‘Jilted partner’: Neil Lawrence on how Europe’s AI strategy must move on from US

DeepMind Professor Neil Lawrence tells the audience at The AI Summit in Turku, Finland that Europe needs to stop behaving like the ‘jilted partner’ in the AI race and find its own unique path….

Known for bridging technical research with societal implications, Professor Neil Lawrence, the DeepMind Professor of Machine Learning at the University of Cambridge, is one of the most respected voices in artificial intelligence at present.

His unique perspective is informed by his academic background and his time as Director of Machine Learning at Amazon. Speaking at the recent AI Summit in Turku, which Tech Digest attended, he shared his candid view on Europe’s response to the US-led AI boom and outlined a path for a distinct, successful European AI identity.

Europe’s Emotional Reaction to the US AI Boom

Professor Lawrence began by detailing the sentiment he observes at high-level European policy meetings regarding the dominance of the American tech sector, describing an ‘underlying sense of fear and panic’.

“It’s an emotional response, really. It feels a little bit like that moment when someone has just been told their partner was thinking of getting a divorce. There’s a sense of loss, a desperate feeling of being left behind by this phenomenal economic growth that the US tech sector, driven by AI, is currently experiencing.”

This emotional analogy, Lawrence explains, highlights the core problem: Europe’s primary motivation has become simply to catch up. The goal is often to replicate the massive, centralized AI factories of Silicon Valley. “We are focusing on US growth metrics as the only definition of success, and that’s a mistake. We are trying to win a race using someone else’s rules and their infrastructure, and that puts us in a fundamentally weak position.”

Growth Versus a Better Society

The Professor argues that the American approach, while achieving rapid growth, is not a model Europe should blindly follow, as it prioritizes scaling and financial returns over social well-being.

“The US model is optimized for rapid scaling and financial growth. It’s about building large, monolithic systems that capture vast amounts of data and attention, and consequently, vast amounts of capital. They have the growth, but the crucial point is that they don’t necessarily have the better society.”

He urges Europe to change its focus entirely: “Our focus, therefore, shouldn’t be on achieving the same growth, but on addressing the needs of our citizens and our social contracts.”

This financially driven, hype-heavy environment in the US also breeds a form of false expertise. Lawrence notes that the investment landscape is often driven by short-term gains, sometimes detached from true scientific understanding. “I’m often reminded of the quote I heard recently: that ‘100% of venture capitalists are experts in AI.’

This speaks to the atmosphere of hyper-confidence and short-term financialization that often overshadows genuine, long-term scientific progress.” He concludes that when the focus is purely transactional, systems are deployed without deep consideration for resilience, privacy, or democratic values.

Defining Europe’s Unique Path in AI

In short, Europe must stop behaving like a “jilted partner” trying to emulate the US and define its own measure of success, which Lawrence believes lies in building an AI ecosystem that serves its “citizens, science, and society – not just shareholders.” This requires a strategic, focused, and deep-rooted core.

The first step is moving away from thinly spread general research funding. “This should not be a $100 million spread across like a horizon, which is what the current initiative seems to be doing right now. We need to define some clear strategic pillars.”

Lawrence identified several key domains where Europe can establish AI leadership aligned with its societal needs. One is the Energy Transition, where AI optimization could be used to make Europe sovereign in terms of its energy supply and efficiency. Another is Semiconductors, a critically important domain for achieving sovereign control over the physical infrastructure of AI.

Finally, Social Sectors like food security, healthcare, and social care align naturally with Europe’s strong social systems, favouring responsible, decentralized, and human-centric AI application.

This focus allows Europe to leverage its deep expertise in systems integration, public governance, and precision engineering, addressing micro-level needs rather than macro-level solutions.

The Need for ‘Many Cerns’

To translate these strategic pillars into actionable R&D, Lawrence proposed creating large, collaborative, long-term research structures: “We need to create, what I would call now, many ‘Cerns.’”

These non-profit, collaborative research institutes would bring together talent to do fundamental research, generate data, and have sufficient compute power. Crucially, they would be mission-driven: “These ‘many Cerns’ would focus on our strategic pillars – applying machine learning to, say, climate modelling or optimizing complex energy grids.”

This approach offers a sustainable model, less prone to the “dizzying volatility of the venture capital hype cycle,” and ultimately more aligned with European values of stability and public good. With existing academic talent, Lawrence concludes, Europe only needs the political will and focused investment strategy to empower its distinct AI future successfully.

For latest tech stories go to TechDigest.tv