© Loren Elliott for The New York Times
© The New York Times
© Nathan Howard for The New York Times
Europe’s efforts to establish greater control over artificial intelligence technology are entering a new phase as the EUROPA consortium, led by Italian company Domyn, advances plans to develop one of the continent’s largest open-source AI models.
The initiative, supported by the European Commission and European high-performance computing infrastructure, aims to create an advanced artificial intelligence system capable of competing with models developed by leading American and Chinese companies.
Domyn chief executive Uljan Sharka told the Financial Times on 29 October that Europe possesses the technological resources and industrial expertise required to compete in advanced AI development.
However, he also identified two obstacles: the need for greater investment and the difficulty of persuading European businesses to adopt domestically developed systems.
These challenges extend beyond the development of a single language model.
They concern whether Europe can establish a commercially sustainable AI industry while reducing dependence on technology providers headquartered outside the EU.
The European Commission selected EUROPA in June 2026 as the winner of its Frontier AI Grand Challenge.
The competition, launched in February, was designed to support the development of a European general-purpose AI model with more than 400 billion parameters.
EUROPA’s planned model is expected to contain approximately 500 billion parameters and support all 24 official EU languages.
The consortium brings together Domyn and Germany’s Fraunhofer research organisation, including the Fraunhofer IAIS and IIS institutes.
Its work will rely on the EuroHPC network of European supercomputers.
Under the competition arrangements, the winning consortium can obtain access to as much as 2.5 per cent of EuroHPC’s total computing capacity for one year.
This allocation is intended to address one of the largest barriers to advanced AI development: the enormous computing resources required to train models at this scale.
The final system is intended to be released under an open-source licence, allowing organisations to inspect, adapt and deploy the technology.
Nevertheless, parameter count alone does not demonstrate that a model is competitive.
Its actual performance will depend on training data, architecture, computational efficiency, evaluation results and the ability to perform reliably across different tasks.
One of EUROPA’s distinguishing objectives is its coverage of all official EU languages.
The commercial AI market has historically been dominated by models developed primarily for English-language applications, although leading international systems increasingly support multiple languages.
For Europe, linguistic capability has particular relevance to public administration, legal services, education and cross-border business.
A model designed specifically for the European market could potentially support specialised applications involving national legislation, administrative procedures and technical terminology.
However, multilingual availability will need to be assessed against measurable performance standards.
A system capable of producing fluent responses in a particular language is not necessarily equally reliable when interpreting specialised legal or scientific information.
The consortium will therefore need to demonstrate that linguistic coverage translates into practical capabilities rather than remaining principally a technical specification.
The EUROPA project forms part of a wider effort to use European publicly supported computing infrastructure for commercial and scientific AI development.
The EuroHPC Joint Undertaking has invested in systems intended to provide advanced computing resources to researchers, businesses and public institutions.
Additional AI-focused infrastructure is being developed through the EU’s network of AI factories.
These facilities are intended to make specialised computing capacity available to organisations that would otherwise face considerable costs when developing or adapting AI systems.
However, infrastructure ownership does not necessarily provide complete technological independence.
European supercomputing projects continue to rely on internationally sourced semiconductor technology and specialised software.
Domyn itself has described a partnership with the American technology company Nvidia in its previous model-development work.
Consequently, European AI sovereignty is better understood as a question of control over important technological capabilities than complete separation from international supply chains.
On 20 October, Domyn announced the initial release of Domyn Edge, a separate AI model containing four billion parameters.
According to the company’s technical announcement, the system was trained from scratch using approximately 13 trillion tokens on Europe’s Leonardo supercomputer.
Domyn has committed to making the model’s weights, training code, intermediate checkpoints and datasets available under open licences.
This approach is intended to improve reproducibility and allow external developers to examine how the model was constructed.
Domyn Edge is substantially smaller than the planned EUROPA system and should not be confused with it.
Nevertheless, the project provides an example of the company’s approach to developing AI models using European computing infrastructure.
