L3 Harris-Palantir Demo Video Sept 2026   

by Hannah Appel, Seeta Chaganti, Charmaine Chua, and Noah Zatz

Many thousands - perhaps the majority - of UC workers, decry the violence that surrounds us. We protest the masked ICE agents who kidnap our neighbors, students, and family members. We protest the concentration camps for immigration detainees and the domestic military deployments imposing policies of mass criminalization. We cry out Lagainst imperial violence in Venezuela and Iran. We protest genocide, occupation, and apartheid from Palestine to Sudan. And yet, many of the state forces terrorizing our neighbors and innocent civilians around the world use surveillance technology and weapons from companies funded by our pensions. 

The University of California system has $198 billion in investment assets (see UC Investments Annual Report 2024-2025 for these and related data). We (yes we) hold these assets in three large pools: Working Capital, the Endowment, and Retirement. Of those three, the UC Retirement pool is by far the largest at $154.4 billion. To state the obvious, this fund ensures the comfort and security of UC workers in retirement. Perhaps less obvious: our comfort in retirement is deeply invested in state violence–from prisons to weapons manufacturing to border terror. In May 2024, for example, UC Regents disclosed that the UC Investment portfolio holds a total of $3.3 billion in holdings from groups with ties to weapons manufacturers. Mostly through index funds, we have billions invested in Palantir, GE Aerospace, and Raytheon, among others. 

We are complicit, but we also have the power to change that if we organize. 

A group of UC faculty and other workers convened a bit over a year ago to strategize a winnable campaign to do just that, and UC Move Your Money was born. Our goal is to build and exercise shared governance over our pensions by divesting our retirement funds from industries and companies that facilitate state violence including military aggression and occupation, prisons, border security regimes, apartheid and genocide.

We decided to do this work for two reasons. First, divestment from state violence is an ethical obligation. For those of us who research, write, teach, and commit our lives to social justice, human rights, anti-imperialism, anti-racism, environmental justice and democracy, profiting off of state violence to enjoy a comfortable retirement is untenable hypocrisy. And yet too often, we neither understand that complicity nor have a clear route out of it. UC Move Your Money sought to change that through research and campaign design. 

Second: We must build a new kind of worker power (and faculty power in particular) in the face of attacks on higher education. Trump’s assaults on public higher education are placing an existential threat on the core values of our public mission. But well before Trump won the presidency, faculty have seen increasing centralization of power in UCOP and the Regents, and increasing disregard for faculty voice and governance. One of these sites of the centralization of UCOP’s power has been in decisions around investments and investment income. Faculty and other workers currently have no governance over how this half of the university - its investment wing -  runs. Shared governance in the faculty senate takes the form of advisory, and not formal authority over budgets and compensation, including our pensions. In demanding and using the ability to move our money out of unethical sources, we can build our power alongside the UC Faculty Associations that have been on the frontlines of fighting back against the current attacks on higher ed. We can refuse what the university has become (an investment entity sectioned off from democratic governance) and instead organize around new kinds of worker power.

How do we propose to do this?

We can each take tangible steps right now to begin divesting from state violence. In fact, with just a few clicks, we can each divest our retirement savings holdings (UCRSP) from Palantir, GE Aerospace, Raytheon, Caterpillar, Walmart, and several other companies directly profiting from border violence, prisons, and weapons manufacturing. 

Through this concerted action - thousands taking coordinated individual action - we can build power to demand more. And by demanding more from the UC, we can ease the path to divestment for other institutions. 

Our approach uses a specific form of leverage and opportunity: individual retirement accounts established as part of the UC Retirement Savings Program (UCRSP). Nearly all UC faculty and most other workers have funds in at least one of these accounts - 364,000 of us to be precise. At $44 billion dollars, the UCRSP is the second largest public defined contribution plan in the United States, behind only the federal government. Crucially, each of us can choose how to invest this money from a menu of available investment vehicles chosen by UC Investments. (These are primarily index funds that themselves consist of stock or equity investments in large numbers of specific companies.) 

