Archived version

The EU’s tech chief has warned that AI has become a geopolitical weapon, pushing for Europe to develop its own alternatives.

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Brussels last month presented a tech sovereignty package to loosen its dependence on US technology by backing European alternatives in sectors from semiconductors and cloud computing to AI.

The plan has incentives to accelerate the construction of European data centres and favour homegrown cloud and AI technologies, such as the AI company Mistral or cloud providers such as Scaleway or OVHcloud.

“It’s so important also that Europe is building up our own capacities and that we are not dependent on third countries for these very critical technologies,” Virkkunen said.

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In order to finance those investments, the EU and the European Investment Bank will set up a new mechanism to make strategic investments in European tech companies.

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The “kill switch” risk is the clearest political image. “If a foreign government can require a provider to cut access, change model availability, restrict compute exports or expose data under national law, European users face a dependency that is not merely commercial. It becomes strategic,” writes EUToday in a an article covering the issue.

If European institutions and governments want domestic or trusted AI capacity, they must create demand. That could mean buying European models for public administration, supporting sovereign cloud frameworks and funding compute infrastructure through EU-European Investment Bank mechanisms.

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Virkkunen’s warning should therefore be read as a policy marker. Europe is no longer discussing AI sovereignty only to create champions. It is asking whether critical functions can be interrupted from outside the Union. That is a harder and more practical question.

The next phase will be judged by execution rather than vocabulary. Europe has no shortage of strategies on cloud, data and AI; the test is whether they translate into compute capacity, procurement demand and viable companies able to serve public and private clients at scale.

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cross-posted from: https://scribe.disroot.org/post/10269370

Archived version

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The angst over China’s latest AI models is missing an important business fact: “open weight” AI is not the same thing as open-source software.

Open-source software, where the code is freely shared, can be an amazing business. Think Red Hat, which IBM bought for $34 billion. Open-weight AI models are different — and, so far, they’re proving to be a terrible business.

Take Z.ai, also known as Zhipu. It’s publicly traded, so we can see its finances. Last year, the Chinese company lost almost $500 million on revenue of about $107 million.

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Zhipu is the lab behind GLM 5.2, an open-weight AI model that wowed the industry when it launched last month. You might expect the stock to have soared. Instead, Zhipu shares have plunged more than 40% over the past month.

MiniMax, one of the only other independent Chinese AI labs that’s publicly traded, lost $250 million last year on revenue of just $79 million. Its shares have fallen more than 50% in the past month.

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Open-weight AI isn’t open-source software

The key difference comes down to economics.

Software can be distributed almost for free. Once it’s written, sending another customer a copy costs practically nothing. Profit margins improve as software companies grow.

AI doesn’t work that way. Every answer requires expensive chips, electricity, and data-center capacity. The next unit of software is nearly free; the next unit of intelligence is not.

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Moonshot AI, another Chinese lab, illustrated the problem last week. Its new Kimi K3 open-weight model impressed the industry with frontier-level performance. But days after launch, the company had to halt new customer sign-ups because it didn’t have enough computing power to run the model.

If Moonshot were selling traditional software, adding millions of users would be relatively easy. Instead, every new customer increases the company’s infrastructure bill, capping its growth.

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Someone else captures the profits

The way open-weight AI models are run, a process known as inference, makes the business situation worse.

Open-weight AI labs give outsiders their models’ trained numerical parameters, allowing them to download and run them. (Parameters are like tiny numerical dials inside a model’s brain that determine how these systems learn from data and what outputs they produce).

After that, these models are usually run by other companies, such as cloud giants Amazon, Microsoft, Google, Oracle, and Alibaba. There are also specialist providers such as Fireworks AI and Baseten, although they largely rent capacity from the big cloud companies.

Companies can also download these open weights and run the models themselves. Or, they can also use the Chinese model maker’s own inference service, but in the Western world, most corporate customers don’t do that for data security reasons.

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Only that last option reliably generates real revenue for the model creator. In the other three cases, the AI lab that spent hundreds of millions of dollars building the model may receive little or no ongoing revenue.

That leaves the model makers in a difficult position. They’ve paid heavily to train the systems, then given away the key assets.

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No wonder Alibaba’s stock is up about 13% over the past month, while AI labs Zhipu and MiniMax have been crushed.

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“Unlike open-source software, open-weight models do not generate significant sums of revenue by selling support, services, and enterprise editions around the free offering (the Red Hat playbook),” William Blair’s Bhatia wrote.

“Instead, they primarily generate revenue by hosting the model and selling inference compute. But inference workloads will flow to whoever can operate the inference infrastructure most efficiently, and this is usually not the model provider,” the analyst added.

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Raimo Lenshow, an analyst at Barclays, recently came back from China after researching the country’s AI sector. He reached a similar conclusion.

“Intense domestic competition has also led to more aggressive pricing competition,” the analyst told investors. “Some major models remain open-source or open-weight, accelerating the pricing pressure throughout the system. While this helps drive faster commercialization, it is also adding uncertainty to long-term profitability for those AI labs.”

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So why give the models away?

Open technology has long been a strategy for challengers trying to catch market leaders. A late starter may not be able to match a leader’s customers or distribution, but it can spread its technology widely, attract developers, and make the leader’s product harder to sell at premium prices.

That may be exactly what China and its AI labs are trying to do. Open-weight models put pressure on OpenAI, Anthropic, and other US leaders by offering capable alternatives at lower prices. Even if the Chinese labs make little money themselves, they can force American competitors to cut prices and make it harder to recover the billions they spend training new models.

Bhatia said Chinese labs may be releasing open-weight models with “little regard for near-term profitability.” In his view, openness can turn advanced AI into a commodity, weakening the business model of US companies that keep their technology closed.

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The financial payoff for the Chinese labs may come much later — or may be less important than the broader strategic benefit to China.

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cross-posted from: https://scribe.disroot.org/post/10253969

Archived version

The release of Moonshot AI’s Kimi K3 and Xi Jinping’s diplomatic offensive mark a pivot in China’s global AI strategy. Unable to match the United States in advanced semiconductor manufacturing due to stringent export controls, China is leveraging its strength in software engineering and algorithmic efficiency to dominate the open-source layer of the AI stack. This strategy effectively turns AI into a digital Trojan horse. While the US attempts to build a walled garden around its proprietary technology, China is building the public roads—roads that lead directly back to Beijing’s digital infrastructure and regulatory influence. India, caught between these two titans, is attempting a delicate balancing act, leveraging its sovereign digital public infrastructure to avoid the Chinese trap while remaining heavily dependent on Western compute power.

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The Trap Mechanism: By offering “free” foundational models, China aims to make the Global South algorithmically dependent on Chinese infrastructure, embedding censorship and surveillance capabilities into foreign digital architectures.

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First, there is the infrastructure trap. Running a model as large as Kimi K3 requires significant inference compute. While the weights are free, the hardware to run them is not. Chinese cloud providers, backed by state subsidies, offer to host these models for developing nations at rates Western cloud giants cannot match. The data generated by these nations then flows through Chinese servers.

Second, there is the alignment and ideological trap. Open-source models from China are trained to adhere to “socialist core values.” While developers can fine-tune the models, the foundational weights contain baked-in biases. The model will inherently struggle with, or refuse to generate, content related to Taiwanese independence, the Tiananmen Square massacre, or critiques of the Chinese Communist Party. By normalizing the use of these models globally, Beijing subtly exports its censorship red lines.

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“Open-source AI from China is not a public good; it is a digital Belt and Road. The code is free, but the geopolitical alignment is expensive.”

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Political and Diplomatic Implications

Beijing’s diplomatic corps has seamlessly integrated AI into its South-South cooperation narrative. By offering Kimi K3 and similar models to BRICS nations and the Shanghai Cooperation Organisation, China is building a technological coalition that inherently aligns with its data governance standards. This fractures the global internet further, creating a “splinternet” where not only the applications differ, but the very cognitive engines processing information operate on divergent ethical and ideological frameworks.

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Military and Intelligence Implications

From an intelligence perspective, the proliferation of Chinese open-source models presents a severe counterintelligence nightmare. Open-source does not mean secure; it means the code is visible, but the training data and potential latent vulnerabilities are not. Integrating Chinese models into NATO or allied telecommunications and defense supply chains—even at the application layer—creates avenues for data exfiltration, model poisoning, and adversarial attacks.

Economic and Trade Implications

The economic strategy is simple: commoditize the complement. If AI models become a cheap, open commodity, the value shifts to the application layer and the compute layer. Because China controls the manufacturing of mid-tier hardware and heavily subsidizes its cloud infrastructure, it can win the application layer in price-sensitive markets. Meanwhile, US tech giants like Microsoft, Google, and Amazon, which expected high-margin returns on their multi-billion-dollar AI investments, face a pricing collapse. If a free Chinese model performs 95% as well as a $20-per-month US API, the commercial model breaks down.

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Counterarguments: The Case for Open Ecosystems

… Many technologists argue that the US push for closed, proprietary AI creates a techno-feudal system where only a few billionaires control humanity’s cognitive engine. From this perspective, China’s release of Kimi K3 democratizes AI, allowing developing nations to build local tech ecosystems without paying tribute to Silicon Valley.

Furthermore, open-source models are auditable. Security researchers can (theoretically) inspect the weights and architecture for backdoors. Proponents argue that the “China trap” narrative is merely a protectionist talking point used by American tech giants to stave off competition.

While these points hold merit regarding the general value of open-source technology, they fail to account for the specific nature of the Chinese state. In China, there is no delineation between private enterprise and state security. The National Intelligence Law of 2017 mandates that all Chinese organizations and citizens must “support, assist, and cooperate with national intelligence efforts.” Therefore, any Chinese AI startup, no matter how independently it markets itself, is ultimately subject to CCP directives. The risk is not in the visible code, but in the invisible training data, the alignment protocols, and the potential for future remote manipulation or data harvesting via associated cloud services.

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The China AI Trap refers to the geopolitical strategy where China offers advanced AI models as open-source and free to developing nations. Once these nations build their digital infrastructure, government services, and private sectors on these models, they become dependent on Chinese cloud infrastructure, updates, and regulatory frameworks, compromising their digital sovereignty.

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The geopolitical contest over artificial intelligence is often framed as a race for compute power and algorithmic supremacy. But the release of Moonshot AI’s Kimi K3 and Xi Jinping’s open-source diplomatic offensive reveal that the true battleground is infrastructure dependency. China has recognized that if it cannot build the best chips, it can build the most used software, thereby capturing the global digital nervous system.

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cross-posted from: https://scribe.disroot.org/post/8843134

Op-ed by Kylie Moore-Gilbert, Research Fellow, Security Studies at Macquarie University.

Archived version

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Once primarily the domain of non-state actors, including terror groups, drug cartels and armed gangs, hostage-taking has become a lucrative bargaining chip in the hands of countries like Iran, Russia, China, North Korea and Venezuela. (I was imprisoned by Iran for more than two years on false charges of espionage.)

It has become an unorthodox yet highly effective means of forcing concessions, including prisoner swaps, financial payments and the removal of sanctions.

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However, very little scholarly research has examined the phenomenon. The data we do have on cases is patchy. This is in part because the governments whose citizens have been taken hostage usually prefer to negotiate in the shadows. We only tend to hear about select cases that attract media coverage.

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Treating state hostage-taking as a consular issue to be solved via traditional diplomacy hasn’t worked. Bad actors haven’t been deterred; rather the opposite. An innovative new approach is long overdue.

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Some of the ideas put forward in our research include:

1) Expanded international legal approaches

This includes reframing state hostage-taking as a form of torture and, under certain conditions, even a war crime or crime against humanity.

UN torture rapporteur Alice Edwards argues this would help open avenues for victims seeking justice … Legal academic Carla Ferstman governments should look to existing models in the US and Canada and consider passing legislation to allow victims of state-sponsored terrorism to sue hostage-taking states in their domestic courts.

2) Stronger government-led responses to hostage-taking

Many countries don’t have a designated office or role within government to coordinate domestic and multilateral responses to hostage-taking.

These positions exist now in the US and Canada. This step was also proposed in a 2024 Australian Senate inquiry into the wrongful detention of Australian citizens overseas. The government has yet to respond to the inquiry.

3) Innovative models for multilateral rapid responses to hostage crises

Several contributors to the journal have proposed new ideas for how states can do this, including former Canadian Justice Minister and Attorney General Irwin Cotler (with international human rights lawyer Brandon Silver) and former hostage Michael Kovrig (with international security and diplomacy expert Vina Nadjibulla).

Their recommendations include:

• developing rapid-response mechanisms to hostage-taking in pre-existing multilateral groupings, such as the G7 or NATO

• strengthening the Declaration on Arbitrary Detention in State-to-State Relations (launched by Canada and now supported by more than 80 nations)

• imposing multilateral sanctions and other tools of economic leverage against states that engage in hostage-taking.

4) Greater investment in post-detention recovery care for both victims and families

Proposals for taking better care of former detainees came from the NGO Hostage International, human rights lawyer Sarah Teich and an Israeli team involved in designing reintegration programs for Gaza hostages.

These proposals include:

• passing legislation to mandate a “duty of care” by governments to former hostages

• developing new strategies for helping former hostages overcome their psychological challenges, based on emerging research in the field.

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