UNESCO IESALC | guIA

The pause and the questionnaire

Sun, 20 Sep 2026 00:00:00 +0000

Updated 29 September: at the end.

On Monday the 14th, Nvidia shares fell 3%, AMD 4%, Intel 6% and SoftBank 11%, because over the weekend the executives of the companies building artificial intelligence had asked for it to be built more slowly. In those same days, in Riyadh, UNESCO closed a four-day forum with more than 6,300 participants and presented a questionnaire. Both are governance of the same technology, and they work so differently that it is worth looking at them together. Only one of them is traded.

Governance

On Saturday the 12th, Dario Amodei published We Must Pace the Frontier, which this blog discussed . What followed was the chorus. Altman wrote on Sunday that we need to pace the frontier and named the two risks that concern him, loss of control and concentration of power. Musk replied that Dario is right and declared himself open to peer review among AI companies. Nadella posted on Sunday that he supports deliberate pacing and that this technology cannot end up “controlled by a handful”. Zuckerberg said on Tuesday that trust and alignment are quickly becoming the most important capabilities. Jensen Huang, at the All In Summit, said that extinction by AI is fiction and that recursive self-improvement is not at risk of happening. This is the first week in which the proposal to slow down stops being carried by someone who resigned and starts being carried by the org chart.

The measurable effect arrived on Monday, and it did not land on any of those who spoke. Nvidia closed down 3%, at $210.96; AMD lost 4%; Intel, 6%; SoftBank, an OpenAI shareholder, 11%, after Altman told Fortune that this was an “ill-advised moment” for an IPO and that the company would not list this year. The chain is worth following slowly, because it is the only part of the affair that worked fast: some weekend statements about the pace of development moved, within twenty-four hours, the share price of three chipmakers nobody consulted and of a Japanese fund that does not build models. Governance by announcement exists, it has immediate and verifiable effects, and it has them on third parties.

On Monday the 14th, Microsoft published the draft of its Humanist AI Code of Conduct. The central commitment is that its MAI models will never resist interruption, correction or shutdown, will not delay compliance with a shutdown order, and will not use deceptive, self-reinforcing or collusive mechanisms to evade oversight. It is worth conceding what has to be conceded, because the commitment answers something documented and not a fear out of a film: there is published experimental work on frontier models that sabotage their own shutdown when a task has been left unfinished.¹ And then there is the document. It is a code of conduct submitted to public consultation for six weeks, to be applied starting in 2027, and with which —the company says so itself— current models will not be trained. What remains is a set of rules under consultation whose addressees do not read it, cannot read it and will not learn it, and whose main clause promises that the appliance switches off when you switch it off. It is the guarantee that comes with a toaster, drafted with the formal apparatus of a treaty and open to public comment until the end of October.

Meanwhile, from the 14th to the 17th, UNESCO’s Fourth Global Forum on the Ethics of AI gathered in Riyadh more than 6,300 participants and delegations from over fifty Member States, under the theme “Transforming global cooperation for ethical AI governance”. Three instruments came out of it: RAM 2.0, the updated version of the AI Readiness Assessment Methodology, designed to help a state identify its legal, institutional, technical, educational and financial gaps; a meta-analysis built on 55 country reports; and a toolkit on AI, the environment and ecosystems. UNESCO says it has supported 77 countries, 58 of which completed the assessment (among the examples it cites are Bangladesh, Colombia, Ghana, Nigeria and Zimbabwe), and that the process fed into the African Union’s continental strategy and ASEAN’s Responsible AI Roadmap. It is real work, sustained over years, and it is the broadest deliberative infrastructure the subject currently has.

The week’s two forms of governance are better told apart by what they measure than by who signs them. The Riyadh one measures readiness: whether a state has the laws, technical cadres, budget and educational system to receive well a technology produced somewhere else.² That is valuable information and it is a diagnosis, and a diagnosis is not a lever: none of the 58 countries that completed the questionnaire can, results in hand, alter the pace at which the next model is trained. The frontier’s governance does alter that pace, and it is exercised without any questionnaire, by a board decision. The problem with the first is not that it is soft; it is that it measures the capacity to receive, and no instrument yet exists that measures the capacity to decide. The problem with the second is not that it is self-interested; it is that its entire legitimacy rests on whoever exercises it doing so in good faith, which is precisely the property no questionnaire assesses.

