Beyond Technological Inevitability: Democratically Remaking the University Without Flooding the Zone, by Angela Valenzuela, Ph.D., July 18, 2026

Beyond Technological Inevitability: Democratically Remaking the University Without Flooding the Zone

by 

Angela Valenzuela, Ph.D.

July 19, 2026

In his provocative essay, “The University as We Know It Is Finished,” Nils Gilman argues that artificial intelligence is accelerating the collapse of the modern “multiversity”—the sprawling research university that combines teaching, research, credentialing, professional preparation, and student life within a single institution. This model, he contends, was already weakened by declining public investment, rising tuition, adjunctification, and an increasing emphasis on marketable credentials over liberal education. 

AI now exposes its deepest contradictions by making conventional lectures, term papers, standardized assessments, and routine information delivery increasingly easy to automate.

Gilman does not regard this disruption solely as a catastrophe. He argues that universities should move away from mass lectures and conventional papers toward seminars, oral examinations, live debate, collaborative problem-solving, and other forms of demonstrated reasoning. Professors would become less like transmitters of information and more like mentors and intellectual interlocutors. 

At the same time, the liberal arts—history, philosophy, literature, and political theory—would become more, not less, important because they cultivate judgment, ethical reasoning, historical understanding, taste, and the distinctly human capacity to determine which goals are worth pursuing.

Gilman is especially critical of the recently published report of the Yale Committee on Trust in Higher Education. Although the report addresses affordability, admissions, intellectual pluralism, academic freedom, classroom practices, and other sources of public dissatisfaction, Gilman argues that its discussion of AI is strikingly cursory. 

It treats faculty members’ struggles with AI largely as a problem of syllabus and classroom redesign rather than confronting AI as a force capable of restructuring the economics, practices, and institutional organization of knowledge itself. Yale’s report presents twenty recommendations intended to rebuild public confidence, including measures concerning affordability, openness, free expression, admissions, teaching, and the university’s public mission.

I share Gilman’s concern about the report, but my own misgivings go further. As I have written previously, the Yale report is serious and welcome, but it feels incomplete (Valenzuela, 2026). It names many of the symptoms of declining trust while largely sidestepping the political conditions producing them. Its framework can consequently read as a technocratic conversation about institutional repair—better communication, greater transparency, more intellectual openness, and renewed attention to affordability—rather than an account of the organized political forces seeking to reshape higher education.

The crisis is not simply that portions of the public have lost confidence in universities. Nor can distrust be understood as a matter of perception alone. We must also examine the political economy of higher education, the intensifying role of the state in regulating knowledge, and the emergence of an anti-democratic coalition seeking to centralize governance, discipline faculty, restrict fields of inquiry, and redefine the university’s public mission. 

The racialized dimensions of this campaign are also crucial. Accusations of ideological “bias” are increasingly used to delegitimize the disciplines and programs that examine race, inequality, gender, history, and power. A discussion of trust that does not adequately address these political developments risks mistaking an organized project of institutional transformation for a public-relations problem.

This omission is especially consequential because the dizzying speed of technological change coincides with the Trump administration’s shock-and-awe, flood-the-zone approach to higher education. Rapid and overlapping investigations, funding pressures, executive actions, lawsuits, and demands for institutional change can overwhelm universities’ capacity to deliberate, organize, and respond. Legal scholars have characterized the use of federal funding to compel ideological conformity and alter university governance as a fundamental threat to institutional autonomy and academic freedom.

The convergence of these forces matters. Technological disruption creates pressure to act quickly, while political disruption weakens the conditions necessary for thoughtful and independent decision-making. Under such circumstances, declarations that the university is “finished” can become self-fulfilling—or provide intellectual cover for those already seeking to dismantle public higher education, weaken faculty governance, narrow academic freedom, and redefine universities according to partisan and commercial priorities. The speed of change is therefore not merely a technological or administrative problem. It is itself a democratic problem.

Ultimately, Gilman predicts that research, teaching, residential life, and credentialing may become separated into different institutions. AI may not destroy higher education, he argues, but it will force universities to reconsider what education is actually for and which distinctly human capacities they are responsible for developing.

Yet his argument raises a larger question: Does Gilman underestimate the university’s democratic, public, and community-serving purposes by treating its transformation primarily as a technological problem? Universities do more than transmit information, develop cognitive skills, or award credentials. At their best, they preserve historical memory, sustain independent inquiry, prepare people for democratic participation, produce knowledge in the public interest, and provide spaces in which society’s most difficult conflicts can be examined rather than suppressed.

How universities respond to AI should therefore not be determined exclusively by technology companies, consultants, governing boards, political appointees, or university presidents operating under emergency conditions. These decisions should be made democratically through meaningful participation by faculty, students, staff, communities, and the broader public. Major changes to curriculum, assessment, faculty roles, research priorities, data governance, and institutional structure require deliberation, transparency, experimentation, and genuine shared governance—not another version of shock and awe.

Trust cannot be restored through messaging or institutional repair alone. We must also talk about power. The defense of higher education must rest on democratic clarity: a clear understanding of who is seeking to transform the university, whose knowledge is being restricted, whose interests are being served, and who will have a voice in determining what comes next.

The challenge posed by AI is therefore not simply to redesign assignments or disaggregate institutional functions. It is to ensure that the remaking of the university does not occur through speed, exhaustion, political coercion, administrative fiat, or claims of technological inevitability. The fundamental question is not only what kind of university can survive AI, but what kind of university a democratic society should choose to preserve—and collectively create.

Reference

Gilman, N. (2026, June 17). The university as we know it is finished: That’s a good thing, Substack. Gilman, N. (2026, June 17). The university as we know it is finished: That’s a good thing. https://www.persuasion.community/p/the-multiversity-is-finished

Valenzuela, A. (2026, April 28). The wrong crisis: What the Yale report misses in the age of manufactured mistrust [Blog post], Educational Equity, Politics and Policy in Texas. https://texasedequity.blogspot.com/2026/04/the-wrong-crisis-what-yale-report.html

The University As We Know It Is Finished
That’s a good thing. by Nils Gilman
Jun 17, 2026 | Substack

Clark Kerr (center), president of the University of California, at Occidental 

graduation, 1958. (Photo by Los Angeles Examiner/USC Libraries/Corbis.)

