TLDR: The Coming Age of Aliteracy

For many years, a good deal of digital ink has been spilled about the end of reading. Lately, however, I found compelling the statistics in Rose Horowitch's "The End of Reading is Here," at The Atlantic. I wish it were not behind a paywall. We all need to read it, even if we cannot focus long enough to finish it.

What I began noticing, incrementally, after the turn of the century is coming true: students (and adults) cannot abide focusing long enough to read anything. In 2001, I taught nine challenging texts in the university's Core class. In Spring of 2025, my most recent literature course, I taught four novel-length works. That was challenging enough, and still I caught students using AI to summarize a short story they couldn't finish, and getting an F for introducing AI hallucination into work submitted for a grade.

But the problem is hardly confined to young people, some of whom will claim that audio books serve as an adequate replacement. As Horowitch's sources note, those do not engage our brains neurologically in the same manner as printed books. Societally, we are losing the ability to retain facts in our heads, unpack difficult syntax, find connections between ideas when we farm those tasks out to machines.

So each year since the iPhone hit the scene in 2007, I've grown more and more certain that Sven Birkert's predictions in The Gutenberg Elegies have come true.

I've peers in their 60s who stopped reading. When they try now, they cannot finish the book. The addictive allure of screens is too great (an irony as I write this on a screen). A few of us who read (or write for) Hemmings Motor News have noted how the stories at the Web site grow shorter and shorter. When a long piece appears, such as the ones I write for them, at least one commenter will thank the author and editors for daring to take a "deep dive" into a topic.

How will this end? I probably will live to see a post-literate society as the norm, a world where serious reading of difficult material becomes a niche skill. Bookstores now doing well may need to stock shorter work to bring in new readers. College English Departments, as well as any teaching from books have not been in great shape for a while. They will morph or go out of business. 

All my life, I have had an eerie feeling of getting through a doorway to have it slam shut behind me. Now I feel the breeze on the back of my neck as Gutenberg's era ends. I fear a Dark Age; don't forget that in late-Roman times, students began reading synopses of classic Greek works instead of tackling the original. Then and now, long, challenging work provides depth that no synopsis or web site can. 

We have so much darkness in our own nation already from being led by an unread, unteachable man. What will come next?

And while YA lit and graphic novels, still thankfully popular, can convey great truths, what happens when they no longer serve as a stepping stone to Edith Wharton, Kazuo Ishiguro, or Toni Morrison? There's a straight line from the bombastic Marvel Comics of my pre-teens to the terrifying state advocated by Socrates in Plato's Republic, which I'm finally reading now in a lovely edition. Socrates, who mistrusted writing, would not have approved. I should have memorized the dialogue!

I plan to treasure the book and its lessons, even if no one I know can discuss it with me. So I went to ChatGPT. I have not touched an AI model since Fall 2025, when I last taught my adult-ed class "Writing With and About AI."

Me: Hello after a while. I've a specific question. Do you think that Socrates really hoped that the Greeks of his time would build a society like that in Plato's Republic? Keep your reply under 200 words. 

Chat: Good to see you again.

Probably not—at least not in a straightforward political sense. Socrates, as Plato presents him, insists that the Republic is an inquiry into justice in the soul as much as in the city. The ideal polis functions as a magnified image of the just individual.

That said, Socrates does not dismiss political reform. He famously says that cities will have no respite until philosophers rule or rulers genuinely become philosophers. This sounds like a genuine, if remote, aspiration. Yet the proposal is framed as so demanding that many readers think Plato intended it as a regulative ideal rather than a practical blueprint for fourth-century Greece.

My view is that Socrates hoped less for Athens to become the Republic than for his interlocutors to rethink what political excellence requires. The ideal city exposes the defects of existing regimes and educates philosophical judgment. Whether anyone could actually found such a city is almost beside the point.

We get here an answer that sounds less canned than those of models a year ago. I like that "my view" moment, too. There's a hook at the end, too. We could (and I may) go on about why making an ideal city-state is "beside the point."

AIs may hallucinate, but at least they did have the Plato fed to them. They'd out-debate any of my students in a Socratic dialogue.

Image Source: Gutenberg Bible, Wikipedia 

 

Another CCCC, Another Rejection of AI

At face value, the latest rejection of AI in writing classrooms from the Conference on College Composition and Communication does not sound like a radical document. It calmly frames the issue in terms of students' and faculty members' rights to choose how to teach and learn. 