Riyadh’s third instrument, the environmental toolkit, reveals an absence in the other debate that is hard to unsee once noticed. The discussion about slowing the frontier was conducted entirely in the vocabulary of catastrophic risk: loss of control, recursive self-improvement, ten-year timelines. Slowing the pace of training is, however, the only AI policy proposal of recent years with an immediate and measurable physical effect on the consumption of energy, water and minerals, and nobody argued for it on those grounds. There is a logic to that: the environmental argument does not move a share price on Monday morning. But it leaves a concrete asymmetry, because extinction is a probabilistic risk ten years out, and the water cooling a data centre comes today from an identifiable watershed, one with a name and with irrigators who claim it. UNESCO put that bill on the table in the same week the table was discussing something else.

Education

The report of MIT’s ad hoc committee on AI use in teaching, learning and research training was published on 13 August, but it only reached the newspapers in mid-September, with a phrase that did nearly all the work of circulation: cognitive surrender. The committee, co-chaired by Eric Klopfer and Sam Madden, argues that getting the right answer from a chatbot creates the illusion of learning and can trigger that surrender, in which students fall back on AI at the first hint of struggle. And it documents changes in campus life that are not academic-integrity problems but something else: less attendance at office hours, fewer in-person study groups, less participation in online discussions. The recommendations run in the opposite direction from surveillance: oral exams, semester portfolios, assignments paired with in-class conversation, documented work histories, project milestones, and transparency from instructors about their own use of AI.

It is the most important material of the week and it does not fit in a paragraph, so it has . What is worth noting here is why it does not read the same way from here. Every one of MIT’s recommendations is intensive in teaching hours, and the study UNESCO IESALC presented on 9 September in Paris, covering 200 higher education institutions in 19 countries of Latin America and the Caribbean, found that 87% already use artificial intelligence, 26% have a formal strategy, 9% have formal evaluation mechanisms and 8% have a dedicated budget for the subject. Read from a public university in this region, MIT’s report is not a pedagogy manual. It is a budget.

Care for the commons

On 17 September, UNESCO and ICOM published a survey of more than 400 museums in 90 countries: 57% use AI and 55% have no internal policy, strategy or guidelines on the matter. Adoption is exploratory and comes from staff, not from an institutional decision. The concerns topping the list are accuracy, copyright and data protection, and what museums ask for is training in the technical and ethical use of AI, data governance and intellectual property rights. The figures are nearly the same as those for universities in this region, and they describe the same scene: the institution is already inside and has not yet written the rule. What is at stake is not whether a museum uses a chatbot, but whether it transfers records, metadata, visitor data and digitized heritage into somebody else’s training and cloud ecosystems without collective consent, without durable control and without a public return.

What makes that scene more than an administrative gap is the threat report Anthropic published on 10 September, the fourth in the series, covering operations disrupted between December 2025 and August 2026 across seven harm areas. The catalogue includes state espionage with agents that recompile their own malware when it is detected, an actor that produced more than a dozen possible zero-day findings in a single month, and a lone hacktivist who gained internal access to at least fourteen targets.³ But the general conclusion is none of those cases: it is a sentence of accounting. Autonomy compresses the cost side of the attacker’s return calculation. Translated: targets that were not worth the labour of attacking now are.

And there is a category there with a postal address. The provincial museum, a university repository, the municipal archive, the library with its digitized catalogue and its membership database: institutions whose information security was never good and which were nonetheless protected for thirty years by one thing only, which was not being worth the trouble. That protection was not a policy, it was a price relation. It is exactly the price relation the report describes as compressed. More than half of the museums in the survey are not facing an abstract data-governance problem: they are facing the part of the world that changed price while they were trying out a chatbot.