When University of California President Clark Kerr delivered the Godkin Lectures at Harvard in 1963, published shortly thereafter as The Uses of the University, he was doing something unusual for an academic administrator: he was offering a sophisticated social theory, and doing so with wit. In these lectures, Kerr coined the term “multiversity” to describe what the postwar American research university had become. In Kerr’s account, the modern university was no longer to be understood as a community of scholars united by a shared ideal of learning, but rather as a sprawling institutional conglomerate serving at once as a research engine, a job-training facility, a credentialing mechanism, a coming-of-age experience, and an incubator of the national technical elite. The University of California, which Kerr had just finished steering through a near-decade of explosive growth, was his exemplar.

Kerr was a droll man. He once observed that the three great problems facing any university president were “parking for the faculty, athletics for the alumni, and sex for the students.” He described the university faculty (and I can confirm from personal experience that this remains accurate) as “a series of individual faculty entrepreneurs held together by a common grievance over parking.” And when Ronald Reagan made good on his 1966 gubernatorial campaign promise to fire him for being too lenient with the Free Speech Movement protesters, Kerr offered one of the great farewell lines in American academic history: “I leave the University of California as I arrived: fired with enthusiasm!”

Despite the jokes, Kerr was a serious man. The argument underneath The Uses of the University was that the multiversity, precisely because of its sprawl and apparent incoherence, was the institutional master key of mid-century American civilization. It was the nexus at which basic scientific knowledge was produced, technical and professional talent was credentialed, democratic citizenship was cultivated, and the national project of technological supremacy was advanced. The multiversity didn’t need to be coherent in order to be functionally useful as a platform for what Kerr called “administering the present.” He wrote with the high modernist confidence of someone who believed that hierarchical technocratic institutions, if competently managed, could keep these various volatile elements in balance.

Kerr’s dismissal by Reagan in 1967 was, in a sense, the first indicator and warning of the crisis of the high modernist technocratic model that he championed and sought to institutionalize through the multiversity.

It is time to acknowledge that Kerr’s model of higher education is finished: long on its last legs, the arrival of AI announces its death-knell. What comes next is disaggregation: the multiversity as we know it being disassembled into its component parts. This need not, however, be a catastrophe for higher education. Actually, in many ways, it represents an opportunity to return to roots, in a classical model of education and in attentive pedagogical instruction. But higher education can only weather this period of disruption if it is clear-eyed about what is happening and moves confidently toward a new model.

The Crisis of the University Is Not New

The present crisis of the American university began building already sixty years ago as the postwar bargain that Kerr’s vision embodied started to fray. What followed was a slow-motion privatization of university finances, producing a slow-motion tuition hyperinflation that has burdened a generation of students with debt while hollowing out the public mission of the university. The shift from grants to loans, from tenured faculty to mass adjunctification, and from a broad education in the liberal arts to vocational credentialism all occurred under the banner of making universities more “responsive to market demands.” In practice, this has meant transferring cost from the public to the individual “student consumer,” while defunding the parts of the institution that didn’t produce monetizable outputs. The net result has been the ever-upward-spiraling costs of undergraduate education, without a corresponding increase in the value of educational training or credentialling, and a loss of political support for the mission of universities. These financial and political travails have heightened the contradictions between the disparate missions and functions of the multiversity.

Into this increasingly unstable compound, add AI.

The arrival of large language models is acting as a catalytic solvent, titrating out the incoherence that was always there. When a student can produce a plausible term paper in twenty minutes using Claude Opus or Google Gemini, what is the point of assigning term papers? When an AI tutor can explain any concept at any level of sophistication with infinite patience, what is the value of a lecturer reading from notes? When AI can ace most standardized professional examinations, what is a credential certifying? These are old problems that AI has made it impossible to ignore.

Beyond the pedagogic challenges posed by the arrival of LLMs, AI is also exposing that the Kerrian bundle held together for as long as it did because its components shared a common and venerable set of technologies of knowledge transmission: the book, the lecture, the problem set, the written examination. In a pre-LLM world, these formats made cognitive demands of students that were difficult to simulate or shortcut. That is no longer true. AI doesn’t just automate some of the tasks associated with these formats; it renders the formats themselves obsolete as instruments of either intellectual discipline or assessment. And when the shared technological substrate dissolves, the contradictions built into the multiversity from the beginning become impossible to paper over. 

The world-class research mission and the undergraduate teaching mission have always been in tension. The prestige economy that rewards publications over pedagogy always distorted faculty incentives. The credentialing function was always only loosely connected to the educational one. These were the open secrets of the American research university. In a post-AI world, these divergences are being rendered untenable.

It is striking, then, that the most widely discussed recent attempt at university self-examination, the April 2026 Report of the Yale Committee on Trust in Higher Education, barely registered any of this reality. The report was in some ways an admirable document. It was clear-eyed about costs, scathing about admissions opacity, and candid about the political monoculture that has eroded public trust across partisan lines. Yet its treatment of AI was cursory to the point of negligence: a few sentences in the section on the classroom, expressing uncertainty about AI’s effects and noting that faculty are “scrambling to redesign syllabi.” It is remarkable that a report tasked with understanding why public trust in higher education is collapsing would fail to reckon with the technology that is restructuring the economics and logic of knowledge work. It suggests that even the most self-aware corners of the academy are still treating AI as a pedagogical inconvenience (or literal cheat-code) rather than what it actually is: the force that is making the entire inherited architecture of the multiversity impossible to sustain.

The Co-curricular Dodge

So how should the university respond to this crisis of purpose, identity, and even faith? The most popular present answer in certain administrative circles to this question is an emphasis on the “co-curricular,” that is, on residential life and human connection as the university’s irreducible value in an age of AI tutors. Perhaps the most cited proposal is Molly Worthen’s New York Times piece from three years ago, “Why Universities Should Be More Like Monasteries,” which argued that universities should offer radically low-tech, high-presence educational environments.

This argument isn’t meritless: there is evidence that learning works differently when embedded in community, that chance hallway encounters with faculty members, late-night bull sessions in the dormitory common room, and heated dining hall debates are often the most generative moments of learning. Students’ own accounts of what matters most in college consistently center on relationships, belonging, and dialogue. The argument for residential education, for the ancient model of the Platonic Academy as gymnasium and garden as much as classroom, is stronger now than it has been in decades.

