Palantir Europe Chief Says the Region Risks Missing AI’s Defining Moment

Preface

Europe may be at risk of repeating a familiar technology-industry pattern: approaching a transformative innovation cautiously while other regions build the businesses and infrastructure that define it. Louis Mosley, Palantir’s head of U.K. and Europe, argues that the region’s response to artificial intelligence resembles its earlier experience with the internet. In his view, hesitation by European companies could limit their ability to benefit from AI and widen the economic gap with the U.S.

Mosley’s comments come amid debate over AI’s economic promise, its effects on work and public services, and Palantir’s controversial defense and law enforcement contracts. This article outlines his argument, the company’s role in AI adoption, and the competing concerns shaping the discussion. The central question is whether Europe can weigh legitimate risks without allowing caution to become inaction.

Lazy bag

Mosley warns that Europe risks “missing the boat” on AI, as he believes it did with the internet. He points to a widening economic divide with the U.S. and says European businesses are often reluctant to adopt unfamiliar technology. Palantir offers tools that help organizations deploy AI models using their own data, though its defense and law enforcement work has generated opposition. Mosley argues AI could raise productivity and reduce administrative work in public services, while acknowledging that it may disrupt jobs. In his view, the main danger is political failure to explain AI’s potential benefits to the public.

Main Body

Louis Mosley, Palantir’s head of U.K. and Europe, has warned that Europe risks repeating what he sees as a past failure to capitalize on the internet. Speaking to CoinDesk, he contrasted the U.S., which he said “got the internet right” and built many businesses that became part of the digital economy, with Europe, which he said “missed the boat.” His comparison frames the debate over artificial intelligence not simply as a question of technology, but as one of economic ambition, investment and willingness to adopt new tools.

Mosley pointed to the long-term divergence between the U.S. and Europe. He said the two regions were roughly comparable 20 years ago in GDP per capita, capital-market depth and the scale of their largest companies. Since then, he argued, the U.S. has compounded growth while Europe has made little progress by comparison. The implication of his argument is that a technology transition can influence economic performance over many years: the places that develop companies and capabilities around a new platform may capture a disproportionate share of its value.

Earlier waves of technology did produce successful European companies, Mosley noted, but he argued that the internet era created few European champions. He cited ASML (ASML) as a rare exception and said it had become by far Europe’s largest company, adding that this illustrates how much value is found in technology. His point was not that Europe lacks technical expertise, but that expertise does not automatically translate into a broad ecosystem of technology businesses with global scale.

Mosley believes AI could prove even more consequential than the internet. He says many European companies remain cautious, adopting a wait-and-see attitude because the technology is new and therefore may appear risky. That hesitation can be understandable: organizations must consider data protection, reliability, workforce impacts, regulation and the costs of integrating new systems. However, Mosley’s concern is that delaying adoption may prevent companies from learning how AI can be used effectively, leaving them less prepared as competitors build experience.

Palantir’s business is closely tied to this adoption question. The company develops an ontology and governance scaffold intended to help governments and businesses organize information and use large language models and agents in areas such as healthcare, industry and defense. Mosley called Palantir “the purest proxy on public markets for the rate of AI adoption in an economy.” In this framing, the company’s business activity offers one way to observe whether complex organizations are moving from interest in AI toward practical deployment.

The company describes its approach as model-agnostic. Customers can use models from OpenAI, Anthropic, Gemini, Grok, open-weight models or Chinese systems, and can fine-tune them on their own data. This flexibility matters because organizations may have different technical requirements, risk policies and preferences about where models are hosted. Rather than relying on one AI model, customers can select among options and connect them to their operations. The broader proposition is that AI adoption involves more than choosing a model: organizations also need ways to govern access, integrate information and oversee how systems are used.

Palantir’s position in Europe is nevertheless contentious. Opposition relates in part to its defense and law enforcement work. The company has provided surveillance software to U.S. Immigration and Customs Enforcement (ICE) under the administration of President Donald Trump and supplied targeting systems to the Israel Defense Forces (IDF). Campaigners have raised ethical objections to using Palantir in systems supporting public services, including the U.K.’s National Health Service (NHS). These concerns involve questions about surveillance, the use of data, military applications and whether a company’s work elsewhere should affect its role in domestic services.

Mosley told CoinDesk that Gaza and Trump are issues that particularly animate critics. He acknowledged Palantir’s contracts in America for Trump’s administration and in Israel with the IDF, as well as its contract with the NHS. He argued that attention to the company’s foreign contracts can distract from domestic policy decisions. To make that case, he asked whether politicians would tell a constituent to wait longer for a hip operation because of a supplier’s unrelated contracts abroad. His example presents a tension between scrutinizing a supplier’s record and evaluating whether its technology can improve a particular public service.

That tension cannot be resolved solely by arguing that AI may be useful. Public agencies and companies still need to assess privacy, accountability, security and the appropriateness of particular uses. At the same time, rejecting a technology or provider without considering the practical consequences may carry costs. Mosley’s argument is that controversy should not prevent decision-makers from asking whether carefully governed AI could improve services or make organizations more productive. A balanced assessment would examine the intended use, safeguards, alternatives and measurable outcomes rather than treating adoption as automatically beneficial or harmful.

Concerns about the resource demands of new technologies also feature in the debate. Mosley compared current criticism of AI’s energy use with criticism directed at bitcoin miners a decade ago. He noted that many miners have since pivoted to AI high-performance computing and now face similar concerns about power consumption. The comparison highlights how technology industries can change their focus while energy questions persist. It does not remove the need to account for electricity use; instead, it suggests that policymakers and the public will continue to weigh the benefits of computing against its infrastructure and environmental demands.

Mosley recognizes that AI will disrupt jobs, but argues that it can also free workers from administrative tasks. He gave nurses and police as examples of public-service professionals who could spend less time on paperwork and more time on core responsibilities. If achieved, that shift could support productivity, but its effects would depend on how systems are introduced, whether they are accurate and dependable, and how organizations redesign work. Productivity improvements are not automatic: they require suitable processes, staff training and appropriate oversight.

Palantir, Mosley said, still has room to grow. He cited around 1,000 customers, set against a much larger addressable market of complex organizations in allied countries, and described the company as being at an early stage. This assessment reflects his perspective as a company executive and should be understood in that context. More broadly, the opportunity for AI depends on whether organizations can turn experimentation into useful, responsible applications, not merely on the availability of models or software.

For Mosley, the greatest risk to AI is political failure to explain why the technology could benefit people. He sees a recurring pattern in which public debate focuses on objections while potential improvements receive less attention. He warned that Europe is “making all the same mistakes again,” while expressing hope that “good wins out over bad.” His comments capture the article’s central tension: Europe must confront real ethical, social and environmental concerns, while also deciding whether excessive hesitation could leave it less able to shape and benefit from an important technological shift.

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