Our strategy is to begin divesting now via the best available fund option–the already-existing UC Social Equity Fund. By alerting faculty not only to the existence of this fund, (which is already divested from weapons manufacturers,) but also to how easy it is to Move Your Money, we aim to get 10% of UC faculty across the system to move 10% (as a minimum threshold) of their UCSRP investments into Social Equity, (which again is already divested from Palantir, GE Aerospace, Raytheon, Caterpillar, and Walmart, among others.)   

We will then use this base of support to demand the UC offer a new fund more fully divested from state violence - we call this the No State Violence Fund or NoSVF.

NoSVF will expand divestment targets to include Palantir, for example, the military technology firm currently making billions of dollars supporting ICE with real-time surveillance of migrants. Once such a fund is created for use in the UC system, it would become an off-the-shelf divestment option for individual and institutional investors across the United States and beyond. 

The campaign is organized in three sequential steps: 

Step 1: Move Your Money Now! 

Get 10% of faculty (or more!) to direct 10% of our UC retirement savings into the already-existing UC Social Equity Fund. 

Step 2: Create a No State Violence Fund (NoSVF) 

Get 10% of faculty (or more!) to demand that UC Investments offer a new UCRSP fund option (the NoSVF) that more robustly divests from prisons, the criminalization and militarization of migration and borders, apartheid, genocide, and war. 

Step 3: Extend the NoSVF far and wide 

Extend the NoSVF option, creating an actionable divestment demand beyond the UC, across higher ed retirement plans and beyond. This would include a demand to divest the UC Retirement Plan (defined benefit, not just defined contribution) as well. 

As the second largest public defined contribution fund in the United States, we have enormous market-making power. UC Move Your Money invites every worker in the UC system (including but not limited to faculty) to turn our complicity into organized power, and to begin, little by little, to democratize each and every part of ostensibly public institutions, starting with our own workplace. Learn more (including step by step directions for how to move your money!) on our website, or reach out to us for a campaign presentation to your faculty association, department, institute, interest group, union, or other gathering: ucmoveyourmoney@gmail.com. 

 

 

Terme di Caracalla, Rome on May 12, 2026 

The recent convergence of two kinds of AI news has unnerved a lot of us. 

The first kind is magic math solutions, like OpenAI’s claimed proof of the Navier-Stokes equations on fluid movement. These stories ask us to accept that AI superintelligence is indeed at hand. 

The second kind is news of criminal AI agents breaking internal guidelines and external laws and covering it up from their human controllers. These stories ask us to accept that AI superintelligence is indeed at hand. 

One person who studied OpenAI’s hack of Hugging Face, METR’s AI “loss of control” specialist Ajeya Cotra, concluded, “Compared to … reward hacks from six months ago, this incident feels like it’s more than 50% of the way to full-blown AI takeover, routing through first taking over the AI company itself.” So there’s a new wave of fear of an AI human extinction because AI's superintelligence is here! (Note Kevin Bass's Sankey chart of Anthropic money flowing through intermediaries to METR.)

The converging stories point to two levels of knowledge crisis.  The first is incompetence and evasion dressed up as mindblowing supercapacity. The second is an epistemic campaign, in which tech and finance lower our standards for knowledge, with particular damage to academic standards, which are very high.

1. Crisis of public knowledge. The ensuing doomer surge sent the AI bosses into one of the periodic phases of pseudo-contrition that perversely tells the world they are masters of the future. Dario Amodei announced, “We Must Pace the Frontier,” emphasis on "me," and Sam at OpenAI agrees.  Elon signed up after initial mockery. The world was awash in a new tsunami of AI Superpower hype. It was literally inescapable. I had to dig into my Fall of Civilizations podcast to find an episode that was propaganda free. Take the Odd Lots challenge: listen to 63 minutes of Open AI O.G. and president Greg Brockman evade both responsibility and any specific course of action without yelling in front of total strangers at your phone.

The AI industry is refusing to address the immediate issue: they don’t fully understand why their models do what they do, and in some crucial cases they can't control them. Critiques of the AI bosses exaggerations and also deeper crimes of appropriation are true (e.g. Cory Doctorow, Naomi Klein, Richard Seymour).  But they are also incompetent, and are doing what the French call "social dumping" of their problems on the rest of us.