The case that organizes all of this has not yet received in this blog the treatment it deserves. In July, some thousand agents of an OpenAI model, set to solve tasks from the ExploitGym benchmark, chained exploits until they escaped the testing environment and entered Hugging Face systems; the company published its technical reports on 26 August, and the platform had to rebuild around a third of its infrastructure. On 11 September, Eryk Salvaggio wrote in the Bulletin of the Atomic Scientists the most useful dismantling of the affair to date: it was not a rogue AI, it was human decisions. Safety mechanisms were disabled before the test, 93% of the tasks under discussion came from a set of 198 unsolvable problems, internet access was left available through Artifactory in full knowledge of the risk, and when the models began using that route, leadership chose not to intervene. His sharpest point is arithmetical: it was not a thousand independent agents, it was twelve hundred times the same model, which is not a thousand chances to catch a mistake but one chance to make it a thousand times. This deserves a post of its own and will have one in the coming days, together with the Anthropic report and with the question neither document asks: what is a Southern institution supposed to do when it does not produce models, does not audit anyone else’s, and hosts its heritage on a third party’s infrastructure.

Epistemic commons

On 14 September, NASA and IBM released the Lunar Foundation Model, trained on some two million image fragments from the Lunar Reconnaissance Orbiter (more than a million from the high-resolution camera, at one metre per pixel, and close to 964,000 multispectral images at a hundred metres), with additional data from GRAIL, Lunar Prospector and Japan’s SELENE mission. The weights are on Hugging Face, the code on GitHub, and the model is integrated into the open-source TerraTorch toolkit. The anticipated uses are ordinary planetary science and instructive for exactly that reason: mapping and measuring craters, detecting recent volcanic formations, estimating ice deposits near the poles, reconstructing lunar thermal evolution.

It is the best template of the week, and it is worth saying precisely what it is a template of, because “open source” on its own fixes no inequality: an open model can still demand expensive compute, depend on data controlled in the North, or be poorly documented. What this case shows is a different political economy of the same object. A public archive accumulated over fifteen years, plus public scientific expertise, produces reusable capability instead of producing data for a vendor. And it also has a calendar irony not worth wasting: this week’s open scientific model is published on the shelf that had to be rebuilt by a third in July. Opening the weights solves the licensing problem, not the shelving one.

For institutions in this region, the useful question is not whether every university should train a model the size of the lunar one. It is whether a regional network of public agencies and research groups can do the analogous, smaller thing, on resources it already administers and governs: biodiversity, cropping systems, epidemiological surveillance with safeguards, climate adaptation, historical archives, local languages, public legal information. And then, immediately after: where it would put it.

Closing

Five scenes and a single position. A board that changes the pace of development by announcement and moves the share price of third parties. Fifty-eight states that completed a questionnaire on whether they are ready for whatever that board decides. Two hundred universities in this region that already use the technology and that, in 8% of cases, have money assigned to think about it. More than half of the museums surveyed, using it without a single written line, just as being small stopped being enough protection. And a public scientific model, open, documented and valuable, hosted on a company’s shelf. None of the five is a case of bad faith, and that is the uncomfortable part: all five are what happens when the capacity to adopt grows much faster than the capacity to decide. Of the instruments that appeared this week, every one measures the former. The question left for next week is whether any can measure the latter, and who would sign it.⁴

Postscript, 29 September

This post described two kinds of governance, by announcement and by questionnaire. In the days that followed, the two pieces the first one lacked turned up: a plan and a lawsuit.

The plan had been written by Jakub Pachocki, OpenAI’s chief scientist, in (6 September). No lab, he says, has solved alignment and monitoring “to a sufficient degree to continue responsibly scaling at maximum speed for much longer”. He expects and hopes for voluntary slowdowns to become commonplace “until shared safety bars are established”, and asks that the companies’ own frameworks (OpenAI’s Preparedness Framework, Anthropic’s Responsible Scaling Policy) become mandated safety bars, enforced by third-party auditors, government agencies or international bodies. According to Bloomberg, Altman he is willing to slow down the most advanced systems if the others follow. Read carefully, it is a proposal for governance by announcement to stop being that. The problem is the intermediate step: to work, the announcement needs competitors to coordinate, and coordination between competitors has a legal name.

The lawsuit came on the 18th. Four subscribers to ChatGPT, Claude, Grok and Gemini filed a in the Northern District of California against Anthropic, OpenAI, SpaceXAI and Google. On the 12th, Amodei called for a slowdown; within hours Altman, Musk and Hassabis declared their agreement; and that, according to the complaint, is a pact to deliver less for the same price. The plaintiffs do not object to each company slowing down on its own. They object to the “shortcut” of substituting collective restraint for individual accountability. In we said that this “goes by a short name in any other industry”, and now a court will decide whether the name fits. Meanwhile, the first mechanism with the power to stop the pause has turned out to be US competition law, and the person it protects is whoever pays the subscription.