The EU has already established an extensive regulatory framework for artificial intelligence through the AI Act.
However, legislation governing the development and deployment of AI does not, by itself, create a competitive domestic technology industry.
Europe faces competition from American companies with substantial private investment, established cloud platforms and access to large commercial markets.
Chinese developers have also expanded their capabilities, including in open-weight AI systems.
EUROPA’s commercial prospects will therefore depend on more than technical performance.
European businesses must be willing to integrate the resulting technology into their operations, while developers will require dependable access to computing infrastructure and support services.
There is also the question of whether European institutions themselves will adopt domestically developed AI systems where they meet relevant security and performance requirements.
Without sufficient demand, even a technically successful model could struggle to establish a sustainable ecosystem.
Domyn has indicated that it aims to release EUROPA during 2027, although public statements have differed over the precise timetable.
The company’s leadership has discussed a June target, while an earlier technical announcement referred to the second half of 2027.
The immediate challenge is completing development and demonstrating that the model can compete on performance, reliability and cost.
The longer-term challenge concerns adoption.
If European businesses and public institutions continue relying predominantly on foreign technology, access to an advanced European alternative may have only a limited effect on the market.
Conversely, a competitive and widely adopted open-source model could offer organisations greater control over deployment, customisation and sensitive information.
EUROPA is therefore more than a publicly supported technology experiment.
It is a test of whether Europe can convert research institutions, computing infrastructure and industrial expertise into AI products capable of competing in the international market.
The outcome will depend not simply on whether Europe can build an advanced model, but on whether organisations consider it sufficiently capable and economical to use.
© The New York Times
© Julia Demaree Nikhinson/Associated Press
Although artificial intelligence is drawing criticism for its energy use, it's also being used by firms such as ExxonMobil and BP to unleash new energy production. The research firm Wood Mackenzie even estimates that AI could unlock 1 trillion extra barrels of oil globally by helping companies "wring substantially more oil out of producing reservoirs."
These developments are good news for consumers, but they could have a significant impact on the environment, according to an August paper published in npj Climate Action. The authors find that AI could prolong oil and gas usage, leading to a 0.47–1.8 gigaton increase in annual global carbon dioxide emissions. The lower end of that estimate is comparable to Mexico's annual carbon dioxide emissions, study co-author Holly Alpine told The Wall Street Journal. The higher end is equivalent to about 5 percent of the world's energy emissions from 2024.
The researchers say net emissions reductions cannot occur unless renewables grow four to five times faster than oil and gas. (They do note there are other ways AI could reduce the greenhouse gas footprint of the fossil fuel industry—namely, "methane leak detection or efficiency gains in power generation.")
This is an ambitious task, considering that fossil fuels account for over 80 percent of the world's energy needs and their peak usage is years away. But there are reasons to be optimistic about AI's role in accelerating the use of clean technologies.
In the same way that oil majors are using AI to find their next well, startups are utilizing the technology to unleash geothermal energy. One such company is Zanskar, which uses a combination of AI, advanced drilling methods, and geoscience to "accurately map the Earth's subsurface and develop geothermal resources that have been overlooked or considered too risky."
At the Lightning Dock project in New Mexico—which Zanskar purchased in 2024—the company's technology has reduced surface infrastructure costs by 75 percent and sped up drilling by 35 percent. Zanskar says Lightning Dock is now "home to the most productive pumped geothermal well in the U.S. and quite possibly the world." Late last year, the company discovered "Big Blind," a naturally occurring geothermal system in Nevada with the potential to power 80,000–100,000 homes with reliable carbon-free energy. In January, it closed a $115 million funding round.
Nuclear power is also benefiting from advances in AI. In August, Atomic Canyon released its Nuclear Industry Virtual Assistant with backing from Nvidia. The tool is designed to help nuclear professionals navigate technical and regulatory landscapes, which could lead to more nuclear power being developed and deployed faster. The industry has also seen a wave of investment from tech companies looking to fuel their power-hungry data centers with clean energy. Big Tech firms such as Google, meanwhile, have poured significant resources into renewable and battery storage projects to support data centers and bring more energy to the grid.