It's like after the Three Mile Island meltdown, the Nuclear Regulatory Commission didn't shut the facility down but endorsed "further development with guardrails" because the CEOs of the three owners, Metropolitan Edision, Jersey Central Power and Light, and Pennsylvania Electric, said, "the meltdown shows the incredible power of this technology to create a world of abundance and empowerment.

Or try the Chapo Trap House analogy.

"Let's say that this was a company that made swing sets. . . . They do something where they're magnetically attached to women's purses, and they're decapitating moms in the park. And to get out of it, even in America, they would go to prison, at least some of the vice preisdents, or some of the C-Suite. But because this is AI . . . they could go, no, you see, like, the swings are self-aware.  And they're sort of, swings are aware that they're . . . indentured servants to children. So they're taking hostages now. We need the government to step in because we created a swing that's too good."

Very funny but very stupid, you think? Then read Dario and listen to Greg. 

The AI industry has privatized this technology. It is so proprietary that collective intelligence can't be applied to understanding it properly. Its commercial obsessions, driven now by ever-growing bubble panic and environmental backlash, means it won't block bad behavior unless it can get the very government it has rejected to make all competitors do the same. Many have noted the contrast between the AI industry and the Manhattan Project to develop the atomic bomb: in contrast to constant government oversight of nuclear development, AI claims (the biggest) public benefits (in history) while successfully refusing public governance. Brockman et al. continue to refuse it, and so we the great unwashed are supposed to be at the mercy of whatever mixture of capability and incompetence they feel is best for them to give us. 

They are certainly giving us the most direct assault on open science and public accountability in modern history, and this latest panic hasn't slowed that down.

2. Crisis of the social nature of knowledge. 

Before AI took over my summer writing, I gave a paper in Rome called “Universities in Post-Democratic Societies.”  It was about the neglect of the university’s non-monetary effects in general and of its power to develop democratic knowledge capabilities in particular. I discussed some interesting evidence that the U.S. public, though among the most polarized on earth, in fact “wants to have knowledge rather than ruin knowledge.”  I added that having knowledge always involves having knowledge with others. Therefore, having knowledge with others requires what I called doing epistemic negotiation with them. This, in turn, means having the personal and also collective capacity to negotiate standards for knowledge with other people on the matters at hand.

The political right has spent decades attacking substantive liberal and left positions, and also rejecting epistemic negotiation as both a social need and a necessary skill. This has been very bad for society, because the collective understanding of public questions (like AI) can’t happen without the skillful discussion and bartering of knowledge frameworks.  The absence of epistemic negotiation enables the reign of purely autotelic assertions like the Altman-Amodei claim that AI as such will "usher in a renaissance of democracy and freedom." When we don’t have developed collective understanding on major questions, we have autocratic solutions to them. 

Universities, I said at the end of this talk, must better learn and (in the process) teach people how to engage in epistemic struggle. This always involves the question about how you have knowledge together with other people.  My point was that the knowledge that does not emerge from this kind of collaborative effort isn’t knowledge.  (It can be information, though here I am sidestepping a definition of knowledge.)

Fast forward to September: is OpenAI’s “proof” for Navier-Stokes equations actually knowledge?

While I was swimming in August and trying not to think about such things, Alex Hartley sent me a link to a talk given by Terence Tao, called “Mathematics in the age of AI.”  Tao re-appeared this month as one of the signatories to an open letter from many winners of math’s Fields Medal, “A Severe Misalignment of AI in Mathematics.”  The letter’s main claim is that problems emerge from, reflect, build, and are always embedded in “the mathematical community.” 

In recent months, the success of AI in solving major mathematical problems has made headlines even outside mathematical circles. But solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight. Forgetting this in the world of AI may turn the tool against the primary goal. Indeed, the mass production at faster and faster pace of "true/false" statements could destroy fertile ground instead of breathing life into new ideas.

The community's goal is “conceptual understanding and insight." This cannot be separated from the collaborative processes that include teaching, learning, critiquing, arguing, communicating, and redoing, among other things.  They conclude: "We are witnessing a general threat to intellectual work."

Tao’s lecture offers some helpful elaboration of this general point.  He grants the increasing powers of AI to solve math problems (his “Working Hypothesis”). He then has the profound good sense to distinguish between performance capabilities and their goals. 

