Several European governments are trying to reduce their dependence on American software, cloud platforms, and digital infrastructure as debates around data control, political influence, and technological independence become more intense across the region.
The situation has exposed contradictions in Europe’s relationship with U.S. technology companies. Microsoft chief executive Satya Nadella has largely stayed away from the kind of political messaging often associated with Alex Karp. Despite this difference, France has started moving parts of its public systems away from Microsoft Windows while simultaneously renewing contracts linked to Palantir Technologies through its domestic intelligence agency.
This complicated approach shows how Europe is attempting to distance itself from American tech firms without fully breaking away from them. Many governments now believe that relying too heavily on foreign technology companies can also mean depending on foreign laws, political priorities, and corporate influence. Still, Europe’s response has not followed one common strategy, with many actions appearing fragmented or reactive.
Much of the debate intensified after the U.S. passed the CLOUD Act in 2018 during President Donald Trump’s first term. The law gives American authorities the ability to request data from U.S.-based technology companies even if that information is stored outside the United States. For European officials, this raised concerns that storing data inside Europe may no longer be enough to fully protect sensitive information from foreign legal access.
Healthcare data quickly became one of the strongest examples used in these discussions. Medical records are considered among the most sensitive forms of information governments hold because they contain deeply personal details tied to citizens. Even after the CLOUD Act came into force, the United Kingdom partnered with companies including Google, Microsoft, and Palantir Technologies during the COVID-19 pandemic for projects involving National Health Service data.
Critics have argued that such partnerships could expose public-sector information to outside influence. France later decided that its Health Data Hub would stop using Microsoft Azure infrastructure and move toward what officials described as a sovereign cloud model. The contract was awarded to Scaleway, a cloud provider owned by French telecommunications group Iliad. Scaleway has also been expanding its network of data centers across Europe.
Scaleway later became one of four companies selected in a €180 million sovereign cloud contract backed by the European Commission. The program is intended to support cloud services that operate under European legal and regulatory standards. Notably, the European Sovereign Cloud initiative launched by Amazon Web Services was not included among the selected providers, even though Amazon created the project to answer European concerns about digital sovereignty.
Questions have also emerged around whether some so-called sovereign alternatives remain partly tied to American technology companies underneath. Some observers pointed to S3NS, a joint venture involving French defense company Thales Group and Google Cloud. Critics worry that arrangements like these could still leave room for indirect U.S. access or legal exposure despite being promoted as trusted European solutions.
Europe has faced similar problems in the search engine market. French search company Qwant was previously recommended for public servants in France while relying on Microsoft Bing’s underlying search infrastructure. The relationship later deteriorated after Qwant accused Microsoft of taking advantage of its dominant position in the market. Although French regulators declined to act against Microsoft, Qwant eventually started searching for alternatives on its own.
Qwant later partnered with German nonprofit search platform Ecosia to launch Staan, a Europe-based search index designed to reduce reliance on Google and Bing technologies. The project focuses on privacy and regional control over search infrastructure. Even so, both companies remain far smaller than their American competitors. Ecosia, despite having around 20 million users, still operates on a completely different scale compared to Google’s global user base.
One of the biggest problems facing European technology firms is market dominance from American companies. U.S. providers continue to control large parts of cloud computing, enterprise software, internet search, and artificial intelligence markets because of their global infrastructure, financial resources, and established ecosystems. European officials hope that large public-sector contracts could help regional providers compete more effectively.
Besides Scaleway, the European Commission’s sovereign cloud program also selected French companies Clever Cloud and OVHcloud, along with STACKIT. STACKIT was developed by the Schwarz Group, the parent company of Lidl, originally for its own internal systems before later being turned into a commercial cloud service.
Supporters of the initiative believe government-backed contracts could encourage more European companies to invest in domestic infrastructure instead of depending on foreign cloud providers. Backers of the program have also said the project aims to encourage digital solutions that align with European laws, governance rules, and privacy standards.
Still, Europe’s strategy of distributing contracts across several companies may create another challenge. While diversification could reduce dependence on one dominant provider and improve resilience, it may also make it harder for Europe to build a single technology giant capable of competing globally with firms such as Microsoft, Amazon, or Google.
Some critics also view sovereign tech partly as an economic strategy meant to keep European spending within the region. However, Europe’s attempts to move away from U.S. technology have not always translated into direct support for startups. In several cases, governments have instead turned toward open-source software alternatives.