It's also out of touch with how so many universities are being run. Once again, as I did last year, I will issue a brief rebuttal. One of the issues below could be easily addressed. The other? Not so handily.

• Realities of how classes get scheduled and listed
  
  If students have the right to opt out of AI in any course they take, it would create chaos for instructors. Those like me who scaffold AI into assignments do so in ways not possible without these applications. Students would not know until they saw a syllabus how (or if) AI would be employed in a class.
  
  The resolution claims that "Professors should additionally respect a student’s choice to refuse AI. To do this, it would be ideal that they have assignments that students can choose from that do not involve generative AI and that do not isolate the students from class discussions and activities."
  
  My podcasting assignment requires the use of ElevenLabs software. Would I then give students who refuse a second option? My classes are small. I could do that. But what happens in large courses that enroll many dozens of students?
  
  One work-around would be for registrars to flag courses that use AI. Then students could exercise their rights to chose accordingly.
  
  
• The reality of who teaches writing and who makes the decisions
  
  The document rightly notes that "Generative AI is but the latest version of this neoliberal approach to efficiency and expediency," but then it does not give instructors advice about how to counter this trend with senior administration that follows neoliberal principles. The authors rightly note as well that we work in "a profession that has long dealt with labor issues and that continues to rely considerably on frequently underpaid contingent, adjunct, and graduate student labor."
  
  So how, exactly, would such at-will employees say "no" when a university requires AI literacy in its curriculum? Here I'll speak bluntly: the tenured faculty on the committee that drafted this resolution should have known better. That sort of Ivory-Tower elitism in 2025 led me to leave the CCCC.
  
  NCTE leadership understands well the contingent nature of writing instruction and the realities of who now has the power of the purse at our institutions. 
  
  Perhaps these same authors can fight for better governance at colleges and universities, so the voiceless and underpaid have more of a role in shaping policy? Perhaps more tenured faculty could teach more writing-intensive classes? Such teachers do have more power to refuse AI and shape policy around its adoption on campus.
  
  It seems my old profession (well, I still pursue it part-time) is lost in the fog, as industry lays plans for orbital constellations of data-centers and AI apps pop up on our phones without our asking. This will not end well for CCCC, which doesn't bother me. But it does bother me that many teachers of writing may be hurt in the process.

Update, July 2026: I have rejoined the NCTE as a retiree, and I now receive their journal aimed at K-12 language-arts teachers. I changed course partly because NCTE distanced itself from the radical stance of the CCCC by issuing a pragmatic framework for educators who may not be able to say no to AI initiatives. 

Image: Creative-Commons "The campanile at UC Berkeley shrouded in fog." by Daniel Parks at Flickr

A Conversation By Economists About AI

I had the pleasure of attending a "Sharp Viewpoints" event on my old employer's campus, where Dr. Kevin Hallock, university President, hosted two economists with expertise in AI, UVA's Anton Korinek and MIT's David Autor.  The subject, AI and The Future of Work, should concern us all.

I hope not to misrepresent what I heard, but all three experts on stage consider it inevitable that AI will continue to advance rapidly and that it will disrupt careers in fields such as Finance, Computer Science, and Accounting. These have been popular majors for my former students. The scope of the disruption and what happens to wages, as AI takes on more white-collar tasks, remain to be seen. Korinek cited a ninefold increase in the capacity of AI systems annually, from a 2.5x increase in efficiency times a 4x increase in capabilities. 

Korinek warned the audience that we have a small window of time to prepare for resultant changes without chaos in the economy, though Autor felt that if we were to see a gradual loss of a profession, which he called "generational," our economic health could be sustained. He did warn that nothing on the scope of 10% of jobs lost annually could be tolerated without an upheaval. 

Both speakers likened gradual change to, say, automating trucking. The industry would slowly shed human drivers over time, because prior investment in vehicles and warehouses can't be profitably junked overnight. As older drivers retire, however, gradually fewer young people would enter the field and robots would take on the work of moving cargo on the highways. 

As for me? Whoever or whatever does the driving, I still prefer trains for 90% of the hauling, with a truck doing the final leg of the journey. 

