Photo courtesy of www.eduba.com.

Dear Commons Community,

Science has an in depth article this morning entitled, “Large AI models are cultural and social technologies.” Well researched and carefully presented, the authors present a cogent argument that the “large language models” (LLMs) that are driving today’s AI applications are not by design the AI agents that might one day take over human life.  The basic definition of an artificial intelligent agent is a digital helper that can think and make decisions on its own. It uses information from its surroundings, learns from its experiences, and acts to accomplish tasks without human intervention. The authors’ thesis is well-founded and built on careful research that references work by the likes of Herbert Simon, Friedrich Hayek, and Claudia Goldin and Lawrence Katz.   The article’s conclusion is:

“Of course, as we note above, there may be hypothetical future AI systems that are more like intelligent agents, and we might debate how we should deal with these hypothetical systems, but LLMs are not such systems, any more than were library card catalogs or the internet. Like catalogs and the internet, large models are part of a long history of cultural and social technologies.”

I highly recommend this article (below) for anyone seriously interested in the issue of AI and its future particularly as related to its evolution as a true intelligent agent. Although long, it is quick read.

Tony

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Science

Large AI models are cultural and social technologies

Implications draw on the history of transformative information systems from the past

By Henry Farrell¹, Alison Gopnik^2,3,4, Cosma Shalizi^4,5,6, James Evans^4,7

March 14, 2025

Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents—perhaps even superintelligent AGI agents. But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us to understand AI systems more accurately. Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.

The new technology of large models combines important features of earlier technologies. Like pictures, writing, print, video, internet search, and other such technologies, large models allow people to access information that other people have created. Large models—currently language, vision, and multimodal—depend on the internet having made the products of these earlier technologies readily available in machine-readable form. But like economic markets, state bureaucracies, and other social technologies, these systems not only make information widely available, they allow it to be reorganized, transformed, and restructured in distinctive ways. Adopting Simon’s terminology, large models are a new variant of the “artificial systems of human society” that process information to enable large-scale coordination [(¹), p. 33].

Our central point here is not just that these technological innovations, like all other innovations, will have cultural and social consequences. Rather we argue that large models are themselves best understood as a particular type of cultural and social technology. They are analogous to such past technologies as writing, print, markets, bureaucracies, and representative democracies. Then we can ask the separate question about what the effects of these systems will be. New technologies that are not themselves cultural or social, such as steam and electricity, can have cultural effects. Genuinely new cultural technologies—Wikipedia, for example—may have limited effects. However, many past cultural and social technologies also had profound, transformative effects on societies, for good and ill, and this is likely to be true for large models.

These effects are markedly different from the consequences of other important general technologies such as steam or electricity. They are also different from what we might expect from hypothetical AGI. Reflecting on past cultural and social technologies and their impact will help us to understand the perils and promise of AI models better than worrying about superintelligent agents.

SOCIAL AND CULTURAL INSTITUTIONS

For as long as there have been humans, we have depended on culture. Beginning with language itself, human beings have had distinctive capacities to learn from the experiences of other humans, and these capacities are arguably the secret of human evolutionary success. Major technological changes in these capacities have led to dramatic social transformations. Spoken language was succeeded by pictures then by writing, print, film, and video. As more and more information became available across wider gulfs of space and time, new ways of accessing and organizing that information also developed, from libraries to newspapers to internet search. These developments have had profound effects on human thought and society, for better or worse. Eighteenth-century advances in print technology, for example, which allowed new ideas to quickly spread, played an important role in the Enlightenment and the French Revolution. A landmark transformation occurred around 2000 when nearly all the information from text, pictures, and moving images was converted into digital formats; it could be instantly transmitted and infinitely reproduced.

As long as there have been humans, we have also relied on social institutions to coordinate individual information-gathering and decision-making. These institutions can themselves be thought of as a kind of technology (¹). In the modern era, markets, democracies, and bureaucracies have been particularly important. The economist Friedrich Hayek argued that the market’s price mechanism generates dynamic summaries of enormously complex and otherwise unfathomable economic relations (²). Producers and buyers do not need to understand the complexities of production; all they need to know is the price, which compresses vast swathes of detail into a simplified but usable representation. Election mechanisms in democratic regimes focus distributed opinion toward collective legal and leadership decisions in a related way. The anthropologist Scott argued (³) that all states, democratic or otherwise, have managed complex societies by creating bureaucratic systems that categorize and systematize information. Markets, democracies, and bureaucracies have relied on mechanisms that generate lossy (incomplete, selective, and uninvertible) but useful representations well before the computer. Those representations both depend on and go beyond the knowledge and decisions of individual people. A price, an election result, or a measure such as gross domestic product (GDP) summarizes large amounts of individual knowledge, values, preferences, and actions. At the same time, these social technologies can also themselves shape individual knowledge and decision-making.

The abstract mechanisms of a market, state, or bureaucracy, like cultural media, can influence individual lives in crucial ways, sometimes for the worse. Central banks, for example, reduced the complexities of the financial economy down to a few key variables. This provided apparent financial stability but at the cost of allowing instabilities to build up in the housing market, which central banks paid little attention to, precipitating the 2008 global financial crisis (⁴). Similarly, markets may not represent “externalities” such as harmful carbon emissions. Integrating such information into prices through, for example, a carbon tax can help but requires state action.

“But these systems do not merely summarize this information, like library catalogs, internet search, and Wikipedia. They also can reorganize and reconstruct…information…”

Humans rely extensively on these cultural and social technologies. These technologies are only possible, however, because humans have distinct capacities characteristic of intelligent agents. Humans, and other animals, can perceive and act on a changing external world, build new models of that world, revise those models as they accumulate more evidence, and then design new goals. Individual humans can create new beliefs and values and convey those beliefs and values to others through language or print. Cultural and social technologies transmit and organize those beliefs and values in powerful ways, but without those individual capacities, the cultural and social technologies would have no purchase. Without innovation, there would be no point to imitation (⁵).