This is continuous with a long-standing function of universities as sites for passage from childhood to adulthood, for coming to a new understanding of oneself. In the 1960s more than four fifths of college freshmen reported that a major goal of college was to help themselves “develop a meaningful philosophy of life,” a number which collapsed by half in the 1970s and 1980s. A reemphasis on the co-curricular could help revivify this ideal, which would in turn help prepare students for the AI-forward world they are entering. As Anthropic cofounder Jack Clark recently argued, the people who will most benefit from AI are those who have first built deep, idiosyncratic human capacities through “repetitive practice and creation.” The machines will work best when helping you to amplify what you’ve already made of yourself.

But by itself, the co-curricular is an evasion. It leaves untouched the question that determines what students and families are paying for: what happens in the curriculum, in the classroom, in the formal educational encounter. That is where reform needs to be most radical, and where the response of universities so far has been most quavering. If the primary response of universities to the most dramatic new knowledge technology in decades, one that employers everywhere are expecting employees everywhere to use, is to demand that students stick cotton in their ears and keep rowing, it will only hasten their decline into institutional redundancy, if not irrelevance.

Cognitive Requirements in the Age of AI

Any reimagining of the university in the age of AI must begin with an honest reckoning with what AI cannot do—and what therefore becomes relatively valuable precisely because AI can do everything else. The key distinction is between work that AI does well (such as synthesis of known patterns, argument elaboration, template instantiation, and generating local coherence) and work it structurally cannot do because of the architecture of the technology as such. AI cannot build the trust on which institutional cooperation depends, because trust is not a conclusion reached by processing information about another agent but instead is a relationship constituted over time between persons who have staked something on each other, and who can be betrayed. AI cannot give a person good taste or style, because taste and style are about personal distinctiveness within a community which shares an aesthetic. AI cannot constitute goals, because that act requires a valuing subject. These are not gaps that more compute will close. They are absences that follow from the ontology of the technology itself.

A curriculum designed around AI’s limitations should be seen neither as an exercise in nostalgia nor as a denial of the burgeoning power of these systems. In fact, given the trajectory of AI capabilities, it is the only curriculum with any hope of finding a stable foundation.

What does this mean in practice? Start with the most obvious casualty: the term paper, as an assessment instrument, is dead. Written homework assignments were meant to push (and test) a student’s ability to produce a well-structured, coherently argued text. But this is exactly what LLMs do effortlessly and without demanding of the user any of the underlying cognitive work for which the traditional term paper was supposed to be a proxy. This included sustained argumentative reason: the ability to construct and maintain a complex argument across an extended piece of discourse, distinguishing claims from evidence, handling counterarguments, and reaching a defensible conclusion. Written assignments also demanded epistemic self-regulation, that is, the metacognitive capacity to monitor one’s own understanding, recognize gaps in evidence, revise positions in response to what the evidence shows rather than what one hoped to find. This pedagogically valuable work always operated below the waterline of the actual output of a term paper; what LLMs do is deliver results that simulate these actions without putting the students through their cognitive paces.

The replacement, as many education researchers are arguing, is live assessment and demonstration: real-time diagnosis of novel situations, design critique, structured adversarial debate, and Socratic examination. These formats test the ability to sense-make under pressure, defend a frame against live challenge, revise a model when evidence contradicts rather than confirms it, and recognize when uncertainty is too high to proceed. In practical terms: collaborative student projects will require documented decision logs tracing reasoning behind commitments, the canonical deliverable shifts from polished artifact to demonstrated live reasoning, and oral examinations and hand-written exams will become the primary assessment instruments. But despite this emerging consensus among education researchers, institutional practice has barely moved.

If the post-AI university’s pedagogic value proposition is the formation of cognitive capacity in conditions that cannot be replicated on a screen, then the function and responsibilities of faculty members must also be reconceived. It clearly no longer makes sense for professors to stand in front of a hall full (or, too often, only half full) of students delivering lectures. As a mechanism of information conveyance, AI can now provide the same at near-zero cost, tailor-made to the specific knowledge gaps of individual students. Instead, professors must reconceive of themselves as interlocutors, serving as performative models of how to calibrate uncertainty and revise frames in real time. The classroom experience should focus on helping students to understand how to constitute a goal rather than generate a text in response to a prompt provided by the professor.

This is something closer to the Oxbridge tutorial system, the clinical ward round, or the seminars of many small liberal arts colleges in the United States. These pedagogies were once defended on grounds of tradition or prestige. The post-AI argument is structural: they are the delivery mechanisms for exactly the cognitive capacities that the architecture of AI cannot replicate, because those capacities are developed only by being exercised, not described. Interestingly, this means that the coming of AI is going to mean there will be demand for more professors, rather than fewer.

None of this implies that faculty should pretend AI does not exist, or that the tutorial and seminar should be conducted in proud ignorance of a tool students will be spending the rest of their professional lives using. The opposite is true. Faculty should integrate LLMs directly and deliberately into their instruction as tools that need to be used correctly in order to not be harmful. (The analogy of a blowtorch or a chainsaw comes to mind: these are useful tools, but you need to learn how to use them safely.) Teaching a student to prompt effectively is teaching them to think precisely about what they want to know and why; it is, in this sense, an exercise in goal constitution. Teaching students to evaluate an LLM’s output critically by scrutinizing the machine’s often over-confident syntheses against evidentiary standards defined by the phenomenological reality of the external and material world is teaching them epistemic provenance tracking and calibrated self-assessment. LLMs can also become objects of critical study in their own right: students should be asked to assess why the model did not produce exactly what they had a priori in mind when they initiated the interaction. Handled this way, LLMs can serve as clarifying instruments in the pursuit of the classical objectives of enlightened education: the inculcation of critical thinking and logical reasoning, rhetorical and communicative competence, aesthetic appreciation and the cultivation of taste, moral and ethical reasoning, and ultimately the ideal of self-knowledge and Bildung.

