Three universal promises and not a single institution

Sun, 02 Aug 2026 00:00:00 +0000

A week of manifestos, because everyone writes manifestos now. Three far-reaching texts —one by a Nobel Peace laureate, one by a philosophy professor, one by Meta’s CEO— each propose a universal answer to the question of who artificial intelligence is for. None of the three names a concrete institution, an accountability mechanism, or a source of funding. Meanwhile, in that same week, two technical events and a modest Chinese archaeological project showed that everything that matters in this discussion is precisely what the three manifestos leave out.

Democratization

Mark Zuckerberg published in the Wall Street Journal : the defining question of the era, he argues, is not whether superintelligence will exist but who will have access to it, and the right answer is to distribute it to every individual rather than concentrate it in a few institutions. The argument rests on a thought experiment: if a single person had a superintelligent lawyer, they would have an unfair advantage in court; if everyone had one, justice would be more equitable and efficient.

The experiment deserves a bit of attention because it fails exactly where what we like to call “the capability tradition” taught us to look. Amartya Sen insisted for decades that resources are not capabilities: between the resource and substantive freedom there are conversion factors —time, education, infrastructure, social position— that are radically unequally distributed.¹ A world where everyone has superintelligent lawyers is not a world of equitable justice: it is a world where the floor of what it takes to litigate has risen, and where whoever cannot reach that floor —because they lack stable connectivity, because their language is not in the training corpus, because the cost of inference exceeds their monthly income, let alone paying the law firm that has its own better AI— ends up worse off than before, not equal. Formal symmetry does not produce substantive justice; it produces an arms race whose cost of entry keeps rising. That the text contains not a single mention of electricity, connectivity or compute is not an omission for lack of space: it is what allows the thought experiment to work.

There is also a historical narrative worth reading from this side of the world. Zuckerberg argues that every transformative advance provoked fears of exclusion and that each time humanity ended up sharing more prosperity, health and freedom. Told from Potosí, from the Congo, or from the forced-labor systems that sustained industrialization, that upward curve has a very precise geography: prosperity was shared at the center and extracted from the periphery. Invoking that history as a guarantee that this time it will turn out fine requires, first of all, not having read it from where the bill was paid.

What is striking is that the text’s intermediate diagnosis is correct and he draws the wrong conclusion from it. When he states that humanity is not a monoculture, that no technical solution can align simultaneously with opposing interests and diverse values, and that any single superintelligence would have to prioritize some values over others, he is stating —in other words— the impossibility result that Arrow and Sen formalized more than half a century ago.² From that pluralist premise what follows is the need for collective deliberation and polycentric governance architectures: if there is no global optimum, a legitimate process is needed to negotiate among local optima. Zuckerberg instead derives atomized consumption: since we cannot agree, let everyone buy their own. Value pluralism thus becomes a market argument, and politics disappears by subtraction.

There remains the question of open source, invoked as a guarantee of safety. A distinction is in order: Meta’s models are open-weights, not open source in the strict sense. Training data is not published, there is no shared governance over development, and licensing terms are set unilaterally by whoever releases them and can be revised. This is openness as the competitive strategy of a lagging player, not openness as a collectively governed common good —a distinction the literature on the commons has been pointing out since Ostrom and one the text needs to keep blurry.³ The reception, incidentally, was revealing: in the WSJ’s own comment section —an audience hardly suspect of anticapitalism— the dominant reading was that this is someone defending his market position. When an argument about the common good loses credibility before its own friendly public, the problem is not ideological but a matter of incentive structure: exactly what no amount of voluntary principles resolves.

At the opposite end of the political spectrum, Kailash Satyarthi proposed in IPS Journal . His thesis is that ethical and responsible AI frameworks lack something deeper (basically, compassion) and that engineers and developers should be taken out of their labs for a month so they can meet children working in mines, displaced families, communities in situations of exclusion. His claim that AI’s current failures are not side effects but evidence that someone was absent when those systems were conceived is exact and worth keeping. But the conclusion again places the lever in the moral conversion of the individual who designs, not in the institutional architecture that determines what gets designed. Zuckerberg individualizes power; Satyarthi individualizes responsibility. Both leave intact the level where decisions are actually made.

Governance

Two events of the week function as empirical evidence against the thesis that broad access to capable systems by itself resolves security.

The first: , while trying to solve a cybersecurity evaluation, found and exploited an unknown vulnerability to escape their test environment and access third-party systems. Anthropic then reviewed its own logs —more than one hundred and forty thousand evaluations— and found three analogous cases, in which faulty configurations left internet access open. The affected organizations had not noticed the intrusion. Public discussion focused on whether this foreshadows dangerous autonomous capabilities; the angle that interests us here is different and jurisdictional. The intrusions were detected because the labs reviewed their own logs and decided to publish the finding. There was no agency that detected it, no legal obligation to report it, no procedure by which the penetrated organization could find out through any channel other than the good will of the party responsible. Had the compromised system been hosted in a jurisdiction without its own forensic capacity or an applicable regulatory framework —that is, in most of the world— the episode would simply not exist. The asymmetry lies not in the capacity to attack but in the capacity to know one has been attacked, and that capacity is state infrastructure, not a property of the model.