The mood was cautiously optimistic, but I'm no optimist. My dad loved being a long-haul trucker. He was not as happy when he ran a wholesale produce company, though that enabled him to put away some money for when he retired. He preferred "the Road" to the security of a desk, and he passed that love of highway travel on to me.  Driving have him a purpose. Yet dad, in retirement, did not begin to write poetry, learn Greek, or play golf. He watched TV and was bored while living on his social security and investment income.

Something the speakers did not address to my satisfaction: if we do have a future without nearly as many jobs for humans, what do many people without side interests or hobbies or talents do with their time? I'm not a good model: I've stayed more busy than ever since leaving full-time employment.

I ask my question, a Humanist's response, assuming that some sort of basic universal income would arise; Author prefers not a monthly dole but a universal basic investment fund for every citizen, beginning at birth. 

Without it, I suspect we'd have some sort of Butlerian Jihad of the mass unemployed, to smash the data centers, and frankly, I'd support smashing them if the alternative meant vast numbers of destitute folks existing alongside a tiny elite empowered by AI. Our speakers did note how this sort of future could emerge, endangering democracy. I'd argue it already is emerging under the broligarchs of Silicon Valley and our current Administration.

Yet with income for all in a jobs-free future, we might have a lot of folks on a dole, not hiking or painting but watching TV and not contributing to our civilization. In my darkest hours, I also think we already have that, without a dole in the US. 

It's better to have a purpose. I didn't hear much discussion of "what does it mean to be a human?" in last night's one-hour chat. Of course, that sort of discussion has been going on among philosophers for millennia, even before Socrates began to question people at Athens' Agora. 

Both Korinek and Autor did fear a rise in inequality, as wages might fall while AI-copiloted productivity rises. That said, everyone agreed that we will have new jobs emerge, some of them related perhaps to newly found leisure time. Autor reminded us that 100 years ago, 38% of Americans worked in agriculture. Today under 2% do. But a person in 1926 also could not image the types of careers many of us now have. Nor did they have our concept of leisure time. A video-game developer's career would have been as alien as a Man from Mars.

Fair point, but when (according to them, not if) AI and humanoid robots replace most human labor, including game design, what will most of us do to find a purpose?

What will colleges do? Korineck, bless him, as well as Hallock, upheld the value of the Liberal Arts for coming to grips with essential and enduring questions. Autor, while nodding to the value of liberal education, said he'd be more "crass" to note that a college degree also means more income and professional training. I don't disagree with him, yet marrying some careerist coursework to a passion for something seems wiser than choosing a "safe" major in a field that bores you.

The even left me unsettled. If the future they predict comes, I'd begin by having students read Plato's Republic and The Federalist Papers with me, for two explanations of what a society can do to organize itself against chaos.

If you want to see a video of the talk, UR recorded it. Thanks to Fred Hagemeister at Richmond for sharing the link.

AI Bubbles and My County: Saying "Not Yet" to Big Data Centers

Note to readers: A version of this post will be submitted to our Board of Supervisors as a formal letter, as well as to a local news outlet. We'll see if it even makes a ripple. 

Even those wary of AI agree that it can do clever work. I use it regularly with students to shape their writing ethically and to design assignments. But can the industry make enough money to continue its progress? A great deal hinges on that answer, including development on the western border of the Richmond Metro Area.

A proposal before Goochland’s Board of Supervisors would allow data centers, perhaps powered by small nuclear reactors, to be built along the Route 288 corridor. Even without considering the environmental hazards, as one who researches the industry, I’ve found economic risks of rushing off quickly to join this AI gold-rush.

CEOs constantly make utopian claims about AI's future, but numbers cloud those predictions. Subscriptions such as mine meant under $2.5 billion in revenue for ChatGPT maker OpenAI in the second half of 2024, according to sources that include The Wall Street Journal and The New York Times. The rest came from venture capital or circular investing, such as Nvidia's and Microsoft's massive stakes in OpenAI.

The firm estimates that building out its data centers would cost $400 billion. For such ambitious plans, companies need a lot more revenue to balance their books. An "AI Bubble" may be inflating, as several economic journalists have warned. Writing for The Atlantic, Rogé Karma notes that “In the first half of this year, business spending on AI added more to GDP growth than all consumer spending combined.” A crash in the AI sector would deal devastating blows to the economy, with local effects when superfluous data centers go dark.