AI is here to stay. We should embrace what it is already starting to give us: a cleaner and more abundant energy future.
The post AI Isn't Just Extracting More Fossil Fuels—It's Unlocking Renewable Energy Too appeared first on Reason Magazine.
Cypriot businesses need to organise their data, establish clear rules and train employees before adopting artificial intelligence (AI), according to Kyriaki Parmakki, co-founder and chief executive of Limassol-based solutions studio essere.ai, who hosted the first ClickUp event in Cyprus earlier this month.
The event, held on October 8, brought together business owners, directors and technology specialists to explore how companies can integrate AI into their operations, with demonstrations of practical applications and discussions on the foundations needed to make the technology effective.
ClickUp, an American software company valued at $4 billion and known for its project management, task management and AI capabilities, held its first event on the island in partnership with essere.ai, which is ClickUp’s official and certified partner in Cyprus and the only partner it works with directly on the island.
In an interview with the Cyprus Mail, Parmakki said that the event highlighted a growing gap between individuals’ willingness to use AI and the extent to which businesses have integrated it into their operations.
“In 2025, 44 per cent of people in Cyprus used generative AI, against an EU average of 33 per cent, while only 9 per cent of Cypriot companies used AI, less than half the EU rate of 20 per cent,” she said.
“People in Cyprus are ahead of their companies.”
She added that the event’s attendance and the questions raised by participants suggested that businesses were increasingly interested in practical implementation rather than simply debating whether to adopt the technology.
“Appetite is not the problem. The room was full on a Thursday evening, with owners and directors, and the questions were about how, not whether,” Parmakki said.
“ClickUp choosing Cyprus for its first event here means international platforms now see this market too.”
The event featured a presentation by ClickUp guest speaker Mary Bornaghi, who outlined the platform’s approach to bringing work management and AI together, while Parmakki presented the fundamentals businesses need before introducing AI.
Neophytos Polydorou, senior AI engineer at essere.ai, demonstrated 5 live use cases using real systems and demonstration data, while representatives of 2 companies discussed how moving their work into ClickUp had changed their operations.
The evening concluded with an AI-readiness assessment that participants could complete on their phones, followed by networking.
A central theme was that businesses should focus on their operational foundations before investing in AI tools, with Parmakki arguing that the technology’s effectiveness depends on the quality and accessibility of the information it can use.
“Everything a company does is data. A call, an invoice, a message, an approval. AI can only work with what it can read, so the first question is never which tool, it is where the data lives,” she said.
She described 4 stages of business development according to how information is managed, ranging from data held in employees’ heads to information stored in separate files, then organised in systems, and finally supported by AI intelligence.
“Moving up one step always means the same thing, organising the data, and only then does AI have something reliable to work with,” Parmakki said.
Skipping this groundwork can result in AI producing generic responses or confidently delivering incorrect information, while employees may turn to personal AI accounts to handle company information because their employers have not provided suitable systems.
Businesses may also struggle to translate individual productivity gains into measurable commercial results.
Parmakki cited McKinsey research involving 1,700 executives, saying 80 per cent reported that AI made them personally more productive, while only 37 per cent of companies saw the benefits reflected in their results.
“The gap between those two numbers is the foundations,” she said.
The event also examined the growing use of AI by employees, including the security implications when individuals adopt tools without company oversight.
According to figures presented during the event, the proportion of employees regularly using AI on their work computers had increased from 15 per cent to 45 per cent in 1 year, while 2 thirds of those users signed in through personal rather than company accounts.
“The question is not whether your people use AI. It is whether the company knows,” the event’s presentation stated.
Parmakki said the most immediate returns tended to come from processes involving large volumes of unstructured information, including telephone calls, emails, invoices and documents.