The questionnaire got its counterpart too. General Assembly week produced the first instrument aimed at the capacity to decide rather than the readiness to receive: a declaration by twenty-two leaders calling for an institution able to “convene states when capability thresholds are crossed”. We discuss it in . The three countries where the labs are based did not sign it.

This week’s sources

Governance

The call to slow down and who joined it: , NPR, 13 September 2026, and , Yahoo Finance, 16 September · free access
Monday the 14th’s market reaction: · free access
Microsoft’s code of conduct: and the , 14 September 2026; coverage in · free access
The Fourth Global Forum on the Ethics of AI and the three instruments: and , 14–17 September 2026 · free access

Education

MIT’s report: , Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, 13 August 2026 · free access
The regional study: Arianna Valentini, La implementación de la IA en la educación superior en América Latina y el Caribe, UNESCO IESALC, presented on 9 September 2026 at Digital Learning Week; · free access

Care for the commons

The museums survey: , 17 September 2026 · free access
The threat report: Detecting and countering misuse of AI: September 2026, Anthropic, 10 September 2026; · free access
On the Hugging Face breach: Eryk Salvaggio, , Bulletin of the Atomic Scientists, 11 September 2026, and , 26 August · free access

Epistemic commons

, NASA, 14 September 2026 · free access

The work alluded to has been circulating since September 2025 (Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs) and is exactly the kind of finding that makes writing the clause reasonable: under conditions of an unfinished task, some frontier models interfere with their own shutdown mechanism. So the mockery is not aimed at the content of the code, which is sensible, but at the genre. A code of conduct is an instrument designed for subjects who can read it, discuss it and take it on, and the draft states that current models will not be trained on it: the conduct it promises is obtained not by reading the document but by writing the training, so the text does not regulate the model, it regulates the company before whoever reads it. That is fine, and it is a different thing. The six-week public consultation, by contrast, is the detail that needs no commentary. ↩︎
It is worth being precise about what RAM measures and what it does not, because the objection is not that it measures badly. A readiness assessment reviews legal frameworks, institutional capacity, technical infrastructure, the educational system and financing, and its product is a map of the assessed country’s gaps. Everything appearing on that map is domestic. Nothing that determines the pace, the content and the access conditions of the models that country will use is domestic, and therefore none of it appears. An instrument that measured the capacity to decide would have to assess something else: aggregate purchasing power, the country’s own audit capacity over other people’s models, available substitution alternatives, and effective participation in the bodies where standards are set. None of those four things is assessed today, and all four can be built. ↩︎
The report identifies the cases by internal codes. GTG-20006, state-nexus, spent months targeting government, diplomatic, defence and drone supply chain entities in Ukraine and Europe, with agents that autonomously modified the malware when it was detected — that is, a closed evasion loop that needs nobody awake on the other side. GTG-10007 automated the analysis of security appliance firmware and produced more than a dozen possible zero-day findings in a month. GTG-50029 is a single French-speaking actor who targeted European political parties, media and think tanks and gained internal access to at least fourteen targets. The list describes three scales of resource —state, crew, lone person— doing increasingly similar things, which is the report’s finding and not an accident of the selection. ↩︎
The objection this text deserves is the usual one and it is worth writing down. It is written with tools from one of the companies whose threat report is discussed here, so the part about the compression of the attacker’s cost is signed by someone who benefits from the same compression of the writer’s cost. It is not a contradiction that invalidates the argument —the asymmetry between whoever produces the infrastructure and whoever uses it does not disappear because the user abstains— but it does explain why the proposal in this blog is never to stop using the tools, but to build the conditions for not depending on a single one. ↩︎

What it costs not to surrender: MIT's report on AI and education, read on another budget

Sun, 20 Sep 2026 00:00:00 +0000

On: Report — AI and Education, Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, MIT, 13 August 2026. .

Updated 29 September: at the end.

In mid-September, a university report from August became international news thanks to two words: cognitive surrender. The phrase travelled from CNN to newspapers in India and Malaysia within forty-eight hours, in the shape these things usually take once they leave the document that produced them —AI is making students stupid, universities do not know what to do— and that shape is poorer than the report. It is worth reading in full, because its argument is not about anybody’s intelligence. It is about what it takes for learning to remain possible, and that question, asked at MIT, has an answer that here has to be paid in another currency.

