European equities in the face of AI – opportunities amid a shifting market
30 APR, 2026
By Jupiter AM
Caroline Cantor, Chief Investment Officer, European Equities at Jupiter
“AI-driven obsolescence” refers to the risk that artificial intelligence – through automation, large language models (LLMs), and AI-native startups – erodes the competitive moats, pricing power, and profit sources of established companies.
In financial markets, this has already been reflected in sharp valuation declines for companies considered vulnerable, even before any real deterioration in earnings. This phenomenon has been amplified by “winners vs. losers” investment strategies. However, we are still at the beginning of the adoption curve. Considerable uncertainty remains, and some companies currently labeled as “AI losers” may possess durable assets—such as proprietary data and integrated workflows – that preserve their relevance. For active investors, this disconnect can create opportunities.
The sectors most exposed are those where AI can replicate or improve existing processes. This includes parts of enterprise software, business services, information providers, and potentially segments of wealth management. While much of the negative sentiment may already be priced in, the risk continues to evolve, and new areas of disruption are likely to emerge as AI adoption intensifies.
The key differentiator is the strength of a company’s competitive moat – for example, proprietary datasets, whose importance we believe is underestimated in companies such as RELX and LSEG; regulatory integration; or positioning within critical workflows (e.g., SAP, where systems tend to be deeply embedded). When these elements are absent, AI can accelerate competition and compress margins.
The concept of “Heavy Assets, Low Obsolescence” refers to companies anchored in the physical economy, where value is supported by infrastructure, capital intensity, or system-critical roles.
This includes areas such as power grids, electrical equipment, industrial infrastructure, logistics, and construction materials. European examples include Schneider Electric and Siemens Energy (electrical systems), Prysmian (cabling), Atlas Copco (industrial equipment), and Saint-Gobain (construction materials).
These companies are less susceptible to disruption due to bottlenecks in the physical world and long investment cycles. In many cases, they also benefit from AI, as growing demand for data centers increases electricity consumption and drives grid expansion.
After a decade dominated by U.S. technology, valuations are elevated and market concentration remains high. At the same time, Europe is entering a capex-driven cycle focused on electrification, energy transition, defense, and infrastructure renewal.
In this context, resilience may be found in sectors such as electrification, grid infrastructure, utilities, industrial equipment, construction, and parts of the financial sector.
For example, electrical cabling has a multi-year growth trajectory as grids are modernized to handle renewable energy and rising power demand. Banks also stand out: while not immune to AI, they can improve efficiency (e.g., in underwriting, compliance, or customer service) while benefiting from higher interest rates, consolidation, and stronger balance sheets. Institutions such as CaixaBank, BBVA, UniCredit, and Nordea illustrate this dynamic.
These areas are also more labor-intensive and linked to domestic investment, meaning they are likely to contribute to economic growth and employment. At the same time, companies benefiting from AI-driven investment are also likely to make a positive contribution.
It remains difficult to fully assess the long-term impact of AI, given how early we are in its development. While large language models are already driving efficiency gains, the distinction between true obsolescence and business adaptation continues to evolve.
Many sectors—including infrastructure, construction, tourism, and hospitality – still require significant human capital and therefore exhibit a degree of resilience. However, disruption will continue within these sectors, particularly in roles such as travel agents or administrative tasks.
From a strategic standpoint, Europe is unlikely to lead frontier AI at the same level as the United States or China, but it does have advantages in enabling systems such as industrial automation, electrification, and energy infrastructure.
Focusing on these strengths, rather than attempting to replicate Silicon Valley, may offer a more effective path to long-term resilience. At the same time, increasing geopolitical fragmentation and supply chain risks reinforce the need for greater self-sufficiency, which may require regulatory adjustments to support industry and local production, as already seen in sectors such as steel.
From an investor perspective, this environment reinforces the importance of diversification and a style-agnostic approach. We focus on companies with high returns on capital, disciplined capital allocation, and exposure to differentiated structural themes, with particular emphasis on the sustainability of those returns.
While we invest in companies that directly benefit from AI-related spending, we also hold businesses whose growth drivers are largely independent of AI, including defense, infrastructure, and financials. We have tilted toward opportunities where the market appears to have overestimated the risk of obsolescence, creating what we consider attractive valuations, while underestimating durable strengths such as proprietary data, embedded positioning, and strong competitive moats.