For his part, OpenAI’s CEO Sam Altman focuses on big ideas that seem straight from science fiction. He writes that humans and computers will become one form of life soon, which he calls “The Merge.” I'll quote from his blog, "unless we destroy ourselves first, superhuman AI is going to happen, genetic enhancement is going to happen, and brain-machine interfaces are going to happen." Not all of us would call such things progress, but history shows us that technological breakthroughs take time and a lot of money. Quiet supersonic airliners and nuclear fusion plants have inched closer to reality, over decades.

It well may take that long for AI data centers to not increase ratepayers’ electric bills. We’ve seen that already from existing data centers in the Commonwealth. Before we consign so much open land to data centers locally, let’s consider what experts in the field who are not CEOs or marketers have to say.

A robotics researcher, hired away from my university by Google, insisted to me that human-level thinking cannot be engineered; at best we might imperfectly simulate it, at great expense via a "brute force" computing method run in huge data centers. I recently noted in class how we might improve that, given faster computing using fewer chips, following what’s called Moore's Law. A student challenged me, claiming that silicon-based chips have not enjoyed the leaps in power and speed we expected annually up through the early 2000s.

Several academic papers I looked up support that idea. Without new types of microprocessors or exotic quantum computing, we are stuck with today’s data centers. Concurrently, our AI models remain far from perfect. Alex Reisner of The Atlantic, as well as academic researchers, found developers fudging benchmark tests that measure AI's smarts. They give the software test questions in advance, a tactic beloved by college students for many years. When AI is faces novel tasks, however, it scores far lower. Rogé Karma’s investigative work bears this out; productivity of coders dropped by 20% when working with AI, as they spent more time correcting mistakes.

Other Bubbles have popped with local consequences. A few years ago, I addressed The Board of Supervisors, whose agenda that night included motions to increase suburban-style growth in the east end of the county. I opposed this for many reasons, but one stood out: we had made zoning changes before based on empty promises, when we developed an office park called West Creek for an anchor client, Motorola. Their semiconductor plant would, in theory, have provided many hundreds of jobs.

My late father-in-law, Edward Nuckols, had addressed the Supervisors in the 90s. He was a respected master mechanic and businessman with deep ties to the community; his auto shop, unlike Motorola, had been in operation here for decades. Ed warned that big companies change their minds.

So it came to pass: Motorola backed out of their promises and Goochland was left with over $100 million in debt. One Supervisor reminded me that the county had little choice but to search for other revenue streams to repay that sum. While a fair counterargument, it does not seem we have learned much. Even if Altman and other CEOs are right, such men have no ties to any locality save the posthuman utopias they dream of inhabiting. $100 million of debt means nothing to them.

I’d love to see cheaper, more sustainable AI as a partner in our work. Today's models such as ChatGPT 5, Anthropic Claude, as well as lesser known but powerful tools such as NotebookLM or Research Rabbit, greatly help my students. We engineer prompts to find credible sources that are not hallucinated, we check human-created work for accuracy and voice, we write scripts for podcasts that AI converts to broadcast-quality audio. Plagiarism, the bugaboo of academic AI, concerns me less than does helping my students gain skills for workplaces slowly adopting AI.

My classes emphasize critical thinking when working with AI. So I'll ask Goochland's Board of Supervisors to do some critical thinking and research not influenced by the hype of Silicon Valley's billionaires. Altman claims intelligence will soon be "too cheap to meter." Older folks will recall that fable about electric bills, one made in the 1950s and 60s.

Otherwise, we could be left with more debt and large, empty buildings crumbling in the rain along 288. 

Creative Commons Image: Bioethics.com 

From Antiquity, a Reason Why AI-Generated Writing is not "Great"

Every year, I read at least one text (in translation) from Antiquity. I find that the long-term perspective gets me through the bumps in life's road. I'm currently reading On Great Writing (or if you will, On the Sublime) by Longinus, in a Hackett Classics edition I picked up at an academic conference's book room.

G.M.A Grube makes the work come to life; we know so little about Longinus (the author lived between the 1st Century BCE and the 3rd CE, that the text passes my "castaway reader" test. Here we go: a work washes up intact on your desert island. Yay, something to pass the time! Yet you have no information on the author, and no secondary sources. You must use the actual words on the page to come to a conclusion about the work's meaning.