This brings us to the content of the curriculum itself. As I recently argued in Noema, if the goal of a college curriculum is (as it should be) to inculcate oral reasoning and persuasion, ethical analysis and moral judgment, historical and comparative thinking, and the cultivation of taste and discrimination, then we are precisely in the domain of the classical curriculum of the liberal arts. Skills such as goal constitution, situated judgment, and value alignment are exactly the capacities that a serious engagement with history, philosophy, literature, and political theory develops. History trains temporal imagination and frame revision; philosophy trains epistemic precision and the discipline of distinguishing solid argument from vapid sophistry; literature sharpens an appreciation for style and a feeling for hidden meaning; political theory trains the recognition of suppressed goal contestation and the conditions for legitimate alignment. Together they enable students to imagine lives unlike their own, a hugely valuable experience in a world changing as fast as ours.

How to convey the content of these disciplines to students is going to have to change dramatically from the homogenous one-to-many mass-delivery model of the postwar multiversity, but the content is perfectly classical. The university’s present crisis of purpose is, in this light, at least in part a crisis of having abandoned its own best tradition in pursuit of vocational or technical training that AI is now rendering obsolete.

But Does It Scale?

The central challenge for universities will be how to move toward this model at scale. The tutorial and seminar model is labor-intensive by design: a professor working as interlocutor rather than lecturer can engage only a fraction of the students she could previously reach from a podium. The skills required of faculty will also need to change substantially. Under the old model, a brilliant researcher delivered expected value simply by speaking one-to-many; the new model requires someone with the pedagogic sensitivity to calibrate each student’s specific confusions and capacities—qualities that research prowess neither produces nor rewards. Elite universities in particular have built their faculties almost entirely around research achievement, with teaching treated as a secondary obligation. Reconceiving the professoriate will mean altering tenure criteria and promotion incentives, and it will face fierce resistance from scholars whose professional identities are bound up in the research function. None of this is impossible, but none of it will be easy. No doubt some tenured faculty will pour boulders and boiling oil down the side of their ivory towers to prevent these changes from taking place.

Longer term, however, we should expect the disruption caused by AI to be not just pedagogical but to the structure of the university as such. Kerr’s great insight was that the multiversity’s incoherence was not a bug but a feature—that a loosely bundled institution mirrored a loosely bundled society by providing something for everyone, from the Nobel laureate to the newbie grad student, from the NIH grant-seeker to the remedial English student. What held those disparate functions together was a social infrastructure of knowledge transmission: the laboratory, the lecture hall, the examination, the credential. Once AI can provide information delivery at near-zero cost there is no longer a compelling reason why research, teaching, and credentialing need be co-located in the same institution. What will replace the multiversity is likely to be not one thing but several: research centers that focus exclusively on the new-knowledge-production business; independent communal residence facilities that know they are in the coming-of-age business; and teaching systems that are honest about what skills they are inculcating. Even credentials from the most exclusive universities may not retain much social signaling value.

Clark Kerr would have recognized this moment. He was no naïf about the multiversity’s contradictions; but he also believed that competent management could hold them in productive tension. What he did not foresee was that the tension would be dissolved not by political upheaval—as it nearly was in 1964, when the student movement that eventually got him fired also signaled the coming fracture of the postwar liberal-technocratic consensus—but by technological rupture. The irony is that the research university, which Kerr celebrated as the engine of American technopolitical supremacy, incubated the very instrument that is now rendering untenable the research university’s inherited form.

What the students who booed the mention of AI at recent commencement ceremonies this spring were registering, in the way that students have always registered institutional failures, is that they were not getting what they came for. But as with more than one student movement before them, just because they rightly identified a structural problem doesn’t mean that they have particularly good ideas about what a better institution would look like. Just as Kerr recast the University of California to match the liberal-technocratic imperatives of the postwar period, so do visionary college leaders today have an opportunity to remake the university to match the requirements of an economy that will be redefined by AI. Achieving this will be a generational project.

Nils Gilman is Senior Advisor to the Berggruen Institute and former Associate Chancellor of UC Berkeley.

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Texas Is Legislating Growth on a Drying Planet—and the $174 Billion Bill for Texas’ Growth Machine Is Coming Due, by Angela Valenzuela, Ph.D.

Texas Is Legislating Growth on a Drying Planet—and the $174 Billion Bill for Texas’ Growth Machine Is 

Coming Due

by 

Angela Valenzuela, Ph.D.

June 24, 2026

Chris Tomlinson’s recent column posted below, “Texas is running out of cheap water, and will need to make salty water sweet,” should land like an alarm bell across the state. His central point is simple and sobering: Texas is outgrowing its freshwater supply. Population growth, drought, fracking, semiconductor production, data centers, and the vast water needs of artificial intelligence are converging into a crisis that can no longer be deferred.

Link to PDF here

According to the Texas Water Development Board’s Draft 2027 State WaterPlan, implementing the recommended water management strategies is estimated to cost $174 billion, more than double the $81 billion estimated in the 2022 plan (Texas Water Development Board, 2026, p. 28). That number should stop us in our tracks. It tells us that the old bargain—grow endlessly, build endlessly, extract endlessly, and assume water will somehow be there—is breaking down.

Tomlinson rightly points to Corpus Christi as the canary in the coal mine. There, the city’s surface water supplies are running low, new groundwater wells have not delivered as hoped, and desalination remains expensive. 

Meanwhile, the endless drive to attract industry is increasingly ill advised, stretching the water supply beyond what residents can reasonably bear. The result is a familiar Texas pattern: public resources get promised to private growth, and ordinary people are left to pay the bill.

This is where the water crisis becomes more than an infrastructure problem. It is a democracy problem. Who gets to decide how much growth is too much? Who benefits from water-intensive development? Who pays when systems fail? Who is asked to conserve while industries expand? And whose communities are treated as sacrifice zones in the name of “economic development”?

These questions echo themes I have raised in other posts about the Hot Earth, climate disruption, and the extractive logic that governs so much of our public policy. Climate change is not some distant abstraction. It is arriving through heat, drought, flood, fire, migration, insurance costs, utility bills, and water scarcity. It is arriving through the quiet violence of unaffordability. It is arriving through the realization that the poor, the elderly, rural communities, and working families will be the first to experience what policymakers long treated as someone else’s future.

Tomlinson’s column is especially important because it pierces the illusion of cheapness. Texas has “run out of cheap water,” as Sen. Charles Perry put it. That statement deserves our full attention. Cheap water was never really cheap. It was subsidized by aquifers, rivers, ecosystems, Indigenous dispossession, rural extraction, underpriced infrastructure, and future generations who were never asked for consent. Now the bill is coming due.

