns can be substantial, with a single use case capable of generating more than $100 million in value during its first year.

When a Ship Is Delayed

To illustrate the difference between automation and intelligence, Abu-Ghazaleh uses the example of a ship arriving several hours behind schedule. Such an event does not simply alter one arrival time. It disrupts the entire operational plan. The berth assigned to that vessel may now be needed for another ship, while the cranes and crews waiting for it remain idle. The containers it carries are linked to trucks, trains, and delivery schedules that are no longer aligned, even as the yard has already been organized according to the original arrival timetable.

Automation can handle some routine tasks, such as sending an alert, updating a schedule, or reallocating a slot based on predefined rules. But when real-world conditions diverge from the plan, executing a fixed rule is no longer enough. What is required is a complete reassessment of the operation and the identification of the best recovery plan across thousands of variables within minutes.

Abu-Ghazaleh said this is precisely the type of decision that artificial intelligence can improve because it "sees the entire operation at once." It can rapidly develop a new operating plan by determining which berth should accommodate the delayed vessel, how cranes and yard operations should be reorganized, and how trucks and trains should be rescheduled together while accounting for real-world constraints. Even so, this does not eliminate the role of human operators. The proposed plan is presented to the operator along with the reasoning behind it, while the final decision remains under human control.

AI as Part of the Operation

The risks change when artificial intelligence moves beyond analysis into recommendation or execution. At the analysis stage, the system serves as an advisory tool. If it makes a mistake, a human can identify the error before any harm occurs. But once it begins making recommendations that influence the operation of an airport, port, or energy asset, it becomes part of the operational process itself.

Abu-Ghazaleh explained that "failure in these environments is not a software bug in a report. It is a crisis." For that reason, the standard for trust becomes significantly higher. He identified three essential requirements: consistent reliability, the ability to explain the reasoning behind every recommendation or action, and the recording of every step so that decisions can be audited and reversed when necessary.

He added that the system must be governed rather than trusted unconditionally, while human operators must remain in control. The system should earn greater autonomy gradually, "one decision at a time."

This also explains why many AI projects struggle to move from the pilot stage to live operations. The gap is not merely technological. It is also operational and institutional. There is a significant difference between a convincing demonstration and a system that an operator is willing to trust alongside an airport runway or on a port berth. As a result, system governance, auditability, and operational continuity become essential requirements for deployment in critical infrastructure rather than optional features that can be added later.

The Challenge of Incomplete Data

Large-scale operations often contend with fragmented or incomplete data, particularly when they rely on legacy systems. Abu-Ghazaleh believes the answer is not to wait for perfect data, but to build integration and data lineage instead.

"You will never get perfectly clean data from systems that are decades old, and you do not need to," he said. The objective is to bring together scattered data sources within a consistent operational model while ensuring that every recommendation can be traced back to the data, logic, and alternative options on which it was based.

He added that data alone cannot capture everything that happens within an operation. A significant portion of operational knowledge exists only in the minds of the people who manage these systems every day and is not stored in any database. According to Abu-Ghazaleh, 1001 uses AI agents to capture that context directly from operational teams and integrate it into the model alongside system data. When data gaps or conflicts between sources emerge, they should be made visible to the operator rather than concealed, and the decision should be escalated to a human instead of having the system rely on guesswork.

Sovereignty Alone Is Not Enough

Sovereignty remains an important part of the discussion, particularly when it comes to critical infrastructure. However, Abu-Ghazaleh cautions against equating resilience with local deployment alone, arguing that "deploying systems locally does not automatically make them resilient." In his view, two distinct risks must be addressed: the risk of foreign control, and the risk of depending on a single component, facility, or supplier, even within the domestic market.