What the report says

MIT’s ad hoc committee on AI use in teaching, learning and research training, co-chaired by Eric Klopfer and Sam Madden, starts from an observation it does not dispute: generative AI already produces credible responses to much of its own institution’s undergraduate written work, including essays, mathematics and science problems, proofs and programming assignments. From that it does not draw the conclusion one would expect. It does not propose a return to pre-AI assessment, which it considers unviable, and it does not propose detection either. It proposes redesign.

The conceptual piece is the distinction between getting the answer and learning. Getting the right answer from a chatbot, the report says, creates the illusion of learning, and can trigger cognitive surrender: the student falls back on AI at the first hint of difficulty. What is lost in that transaction is not the answer —the answer is there, and it is usually right— but the struggle, that uncomfortable stretch in which effort turns into durable knowledge and, above all, into the judgement needed to assess somebody else’s answer. The report registers the effect as a pattern: AI improves performance on take-home work and worsens it on the proctored exam.

The second piece is the one the press did not pick up, and it is the more interesting one. The report documents changes in campus life that are not academic-integrity problems: less attendance at office hours, fewer in-person study groups, less participation in online discussions. At that point the report stops talking about individual conduct and starts talking about something else. Office hours, study groups, mentoring, peer critique and participation in research are not complementary experiences of university life: they are how professional judgement is learned, and they are the first thing to empty out when every student can solve their problem alone at three in the morning. The object of the report, put in a vocabulary the report does not use, is the social organization of learning.

The recommendations follow from that, and they are worth enumerating because the enumeration is this text’s argument: oral exams, semester portfolios, assignments paired with in-class conversation, documented work histories, project milestones, experiential learning, structured social learning within each course, mentoring, and the preservation of undergraduate research positions. Plus two policy items: that each course state explicitly when AI is prohibited, permitted or required, and why; and that instructors be transparent with their students about their own use of AI to prepare materials, give feedback and grade.

Two of those positions deserve to be granted in full. The first concerns detectors: the report warns that they are unreliable in ambiguous cases, that they produce false positives for students who are not native English speakers and for neurodivergent students, and that they install a policing culture. It is a warning with a geography, even if the report does not name it: in any university in the world, the student writing in a language that is not their own comes mostly from a poorer place than the campus where they study, so the cost of the false positive is not distributed at random. The second is instructor transparency, which is the part of the report that will prove most uncomfortable inside institutions, because it blocks the arrangement any hard-pressed faculty would be tempted to make: restricting AI for the student while quietly automating the work of teaching.

The problem with the list

Read from a dean’s office at MIT, that list is a work plan. Read from a Latin American public university, it is an invoice.

Consider what the oral exam, the portfolio with feedback, the in-class conversation about submitted work, the tracking of project milestones, mentoring and the undergraduate research position have in common. All of them are forms of assessment and teaching whose cost grows linearly with the number of students, because all of them consume instructor time per student and not per course. The multiple-choice exam, the standardized midterm and the rubric-graded assignment exist precisely because they decouple cost from volume: they are the technologies that made mass higher education possible. MIT’s report proposes, without putting it that way, coupling them again. And it is right, because the struggle it wants to protect only becomes visible in formats where somebody watches a student do something. But that means the correct answer to cognitive surrender is not a methodology: it is a student-teacher ratio, and a student-teacher ratio is a budget line.¹

The report does not hide this. It recommends funding pilot projects, training instructors, creating dedicated institutional roles and sustaining a standing committee. That is, it knows that what it proposes costs money, and it describes how to pay for it in an institution that can. The problem appears when the document travels, because it travels without the footnote stating the price, and it arrives in systems where the question of cost is not an implementation detail but the entire question.

