Pax Silica

Sun, 23 Aug 2026 00:00:00 +0000

The draft comes from the State Department and has not been sent yet: the thirty-five signatories of a June declaration would be warned that joining China’s framework puts them outside the US-led coalition. Six days later, Brazil announced 2.3 billion reais split between Huawei and Nvidia. And in between, the two supposed sides — OpenAI and Z.ai — halted their most capable models for the same reason, for the same two weeks, with no outside party reviewing the decision.

Governance

On 18 August OpenAI announced it had paused reinforcement-learning training on deployment-bound models for two weeks, and put its largest planned run on hold, after internal signals suggested that Astra — an unreleased system — might be crossing the “Critical” cyber-capability threshold in its own Preparedness Framework. Four days earlier, Z.ai had launched GLM-5.3 while withholding the weights, after measuring 84.5% on CyberGym, a vulnerability-discovery benchmark. Two labs, one on each side of the line the State Department wants to draw, reached the same conclusion in the same week using the same instrument: a threshold they defined themselves, measured themselves and enforced themselves.

It beats the alternative, and that deserves to be said without irony: stopping costs money, and they stopped. But it is not governance, and that shows most clearly when read against the safety index the Future of Life Institute published in July, where the industry’s top grade was a C+ — Anthropic, at 2.66 — with OpenAI at C, Meta at D+, and xAI, DeepSeek and Mistral at F; and which documents that several companies, the best-graded ones included, had weakened or dropped precisely the commitments to halt when hard limits are approached. That same week Google made the visible watermark optional in Gemini and Flow, keeping only the invisible SynthID. The pause and the unmarking run in opposite directions and share a structure: they are commitments the party making them can edit without telling anyone. For any state without an evaluation capacity of its own — that is, for almost all of them — the difference between a threshold and a press release is exactly zero.

Care for the commons

Three releases in one week, three different layers opened. On 12 August Alibaba released the weights for Qwen3.8-2.4T-A95B — 2.4 trillion total parameters, 95 billion active per token — the first time a Qwen-Max-class model has shipped with available weights; but the checkpoint is text-only, without the vision and the million-token context that make the hosted product worth having, and it ships under a custom licence with a revenue-share clause. Z.ai published GLM-5.3 without weights, promising to release them around 28 August under the same permissive licence as before. DeepSeek released Harness, its agent scaffolding, under a genuine MIT licence, provider-agnostic, with every layer — inference, tools, session state, the agent loop itself — replaceable as a plugin; and on the same day raised the price of the V4-Pro API.

None of the three is closing down. All three are choosing which layer to open, and the choice follows a pattern: what gets opened is the layer whose marginal copying cost is zero, and what gets held back is the one that costs money to sustain.¹ The best thing of the week here is DeepSeek’s harness, precisely because it does not depend on DeepSeek: a lab in Bogotá or Accra can run it against whichever model it can afford, including one of its own. But the question left open last week is still there, merely displaced: it is no longer whether the weights are available, but which of the system’s layers came out free and which one is billed. Openness has stopped being a state of the artefact and become a dial, adjusted layer by layer — and the hand on the dial is always the same one.

Epistemic commons

A Northwestern team published in PNAS the largest analysis so far of what language-model use does to the public funding of science. They combined confidential proposals from two large US R1 universities — roughly 1,600 to the NSF and 4,100 to the NIH, including rejected and pending ones — with the full population of awards granted between 2021 and 2025: 57,000 from the NSF and 74,000 from the NIH. Model use rises sharply from 2023 and is bimodally distributed: either almost none, or a great deal. And across every dataset, higher model involvement is associated with lower semantic distinctiveness: proposals sit closer to what that same agency has recently been funding. The consequences, however, are agency-dependent. At the NIH, moving from the 25th to the 75th percentile of use corresponds to roughly 4 percentage points more funding probability and 5% more publications; at the NSF there is no significant association. And the NIH productivity gain is concentrated in papers that are not among the most cited.

