The narrative is seductive and paralysing.
The US has the models. China has the scale. Everyone else is a customer. Europe and the Nordics missed the boat. The only rational move is to rent from the winners and focus on regulation.
This is defeatism dressed up as pragmatism. And it is wrong.
The AI landscape is not a finished race. It is a shifting terrain. The leaders carry enormous costs, geopolitical baggage, and architectural debt. The followers, if they are strategic, carry none of it. Catching up is not about matching OpenAI parameter for parameter. It is about understanding which parts of the stack are commoditising, which are still open to disruption, and where a smaller, more focused player can build asymmetric advantage.
01. Problems
The analysis
The defeatism trap
The most dangerous constraint is not technical. It is psychological.
When decision-makers believe the gap is unclosable, they stop investing. When they stop investing, the gap actually becomes unclosable. The prophecy fulfils itself. Every European minister who says “we can’t compete with the US hyperscalers” makes it slightly more true by saying it.
China faced a similar narrative a decade ago. Its response was not to wait for permission. It was to train on what existed, build on what was open, and invest ferociously in what was missing. The playbook is not secret. It is just uncomfortable for cultures that prefer consensus over speed.
The compute gap
Training frontier models requires GPU clusters that cost hundreds of millions of dollars. The US dominates supply through NVIDIA. Export controls restrict what China, and by extension the rest of us, can access. Europe has almost no domestic GPU manufacturing. The Nordics have energy, talent, and cooling. Not the silicon.
This is real. It is also narrower than it appears.
Training a frontier model from scratch is one activity. Fine-tuning, distilling, and deploying existing open-weight models is another. The second requires orders of magnitude less compute. The question is not “can we build GPT-5 from scratch?”. The question is “do we need to?”
Smaller models, sharper edges
The arms race for the largest model is a game with diminishing returns and escalating costs.
Meanwhile, something quieter is happening at the other end of the scale. Models with 7 to 14 billion parameters, a fraction of frontier size, are increasingly competitive on real-world tasks when properly fine-tuned. They are cheaper to train by orders of magnitude. Fast enough to run on consumer hardware. Small enough to deploy on-premises without a data centre.
If Europe cannot afford to compete at 1 trillion parameters, it does not have to. It can invest in becoming the best in the world at small, task-specific models. Models that beat the giants on the domains that matter: local languages, regulated industries, edge deployment, privacy-sensitive applications. This is not a consolation strategy. It is where the market is already moving. Even the hyperscalers are racing to make their models smaller. Europe can start where they are trying to arrive.
The talent drain
Europe’s best AI researchers leave. Not because they can’t do the work here. Because the work is not here to do.
When the most ambitious projects are in San Francisco, the most ambitious people follow. This is not a salary problem alone. It is a gravity problem. Large clusters of talent attract more talent. Breaking the cycle requires building something worth staying for.
The standards vacuum
Europe excels at regulation. It is less good at setting technical standards that the rest of the world adopts voluntarily.
The AI Act is a legal framework. What is missing is a European-led open technical stack: model formats, evaluation benchmarks, deployment standards, interoperability protocols. Something with gravity of its own.
You do not catch up by running the same race faster. You catch up by finding a shorter course.
What policy-makers must do
Kill the defeatist narrative publicly. Every speech that opens with “we are behind” reinforces the problem. Reframe. Europe is not behind in AI. It is early in sovereign AI, and early is where strategic investment has the highest return.
Audit the actual gap. Commission an honest, technical assessment of what European organisations can and cannot do today. Not a consultancy slide deck. A practitioner-led audit. The gap in model training is real. The gap in deployment, fine-tuning, and application is much smaller. Policy should target the real gaps, not the imagined ones.
Stop subsidising dependence. Every grant that helps a European company buy more US cloud compute deepens the dependency. Redirect subsidies toward domestic compute infrastructure, open-source model development, and hardware supply chain diversification.
Speaking partners
EuroHPC Joint Undertaking. Nordic AI research institutes (WASP in Sweden, FCAI in Finland, NORA in Norway). National science foundations. The European Innovation Council. And the CTOs of European AI startups who are shipping products today, not waiting for permission.