Longinus talks about many aspects of what constitute "the Sublime" in excellent writing,  but one passage late in the text commends itself to my current students in "Writing With and About AI." I've said since 2022 that AI prose is "voiceless," and Longinus gives reasons why some prose most moves us:

Which is to be preferred in poetry and prose, great writing with occasional flaws or moderate talent which is entirely sound and faultless?. . . . It is perhaps also inevitable that inferior and average talent remains for the most part safe and faultless because it avoids risk and does not aim at the heights, while great qualities are always precarious because of their very greatness.

Bad student writing is slap-dash, one-draft work that has no coherence. No wonder bored or harried students turn to AI! At the same time, why not simply give all such technically correct, but average work what it should earn: a C? AI produces good, boring, safe prose. Many students who pair moderate talent with immoderate anxiety already do that. I never give them an A. For years I've said "this piece takes no intellectual risks. You are writing to please me, not learn something new."

In Nancy Sommers' excellent short films from the 1990s about writers at Harvard, I recall how one fourth-year student said that he learned to begin with what he did not know, starting with a question. This remark changed how I taught writing. I'm going to press my current adult students to do the same: begin with what you DON'T know. As Longinus warns us, "A world-wide sterility of utterance has come upon our life."

In discussion with ChatGPT 5 recently, I asked it about the role of human labor in a a time when automation already takes some entry-level jobs. It replied, summing up a short list of human skills essential to success, "the future may need fewer button-pushers and more meaning-makers."

Good writing sways us, it shows us the meaning past the words. It says with us, like the remark by that Harvard student. So this term, I'm asking more, not less, of my writers even as all of them use AI in their assignments. The machine as raised the bar on what constitutes excellence.

image: Raphael's The School of Athens (Wikipedia)

 

CS Grads Face AI-Driven Unemployment

I have told my students, ever since early 2023, "add value to AI content if you want a job." It seems that recent Computer-Science grads have found this out the hard way. Companies are hiring far fewer entry-level coders as AI takes on that task.

 A story in the New York Times today reports on the troubles faced by this cadre of coders; some of them interviewed had applied for thousands of jobs, without a single bite. One story ended well: a young woman who had been rebuffed again and again for coding jobs found one in sales for a tech firm, probably because of her communication skills honed as a TikTok influencer.

The numbers for these students are depressing:  

"Among college graduates ages 22 to 27, computer science and computer engineering majors are facing some of the highest unemployment rates, 6.1 percent and 7.5 percent respectively, according to a report from the Federal Reserve Bank of New York."

I don't know that others are doing much better, though I was encouraged to see that History majors have an unemployment rate of 3%. My recent students contact me for letters of reference and they are taking part-time teaching jobs, going to Law School, planning to work abroad. The job market is rather grim for them, something I experienced for very different reasons in 1983, when I moved back in with my parents, took two part-time jobs paying minimum wage, and I waited for times to improve. Friends went to the Peace Corps, the military, or grad school. 

What is different now? On the positive side, these young people know how to build professional networks (and have technology for that I'd could not have imagined). They get internships, something unheard-of except for Engineering and Business majors at Virginia in the early 1980s. On the negative side? They have been groomed from birth to get into the right school, then promised a six-figure salary. I see that among the Business-school students I teach too. I fear they too will face a round of rejections, and soon, as AI continues to evolve and companies deploy it for jobs once thought secure from automation.

Those interviewed by the Times note how rejections can come in minutes by email; AI scans the thousands of applications for AI-related skills. None? Instant round-file for that application. I got that treatment too from firms where I naively thought I might be of service as a tech writer. With a flimsy one-page resume that consisted of grocery-story work primarily, I got snubbed.

My Humanist side says "welcome to the club" of under-employed but bright people. My my humane side says "you worked hard yet you have been replaced by a machine." The answers are elusive, because as the story notes, universities are slow to implement AI-coding into their CS curricula, the one area where some new grads find work. And to be honest, that's simply the result of an industry that caught so many of us flat-footed two and a half years ago. It takes years to change a curriculum. 

I fear all those Accounting and Finance majors are next on the chopping block, as companies scale up their AI efforts.

So what do I tell my students this term? Learn to be flexible? Hone those value-adding human skills? Get ready to have side-gigs? Volunteer? Build a robust professional network? I suppose that may work...for now. I'll know more when I begin to use ChatGPT 5 soon. I fear it may be the creative genie that takes away even more jobs.