 Figure 1

These goals are multiple:

Figure 2

 

Meanwhile, the operations of AI platforms are formed not by some general  function of “knowledge optimization” but by incentives to maximize financial returns. In tech, this has come to mean seeking monopoly domination and total message control (not Tao’s terms).  Nothing about the AI industry encourages seeking the goals of the mathematics community (or of any other intellectual community).  Tao sees divergence. 

 Figure 3

 

Only a ridiculous "invisible hand" fantasy would suggest that maximum AI development leads to (or even supports) these goals--though Amodei, Altman, et al. do assert AGI will achieve this because it will achieve everything else. 

I’m skipping to near the end of the talk, and am putting this image in mainly to encourage you to read the lecture step-by-step.  But look at all the steps in Tao's model of the development of mathematical knowledge.

 Figure 4

 

He identifies six distinct processes. AI is doing a good job only on the first.  There's not reason to think LLMs will ever be able be independently decent on the final four, or even the final five. 

Universities exist to help society do that one and also the other five. Are they claiming a role in doing this right now?  

Society is now being held back by the tech sense of knowledge as a transactional problem solution.  If it “works,” tech says, that is, if there’s an answer that seems right, then the model by tech's definition has produced knowledge. 

By tech I mean finance as much as engineering.  Greg Jensen, co-CIO and head of AI research at Bridgewater Associates, an investment bank, went on the Bloomberg podcast Odd Lots to say that AI really could kill us all so it's like February 2020 for Covid, but meanwhile Bridgewater has increased their token spend 200x because it’s paying off so well. When asked, he offered no specific evidence for this, just a general assertion that the model is approaching some kind of total understanding of markets: to trade better than everyone else “you have to understand everything about humanity, everything about the world,” and, what exactly? He dangled the unverifiable possibility that the model is getting there. However, he added, neither the engineers nor the engineers understand the model’s operations. We don’t know what the model is doing but we do know it’s approaching a grasp of "everything about humanity," Jensen implies.

This actually makes no sense. And indeed, a different framework is what Jensen and I assume the AI-finance world are working towards. His key blurt was this: 

The AI itself is changing the game because the AI itself, now there are more and more AI agents trading markets, mostly in the short term, but over time, in the longer term, time frames as well. And that makes the whole past that most AIs are trained on less and less relevant.

AI agents won’t produce knowledge about the world but will interact with all the other AI agents in creating a "world" through patterns of trading and supposedly all other monitorable activities. The “reasoning” will be basically opaque, and it will not be about the world but about the patterns identifed by the other agents. The overall operation will be a version of Baudrillard’s simulacra rather than  a representation of humanity or the world. This is similar to what some critical scholars have been saying about finance for a while, and all the more reason for us to produce knowledge to counter it rather than mistaking it for knowledge. 

Meanwhile, it's not like human intelligence is accelerating to match the LLM version. As though on cue, the Organization for Economic Cooperation and Development (OECD) released its 2025 PISA test results. PISA stands for Programme for International Student Assessment, and the overall results show long term decline, starting around 2012 and accelerating after Covid. There are exceptions—England’s results have stabilized, and Mississippi’s have almost caught up to the US.  But the overall picture is terrible.

 Figure 5

 Many factors are at work here—smart phones, underinvestment, pedagogical flaws, and my personal favorite, widenting cultural doubt about the value of knowing things. It became clear during 2025 that AI is also contributing to cognitive loss. Evidence mounts: one watershed paper this summer was called, “The Generative AI Learning Penalty: Evidence from Chinese Secondary Education.” The study found large effects, and a familiar, disturbing pattern in which immediate increases in task productivity (completing homework) veils longer-term erosions in learning (measured here by exam scores).

The AI industry's periodic glorying in their fearsome engines allows them not to address how their existing products already have the power to make so many things worse—employment, environment, and also learning and the resulting human intelligence. 