France has already started replacing parts of its Windows-based systems with Linux. Public institutions in Germany, Denmark, Austria, and Italy are also exploring alternatives to Microsoft’s office software products through platforms such as LibreOffice.
Several governments have also embraced a “build instead of buy” approach by creating internal software tools. That strategy has faced criticism from parts of the technology and financial sectors. France’s Court of Auditors reportedly questioned spending linked to Visio, an internally developed platform intended to act as an alternative to Zoom and Microsoft Teams.
French newspaper Les Echos also reported frustration from parts of the country’s technology sector. Some critics argued that if governments themselves do not consistently adopt domestic technology tools, it becomes difficult to convince large private companies to do the same.
Many giants of European businesses continue selecting American technology providers when they offer stronger technical or commercial advantages. German airline Lufthansa chose Starlink for onboard internet services. Air France also selected Starlink despite partial ownership ties to the French and Dutch governments. Reports have additionally suggested that France’s national railway operator SNCF may eventually adopt similar services.
The debate around European alternatives has become particularly visible in satellite communications. During a disagreement involving Poland, Elon Musk stated publicly that “there is no substitute for Starlink.” European governments are now trying to prove otherwise by investing in domestic telecommunications and space infrastructure projects.
Public sentiment has also started influencing the discussion. After President Trump threatened to take control of Greenland, applications encouraging consumers to boycott American products surged in popularity on Denmark’s App Store rankings. The reaction showed that calls to reduce dependence on U.S. companies are no longer limited to policymakers and regulators.
Pressure is also building on European governments to reconsider contracts involving controversial American firms. Palantir’s recent public messaging and political positioning have drawn criticism inside parts of the European Union and the United Kingdom. At the same time, many European officials and citizens have started distancing themselves from X, formerly Twitter, because of growing dissatisfaction around platform governance and political discourse.
American technology companies have also shown that Europe is not always their top commercial priority. When Meta delayed the European release of Threads because of regulatory concerns tied to EU laws, it reinforced the perception that large U.S. firms can afford to deprioritize the region when legal requirements become too restrictive.
At the same time, this environment is opening new opportunities for companies building products specifically designed for European markets, languages, and legal standards. Supporters of the EuroStack initiative are pushing for rules that would encourage or require public institutions to purchase locally developed technology whenever possible.
Backers of sovereign tech also hope European companies can eventually compete internationally rather than only within domestic markets. French artificial intelligence company Mistral AI has reportedly experienced strong revenue growth as some businesses search for alternatives to OpenAI. Meanwhile, the governments of Canada and Germany are supporting cooperation between Cohere and Aleph Alpha to create what supporters describe as a transatlantic AI platform for governments and businesses.
As geopolitical tensions continue reshaping the global technology industry, some companies are discovering that not being American, Chinese, or Russian is itself becoming a commercial advantage in international markets.
A contemporary study conducted by researchers at Harvard University has revealed that advanced artificial intelligence systems are now capable of exceeding human doctors in both diagnosing medical conditions and determining treatment strategies, including in fast-paced and high-stakes emergency room environments. The research specifically accentuates the potential capabilities of modern AI systems in handling complex clinical reasoning tasks that were traditionally considered exclusive to trained physicians.
The findings, published in the peer-reviewed journal Science, are based on a controlled comparison between OpenAI o1 and experienced attending physicians. To ensure realistic testing conditions, the study used 76 actual emergency department cases sourced from Beth Israel Deaconess Medical Center. These cases were evaluated across multiple stages of the diagnostic process, allowing researchers to assess performance under varying levels of available patient information.
At the earliest stage of patient assessment, commonly referred to as initial triage, where clinicians typically have only limited details about a patient’s condition, the AI model demonstrated a notable advantage. It was able to correctly identify either the exact diagnosis or a closely related condition in 67.1 percent of the cases. In comparison, the two physicians involved in the study achieved accuracy rates of 55.3 percent and 50 percent respectively. This suggests that even with minimal data, the AI system was more effective at narrowing down potential diagnoses.
As the diagnostic process progressed and additional clinical information became available during the emergency room evaluation phase, the model’s performance improved further. Its diagnostic accuracy increased to 72.4 percent, reflecting its ability to refine its conclusions with more context. The physicians also showed improvement at this stage, but their accuracy remained lower, at 61.8 percent and 52.6 percent. This stage is particularly important as it mirrors real-world conditions where doctors continuously update their assessments based on new findings.