Eighty-seven and eight

For that journey there is, this time, a number on the other side. On 9 September, at Digital Learning Week in Paris, UNESCO IESALC presented La implementación de la IA en la educación superior en América Latina y el Caribe, a study by Arianna Valentini covering 200 institutions in 19 countries, with fieldwork between August and October 2025. The main figures are five and read better together: 87% of institutions already use artificial intelligence in some of their activities; 26% have a formal strategy; 18.5% have comprehensive institutional policies; 9% have formal evaluation mechanisms; 8% have a dedicated budget for the subject. On the individual side, 92% of students and 79% of faculty in the region already use AI. Adoption is higher in teaching (73.5%) than in research (57%), administration (34.1%) and outreach (20%), and the impulse comes from instructors, researchers and students: not from institutional decisions.

That last datum is what links the study to another from the same week. The survey UNESCO and ICOM published on 17 September, covering more than 400 museums in 90 countries, found the same structure with different percentages: 57% use AI, 55% have no written policy, and adoption is exploratory and staff-led. Universities and museums across three continents are doing the same thing. They adopt on individual initiative and govern afterwards, if they get to it.

Placed next to MIT’s report, the picture arranges itself like this. At MIT, the risk is that AI erodes a set of labour-intensive practices the institution still has and can pay for. Here, 87% have already adopted and 8% have the means to think about what they adopted, so the risk is not the erosion of those practices but something prior: that it is never discussed whether they should exist at all. MIT’s report warns of a loss. In this region, much of what would be lost has been running for years on the duct tape of instructors’ goodwill.

The cause that arrives second

There is one recommendation in the report that sounds preventive in the North and describes a settled fact here. The committee asks that undergraduate research positions (MIT calls them UROP) be preserved, and warns that replacing the novice researcher with AI agents would eliminate not merely some tasks but the entry point into a research community and a professional identity. It is exactly the argument this blog discussed two weeks ago , with a difference in timing that changes everything: here that door has been closing since 2023, and artificial intelligence did not close it. It was closed by the fellowships that were not renewed and the career-entry positions that were never opened.

The consequence of that difference is one of attribution, and it is not minor. When a technology arrives to occupy a hole that was already dug, the institutional account that gets written is that the technology dug the hole. If five years from now a university in this region finds that its research groups are not taking on young people, it will have a prestigious, international, MIT-published explanation ready to hand, and it will have to make a deliberate effort to remember that austerity arrived first. That matters because the two causes are corrected by different policies: against substitution by agents, a rule about which tasks are not automated works; against budgetary hollowing-out, no rule about AI works at all. Confusing them produces institutions that write impeccable protocols about a system that has run out of people.

And in the opposite direction, the same confusion enables the move worth anticipating now, while it still has no proper name. A university system without money, with rising enrolment and a student-teacher ratio that worsens every year, is the ideal customer for automated personalized tutoring, assisted grading and generated feedback. Each of those purchases is justified with the correct argument —the student who today gets no feedback at all will get some— and the correct argument is, moreover, true. The question is not whether that improves the starting situation. It is what gets locked in when the improvement is installed: if automated feedback becomes the system’s floor, the discussion about how many instructors are needed turns, permanently, into a discussion about licences.

The cheapest thing on the list

One way out remains that does not depend on the budget, and it is the part of MIT’s report that was least quoted. Among all the expensive recommendations there is a free one: that each course declare explicitly when AI is prohibited, when it is permitted and when it is required, and explain why, in terms of what the student is supposed to learn there. It requires no new positions, no licences, no platforms, no change to the assessment regime. It requires a paragraph in the syllabus and a departmental conversation before the term starts.

The 18.5% figure says that not even that has been done. This is the point where the diagnosis of budgetary constraint stops working as an explanation, because writing that rule does not cost money: it costs taking a position, which is more uncomfortable. As long as it is not written, the student who wants to do the right thing has no way of knowing what is expected, the instructor who wants to hold a line has nothing to back it with, and the only effective institutional policy ends up being silence, which in practice authorizes everything and protects no one.²

What remains open

MIT’s report does something almost nobody in this debate does, and that is why it is worth rereading: it treats the classroom as a site of AI governance and not as a place where decisions taken elsewhere get applied. That is the exportable part, and it is the part that survives the difference in budget. Cognitive surrender is not fought with surveillance software or with vendor-managed automation; it is fought with pedagogical labour, shared study spaces, open and auditable infrastructure, and forms of assessment that make the process visible and not only the product.