Some AI systems—in robotics, for example—do attempt to instantiate similar truth-finding abilities. There is no reason, in principle, why an artificial system could not do so at some point in the future. Human brains do, after all. But at the moment, all such systems are far from these human capacities. We can debate how much to worry now about these potential future AI systems or how we might handle them if they emerge. But this is different from the question of the effects of large models at present and in the immediate future.

LARGE MODELS

Large models, unlike more agentive systems, have made notable and unexpected progress in the past few years, making them the focus of the current conversation about AI in general. This progress has led to claims that “scaling,” simply taking the current designs and increasing the amount of data and computing power they use, will lead to AGI agents in the near future. But large models are fundamentally different from intelligent agents, and scaling will not change this. For example, “hallucinations” are an endemic problem in these systems because they have no conception of truth and falsity (although there are practical steps toward mitigation). They simply sample and generate text and images.

Rather than being intelligent agents, large models combine the features of cultural and social technologies in a new way. They generate summaries of unmanageably large and complex bodies of human-generated information. But these systems do not merely summarize this information, like library catalogs, internet search, and Wikipedia. They also can reorganize and reconstruct representations or “simulations” (¹) of this information at scale and in new ways, like markets, states, and bureaucracies. Just as market prices are lossy representations of the underlying allocations and uses of resources, and government statistics and bureaucratic categories imperfectly represent the characteristics of underlying populations, so too are large models “lossy JPEGs” (⁶) of the data corpora on which they have been trained.

Because it is hard for humans to think clearly about large-scale cultural and social technologies, we have tended to think of them in terms of agents. Stories are a particularly powerful way to pass on information, and from fireside tales to novels to video games, they have done this by creating illustrative fictional agents, even though listeners know that those agents are not real. Chatbots are the successor to Hercules, Anansi, and Peter Rabbit. Similarly, it is easy to treat markets and states as though they were agents, and agencies or companies can even have a kind of legal personhood.

But behind their agent-like interfaces and anthropomorphic pretensions, large language models (LLM) and large multimodal models are statistical models that take enormous corpora of text produced by humans, break them down into particular words, and estimate the probability distribution of long word sequences. This is an imperfect representation of language but contains a surprisingly large amount of information about the patterns it summarizes. It allows the LLM to predict which words come next in a sequence and so generate human-like text. Large multimodal models do the same with audio, image, and video data. Large models not only abstract a very large body of human culture, they also allow a wide variety of new operations to be carried out on it. LLMs can be prompted to carry out complex transformations of the data on which they are trained. Simple arguments can be expressed in flowery metaphors, while ornate prose can be condensed into plain language. Similar techniques enable related models to generate new pictures, songs, and video in response to prompts. A body of cultural information that was previously too complex, large, and inchoate for large-scale operations has been rendered tractable.

In practice, the most recent versions of these systems depend not only on massive caches of text and images generated and curated by humans but also on human judgment and knowledge in other forms. In particular, the systems rely on reinforcement learning from human feedback (RLHF) or its variants: Tens of thousands of human employees provide ratings of model outputs. They also depend on prompt engineering: Humans must use both their background knowledge and ingenuity to extract useful information from the models. Even the newest “chain of thought” models regularly begin from dialogue with their human users.

The relatively simple though powerful algorithms that allow large models to extract statistical patterns from text are not really the key to the models’ success. Instead, modern AI rests atop libraries, the internet, tens of thousands of human coders, and a growing international world of active users. Someone asking a bot for help writing a cover letter for a job application is really engaging in a technically mediated relationship with thousands of earlier job applicants and millions of other letter writers and RLHF workers.

CHALLENGES AND OPPORTUNITIES

The AI debate should focus on the challenges and opportunities that these new cultural and social technologies generate. We now have a technology that does for written and pictured culture what large-scale markets do for the economy, what large-scale bureaucracy does for society, and perhaps even comparable with what print once did for language. What happens next? Like past economic, organizational, and informational “general purpose technologies,” these systems will have implications for productivity (⁷), complementing human work but also automating tasks that only humans could previously perform, and for distribution, affecting who gets what (⁸).

Yet they will also have wider and more profound cultural consequences. We do not yet know whether these consequences will be as great as those of earlier technologies such as print, markets, or bureaucracies, but thinking of them as cultural technologies increases rather than decreases their potential impact. These earlier technologies were central to the extensive social transformations of the 18th and 19th centuries, both as causes and effects. All of these technologies, like large models, supported the abstraction of information so that new kinds of operations could be carried out at scale. All provoked justified concerns about the spread of misinformation and bias, cultural homogenization or fragmentation, and shifts in the distribution of power and resources. The emergence of new communications media, including both print and television, was accompanied by reasonable worries that the new media would spread misinformation and strengthen malign cultural forces. Similarly, the categorization schemes that bureaucracies and markets deploy often embed oppressive assumptions.

At the same time, these technologies generated new possibilities for recombining information and coordinating actions among millions of people at a planetary scale. Emerging debates over the social, economic, and political consequences of LLMs continue deep-rooted historical worries and hopes about new cultural and social technologies. Orienting these debates requires both recognizing the commonalities between new arguments and old ones and carefully mapping the particulars of the new and evolving technologies.

Such mapping is among the central tasks of the social sciences, which emerged from the social, economic, and political upheavals of the Industrial Revolution and its aftermath. Social scientists’ investigation of the consequences of these past technologies can help us think about less obvious social implications of AI, both negative and positive, and to consider ways that AI systems could be redesigned to increase the positive impacts and reduce the negative. As media, markets, and bureaucratic technologies expanded in the 19th and 20th centuries, they generated economic losers and winners, displacing whole categories of workers, from clerks and typists to “human computers.” Today, there are obvious worries that large models and related technologies may displace “knowledge workers.”

There are also less obvious questions. Will large models homogenize or fragment culture and society? Thinking about this in historical context can be particularly illuminating. Current concerns resemble 19th- and 20th-century disagreements over markets and bureaucracies. Weber worried (⁹) about the deadening homogenizing consequences of economic and bureaucratic “rationalization,” whereas Mill (¹⁰) thought that market exchanges would expose participants to different forms of life and soften impulses to conflict (“doux commerce”).

