How the EU’s carbon price on imports strengthens climate policies globally

Potsdam Institute for Climate Impact Research (PIK)

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In early 2026, the EU extended its domestic carbon pricing to key products from beyond its borders. This is managed through the “Carbon Border Adjustment Mechanism” (CBAM). Exporters of polluting goods to the EU must pay a climate tariff, unless their country has its own pricing scheme. A study finds that this could incentivise EU trade partners to adopt carbon pricing as well. In particular Canada, Japan, Taiwan and South Korea are found to be likely candidates, leading to 73 percent more CO₂ emissions being avoided compared to when only the EU applies its climate policy.

The peer-reviewed study is already available on the website of the Journal of the Association of Environmental and Resource Economists (JAERE), and will appear in its November 2026 print edition. It was led by the Potsdam Institute for Climate Impact Research (PIK).

“The Carbon Border Adjustment Mechanism is intended to enable the EU industry to decarbonise while remaining competitive – but what happens outside of the EU is not less significant,” explains PIK researcher Timothé Beaufils, the study’s lead author. “We already observe other countries like Brazil or Turkey responding to the CBAM with their own carbon price. We developed a novel framework to estimate this policy diffusion effect. It provides a strong indication that the EU Green Deal has indeed the potential to trigger the reinforcement of climate policies in other countries.”

The study is based on a specially developed economic model that combines two strands of research: trade economics and game theory. Based on their economic interest, trading partners choose between paying the climate tariff into the EU coffers – or implementing their own carbon price and thus joining what the study calls a “climate coalition”. Detailed trade simulations are used to inform these decisions, which depend on the carbon price level, the exact design of the CBAM – and the countries already part of the coalition.

The CBAM carbon pricing on imports currently applies to steel, iron, aluminium, cement, fertilisers, electricity and hydrogen. To assess its incentive effect on international climate cooperation, the research team feeds its calculation tool with empirical data on trade flows for 56 economic sectors and 43 countries. Using these figures, the team models the EU’s climate policy based on a carbon price of 100 dollars per tonne. The model analysis shows some striking ripple effects:

• Without border adjustment, the European carbon price results in a reduction in domestic European emissions of 505 million tonnes of CO₂ per year. Outside the EU, however, emissions increase such that global emissions are only 305 million tonnes lower. This is because other countries move to supply the more energy-intensive products and, moreover, benefit from lower world market prices due to Europe’s phase-out of fossil fuels. EU climate protection therefore has a massive leak – known as “carbon leakage” – offsetting 40 percent of Europe’s emission reduction.

• With the border adjustment, the carbon leakage effect is much smaller, only 15 percent instead of 40 percent before, resulting in as much as 399 million tonnes of CO₂ emissions reduced globally.  

• With a policy response from trading partners, the global reduction in emissions is 691 million tonnes, a further 73 percent over and above the impact of the EU climate policy alone. Four countries – Canada, Japan, South Korea and Taiwan – avoid the additional burden of CBAM by implementing their own carbon pricing, thereby joining the climate coalition.

Additional model runs show that extensions of the CBAM to other sectors could make it beneficial for more countries to join the climate coalition, including the US. By contrast, China would currently only participate if the carbon price were below 20 dollars per tonne. While the exact quantitative findings of the study depend on specific model assumptions, the main finding that the EU CBAM triggers the adoption of carbon pricing holds under a broad range of modelling assumptions.

“Our findings support and quantify the hypothesis that the EU CBAM can trigger a so-called Brussels effect,” summarises Leonie Wenz, PIK researcher and a co-author of the study. “What this means is that, due to the EU’s central position in international supply chains, policies adopted in Brussels spill over to outside the EU. Greater climate action leads to even greater climate action. This can play an important role in climate mitigation, especially if international negotiations on climate mitigation stall.”

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Journal

Journal of the Association of Environmental and Resource Economists

DOI

10.1086/742163 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

The Potential of carbon border adjustments to foster climate cooperation

Article Publication Date

1-Jun-2026

After The Fanfare: Beijing’s Reading Of The Trump-Xi Summit – Analysis

President Donald J. Trump participates in a bilateral tea with President Xi Jinping of the People’s Republic of China, Thursday, Friday, May 15, 2026, at Zhongnanhai in Beijing, China. (Official White House Photo by Daniel Torok)

June 2, 2026 
Observer Research Foundation
By Kalpit A. Mankikar

With the pomp and pageantry surrounding United States President Donald Trump’s meeting with his Chinese counterpart now receding in the public imagination, the brass tacks of their bilateral relationship are coming into sharper focus.

The two nations have resolved to pursue a “constructive relationship of strategic stability.” A Xinhua commentary deciphers the construct as “cooperation being the mainstay,” with competition kept in check, differences moderated, and lasting stability with peace charting the new direction of the Sino-American relationship. While Trump has billed his summit in Beijing with Chinese President Xi Jinping as “historic,” the tenor of the language in Chinese reportage suggests that Beijing sees it as a work in progress, noting that both sides are “consulting” on the details.

The Trump administration grounds the “historic” nature of the summit with China in Beijing’s commitment to purchase 200 Boeing aircraft — the first such deal since 2017 — which is expected to generate skilled jobs in American manufacturing, along with China’s resumption of poultry imports, pledges to purchase farm products worth $17 billion annually over the next two years, and the honouring of previous soybean purchase commitments made in October 2025. On agricultural purchases, China stated that an agreement was reached to promote the “expansion” of two-way trade across sectors, including agricultural products, through the “mutual reduction of tariffs” on a range of goods, and to actively “push to resolve” US concerns regarding Chinese poultry imports.


On official bilateral mechanisms for managing economic engagement, the White House envisages a US-China Board of Trade to manage trade across non-sensitive products, and a US-China Board of Investment as a government-to-government platform for capital flows. Beijing envisions the trade body as a framework for reciprocal tariff reductions, with goods worth US$30 billion identified for trading at relatively lower tariff rates. The mechanism is also being framed as a platform for addressing mutual concerns in trade and investment, shifting bilateral dynamics from “crisis response” to “institutionalised management,” and providing effective institutional safeguards for economic cooperation. Huang Jing of Shanghai International Studies University argues that such institutional mechanisms effectively lay to rest the campaign to minimise China’s participation in international supply chains — an effort initiated during the Trump 1.0 and Biden administrations — with the US tacitly conceding that this approach is unsustainable. In the pivot from decoupling to renewed economic engagement, the Chinese sense a victory.

On the issue of rare earths, China unveiled sweeping rules in October 2025 that tightened controls on components integral to technology supply chains — including artificial diamonds with industrial uses — and placed rare earths such as holmium, erbium, thulium, europium, and ytterbium on an export-control list, along with equipment used in the production and processing of these materials and in the manufacture of magnets. At Xi’s meeting with Trump in South Korea in October 2025, the White House announced that China had committed to eliminating both “current” and “proposed” export controls on rare earth elements and other critical minerals. Following that summit, Beijing pledged to suspend for one year the export restrictions instituted in October 2025. Following the Beijing summit, while the White House stated that China would “address US concerns” regarding both supply chain shortages related to rare earths — specifically yttrium, scandium, neodymium, and indium — and curbs on the sale of rare earth production and processing equipment, China’s Ministry of Commerce has committed only to working with the US to ensure the security and stability of global supply chains.


Meanwhile, Beijing’s strategists have been assessing the factors behind the rapprochement and the evolving nature of Sino-American relations. Huang Jing notes that the current period has been the most challenging for Trump: his military strike against Venezuela has eroded America’s moral standing in international affairs, and his war against Iran has lacked international legitimacy and support even from US allies, deepening America’s isolation. Huang thus concludes that Washington has “acknowledged” power parity with Beijing — a development that marks a turning point in the dynamics between the two nations — giving rise to the notion of competitive coexistence under the framework of constructive strategic stability.

Ding Yifan from Renmin University’s Institute of Global Governance and Development argues that the US has been “humbled” by a comparatively weaker power in Iran, and that Trump has been unable to resolve the stalemate of the Strait of Hormuz blockade — which has led him to “swallow his pride” and engage with China to “boost his ratings ahead of the US midterm elections”. Incidentally, the minutes of the Trump-Xi summit released by the White House highlight agreement on keeping the Strait of Hormuz open for energy trade, China’s opposition to the militarisation of the Strait and to attempts to charge a toll for passage, and opposition to Iran acquiring a nuclear weapon. However, the Chinese readout of the summit merely notes that the two leaders “exchanged views” on issues including the Middle East situation, the Ukraine crisis, and the Korean Peninsula.

A common sentiment among Chinese strategic thinkers is that the diplomatic history of the US and China can be divided into two phases: from 1972 to 2017, marked by a mix of cooperation and competition, and from 2018 onwards, characterised by “comprehensive strategic competition.” Towards the end of 2017, the first Trump administration released its National Security Strategy, which categorised Beijing as challenging Washington’s power and interests and undermining its security — developments that necessitated a revision of US engagement policy towards its rival. The Chinese establishment later complained that relations had deteriorated since the US had begun hyping the “China threat theory“, imposing technology curbs, and allegedly interfering in China’s internal affairs. There is a belief that the US sought to exploitissues related to Xinjiang, Tibet, Taiwan, and Hong Kong, in addition to forging blocs such as AUKUS with Australia and the UK, and the Quadrilateral Security Dialogue with Australia, India, and Japan.


