Santa Barbara, CA on October 15, 2026   

Tenured faculty positions historically marked their university as important, even elite. That era is now over, but university officials are generally misreading what that means. 

Many universities have been using educational technology to replace or deskill instructors for years. AI has now given research universities the idea that AI can replace or deskill researchers.   Their faculty are often encouraging this idea. In her superb piece, “AI was great until it was not,” physics grad student Nia Robles describes a Harvard physics corridor in which grads and senior professors work with their models rather than each other. Prominent UC Berkeley mathematician Richard Borcherds told the Daily Cal that he no longer grades problem sets because they represent AI work not student work.  “’If AI continues to improve at present rate, then, in a few years, there’ll be nothing human mathematicians can do that AI can’t do better.’” In a future where all research is done by OpenAI-Anthropic models or by managers using them, the

But back in reality, if you want your university to conduct research in a time of private science you need more tenure-line faculty, not fewer.  If you want to help students to have powerful human intelligence on AI Planet, you need more tenure-line faculty, not fewer.  And yet these truths are no longer self-evident.

In fact, tenure has never enjoyed a national consensus. Ten years ago, David Laurence’s foundational research showed that 40% tenure density defined only a quarter of U.S. universities. In addition, “Institutions where 70% or more of the faculty is tenured or on the tenure track are reached at . . . the last 136 (3.2%) of the 4,279 degree-granting institutions.”  In 2019, the University of California “general campuses” had a 76% tenure-track (TT) rate (except for the Arts and Humanities, where TT density was just above 50%). Nationally, tenure has fallen from two-thirds to one-third of the faculty. High levels of tenure were correlated with high status, which in my view is mostly why governing boards endorsed it, but undoubtedly also with high research output and high levels of the intellectual development appropriate to those students who would become “the future leaders of the society.”  Tenure had a large non-pecuniary and social value that was weakly theorized and never really explained to the wider public.

Many people are now thinking that this conceptual and social value can be generated by AI instead of intensive cognitive labor under the stable, supportive working conditions that tenure can bring. This conventional wisdom about AI is undermined by at least one of the AI companies: in a new report, Anthropic claims that the most valuable quality in their workplace is intellectual “autonomy and judgment.” That is best created by active learning with continuous study both alone and in groups (as Robles nicely explains). And that requires de-massification – more small-scale intellectual interactions—not further automation and offloading of thought.

At the moment, universities are going in the opposite direction. Dissertations in the humanities disciplines—which have taught autonomy and judgement since antiquity-- have already fallen by half, according to a new report, “Humanities in Crisis.” Even wealthy universities have paused or scaled back their doctoral programs in response to declines in faculty hiring.  Meanwhile, the tech-mogul marketing claim that “we are pretty much in the superintelligence singularity”  has sapped the general will to increase human brainpower. This falling confidence in mental development afflicts university people as much as, if not more than, anyone else.

It’s in this context that the University of California Faculty Association (formerly CUCFA), has launched a systemwide campaign called “Reinvest in the Mission.”  A key demand is “Hire More Faculty”—tenure track faculty in particular.  I don’t know of other campaigns to add a set number of tenure-line faculty by a specific deadline, and this one should be tested and developed as a national model. 

UCFA has published a white paper, “Broken Promises: How UC’s failure to hire faculty is undermining higher education.” They have set up a Town Hall series for the fall term. The first three topics are, “The Challenges Facing Teaching Professors (October 28^th), “AI, Overwork, and Shared Governance” (November 9th), and “Protect Research Budgets (November 12^th).  These are essential topics where faculty’s impact on policy depends on strong participation.  If you teach at UC please go to one of these. Please offer as much input as you can stand in whatever mode suits.

I’ll come back to the UC case and the UCFA report but first want to note (again) that we are in another period of enormous danger for education as such.  Fifteen years ago, Massive Open Online Courses (MOOCs) failed to match face-to-face course instruction in developing these mental powers with technology. But MOOCs exposed the weaknesses of passive learning and of the weak or non-existent sequencing of skill and knowledge development in the massified university. Now AI advocates have raised the prospect that models can teach better than humans and also just do the cognitive stuff humans may not need to do anymore. Even strong defenders of the obvious and essential need for the best possible human cognition do find AI very useful for learning. Minor flubs of interpretation can lead to massive disinvestment.

Another interesting feature of the Anthropic paper is that their frenzied use of AI agents has not reduced their human workforce.  They are hiring fewer entry-level people—very bad for recent college grads—while tripling their workforce.  AI-intensive work is labor-intensive.  It is cognition-intensive, let’s say.  Teaching the power of cognitive intensity is also labor-intensive. It requires intensity of teaching labor—and learning labor, meaning students need to be studying, practicing, and collaborating, not working at Home Depot 25 hours a week.

