Open Source AI: The DeepSeek Takeaway for Europe

On January 20th, DeepSeek sent shockwaves through the AI community by releasing models matching or exceeding OpenAI's frontier models on reasoning tasks, while demonstrating how to distill advanced capabilities into smaller, efficient models. The most impressive part? DeepSeek's reported model training costs were just $5.6 million compared to OpenAI's reported $78 million for ChatGPT-4o.

While many focus on the US-China AI race or interpret DeepSeek as a "Sputnik moment”, they're missing the real story. What DeepSeek did wasn't about China versus the USA (or Europe!) – it was about the fundamental power of open innovation to reshape markets and facilitate collective progress and community-driven collaboration at the frontiers of science and technology. As Nathan Lambert from the Allen Institute for AI succinctly put it: DeepSeek is an example of “the [open source] system working as intended.” 

What DeepSeek accomplished wasn't magic. There were no massive algorithmic breakthroughs – just clever engineering and a return to first principles. As they explain in their technical report, they applied existing techniques like reinforcement learning and fine-tuning in novel ways to develop models on par with frontier proprietary models at a fraction of the cost, at least according to reported costs, and they showed that the advanced capabilities of large models can be distilled into much smaller but nonetheless extremely powerful models. Going one step further, they released their code and distilled models under the MIT license for the community to use and build on.

In a nutshell: they showed that breakthrough innovations in AI development don't always require massive capital investments or groundbreaking new theories – sometimes they just need a fresh perspective and clever engineering, a metaphorical reshuffling of the cards. 

This brings competitiveness back to the realm of clever engineering rather than just massive investments, democratizing access to and the development of state-of-the-art AI models. Crucially for Europe, DeepSeek’s models highlight that the AI race is far from over and have created an opening: if we double down on open source, we can become a leader in open source AI.

Europe’s Open Source AI Opportunity

Europe is uniquely positioned to leverage this opportunity in at least three distinct ways.

First, we are not starting from scratch: European developers are already leaders in the open source AI community, building on our robust ecosystem of AI talent and our history of open innovation (remember both the Internet and Linux were created here). In fact, many of the most popular model creators on Hugging Face Hub stem from Europe, and startups like Mistral AI, Aleph Alpha, and Black Forest Labs are gaining ground in the leaderboards. What is more, our universities and research institutions have long histories of collaboration across borders, making them ideal incubators of open source AI that serves diverse societal needs and cultural perspectives. Case in point: The EuroLLM project, a coalition of labs and universities from various countries co-funded by the EU, has developed and openly released multilingual and multimodal LLMs for all 24 official European languages, and their EuroLLM 9B Instruct model ranks among the top in the European LLM leaderboard.

Second, Europe's approach to AI can deliver global benefit by leveraging our core values and strengths in transparency, privacy, and responsible development. For example, while DeepSeek's models impress, the community have noted that they avoid discussing topics that are censored in China. This was a troubling revelation, as this is not the kind of AI we want in our democratic society. So, while we can learn key engineering lessons from DeepSeek, we should apply them in a manner that both protects and promotes our democratic values and fundamental rights. It's also worth highlighting that in little time developers uncensored DeepSeek’s open models, which they in turn released as open models, and documented their process for others to learn from and build on. This is open source working as open source works best.

Third, Europe can lead by building specialised models and applications. The advantage is twofold: European companies can focus on solving real-world problems in our society and our sectors rather than competing to build foundation models that chase after artificial general intelligence, and by building applications on top of models that can align with EU values and regulations from the start. For example, Nick Clegg made this point at the World Economic Forum last month: building AI applications on top of models is an area where Europe can develop its competitive edge. To do so, we need to drive down the cost of model inference and the growing momentum of small but powerful models gives us reason to be optimistic.

The Road Ahead

Recently, we’ve heard of a number of grand investments in AI infrastructure. For example, at the AI Action Summit, President Macron announced a €109 billion plan for AI compute infrastructure, matching the recently announced US Stargate program per capita. It is great to see this ambition in Europe. As per the Draghi report, Europe must make unprecedented economic investment in digital innovation if it wants to be competitive in the global economy. 

But I want to highlight that the key to success isn't just about money – it's about how we invest it strategically across both the public and private sectors. And open source cannot be just a part of Europe’s digital strategy; it’s the only way for Europe to achieve its goals of competitiveness and promotion of its democratic values at a global scale, especially in the era of AI. Specifically, our investment strategy must support our digital commons that is key to AI research and development — the open source software, open data, and open models that are developed, maintained, and governed by global online communities.

We should be investing in the sustainability of the open source developer communities that have given us invaluable frameworks and libraries like scikit-learn and PyTorch without which AI research and development would simply not be possible today. We should also be investing more in open source tooling that facilitate responsible and ethical AI development. I point to examples like the UK AI Security Institute's Inspect and ETH Zurich's Compl-AI, two open source frameworks for large language model evaluations, as well as the newly launched ROOST initiative which will build open source tooling for safe AI. And we should also be investing in the growth of a strong commercial ecosystem that has favourable conditions for open source businesses to thrive, grow, and exit. 

It goes without saying that data is key to AI and as such we should also be investing more in the communities that develop, maintain, and govern our open data commons. For example, in Europe we could invest more in the creation and curation of open data that capture Europe's rich linguistic and cultural diversity, from the EU’s 24 official languages to regional dialects, or ones that facilitate innovation in industries and sectors where Europe has a competitive advantage like automotive or renewable energies. 

This is all to say: DeepSeek's success hasn't closed doors for Europe – it's opened them. By embracing open source, Europe can lead in AI development that reflects our values while driving innovation. This isn't about catching up to the US or China – it's about leading the world toward a more open, transparent, and democratic future.

To learn more about the topic watch the keynote delivered at the Open Source Policy Summit 2025: