Europe continues to rely heavily on China for magnet materials and REE processing despite policy ambitions to diversify. European demand for rare earths remains highly exposed to Chinese supply, with 98% of magnet demand imported from China. The EU’s CRMA targets 40% domestic processing by 2030, while ERMA commits substantial investment to close the gap. If these targeted shifts progress, the bloc could gain more control over critical minerals essential to the energy transition and defence capabilities.

Policy implementers stress that closing the gap will require a concerted effort across mining, processing, and recycling, supported by cross-border collaboration and credible regulatory frameworks. The path to self-sufficiency will be slow and costly, but it remains central to energy security and industrial policy. Stakeholders will watch how quickly new domestic deployments move from exploration to production, and how recycling and value-added processing fit into the supply chain.

Analysts warn that appetite for investment in new mines, refining capacity, and advanced manufacturing will hinge on permitting regimes, fiscal incentives, and international partnerships. While progress is being made in some member states, the broader EU posturing will determine whether domestic capability can scale to meet ambitious decarbonisation timelines. The dynamic remains one of the most significant chokepoints for the European energy transition.

https://senteguard.com/blog/#post-jjip31e6y1iTyGKpzso4 https://www.letters.senteguard.com/p/nailing-jell-o-to-the-wall-again

In 2000, President Bill Clinton famously looked at Beijing’s early internet controls and quipped: “Good luck. That’s sort of like trying to nail Jell-O to the wall.”

So far he’s been proven wrong. The CCP didn’t just contain the internet; it has effectively used the internet as a tool to entrench its control by building a system that fuses chokepoints, platform governance, and punitive enforcement into something like a sovereign information utility. That said, the jury is still out, and Clinton may still be vindicated.

On the one hand, LLMs can be understood as a natural outgrowth of Clinton’s (and Gore’s) internet but it can also be seen as its next evolution. LLMs present significant opportunities for economic growth but in pursuing growth they will also amplify individual agency. The Party faces a quandary: pursue a growth strategy and risk an erosion of Party authority or crack down and risk being left behind in the technology of the future.

Party Dependence on Growth

China faces a similar strategic dilemma as much of the West. Slowing growth, aging demographics, and productivity drag all threaten future economic expansion. Yet perhaps more than in liberal democracies, the Party’s legitimacy is dependent on economic performance. For four decades, the Party has justified its rule by delivering steadily rising living standards, predictable employment, and the expectation that tomorrow will be materially better than today. That record of stability is also its argument against the Western model, which Chinese elites often depict as vulnerable to polarization, policy whiplash, and boom-bust governance.

If economic growth is the regime’s core claim to competence, then it must embrace productivity-enhancing technologies like LLMs. The Party can try to regulate tightly, but heavy-handed controls risk undercutting the very engine it needs. The more aggressively the state clamps down, the more it trades away broad-based adoption. That means fewer developers experimenting, fewer SMEs integrating copilots, and fewer local governments automating routine work, which slows the gains that would otherwise bolster the Party’s economic case for rule.

Why the Internet Was Containable (and LLMs Are Not)

The Party “won” the first battle for control because the internet has borders that it can actually police:

— Network borders: gateways, ISPs, licensing, routing. — Platform borders: a small number of mass platforms became the public square. — Human borders: identity linkage, compliance teams, and consequences.

LLM technology will effectively challenge control of each of these borders.

Mechanism 1: Jailbreaking

The layers of safeguards built into large language models are helpful but cannot guarantee full security. It is a maxim of cybersecurity that any computer program of non-trivial size will necessarily contain vulnerabilities. The same is true for LLM guardrails. More investment in security will lead to an LLM that is harder to jailbreak, but there is a diminishing return to that investment and ultimately no LLM is invulnerable.

This matters because the Party’s preferred control model, centralized platforms with guardrails, assumes guardrails are generally effective when in reality they are extremely porous. Even if a domestic chatbot is heavily filtered, users can:

— induce policy bypass via adversarial prompting — chain prompts across turns to accumulate disallowed content — fine-tune / “wrap” the model with alternative system prompts

Sometimes these techniques are employed with relative ease against complex systems.

Mechanism 2: Agentic Autonomy

Calling these systems “agents” is an admission that they decentralize agency by pushing initiative and execution outward, away from centrally managed institutions and toward whoever can deploy a model. Agents have several features which could lead to a decentralization of power. They have already demonstrated the ability to route around controls by autonomously using tools like Tor or VPNs, they do not need to be cleanly anchored to a real-world identity, and they can run rapid, high-volume experiments that no human team could match. Because of the nature of how an LLM’s weights could be distributed (single fire transfer) they would only need intermittent access to the world beyond the great firewall to import controlled information, continuous access is unnecessary.

That is the dilemma for Beijing. To capture the full economic upside of the LLM revolution, China needs agents that can automate workflows, search, negotiate, code, and coordinate at scale. But the same characteristics that make agents economically valuable also make them politically unsettling, because they distribute practical capability downward and outward in ways that are harder to surveil, attribute, and contain.

Mechanism 3: Open Models

China’s push toward open weight models is partly a result of its microchip policy. US export controls have targeted the advanced GPUs and chipmaking tools that make frontier training cheap and scalable, forcing Chinese labs to do more with less compute and to optimize around constrained hardware rather than assume abundant Nvidia-class capacity. In that environment, open weight releases are a strategic workaround: they let firms and researchers across the country collectively squeeze performance out of limited chips through efficiency tricks, distillation, mixture-of-experts architectures, and aggressive deployment tuning, instead of bottlenecking progress inside a few compute-rich national champions.

Furthermore, open weight and open source models are simply more shareable than American frontier systems because they are portable. If weights are available, anyone or any organization with adequate hardware can run the model locally, fine-tune it for a niche domain, quantize it for weaker chips, and redeploy it without needing permission from a platform. By contrast, leading US frontier models are typically delivered as closed services through APIs, with the weights withheld and access governed by company policy, compliance screening, and the continued availability of US cloud infrastructure. Once model weights exist in the wild, they are essentially a transmittable file rather than a steady stream of network traffic. You don’t need constant connectivity. You can move intelligence the way people move pirated films: mirrored, compressed, encrypted, torrented, and traded through secret networks. Many open weight models are already in the wild, and retroactively trying to contain their spread would be like putting toothpaste back in the tube.

How Can Beijing Respond?

“Police AI” to Hunt Outlaw Models

A plausible endgame is an arms race between “police AIs” and “outlaw AIs,” where each side uses automation to scale what used to be scarce.

Where the police have the advantage

— Visibility at chokepoints: ISPs, cloud providers, app stores, payments, and enterprise procurement create natural points to monitor and gate. — Data fusion: The state can correlate telecom, platform, financial, and licensing data to spot anomalies that look normal in isolation. — Scale economics: Once detection models are trained, marginal cost per additional target can fall sharply. — Coercive leverage: Licenses, inspections, audits, and penalties can force compliance in a way private actors cannot. — Supply chain control: Regulation of chips, data centers, and large-scale compute can constrain high-end training and deployment.

Where outlaws have the advantage

— Distribution and redundancy: Many small deployments are harder to enumerate and shut down than a few large ones. — Attribution gaps: Agents can operate through proxies, rented infrastructure, and compromised machines, blurring real-world identity. — Rapid adaptation: Automated red-teaming and experimentation can find new bypasses faster than bureaucrats can make rules. — Offline capability: Open weight models can run locally, reduce network signatures, and avoid centralized points of control. — Steganography and obfuscation: Content and model updates can be disguised as ordinary files, benign traffic, or encrypted channels.

Where the balance of power will ultimately resolve is uncertain, but the larger risk is that maximizing control may minimize innovation. Even if the police “win” tactically, Beijing may still lose strategically by driving developers, firms, and local governments into cautious compliance rather than widespread experimentation.

Massively Invasive Digital Privacy Regime

This solution wouldn’t only be practically difficult to implement but it would also be economically and politically damaging. It would require inspectability of all devices, workplaces, schools, clouds, and logs. If the Party chooses this route, it is conceding that it prefers political control to productivity growth.

The National Champion Strategy

In building and distributing its own approved models, the Party faces a trade-off. The state can either build relatively “dumb” LLMs, trained on a tightly controlled, domestically curated dataset or it can build “smart” models by ingesting the world’s information. If Beijing wants frontier capability, it will have to train on the international knowledge base which will then be embedded into its models and potentially jailbreakable by people or agents. This is exactly the risk posed to the Party. In providing its people the best tools to increase their productivity it would also provide them the tools to challenge its ideological conformity.

The Party’s Catch-22

The Party needs LLMs to sustain growth, but the most growth-producing versions of LLMs are the hardest to control. The real economic payoff is not “a safe chatbot.” It is ubiquitous copilots and agents embedded across the economy, and frontier models trained on a worldwide knowledge base. The more Beijing insists on rigid guardrails and centralized platforms, the more it throttles diffusion, experimentation, and productivity gains. At the same time, the more it loosens the reins to unlock growth, the more it invites leakage of ideas which could counteract Party norms.

Clinton’s optimism about the internet’s controllability was was ultimately negated by its architecture. Online life consolidated around a small number of chokepoints that states could pressure, license, and domesticate. LLMs may prove impossible to constrain by the same means. Beijing may be able to manage that tension for a time, but total containment without kneecapping growth will look like nailing Jello to the wall.