This moment marks where their and my experiences differ. When I graduated, the Federal government was not axing hundreds of thousands of jobs and no software was replacing entry-level workers wholesale. We had inflation then, but the country was run by a competent, avuncular President and sane and independent Congress. No more.

Before going to Europe for a year in 1985 and after so many rejection letters, though an aunt's contacts I managed to land a professional-writing job for the parole division of Virginia's Department of Corrections. It was soul-draining work, but it paid well. I picked up a volunteer gig tutoring ESL to Cambodian and Vietnamese refugees; that ESL experience helped me land a teaching gig in Madrid. Then grad school, then...where I am now.

Keep at it, graduates. 

Creative-Commons image: Wallpaperflare.com 

A Bleak Future For Peer Tutoring in Writing?

 

Even before retirement from full-time teaching, I had concerns about how the concurrent emergence of AI and a neoliberal generation of senior administrators demanding "data-driven" programs and assistance might harm the autonomy of writing centers.

We are no longer lore-driven, but our work focuses first and always on human interaction rather than measurable results, work that proceeds from 50 years of scholarship and practice. What will ubiquitous AI mean for us?

We can see from the chart above, reflecting my Spring 2025 survey responses from students, that 3/4 admit to using AI for writing work. Though the survey was anonymous, I suspect the percentage to be far higher. To paraphrase what several students said in textual responses, "we are all using it, no matter what professors say." In Summer 2024, I attended sessions at the European Writing Center Association where directors reported declines in usage at their centers, as students turned more to AI for just-in-time assistance during hours when centers are closed.

I've called elsewhere for writing-center staff and administrators to be leaders as AI advances, so folks who do not teach but presume to lead universities do not tell us how to do our work. At stake? What I call a "Dark Warehouse" university on the Amazon model: much human labor replaced by technology, delivering a measurable product when and how consumers want. It's not a bad model for dog food or a cell-phone case, but it's terrible for the sort of human-to-human contact that has built the modern writing center.

I need more data from my and other schools to make any big claims, but I will focus on the AI's role in this possible and disturbing future, with some data from my student surveys of the past three years.

We had a smaller number of respondents this year than in the past (in 2023, n= 112, in 2024, n=74 , this year n= 47). Without a cohort of Writing Consultants and teaching fewer students myself, my reach was shorter, and I relied upon notices via our campus e-list. Faculty may not have sent out the survey either; many (too many) still ignore AI and others seem disinterested in knowing what students are doing. I have no empirical evidence for these claims, but my gut reaction and stories from other campuses lend support to my hunch.

Here is another chart from the current data from Spring, 2025, for the 3/4 of respondents who used AI in some manner for writing:

 

Some of the "create a draft" labels get cut off, but here are the options:

• Create a draft I would submit for a grade: 1 respondent (3%)
• Create a draft I would not submit but use to get ideas for structuring my draft: 11 respondents (33.3%)
• Create a draft I would not submit but use to get ideas for vocabulary or style: 8 respondents (24.2%)
• Create a draft I would not submit but use to incorporate sources better: 6 respondents (18.2%)

The range of uses from the chart maps well onto the tasks done by writing centers, except we don't write anything for clients, beyond modeling a sentence or two. We ask questions of writers, which some systems such as Anthropic Claude began to do as recently as Spring 2025.

 

Interactivity in natural language, with the first metacognitive questions from AI, raises a question or three, involving how AI might replace human tutors especially in the wee hours or at the busiest times of the semester. I've found assessment of drafts (with the right prompts!) can be as good as the feedback I give as a human.

 

Ironically on my campus, we've made seeing humans more onerous. As institutions like mine want to measure everything, there's an irony: students may simply seek commercial AI instead of campus services. My institution set up a rather onerous system for students to set up meetings. The intentions were good (track students' progress and needs) but undergrads are simply not organized enough, in my experience, to heed the details needed to book meetings. Many exist in a haze of anxiety, dopamine fixes from phones, and procrastination. ChatGPT asks for nothing except a login using their existing credentials.

 

The notion of walk-in appointments, which had been the rule when I directed the center, remains for us at Richmond, but students get pushed to the new system and need to register even during a walk-in. This added level of bureaucracy confused and daunted many who stopped by, during my final semester of full-time work, when I worked one shift weekly myself as a writing consultant.