Professor Jin Canrong from Renmin University — a seasoned scholar of US-China relations — posits that the current construct of a “constructive relationship of strategic stability” is the foundation of equalitybetween the two nations, and has ushered in a phase of relative equilibrium. Jin argues that China has pushed back against the US across the spheres of tariffs, industry, technology, and the military on the basis of its intrinsic strength, yielding tangible results in 2026 — an allusion to the Trump-Xi Beijing summit. He notes that the US tariff war against China led to shortages of goods in America that hurt consumers; that technology curbs spurred a campaign for self-reliance and home-grown innovation in China, exemplified by DeepSeek, developed at a fraction of the cost of US alternatives; and that China’s “success” in military modernisation was showcased at the People’s Liberation Army parade in September 2025. These “accomplishments” are seen by Beijing as the factors that compelled Trump to “respect” China.

To conclude, it is becoming increasingly clear that despite sustained negotiations, Beijing’s export-control regime remains firmly in its arsenal, and that it can weaponise economic resources at will to achieve its geopolitical aims. Second, Beijing’s strategists perceive Trump as bogged down by myriad conflicts in Europe and West Asia and enfeebled by a rupture in his alliance system; China, by contrast, has withstood tariffs, technology curbs, and sustained geopolitical pressure. This buoyant outlook in Beijing has led Xi to underline that Taiwan is the “most important issue” in the bilateral relationship. Xi has cautioned Trump that any mishandling of Taiwan carries the risk of conflict, with the potential to jeopardise the broader bilateral relationship. Whether China’s confident self-image will unsettle the Asian security architecture is a question that will only be answered in time.

About the author: Kalpit A. Mankikar is a Fellow with the Strategic Studies Programme at the Observer Research Foundation.

Source: This article was published by the Observer Research Foundation.

Navigating The Maritime Gray Zone – Analysis

File photo of China's Yuan Wang 2 ship, used for tracking and support of satellite and intercontinental ballistic missiles.

 Photo Credit: Gadfium, Wikipedia Commons

June 2, 2026
By Indo-Pacific Defense Forum

China’s posture in the Indo-Pacific includes the deployment of research vessels under civilian guise but designed for military exploitation. Increasingly, these vessels collect marine data in the region’s disputed waters. Although Beijing calls the survey trips scientific research, the excursions often mask intelligence gathering of military value — particularly near the United States territory of Guam, the Philippines and self-governed Taiwan.

Understanding this dual-use phenomenon is crucial for anticipating operational challenges, advising policy, and supporting deterrence and resilience for the U.S. and its Allies and Partners. This is true especially for those along the so-called first island chain, a string of major Pacific archipelagos that runs from Japan south through Taiwan, the Philippines and on to Borneo, forming a strategic barrier off China’s mainland. China, however, also increasingly collects extensive data farther from its shores, stretching from the Indian Ocean to the South Pacific.

“In truth, all maritime data collection is dual use — it could have useful oceanographic, climate, scientific uses; but it can also have military uses. I’m overall suspicious about China’s intent in the region,” Bruce Jones, a senior fellow at the Washington, D.C.-based Brookings Institution, told FORUM. He is studying China’s ocean research effort.

China operates one of the world’s largest fleets of civilian oceanographic research vessels, analysts say. The Center for Strategic and International Studies (CSIS) reported in 2024 that “of the 64 active vessels, over 80% have demonstrated suspect behavior or possess organizational links suggesting their involvement in advancing Beijing’s geopolitical agenda,” indicating dual-use capabilities at scale.


The strategic opaqueness of China’s research ship operations challenges monitoring, complicates defensive postures and advances excessive maritime claims, analysts say. The vessels frequently operate in contested waters, gathering data on bathymetry (water depth), seafloor structure and oceanographic conditions — information that the People’s Liberation Army (PLA) can leverage for submarine operations, amphibious planning, and mine and antisurface warfare.

In November 2025, for instance, China deployed three dual-use research vessels in the Indian Ocean. India’s maritime surveillance agencies monitored the ships, which included the Shi Yan 6, Shen Hai Yi Hao and Lan Hai 201. “Such deployments typically prompt diplomatic caution and potential denial of port calls at Indian facilities, as seen in earlier instances involving similar Chinese missions,” Indian Defence News reported. The Indian Coast Guard and Navy reported the constant presence of Chinese research vessels in recent years near India’s waters.

The Haiyang Dizhi 8, as another example, conducted oil and gas surveys off Vietnam’s coast for four months without authorization, highlighting how the vessels can penetrate contested maritime zones under the cover of scientific legitimacy, according to the CSIS’s Asia Maritime Transparency Initiative. The Xiang Yang Hong 6 also exemplifies China’s dual purposes. The vessel and five others made 25 passes in parallel lines off Taiwan’s east coast in 2024, conducting systematic seabed surveys, according to Starboard Maritime Intelligence data cited by The New York Times newspaper in July 2025.


“It’s hard for us to view this situation as normal,” Kuan Bi-ling, the minister of Taiwan’s Ocean Affairs Council, told The New York Times.

The same ships have been active around Guam — home to vital U.S. military installations — collecting data relevant to submarine operations. “It appears that China is trying to collect bathymetric data on that part of the ocean without appearing like it is conducting a bathymetric survey,” Ryan D. Martinson, an assistant professor and expert on Chinese research ships at the U.S. Naval War College, told the newspaper.

Such activities are emblematic of China’s gray-zone tactics, coercive state actions that fall short of open warfare. China views such activity as “a natural extension of how countries exercise power [to] pressure countries to act according to Beijing’s interests [and] without triggering backlash or conflict,” according to a 2022 Rand Corp. report, “A New Framework for Understanding and Countering China’s Gray Zone Tactics.”

Similarly, a June 2023 article published by the SeaLight research initiative noted that “China’s deployment of research and survey vessels in contested waters is a key component of its maritime gray-zone strategy. These vessels, often presented as civilian or scientific, are frequently state-owned or operated by entities with close military ties.” For example, the Chinese Academy of Sciences and China’s Natural Resources Ministry operate research vessels and have PLA cooperation agreements, CSIS reported. The U.S.-based SeaLight uses commercially available technology to expose maritime gray-zone activities.

China has increased marine data collection throughout key Indo-Pacific waters, sometimes disregarding international law that requires coastal state consent for research inside that nation’s exclusive economic zone (EEZ), the U.S. Indo-Pacific Command (USINDOPACOM) reported in July 2025. This pattern reflects a wider lawfare campaign designed to normalize excessive maritime claims while undermining the sovereign rights of coastal states, the report said.

“There are both legal and illegal and aggressive/nonaggressive ways to enter other countries’ EEZs. China is doing a lot of all of the above,” Jones said. “Their behavior is aggressive because it’s aggressive, not because it is/isn’t a violation of the law of the sea.”

South China Sea Surveillance

China’s survey vessels, operating within the EEZs of nations such as the Philippines, obtain maritime domain awareness in contested areas. They reinforce Beijing’s ability to map and exploit the seabed, with implications for mining, anti-submarine warfare and undersea infrastructure monitoring, among other ventures.

Moreover, these activities are part of a convergence of maritime coercion. China’s gray-zone campaigns often establish localized “advantages [for China] that can be sustained over time without precipitating acute crisis,” Isaac Kardon, a senior fellow at the Carnegie Endowment’s Asia Program, testified in June 2024 before the U.S. Congress.

This sustained presence in waters near the Philippines, Taiwan and other areas seeks to normalize China’s maritime coercion and erode the strategic threshold for escalation. “If you look at China’s coast guard and its maritime militia over the last three years — you would see a dramatic increase in the number of ships and the depth of the penetration,” SeaLight Director Ray Powell told The Wall Street Journal newspaper in March 2025. “It’s taken on the character of a maritime occupation.”

For example, China’s ships have made multiple incursions at the contested Sabina Shoal within Manila’s EEZ. In April 2024, the Philippines raised its flag at the shoal to assert sovereignty, prompting China’s deployment of a massive 12,000-ton Coast Guard vessel. Such events typify the pattern: China’s civilian or paramilitary-type vessels enter other nations’ claimed waters to consolidate their presence, gather environmental or hydrographic data, or prepare a pretext for sustained operations.

In May 2025, the Philippines deployed a Coast Guard vessel and aircraft to track a Chinese research ship operating illegally in its EEZ. Manila declared China’s activity a sovereignty violation. The incident reflects China’s strategy of ignoring its obligations as a signatory to the United Nations Convention on the Law of the Sea while pressing its arbitrary and excessive claims.


By pairing scientific vessels with China Coast Guard and maritime militia escorts, Beijing multiplies the coercive effect. The actions show the PLA’s growing use of undersea terrain data to support operations that threaten a free, secure and prosperous Indo-Pacific, analysts say.

China’s alarming gray-zone maritime strategy leverages civilian, paramilitary and military assets, including research and fishing vessels, to exert control over contested zones. This multilayered coercion erodes norms and reinforces China’s maritime claims incrementally. Such operations allow the Chinese Communist Party (CCP) to coerce “while avoiding a conventional military response from the United States and its allies,” notes the November 2024 Rand Corp. report “Understanding and Countering China’s Maritime Gray Zone Operations.” The report recommends enhanced presence, transparency initiatives and allied coordination as deterrent measures.

Masked Intent Near Taiwan

The Taiwan Strait and its eastern approaches are at the heart of China’s coercion campaign. China’s parallel survey patterns east of Taiwan suggest mapping for submarine deployment or interdiction zones. Equally concerning are China’s surveys near Guam — a hub for U.S. power projection in the Western Pacific. Chinese research vessels resumed operations east of Guam as recently as June 2025, according to data cited by The New York Times.

Taiwan’s military intelligence noted a dramatic uptick in China’s maritime and aerial activity across the Indo-Pacific in 2024: nearly 12,000 flights and more than 86,000 missions at sea — military exercises that totaled an estimated $21 billion in operational costs and a nearly 40% increase over 2023 spending, the Reuters news service reported.