That contrast between agencies is the finding that matters, because it relocates the problem. It is not the model that rewards convergence: one review culture rewards it and another does not, with the same tool in the middle. For someone writing in a second language — most researchers in the Global South — a language model is a real equaliser: it removes the accent penalty a grant form has always charged. But the same instrument that lowers that barrier pushes the content toward the centre of what has already been funded, and that centre has a geography. The practical conclusion is not to ban anything. It is that the region’s agencies — CNPq, CONICET, Minciencias — still have time to decide whether their evaluation criteria reward distinctiveness or conformity, before redesigning their processes around detecting model use, which is the easy answer and the wrong one.

The week’s other finding runs in the opposite direction, and both have to be held at once. A Stanford-led team published in Science the creation of sixteen viable bacteriophages that do not exist in nature, designed by generative models trained on millions of genomes: they chemically synthesised close to three hundred candidates, and the cocktail of the sixteen that worked overcame resistance that had defeated the natural phage. The burden of antibiotic-resistant infection falls overwhelmingly on the Global South, and phage therapy is one of the few things in biomedicine that can be produced cheaply and locally. This is exactly what the promise of AI for science says will happen. It is also, in the same breath, a pathogen-design capability, and the predictable response — export controls on biological design models — would enclose that capability precisely where the need is greatest. The same technology produces convergence in a grant application and genuine novelty in a genome; what differs between the two cases is not the model but what the selection mechanism on the other side rewards.

Democratization

In mid-August Reuters obtained a State Department draft addressed to the thirty-five signatories of a June “AI Opportunity Statement”: a warning that joining Beijing’s competing framework leaves them outside the US-led coalition. The framework is called Pax Silica, was launched last year to secure supply chains for models, semiconductors and critical minerals, and already has some two dozen members, among them Japan, Australia, South Korea and Kazakhstan — which is also in the Chinese coalition. On 19 August spokesperson Lin Jian replied that China opposes taking sides and forming camps on AI, and that “each country has the right to choose its partners based on its national conditions and development needs.”

The two positions are formally symmetric — both are bids for alignment — and materially they are not, because they do not ask for the same thing. Pax Silica is, before it is an agreement about models, an agreement about critical minerals: about what the Global South has in the ground. The name is neither an accident nor an in-joke; it is the thesis. A pax is what the party holding the perimeter grants, and what it grants is predictability in exchange for exclusivity. Which is why Kazakhstan is the week’s most instructive case: being in both coalitions is not indecision, it is the rational strategy of an input supplier, because the value of what it sells comes precisely from not being committed. The exclusivity clause exists to eliminate that margin. Non-alignment, here, is not a moral posture inherited from the sixties: it is a bargaining position, and the letter is an attempt to make it contractually impossible.

Public sector opportunities

On 20 August Brazil announced 2.3 billion reais ($444.2 million) for its AI ecosystem, deliberately split. Just over half — 1.3 billion — funds supercomputing infrastructure in Rio de Janeiro with Huawei and iFlytek, explicitly aimed at developing general and sector-specific language models. The other billion goes to a tender for a machine the government expects to rank among the world's ten most powerful for AI processing, to be installed in Rio Grande do Norte, and which Nvidia is expected to win. The next day South Korea announced a "Future Response Fund" financed by the tax windfall from the semiconductor boom — whatever exceeds a benchmark based on the past decade's average growth — and directed at youth employment, housing, regional development and AI investment; local press estimates it could exceed 100 trillion won ($72.28 billion).