Large models are designed to work well—to faithfully reproduce the actual probabilities of sequences of text, images, and video—on average. They therefore have an intrinsic tendency to be most accurate in situations most commonly found in their training data and least accurate in situations that were rare in data or entirely new. This might lead large models to worsen the kind of homogenization that haunted Weber.

On the other hand, large models may allow us to design new ways to harvest the diversity of the cultural perspectives they summarize. Combining and balancing these perspectives may provide more sophisticated means of solving complex problems (¹¹). One way to do this may be to build “society-like” ecologies in which different perspectives, encoded in different large models, debate each other and potentially cross-fertilize to create hybrid perspectives (¹²) or to identify gaps in the space of human expertise (¹³) that might usefully be bridged. Large models are surprisingly effective at abstracting subtle and nonobvious patterns in texts and images. This suggests that such technologies could be used to find patterns in text and images that crisscross the space of human knowledge and culture, including patterns invisible to any particular human. We may require new systems that diversify large model reflections and personas and produce the same distribution and diversity as do human societies.

Diversifying systems like this might be particularly important for scientific progress. Formal science itself depended on the emergence of the new cultural technologies of the 17th and 18th centuries, from coffee houses and rapid mail to journals and peer review. AI technologies have the potential to accelerate science further, but this will depend on imaginative ways of using and rethinking these technologies. By wiring together so many perspectives across text, audio, and images, large models may allow us to discover unprecedented connections between them for the benefit of science and society. These technologies have most commonly been trained to regurgitate routine information as helpful assistants. A more fundamental set of possibilities might open up if we deployed them as maps to explore formerly uncharted territory.

There are also less obvious and more interesting ways that new cultural and social technologies influence economic relationships. The development of cultural technologies leads to a fundamental economic tension between the people who produce information and the systems that distribute it. Neither group can exist without the other: A writer needs publishers as much as the publisher need writers. But their economic incentives push in opposite directions. The distributors will profit if they can access the producer’s information cheaply, whereas the producers will profit if they can get their information distributed cheaply. This tension has always been a feature of new cultural technologies. The ease and efficiency of distributing information in digital form has already made this problem especially acute, as evidenced by the crisis in everything from local newspapers to academic journals. But the very speed, efficiency, and scope of large models, processing all the available information at once, combined with the centralized ownership of those models, makes these problems loom especially large. Concentrated power may make it easier for those who own the systems to skim the benefits of efficiency at the expense of others.

“…large models are themselves best understood as a particular type of cultural and social technology.”

There are crucial technical questions: To what extent can the systematic imperfections of large models be remedied, and when are they better or worse than the imperfections of systems based around human knowledge workers? Those should not overshadow the crucial political questions: Which actors are capable of mobilizing around their interests, and how might they shape the resulting mix of technology and organizational capacities? Very often, commentators within the technology sector reduce these questions into a simple battle between machines and humans. Either the forces of progress will prevail against retrograde Luddite tendencies, or on the other hand, human beings will successfully resist the inhuman encroachment of artificial technology. Not only does this fail to appreciate the complexities of past distributional struggles, struggles that long predate the computer, it ignores the many different possible paths that future progress might take, each with its own mix of technological possibilities and choices (⁸).

In the case of earlier social and cultural technologies, a range of further institutions, including normative and regulatory institutions, emerged to temper their effects. These ranged from editors, peer review, and libel laws for print, to election law, deposit insurance, and the Securities and Exchange Commission for markets, democracies, and bureaucracies. These institutions had varied effectiveness and required continual revision. These countervailing forces did not emerge on their own, however, but resulted from concerted and sustained efforts by actors both within and outside the technologies themselves.

LOOKING FORWARD

The narrative of AGI, of large models as superintelligent agents, has been promoted both within the tech community and outside it, both by AI optimist “boomers” and more concerned “doomers.” This narrative gets the nature of these models and their relation to past technological changes wrong. But more importantly, it actively distracts from the real problems and opportunities that these technologies pose and the lessons history can teach us about how to ensure that the benefits outweigh the costs.

Of course, as we note above, there may be hypothetical future AI systems that are more like intelligent agents, and we might debate how we should deal with these hypothetical systems, but LLMs are not such systems, any more than were library card catalogs or the internet. Like catalogs and the internet, large models are part of a long history of cultural and social technologies.

The social sciences have explored this history in detail, generating a distinct understanding of past technological upheavals. Bringing computer science and engineering into close cooperation with the social sciences will help us to understand this history and apply these lessons. Will large models lead to greater cultural homogeneity or greater fragmentation? Will they reinforce or undermine the social institutions of human discovery? As they reshape the political economy, who will win and lose? These and other urgent questions do not come into focus in debates that treat large models as analogs for human agents.

Changing the terms of debate would lead to better research. It would be far easier for social scientists and computer scientists to cooperate and combine their respective strengths if both understood that large models are no more—but also no less—than a new kind of cultural and social technology. Computer scientists could bring together their deep understanding of how these systems work with social scientists’ comprehension of how other such large-scale systems have reshaped society, politics, and the economy in previous eras, elaborating existing research agendas and discovering new ones. This would help remedy past confusions in which computer scientists have adopted overly simplified notions of complex social phenomena (¹⁴) while social scientists have failed to understand the complex functioning of these new technologies.

It would move policy discussions over AI decisively away from simplistic battles between the existential fear of a machine takeover and the promise of a near-future paradise in which everyone will have a perfectly reliable and competent artificial assistant. The actual policy consequences of large models will surely be different. Like markets and bureaucracies, they will make some kinds of knowledge more visible and tractable than they were in the past, encouraging policy-makers to focus on the new things that they can measure and see at the expense of those less visible and more confusing. As a result, reflecting past cases of markets and media, power and influence will shift toward those who can fully deploy these technologies and away from those who cannot. AI weakens the position of those on whom it is used and who provide its data, strengthening AI experts and policy-makers (¹⁴).