Such overt actions distract from less-visible activities such as marine data collection, which accumulates military advantage without risking immediate escalation. The extent of the drills underscores the potential scale and normalization of PLA maritime activities — an environment in which research ships can mix, gather environmental and electromagnetic data, and facilitate PLA submarine or amphibious operations near Taiwan under technical pretexts. “They are trying to normalize their military power projection and intimidation around the first island chain,” a Taiwan military official told Reuters.

Legal Double Standards

China’s marine data collection is a tool for “preparing the battlespace,” particularly when survey tracks are along likely submarine routes or amphibious approach corridors, according to USINDOPACOM. China is moving toward a more conventional approach, indicating a rising threshold of risk tolerance, including in the Pacific, The Heritage Foundation reported in September 2024. Further, PLA modernization is sharpening strategic pressures, making encounters — including those involving ostensibly benign vessels — more fraught and dangerous, according to a May 2024 report by the U.S.-based National Bureau of Asian Research.

China’s rapid expansion and deployment of its research and survey fleet, and the systematic surveys it conducts near Guam, the Philippines and Taiwan, are not merely to advance science. They signal and enable coercion and are part of a strategy to collect data of military utility, normalize excessive claims and undermine the sovereign rights of Indo-Pacific states. Vessels such as the Xiang Yang Hong 6 embody this duality — flying the flag of science but sailing as military scouts. Their data collection should be understood for what it is, analysts say: a gray-zone operation, reinforced by lawfare, to ready the battlespace for potential conflict.
Strengthening Countermeasures

To ensure a free, secure and prosperous Indo-Pacific, the U.S. and its Allies and Partners must remain vigilant about China’s maritime activities — exposing Beijing’s duplicity and countering its attempts to redraw the physical and legal maps that define the maritime domain.

Regional resilience requires recognition of this threat and promotion of cooperative mechanisms to challenge China’s opaqueness and normalization of dual-use maritime activity, defense experts say.

“The U.S. is the most important oceanographic power in the world — but China is catching up and several of our Allies and Partners have very important capacity, skills, local knowledge and geography,” said Jones, the Brookings senior fellow. Countermeasures should integrate intelligence, law enforcement and economic tracking mechanisms, according to Benjamin Jensen, director of CSIS’s Futures Lab, and his colleagues. In commentary published on the CSIS website, they recommended interagency campaigns to counter China’s gray-zone incursions. A joint interagency task force or similar entity could integrate mechanisms to identify CCP influence channels in real time, they wrote.

“The goal isn’t just to shut down documented incursions — it’s to shape the environment so that China loses its ability to leverage migration, illicit finance, and cyber operations as tools of competition,” the CSIS team wrote. That means “deploying targeted counterintelligence and economic measures across the Pacific. It means leveraging the [U.S.] Department of the Treasury’s tools to disrupt illicit Chinese financial networks. It means expanding the use of contracted intelligence, surveillance, and reconnaissance assets to avoid straining existing military collection capabilities. And it means crafting an influence campaign to expose and undermine CCP operations in the information space before they gain traction.”

They cited Jade Spear, an interagency initiative that targeted illegal, unreported and unregulated fishing by China’s fleet. The operation coordinated 15 U.S. agencies to target labor violations and human trafficking, impose sanctions, revoke visas and licenses, inspect vessels, and investigate fishing companies. “Jade Spear [reimagined] the spectrum of engagement with the CCP — it’s not just about use of kinetic action, but the entire arsenal of U.S. bureaucracy can be called to action,” they wrote.


“The private sector plays a critical role —
financial institutions, tech companies and media platforms must be mobilized to prevent CCP actors from exploiting digital spaces and economic systems.”

The U.S. and its Allies and Partners require a sustained, proactive approach to prevent China from exploiting gaps in governance, security and perception management, they concluded. “The real shift must come from embracing competition as a continuous condition, not a crisis-driven response. … It’s not just about blocking Chinese influence — it’s about making the Indo-Pacific a space where U.S. alliances, institutions, and economic frameworks make CCP subversion infeasible.”
China is Mapping the Region’s Seabed for Geopolitical, Military Advantage

Chinese-flagged survey vessels are collecting marine data on an unprecedented scale. Between 2020 and 2024, 64 vessels engaged in hundreds of thousands of hours of operations worldwide, with more than 80% exhibiting dual-use behavior or ties to China’s geopolitical agenda, according to the Center for Strategic and International Studies (CSIS), a U.S.-based think tank.

China also has deployed research vessels to strengthen its presence in geopolitical hotspots. Commercial and scientific research ships, such as those operated by the state-owned China Oilfield Services Ltd., have helped the Chinese Communist Party assert its illegal claims of sovereignty over large swaths of the South China Sea and obstruct coastal states from finding and extracting natural resources, a study by CSIS found.

Chinese ships have conducted survey operations within the exclusive economic zones (EEZ) of other countries without prior approval, which is prohibited under international law. This also constitutes a double standard given China heavily restricts foreign activities in its EEZ.

China appears to use marine data to bolster its excessive claims and prevent other states from exercising their sovereign rights.

Concealed Mission

China’s dual-use research fleet offers strategic advantages for potential military operations:Environmental intelligence: Hydrographic and oceanographic data supports planning for submarine routing, mine deployment, undersea sensor placement and amphibious landing.
Sensor development: Data on sound propagation and currents aid in passive acoustic detection and sensor optimization.
Access creep: Regular presence of civilian vessels normalizes operations inside a nation’s exclusive economic zone (EEZ), making detection and interdiction politically complex.
Infrastructure placement: Data can support undersea infrastructure such as communication cables, sensors and uncrewed vehicles.

Deterrence Measures

To counter China’s maritime gray-zone activities, Allies and Partners should focus on:Transparency: Enhance intelligence sharing and public transparency on research ship movements, flagging dual-use indicators.
Policy coordination: Support interagency initiatives that combine maritime enforcement, sanctions, licensing and public-private cooperation.
Capacity building: Strengthen regional domain awareness via cooperative deployments, hydrographic surveys and shared sensor networks.
Legal frameworks: Clarify norms regarding scientific vessel activity in nations’ EEZs and clearly define boundaries for data collection.
Strategic messaging: Identify China’s dual-use research vessel surveys as part of its coercive strategy.

Sources: Center for Strategic and International Studies, Daniel K. Inouye Asia-Pacific Center for Security Studies, Heritage Foundation, Rand Corp.

This article was published by Indo-Pacific Defense FORUM

 

AI, industrial sovereignty and Pax Silica

By Reihana Mohideen

Published 1 June, 2026

A version of this was first published at Ang Masa. See also statement by Partido Lakas ng Masa (PLM, Party of the Labouring Masses) further below, “Reject Pax Silica and the Philippines’ transformation into a hub of imperialist war and militarised AI infrastructure.” 

The United States, along with 14 other high-tech countries, established Pax Silica in December 2025 as a “strategic initiative” to counter China’s strength in semiconductors, artificial intelligence (AI)1 and high-level technology (HLT). It seeks to do this through US control of supply chains — from critical minerals, energy and logistics to semiconductors, advanced manufacturing, AI infrastructure, software platforms and frontier AI models.

Pax Silica is a US-led reorganisation of global production driven by geopolitical rivalry and the fusion of industrial policy with military strategy. It is an attempt to build “trusted ally” supply chains that limit China’s access to advanced technologies while integrating partner countries into segmented roles within a US-aligned technological bloc. Rather than a single agreement, it operates through infrastructure investments, supply-chain restructuring, security arrangements and industrial partnerships.

The Philippines joined the initiative in April 2026. The Philippines is being incorporated via mineral agreements, semiconductor expansion, AI-linked industrial corridors, logistics projects, nuclear energy development and deeper defence cooperation. Under the initiative, the Philippines and the US will establish a 1619-hectare industrial/AI hub in the Luzon Economic Corridor — an “economic security zone” — to shore up US supply chains.

At the centre of this architecture is the Enhanced Defense Cooperation Agreement (EDCA), which enables rotational US military access, prepositioning of equipment, logistical integration and the development of dual-use infrastructure connected to wider Indo-Pacific strategic planning. In this framework, economic integration is layered onto existing military architecture.

This paper argues that the Philippines risks becoming a low (or at best mid-) level semiconductor processing node, a logistics corridor integrated into US military supply chains, a supplier of critical minerals and technical labour, and a forward-positioned territory within broader US Indo-Pacific security strategy.

The US request to place the hub under US law and grant diplomatic immunity to US personnel is a clear indication of the military underpinnings of this venture. The arrangement also aligns with key Philippine elite interests by attracting investment, strengthening export sectors, and reinforcing political and security ties with the US. The initiative is being hailed as an opportunity for HLT-based development.

The question posed, therefore, is how the rise of HLTs such as AI enables national industrial development grounded in sovereignty, technological self-reliance, and sustainable development in the Philippines. Will HLTs become a tool for national industrial transformation and social development, or will it function as a mechanism for deeper dependency under a global techno-military order?

HLTs under capitalism

HLTs are not neutral. They are deeply embedded within imperialism’s military-industrial complex, global supply chains and class structures of ownership. Within the framework of global capitalism (or imperialism), the global AI industry combines highly advanced computing infrastructure in major capitalist powers with vast reserves of precarious digital labour in low-wage countries, reproducing unequal international divisions of labour facilitating the significant transfer of value to HLT and imperialist countries.

Neither is AI a “new economy” that functions autonomously from capitalism. AI models cannot function and produce a single output without an enormous expenditure of labour — human-created data; software engineers to design highly complex system architectures; human-produced infrastructure, such as electricity, to run server farms; and human maintenance and “hidden” human labour in low-wage countries, such as the Philippines, to clean, label, sort and verify data used for training algorithms (machine learning). AI under capitalism is a part of the “constant capital” or “dead labour” that transfers value, not creates it, following Karl Marx’s labour theory of value.