I won’t say more about this here: I wrote about the links between tenure-track faculty and cognitive development in July (“Invest or Die”); that Note included my updated chart of 14 intellectual capabilities that university graduates should possess.  Now it’s true that more TT faculty is our guild preference. But the preference is grounded in the labor required by the cognitive development that universities are supposed to deliver. If universities want to walk the talk of AI-era human learning, they will need to support the face-to-face curation of individual and group capabilities by faculty who have the time, the working conditions, the workplace authority, and the combined scholarly-instructional skills to make this a reality.

This gets us back to the UCFA report: their survey suggests that UC faculty generally don’t have those working conditions now.

Figure 1

 Figure 2

 

 n other words, the permanent austerity on the campuses that is built into budgeting by UCOP and other finance offices hurts faculty productivity and faculty morale. 

What has been the official response? In the late 2010s, UCOP acknowledged that growing student enrollment had been handled by growing the non-tenure track (NTT) faculty.  In 2019 and 2020, they announced “UC seeks to grow non-recall LRE faculty by 1,100 over the next four years” – that is, by 2022-23.  Covid happened, freezing hiring for a year.  But they then delayed hiring 1,100 TT faculty (which would increase that group by 10%) from 2023 to 2030.

What has UC been doing instead?  Not growing any category of academic personnel.  Lecturer numbers are slightly lower in 2026 than in 2021.  Clinical faculty are below 2021 levels. Graduate student employees (teaching and research) are down 5%.  All academic employees together haven’t budged in 5 years (at just above 50,000 for the system). Student growth also slowed—from a cumulative 14% in the second half of the 2010s to 2% in the first half of the 2020s.  But the impacts of unsupported growth are now frozen in place: oversized classes, reduced faculty research and teaching time, reduced personal contact, more administrative duties, etc.

What did keep growing? Non-academic hiring. That went from 118,667 in April 2021 to 152,901 in April 2025 before dropping back to 145,172 in the following year, for accumulated growth of over 22%. The UCFA report has a gripping, gruesome chart.

Figure 3

 

This is a university, so naturally it spends its payroll money hiring non-academics!

This is of course absurd, and damaging to the academic mission.  In response, UCFA wants to set up “An elected, representative faculty committee to negotiate with UCOP” that will “return to the 25.5:1 ratio.”  I certainly endorse the committee and the goal. Shockingly, the Academic Senate has never set a student:faculty ratio target based on their expertise, even as typical class sizes soared far beyond today’s averaged ratio of nearly 30:1 (Figure 2) and UC fell far behind even the public universities in its comparative group (SUNY Buffalo is about 24:1, and the University of Michigan-Ann Arbor is 18:1; Figure 4).

As we’ve seen across many posts, this perverse neo-massification on the general campuses is built into the funding model.  UCOP finance has constructed a political economy that supports the medical operations more the campuses. The plan is Blue-and-Gold Brostrom Borrowing, meaning borrow (and fundraise, which entails more borrowing) for capital projects that support clinical ROI and technology transfer, while condoning inadequate tax-based state allocations for teaching and research on the campuses (see Notes like “Selling,” “Stop Hurting,”  “Peak UC?” etc.).

UCFA’s precursor, CUCFA, linkd educational need to the political economy of educational revenues: it developed The $66 Fix: Quality Affordable Higher Education. UCFA may want to start from a different place.  This time the plan(s) will need to evolve into a systemwide faculty movement. A good start on that is the Town Hall series.  All types of UC academic workers should go!

 Golden Gate on October 7, 2026

This is the text of a short talk I gave in a UC Berkeley Mellon Sawyer seminar last Friday. The seminar's overall title is “Neutrality: The Problems and Challenges of the Public University.” Friday’s topic was “institutional neutrality,” and the reading included the Kalven Report (1967), discussed below, the Woodward Report (pre-demolished by legal scholar Robert C. Post), and UC Berkeley’s Senate report on academic freedom (2026), mercifully left to one side. The other guest was Joy Connolly, president of ACLS, who said inspiring things about universities as generative sites of intellectual freedom that I hope she publishes at some point. The discussion was truly interesting and also heartening, as participants were less wary of the potential public popularity of academic freedom and of democratized governance than has been my past experience.  It was also really fun, and reminded me that a far more open university, where governance includes batting things around in an atmosphere of Tomkins-like mutual interest, would also be very fun to have.  Education is really very fun. Happy thank yous to the participants and the organizers and the staff still doing the paperwork.