In the final phase of care, when patients were admitted either to general hospital wards or intensive care units, the AI model continued to outperform its human counterparts. It achieved an accuracy rate of 81.6 percent, compared to 78.9 percent and 69.7 percent for the physicians. Although the performance gap narrowed slightly at this stage, the AI still maintained a measurable edge, indicating consistency across the full diagnostic timeline.
Beyond identifying illnesses, the study also evaluated how effectively the AI system could design clinical management plans. This included decisions such as selecting appropriate medications, including antibiotics, as well as handling complex and sensitive scenarios like end-of-life care planning. Across five evaluated case studies, the AI achieved a median performance score of 89 percent. In contrast, physicians scored significantly lower, averaging 34 percent when relying on traditional clinical resources and 41 percent when supported by GPT-4. This underlines a substantial gap in structured decision-making support.
The researchers acknowledged that while integrating AI into clinical workflows is often viewed as a high-risk approach due to patient safety concerns, its potential benefits are significant. They noted that wider adoption of such systems could help reduce diagnostic errors, minimize treatment delays, and address disparities in access to healthcare services. These factors collectively contribute to both improved patient outcomes and reduced financial strain on healthcare systems.
At the same time, the study emphasizes that current AI systems are not without limitations. Clinical medicine involves more than text-based data. Doctors routinely rely on non-verbal and non-textual cues, such as observing a patient’s physical discomfort, interpreting imaging results, and making judgment calls based on experience. These aspects are not fully captured by existing AI models, which means human expertise remains essential.
The authors further concluded that large language models have now surpassed many traditional benchmarks used to measure clinical reasoning abilities. However, they stress the urgent need for more detailed research, including real-world clinical trials and studies focused on human-AI collaboration, to determine how these systems can be safely and effectively integrated into healthcare settings.
In comments shared with The Guardian, lead researcher Arjun Manrai clarified that the findings should not be interpreted as suggesting that AI will replace doctors. Instead, he described the results as evidence of a major technological shift that is likely to transform the medical field in the coming years.
From a macro industry perspective, this study reflects a developing trend in which AI is increasingly being used to augment clinical decision-making. However, experts continue to caution that challenges such as data bias, accountability, regulatory oversight, and patient trust must be addressed before such systems can be widely deployed. The future of healthcare, therefore, is likely to involve a collaborative model where AI amplifies efficiency and accuracy, while human doctors provide critical judgment, ethical oversight, and patient-centered care.
When Nataliya Kosmyna reviewed applications for internships, she noticed a pattern that stood out. Many cover letters were structured in nearly identical ways, written in polished language, and included vague or forced connections to her research. The consistency suggested that applicants were relying on large language models, the technology behind tools such as ChatGPT, Google Gemini, and Claude.
At the same time, while teaching at the Massachusetts Institute of Technology, Kosmyna began noticing that students were finding it harder to retain what they had learned. Compared to previous years, more students struggled to recall material, which led her to question whether growing dependence on AI tools could be influencing cognitive abilities.
Researchers studying human-computer interaction are increasingly concerned that relying too heavily on AI may alter not just how people write but how they think. This phenomenon, often described as “cognitive offloading,” refers to shifting mental effort onto external tools. While this has existed for years with calculators and search engines, experts warn that AI systems may deepen the effect because they generate complete responses rather than simply helping users find information.
Earlier research on internet usage identified what is known as the “Google effect,” where people became less likely to remember facts because they could easily look them up. Some researchers argued that this allowed the brain to focus on more complex tasks. However, AI tools now go a step further by producing answers, arguments, and even creative content, reducing the need for active thinking.
To better understand the impact, Kosmyna and her team conducted an experiment involving 54 students. Participants were divided into three groups. One group used AI tools to write essays, another relied on search engines without AI-generated summaries, and a third completed the task without any digital assistance. Their brain activity was monitored while they worked on open-ended topics such as happiness, loyalty, and everyday decisions.
The differences were clear. Students who worked without any tools showed strong and widespread brain activity across multiple regions. Those using search engines still demonstrated notable engagement, particularly in areas related to visual processing. In contrast, the group using AI tools showed comparatively lower brain activity, with levels dropping by as much as 55%. Activity in areas linked to creativity and deeper thinking was especially reduced.