Last, thinking in this way might reshape AI practice. Engineers and computer scientists are already aware of the problem of large model bias and are thinking about their relationship to ethics and justice. They should go further. How will these systems affect who gets what? What will their practical consequences be for societal polarization and integration? Can large models be developed to enhance human creativity rather than to dull it? Finding practical answers to such questions will require an understanding of social science as well as engineering. Shifting the debate about AI away from agents and toward cultural and social technologies is a crucial first step toward building that cross-disciplinary understanding (¹⁵).

¹SNF Agora Institute and School of Advanced International Studies, Johns Hopkins University, Baltimore, MD, USA.

²Department of Psychology, University of California, Berkeley, CA, USA.

³Department of Philosophy, University of California, Berkeley, CA, USA.

⁴Santa Fe Institute, Santa Fe, NM, USA.

⁵Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.

⁶Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.

⁷Department of Sociology, University of Chicago, Chicago, IL, USA.

Email: [email protected]

REFERENCES AND NOTES

1. H. Simon, The Sciences of the Artificial (MIT Press, 1996).
2. F. A. von Hayek, Am. Econ. Rev. 35, 519 (1945).
3. J. C. Scott, Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (Yale Univ. Press, 1998).
4. D. Davies, The Unaccountability Machine (Univ. Chicago Press, 2025).
5. E. Yiu, E. Kosoy, A. Gopnik, Perspect. Psychol. Sci. 19, 874 (2024).
6. T. Chiang, New Yorker 9 (2023).
7. C. Goldin, L. Katz, Q. J. Econ. 113, 693 (1998).
8. D. Acemoglu, S. Johnson, Power and Progress: Our 1000 Year Struggle over Technology and Prosperity (Hachette, 2023).
9. M. Weber, Wissenschaft Als Beruf (Duncker & Humblot, 1919).
10. J. S. Mill, Principles of Political Economy (Longmans and Green, 1920).
11. L. Hong, S. E. Page, Proc. Natl. Acad. Sci. U.S.A. 101, 16385 (2004).
12. S. Lai et al., Proc. 41st Int. Conf. Mach. Learn. 235, 25892 (2024).
13. J. Sourati, J. A. Evans, Nat. Hum. Behav. 7, 1682 (2023).
14. S. L. Blodgett, S. Barocas, H. Daumé, H. Wallach, arXiv:2005.14050 [cs.CL] (2020)
15. L. Brinkmann et al., Nat. Hum. Behav. 7, 1855 (2023).

ACKNOWLEDGMENTS

All authors contributed equally to this work. J.E. began a visiting researcher affiliation with Google after this manuscript was submitted.

Judge William H. Alsup

Dear Commons Community,

A federal judge called the administration’s justification for the firings of workers with probationary status a “sham.”

Judge William H. Alsup of the U.S. District Court for the Northern District of California yesterday ordered six federal agencies to rehire thousands of workers with probationary status who had been fired as part of President Trump’s government-gutting initiative.

Ruling from the bench, Judge Alsup  went further than a previous ruling. He found that the Trump administration’s firing of probationary workers had essentially been done unlawfully by fiat from the Office of Personnel Management, the government’s human resources arm. Only agencies themselves have broad hiring and firing powers, he said. As reported by The New York Times.

He directed the Treasury and the Veterans Affairs, Agriculture, Defense, Energy and Interior Departments to comply with his order and offer to reinstate any probationary employees who were improperly terminated. But he added that he was open to expanding his decision later to apply to other agencies where the extent of harms had not been as fully documented yet.

His order stemmed from a lawsuit brought by federal employee unions that challenged the legality of how those agencies went about firing probationary workers en masse. The unions argued that those workers were swept up in a larger effort by Mr. Trump and his top adviser, Elon Musk, to arbitrarily ravage the federal government and demoralize its employees.

Judge Alsup said he was convinced that federal agencies followed a directive from senior officials in the Office of Personnel Management to use a loophole allowing them to fire probationary workers by citing poor performance, regardless of their actual conduct on the job. He concluded that the government’s actions were a “gimmick” intended to expeditiously carry out mass firings.

“It is a sad day when our government would fire some good employee and say it was based on performance when they know good and well that’s a lie,” he said.

“It was a sham in order to try to avoid statutory requirements,” he added.

He also extended his restraining order issued last month blocking the Office of Personnel Management from orchestrating further mass firings. But before handing down his ruling on Thursday, Judge Alsup was careful to make sure the lawyers representing the unions understood its limits.

Agencies planning to conduct large-scale layoffs, known as a “reduction in force,” can still proceed in accordance with the laws that govern such processes — meaning that the reprieve for workers may only be temporary. The Office of Personnel Management had set a deadline of Thursday for agencies to submit reduction in force plans.

“If it’s done right, there can be a reduction in force within an agency, that has to be true,” Judge Alsup said.

As the Trump administration continues to contest the judge’s order, a substantial portion of the hearing on yesterday also focused on the ways the government has moved to circumvent the courts and sideline workers by any means available.

In other cases focused on the administration’s suspension of federal contracts and grants, judges have similarly fretted that agencies have forged ahead to terminate those programs faster than courts could order the funding unfrozen.

Danielle Leonard, a lawyer representing the unions, said that even after an independent agency that protects government workers in employment disputes ordered the Agriculture Department to reinstate 6,000 probationary workers this month, the agency had kept many on paid leave, restoring their salaries but not their jobs.

“We do not believe that they are going to return any of these employees to actual service,” she said.

In a statement on Tuesday, the Agriculture Department said it was working on a “phased plan for return to duty” for those workers.

Judge Alsup had originally planned to have Trump administration officials appear to testify on Thursday about the process through which the layoffs were planned. But the government made clear on Wednesday that Charles Ezell, the acting head of the Office of Personnel Management, would not appear.