What individuals & companies can do
Stop waiting for a European GPT. You do not need one. Open-weight models from Mistral, Meta (LLaMA), Alibaba (Qwen), and others are available now, run on European infrastructure, and can be fine-tuned for your domain in days. The best European AI companies are already building on this stack. Join them.
Refuse the “we need the big clouds” default. Every architectural decision that routes through a US hyperscaler is a decision that could have routed through a European provider, a Nordic data centre, or your own hardware. The performance difference is often negligible. The sovereignty difference is not.
Hire and retain locally. If you are a European AI company paying Silicon Valley salaries to attract Silicon Valley talent, you are competing on their terms. Instead: build the most interesting projects. Offer the best working conditions. Create the research environment that makes people stay. Talent follows ambition, not just money.
02. Solutions
The analysis
The open-weight playbook
China’s AI ecosystem did not emerge from a vacuum. It bootstrapped aggressively from open research, open code, and pre-trained models. When DeepSeek demonstrated that competitive results were possible at a fraction of the frontier compute budget, it dented the assumption that only frontier labs could produce frontier capability.
The technique, at a high level, is pragmatic. Take an open-weight model. Adapt it (through fine-tuning, LoRA, quantisation, or knowledge transfer). Optimise for inference efficiency. Ship something small, fast, and task-specific. For most real-world applications, this is not a compromise. It is the right engineering decision.
Europe and the Nordics can do this legally, at scale, and without asking permission. The permissive open-weight models are there. Mistral ships under Apache 2.0. Qwen’s flagship models under Apache 2.0. Meta’s LLaMA under a community license that allows commercial use below a very high user threshold, which no European AI startup is likely to hit any time soon. What is missing is not capability. It is organisational will.
Hardware openings
NVIDIA’s dominance is real but not permanent. Several forces are converging to create space:
RISC-V and open silicon. The RISC-V instruction set is open, royalty-free, and increasingly viable for AI accelerators. RISC-V International, the governing body, is now headquartered in Switzerland. European companies like SiPearl in France are designing AI-capable chips on open architectures. The EU Chips Act has mobilised investment commitments on the order of tens of billions of euros, though much of that figure is private capital and existing state aid rebadged, not fresh EU budget. The question is whether this money reaches the right projects or disappears into legacy fabs building yesterday’s chips. The cancelled Intel Magdeburg fab and the cancelled Wolfspeed Saarland plant are early warnings that it often does.
Nordic energy as a competitive advantage. Training AI models is, at its core, an energy problem. The Nordics have some of the cheapest and cleanest electricity in Europe, abundant natural cooling, and stable political environments. A GPU cluster in northern Sweden or Finland costs less to operate than one in most of central Europe, and emits a fraction of the carbon. At scale, energy cost is a dominant variable.
Alternative architectures. The assumption that AI requires NVIDIA GPUs is already weakening. Cerebras, with its wafer-scale design. Graphcore, the UK-founded IPU company now owned by SoftBank. An expanding class of custom AI ASICs showing that different architectures can outperform GPUs on specific workloads. Europe does not need to win the GPU race. It needs to be ready for the post-GPU era, and invest accordingly.
Automation as cost collapse
The cost of building AI systems is not fixed. It is falling. Automation is the mechanism.
Data preparation. Synthetic data generation, automated labelling, and AI-assisted cleaning are reducing the human labour cost of dataset creation by an order of magnitude or more. A small European team with good tooling can build training datasets that previously required a room full of annotators.
Model development. Automated architecture search, hyperparameter optimisation, and distillation pipelines mean that a small team with the right toolchain can iterate at the pace of a large team without one. The leverage is in the tooling, not the headcount.
Deployment and operations. AI-assisted DevOps, automated monitoring, and self-healing infrastructure mean that running a model in production no longer requires a dedicated ops team for every deployment. One engineer with good automation now does what recently took a small team.
The implication is that the cost of being a fast follower is dropping faster than the cost of being a leader. Every year that passes makes catching up cheaper, provided you are building the automation infrastructure to take advantage of it.
China did not ask for permission to train on open models. It did not wait for a domestic GPU industry to mature before starting. It used what was available, improved what it could, and built what was missing. The strategy was not elegant. It was effective.
What policy-makers must do
Fund fine-tuning and adaptation centres. Not every country needs to train a frontier model. Every country needs the infrastructure to take an open-weight model and make it their own. Fund national AI centres whose explicit mandate is: take the best open models, adapt them on domestic data, evaluate them against local benchmarks, and make them available to the public sector and SMEs.