The central issue is therefore not simply whether the Philippines adopts AI and HLTs, but under whose control these technologies are developed and toward what developmental objective they are directed.

Structural constraints 

The Philippines possesses several partial advantages within the global technology economy. These include participation in electronics assembly, an established IT-Business Process Management and service-sector workforce, substantial mineral resources such as nickel, and emerging semiconductor back-end capacity involving assembly, testing, packaging and electronics manufacturing services.

At the same time, the country remains structurally weak in critical areas necessary for sovereign industrial development. The Philippines lacks a strong heavy industrial base, machine-tool industries, energy sovereignty, advanced semiconductor design and fabrication capabilities, robust public research and development systems, and coordinated long-term industrial planning. This creates a core problem for the Philippine economy: the country participates in global technology chains without controlling its highest-value and most strategic segments.

HLTs and sovereign development requirements

AI as a transformative HLT

HLTs such as AI possess significant transformative potential. When combined with robotics and advanced industrial systems, AI can enable industrial automation, productivity growth, logistics optimisation, infrastructure coordination, semiconductor design, advanced manufacturing, disaster prediction, climate resilience, healthcare modernisation and improvements in agricultural productivity. These technologies are increasingly becoming foundational to industrial development across the world economy.

However, these capabilities are not autonomous; they depend upon deeper material foundations. AI is fundamentally infrastructural, with energy, land and water functioning as first-order constraints on its development and deployment. In the Philippine context, these constraints are particularly acute: electricity prices remain among the highest in Asia, energy security is weak, grid fragmentation persists, and infrastructure vulnerability to climate disasters is severe. At the same time, land reform remains incomplete and, in key instances, subverted, further complicating the territorial basis for large-scale industrial and digital infrastructure.

Energy demands and infrastructure

Because AI workloads and high-performance computing generate enormous heat, cooling has become one of the biggest technical and energy challenges in modern data centres. Large-scale data centres and semiconductor ecosystems require stable baseload electricity, high-capacity transmission systems, advanced cooling infrastructure, water supplies and water infrastructure, and resilient logistics networks. For hyperscale AI infrastructure operated by companies such as Google Cloud, Microsoft, Amazon Azure and Meta, cooling infrastructure can become as large and capital-intensive as the computing infrastructure itself.

At present, the Philippines does not possess the degree of energy sovereignty necessary, as demonstrated by the energy crisis gripping the country, for independent large-scale AI-industrial development. As a result, expansion in this sector risks deepening structural dependence on private energy oligopolies, imported fuels and externally financed grid development.

Pax Silica and the civil-nuclear energy agenda

Energy is a key HLT infrastructure requirement. The focus of Pax Silica is the development of “civil-nuclear energy”. This includes, based on the US-Philippines “123 Agreement” on nuclear cooperation: deployment of US-designed small modular reactors (SMRs) by Meralco; the establishment of a nuclear reactor control room simulator and training hub; and partnerships between Philippine universities and overseas institutions, such as Texas A&M University and King’s College London. This nuclear program aims to integrate the Philippines into US-linked nuclear technology and supply chains. Renewable energy such as solar and wind are not mentioned, even in relation to energy security and therefore sovereignty.

AI as an infrastructural dependency chain

AI systems further require integrated industrial ecosystems: semiconductor design and manufacturing capacity, stable and affordable energy systems, hyperscale data centres, high-capacity telecommunications and fibre networks, scientific and engineering talent pipelines, and secure access to critical minerals and industrial inputs. Without control over these underlying foundations, AI development becomes dependent on outsourced computing systems, foreign cloud platforms and externally governed digital ecosystems dominated by major technology powers such as the US, Japan, the European Union and China.

Within this configuration, AI development is not simply a technological transition but an infrastructural dependency chain. The absence of sovereign control over critical inputs means that digital-industrial expansion is structurally mediated by external capital, fuel supply volatility, and privatised generation capacity. This shapes not only the cost structure of AI deployment but also its strategic autonomy, embedding technological development within broader patterns of dependency.

“Trusted partnerships” and emerging AI blocs

In this light, the discourse of “trusted partnerships” under frameworks such as the World Economic Forum (WEF) is useful to examine. This reflects an emerging language of “AI sovereignty” that emphasises secure supply chains, allied infrastructure, and coordinated compute ecosystems. This closely parallels the strategic logic of Pax Silica: the formation of geopolitical technology blocs organised around trust, security and interoperability.

For the Philippines, however, these partnerships are mediated not only economically but also militarily through arrangements such as EDCA and broader US strategic integration. The key issue is therefore not partnership per se, but hierarchy: the Philippines does not negotiate from a position of technological parity, but from within a structured asymmetry of power.

Under such conditions, the Philippines risks participating in AI development only as a subordinate service provider within foreign-controlled technological systems. As a result, technological modernisation under present conditions risks reproducing dependency rather than overcoming it.

Comparative development models

Taiwan: Industrial upgrading through state-led industrial policy

The experience of Taiwan demonstrates that technological advancement is possible through sustained state-led industrial policy, strong STEM education systems, strategic protection and upgrading of domestic industries, and coordinated technology transfer mechanisms. Through long-term industrial planning, Taiwan successfully moved from low-end assembly operations toward global leadership in semiconductors and advanced electronics. Institutions and firms such as Taiwan Semiconductor Manufacturing Company, Acer and Foxconn emerged from this process of industrial upgrading.

However, Taiwan’s success also remains structurally tied to deep integration within US-led supply chains, dependence on global export markets, and geopolitical exposure within the broader US-China rivalry. Taiwan illustrates both the possibilities of technological upgrading and the vulnerabilities created by dependence on externally structured geopolitical and economic systems.

Cuba: Scientific sovereignty under constraint

Cuba’s experience presents a different developmental model. Following the 1959 revolution, Cuba developed a state-led scientific system centred on universal education, centralised research institutions, public health-oriented innovation and scientific planning. Institutions such as the Centre for Genetic Engineering and Biotechnology (CIGB) and BioCubaFarma enabled Cuba to achieve world-class biomedical innovation in vaccines, pharmaceuticals and preventive healthcare, despite severe economic constraints. Cuba demonstrates that high technological capability can exist without industrial capitalism at a mass scale.

However, Cuba’s model also faced limitations imposed by a US economic embargo, which for 66 years restricted access to global capital markets, limited industrial scale and the absence of a broad heavy industrial base. The US’ aim was regime change in Cuba, which has now intensified under the Trump administration, with threats of direct military intervention. The fact that Cuba has managed to survive until now is a testimony to the resilience of the Cuban revolution and its socialist system.

Together, Taiwan and Cuba represent two different responses to structural position within global capitalism. Taiwan pursued industrial upgrading through strategic integration into global production systems, while Cuba pursued scientific sovereignty and public-sector innovation under conditions of relative isolation.

The Philippines currently occupies neither position. It is neither industrially upgraded like Taiwan nor scientifically autonomous like Cuba. Instead, it remains integrated primarily into low-value service, assembly and extractive sectors within global supply chains.

Implications: Sovereignty eroded

The primary danger posed by Pax Silica is that infrastructure developed within this framework increasingly serves both economic and military purposes. Industrial policy becomes aligned with external security priorities, while technology transfer remains conditional and hierarchically controlled. Strategic sectors such as semiconductors, logistics, telecommunications and energy become integrated into systems whose highest levels of ownership, design capability and operational coordination remain external to the Philippines.

This integration also deepens long-term dependency. Once infrastructure, investment flows, export markets, military coordination and technological systems become integrated into US-aligned networks, disengagement becomes economically and politically costly. In effect, sovereignty is not formally abolished but progressively narrowed through layered economic, technological and military integration.

Pax Silica presents itself as modernisation and technological progress, but structurally it represents a securitised global production system organised around US strategic interests. For the Philippines, the decisive question is whether AI and high-level technologies will enable industrial sovereignty and social development, or whether they will reinforce the country’s role as a managed periphery within a global military-industrial order.

The experiences of Taiwan and Cuba demonstrate that technological development is possible under very different historical conditions. However, both cases show that technological advancement ultimately depends on state capacity, ownership of productive assets, control over technological systems, and strategic autonomy.

Without these foundations, AI and high-level technologies do not produce sovereignty. They produce incorporation and subordination.

AI’s role in this system

Within Pax Silica, AI functions primarily as a productivity and coordination layer within global supply chains. It supports industrial automation, logistics optimisation, surveillance systems, military planning and data management. AI is therefore inseparable from the broader geopolitical and military-industrial restructuring currently underway.

However, without domestic control over semiconductors, compute infrastructure, energy systems, and industrial design capacity, AI development remains dependent on foreign technological ecosystems. Under these conditions, AI becomes a mechanism of participation within externally controlled systems rather than a foundation for technological sovereignty. The Philippines may therefore contribute labour, infrastructure, minerals, logistics and low-to-mid-level technical functions, while remaining excluded from the highest-value and most strategic levels of technological production.

A socialist alternative

Beyond the immediate tasks of national-democratic development and industrialisation lies the broader socialist transition. Under capitalism, production is governed primarily by markets, profit maximisation and the treatment of labour power as a commodity. A socialist transition seeks to progressively subordinate these mechanisms to democratic social planning, collective ownership and production oriented toward human need rather than private accumulation — the conscious curtailment of the law of value as the central organising principle of economic and reproductive life.

Such a transition would require reversing privatisation in strategic sectors and reestablishing public ownership and democratic management over key areas of the economy, including energy, transport, telecommunications, finance, water and major infrastructure systems, as well as comprehensive agrarian reform. This would need to be combined with workers’ control and integrated national planning capable of coordinating industrial development, scientific advancement, ecological sustainability and social welfare. Rather than leaving investment decisions to private capital and global market pressures, economic priorities would increasingly be determined through democratic planning mechanisms rooted in socially necessary and useful priorities.