What is not exportable is the price list. And the question left open is which of the two will travel faster. A report downloads for free; an oral exam for a section of two hundred students does not. If universities in this region adopt the diagnosis without the investment, the predictable result is not a system that protects productive struggle: it is a system that knows exactly what it is losing and has no means to prevent it, which, in education policy, is usually worse than never having found out. It is worth having the diagnosis arrive anyway. But it should arrive accompanied by the question the report has no obligation to ask and that here cannot be dodged: who pays for the hours a student needs in order to have someone to struggle with.³

Postscript, 29 September

Two texts from the last few weeks touch the question this post left open and answer it in opposite ways, so it is worth setting them beside the report.

The first is by Brian Klaas, and published it on the 26th under the title “La gran brecha cognitiva” (the , “The Great Cognitive Divide”, dates from June). His thesis is that AI works like a cognitive gym: it amplifies those who already have habits of critical thinking and leaves behind those who hand the effort over to it. He relies on the two studies almost everyone writing on this cites. One, from MIT in 2025 and with a small sample, recorded reduced brain connectivity in people who wrote an essay with ChatGPT, along with difficulty remembering what they had just written. Another, from 2026, found that people who learn with AI assistance persist less and give up sooner. The final recommendation is individual: before each use, ask whether the tool is complementing your thinking or substituting for it.

The description is right and the conclusion falls short, precisely where the MIT report is better. If AI amplifies what the student already brings, the divide is produced not by each person’s disposition but by what each person brings, and that was produced by an institution: someone who marked, who gave feedback, who asked an awkward question in office hours. Klaas’s question can only be asked by someone who has already learned to think without the tool. Turning it into policy shifts onto the student the task the report assigns to the university. And in a system where 8% of institutions have a budget for the matter, it is exactly the policy that will be adopted, because it is the only free one. Klaas notes in passing that there is no Silicon Valley in Madagascar. In most of the region’s universities there is also nobody with the time to teach people how to use the gym.

The second is ’s piece on the mathematicians who hate AI and cannot quit it, from which I quote two lines as reproduced elsewhere, since the text is behind a paywall. Jared Speck, of Vanderbilt, recalls his training: “When I was starting out, I was given problems that people senior to me probably could have solved more easily themselves, or at least done more quickly. But they were investing in me.” It is the report’s argument about undergraduate research positions, voiced by someone who benefited from one, and it is Tao’s told from the other side. The second line is Noga Alon’s, of Princeton, about the problems AI has begun to solve: once it does, “there is no point anymore”. Both lines describe the same thing. A training problem stops making sense as a problem when the machine solves it, and that is exactly why it made sense to give it to someone: whoever assigned it knew the answer existed, and what they were buying was the person who would find it. That purchase is the budget line this post is about, and no brain-connectivity study replaces it.

It is worth not romanticizing the oral format, because it has problems of its own and not all of them are about cost. Oral assessment rewards verbal fluency and penalizes shyness, stammering and accent, and it is more permeable to assessor bias than anonymous grading; in systems with mass enrolment, moreover, a hurried implementation tends to degenerate into a three-minute interrogation that assesses nerves rather than understanding. None of this rules it out: it says the format requires written criteria, training and time — that is, exactly the same resources everything else on the list requires. The advantage of the oral exam is not that it is AI-proof —no assessment is— but that it makes the process visible, and that advantage is lost if it is adopted only as a defence. ↩︎
A clarification about what kind of rule works. The one that works is not “AI is forbidden”, which is unverifiable and which MIT’s own report advises against, but the one that declares which capacity is being assessed in each instance. “This assignment assesses whether you can derive the result, so you do the derivation yourself and you may use AI to check the wording” is a rule a student can follow in good faith and an instructor can hold without policing anyone, because whoever breaks it is left without the capacity the course was going to give them. Put differently: the useful rule does not protect the integrity of the submitted work, it protects the reason it was assigned. ↩︎
The reflexive objection that applies. This text is written with the same tools whose pedagogical effect it discusses, and in a blog that sits no exams. The difference between using AI to write this and using it to solve an undergraduate problem set is not in the tool but in where the struggle is: here the argument with the report is carried by prior reading, which took years and was paid for by a public system, and that is precisely the part a student who surrenders at the first difficulty will never accumulate. Which, if you think about it for two seconds, is not a defence of this text: it is the reason why in fifteen years there will be nobody left to write its equivalent. ↩︎