A European venture capital firm has narrowed its focus to concentrate on a "resilience" strategy that culminated in Resilience I, a $300 million fund closed in July 2026 .

The world's leading AI companies are investing unprecedented sums in supporting infrastructure. The central question is whether this infrastructure can be financed—and ultimately generate sufficient economic returns to justify the capital committed.

August 23 Update here

AMI Labs is, by its own leadership's description, a research organisation first and a product company second. This report sets out what is known about the company's technology, team, capitalisation, market context, and principal risk factors, to support an informed view of the opportunity it represents.

Artificial intelligence may now be capable of participating directly in the recurring cycle science has advanced through.

Has European integration promoted global economic openness, or created new regulatory and commercial barriers for foreign firms?

This report maps the segment by category and by player, rather than through any single company's lens, .Europe's clinical AI market is not one race but several running in parallel, and no single company — European or American — currently leads all of them. Ambient documentation is dominated by Nabla, a French startup carrying a valuation most European AI companies never reach. The largest single population-health data contract in Europe belongs to Palantir, a US defense-adjacent firm now facing sustained political pressure to be removed.

ICEYE is a rare case among European growth-stage technology companies: profitable, revenue-generating, backlog-supported, Its core strengths are a genuine, defensible manufacturing cost advantage in SAR satellite production, an increasingly diversified sovereign-government customer base spanning at least seven European nations plus Japan.

Doctolib is a reasonably strong candidate to become "the French champion" in the documentation segment of Clinical AU given its size, though it would have to out-compete a better AI-funded domestic rival (Nabla) to get there. Its bid to become a genuine population-health analytics player is much earlier-stage and far less certain, and depends on two things outside its control: whether Europe's shared data rules actually arrive on schedule, and whether it can rebuild enough public trust in the meantime to be handed the data such a product would require.

The report deliberately avoids predicting either success or failure. Instead, it identifies a series of questions that deserve closer examination. Can AI infrastructure ultimately pay for itself? Who should own the physical infrastructure on which AI depends? Will specialised infrastructure companies gradually replace hyperscalers as owners of data centres? How much of today's AI wealth reflects productive assets and sustainable future cash flows, and how much reflects expectations embedded in market capitalisations? If AI investment disappoints, could financial institutions become the channel through which losses spread more widely through the economy?

Imagine a government must suddenly decide whether autonomous weapons should be allowed on the battlefield. Or perhaps it must determine how artificial intelligence should be regulated, how Europe should respond to an ageing population, or whether billions of euros should be invested in nuclear energy. None of these questions has a simple answer. Each involves science, economics, law, ethics, international relations and politics. Before any decision can be made, someone has to study the problem in depth. That is where think tanks come in.