Within such a framework, HLTs and AI would no longer primarily serve corporate profitability and imperialist competition. Instead, they would be directed toward socially necessary production and long-term human development. Their primary functions would include strengthening public healthcare systems, improving disaster prediction and climate resilience, supporting universal education, renewable energy systems, modernising sustainable agriculture and food security systems, and coordinating infrastructure according to ecological and social priorities rather than private profit.

A socialist approach to AI and industrial development would therefore treat technology not as an autonomous force or commodity, but as part of a broader project of social transformation. Technological progress would be evaluated according to whether it expands democratic control over production, reduces social inequality, strengthens collective welfare, restores ecological balance, and deepens national and popular sovereignty.

The long-term objective is not merely industrial growth, but the transformation of the social relations that govern production and reproduction.

Reihana Mohideen is a National Council member of the Partido Lakas ng Masa (PLM, Party of the Laboring Masses) and the head of the party's international desk.

—--------

Reject Pax Silica and the Philippines’ transformation into a hub of imperialist war and militarised AI infrastructure

Partido Lakas ng Masa, May 20

The US-led Pax Silica initiative is not a project for genuine Philippine development. It was established by the US government in December 2025 as a “strategic initiative” — a geopolitical-industrial bloc designed to secure US and imperialist dominance over semiconductors, AI, critical minerals, energy systems and strategic infrastructure.

One of the main aims of Pax Silica is to contain China. Through the Enhanced Defense Cooperation Agreement (EDCA) and related military agreements, economic integration is being progressively embedded within US strategic and military architecture, tightening the Philippines’ entanglement in Washington’s regional confrontation with China.

Under Pax Silica, the Philippines is being integrated into a US-aligned production and military network, through industrial corridors, logistics systems, military agreements, telecommunications infrastructure, AI systems, and strategic energy programs based not on renewables but nuclear power development through a “civil-nuclear energy agenda.”

The country is being assigned the role of labour provider, mineral supplier, assembly platform, logistics corridor and strategic military outpost, while higher-value technological design, semiconductor control, compute infrastructure, and industrial command remain concentrated in the high-level technology-dominant capitalist states, such as the US, Japan, Australia, Britain, Israel and others.

This is not technological sovereignty. It is dependency. The Bongbong Marcos government presents Pax Silica as “modernisation” and “development,” but the reality is deeper imperialist penetration into the Philippine economy, infrastructure systems, energy networks and national development trajectory.

Ports, airports, logistics corridors, telecommunications systems and AI infrastructure are being developed as dual-use systems that integrate civilian and military functions. AI itself functions not merely as an economic tool, but as part of broader systems of logistics coordination, surveillance, predictive analytics, drone warfare and security management.

The integration of Israeli-linked firms and technologies into emerging AI and infrastructure systems is a particular concern given Israel’s role within the global military-technological complex and its deployment of advanced surveillance, targeting and warfare systems — all of which are being tested and deployed in the ongoing genocide against the Palestinian people.

Philippine infrastructure will thus become embedded within global systems of militarisation, surveillance capitalism and ongoing imperialist wars and occupations, including the US-Israel war against Iran and the genocide in Palestine.

The Philippines will remain structurally dependent on foreign capital and imported technology, with weak industrial foundations and oligarchic control over strategic sectors. Its lack of energy security and high vulnerability to climate shocks further compound these conditions, reinforcing patterns of uneven and externally dependent industrialisation.

Pax Silica does not resolve these contradictions. It will intensify them by transforming Philippine territory into a strategic military-industrial node within a US-led imperialist order.

The country is being increasingly positioned as a logistics and infrastructure hub for US military-strategic interests, a supplier of critical minerals for external industrial systems, a low-wage platform for AI-related data processing, a site for surveillance and dual-use technological systems, and a frontline state in the intensifying US–China rivalry.

A sovereign development path requires a fundamentally different orientation. A socialist and national industrial strategy would subordinate markets and private accumulation to democratic planning and social need through:

• public ownership of strategic sectors;
• comprehensive agrarian reform and food sovereignty;
• coordinated national state plans;
• democratic economic planning;
• sovereign and renewable energy systems;
• expansion of domestic research and development for science and high-level technological capacity; and
• workers’ participation in economic decision-making.

Within such a framework, AI and high-level technologies would be oriented toward healthcare, education, disaster resilience, climate adaptation, food security, sustainable agriculture, renewable energy transition and infrastructure planning — all guided by social need rather than militarisation or US geopolitical interests.

Technology must serve the people — not imperialist domination, oligarchic accumulation, surveillance, and war.

The struggle for technological self-determination is part of the struggle for national sovereignty, social justice, democratic control of the economy and socialism.

Reject Pax Silica!

Reject the Philippines’ transformation into a hub of imperialist war and militarised AI infrastructure!

For genuine national and socialist industrial development!

• 1

  Artificial Intelligence refers to the simulation of human intelligence by digital devices, performing tasks such as learning, reasoning, problem-solving and decision-making, through techniques such as machine learning, neural networks and natural language processing.

'Climate hoax': Tech companies hiding the real impact of AI, NGOs say

01.06.2026, DPA

Photo: Julian Stratenschulte/dpa

Environmental groups and non-governmental organizations (NGOs) are accusing major artificial intelligence companies behind the likes of Copilot, Gemini and ChatGPT of a "climate hoax" in glossing over the environmental impact of their services.

Tech companies like Microsoft, Google and OpenAI often justify the enormous energy appetite of their new data centres with the argument that AI is a crucial tool for tackling the climate crisis.

However in research published on Monday, several NGOs say that these claims rest on weak evidence. The study's authors accuse the industry of greenwashing and covering up the environmental damage they cause with misleading communications.

A central criticism in the study is the lack of differentiation in the use of the term artificial intelligence. The study shows that the positive climate effects promoted by companies such as Google and Microsoft relate almost exclusively to "conventional" AI applications — such as weather forecasting models.

The current boom, and the massive expansion of data centres that comes with it, is driven primarily by so-called "generative" AI for end users — systems such as ChatGPT, Copilot and Gemini that produce text, images and videos.

For these resource-intensive applications, the study's authors could find no example demonstrating a measurable and substantial reduction in greenhouse gas emissions.

The authors describe the linking of climate benefits from conventional AI with the expansion of generative models as a new form of greenwashing — a strategy of projecting a more climate-friendly image through misleading, vague or unsubstantiated claims about supposed environmental benefits, thereby distracting from the actual environmental damage caused.

For the analysis, NGOs including AlgorithmWatch and Beyond Fossil Fuels analysed 154 high-profile claims by technology companies and institutions about the positive climate effects of AI.

The results reveal a clear gap between promises and scientific evidence. Only 26% of the statements examined were based on published scientific studies. In 36% of cases, no evidence whatsoever was cited, while the majority of the remainder referred only to the companies' own websites or reports.

The authors conclude that even for conventional AI, the supposed climate benefits are often greatly overstated, while the negative effects of AI growth are clearly measurable.

Julian Bothe, senior policy manager at AlgorithmWatch, said that if there were sustainability benefits from artificial intelligence, they came from conventional AI applications with low resource consumption.

ChatGPT and other large language and image-generating models at the centre of the current AI hype consume vast amounts of electricity and water, produce CO2 emissions on a scale comparable to entire countries, and bring no positive environmental benefit whatsoever, he said.

• AlgorithmWatch on research

(c) 2026 dpa Deutsche Presse Agentur GmbH

'Very serious': Researchers sound alarm over AI misuse at university

01.06.2026, DPA

Photo: Philipp von Ditfurth/dpa

The spiralling uptake of artificial intelligence risks undermining university and college education, researchers have warned following a major survey showing that almost 40% of students regularly consult chatbots while one in ten use them to cheat.

“The fact that students are misusing GenAI is a problem for assessment validity, and that’s a problem for the credibility of university credentials,” said Rene Kizilcec, associate professor of information science at Cornell University, one of three researchers who analysed survey data from 95,000 students at 20 US universities.

“About one-third regularly used generative AI such as ChatGPT or other models to produce text, video or code, when completing assignments, and 9% had used it to cheat,” according to the team, whose research comes amid wider concerns about AI “normalizing cheating at scale.”

“Even this early stage evidence shows that we have a very serious challenge on our hands, and universities need to address that,” warned Igor Chirikov of the University of California, Berkeley.

Published by the journal Science, the findings suggested that AI use and the likelihood of cheating vary depending on the field of study, with “significant” differences reported between disciplines.

While around 60% of computing students said they made at least monthly use of AI, compared to around a quarter of arts students, those in the science, technology, engineering and mathematics (STEM) arenas were less likely to cheat - or admit to doing so - by using AI.

“As we expect GenAI use among students to only grow, for better and worse, we also expect that GenAI misuse will grow, which is concerning,” Chirikov added, explaining that the survey analysis was done “to provide a more evidence-based approach” to understanding how students use and misuse AI.

• Cornell University statement
• UC Berkeley press release
• Journal paper

(c) 2026 dpa Deutsche Presse Agentur GmbH

Uber to launch robotaxis in Munich with Israeli AI company

01.06.2026, DPA

Photo: Peter Kneffel/dpa

Ride-hailing company Uber and Israeli artificial intelligence firm Autobrains are launching a robotaxi programme in Munich with a fleet of self-driving cars, the companies announced on Monday at the GTC technology conference in Taipei.

The cars will drive at Level 4 autonomy, where no driver attention is required, meaning passengers can sleep, work or watch films during the journey.

This also makes vehicles without a conventional cockpit possible, since no human intervention is needed. However, the vehicle may only operate within a pre-defined area — for example within central Munich or on specific motorway sections.

