In 2026, one of Europe’s most ambitious scientific ventures, Horizon Europe, a seven-year, roughly €93 billion framework dedicated to research and innovation, underwent a quiet but significant transformation. 

What had once been an open invitation to researchers across the globe now carries a more guarded tenor. 

In critical areas such as artificial intelligence, semiconductors, quantum technologies, and biotechnology, organisations based in China are no longer automatically eligible to receive EU funding, a sharp deviation from earlier years when Chinese participation was possible, albeit under evolving conditions. 

This change is neither arbitrary nor purely technical. It reflects the culmination of years of negotiation and strategic signalling in Brussels. 

According to the European Commission’s own international cooperation guidance, cooperation with third countries like China has always been conditional; Chinese researchers may contribute, but they are required to enter as Associated Partners and often must bring their own funding where EU funding does not automatically apply. 

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Yet the updated participation rules go further. 

In late 2025, the Commission codified conditions that essentially block Chinese institutions from receiving core Horizon Europe grants in sensitive clusters of research and innovation. 

In policy terms, the threshold for inclusion has shifted: European partners must now demonstrate that their collaborators are not owned or controlled by Chinese entities, creating de facto barriers for significant portions of bilateral work in cutting-edge fields. 

While cooperation is not extinguished outright, joint work continues in areas like climate science and agriculture under bilateral road-map mechanisms; this recalibration is telling. 

It amounts to Europe drawing boundaries around where it will share its most prized scientific infrastructure and intellectual capital and where it will withhold it. 

The official justifications, as framed by Commission texts, lean heavily on concerns about research security, intellectual property protection, and the perceived risk of unintended transfers of strategic technology where civil and military boundaries blur. 

Viewed in isolation, these adjustments might read as bureaucratic fine-tuning. But in the broader context of EU policy, which straddles an ambition for open scientific cooperation and an emergent emphasis on strategic autonomy, they underscore a fundamental tension. 

Europe still champions collaborative discovery across borders, yet it acknowledges nowadays research ecosystem is intertwined with global power dynamics in once unimaginable ways.

Beyond the sharp edges of eligibility rules lies a deeper question: why does this particular rebalancing matter in practice? 

Over the past decade, China has become increasingly visible in global scientific networks. Its researchers regularly co-authored papers with European counterparts, and its rapidly expanding domestic science base, often supported through state mechanisms, moved from peripheral to central positions in disciplines ranging from materials science to computational biology. 

Yet in the architecture of Horizon Europe that emerges in 2026, participation is no longer synonymous with access to EU funds.

Chinese entities still can contribute to research proposals, but they do so as Associated Partners and typically must bring their own financing, a distinction that subtly but fundamentally changes the incentives and power dynamics of collaboration.

In practical terms, the new rules change how research consortia form and operate. European institutions seeking to work at the frontier of emerging technologies must now factor in eligibility constraints when structuring partnerships. 

Where once multinational consortia could mix researchers from across continents with minimal procedural friction, they now must design collaborations that either exclude certain partners from funding streams or justify their presence through alternative mechanisms. 

This places a renewed premium on legal expertise, consortium management, and alignment with EU strategic priorities, an additional administrative layer that did not exist to the same degree in earlier cooperation frameworks. 

These restrictions could have unintended intellectual or scientific consequences. When large research systems are pushed to the margins, there is a risk that parallel ecosystems evolve, with reduced interoperability between them. In the long term, this could alter citation networks, collaborative norms, and research mobility patterns. 

It could also prompt other powerful actors to adopt similar measures, reshaping the landscape of global science into distinct blocs defined by policy fences rather than open inquiry.

It’s important to emphasise that the EU has not abandoned bilateral scientific engagement outright.

Mechanisms outside Horizon Europe,  including mobility schemes and targeted co-funding instruments designed to support researcher exchanges, continue to exist, and cooperation on transnational challenges such as climate change and biodiversity remains active. 

What has changed is the weight of strategic calculation in decisions about where and how to invest EU funding. As a result, science policy in Europe now sits at the intersection of research excellence, economic sovereignty, and geopolitical strategy.

For Europe’s research community, this presents a complex set of questions. Does tighter control over strategic collaborations strengthen the European innovation base? Or does it risk isolating European science from talent and knowledge flows? 

The answer is unlikely to be binary. 

What is clear, however, is that Horizon Europe,  once known chiefly as a vehicle for excellence and discovery, is now also a mirror of shifting geopolitical realities, showing how science policy has become part of broader efforts to navigate uncertainty in a multipolar world.