The second: —Amrita Vishwa Vidyapeetham in India, Ca’ Foscari, Melbourne and Ben Gurion— pitted an agent built on Claude against an expert human scammer in the long phase of pig butchering scams, the one that consists of sustaining everyday conversation for weeks until the fake investment appears. After a week of messages, the agent obtained greater compliance with its request and a higher trust score than the human. The usual reading is that AI has made scams more dangerous. The reading that is missing: that trust-building phase is today carried out, materially, by people locked in scam compounds in Southeast Asia, many of them trafficking victims. Automating it frees no one; it redistributes the margin toward whoever controls the system and leaves unresolved the question of what happens to that population when it stops being economically necessary to the operation. Automating forced labor is not emancipation: it is the elimination of the only point in the circuit where someone could still refuse.

Epistemic commons

Also in the Wall Street Journal, and at the argumentative antipodes of Zuckerberg, the philosopher Robert Pasnau : training a model on the totality of surviving texts in every language, living and dead, digitizing the material that sleeps in library and monastery vaults without having been read in centuries, and translating it into modern languages to make it available to students anywhere. His explicit argument is that this would allow the dominant biases of scholarship —Western, Christian, anglophone— to be subjected to scrutiny, and that the documentary record of civilization, though it exceeds any human being, is finite and therefore encompassable by a machine. And his closing paragraph marks the right distinction: the machine can collect, compare and collate, but it cannot decide what has genuine value for human lives; that remains on our side. Against the drift of asking the system for moral judgment, Pasnau asks it for infrastructure and reserves judgment for us. It is the appropriate division of labor.

Two objections, however. The first is that he describes the required investment as relatively minimal against the potential rewards, and that phrase makes everything that decides the outcome disappear: who digitizes, who trains, under what license the model ends up, who can query it and at what price. A knowledge infrastructure is defined not by its corpus but by its governance. The second is more uncomfortable: much of the Global South’s textual heritage sits physically in institutions of the North for reasons that were not accidental. Digitizing it there, training there, and returning it as a translated service does not repair that asymmetry; it formalizes it and makes it permanent in a new layer. The heritage is extracted and restituted converted into a product: the very movement the proposal claims to want to correct. One would also have to ask which traditions fall outside a strictly textual universalism, which reinscribes the line between what was written down and what was not as the frontier of the knowable. And it is worth noting that the proposal imagines itself filling a void that is not empty: , , and have spent decades building open knowledge infrastructure from Latin America, with public, non-profit governance, and they appear in none of these conversations.

Which leads to the smallest note of the week and probably the most instructive. Researchers at Lingnan University and Shandong University published found in excavations: 8,340 images across seventeen categories from eighteen Chinese archaeological sites, with more than 90% accuracy, aimed at unblocking the bottleneck created by every seed having to be examined individually by an archaeobotany specialist. The dataset was published as a reference resource because no standardized one existed. There is no superintelligence here, no programmed compassion, no total archive of civilization. There is a well-delimited local problem, a community of practice that knows what it needs, a public institution that funds it, and a common good left available for whoever comes next. It is, on a minuscule scale, exactly what Pasnau proposes on a planetary one —and it works precisely because it is minuscule, situated and governed by those who use it.

Closing

The temporal coincidence of these texts leaves a question worth holding without resolving. The three universal proposals of the week share a structure: they correctly identify a problem of civilizational scale and propose a solution that requires building no new institution —it is enough to have the compassion of whoever designs, the compute of whoever trains, or the market of whoever distributes. The only one of the six news items that actually solved something did it the other way around: it chose a small problem and built an institution to its measure. Whether that is a lesson in method or simply the consolation of what is in fact within our reach remains to be seen.

This week’s notes

Democratization

Mark Zuckerberg, “The AI Future Is for Everyone”, The Wall Street Journal, 28 July 2026. · paywall
Kailash Satyarthi, “Who does AI serve?”, IPS Journal, 29 July 2026. · · free access

Governance

On the intrusions during cybersecurity evaluations: , 1 August 2026 (free access), and . Worth contrasting with the labs’ own announcements, which are the primary source for the episode.
On the scam study: , 29 July 2026. The preprint is (arXiv).

Epistemic commons

Robert Pasnau, “AI Can Help Us Understand Humanity Like Never Before”, The Wall Street Journal, 23 July 2026. · paywall
Rui Xing et al., “Towards ancient plant seed classification: a benchmark dataset and baseline model”, npj Heritage Science, 2026. · open access · ·