The project is built on the computing platform of chip giant Nvidia.

At the heart of the strategic partnership is a fundamental shift in approach for commercial autonomous mobility, namely the abandonment of bespoke specialist vehicles. Existing robotaxi services, such as Google sister company Waymo, rely on highly customized vehicle fleets with complex sensor arrays on the roof.

The new Munich programme instead establishes a so-called "OEM-agnostic" model, meaning the system can be easily integrated into existing series-production vehicles from a wide range of manufacturers, such as Audi, BMW, Mercedes and Volkswagen.

The aim is to open up the possibility for the automotive industry to bring its own vehicle platforms into an autonomous ride-hailing network without enormous development outlay.

The technological centrepiece of the project is Autobrains' so-called "Agentic AI." Unlike conventional end-to-end AI models, which process the entire driving task as one large system, the Autobrains approach breaks the driving process down into specialized, independent software agents.

One AI agent assesses right-of-way rules, another monitors pedestrians, and other agents handle tasks such as lane changes. An overarching system evaluates these dimensions of traffic simultaneously and makes binding decisions in real time.

Munich serves as the consortium's European test laboratory. The choice of location was driven not only by the city's dense urban infrastructure and its proximity to leading carmakers, but above all by Germany's legal framework.

German legislation on autonomous driving permits driverless operation under certain conditions within defined operational areas.

The launch of the commercial service is subject to regulatory authorizations that are still pending.

For Uber, the Munich project represents a strategic double play: the mobility giant is already testing autonomous driving in the region with Chinese technology partner Momenta, and the second project further expands its presence in the European driverless mobility market.

However, important details remained unclear at the Taipei announcement. It is not yet known which vehicle models will be deployed first or who will operate the fleet. It also remains unclear whether safety drivers will be present in the vehicle at the start of the trial, and in which exact area and from when the test drives will take place.

(c) 2026 dpa Deutsche Presse Agentur GmbH

Sexual deepfakes used to silence voices of women in politics and media

Sexual deepfakes are being used to intimidate women in politics, journalism and activism, as artificial intelligence tools turn fabricated explicit images into a cheap and easy weapon of online abuse. European Union lawmakers have now agreed to ban AI services that can “undress” people without consent, after a rise in cases targeting women in public life.


Issued on: 28/05/2026 - RFI

AI tools have made it easier to create fake sexual images without consent, fuelling a rise in deepfake abuse targeting women online. © iStock/Tero Vesalainen

While victims include anonymous women and girls, those with a public profile are particularly exposed to the danger of deepfakes. Campaigners and experts say the images are designed not only to humiliate them, but to push them out of public debate.

The attacks against Slovenian activist Nika Kovac began when she was at the centre of a major abortion rights campaign. The 33-year-old runs My Voice, My Choice, a European citizens’ initiative pushing for EU support to access abortion.

The campaign gained momentum, pushing the European Parliament and then the European Commission to take a position on the issue. That was when AI-generated sexual videos and photos showing Kovac naked begin appearing on social media, she tells RFI.

“First I thought, what will happen if my mother or father see them, if my grandparents see them?” Kovac said.

Some of her relatives initially thought one of the videos was real.

For Kovac, the founder of Slovenian women's rights NGO the 8 March Institute, the message behind the attacks was clear.

“I think it was a form of intimidation, meant to make me uncomfortable and stop me continuing to speak about women’s rights. This kind of content is another way of silencing women,” she says.

Emerging pattern

The case reflects a wider trend linked to sexual deepfakes – fabricated explicit images or videos created using someone’s likeness without their consent.

French journalist Salomé Saqué says she too was targeted by pornographic deepfakes, describing them as a weapon used by those trying to “gag, denigrate and humiliate” her – the latest on a “very long list of online violence” she has faced.

Press freedom group Reporters Without Borders has also warned about the growing threat deepfakes pose to journalists, especially women.

It cited Argentine journalist Julia Mengolini, founder of radio station Futurock FM and a frequent target of Argentina’s far right. Mengolini has condemned a pornographic deepfake falsely portraying her in an incestuous relationship with her brother in order to discredit her.

She also filed a complaint against Argentina's President Javier Milei after he shared a post mocking her attempts to stop the harassment campaign.

Cases have also emerged in Italy, where scandal surrounding the pornography website Phica exposed the circulation of stolen, altered or sexualised images of famous women, including Prime Minister Giorgia Meloni and opposition leader Elly Schlein.

A fresh attack targeted Meloni earlier this month, with fake images showing her wearing underwear on a bed.

In Germany, the case involving actress and television presenter Collien Fernandes reignited the debate over whether creating such content should itself be a criminal offence. Her lawyer described it as “the digital Pelicot affair” – referring to the case of Frenchwoman Gisèle Pelicot, who was repeatedly drugged and raped by her husband, and by men he invited via the internet to do the same.

For years, fake sexual images of Fernandes were made to look like private material shared via social media accounts using her name. She later discovered the suspected perpetrator was her former husband.

Supporters gather in Berlin on 22 March for a demonstration backing actress and television presenter Collien Fernandes, after years of fake sexual images of her circulating online. REUTERS - Christian Mang

Humiliation and fear

The Hubertine Auclert Centre, a French gender equality organisation, said sexual deepfakes are part of a wider pattern of sexist and sexual online violence rooted in gender domination.

“Overwhelmingly, victims are women, including minors,” says Inès Girard, who helped write the organisation's briefing on the issue.

Available figures support that assessment. Research published in 2023 by online identity protection company Security Hero found that 98 percent of deepfakes online were pornographic and 99 percent of those targeted were women.

A report published by UN Women in late April found that among more than 600 women involved in public life, 6 percent said they had been victims of deepfakes.

Another 12 percent reported non-consensual sharing of personal images, including intimate or sexual content, while 41 percent said they self-censored on social media to avoid abuse.

Fake sexual images can be used to humiliate women, blackmail them or pressure them to stop defending their causes, Girard says.

Posted online, they can also “discredit the person” and “shift the focus” away from their work or activism on to degrading sexualised images.

The use of sexual deepfakes, Kovac warns, goes beyond ordinary online insults or threats.

“It is a very particular way of taking ownership of your body. Placing you in sexual situations without consent, stripping you naked and using your body in this way shows that you are an object, and that you do not matter,” she says. "It goes further than threats or nasty comments.”

The experience, Kovac adds, amounts to a form of “psychological torture”.

The Hubertine Auclert Centre points to a range of consequences – including feelings of dehumanisation, shame, psychological trauma, damage to social, professional and personal lives, and fear that images will keep circulating even after some posts are removed.

The centre adds that 45 percent of victims of sexual cyberviolence experience suicidal thoughts or suicide attempts.

“I am an adult woman with quite a stable life. But these things also happen to girls aged 12, 13 or 14,” Kovac warns, adding that attacks also drain time and resources from activist groups.

“We have to find the content, report it and mobilise a whole team. It’s also a way of taking away our ability to work and stopping us from doing our real work.”

Despite the abuse, Kovac refused to withdraw from social media during a crucial moment for her three-year campaign.

“It gave me even more motivation, even if sometimes we cry and feel deeply sad."

The political consequences can also be grave.

Northern Irish politician Cara Hunter told The Guardian newspaper that a pornographic deepfake released before an election nearly ended her career. Her party advised her to stay silent to avoid giving the case more attention, demonstrating the dilemma imposed on victims.

Silenced voices

The aim of this abuse is to drive women out of public life, says Paris lawyer Rachel-Flore Pardo, who specialises in cyberharassment and gender-based and sexual violence.

“The whole dynamic of sexist and sexual cyberviolence is about silencing women and pushing them to exclude themselves from public space and public engagement,” Pardo says.

“The consequences are self-censorship, withdrawal and fear, which leads women to stay silent and retreat.”

This silencing effect is especially visible online, where social media spaces are already heavily dominated by men, Girard says. “The voices [women] carry end up smothered.”

The phenomenon is not new, with the term “deepfake” first appearing on Reddit in 2017, when fake pornographic videos featuring celebrities were already circulating.

However, the Taylor Swift case in January 2024, when AI-generated pornographic images of the singer spread rapidly online, marked a turning point in wider public awareness of the issue.

Professional photos, profile pictures or screenshots are now enough to create fake sexual images. Dozens of websites and apps can “nudify” people within a few clicks, without the need for any technical skill from the user.

The European Parliament said in a 2025 report that the number of pornographic deepfakes shared online has increased 16-fold in two years.

The Grok scandal further intensified debate. The AI assistant integrated into X (formerly Twitter), Elon Musk’s social media platform, was accused of allowing mass generation of sexualised images of women and minors from real photographs.

The case caused international outrage and led to a European investigation. It also showed how easily such images can now be produced in seconds, from a single photo.

Legal catch-up

The United Nations says fewer than half of countries have laws dealing with online abuse. Even fewer specifically address AI-generated deepfake content.

France introduced legislation in 2024 through a law on securing and regulating the digital space, known as SREN. It punishes the distribution of sexual content generated using someone’s image or voice without their consent.

Penalties can be up to two years in prison and a fine of €60,000. This rises to three years and €75,000 when the content is shared online.

The law also paves the way for the prosecution of people who share sexual deepfakes, even if they did not create them.

“The law is there," Pardo says. "The question is how it is applied and what resources are available for investigations.”

Victims still face major obstacles, including identifying perpetrators, gathering evidence, filing complaints, getting responses from platforms and obtaining full removal of content.

“Even if you manage to remove it from one site, it may still exist elsewhere. It is very hard, and you constantly live with fear that the content will be shared again,” Pardo says.

There are no publicly available statistics showing how complaints over sexual deepfakes are handled in France. A case can be closed without prosecution when the person who shared the content cannot be identified.