The impact extended beyond brain activity. Students who used AI struggled to recall what they had written shortly after completing their essays. Several participants also reported feeling disconnected from their work, as if they had not fully contributed to it. Similar findings from other studies suggest that frequent use of AI tools can weaken memory retention and recall.
Research from the University of Pennsylvania introduces another concern described as “cognitive surrender,” where users accept AI-generated responses without questioning them. In such cases, individuals may rely on the system’s output even when it conflicts with their own understanding.
The effects are not limited to academic settings. A multinational study found that medical professionals who relied on AI tools for detecting colon cancer became less accurate when asked to identify cases without assistance after several months of use. This suggests that repeated dependence on AI may reduce independent decision-making skills, even in critical fields.
Kosmyna also observed that essays written with AI tended to be highly similar, lacking variation in style and depth. Teachers reviewing the work described it as uniform and lacking originality. In some cases, the responses were so alike that it appeared as though students had collaborated, even when they had not.
Follow-up observations months later revealed further differences. Students who had previously relied on AI showed weaker neural connectivity when asked to complete tasks without it, compared to those who had worked independently earlier. This may indicate that they had engaged less deeply with the material from the start.
Vivienne Ming, author of Robot Proof, has raised similar concerns. In her research, students asked to make real-world predictions often defaulted to copying answers from AI systems instead of forming their own conclusions. Brain measurements showed low levels of gamma wave activity, which is associated with active thinking. Reduced gamma activity has been linked in other studies to cognitive decline over time.
However, not all users showed the same pattern. A small group, fewer than 10%, used AI differently by treating it as a source of information rather than a final answer. These individuals analysed the output themselves, showed stronger brain engagement, and produced more accurate results.
The concerns echo earlier findings related to navigation technology. Increased reliance on GPS has been associated with reduced spatial memory in some studies. Weak spatial navigation skills have also been explored as a possible early indicator of conditions such as Alzheimer's disease. These parallels suggest that reduced mental effort over time may have broader cognitive consequences.
Researchers emphasize that AI itself is not the problem but how it is used. Ming advocates for a more deliberate approach, where individuals think through problems first and then use AI to test or refine their ideas. She suggests methods such as asking AI to challenge one’s reasoning or limiting it to providing context instead of direct answers, encouraging deeper engagement.
Kosmyna similarly recommends building a strong understanding of subjects without AI assistance before integrating such tools into the learning process.
The alarming takeaway from the current research is clear. While AI offers efficiency and convenience, it may also encourage mental shortcuts. Human cognition depends on regular effort and engagement, and reducing that effort could carry long-term consequences. As these tools become more integrated into daily life, the challenge will be to use them in ways that support thinking rather than replace it.
Salesforce has introduced what it describes as the most crucial architectural overhaul in its 27-year history, launching a new initiative called “Headless 360.” The update is designed to allow artificial intelligence agents to control and operate the company’s entire platform without requiring a traditional graphical interface such as a dashboard or browser.
The announcement was made during the company’s annual TDX developer conference in San Francisco, where Salesforce revealed that it is releasing more than 100 new developer tools and capabilities. These tools immediately enable AI systems to interact directly with Salesforce environments. The move reflects a deeper shift in enterprise software, where the rise of intelligent agents capable of reasoning and executing tasks is forcing companies to rethink whether conventional user interfaces are still necessary.
Salesforce’s answer to that question is direct: instead of designing software primarily for human interaction, the platform is now being rebuilt so that machines can access and operate it programmatically. According to the company, this transformation began over two years ago with a strategic decision to expose all internal capabilities rather than keeping them hidden behind user interfaces.
This shift is taking place during a period of uncertainty in the broader software industry. Concerns that advanced AI models developed by companies like OpenAI and Anthropic could disrupt traditional software business models have already impacted market performance. Industry indicators, including software-focused exchange-traded funds, have declined substantially, reflecting investor anxiety about the long-term relevance of existing SaaS platforms.
Senior leadership at Salesforce has indicated that the new architecture is based on practical challenges observed while deploying AI systems across enterprise clients. According to internal insights, building an AI agent is only the initial step. Organizations also face ongoing challenges related to development workflows, system reliability, updates, and long-term maintenance.