Mr. Ezell became a central character in the lawsuit because of memos and meetings he held with agency heads in February that included detailed guidance on how to conduct the mass layoffs of probationary workers. Lawyers representing the federal workers’ unions that sued called the Mr. Ezell’s guidance “insidious” and clearly devised to enlist Mr. Trump’s appointees into a broader effort to decimate the federal work force.

Judge Alsup said he had hoped testimony from the officials involved in the firings would provide clarity about the conception and execution of those plans. He also excoriated a lawyer from the Justice Department for failing to produce him and other potential witnesses.

As part of his ruling, Judge Alsup specified that the government must allow Noah Peters, a lawyer working with Mr. Musk’s team who was detailed to the personnel office, to be deposed in Washington about the impetus behind the firings.

“You will not bring the people in here to be cross-examined,” he said. “You’re afraid to do so, because you know cross-examination would reveal the truth.”

Kelsey Helland, the lawyer present from the Justice Department, said the government had submitted ample evidence that agencies were acting on their own and were never beholden to orders from Mr. Ezell.

On Wednesday, the government filed news releases from a variety of agencies. They included language indicating that the appointed leaders of those agencies made the decisions to shrink their work forces independently and in line with Mr. Trump’s political agenda — not based on any directive from the Office of Personnel Management.

“These were the actions of the political leadership of these agencies in response to a priority, a clearly communicated public priority of the administration, rather than an order from O.P.M.,” Mr. Helland said.

Mr. Helland added that it was not unusual for the Trump administration to try to shield its top officials from appearing in court.

“Every presidential administration in modern history has jealously guarded their agency heads against being forced to give testimony,” he said.

But Judge Alsup grew increasingly riled by those explanations, saying he felt “misled by the U.S. government” about the way it had proceeded.

He said the Trump administration appeared determined to “decimate” agencies such as the Merit Systems Protection Board, a venue through which workers can appeal adverse personnel decisions, and the Office of Special Counsel, which used to be well positioned to assist probationary workers in this case.

All the while, Judge Alsup said, the government appeared to have obfuscated its intentions with regard to federal workers while sidestepping his orders to have top officials testify.

“It upsets me; I want you to know that,” he said.

“You’re not helping me get at the truth,” he added. “You’re giving me press releases — sham documents.”

Unfortunately, this ruling will only delay not stop Trump’s plans.

Tony

Dear Commons Community,

The first total lunar eclipse of the year is set to be visible starting tonight (March 13) through the early morning of March 14.

According to NASA, lunar eclipses “occur when the Sun, Earth, and moon align so that the moon passes into Earth’s shadow.” During a total lunar eclipse, the moon passes by the darkest part of Earth’s shadow known as the umbra and makes the moon appear a red-orange color, giving it the “blood moon” nickname.  As provided by PEOPLE.

Unlike a total solar eclipse — which can only be seen in certain locations for a few minutes — a total lunar eclipse is visible in the sky for a few hours and can be seen in larger parts of the world, per the National Weather Service. Lunar eclipses also occur more frequently than solar eclipses and are safe to view with the naked eye. That being said, this will be the first total lunar eclipse since November 2022, making it a rare celestial event.

In astrology, eclipses are known to bring plenty of change in a person’s life and astrologer Valerie Mesa tells PEOPLE this lunar eclipse is “all about illumination and powerful endings,” adding that “they’re known to shed light on what’s been hidden beneath the surface so we can purge and release what doesn’t serve us.”

Here’s everything you need to know about this upcoming total lunar eclipse, including where it will be visible.

When is the 2025 total lunar eclipse?

The first total lunar eclipse of the year will take place in the evening between March 13 and March 14.

Where will the total lunar eclipse be visible?

Per Space.com, the total lunar eclipse will be visible across North America — including all 50 states — South America, Europe, Africa and Oceania — which includes New Zealand.

What time does the total lunar eclipse start?

Getting a good glimpse at the total lunar eclipse will depend on your location. Per NASA, the lunar eclipse will begin at 11:57 p.m. EDT, and reach totality 2:26 a.m. The moon will stay in totality for a little over an hour, ending at 3:31 a.m. The lunar eclipse will then end at 6:00 a.m.

How do you watch the total lunar eclipse?

The total lunar eclipse is safe to view with the naked eye, unlike a solar eclipse which requires special glasses or other tools to safely view.

During the lunar eclipse, NASA recommends observing the eclipse in a dark spot away from bright lights and using binoculars or a telescope for the best view. While you’re observing the lunar eclipse, stargazers may also be able to catch a glimpse at Jupiter and Mars in the western part of the night sky.

It should be a good show assuming the weather cooperates!

Tony

Dear Commons Community,

President Donald Trump’s dream of U.S. control over Greenland may be on hold after this week’s election in the autonomous Danish territory.  As reported by The Huffington Post and The New York Times.

During a debate earlier this week, leaders of six parties vying in the election were asked if they trust Trump.

Five said no.

The one yes, Karl Ingemann of the relatively new Qulleq party, didn’t even win a seat in Parliament after his party flopped with just 1.1% of the vote.

The biggest winner this week was the center-right Demokraatit Party, which took nearly 30% of the vote.

Its leader, Jens-Frederik Nielsen, has been blunt in addressing Trump’s ambitions for the territory, calling his rhetoric “a threat to our political independence.”

The New York Times said the party was likely to enter a coalition with Inuit Ataqatigiit, a moderate party that came in third in this week’s election with 21.4% of the vote. Its leader is the current prime minister, Múte Egede, who has also dismissed joining the United States.

“I think in the future, we have a lot to offer to cooperate with, but we want to also be clear. We don’t want to be Americans, we don’t want to be a part of U.S., but we want a strong cooperation together with U.S.,” Egede told Fox News earlier this year.

Both parties favor a gradual approach to the territory’s independence from Denmark.

The second-place party, Naleraq, won nearly a quarter of the vote. Its leader, Pele Broberg, wrote an editorial this week calling for a more rapid independence from Denmark and a pact with the United States but “without becoming a U.S. territory.”