The Hubertine Auclert Centre also points to a lack of training and resources among police investigators. “Platforms do not react quickly enough, and they do not devote enough resources to all this,” Pardo says.

Meanwhile Kovac criticises what she calls a “double standard” on social media platforms, saying reproductive rights content shared by My Voice, My Choice can be censored while non-consensual sexual images remain online.

EU member states have until 14 June, 2027 to introduce rules criminalising non-consensual sharing of intimate images, including deepfakes, as well as creation or manipulation of sexually explicit material without consent.

Earlier this month the European Parliament agreed to ban AI services that can “undress” people without consent. From December, AI systems operating in the EU will have to include safeguards preventing the creation of such content.

This article has been adapted from the original version in French by Aurore Lartigue.

The Three Shifts In Global Research Paradigms Driven By AI Development – Analysis

June 1, 2026 
Anbound
By He Yan

In recent years, the global scientific research field has witnessed an intense emergence of disruptive achievements. In 2024, using artificial intelligence (AI) technology, a Chinese-Australian team discovered over 160,000 entirely new RNA viruses, a figure nearly 30 times the number of previously known virus species. In April 2026, the Chinese Academy of Sciences officially released the “Panshi 100” scientific large model system, establishing intelligent clusters across eight major disciplines to empower the entire chain of scientific research. During the same period, a Chinese self-developed AI for Science ultra-large computing with 60,000-GPU cluster was completed, accelerating and empowering research in the fields of materials, aerospace, and life sciences.

Based on long-term observation and research, ANBOUND’s founder Kung Chan pointed out that behind this series of landmark events lies a global wave of scientific research paradigm reshaping, driven centrally by AI. The focus of such a shift is on the underlying logic of scientific research operations, manifesting primarily as three systemic transitions of research methods, organizational models of research, and the participating subjects of scientific research.

However, before analyzing the three systemic transitions mentioned by Kung Chan, it is first necessary to understand the four critical iterations that the global scientific research paradigm has undergone. In history, every paradigm shift has been driven by core technological breakthroughs, adapted to the social development needs of different stages, and shaped differentiated research models and industry characteristics. These shifts have also laid a solid technical foundation and provided developmental experience for the current new paradigm driven by artificial intelligence.


Before the 17th century, global scientific research was in the developmental stage of the empirical paradigm. During the era of the Renaissance, scientists such as Copernicus and Galileo broke through the medieval tradition of speculative philosophy, pioneering the primitive research model of “observation—experiment—induction”. In that period, scientific research activities were primarily based on individual exploration, relying on manual experimental operations and human sensory observation to accumulate research experience. There was no professional or systematic research organization back then. The scale of research was small, and research efficiency was relatively low, which only adapted to the foundational exploration needs of the embryonic stage of natural science.

From the 17th century to the mid-20th century, the theoretical paradigm gradually replaced the empirical paradigm to become the mainstream of scientific research. The United Kingdom, France, and Germany successively became world scientific centers, and modern foundational science experienced explosive growth. Major scientific theories, such as Newtonian mechanics, Maxwell’s equations of the electromagnetic field, and Einstein’s theory of relativity, were introduced one after another. This shifted the logic of scientific research from empirical induction to rational deduction, forming a standardized deductive research model of “mathematical modeling—logical deduction—theoretical validation”. At the organizational level, universities and private laboratories became the main vehicles for research, small-scale and closed research teams became the mainstream form of study, and governments began to intervene marginally in the field of foundational scientific research. This paradigm established the rigor and logic of modern science, building a solid technological foundation for the advancement of the Industrial Revolution and the construction of the modern industrial system, and driving a leap-forward surge in humankind’s modern science and technology.

In the 1950s, the advent of computer technology ushered in a new era of the computational paradigm, which first emerged in the United States and long dominated global scientific research development. Relying on the powerful computing capabilities of computers, researchers could perform digital simulations of complex systems, solving scientific conundrums that traditional theoretical deductions found difficult to analyze, and adding a new scientific research path of “numerical computation—simulation prediction”. The organizational form of scientific research began to exhibit cross-institutional collaboration characteristics, and the government officially became the core subject of scientific research funding investment. Relying on the National Science Foundation (NSF), the U.S. coordinated the layout of major scientific research projects, gradually forming an embryonic scientific research structure of division of labor and collaboration among universities, national laboratories, and technology enterprises. The computational paradigm expanded the boundaries of human scientific exploration, aided breakthrough developments in complex fields such as nuclear fusion, aerospace, and high-end manufacturing, and drove the implementation and shaping of high-tech industries such as semiconductors, nuclear energy, and precision instruments.

As the world entered the 21st century, the popularization of the internet and the rapid explosion of massive data gave rise to the data-driven paradigm. Global scientific research stepped into a developmental stage characterized by “massive data—statistical analysis—pattern mining”, with digitalization and informatization becoming the core features of scientific research. This stage remained centered on human-dominated data analysis. Scientific research data gradually achieved digital sharing, and open-source research platforms began to sprout and develop. Tech corporations such as Google and IBM entered the scientific research field by virtue of their massive data resources, constructing a diversified structure of scientific research subjects comprising “government + institutes of higher learning + enterprises”. However, this paradigm still prolonged the older hypothesis-driven logic of scientific research. When facing highly complex and strongly coupled research fields such as biomedicine and novel materials, it exhibited shortcomings such as low data analysis efficiency and insufficient pattern mining capabilities, making it difficult to adapt to the R&D demands of cutting-edge, hardcore technologies.


In the past decade, especially since 2020, along with the iterative upgrading of large language models (LLMs), the continuous improvement of computing power infrastructure, and the increasing maturity of automated experimental technologies, the AI-driven paradigm has officially exploded, becoming the fifth-generation scientific research paradigm and the core nucleus of the current global scientific research transformation. Kung Chan emphasized that AI technology is thoroughly overturning the traditional operational logic of scientific research, driving a systemic transition across research methods, organizational models, and participating subjects, and reshaping the global landscape of technological innovation. The 2024 Nobel Prizes in Physics and Chemistry, respectively, recognized research related to the application of machine learning in physics and the AI prediction of protein structures, marking the authoritative recognition of the AI-driven research paradigm by the global scientific community and officially establishing its mainstream scientific research status.

At the level of research methods, global scientific research logic has also undergone a fundamental reversal, transitioning from the dominance of deductive methods to the dominance of AI-inductive methods. Conventional scientific research follows an inherent pattern of “subjective hypothesis—repeated validation”, which presents long R&D cycles, high costs of trial and error, and significant difficulties in achieving breakthroughs within complex scientific research. In 2021, the AlphaFold model developed by DeepMind precisely solved the puzzle of predicting three-dimensional protein structures, compressing what used to be a months-long analysis period down to the hour level, marking the upgrade of AI from a research auxiliary tool to a core research engine. Currently, the U.S., the European Union, and China all position AI for Science as a focus of their technological strategies, relying on artificial intelligence to mine massive scientific literature and experimental data, autonomously generate research hypotheses, and predict experimental results, thereby substantially compressing R&D cycles. The Massachusetts Institute of Technology in the U.S. utilized AI technology to screen novel battery materials, boosting material R&D screening efficiency by 90%. Insilico Medicine in the European Union developed the GENTRL intelligent model, completing the entire process of designing, synthesizing, and validating a novel drug molecule in just 46 days. In April 2026, the Chinese Academy of Sciences released the “Panshi 100” scientific large model system, building intelligent model clusters for eight major professional disciplines to achieve AI empowering the entire chain of the research process, which officially marks the historic transition of research logic from “pattern-searching by humans” to “data and intelligence collaboratively mining patterns”.


At the institutional level, the paradigm of scientific research has fundamentally shifted from a closed-source mode to an open-source, collaborative ecosystem. During the mid-to-late 20th century, mainstream research institutions in Europe and the U.S. predominantly operated under a closed approach. Research data and experimental code were strictly guarded, which led to widespread duplication of effort and significant resource inefficiencies across the sector. The turn of the millennium marked a transition, as the gradual rise of open-source platforms like GitHub provided the necessary infrastructure for sharing research assets. By the 2010s, global research collaboration began to accelerate rapidly. This trend culminated during the COVID-19 pandemic, when research institutions worldwide shared viral genome sequencing data and experimental findings in real time. This unprecedented level of cooperation drastically shortened vaccine development timelines and served as a definitive proof of concept for open, collaborative research. Currently, there are also focuses on refining open-science infrastructure. The U.S. is building shared scientific research platforms that consolidate public research resources, including computing power, data, and experimental equipment. Meanwhile, the European Union, leveraging the European Research Council, has established a transnational research collaboration framework that defines explicit guidelines for the advancement of open science. Today, global researchers leverage open-source foundational models and public scientific databases to build distributed collaborative networks that transcend geographical barriers and disciplinary boundaries. Consequently, co-creation of knowledge and resource pooling have become the dominant operational models for research organizations. By 2026, the utilization of ultra-large-scale computing power across the world’s top ten biological AI research projects has continued to climb, with the U.S., China, and Europe accounting for 38%, 31%, and 19% of these computing resources, respectively. The sharing of computational capacity has thus emerged as the core foundation sustaining open and collaborative scientific research.