Desalination may become part of the state’s water future, but it is not a magic wand. Making salty water sweet requires enormous energy. It also produces concentrated brine that must be handled carefully to avoid serious environmental harm. If desalination becomes another excuse for unrestrained growth, we will have learned nothing. We will simply be using more energy to solve the water crisis while worsening the climate crisis that intensifies drought in the first place.

The same concern applies to artificial intelligence and data centers. As I have written elsewhere in reflecting on AI and extraction, artificial intelligence is not weightless. It is not merely “in the cloud.” It is physical infrastructure: land, water, energy, minerals, labor, servers, cooling systems, and communities asked to absorb the costs. The language of innovation often hides the material reality of extraction. In Texas, that reality is becoming increasingly visible as rural communities confront large-scale data centers that demand huge amounts of water and electricity.

It is telling that even Texas Agriculture Commissioner Sid Miller has called for a moratorium on new data centers, warning that rural communities that have conserved water and land for generations are now being asked to compete with corporate giants whose demands could overwhelm local resources. 

That alone should widen the conversation beyond partisan politics. This is not simply a left-right issue; it is a question of stewardship, fairness, and whether Texas will protect the communities that have long sustained the state before handing scarce resources over to the highest bidder.

Water also forces us to rethink what we mean by prosperity. For too long, Texas leaders have equated growth with success. More highways. More subdivisions. More warehouses. More data centers. More fossil fuel extraction. More tax incentives. More ribbon cuttings. But growth without ecological limits is not prosperity. It is debt. It is risk. It is a form of policy denial.

And as with so many policy debates in Texas, inequality sits at the center. Wealthier households will absorb higher water rates more easily. Poor and working-class families will not. Rural communities may see their aquifers strained by development they did not invite. Colonias and historically underserved communities may face the worst infrastructure deficits. Urban residents may be told to conserve while industry receives favorable treatment. This is how environmental crisis becomes social crisis.

We should be deeply concerned that Texas is discussing water scarcity largely as an engineering challenge rather than a justice challenge. Pipes matter. Treatment plants matter. Desalination facilities may matter. But governance matters, too. Public voice matters. Conservation matters. Climate policy matters. Land-use planning matters. Accountability matters. Without these, water policy will reproduce the very inequities that brought us here.

The Legislature will have to confront this in 2027. But Texans should not wait for lawmakers to frame the issue for us. We need a broader public conversation now—one that includes educators, scientists, farmers, Indigenous communities, rural residents, environmental advocates, labor, students, and families already struggling with the cost of living.

Water is life. It is also memory, land, culture, food, health, and public trust. It is the connective tissue of ecology and democracy.

Texas can still choose a different path. We can invest in infrastructure, conservation, reuse, repair, and fair pricing. We can scrutinize water-intensive industries before granting them public support. We can protect communities from being overrun by extractive development. We can treat climate disruption as real rather than optional. We can insist that economic development serve the public good rather than private accumulation alone.

But we cannot keep pretending that endless growth is compatible with finite water.

Tomlinson’s column is a warning. Corpus Christi is a warning. The draft State Water Plan is a warning. The Hot Earth is warning us, too.

The question is whether Texas will listen before scarcity becomes policy, before crisis becomes normal, and before ordinary Texans are told—once again—that there is no alternative but to pay for decisions they did not make.

References

Texas Water Development Board. (2026). 2027 state water plan: Draft, phase 1. https://www.twdb.texas.gov/waterplanning/swp/2027/docs/DraftSWP27-Water-For-Texas.pdf

Tomlinson, C. (2026, May 21). Texas is running out of cheap water, and will need to make salty water sweet. Houston Chronicle. 

Texas is running out of cheap water, and will need to make salty water sweet

Texas faces a costly water crisis as growth, drought and industry outpace freshwater supplies statewide, writes columnist Chris Tomlinson.

By Chris Tomlinson,ColumnistMay 21, 2026

Texas is outgrowing its freshwater supply.

Communities across the state need billions of gallons more water for growing populations and thirsty industries like oil and gas fracking, computer chip etching and artificial thinking. What happens this summer in Corpus Christi is a sign of what’s coming for all Texans.

The price of creating freshwater and keeping the economy growing will leave consumers with sticker shock, state lawmakers recently acknowledged. A draft of the 2027 State Water Plan says Texas will need to spend $174 billion to meet the water needs of the next 50 years — twice as much as lawmakers estimated just four years ago.

“The taxpayer is going to pay for this stuff one way or the other, be it property tax or be it fees, or be it insurance cost increases,” state Sen. Charles Perry, chairman of the Senate Water, Agriculture and Rural Affairs Committee, said during a hearing last week. “There is no free lunch here, or if they don’t pay, we end up with a Third World state.”

Inconvenient truths

Lawmakers will need to have “a big conversation” when they gather in Austin next year, Perry predicted. The level-headed Lubbock Republican is not prone to hyperbole or known for rhetorical flourish. When he speaks up, Texans best listen up.

Droughts and floods have complicated life in Texas for eons. The population and economy only began to grow once people started damming rivers and drilling into aquifers. But experts say those water sources are insufficient for a growing state.

Corpus Christi is the canary in our coal mine. The city’s surface water supplies are running low, new groundwater wells have disappointed, and citizens can’t afford to make seawater drinkable.

In the City Council’s drive to attract industry and bring in new jobs and revenue, it stretched the water supply too thin. Now that the inevitable drought has arrived, the council will likely declare an emergency and impose strict rationing.

Experts told the Senate committee on May 11 that while 57% of city-owned water utilities say they have long-term plans with sufficient funding, 10% have no plan, and 43% do not have enough money.

This year was the first time the State Water Implementation Fund for Texas fell short, capable of providing only $1.28 billion of the $4.2 billion that water utilities requested. The Legislature created the fund 11 years ago to help local authorities pay for water projects.

The Texas Water Development Board, which administers the fund, rejected the Nueces River Authority’s request for help financing a Corpus Christi desalination plant, claiming it had to prioritize other projects. 

Statewide, Texas doesn’t know where it will find 10 million to 12 million acre-feet of the 17 million the state will need over the next 50 years, Perry warned (an acre-foot is about 325,000 gallons). Making existing water treatment plants and pipelines more efficient will only meet 3% of the need.

“If I have to pick between spending all of my dollars on leaky pipes or all my dollars on supply, I will pick supply every day,” Perry said.

Water users will need a lot of dollars.

Well runs dry

Producing drinking water from ground or surface sources typically costs between $1.10 to $4.30 per 1,000 gallons, according to the Texas Comptroller’s Office. Desalination costs range from $7.50 to $11 per 1,000 gallons.

“Everyone in the audience is going to say, ‘That’s too much, that’s double what we’re paying,’” Perry said. “But that’s the cost of new water in Texas … we have run out of the cheap water.”

If Texans will pay those rates, utilities will deliver. Companies are working to permit three seawater desalination plants, with state officials anticipating seven plants along the coast over the next decade.

Arid nations around the world use desalination, but in addition to requiring a lot of energy, they produce a brine that is twice as salty as the water going in. If the waste stream isn’t handled properly, it can cause severe environmental problems.

Critics question the wisdom of trying to grow Texas beyond what its natural resources can sustain. Community activists want to prioritize residents’ quality of life over industrial development, and environmentalists want conservation over development.

Sid Miller, the Republican agriculture commissioner, called for a moratorium on data centers to conserve water.

“They draw massive volumes of water for cooling, even amid ongoing drought,” Miller wrote in a press release on Monday. “Rural communities that have conserved resources for generations now compete with corporate giants.”

Should a higher power grant South Texans' prayers and bring a week or more of steady rain, a heaven-sent storm will not solve the state’s long-term water shortage. We’ll still need to turn salty water sweet.

“We have a lot of poor people that aren’t going to be able to live in Texas much longer if we don’t figure this out, and there needs to be a sense of urgency about it,” Perry warned. “The status quo has to break.”

Award-winning opinion writer Chris Tomlinson writes commentary about money, politics and life in Texas. Sign up for his “Tomlinson’s Take” newsletter at houstonchronicle.com/tomlinsonnewsletter or expressnews.com/tomlinsonnewsletter.

Chris Tomlinson

Business Columnist

Chris Tomlinson writes commentary about money, politics and life in Texas for Hearst Newspapers. He can be reached at ctomlinson@hearstcorp.com.

In 2025, Tomlinson was inducted into the Texas Institute of Letters, an honor society that recognizes distinctive literary achievement. In 2021, the Texas Association of Managing Editors awarded him columnist of the year, and the Headliners Foundation named him Texas's Star Opinion Writer. He’s authored two New York Times Bestsellers, “Forget the Alamo: The Rise and Fall of an American Myth” and “Tomlinson Hill: The Remarkable Story of Two Families Who Share the Tomlinson Name - One White, One Black.”

Before joining the Houston Chronicle in 2014, he spent 20 years with The Associated Press reporting on politics, economics, conflicts and natural disasters from more than 30 countries in Africa, the Middle East and Europe.

America Isn’t Ready for What AI Will Do to Jobs—Should We Be Worried?

The short answer as to whether we should be worried about AI is—yes! And this is not because mass AI-driven unemployment is already a settled fact, but because the speed of change, the incentives driving corporate adoption, and the absence of serious public planning together create a dangerous vacuum. 

As Josh Tyrangiel writing for The Atlantic makes clear, the real threat is not simply that AI may displace workers, but that it could do so faster than our institutions can respond, leaving millions vulnerable while political leaders, CEOs, and policymakers look the other way. 

Even economists who disagree on timing acknowledge the stakes: if AI compresses years of labor-market disruption into months, the damage will extend far beyond jobs to democracy itself, deepening inequality, anxiety, and political instability. What should concern us most is not only the technology, but the nation’s striking lack of preparation for a transition that may already be underway.

-Angela Valenzuela

America Isn’t Ready for What AI Will Do to Jobs

Does anyone have a plan for what happens next?

By Josh Tyrangiel

Illustrations by Stephan Dybus March, 2026 | The Atlantic

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In 1869, a group of Massachusetts reformers persuaded the state to try a simple idea: counting. The Second Industrial Revolution was belching its way through New England, teaching mill and factory owners a lesson most M.B.A. students now learn in their first semester: that efficiency gains tend to come from somewhere, and that somewhere is usually somebody else. The new machines weren’t just spinning cotton or shaping steel. They were operating at speeds that the human body—an elegant piece of engineering designed over millions of years for entirely different purposes—simply wasn’t built to match. The owners knew this, just as they knew that there’s a limit to how much misery people are willing to tolerate before they start setting fire to things.

Still, the machines pressed on.

So Massachusetts created the nation’s first Bureau of Statistics of Labor, hoping that data might accomplish what conscience could not. By measuring work hours, conditions, wages, and what economists now call “negative externalities” but were then called “children’s arms torn off,” policy makers figured they might be able to produce reasonably fair outcomes for everyone. Or, if you’re a bit more cynical, a sustainable level of exploitation. A few years later, with federal troops shooting at striking railroad workers and wealthy citizens funding private armories—leading indicators that things in your society aren’t going great—Congress decided that this idea might be worth trying at scale and created the Bureau of Labor Statistics.

Measurement doesn’t abolish injustice; it rarely even settles arguments. But the act of counting—of trying to see clearly, of committing the government to a shared set of facts—signals an intention to be fair, or at least to be caught trying. Over time, that intention matters. It’s one way a republic earns the right to be believed in.

The BLS remains a small miracle of civilization. It sends out detailed surveys to about 60,000 households and 120,000 businesses and government agencies every month, supplemented by qualitative research it uses to check and occasionally correct its findings. It deserves at least some credit for the scoreboard. America: 250 years without violent class warfare. And you have to appreciate the entertainment value of its minutiae. The BLS is how we know that, in 2024, 44,119 people worked in mobile food services (a.k.a. food trucks), up 907 percent since 2000; that nonveterinary pet care (grooming, training) employed 190,984 people, up 513 percent; and that the United States had almost 100,000 massage therapists, with five times the national concentration in Napa, California.

These and thousands of other BLS statistics describe a society that has grown more prosperous, and a workforce endlessly adaptive to change. But like all statistical bodies, the BLS has its limits. It’s excellent at revealing what has happened and only moderately useful at telling us what’s about to. The data can’t foresee recessions or pandemics—or the arrival of a technology that might do to the workforce what an asteroid did to the dinosaurs.

I am referring, of course, to artificial intelligence. After a rollout that could have been orchestrated by H. P. Lovecraft—“We are summoning the demon,” Elon Musk warned in a typical early pronouncement—the AI industry has pivoted from the language of nightmares to the stuff of comas. Driving innovation. Accelerating transformation. Reimagining workflows. It’s the first time in history that humans have invented something genuinely miraculous and then rushed to dress it in a fleece vest.

There are gobs of money to be made selling enterprise software, but dulling the impact of AI is also a useful feint. This is a technology that can digest a hundred reports before you’ve finished your coffee, draft and analyze documents faster than teams of paralegals, compose music indistinguishable from the genius of a pop star or a Juilliard grad, code—really code, not just copy-paste from Stack Overflow—with the precision of a top engineer. Tasks that once required skill, judgment, and years of training are now being executed, relentlessly and indifferently, by software that learns as it goes.

AI is already so ubiquitous that any resourceful knowledge worker can delegate some of their job’s drudgery to machines. Many companies—Microsoft and PricewaterhouseCoopers among them—have instructed their employees to increase productivity by doing just that. But anyone subcontracting tasks to AI is clever enough to imagine what might come next—a day when augmentation crosses into automation, and cognitive obsolescence compels them to seek work at a food truck, pet spa, or massage table. At least until the humanoid robots arrive.

Many economists insist that this will all be fine. Capitalism is resilient. The arrival of the ATM famously led to the employment of more bank tellers, just as the introduction of Excel swelled the ranks of accountants and Photoshop spiked demand for graphic designers. In each case, new tech automated old tasks, increased productivity, and created jobs with higher wages than anyone could have conceived of before. The BLS projects that employment will grow 3.1 percent over the next 10 years. That’s down from 13 percent in the previous decade, but 5 million new jobs in a country with a stable population is hardly catastrophic.

And yet: There are things that economists struggle to measure. Americans tend to derive meaning and identity from what they do. Most don’t want to do something else, even if they had any confidence—which they don’t—that they could find something else to do. Seventy-one percent of respondents to an August Reuters/Ipsos poll said they’re worried that artificial intelligence will “put too many people out of work permanently.”

This data point might be easier to dismiss if the modern mill and factory owners hadn’t already declared that AI will put people out of work permanently.

In May 2025, Dario Amodei, the CEO of the AI company Anthropic, said that AI could drive unemployment up 10 to 20 percent in the next one to five years and “wipe out half of all entry-level white-collar jobs.” Jim Farley, the CEO of Ford, estimated that it would eliminate “literally half of all white-collar workers” in a decade. Sam Altman, the CEO of OpenAI, revealed that “my little group chat with my tech-CEO friends” has a bet about the inevitable date when a billion-dollar company is staffed by just one person. (The business side of this magazine, like some other publishers, has a corporate partnership with OpenAI.) Other companies, including Meta, Amazon, UnitedHealth, Walmart, JPMorgan Chase, and UPS, which have recently announced layoffs, have framed them more euphemistically in sunny reports to investors about the rise of “automation” and “head count trending down.” Taken together, these statements are extraordinary: the owners of capital warning workers that the ice beneath them is about to crack—while continuing to stomp on it.

It’s as if we’re watching two versions of the same scene. In one, the ice holds, because it always has. In the other, a lot of people go under. The difference becomes clear only when the surface finally gives way—at which point the range of available options will have considerably narrowed.

AI is already transforming work, one delegated task at a time. If the transformation unfolds slowly enough and the economy adjusts quickly enough, the economists may be right: We’ll be fine. Or better. But if AI instead triggers a rapid reorganization of work—compressing years of change into months, affecting roughly 40 percent of jobs worldwide, as the International Monetary Fund projects—the consequences will not stop at the economy. They will test political institutions that have already shown how brittle they can be.

The question, then, is whether we’re approaching the kind of disruption that can be managed with statistics—or the kind that creates statistics no one can bear to count.

AUSTAN Goolsbee is the president of the Federal Reserve Bank of Chicago, the Robert P. Gwinn Professor of Economics at the University of Chicago’s Booth School of Business, and a former chair of the Council of Economic Advisers under Barack Obama. He’s also one of the few economists you would not immediately regret bringing to a party. When I asked Goolsbee if any conclusive data indicated that AI had begun to eat into the labor market, he delivered an answer that was both obvious and unhelpful, smiling as he did it. The nonanswer was the point.

I’ve known Goolsbee long enough to enjoy these moments, when he makes fun of our shared uselessness. Economists are rarely equipped to give straight answers about the present. Journalists hate when the future won’t reveal itself on deadline.

We spoke in September, shortly after the release of what’s come to be known as “The Canaries Paper,” written by three academics from the Stanford Digital Economy Lab. By crunching data from millions of monthly payroll records for workers in jobs with exposure to generative AI, the authors concluded that workers ages 22 to 25—the canaries—have seen about a 13 percent decline in employment since late 2022.

For several days, the paper was all anyone in the field wanted to talk about, and by talk about I mostly mean punch holes in. The report overemphasized the effect of ChatGPT. Youth employment is cyclical. The same period saw a sharp interest-rate spike—a far more likely source of turbulence. “Canaries” also contradicted a study released a few weeks earlier by the Economic Innovation Group, which argued that AI is unlikely to cause mass unemployment in the near term, even as it reshapes jobs and wages. That paper was knowingly titled “AI and Jobs: The Final Word (Until the Next One).”

This was the point Goolsbee wanted to emphasize: Economists are constrained by numbers. And numerically speaking, nothing indicates that AI has had an impact on people’s jobs. “It’s just too early,” he said.

A lack of certainty should not be mistaken for a lack of concern. The Fed’s mandate is to promote maximum employment, so the corporate pronouncements about imminent job loss have Goolsbee’s attention. But the numbers don’t add up. It’s possible that the labor market is softer than it looks, but that the softness is being absorbed within firms rather than showing up in the unemployment rate. If companies are sitting on more workers than they need, however—a phenomenon known as labor hoarding—you’d expect that to reveal itself as weak productivity growth. It’s as predictable as a hangover: too many workers, not enough work, sagging productivity. “But it’s been totally the opposite,” Goolsbee said. “Productivity growth has been really high. So I don’t know how to reconcile that.”

