Category: News
Headline & intro
Seventeen U.S. AI startups have each raised at least $100 million in just the first weeks of 2026. Two of them pulled in tens of billions. This is no longer a hot funding cycle; it’s an industrial policy by venture capital. For European founders, policymakers and corporate IT leaders, these rounds are not just Silicon Valley gossip — they define who will own the next decade of infrastructure, talent and standards. In this piece, we’ll unpack what this funding wave really means, who gains, who’s squeezed out, and why Europe cannot afford to treat it as a distant spectacle.
The news in brief
According to TechCrunch, 17 U.S.-based AI companies have already closed funding rounds of $100 million or more in 2026, across January and the first half of February.
The list ranges from research labs and infrastructure providers to application-layer startups in voice, robotics and healthcare. Notable deals include Anthropic’s gigantic Series G of around $30 billion at a roughly $380 billion valuation, and Elon Musk’s xAI with a $20 billion Series E before being acquired by SpaceX.
Infrastructure and tooling players like Baseten, Inferact, PaleBlueDot AI and Arena have raised large rounds to build inference, compute and evaluation platforms. Application-focused startups such as Runway (media generation), ElevenLabs and Deepgram (voice AI), SkildAI (robotics) and OpenEvidence (medical chatbot) all reached or expanded unicorn-level valuations. In 2025, U.S. AI startups had already raised more than $76 billion in mega-rounds; 2026 is on track to at least match that pace.
Why this matters
These 17 rounds are not just about big numbers; they redraw the map of power in AI.
First, capital is concentrating at unprecedented scale in a very small circle of U.S. labs and infrastructure providers. A single company raising $20–30 billion in one go puts it in the same financial weight class as big tech incumbents. That effectively turns leading labs into quasi-hyperscalers, with the ability to lock in GPU capacity, top researchers and strategic partnerships for years.
Second, this funding spree hardens the moat around frontier model development. Training cutting-edge models is already a nine- or ten-figure exercise. With Anthropic, xAI and a cluster of well-funded research labs (Goodfire, Fundamental, humans&) armed with fresh billions, the number of players who can credibly compete at the frontier shrinks even further. New entrants without privileged access to capital and compute are pushed down to the application layer.
Third, mega-rounds at valuations in the tens or hundreds of billions raise the systemic risk. If even a few of these companies fail to grow into their valuations, it could sour investor appetite for the entire AI category, just as we saw with crypto and consumer social in past cycles. Right now, however, momentum is strong: enterprises are experimenting with AI across workflows, and infrastructure revenues are real and growing.
Finally, Nvidia’s appearance on multiple cap tables underlines how vertically entangled this ecosystem has become. The supplier of GPUs is also an investor in many of the companies that will consume those GPUs, reinforcing a tight feedback loop between chip supply, model training and valuation hype.
The bigger picture
This wave of funding fits into a broader pattern that has been building since 2023: a shift from AI as a research curiosity to AI as critical infrastructure.
In the first phase (2020–2022), money flowed mostly into core model labs and platform bets like OpenAI and Anthropic, and into enabling hardware like Nvidia. In the second phase (2023–2024), we saw a Cambrian explosion of application startups layering chatbots, copilots and generative features on top of those models.
The 2026 deals suggest a third phase: consolidation and industrialisation.
On one side, you have the frontier labs scaling into multi-hundred-billion-dollar entities. On the other, you have a maturing stack of "picks-and-shovels" companies: inference providers (Inferact), deployment and infra orchestration (Baseten), evaluation platforms (Arena), and specialised compute (PaleBlueDot AI). This is classic enterprise tech pattern: once the underlying capability is proven, the business opportunity shifts to making it reliable, efficient and compliant.
At the same time, the application winners are getting sharper. Instead of "AI for everything", we see deep verticalisation: Runway for media creation, ElevenLabs and Deepgram for voice, SkildAI for robotics, OpenEvidence for medicine. These are domains where access to data, distribution and domain expertise matter as much as model quality.
Historically, similar patterns appeared in cloud computing: a few hyperscalers (AWS, Azure, Google Cloud) plus an ecosystem of specialised infra startups and vertical SaaS. The difference now is speed and capital intensity. What took cloud a decade is being compressed into three to five years in AI, with venture capital acting as an accelerator rather than waiting for organic cash flows.
The European / regional angle
From a European perspective, this U.S. capital surge is both a warning and an opportunity.
The warning is obvious: the gap is widening. Europe has produced promising AI players — from Mistral AI in France to multiple applied-AI scaleups across fintech, health and industry — but the absolute size of rounds remains smaller by an order of magnitude. When a single U.S. lab can raise more in one round than many European countries deploy in national AI strategies over several years, bargaining power shifts decisively toward American platforms.
This has practical consequences. European enterprises, including highly regulated sectors like banking and healthcare, increasingly depend on U.S.-hosted models and infrastructure. That raises questions under the GDPR, the Digital Services Act and the upcoming EU AI Act: data transfer, model transparency, systemic risk and vendor lock-in.
At the same time, EU regulation can be a differentiator. The AI Act will force providers to document datasets, risk controls and human oversight for high-risk use cases. U.S. giants may treat this as a compliance cost; European startups can build "born-compliant" offerings as a selling point.
For local ecosystems — from Berlin and Paris to Ljubljana, Zagreb and Barcelona — the message is stark: competing head-on with $20–30 billion war chests on general-purpose models is unrealistic. The rational strategy is to specialise: sector-specific models, on-prem and sovereign deployments, and tooling that makes U.S. foundation models usable within strict European legal and cultural constraints.
Looking ahead
Over the next 12–24 months, several dynamics are worth watching.
First, consolidation. With this much capital deployed into training, infra and applications, M&A is inevitable. Some of today’s well-funded labs will end up inside hyperscalers or major industrial groups, especially if GPU access becomes political or regulatory pressure tightens.
Second, the GPU bottleneck. The capacity to convert capital into compute is not infinite. If Nvidia and its competitors cannot keep up with demand, we may see delays in model releases and more aggressive long-term supply contracts — further favouring the best-funded players and squeezing everyone else.
Third, regulation and geopolitics. The EU AI Act, U.S. export controls on advanced chips, and potential antitrust actions against Nvidia and large AI labs could reshape who can train what, where. European policymakers must decide whether to treat AI labs as critical infrastructure, similar to telecoms or energy, with corresponding rules and support.
For European founders, the opportunity is to position themselves where the giants are weakest: hybrid deployments, on-prem solutions, safety, evaluation, fine-tuning for local languages and sectors, integration into legacy IT. These are not headline-grabbing $20 billion rounds, but they can become durable, profitable businesses.
The bottom line
The 17 mega-rounds in early 2026 show that AI is no longer a speculative bet; it is the new backbone of digital infrastructure — and the U.S. is doubling down. This funding frenzy will produce both enduring platforms and painful failures, but Europe cannot sit it out. The real question for our region is simple: will we try to clone American labs, or will we build the indispensable layer that makes their technology usable, safe and sovereign on European terms?