Trump has repeatedly said he wants Greenland to join the United States.

“We need [Greenland] really for international world security, and I think we’re going to get it,” he said during last week’s address to Congress. “One way or the other, we’re going to get it.”

Polls show it’s not a popular notion in Greenland, with 85% of respondents opposed in a January survey.

Trump reportedly floated purchasing the island during his first term in office but didn’t say much about it publicly. Then after he won a second term last year, he called control over the territory an “absolute necessity” and suggested taking it by force if needed.

Trump’s scheme isn’t popular in Denmark, either, with Prime Minister Mette Frederiksen insisting that the territory “is not for sale.”

Others have been more blunt, with Danish politician Anders Vistisen telling the president he can “f*** off.”

Tony

Dear Commons Community,

Alaska and four southern states rank in the top five nationally for their dependence on federal dollars, according to a new report by WalletHub.  The Southern states ranking second through fifth are: Kentucky, West Virginia, Mississippi and South Carolina. As reported by USA Today.

WalletHub compiled the ranking “just to point out that not all states are equal when it comes to federal funding,” said Chip Lupo, a writer and analyst at the personal finance site.

The report has currency in light of the Trump administration’s recent efforts to freeze federal funding to programs that do not align with the Republican agenda. The freeze was announced in a January memo, subsequently rescinded. It is now the subject of an ongoing court battle.

Meanwhile, Congressional budget resolutions call for massive cuts in federal funding, potentially forcing states to make “incredibly hard decisions” about Medicaid and other programs, according to a report from the left-leaning Center for Law and Social Policy.

Those predictions echo warnings from Democrats in Congress. Republicans contend that Medicaid and other state-level programs are safe.

Federal dollars fund up to half of state budgets

Federal dollars make up 18% to 50% of state budgets, depending on the state, according to data from the National Association of State Budget Officers. On average, about one-third of state dollars come from the federal government.

Millions of Americans in every state rely on federal funds, including 72 million Medicaid recipients, 30 million students eating subsidized meals, and 43 million food stamp recipients, according to a report from the left-leaning Center for American Progress.

States receive federal aid for many other purposes, from disaster relief to covering the costs of improvements in education, transportation and other infrastructure needs.

To measure each state’s reliance on the federal government, WalletHub looked at three factors: How much of each state’s revenue comes from the federal government; what share of the state’s workforce is employed in federal jobs; and how much federal money the state receives for every dollar paid in federal taxes.

Many of the most federally dependent states, as it turns out, are relatively rural, sparsely populated and “need federal funding for infrastructure,” Lupo said.

Some, like Alaska, have large tracts of federally owned land. Others, like South Carolina, have relatively large military populations.

Others have powerful, long-tenured representatives in Congress who have steered federal dollars to their constituents.

“Kentucky, for example: Mitch McConnell, he’s an institution in Congress,” Lupo said of the long-serving Republican senator.

The five most federally dependent states

Here are the five most federally dependent states, in order:

Alaska

More than half of Alaska’s revenue comes from the federal government. One reason is the challenge of maintaining infrastructure in an enormous state with difficult weather and a small population. Other factors include the state’s vulnerability to natural disasters, and its military value.

Nearly 5% of Alaska’s workforce is employed by the federal government. In most other states, the share ranges from 1% to 3%.

Kentucky

Kentucky receives $3.35 in federal funds for every dollar Kentuckians pay in federal taxes. Federal funding makes up roughly 46% of Kentucky’s revenue, one of the highest shares in the nation.

West Virginia

This state receives $2.72 in federal funds for every dollar residents pay in federal taxes. Federal funding makes up roughly 45% of West Virginia’s revenue. About 3.7% of the state’s workforce is employed by the federal government, one of the highest rates in the nation.

Mississippi

Federal funding makes up 45% of state revenue in Mississippi. The state reaps $2.34 in federal funds for every dollar paid in federal taxes.

South Carolina

South Carolina ranks first among states for the ratio of federal dollars it receives — $3.42 — per federal tax dollar collected.

In political terms, WalletHub found that red states are more federally dependent than blue states. The average Republican-leaning state ranks 21 among the 50 states, while the average Democrat-leaning state ranks 32.

Red States rely more on federal funds

New Jersey, a blue state, ranks as the least federally dependent state, WalletHub reports. Only 1.2% of state residents work in federal jobs, and federal dollars make up only 30% of state revenue.

California ranks second among states least dependent on federal funding. Federal funding makes up only 28% of state revenue, and only 1.4% of residents work for the federal government.

The ranking of all of the states is available at  WalletHub.  

Tony

Linda McMahon (AP Photo/Jacquelyn Martin)

Dear Commons Community,

 The US Education Department plans to lay off more than 1,300 of its employees as part of an effort to halve the organization’s staff — a prelude to President Donald Trump’s plan to dismantle the agency.

Department officials announced the cuts yesterday, raising questions about the agency’s ability to continue usual operations.

The Trump administration had already been whittling the agency’s staff, though buyout offers and the termination of probationary employees. After Tuesday’s layoffs, the Education Department’s staff will sit at roughly half of its previous 4,100, the agency said.

The layoffs are part of a dramatic downsizing directed by Trump as he moves to reduce the footprint of the federal government. Thousands of jobs are expected to be cut across the Department of Veterans Affairs, the Social Security Administration and other agencies.

The department is also terminating leases on buildings in cities including New York, Boston, Chicago and Cleveland, officials said.

Department officials said it would continue to deliver on its key functions such as the distribution of federal aid to schools, student loan management and oversight of Pell Grants.

Education Secretary Linda McMahon said when she got to the department, she wanted to reduce bloat to be able to send more money to local education authorities. As reported by The Associated Press.

“So many of the programs are really excellent, so we need to make sure the money goes to the states,” McMahon said in an interview yesterday on Fox News.

McMahon told employees to brace for profound cuts in a memo issued March 3, the day she was confirmed by the Senate. She said it was the department’s “final mission” to eliminate bureaucratic bloat and turn over the agency’s authority to states.