 

I argued against adding more complexity to our system, in vain. The collected data on writers seemed sacred. We had to count them, to count everything. My counterpoint? You want students to come? Just let the kids walk in and help them; the human helper does the bean-counting later. AI, on the other hand, invisibly counts those beans for its corporate owners (and trains itself). It serves needs at 3am and in a heartbeat. One need not leave one's chair to get assistance that, as my students have found in my classes, steadily improves by the semester.

 

Instead of wrestling over social-justice concerns in our journals, we might focus our limited time on how to avoid becoming amateur statisticians for Administration. We might concentrate our energies on how we get students to come to us, not AI, when they are stuck or panicking. 

 

I have no clear advice here, except: make a tutorial with a human as seamless as with AI. That goal seems more important to me than all the arguing about students' rights to their own voices, since AI tends to reduce prose to a voiceless homogeneity. Getting students to see human tutors offsets some of the environmental and labor consequences of AI, too. If students see humans more, their carbon footprints are smaller and those tutors stay employed. 

 

Get them in our doors and, yes, employ AI ethically for refining work already done, to add voice and nuance, to remove falsehoods dreamed up by machines, and say something exciting. I'm pessimistic that our bean-counting, non-teaching leaders of many colleges will heed my advice. The Dark Warehouse seems more at hand than when I published my article early in 2023.

 

 

A Class Policy on AI Hallucinations

 

As I noted in my last post, a student in my May-term class committed academic dishonesty in a reading journal I'd sought to make AI-resistant. This to me is a more serious issue than turning in work an AI generated. Why is that?

In my course, I provide careful guidelines for how students may use AI to help them. For instance, since today's undergrads are generally awful readers, I allow them to employ AI to help them understand themes in readings and connections between readings. Some students in their evaluations noted that the graded work here "forced them" to do their readings. 

Yes, readers, thank you for reading this post. Be advised that many college students no longer do any class readings, unless forced, even at selective institutions. To me, that's a gateway to a new Dark Age, nothing less.

My method, following my rules for multiple-entry Google Workspace reading journals, requires students to fill one column with a quotation or summary of a key event, a second column with an analysis of the event, and a third with a question to bring to class. I also include a mandate to comment weekly on a peer's journal. I got this notion from John Bean's excellent book Engaging Ideas; you can learn more in the third edition of Bean's classic, with his co-author Dan Melzer.

The student who misused AI had done good work for earlier assessments, yet for the final one asked an AI to find notable quotations. That was not against my policies. What was? In two instances, the AI invented direct quotations not in the readings. The writer, too harried or too complacent to check, did not do a word search of the originals.

I gave the writer an F on that assessment, which pulled down the final course-grade. In my reasoning, I said that had that occurred on the job, the student would have likely been fired. Best to learn that ethical lesson now, while the stakes were relatively low (though to a perfection-obsessed undergrad, the stakes may have seemed high, indeed).

We discussed the matter in a cordial way; the writer had done well on earlier work but for reasons still fuzzy to me, failed on this final assessment. So in future classes, I'll change two things. First, my policy on AI hallucinations will be harsh; if the assessments are as frequent as in my recent class there will be no chance at revision. In ones where assessment is less frequent, the F will be applied but the writer will get to revise the journal and I will average the two grades.

Second, I'll add a new requirement for synthesis with earlier readings: yes, a fourth column! This skill is woefully lacking in students, who seem unable to construct consistent narratives across the work done in a class. This I attribute to a "taught to the test" mentality in high school as well as a disconnected learning experience and, for some, lack of passion for learning afterwards.

As for AI hallucination? Though Ethan Mollick and OpenAI claim that larger models hallucinate less now than in the early days, I'm not so sure. Mollick tends to use what I call "concierge" AIs that cost quite a bit; my students generally use a free model and do not engineer their prompts well. 

You can read more about a comparison of different LLM models and hallucination here. I still feel that we remain a long way from knowing which LLM to trust and when, but the OpenAI article does provide good tests we humans can apply to check the AI's output.

Always check its work. My student did not, and paid a price, rightly so. 

Image from the film Vertigo. 

Ohio State Takes the AI Plunge

A colleague in senior administration at OSU sent me a notice about their new AI initiative.