Make small models a strategic priority. Allocate research funding specifically for efficient architectures. Models that maximise capability per parameter, not parameters per headline. Europe’s comparative advantage is not brute compute. It is engineering precision. Fund the teams building 7B models that beat 70B models on specific tasks. That is where the leverage is.
Accelerate the EU Chips Act toward open architectures. Ensure that a meaningful share of semiconductor investment goes to RISC-V and open-silicon AI accelerator projects. Not just to conventional fabs replicating what TSMC already does better. The Magdeburg and Saarland cancellations should be a lesson. The goal is not to copy the US chip industry. It is to build the next one.
Create Nordic AI infrastructure zones. Designate areas in northern Scandinavia and Finland as AI compute zones with fast-tracked permitting, energy subsidies, and fibre connectivity. Market them internationally. The value proposition of cheap green energy, political stability, GDPR compliance, and Arctic cooling is rare globally.
Invest in automation R&D as a force multiplier. Every euro spent on AI development automation (data pipelines, evaluation frameworks, deployment tooling) reduces the cost of every subsequent AI project. This is infrastructure spending with compound returns. Prioritise it over individual model training projects.
Speaking partners
EuroHPC. WASP (Sweden), FCAI (Finland), NORA (Norway). DFKI (Germany). SiPearl and other European chip designers. RISC-V International. Nordic data centre operators (including those in Luleå, Kajaani, and along the LUMI corridor). Mistral AI. The SiloGen research group (now operating inside AMD after the Silo AI acquisition, but still shipping Poro and Viking Nordic language models). And the growing DeepTech ecosystem in the Nordics and Baltics.
What individuals & companies can do
Start adapting today. If you are building an AI product, you do not need to train from scratch. Take Mistral, LLaMA, or Qwen. Adapt it for your domain. Deploy on European infrastructure. This is not a shortcut. It is state of the art. The best Chinese labs do exactly this, and they are not embarrassed about it.
Bet on small. A 7B model fine-tuned on your domain data, running on a single GPU, responding in milliseconds, deployable on-premises with no API dependency. That is a product. A product you own, a product you control, a product no hyperscaler pricing change can take away from you. Stop chasing parameter counts. Start chasing usefulness per watt.
Invest in automation before headcount. Before hiring your tenth ML engineer, ask a simple question: could better tooling make your existing five twice as productive? The answer is almost always yes. Build data pipelines that run without human intervention. Automate evaluation. Automate deployment. The team that automates first wins, regardless of geography.
Use Nordic infrastructure. If you are running GPU workloads, look north. Swedish and Finnish data centres offer electricity at a fraction of the cost of Frankfurt or Amsterdam, with lower carbon emissions and natural cooling. Several now offer GPU-as-a-service with GDPR-compliant hosting. The cost advantage is real and growing.
Contribute to open hardware. If your company has chip design, firmware, or systems engineering expertise, engage with RISC-V AI accelerator projects. The open silicon ecosystem is where Linux was in 2003. Early, messy, and about to become very important. Early contributors will shape the architecture.
03. Opportunities
The analysis
The follower’s advantage
Leaders pay the R&D tax. They explore dead ends, burn capital on failed architectures, carry the technical debt of being first. Followers see which paths worked and skip the rest. This is not cheating. It is how technology has always diffused. Japan did it with manufacturing. South Korea did it with semiconductors. Europe and the Nordics can do it with large parts of the AI stack.
The key insight: you do not need to reproduce the leader’s journey. You need to reproduce their current position. And that position is increasingly built on open components that anyone can assemble.
Sovereignty as a market differentiator
In a world of increasing geopolitical fragmentation, “made in Europe” is becoming a selling point, not a limitation. European data residency, GDPR compliance, and political neutrality are features that US and Chinese providers cannot replicate. Every industry with regulatory constraints (healthcare, finance, defence, public administration) is a market where sovereign AI is not optional. It is the product.
The total addressable market for “AI that does not route through a US hyperscaler” is not niche. It is the entire regulated economy of Europe, plus every country that prefers European values to American commercial terms or Chinese state oversight.