These are two different state capacities and it is worth not conflating them. Korea’s is fiscal and institutional: a countercyclical rule that turns a boom into a reservoir — that is, a decision about time. Brazil’s is procurement: turning money into machines, now. Brazil is doing the harder thing with far less — $444 million is roughly what one hyperscaler spends in a fortnight — and the detail that matters is not the amount but that over half of it goes to developing models rather than renting capacity to consume them. The answer to Pax Silica was not a communiqué but a divided budget, and it arrived six days after the draft, from a country that is not among the thirty-five. One question neither announcement answers is the one that decides whether this is sovereignty or mere acquisition: who governs that compute afterwards. How it is allocated, on what criteria, and whether a public university in the Northeast will get hours on the Rio Grande do Norte machine or watch it from outside the fence, the way one watches a pipeline go past.

Environmental impact

Rio Grande do Norte was chosen, according to the announcement itself, for its energy potential. In Brazil’s Northeast that phrase means wind, and it should be said that this is a good reason: it is probably the best siting decision available in footprint terms. But the phrase leaves unanswered the two questions that turn it into a policy rather than a postcard: at what price the data centre buys that energy, and who pays for the grid that carries it.

The week’s most transferable answer came from an unlikely place and never mentions models at all. On 18 August Pennsylvania’s governor signed an executive order removing AI data centres from the fast-track permitting programme, requiring binding grid commitments before the environmental authority even evaluates the permit, mandating local hiring and a community benefits agreement, and establishing two things worth more than all of the above: that without local community approval the state does not approve the project, and that infrastructure costs the centre creates are paid by the centre and not by residential ratepayers, even if it later closes and cannot pay them. This is polycentric governance in its least glamorous form: not a national AI framework, but permits, land and who pays for the substation, decided at the scale where the affected people actually are.² The last clause matters most for the region, because the standard extractive contract in Latin America has always externalised exactly that: the cost of what remains once the operation leaves.

Closing

The week left two gestures that resemble each other and are not the same. Two labs decided to stop, and no one outside could verify why, by what measure, or for how long. A governor decided that a data centre does not get built if the local community does not approve it, and that is verifiable, appealable and copyable. The question for next week is not whether the race will have rules, but how many of those rules will be written in a framework the company can edit, and how many in a permit someone can deny.

This week’s sources

Governance

On OpenAI’s pause: , 18 August 2026, and the · open access
On the GLM-5.3 weight embargo and its CyberGym score: and · open access
On the safety index: , Future of Life Institute, with the · open access
On the optional watermarks: , 14 August 2026 · open access

Care for the commons

Qwen3.8-2.4T-A95B: of what the checkpoint and the licence actually cover · open access
DeepSeek V4-Pro and Harness: , 13 August 2026 · open access

Epistemic commons

The study: Qian, Wen, Furnas, Bai, Shao and Wang, , PNAS, 2026. The has the full text · preprint openly accessible
On the bacteriophages: and , August 2026 · open access

Democratization

On the Pax Silica draft: , 15 August 2026 · open access
China’s response: and · open access

Public sector opportunities

On the Brazilian investment: , 20 August 2026, and · open access
On the Korean fund: and , 21 August 2026 · open access

Environmental impact

The Pennsylvania executive order: and , Pennsylvania Capital-Star · open access

The idea of analysing an informational system by layers — physical, logical, content — and asking at each one whether it is open or closed comes from Yochai Benkler, The Wealth of Networks (2006), and before him Lawrence Lessig. What this week adds is that all three companies use the layer separation as a management instrument: the decision is not between opening and closing, it is about where to put the boundary. An MIT-licensed harness on top of a metered model, or available weights stripped of the modalities that make the product useful, are not partial openings forced by technical limits; they are chosen configurations, and the criterion ordering them is which layer can be copied at no cost. ↩︎
In Governing the Commons (1990), Ostrom lists among her design principles the collective-choice arrangements — those affected by the rules take part in modifying them — and nested enterprises, which distribute authority across levels. The local veto clause in the Pennsylvania order is exactly the first, and the fact that the state environmental authority is subordinated to that approval is the second. It is striking that the week’s most Ostromian instrument in AI regulates no model at all: it regulates a shed, a permit and an electricity bill. ↩︎

Brazil | guIA