  Cambridge, England on September 26, 2026  

With everything going on in the world, from war to AI grasping at everything, it’s more important than ever for universities to have the money to think and work independently.  That’s not where they are or where they’re headed.  This is particularly clear in my case study, the most recent University of California Board of Regents’ Finance and Capital Strategies meeting.  The finance model doesn't bring in enough money to support the academic core, as officials now admit in passing.  And this month, they suggested that researchers help fund general operations by giving more research control to venture investors. 

Sounds really strange, I know, but that's what I heard, as I'll explain.

Previous posts have confirmed that under current conditions, UC (and others)

     L3 Harris-Palantir Demo Video Sept 2026   

By: Hannah Appel (UCLA), Seeta Chaganti (UCD), Charmaine Chua (UCB), Noah Zatz (UCLA)

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 against 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.

 

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.)

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.

Banff, Alberta back country on June 5, 2026  

by Dennis M. Hogan, Lecturer on History & Literature, Harvard University

This is the third of the talks from the CHCI panel, "The Humanities We Can Build Right Now" in Banff, Alberta. The others were Chris Nealon’s and Asheesh Kapur Siddique’s is here, along with my introduction to the panel.

∞∞∞

This paper is a response to two trends I've noticed over the past couple of years.

Landing Santa Barbara Airport on Jan 30, 2026   

 by Charlie Hale, Dean of Social Sciences and Professor of Anthropology and Global Studies, UC Santa Barbara. This is his response as a scholar, whose writings were called into question by the Report.  The response does not represent the views of the dean's office, or of the university. 

Chris here: I'm posting a set of answers to a reporter’s questions from Charlie Hale, one of the authors cited for criticism in the Vanderbilt-Washington University “Report on the State of Scholarship in the Humanities and the Humanistic Social Sciences.”  The reporter, Emma Whitford, wrote a good piece in Inside Higher Ed about the scholars who were cited as examples of the alleged decline of qualitative scholarship. None of the scholars she’d reached had been told that they were cited in it. 

From Tunnel Mtn, Banff on June 6, 2026

By Asheesh Kapur Siddique, Associate Professor of History, UMass Amherst

 

This is the second of the talks from the CHCI panel, "The Humanities We Can Build Right Now" in Banff, Alberta. Chris Nealon’s is here, my introduction to the panel is here.

Society needs the cultural knowledge that the humanities produces in its research and disseminates in its scholarship and pedagogy to make sense of the crises of the present. Chris has written about this need and convincingly argued for it, so I don’t have to. Instead, today I want to talk about politics: how do we make this happen?

                          

Society has a need for humanistic knowledge. But there is no political movement in support of the academic humanities. There is a political movement about the humanities in the US-- on the political Right and it is deeply destructive to the project of democracy. While the Right over the last decade has built institutions and invested in undermining us by stealing our resources, destroying humanities programs and departments, and sowing doubt in our scholarship, the Resistance has not responded with anything near the militancy required. We need something different.  I want to begin a discussion about what building a political movement looks like. The political structure of democracies is partisan: parties are the central vehicle through which politics happens. In the US, the ideologies of “Right” and “Left” are not exact correlates to “Republican” and “Democrat.’ These parties are, however, the only way that ideological visions get enacted in public institutions through legislation and bureaucracy.

 

My remarks will proceed in three parts. First, I will describe the political Right’s movement to supplant the academic humanities and why it is threatening to the work we do. Second, I will turn to the political Left in the United States and explain why it, in its own way, is anti-humanistic in important respects. The point of the first two parts is to argue that there is currently no meaningful political support for building the present we want. Third, and finally, I want to end on a hopeful note, by thinking about what it would take to build political support on the Left for robust publicly funded humanities research.

 

So to begin: the political Right has a project for the present and future of the humanities in higher education – the creation of parallel institutions to traditional academic departments that teach courses and sponsor research in humanities fields, though especially political and economic thought, that aligns with various center-right policy objectives. In the US, while there were early predecessors, like the Institute for Humane Studies at George Mason University (founded 1961; part of GMU since mid-1980s), and the James Madison Program in American Ideals and Institutions (2000) at Princeton University, these are so-called ‘civic centers’ that began to proliferate around 2015, often funded by right-wing state legislatures and/or right-wing billionaires like the Koch brothers. Their creation was co-extensive with a renewed assault on higher ed’s institutional autonomy.