To address these challenges, Headless 360 is structured around three foundational pillars.
The first pillar focuses on development flexibility. Salesforce has introduced more than 60 tools based on Model Context Protocol, along with over 30 pre-configured coding capabilities. These allow external AI coding agents, including systems such as Claude Code, Cursor, Codex, and Windsurf, to gain direct, real-time access to a company’s Salesforce environment. This includes data, workflows, and underlying business logic. Developers are no longer required to use Salesforce’s own integrated development environment and can instead operate from any terminal or external setup.
In addition, Salesforce has upgraded its native development environment, Agentforce Vibes 2.0, by introducing an “open agent harness.” This system supports multiple agent frameworks, including those from OpenAI and Anthropic, and dynamically adjusts capabilities depending on which AI model is being used. The platform also supports multiple models simultaneously, including advanced systems like Claude Sonnet and GPT-5, while maintaining full awareness of the organization’s data from the start.
A notable technical enhancement is the introduction of native React support. During demonstrations, developers created a fully functional application using React instead of Salesforce’s traditional Lightning framework. The application connected to Salesforce data through GraphQL while still inheriting built-in security controls. This significantly expands front-end flexibility for developers.
The second pillar focuses on deployment. Salesforce has introduced an “experience layer” that separates how an AI agent functions from how it is presented to users. This allows developers to design an experience once and deploy it across multiple platforms, including Slack, mobile applications, Microsoft Teams, ChatGPT, Claude, Gemini, and other compatible environments. Importantly, this can be done without rewriting code for each platform. The approach represents a change from requiring users to enter Salesforce interfaces to delivering Salesforce-powered experiences directly within existing workflows.
The third pillar addresses trust, control, and scalability. Salesforce has introduced a comprehensive set of tools that manage the entire lifecycle of AI agents. These include systems for testing, evaluation, monitoring, and experimentation. A central component is “Agent Script,” a new programming language designed to combine structured, rule-based logic with the flexible reasoning capabilities of AI models. It allows organizations to define which parts of a process must follow strict rules and which parts can rely on AI-driven decision-making.
Additional tools include a Testing Center that identifies logical errors and policy violations before deployment, custom evaluation systems that define performance standards, and an A/B testing interface that allows multiple agent versions to run simultaneously under real-world conditions.
One of the key technical challenges addressed by Salesforce is the difference between probabilistic and deterministic systems. AI agents do not always produce identical results, which can create instability in enterprise environments where consistency is critical. Early adopters reported that once agents were deployed, even small modifications could lead to unpredictable outcomes, forcing teams to repeat extensive testing processes.
Agent Script was developed to solve this problem by introducing a structured framework. It defines agent behavior as a state machine, where certain steps are fixed and controlled while others allow flexible reasoning. This approach ensures both reliability and adaptability.
Salesforce also distinguishes between two types of AI system architectures. Customer-facing agents, such as those used in sales or support, require strict control to ensure they follow predefined rules and maintain brand consistency. These operate within structured workflows. In contrast, employee-facing agents are designed to operate more freely, exploring multiple paths and refining their outputs dynamically before presenting results. Both systems operate on a unified underlying architecture, allowing organizations to manage them without maintaining separate platforms.
The company is also expanding its ecosystem. It now supports integration with a wide range of AI models, including those from Google and other providers. A new marketplace brings together thousands of applications and tools, supported by a $50 million initiative aimed at encouraging further development.
At the same time, Salesforce is taking a flexible approach to emerging technical standards such as Model Context Protocol. Rather than relying on a single method, the company is offering APIs, command-line interfaces, and protocol-based integrations simultaneously to remain adaptable as the industry evolves.
A real-world example surfaced during the announcement demonstrated how one company built an AI-powered customer service agent in just 12 days. The system now handles approximately half of customer interactions, improving efficiency while reducing operational costs.
Finally, Salesforce is also changing its business model. The company is shifting away from traditional per-user pricing toward a consumption-based approach, reflecting a future where AI agents, rather than human users, perform the majority of work within enterprise systems.
This transformation suggests a new layer in strategic operations. Instead of resisting the rise of AI, Salesforce is restructuring its platform to align with it, betting that its existing data infrastructure, enterprise integrations, and accumulated operational logic will continue to provide value even as software becomes increasingly autonomous.