The department sent an email to employees Tuesday telling them its Washington headquarters and regional offices would be closed Wednesday, with access forbidden, before reopening Thursday. The only reason given for the closures was unspecified “security reasons.”

Trump campaigned on a promise to close the department, saying it had been overtaken by “radicals, zealots and Marxists.” At McMahon’s confirmation hearing, she acknowledged only Congress has the power to abolish the agency but said it might be due for cuts and a reorganization.

Whether the cuts will be felt by America’s students — as Democrats and advocates fear — is yet to be seen. Already there are concerns the administration’s agenda has pushed aside some of the agency’s most fundamental work, including the enforcement of civil rights for students with disabilities and the management of $1.6 trillion in federal student loans.

McMahon told lawmakers at her hearing that her aim is not to defund core programs, but to make them more efficient.

Even before the layoffs, the Education Department was among the smallest Cabinet-level agencies. Its workforce included 3,100 people in Washington and an additional 1,100 at regional offices across the country, according to a department website.

The department’s workers had faced increasing pressure to quit their jobs since Trump took office, first through a deferred resignation program and then through a $25,000 buyout offer that expired March 3.

Jeanne Allen of the Center for Education Reform, which advocates for charter school expansion, said the cuts were important and necessary.

“Ending incessant federal interference will free up state and local leaders to foster more opportunities to give schools and educators true flexibility and innovation to address the needs of students, wherever they are educated,” Allen said.

Some advocates were skeptical of the department’s claim that its functions would not be affected by the layoffs.

“I don’t see at all how that can be true,” said Roxanne Garza, who was chief of staff in the Office of Postsecondary Education under President Joe Biden.

Much of what the department does, like investigating civil rights complaints and helping families apply for financial aid, is labor intensive, said Garza, who is now director of higher education policy at Education Trust, a research and advocacy organization. “How those things will not be impacted with far fewer staff … I just don’t see it.”

I agree!

Tony

Members of the Columbia University Apartheid Divest group, including Mahmoud Khalil, center, on April 30, 2024, in New York City.  Mary Altaffer via Associated Press

Dear Commons Community,

Trump confirmed yesterday that federal immigration agents arrested and detained Mahmoud Khalil, a Palestinian activist and recent Columbia University graduate who was taken this weekend — despite being a permanent legal resident of the United States — for helping peacefully lead antiwar protests on campus last year.

Despite not having a warrant, plainclothes agents abducted Khalil Saturday night as he returned to his university-owned apartment with his wife, a U.S. citizen who is eight months pregnant. Agents claimed they were revoking Syrian-born Khalil’s green card and also threatened to detain his wife, according to a habeas corpus petition his attorney Amy Greer filed on his behalf.

Greer says that the arrest, which was confirmed by the Department of Homeland Security, violates Khalil’s First Amendment and due process rights. On Monday evening, a district judge ruled against deporting him from the U.S. pending further legal action in his case.As reported by The Huffington Post and The Associated Press.

“The remarks by government officials, including the President, on social media only confirm the purpose – and illegality – of Mahmoud’s detention,” Greer said in a Monday evening statement. “He was chosen as an example to stifle entirely lawful dissent in violation of the First Amendment. While tomorrow or thereafter the government may cite the law or process, that toothpaste is out of the tube and irreversibly so. The government’s objective is as transparent as it is unlawful, and our role as Mahmoud’s lawyers is to ensure it does not prevail.”

Trump announced the arrest himself yesterday afternoon, with the president labeling the Palestinian man a “Radical Foreign Pro-Hamas Student” without providing any evidence to support that claim. The arrest was first reported by Zeteo.

“This is the first arrest of many to come. We know there are more students at Columbia and other Universities across the Country who have engaged in pro-terrorist, anti-Semitic, anti-American activity, and the Trump Administration will not tolerate it,” Trump posted on Truth Social, again claiming without evidence that antiwar activists on college campuses are “paid agitators” and not students.

Khalil, who received his master’s degree from Columbia’s School of International and Public Affairs, was a prominent figure in last spring’s protests on college campuses nationwide. Students and faculty of all faiths nonviolently called on the U.S. to stop supporting Israel’s deadly military offensive in Gaza and for their universities to divest from companies that work with the Israeli military and government.

Columbia University, in particular, came under scrutiny for its aggressive response that included allowing New York police to destroy solidarity encampments and arrest protesters. Khalil was a lead negotiator in the university’s encampment and just last week told The Associated Press that he was among those under investigation by a new Columbia office that has disciplined dozens of pro-Palestinian student activists.

“There have been reports of ICE around campus. Columbia has and will continue to follow the law,” the university said in a Sunday statement. “Consistent with our longstanding practice and the practice of cities and institutions throughout the country, law enforcement must have a judicial warrant to enter non-public University areas, including University buildings.”

Three ICE agents also visited a second foreign student at Columbia over the weekend and attempted to enter her university-owned apartment without a warrant, according to a graduate student union representing the unidentified woman. The Student Workers of Columbia said the agents were “rightfully turned away at the door,” according to the AP.

Columbia University did not say whether it had prior knowledge of Immigration and Customs Enforcement agents arresting Khalil without a warrant. But the school announced its Morningside campus is essentially locking down beginning Monday and will have additional public safety guards at the gates and around the perimeter, according to an email obtained by Zeteo.

“It’s not a criminalized activity to speak out against genocide. In fact, it’s antiracist, it’s for justice and it’s for the wellbeing of everyone across the world, including this country,” Layan Fuleihan, education director for the People’s Forum, told HuffPost at a peaceful New York City protest for Khalil’s release. “I would say this is something we’re not intimidated by, we’re going to be fighting for Mahmoud’s release and for anyone who is targeted in this outrageous witch hunt coming from the U.S. administration.”

Immigration experts say DHS can start deportation proceedings against green card holders for alleged criminal activity, but the legal basis for detaining a legal permanent resident without criminal charges is shaky.