 

These civic centers, such as the Hamilton School at the University of Florida (initially funded by $3 million in taxpayer dollars by the Florida legislature), the Chase Center at Ohio State University (initially funded by $24 million in taxpayer dollars by the Ohio legislature), and the Civitas Institute (now School of Civic Leadership) at UT-Austin (funded both by the state and private donors), work within universities in the same way that tobacco and tech companies work to subvert publicly accountable, democratic knowledge structures and institutions.  As Alondra Nelson pointed out in her keynote lecture, they do this by casting doubt on the humanistic knowledge produced in universities, attacking it as “woke,” “political,” invalid, and unserious. They create parallel institutions that exist to produce politicized ideology disguised as “objective” scholarship and often cast in the rhetoric of “civics” that is squarely aimed at destroying the departments and cultures of teaching and scholarship in the humanities and the very idea of public funding for what we do.

 

Through civic centers, the right-wing is seizing on the crisis of funding and adjunctification in our universities for counter-majoritarian ends. As the historian Lauren Lassabe Shepherd has written of these centers, “The right’s approach to higher education has been three-pronged: it has sought to create competing and parallel institutions, to wrest control from existing colleges, and ultimately, to defund public education entirely.”

 

None of the rhetoric is new, of course; if the ‘culture wars’ of the late 20^th century was the ‘new McCarthyism,’ this is the Red Scare 3.0 - the political right has not had a new idea about universities since Joe McCarthy. It  is the political right’s fixed vision of the future of the humanities in higher education go at least since Ronald Reagan became governor of California in 1967. You may know that, during a press conference in 1967 after being elected governor of California, and in the context of his push to introduce tuition into public higher ed in California, Reagan stated “we do believe that there are certain intellectual luxuries that perhaps we could do without for a year or two without hurting the cause of education.” Pressed to define these “intellectual luxuries,” Reagan pointed to only two examples: first, he referred to a course at UC Davis “where they teach you to hang the Governor in effigy.” Second, Reagan referred to “a state back in the Midwest where they discovered that a state university was offering a master's degree in the repair of band instruments, and I thought that this was sort of subsidizing intellectual curiosity.” We cannot give any concession or quarter to this continuing tradition of blanket hostility: it is completely dangerous and invalid. It is actually worse to have right-wing humanities than no humanities at all.

 

So far so retro culture war. Turning from the right’s attacks, I note that we have increasingly lost the center-left, the professional-managerial class (PMC) that once embraced what we do in public higher education.

 

As bad as the right has continued to be since 2020, I would argue that their attacks are not actually the politically worst for us: the center-left attacks have been far more damaging. Since the racist backlash to Black Lives Matter, followed by the backlash to campus protests opposing Netanyahu’s genocide in Gaza, the center-left has joined this campaign. Deeply influential institutions within American political liberalism like The New York Times and The Atlantic have peddled a version of the right-wing narrative aimed at casting doubt on the project of the university and indicting the humanities in particular as the source of the university’s now-dubious project. They are reshaping PMC opinion against the idea of publicly-funded humanities teaching and scholarship.

 

There is so much to say about this; I can talk more about how this is happening in the Q & A. As for higher-ed media, an event earlier this week encapsulated everything wrong with it: the Chronicle of Higher Education just re-published an essay that originally appeared at the right wing outlet Persuasion, arguing, and I am not making this up, that humanists should stop doing and publishing research because what we produce is trash. The literal title of the article is “Most Humanities Research Should Stop.” I’ll say it again: this is in the Chronicle of Higher Education.  Maybe we need a panel at the next CHCI about why the media coverage of humanities research and teaching is so bad and destructive.

 

So the center-left has, in its own way, abandoned the idea of robust public support for curiosity-driven humanistic education and scholarship and has fully embraced neoliberalism. Where do they think the humanities fit in our educational landscape? The center-left has decided that the purpose of public higher education is to supply workers for the economy, not citizens to participate in democracy. The value of any public investment in public education – whether from the teaching side or the research side – must be justified in terms of “return on investment” where “return” is defined in purely monetary terms, with all the non-monetary returns rendered illegible according to neoliberal reason. According to this vision, the humanities are fine in private institutions but their role in public education is less clear. This is important: Democrats do not have any problem with, and indeed support, the teaching of the humanities in private colleges and universities.  This points to the center-left having no problem, per se, with adjunctification and contingency in humanities instruction in private institutions, and especially highly selective ones – look at the reliance on contingent faculty in core curriculum programs at UChicago and Columbia, or in History & Literature and Social Studies at Harvard; look, perhaps most egregiously, at contingent faculty in college writing programs, again, especially private ones. Do any private college or university writing programs have tenure-line appointments?