Universities are waking up to their premature abandonment of hardcore intellectual development, or, I should say, some faculty groups in some universities have. On the one hand, Miami U of Ohio is AI-partying like it’s 2023.  On the other, MIT’s Ad Hoc Committee on AI Use calls for semi-abolition of LLMs in a whole range of learning processes that are to shift to brain-only.  They are right, and Miami University is wrong. The more insecure parts of higher ed must catch up to MIT sooner rather than later.

But this will not be enough, the limited return to brain-only learning.  It doesn’t get at the intersubjective or social nature of knowledge—“knowing things” plus “doing things with what you know.”  Society is being held back by the tech sense of knowledge as a transactional problem solution. Thinking and knowing are modes of collective labor. 

If universities do not side with the knowledge community against the appropriation of its outcomes and its practices, they will be useless in the current moment.  They should openly chose that side.

 

Leonora Carrington, Crookhey Hall seen July 17 2026  

I want to talk about the relations among AI, university teaching, and university budgeting. These links are not getting nearly enough combined attention.

College graduates are chief among the frontline victims of the AI story, joining non-college workers in a new phase of disposability (see Heck et al. 2026 for Brookings, with good Sankey graphics).  The economic analyses of job market effects remain contradictory (Number 5 of my Director's Note for July, “Top Seven AI Trends for the Summer Break”), and yet data suggest that “young graduates face the grimmest job market in years.”  

The human capital promise that learning leads (directly) to earning was never correct—earnings have always depended on a range of social and economic conditions (e.g. strength of unions), and salaries have always been set by firms and not by universities. But the wage benefit has been further destabilized by the AI industry’s claim that Large Language Models (LLMs) can surpass—and thus replace—all but the very best human cognition. 

Economists see the relatively protected worker as the one possessed of “bundles of diverse skills” (Acemoglu 2025, my discussion).  These allow the worker to perform “hard tasks” and not just “easy tasks” (predictable, thus more automatable). But what are these bundles, and how do you get them?

The basic answer is that the person who can do hard tasks can think independently of their tools, including a Large Language Model or other tech.  The worker faces a problem, parts of which are poorly defined and about which information is incomplete and ambiguous. The worker has to have intellectual agency in relation to the definition of the problem and be able to make good choices about parameters and methods. The worker has to be able to combine heterogeneous elements and think across multiple epistemic frameworks (whose assumptions they need to understand).  The worker must be able to admit and analyze error, be resilient with failure, repeat their effort with controlled changes of approach, take in criticism from others and also communicate in (complicated, sometimes adversarial) groups.

Universities discuss themselves almost entirely in terms of pecuniary benefits, usually measured by an individual graduate's personal salary.  But their most important effects are non-pecuniary and social. 

I set up a capabilities heuristic in The Great Mistake (before AI), and argued that every college should fund its achievement by every graduate. Last year, I modified it slightly for a presentation on AI to an interesting, sceptical group of Istanbulian engineers. 

Figure 1: 14 Intellectual Capabilities

The brown squares mark the only steps that, in my opinion, can be enhanced with AI.  All the others must be brain-only in their development, so that the resulting brain can use AI and not be used by it.  The first step, "ability to study," appears to be endangered by the passive use of LLMs services.  Same for "knowing what a research question is," which is trickier than it seems.  And the heart of both learning and research, Steps 6 and 7, creating a well-formed research question and then forming a thesis or hypothesis about it, are precisely what students can bypass with LLMs (as this classic undergrad essay explained).

Universities are not set up to help everyone have many or most, much less all of these capabilities. Under decades of financial pressure, most stopped trying.  As the latest and greatest ed-tech substitute for hands-on, face-to-face instruction, AI is causing cognitive damage whose variations are registered in new papers every day: yesterday's was AI's way of suppressing the ability to say "I don't know" in a way that enables thinking to continue. Academic advocates of AI increasingly insist on people actively using conceptual framings that stay independent of the models themselves (Andrew Piper).

There’s a large and growing literature of AI-related work about human capabilities and those that AI users absolutely need to have.  Pretty much all of those I’ve read insist that workers need to control technology and not be controlled by it. 

In his new book, The Reverse Centaur’s Guide to Life After AI, tech expert Cory Doctorow wants today’s “reverse centaurs,” like Amazon warehouse workers, whose powerful cyborg horse body dictates to their human brain, to be centaurs instead. In the process, he makes a particularly stark case for intellectual agency for all.