While DHS accused Khalil of engaging in “activities aligned to Hamas,” the agency gave no evidence of him providing material support to the militant group.

Greer said she was initially told that Khalil was transferred to an ICE detention center in New Jersey. Upon visiting the facility, his wife was told he was not there. According to the ICE detainee tracker, Khalil has been transferred to the Jena/LaSalle Detention Facility in Louisiana.

DHS spokesperson Tricia McLaughlin did not respond to HuffPost to say why agents did not inform Khalil’s wife of his location, nor why agents ignored Khalil’s constitutional rights by taking him, a legal permanent resident of the U.S., without a warrant.

Columbia and the federal government soon faced intense backlash over Khalil’s arrest, with faculty, advocacy groups, lawyers and students all demanding ICE release him and end Trump’s ongoing free speech crackdown disguised as fighting antisemitism.

“My committed Jewish faculty colleagues and I have warned that the false characterization of Columbia as a hotbed of antisemitism would be used as an alibi for what’s actually at stake, for the Republican establishment and now the Trump administration,” Marianne Hirsch, a professor emerita at Columbia, said on Monday. “Strict control of speech, protest and higher education at large.”

The Council on American-Islamic Relations, a Muslim American advocacy group, said it will join other civil rights organizations in helping fight Khalil’s detention, which is the first publicly known deportation effort under Trump’s ongoing crackdown on free speech that he’s portrayed as fighting antisemitism.

“It is utterly despicable that they are carrying out this authoritarian lurch under the guise of fighting for Jewish safety. Let’s be perfectly clear: not only does destroying higher education and abducting students for political speech not keep Jews safe, it actively endangers us,” said Eva Borgwardt, spokesperson for progressive Jewish American group IfNotNow, on Monday.

“If we do not all strongly oppose this dystopian crackdown on freedom of expression, directed by a government with many neo-Nazi ties and members, any of Trump’s political opponents could be its next victims,” she continued. “Advocates for reproductive justice, climate action, trans rights, immigrant rights and other progressive causes.”

Freedom of speech anyone!

Tony

Dear Commons Community,

Ronald Reagan gave a radio address in 1987 (see video below) which is going viral on the Internet.  His comments especially those about tariffs should be heeded by Trump and the Republicans in Congress.  The video is about five minutes long.  His comments on tariffs appear at about the 2:35 mark.

Tony

Mark Carney

Dear Commons Community,

After yesterday’s  Liberal Party leadership vote. former central banker Mark Carney will will succeed Justin Trudeau to become Canada’s next prime minister.

Carney is 59. He was born in Fort Smith, Northwest Territories, on March 16, 1965, and raised in Edmonton, Alberta.

Credentials

Carney ran the Bank of Canada from 2008 to 2013 and the Bank of England from 2013 to 2020. After helping Canada manage the worst impacts of the 2008 financial crisis, he was recruited to become the first non-Brit to run the Bank of England since it was founded in 1694.

In 2020, he began serving as the United Nations’ special envoy for climate action and finance.

Carney is a former Goldman Sachs executive. He worked for 13 years in London, Tokyo, New York and Toronto, before being appointed deputy governor of the Bank of Canada in 2003. He has no experience in politics.

Education

Carney received a bachelor’s degree in economics from Harvard University in 1988, and master’s and doctoral degrees in economics from Oxford University. Like many Canadians, he played ice hockey, serving as a backup goalie for Harvard.

Citizenship

Carney has Canadian, U.K. and Irish citizenship. He has moved to eventually have solely Canadian citizenship, which is not required by law but seen as politically wise.

Family

His wife Diana is British-born and he has four daughters.

Polls

His chances of remaining prime minister for more than a few weeks seem to be improving. In a mid-January poll by Nanos, the Liberals trailed the opposition Conservatives and their leader Pierre Poilievre 47% to 20%. This week the latest poll has Liberals at 34% and the Conservatives at 37%.

We wish him luck!

Tony

Dear Commons Community,

The job market is becoming more challenging but prospective employees in certain career fields are faring better than others. To find the jobs that have the most demand, Resume Genius looked at projected annual job openings as well as the estimated job growth over the next decade. Among these jobs, there are several that pay over $100,000. Here is the list of the positions, average salaries, growth potential, and education requirements as presented at:  GOBankingRates.com: 4 Six-Figure Jobs That Are in High Demand in 2025

1. General and Operations Managers

• Median annual salary: $101,280
• Projected annual job openings: 320,800
• Number of jobs (2023): 3,507,810
• Estimated job growth (2023-2033): 6%
• Typical educational requirements: Bachelor’s degree (varies by industry)

These managers oversee multiple departments or locations to ensure that a business is staying on track. Operations managers, in particular, are in high demand, as they help look for ways to cut down on inefficiencies and streamline business operations.

2. Nurse Anesthetists, Nurse Midwives and Nurse Practitioners

• Median annual salary: $129,480
• Projected annual job openings: 29,000
• Number of jobs (2023): 349,600
• Estimated job growth (2023-2033): 40%
• Typical educational requirements: Master’s degree

We are currently in the midst of a nursing shortage, so any registered nurse should have no trouble finding work. However, nurses with any of these specialties are especially in high demand this year.

3. Software Developers

• Median annual salary: $130,160
• Projected annual job openings: 125,100
• Number of jobs (2023): 1,897,100
• Estimated job growth (2023-2033): 17%
• Typical educational requirements: Bachelor’s degree or related training

Software developers create applications and programs that run mobile apps, business tools and more.

4. Education Administrators

• Median annual salary: $111,020
• Projected annual job openings: 15,200
• Number of jobs (2023): 302,580
• Estimated job growth (2023-2033): 3%
• Typical educational requirements: Master’s degree

Education administrators manage the academic, administrative and other functions of schools and school districts. They are typically in charge of academic planning, staff supervision, budgeting and student services.

As someone who has taught in a graduate school leadership program since 1986, I can attest to the positive job prospects for education administrators keeping in mind that a school principal or district administrator are not easy jobs.

Tony