The small-language moat
Frontier models are trained primarily on English and Chinese text. Their performance in smaller languages (Swedish, Finnish, Norwegian, Danish, Estonian, Icelandic, the rest) improves every generation but still lags behind their English performance on domain-specific tasks. This is not a bug to complain about. It is a market opportunity.
A model fine-tuned on high-quality Swedish legal text, operated by a team that speaks the language and knows the courts, will outperform any generic frontier model on Swedish legal tasks. A model trained on Finnish healthcare records will do the same in Finnish healthcare. Language specificity is a moat that geography gives you for free.
The green compute premium
AI training is becoming one of the largest energy consumers on the planet. As carbon pricing tightens and ESG reporting becomes mandatory, the cost of running AI on fossil-fuel grids will rise. The Nordics, running largely on hydro, nuclear, and wind, will offer compute at a structural cost advantage that grows every year. This is not a trend. It is physics and policy converging.
The leaders built the road. The followers get to drive on it, faster, lighter, and without the construction debt. The only mistake is standing on the roadside and admiring the traffic.
What policy-makers must do
Brand sovereign AI as an export product. European AI sovereignty is not just a domestic policy goal. It is a product the rest of the world wants to buy. Position European AI infrastructure, models, and standards as the alternative for nations that want AI capability without US or Chinese dependency. This is a foreign policy opportunity as much as an industrial one.
Fund language-specific AI as critical infrastructure. Allocate dedicated budget for training, fine-tuning, and evaluating models in every official EU language. Do it with public data, open weights, and open benchmarks. A Swedish-language model trained on Swedish public sector data is not a research project. It is national infrastructure, as essential as roads.
Set the global standard for green compute. Propose international benchmarks for carbon-per-inference and energy-per-training-run. If Europe defines these metrics first, it defines the playing field. And the playing field tilts toward Nordic energy. This is standards-setting as competitive strategy.
Create fast-track visas for AI talent. If the US restricts immigration and China restricts information, Europe should do the opposite. Be the easiest place in the world for a talented AI researcher to move to, work in, and build a company. Talent follows opportunity, and opportunity follows policy.
Speaking partners
Nordic Council of Ministers. European Commission DG Connect. National energy regulators. Nordic and Baltic AI startup ecosystems. The European Open Source AI community. Language technology institutes (Språkbanken in Sweden, Kielipankki in Finland). And trade representatives from countries actively seeking alternatives to US and Chinese AI dependency, particularly in the Middle East, Southeast Asia, Africa, and Latin America.
What individuals & companies can do
Position yourself as the sovereign alternative. If you are a European AI company, your pitch is not “we are almost as good as OpenAI”. Your pitch is: “we are better for your use case, your language, your regulation, and your data stays in your jurisdiction”. That is not a consolation prize. For a growing number of buyers, it is the deciding factor.
Build language-specific products. The English-language AI market is crowded. The Swedish, Finnish, Norwegian, Dutch, and Polish AI markets are much less so. If you can fine-tune a model that handles Swedish tax law, Norwegian fisheries regulation, or Finnish patient records better than any generic frontier model, you own that market. No one in San Francisco is coming for it.
Market your energy story. If you run AI workloads on Nordic green energy, say so. Loudly. As carbon reporting becomes mandatory for enterprise procurement, your sustainability credentials become a competitive advantage that compounds with every new regulation.
Collaborate across borders. The Nordics individually are small markets. Collectively, they are close to 30 million people with high digital literacy, excellent infrastructure, and compatible regulatory frameworks. Build products that work across Nordic languages. Form consortia that bid for EU-wide contracts. The scale is there. It just requires coordination.
Conclusion
Catching up is not about matching the leaders step for step. It is about recognising that the game has changed.
The cost of AI capability is falling. The tools are open. The architectures are published. The training techniques are documented. What took billions to create can be adapted for millions, deployed for thousands, and operated for a fraction of what the original builders spend.
The Nordics and Europe have assets no amount of venture capital can buy. Clean energy. Political stability. Regulatory credibility. Linguistic diversity. A population that expects technology to serve public interest, not just shareholder value.
The only thing that can stop Europe from catching up is the belief that it cannot. And that belief is a choice, not a fact.
They built the models. We have the energy, the standards, and the will. The only thing we were missing was the nerve to start. Consider this the starting signal.