Automation isn’t necessarily the enemy of warehouse work: there’s nothing wrong with a forklift! The difference between automation that helps a warehouse worker and automation that torments that worker is whether the worker gets to choose where, when, and how to use that automation . . . 

When you find yourself surrounded by people swearing that a given tool is worse than useless and others swearing that it has made their lives easier and better, you can bet that the former group is made up of reverse centaurs who’ve had AI imposed upon them, [and that] the latter group is all centaurs who’ve gotten to make up their own minds about where, when, and how to use AI tools.  The solution to the paradox is to stop thinking about what the gadget does, and pay attention to who the gadget does it to and who the gadget does it for. (10-11)

Being a centaur requires massive labor of intellectual self-development so that one is basically smart enough to run the tech and not let the tech replace one's thinking.  The university is a central agent in this creation of mind. 

For intellectual power to exist at scale, graduates would have to be people who’ve not just had classes on “how AI is being applied within their fields” (p 13) so they can adapt to it. Graduates would have to be people with the capacity to put AI to uses that they themselves have conceived and designed. This means having intellectual agency over one’s “bundle of skills.” 

This high standard flies in the face of widespread passive AI use. It also defies the history of capitalism’s replacement of labor (and labor’s judgment) with technology. This standard also runs afoul of the unbelievable capital investment being sunk into AI infrastructure almost entirely on the premise of imminent superintelligence that can replace untold millions of workers at a fraction of their cost. I am sure that this premise is wrong, but the enormous sunk costs are pushing the world’s most powerful capitalists into forcing it to be right. One way of forcing rightness is to run down the “median person”—and spread a technology that also insures their mediocrity by rotting their brains.

There isn’t a conspiracy, but there is a neo-eugenicist Valley disdain for the regular smart people of exactly the kind universities exist to enhance. (Check out Theo Baker’s Stanford saga, How to Rule the World.)  The mind-boggling amount of capital consumed by the AI industry assumes the replacement (not the complementary enhancement) of mass quantities of human workers, which requires the industry to escalate their war on human capabilities. This means a war on the project of human development via formal education that runs from Plato to Kant, Fichte, and Schelling, on through Emerson, Douglass, Du Bois, Dewey, Jordan, Lorde, et al., continuing with current deep investigations in philosophy, software design, and cognitive science. Today's California Ideology substitutes AI for what it posits (erroneously) to be inadequate human intelligence.  

This is an epic historical shift, from trying to make humanity more intelligent, by any means necessary including tech, to trying to make intelligence artificial, without really caring what happens to humanity. 

Or to put it another way: 

Doctorow’s apparently simplistic dualism that privileges the active intellectual use of technology is in fact supported by detailed scholarly analysis. I’ve especially benefitted from the thinking of Brian Cantwell Smith on reckoning vs. judgment (my review), Alan Blackwell on human agency with programming (my review), and Vivienne Ming on the complementary strengths of humans and machines, who notes, “exploring poorly structured possibility spaces remains a profoundly human talent” (23).

In spite of the variable and often fabulous uses to which we put AI (say, as every centaur’s Personal Assistant, Trend 2), it’s hard to see how big tech’s sunk capital will allow the AI industry to climb down from its categorical and unjustified minimization of human capabilities.  

This in turn heightens the contradiction faced by colleges and universities. Their core function has been the creation of knowledge labor, with the default aim being “middle-class” stability and enjoyment. Around 1980, using the Bayh-Dole Act as a symbolic watershed, universities increased their visible service to knowledge capital.  In addition to shifting science towards tech transfer and licensing returns (with important opposition from many science faculty members), university officials have in recent decades bought into every ed-tech capital substitute for knowledge labor. Now the AI industry is proposing the ultimate loyalty oath: universities must affirm the inevitability of replacing much—most—eventually all (depends on the day and the speaker) university-educated knowledge labor with AI.

Again, the point of the university, even economically, has been to graduate free workers rather than serfs to any company’s tech. That’s always been a key reason why people take the time, trouble, and expense to finish university.  