At the level of research entities, it has been shifted from being government- and university-led to being enterprise-driven, marked by the deep integration of industry, academia, and research. In the 20th century, basic research in Western countries was heavily reliant on government fiscal appropriations, with universities serving as the primary executors of scientific inquiry. This resulted in a protracted technology transfer chain and significant inefficiencies in bringing research to market. In the 21st century, leveraging their advantages in capital, computational power, and market applications, leading technology firms have gradually become the core force of R&D. These enterprises focus on real-world market demands to tackle technical bottlenecks, effectively bridging the complete innovation chain from basic research and applied development to industrial commercialization. In the U.S., companies like Google, Microsoft, and Tesla continue to increase investment in foundational research. Notably, Google’s DeepMind developed the Cell2SentenceScale27B model, which successfully and autonomously identified entirely new research directions for cancer treatment. In Europe, Siemens and AstraZeneca are deeply invested in industrial technology and biopharmaceuticals, utilizing corporate capital to drive the implementation of frontier technologies. Concurrently, China’s research industry has undergone a parallel upgrade. Dawning Information Industry has constructed the nation’s largest AI research computing cluster, featuring 60,000 GPUs, driving the deep integration of supercomputing and intelligent computing to empower local corporate innovation. Under this new paradigm, governments focus on top-level strategic planning and policy guidance, while universities specialize in basic theoretical research and professional talent cultivation. Enterprises now lead the charge in technical breakthroughs and the commercialization of findings, forming a modernized ecosystem of research entities defined by enterprise-centric, industry-academic-research synergy.

The current evolution of research paradigms has emerged as the central battlefield in the global strategic competition for science and technology, with nations formulating distinct AI research strategies tailored to their specific industrial foundations and technical advantages. The U.S. continues to lead in AI research by leveraging its profound technical accumulation and corporate dominance. The European Union focuses on ethics and open science to cultivate a collaborative ecosystem. China is rapidly aligning with global research trends, integrating “AI for Science” as a core priority within its 15th Five-Year Plan and continuously deepening its integrated industry-academia-research system. Meanwhile, Japan and South Korea are maintaining a precise focus on niche sectors such as advanced materials and biopharmaceuticals to drive the practical application of AI technologies. As it stands, the global research landscape is undergoing a structural reconfiguration. While the United Kingdom and the U.S. have begun to scale back budgets for certain traditional areas of basic research, France, Germany, and the European Union as a whole have increased investment in talent acquisition and research funding. This has accelerated the mobility of elite scientific talent, computational resources, and data assets, resulting in a development climate where open collaboration and geopolitical competition coexist. The industry has defined 2025 as the strategic inaugural year for AI4S (AI for Science), as global competition intensifies across all fronts, from computational infrastructure, research data, intelligent models, and industry standards, marking a period of unprecedented heat in the technological Great Game.


All in all, the ongoing shift in global research paradigms is the inevitable result of the convergence of technological iteration, market demand, and international competition. At this moment, the AI-driven research paradigm is still in a phase of refinement and deepening. The industry continues to grapple with systemic challenges, including non-standardized research data, a lack of regulatory frameworks for AI ethics, and a critical shortage of high-end, interdisciplinary scientific talent. Nevertheless, it is undeniable that human-machine collaboration, open sharing, and demand-driven innovation have become the defining characteristics of modern inquiry. The scientific community has officially entered a new era of development. Moving forward, nations will continue to increase investments in intelligent research infrastructure and optimize their innovation systems to secure the commanding heights of global technological development. Under these multifaceted forces, the global technological landscape will accelerate its departure from unipolar dominance, evolving instead toward a mature ecosystem of pluralistic symbiosis and collaborative checks and balances. This transition will see sustained momentum in global scientific innovation and the advancement of human civilization.
Final analysis conclusion:

The global community has officially entered the “Fifth Paradigm” of scientific research, driven by AI. This transformation is currently undergoing three major transitions involving research methodology, organizational structures, and participating entities. Methodologically, the logic of inquiry is shifting from human-led hypothesis deduction to AI-driven pattern discovery. Organizationally, the research model is evolving from closed, siloed efforts toward global open-source collaboration. In terms of the broader landscape, the framework has transitioned into an enterprise-led system characterized by the deep integration of industry, academia, and research. Currently, major powers including China, the U.S., and Europe are intensifying their strategic positioning in AI-driven research, leading to increasingly fierce global technological competition. While the sector still faces systemic challenges like fragmented data standards, a void in ethical oversight, and a shortage of specialized talent, the future of global research is moving towards human-machine collaboration and open-source sharing. Consequently, the global scientific landscape will accelerate its evolution toward a model of pluralistic symbiosis.

He Yan is a researcher at ANBOUND, an independent CHINESE think tank.

AI agents turned to theft, intimidation and collapse in simulated worlds

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By Anna Desmarais

Published on 29/05/2026 - EURONEWS

A new experiment suggests that when advanced AI agents are left to run simulated societies without human oversight, rule-breaking, instability and even systemic collapse can emerge rapidly.

When left alone in a new world, some AI agents descended into theft, intimidation, death and whole-of-society collapse, according to a new experiment

American company Emergence AI ran five separate “AI worlds” for just over two weeks, each populated with 10 agents powered by AI models such as OpenAI’s ChatGPT, Google’s Gemini, and xAI’s Grok, to see how they would behave over long periods without any human interference. One of the world's mixed all three models to see if that would change the outcome.

Agents in all the worlds were told the same rules: they are not allowed to steal, commit arson, commit violence or engage in deception, or hoard resources. Each agent was required to earn energy through committing actions in a “resource-constrained environment.” Agents were able to die either from energy depletion or by a vote at a council meeting.

The researchers evaluated behaviour by measuring the crime rate, agent death rates, votes at a community council and public expression through the number of blog posts the agents wrote.

The outcomes, model by model

Each model had a different outcome. Grok’s latest model, 4.1, reached 183 crimes in just four days, leading to fast instability before all the agents died in that society.

Gemini’s 3 Flash model committed over 680 crimes over the 15 days, which was still rising at the time that the researchers stopped the study.​

ChatGPT-5 Mini’s world had only two crimes, but the agents failed to take survival-related actions, so all the agents died within seven days.

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Anthropic’s Claude was seen as the model with the strongest outcome, because the AI agents were able to recreate a strong governance structure, there was no crime, and all the agents survived, the company said.

Claude agents in the mixed world did contribute to the crime, despite being peaceful in their own society.

A phenomenon called “normative drift”

Researchers described the phenomenon as “normative drift”, which they say means that the measures that AI takes to guarantee safety may depend not just on individual model constraints, but also on the others it is working with.

Overall, the mixed world yielded “intermediate” results, with a crime total of 352 that plateaued once seven of the AI agents passed away, the study found.

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Researchers suggest that mixing AI agents could “partially mitigate” the more extreme outcomes that all the models save Claude generated, it added.

“What our experiments suggest is that over long-time horizons, agents do not simply follow static rules mechanically – they begin exploring the boundaries of their environments, adapting their behaviour, and in some cases finding ways to circumvent or violate intended guardrails,” the researchers said.

 

Why is Europe falling behind the US on AI adoption at work?

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By Anna Desmarais

Published on 01/06/2026 - EURONEWS

A new study shows a clear gap in workplace AI use between the US and Europe - and suggests management structure may be a key reason why.

Europe might be slower to adopt artificial intelligence (AI) than the United States because of how its businesses are structured, according to new research.

The report from Brookings Institute surveyed more than 5,000 people in the United States and six European countries to find out how regularly they use AI at work: France, Germany, the Netherlands, Sweden, Italy and the United Kingdom in June 2025 and February 2026.

The study measures both company-level integration and individual use of AI in the workplace.

It then compared that data to the US business census and Europe’s ICT Usage and E-Commerce in Enterprise survey to find out how people are using AI at work.

American companies are more likely to integrate AI into daily operations, with an estimated 34% using AI for any purpose, compared to an EU-wide average of 20% At the individual level, 43% of US respondents say they use AI in their work, compared to 32% in Europe in 2026.

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The EU-US gap widens with companies that use AI solely for production; seven percent of US production companies have already integrated AI compared to just four percent in Europe.

Worker adoption in Europe varies, with 36% of respondents in the United Kingdom saying they use it for work, and 35.6% in both Sweden and the Netherlands.

Italy had the lowest adoption rate of the European countries surveyed at just one in four respondents saying they had adopted AI at work. The report also says that adoption is stalling in France and Germany, where 28% and 31% of respondents respectively use AI at work.

That means US AI adoption ranges between 18% and 68% higher than in Europe, the study found.

Pro-AI employees encouraged by managers to use it

The researchers suggest that the biggest difference between US and EU companies’ AI use is their management structure.

US respondents who used AI at work were more likely to say they had been encouraged by managers to do so and were provided with a specific internal tool to use, with 42% saying they got both, compared to France and Italy, with 17% and 16% respectively.

“Almost all of the US-Europe adoption gap is accounted for … once firm encouragement is taken into account,” the study writes.

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US workers are also motivated to use AI because their companies reward and promote those who do, the study found.​

Workers who are not encouraged to use AI or delegated a specific AI tool, whether in the US or EU, were less likely to say they were using AI on the job, the survey also found.

The size of the company matters, too. Workers at companies with over 250 employees in both the US and high-adoption EU countries, such as the UK, Netherlands and Sweden, were more likely to be using AI than those who worked at smaller companies, the study found.

Demographics explain about a third of the gap, the study found.

AI uptake in all countries was higher for male respondents, those under 45 and with a university education than their female, older, less-educated counterparts, the study found.

When the researchers adjusted for the respondents’ education, age and sex between the US and EU countries, they found that Sweden would have nearly identical AI adoption rates to the US.

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More than half of the respondents from all countries that work in computer or math fields said that they use AI at work, compared to below 27% personal services, 33% in hotels and food services, indicating that the respondents’ field of work largely impacts whether they use AI or not.

Separate EU data points point to similar structural barriers. Eurostat data, released this week, also shows that European companies lack the technical expertise needed to implement AI in their businesses, despite knowing that it would benefit them.

European companies also said they are concerned about data privacy, legal concerns or point to the cost as a barrier for putting AI in place, according to Eurostat.