 

Whether the center-left supports research for the humanities in private colleges and universities is a more muddled question.  What is clear is that in public education, the Democrats post-Reagan have emphasized STEM above all. The Clinton administration began this with its pivot to STEM education as the focus of its priorities and public investment. Barack Obama famously quipped in 2014 that “folks can make a lot more potentially with skilled manufacturing or the trades than they might with an art history degree.” The Obama administration proposed reductions to NEH and NEA funding in 2011 and 2012. Joe Biden was better, but still, annual NEH funding even under Democratic administrations has never been anywhere near $1 billion. Prior to DOGE, the NSF spent 17.5 times more on undergraduate research than the Congressional funds available to the NEH for individual fellowships to scholars.

 

In 2022, Obama’s Secretary of Education, Miguel Cardonna, tweeted, “Every student should have access to an education that aligns with industry demands and evolves to meet the demands of tomorrow’s global workforce.” Note the language here: the purpose of the public education system is to train workers for industry. Whatever industry demands, the taxpayer should supply. An echo of this came again in the context of the debate over student loan forgiveness during the Biden administration. In 2023, when the US House of Representatives voted to overturn Biden’s student debt relief program, the Democratic congresswoman and Reed College graduate Marie Gluesenkamp Perez, explained why she supported overturning the Biden student loan forgiveness program:

 

“Expansions of student debt forgiveness need to be matched dollar-for-dollar with investments in career & technical education. I can’t support the first without the other. The severe shortage of trades workers needs to be seen & treated as a national priority. It’s about respect.

I’m all for repairing what’s busted but the higher education system is totaled. College costs too much & the credentials produced get unwarranted social status, justifying more cost increases by our country’s elite. They need to snap out of it & the system needs a total overhaul.”

 

Let me underscore again that Congresswoman Perez went to Reed. She had the humanities experience we want students to have. But that experience does not seem to have led her to believe in the value of publicly-funded intellectual curiosity. There is no necessary correlation between classroom exposure to what we do and the willingness of either the college-educated public or college-educated politicians to then go out and support the university we want to build.

 

This is not because pedagogy is not important; far from it. It is because pedagogy in itself is not enough: we need politics – and we need politicians – to make political change happen. 

 

Now for a very brief part three. The construction of a robust public infrastructure for funding humanities research depends in the United States on winning political support. We don’t have that now. But I strongly believe we have a chance right now, even though things are so bad, to build this political support: the next Democratic administration will need to rebuild the federal government knowledge infrastructure, completely and totally. It’s time for us, as humanists, to lay out what we need in terms of public support.

 

We need to be talking to the administration in exile. Media is a huge part of this: the turn by center-left media outlets that influential Democrats read, like the New York Times and The Atlantic, against humanistic scholarship and teaching, is a big problem. The retreat of private funding for university-based humanities research is also a big problem, one that I have written about, but of course in electoral democracies, private foundations are not accountable to publics in the same way as state institutions are, and while we can and should say things about the retreat of private funding (and I have) we actually have an opportunity right now to articulate what a reconstructed NEH should look like and how much it should be funded. The center-right will not let this serious crisis go to waste; neither should we.

 

by Christopher Nealon, John Dewey Professor of English, Johns Hopkins University

This is one of talks from the CHCI panel, "The Humanities We Can Build Right Now" in Banff, Alberta. My explanation of the context and my introduction to the panel are here.

** 

Thanks for having me, folks. I had been planning to devote my short presentation to an outline of the crisis facing STEM fields, and how understanding that crisis can make a difference for how we think about the assaults on the humanities. I’d be happy to talk about that in our conversation after this panel. But after listening to Professor Alondra Nelson’s fantastic opening keynote on Monday, I found myself wanting to pick up on her insights, and run with them a little bit.

 

As you recall, Professor Nelson walked us through a distinction between stochastic and epistemological frames for knowledge, where the stochastic names the random, unpredictable play of the material world, which nonetheless always holds out the promise of the predictability of phenomena, while epistemological language holds out a different promise, the knowledge of how and why things work the way they do. And she pointed us to the idea of agnotology, which if I followed right, describes both the production of obfuscatory anti-knowledge, and study of that obfuscation.

 

Banff, Alberta back country on June 3, 2026  

This piece is my introduction to a panel at the Annual Meeting of the Consortium of Humanities Centers and Institutes in Banff, Alberta. 

 

CHCI funds research initiatives, among other things--see their Climate Futures call and other initiatives. The theme of this year's meeting was "Building the Future We Want," which is also an implicit theme of this blog. Building that future means putting humanities research into modes of influence in the world, which involves transforming our existing knowledge system, an issue with which I'm a bit obsessed.