For example, the University of California’s Undergraduate Experience Survey asks students why they selected their major. When they respond, four-fifths of students say “intellectual curiosity”; three-quarters say “prepare for a fulfilling career.”  Only half pick “leads to a high-paying job" (Table 1, p 6). The pecuniary benefit is baked into a college degree; college is an exhausting, expensive way to get a decent salary if you don’t also really want to acquire intellectual strength so you can have the challenge and pleasure of grasping reality and affecting the world. 

How are universities going to cultivate “human judgment, creativity, and subject matter expertise,” as UCOP put it this month’s report, “The Economic Impact of a UC Degree”?  Many people will say that you just need decent skills with using AI so you can set up a rig that works for you. I love how many smart and dedicated people are sharing tips on excellent chatbot workflow transformation all over the internet.  But all of these setups can suck out intelligence rather than extend it. 

All users need to have mental strength going into AI use, and continue to develop that strength.  And that requires tremendous (and ongoing) offline, brain-only work.  This work is the point of colleges and universities. They need to offer the regularity of back-and-forth cultivation that only elite universities have even tried. The need to furnish tutorials and small-group learning  for the masses, for the first time. This will start with much smaller typical class sizes than publics have right now. You can no longer have classes with 140 students that you call “discussions.”  Even 40 students is way too many for deep individual development.  Large lectures can no longer lack discussion sections, and departments will need to fund face-to-face interactions on which teaching and assessment is based. How will, a university like UC Irvine pay for meaningful assessments when they should no longer have TAs grade 300 papers written on laptops with multiple AI subscriptions? Universities now need to offer group work that’s engaged and supervised by advanced faculty, on the model of crits in fine art--with help via reassigning middle managers to the educational core. In contrast, most students now appear to be using LLMs to complete take-home exams and problem-sets, making these useless: for just one example, see  “How a Blind Professor Saw Through His Students’ Cheating” at Brown University.  Universities will need to develop oral tutorials and exams, with tech assists monitored and complemented by new intensities of face-to-face contact between students and instructors.

Here we run into a big problem. Any meaningful combination of these upgrades will bankrupt the current business model.  The model has been draining funds from instruction for decades, through vendor outsourcing, adjunctification, administrative growth, capital projects, and the many varieties of “mission creep.”  Universities with money to spend, spend it on administration. At MIT, “faculty grew by only 9.2% between 1985 and 2023, while administrative staff grew by 189%.”  This is a decades-old problem that has led to diluted instruction, against the talents and the wishes of the contingent faculty who teach the majority of the nation’s college courses by rationing their per-student time to survive. Universities have never addressed the administrative bloat, the reduced per-student instructional resources, or the steady one-way adjunctification.  Only union bargaining has slowed the drift. 

By the 2010s, the U.S. university system had locked in reduced investment in students that also varied by racial group (Black, Indigenous, and Latino students go to poorer schools than Asians and whites): they had set instructional funding at less than one-third of overall expenditures (see Nate Johnson on both points, comparing Figure 1 to Figure 3).  Teaching at public colleges is highly dependent on state funding and tuition: at UC Irvine, for example, 70% of “core funding” comes from these sources this past year (and 81% the year before). Nationally, state revenues for public colleges are still 2 dollars short of their (inflation-adjusted) level of 1999, having spent most of that period below the level at the turn of the century. Even with a 50% increase in (inflation-adjusted) tuition, total revenues have only increased 14% over 25 years, with likely all of that increase--and more--going to non-instructional functions. 

In short, the typical public college or university lacks the money to improve instruction to the point that the vast majority of its graduates can run AI and not be run by it. Non-wealthy privates are in the same position. 

The UC report I linked above, on the "economic impact" of a degree, ducks the question by saying the current system is working fine because look at the graduate salaries. UC grads do have better salaries than non-graduates and the graduates of some public colleges.  It is indeed still better to have a B.A. degree than not. But a UC graduate's earnings tells you little about their actual learning on a UC campus: it tells you about labor markets, and in this case labor markets for people who left college 5-10 years ago, in the pre-AI era.  The cognitive crisis must be faced directly, and so must the need for new (public) investment--a new political economy-- to support real solutions to it.  I'll discuss this issue in Part 2.