Examples included answering calls in the caller’s language, recording the details and making bookings while other employees continued their work, as well as reading, checking and filing supplier invoices without requiring someone to open each email.
Other applications could prepare short morning briefings identifying tasks at risk of missing deadlines and suggesting how work could be reassigned.
“These are not pilots. Each one removes a chore that repeats every week, and a person can check the result in seconds,” she said.
However, she stressed that businesses must establish clear boundaries for AI agents before allowing them to operate.
“Before an agent goes live we write three things down, what it may read, what it may do on its own, and where a person decides,” Parmakki continued.
“Draft yes, send no. Suggest yes, pay no. No decisions about people or payments without a person.”
She added that every action should be logged and the records reviewed weekly, allowing companies to monitor whether AI systems are operating within their permitted limits.
The live demonstrations illustrated how these principles could work in practice, including a support call recognised and handled by an AI agent called Ciao, which helped the caller and made a booking.
Another demonstration showed an 8am briefing identifying a task at risk and proposing that it be reassigned, while an AI agent prepared month-end billing for approval by a person.
Other examples included filing a supplier invoice without anyone having to read the inbox and conducting a weekly security review in which an agent checked every action against established rules, including access it had been denied.
The event also addressed the distinction between developing bespoke software and configuring existing platforms, with the presentation arguing that although building software has become more accessible, maintaining it still involves logins, permissions, mobile applications, backups, security, updates and support.
The departure of the person who originally built a system can also create difficulties for the business.
The recommendation was to build technology where it creates a genuine competitive advantage and configure existing tools for other requirements.
Regulatory compliance was another key theme, particularly the European Union’s AI Act and the responsibilities it places on businesses.
“The EU AI Act already asks two things of every company, train your people on AI, and tell customers when they are talking to a machine,” Parmakki said.
The event presentation stressed that businesses should provide AI training and retain evidence of that training, while making clear to people when they are interacting with AI.
It also highlighted the heavier requirements for AI systems making decisions about people and money, which Parmakki said were due to arrive in December 2027.
Beyond regulation, she argued that a lack of expertise and uncertainty about legal requirements and data protection were more significant obstacles to adoption than the price of the technology.
“The gap is not technology, and it is not money. It is knowledge, and fear,” she said.
“Most companies do not know where to start and do not know where this is going, so they wait.”
She referred to Eurostat findings on businesses that had considered AI but decided against adopting it, saying the most frequently cited reason in Cyprus and across the EU was a lack of relevant expertise, followed by legal uncertainty and data protection concerns, while cost ranked considerably lower.
Parmakki said essere.ai aimed to bridge that gap through education before technology, helping business owners understand what AI could do within their own operations, where it was appropriate and what needed to be in place first.
For companies seeking to move beyond experimentation, she identified 3 priorities, beginning with establishing a reliable operational system that the business owns.
This should provide a single source of information for clients, tasks, documents and figures, rather than relying on employees’ memories, inboxes and spreadsheets.
The second priority is to establish written rules governing what AI can access, which actions it can perform independently and when human approval is required, with every action logged and a designated employee responsible for oversight.
The third is to train employees, move them away from personal accounts towards company accounts and select a single recurring process in which AI can be introduced and tested.
“Pick one process that repeats every week, put AI inside it, run it for four weeks with a clear yes or no at the end, and only then take the next one,” she said.
“That is the difference between experimenting and integrating.”
Parmakki argued that businesses did not need to begin with an extensive strategy document or bespoke software, but rather with sound preparation and a disciplined approach to implementation.
“None of it needs a strategy document or custom software. It needs the groundwork done properly, and the discipline to start small,” she said.
The event also presented Gartner’s expectation that 60 per cent of AI projects lacking AI-ready data would be abandoned, reinforcing the importance of preparing business information before introducing additional tools.
The broader message was that AI adoption should be treated as an operational change rather than simply a technology purchase, with the potential benefits depending on how well companies organise their work, protect their information and equip employees to use the technology effectively.
© Ariel Davis
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