Productivity is the cheat code for a more prosperous society. If each worker can produce more in the same hour—more goods, better services, faster results—then the total economic pie grows, even if the number of workers doesn’t. It’s the rare efficiency gain that expands the pie rather than merely redistributing slices.

America has been on a productivity tear for the past few years. It might be temporary, the result of a onetime boost, such as the COVID-era boom in new small businesses. But with the special joy of someone paid to complicate everything, Goolsbee pointed out that general-purpose technologies such as electricity and computing can create lasting productivity gains, the kind that make whole societies wealthier.

Whether AI is one of those technologies will only become clear over time. How long before we’ll know? “Years,” Goolsbee said.

In the meantime, there’s another complication. The immediate risk to employment may not be AI itself, but the way companies, seduced by its promise, overinvest before they understand what it can actually do. Goolsbee reached back to the internet bubble, when companies spent wildly on laying fiber cables and building capacity. “In 2001, when we found out that the growth rate of the internet is not going to be 25 percent a year, but merely 10 percent—which is still a pretty great growth rate—it meant we had way too much fiber, and there was a collapse of business investment,” Goolsbee said. “And a bunch of people were thrown out of work the old-fashioned way.”

A similar crash in AI investment, if it comes, would likely look familiar: painful, destabilizing, and accompanied by surges of CNBC rants and recriminations. But it would amount to a financial reset, not a technological reversal—the kind of outcome economists are especially good at recognizing, because it resembles a thing that’s happened before.

This is the paradox of economics. To understand how fast the present is hurtling us into the future, you need a fixed point, and the fixed points are all in the past. It’s like driving while looking only at the rearview mirror—plenty dangerous if the road stays straight, catastrophic if it doesn’t.

David Autor and Daron Acemoglu are among the most accomplished rearview drivers. Both are at MIT, and both excel at understanding previous economic disruptions. Acemoglu, who won the Nobel Prize in Economics in 2024, studies inequality; Autor focuses on labor. But both insist that the story of AI and its consequences will depend mostly on speed—not because they assume lost jobs will automatically be replaced, but because a slower rate of change leaves societies time to adapt, even if some of those jobs never come back.

Labor markets have a natural rate of adjustment. If, over the course of 30 years, 3 percent of employees in a profession retire or have their jobs eliminated annually, you’d barely notice. Yet a decade later, a third of the jobs in those professions would be gone. Elevator operators and tollbooth attendants went through this slow fade to obsolescence with no damage to the economy. “When it happens more rapidly,” Autor told me, “things become problematic.”

Autor is most famous for his work on the China shock. In 2001, China joined the World Trade Organization; six years later, 13 percent of U.S. manufacturing jobs—about 2 million—had disappeared. The China shock took a disproportionate toll on small-scale manufacturing—textiles, toys, furniture—concentrated primarily in the South. “Many of the workers in those places still haven’t recovered,” Autor said, “and we’re obviously living with the political consequences.”

But AI isn’t a trade policy. It’s software. Even if it hits some professions and places first—a lawyer in a large urban firm, say, may feel the impact years before a worker in a less digitized industry—the technology won’t be constrained by geography. Eventually, everyone will be affected.

All of this sounds foreboding, until you remember the most important thing about software: People hate it, almost as much as they hate change.

This is what gives many economists confidence that the AI asteroid is still at least a decade away. “These tech CEOs want us to believe that the market for automation is preordained, and that it will all happen smoothly and profitably,” Acemoglu said. He then made a disdainful noise from his Nobel Prize–winning bullshit detector. “History tells us it’s actually going to happen much slower.”

The argument goes like this: Before AI can transform a company, it has to access the company’s data and be woven into existing systems—which sounds easy, provided you’re not a chief technology officer. A trade secret of most Fortune 500 companies is that they still run many critical functions on lumbering, industrial-strength mainframe computers that almost never break down and therefore can never be replaced. Mainframes are like Christopher Walken: They’ve been going nonstop since the 1960s, they’re fantastic at performing peculiar roles (processing payments, safeguarding data), and nobody alive really understands how they work.

Integrating legacy tech with modern AI means navigating hardware, vendors, contracts, ancient coding languages, and humans—every one of whom has a strong opinion about the “right” way to make changes. Months pass, then years; another company holiday party comes and goes; and the CEO still can’t understand why the miracle of AI isn’t solving all of their problems.

Every new general-purpose technology is, for a time, held hostage by the mess of what already exists. The first electric-power stations opened in the 1880s, and no one debated whether they were superior to steam engines. But factories had been built with steam engines in their basements, powering overhead shafts that ran the length of the buildings, with belts and pulleys carrying power to individual machines. To adopt electricity, factory owners didn’t just need to buy motors—they needed to demolish and rebuild their entire operations. Some did. Most just waited for their infrastructure to wear out, which explains why the major economic gains from electrification didn’t show up for 40 years.

NONE of this is reassuring enough for the economist Anton Korinek. He’s “super worried,” he told me. He thinks that America will see major job losses—“a very noticeable labor-market effect”—as soon as this year.

“And then those economists you’ve been talking to, they’re going to say, ‘I see that in the data!’ ” Korinek paused. “Let’s not joke about it, because it’s too serious.”

Korinek is a professor and the faculty director of the Economics of Transformative AI Initiative at the University of Virginia. Last year, Time magazine put him on its list of the most influential people in AI. But he did not set out to become an economist. He grew up in an Austrian mountain village, writing machine code in 0s and 1s—the least glamorous form of programming, and the most unforgiving. It teaches you where instructions bottleneck, where systems jam, and what breaks first when pushed too hard.

He’d kept a close watch on developments in AI since the deep-learning breakthroughs of the early 2010s, even as his doctoral work focused on the prevention of financial crises. When he got his first demo of a large language model, in September 2022, it took “about five seconds” before he considered its consequences for the future of work, starting with his own.

We met for breakfast in Charlottesville in the fall. Korinek is youthful and slender, with delicate wire-frame glasses and a faintly red beard. My overall impression was of someone who’d rather be customizing Excel tabs than prophesizing doom. Still, here he was, saying the five words economists disdain the most: This time may be different.