From this perspective, sovereignty addresses the risk of an external "kill switch" by ensuring that data, models, and infrastructure remain under national jurisdiction. Resilience, however, also requires an architecture that is not locked into a single model, vendor, or location. Abu-Ghazaleh told Asharq Al-Awsat that this is why model- and vendor-neutral infrastructure is essential, along with the ability to operate across both cloud and on-premises environments. He also stressed the importance of dividing systems into distinct components so that the failure of one does not bring down the entire operation.

He summarized the relationship succinctly: "Local control without redundancy and alternatives is fragile, while redundancy without control leaves you exposed." In other words, intelligent national operations require both sovereignty that reduces external dependence and resilient system design that minimizes domestic points of failure.

The Skills That Matter After Computing Infrastructure Is Built

In recent years, many countries have focused on building data centers and expanding computing capacity. Abu-Ghazaleh argues, however, that leadership in artificial intelligence will not be determined by computing power alone, but by applied AI, meaning the integration of intelligence into real-world operations.

"Applied AI is not won in the laboratory," he said. "It is won inside the most demanding live operations."

In his view, Saudi Arabia holds several advantages, including major infrastructure across aviation, ports, energy, and logistics, the ability to move quickly when institutions are aligned, new projects into which intelligence can be embedded from the outset, and the capacity to invest at scale. Turning those advantages into measurable performance, however, requires three capabilities: a data foundation that transforms fragmented systems into a unified decision-making asset, engineers who understand both software and operational environments, and institutional leadership willing to redesign decision-making processes while maintaining governance, security, and auditability.

If these capabilities are in place, computing infrastructure becomes a productive investment. Without them, it risks becoming expensive but underutilized capacity. As Abu-Ghazaleh put it, shortcomings in these areas turn investment into "costly idle capacity."

The Next Measures of Success

Abu-Ghazaleh believes Saudi Arabia's success will be measured by its ability to move beyond building AI infrastructure and begin embedding artificial intelligence into national operations. The true indicators, he said, will be operational rather than experimental.

The first indicator is that AI becomes part of the daily operations of ports, airports, and energy infrastructure instead of remaining confined to pilot projects. "Ninety-five percent of AI never reaches production," he said. The real test is whether day-to-day deployment is reflected in measurable outcomes such as productivity, on-time performance, uptime, and cost.

The second indicator is sovereignty across data, computing infrastructure, and AI models. When critical systems operate using intelligence built on the Kingdom's own data and models, that intelligence becomes a strategic asset that grows with every decision rather than a service that can be priced, restricted, or switched off from abroad.

The third indicator is ensuring that expertise remains within the domestic market through Saudi engineers and accumulated applied experience gained from each successful deployment.

In this sense, the next phase is not simply about adding artificial intelligence to existing infrastructure. It is a test of whether the entire system can transform major physical assets into smarter, more intelligent operations.


Cerebras Says to Invest Billions in Europe

A person is silhouetted next to the logo of the first Global Dialogue on AI Governance, in Geneva on July 6, 2026. (Photo by Fabrice COFFRINI / AFP)
A person is silhouetted next to the logo of the first Global Dialogue on AI Governance, in Geneva on July 6, 2026. (Photo by Fabrice COFFRINI / AFP)
TT

Cerebras Says to Invest Billions in Europe

A person is silhouetted next to the logo of the first Global Dialogue on AI Governance, in Geneva on July 6, 2026. (Photo by Fabrice COFFRINI / AFP)
A person is silhouetted next to the logo of the first Global Dialogue on AI Governance, in Geneva on July 6, 2026. (Photo by Fabrice COFFRINI / AFP)

US chip maker Cerebras, a US rival of Nvidia, told AFP on Thursday it will invest "several billion dollars" in Europe to boost the computing capacity of its AI data centers on the continent.

"This is a massive expansion" to meet the "rapidly growing" needs of European customers, chief executive Andrew Feldman told AFP in an interview on the sidelines of a Paris artificial intelligence conference.

The Californian company operates three data centers in France, Finland and Norway, which are to be expanded to reach 200MW of computing capacity by 2027.