In the end, the EU’s decision to redraw the terms of research partnership with China feels less like a closing door and more like a recalibration of Europe’s compass. It acknowledges a world in which scientific discovery and geopolitical currents are no longer parallel tracks but deeply intertwined. 

The Horizon Europe programme, once the grand symbol of open scientific cooperation, now also stands as a marker of strategic foresight,  a space where Europe seeks to balance openness with caution, curiosity with control.

This turning point doesn’t signal a retreat from global engagement. 

What it does reflect is a modern realpolitik of research: where funding decisions are informed not only by scientific merit but by questions of security, reciprocity, and long-term technological sovereignty. 

In a scenery defined by rising competition over frontier technologies, Europe is choosing to hedge its bets, opening some doors wider, while tightening others. The future of scientific collaboration may be neither total isolation nor full openness but a nuanced choreography between cooperation and strategic self-interest.

• Russian hacker brute-forced FortiGate firewalls using weak credentials
• AI-generated scripts enabled data parsing, reconnaissance, and lateral movement
• The campaign targeted Veeam servers; attacker abandoned hardened systems

A Russian hacker was recently seen brute-forcing their way into hundreds of firewalls – but what makes this campaign really stand out is the fact that the seemingly low-skilled threat actor was able to pull off the attacks with the help of Generative Artificial Intelligence (GenAI).

In a new analysis, Amazon Integrated Security CISO CJ Moses explained how researchers observed a threat actor “systematically” scanning for exposed FortiGate management interfaces across ports 443, 8443, 10443, and 4443.

AI has driven an explosion of new number formats—the ways in which numbers are represented digitally. Engineers are looking at every possible way to save computation time and energy, including shortening the number of bits used to represent data. But what works for AI doesn’t necessarily work for scientific computing, be it for computational physics, biology, fluid dynamics, or engineering simulations. IEEE Spectrum spoke with Laslo Hunhold, who recently joined Barcelona-based Openchip as an AI engineer, about his efforts to develop a bespoke number format for scientific computing.

What makes number formats interesting to you?

Laslo Hunhold: I don’t know another example of a field that so few are interested in but has such a high impact. If you make a number format that’s 10 percent more [energy] efficient, it can translate to all applications being 10 percent more efficient, and you can save a lot of energy.

Why are there so many new number formats?

Hunhold: For decades, computer users had it really easy. They could just buy new systems every few years, and they would have performance benefits for free. But this hasn’t been the case for the last 10 years. In computers, you have a certain number of bits used to represent a single number, and for years the default was 64 bits. And for AI, companies noticed that they don’t need 64 bits for each number. So they had a strong incentive to go down to 16, 8, or even 2 bits [to save energy]. The problem is, the dominating standard for representing numbers in 64 bits is not well designed for lower bit counts. So in the AI field, they came up with new formats which are more tailored toward AI.

Why does AI need different number formats than scientific computing?

Hunhold: Scientific computing needs high dynamic range: You need very large numbers, or very small numbers, and very high accuracy in both cases. The 64-bit standard has an excessive dynamic range, and it is many more bits than you need most of the time. It’s different with AI. The numbers usually follow a specific distribution, and you don’t need as much accuracy.

What makes a number format “good”?

Hunhold: You have infinite numbers but only finite bit representations. So you need to decide how you assign numbers. The most important part is to represent numbers that you’re actually going to use. Because if you represent a number that you don’t use, you’ve wasted a representation. The simplest thing to look at is the dynamic range. The next is distribution: How do you assign your bits to certain values? Do you have a uniform distribution, or something else? There are infinite possibilities.

What motivated you to introduce the takum number format?

Hunhold: Takums are based on posits. In posits, the numbers that get used more frequently can be represented with more density. But posits don’t work for scientific computing, and this is a huge issue. They have a high density for [numbers close to one], which is great for AI, but the density falls off sharply once you look at larger or smaller values. People have been proposing dozens of number formats in the last few years, but takums are the only number format that’s actually tailored for scientific computing. I found the dynamic range of values you use in scientific computations, if you look at all the fields, and designed takums such that when you take away bits, you don’t reduce that dynamic range

This article appears in the March 2026 print issue as “Laslo Hunhold.”

I saw some of the best stereo speakers while wearing my boots down at Bristol Hi-Fi Show between February 20-22, and I wrote a whole listicle on some of the premium audio gadgets which will absolutely shatter your bank account.

But at the annual show, I also saw a decent amount of kit that wasn’t so wallet-shredding. I enjoyed listening to plenty of headphones and speakers which you and I could even end up buying — I mean, if my landlady decided I could be exempt from rent for a month or two.