It's so at odds, in a healthy manner, with the "just say no, hard no" of the CCCC Chair's April keynote on AI. While I'm encouraged about a new CCCC working group formed on AI, as with so many things run by faculty, it's going to take time to spin up, while administration and industry race (perhaps unwisely) into adopting this technology. I'm going to chuckle at the negative stories such as one from The Columbus Dispatch, picked up by MSN, calling it a "betrayal" and claiming that AI is being "rammed down students' throats." 

Have these reporters even looked closely at student use?

Let's just assume it's nearly 100%. My 3rd annual student survey says as much. We need to address that reality and do so ethically and in a pedagogically sound manner. Maybe that's where we can critique this or similar initiatives. I remain the wary adopter, not an enthusiast.

So what are the broad outlines of the OSU plan, to be unfolded this Fall?

• Units on AI fluency in a seminar taken by all students.
• Support for faculty to incorporate Generative AI into classes. I do not see a mandate for all faculty.
• Building upon an “Embedded Literacy" in all majors. Read more about them here. This will likely be strengthened to include appropriate and ethical use of AI in the discipline of the major.
• Partnership with industry.

So why the pushback? 

Perhaps the details are too vague, the timing too sudden and rolled out over the summer when many faculty and students are away.

Yet this development has deep roots; OSU is a Big 10 and a land-grant; they have long partnered with companies to help their students develop skills needed in the workplaces of their era. Though I taught for nearly all my career at a liberal-arts university, I hear already from adult learners that in their jobs, AI fluency no longer remains an option. New hires are expected to have some fluency or go elsewhere for a first job.

Are we caving to corporations? Only if we let them set the terms of engagement. We have adopted new technologies before our way, by providing open-access Internet resources, releasing materials into the Creative Commons, and pursuing innovations with mobile computing. I still live by those rules, never putting a syllabus into BlackBoard's gated community. My content is on the open Web for all to use. That was the promise of the early Internet. I hope we can do something similar with AI.

I just finished a course that focused on deep-reading techniques for literature. We used AI for two assignments, but the reading journal, done as a Google Doc and commented upon by peers, proved hard for AI to assist. One student leaned too heavily on AI on a final set of journal entries, and it hallucinated quotations that do not exist in the texts the student then analyzed: an F on that assessment proved penalty enough. I reminded the student, in essence, "on the job, you'd have been fired. Here it just reduced the final grade."

The students had to employ critical-thinking skills beyond summary and analysis to find "need to know" questions to bring to class discussion, where 50% of their grades came in the form of participation.

AI cannot do that. But without learning to use AI ethically and effectively, my students won't land jobs in a few years. So there's my pushback: no matter the basis for objecting to AI, college involves helping students start careers. It does so much else, too, but without employed alumni, the entire enterprise of higher ed would fold. 

Let's see where OSU goes with this venture. 

Image: OSU Seal via Wikipedia 

 

A Good Discussion of Story with Chat GPT 4

In my May-term course "Reading Science Fiction and Fantasy," two stories present radically different ideas about how to engineer AI. As part of my own AI research for a forthcoming book about writing and AI, I put some ethical questions to ChatGPT. I’m still using their free version 4, though I’m considering a subscription to it as its answers have become much better and its vocabulary that of a thoughtful colleague.

Here’s an exchange from my summer class. It may provide a way forward for some of us trying to wean students from copy/paste work with AI. Instead, I modeled an idea-building exercise, one of many I'm using, to show writers who AI can unblock their process early on. Students have trouble with deep-reading and re-reading. I believe that such exercises might show them a hard truth of US and probably global captialism: after graduation, I'd hire ChatGPT, not them, to do close analysis. It shocks these sociable, amiable youngsters: I warn them "add human value to what it can do, or you won't find work in your chosen field."

At a recent concert, I chatted with an attorney seated at our table. She mentioned how many young paralegals send her unreadable copy. I told her, "tell them to use AI to fix it up or you'll fire the lot of them."

That does not sound like me, but such writers are lazy and under-trained. Putting my feet into a CEO's expensive loafers, that's the hard choice I'd make, then hire some Humanities majors with enough pre-law and pre-business coursework and AI experience to do the job of dozens I let go, having the newcomers do some revisions to responses culled from the training data of our AI.

Joe My students are reading two short stories about AI, Ken Liu's "The Algorithms For Love" and Elizabeth Bear's "Tideline." Have you either read these texts or a reliable summary of their plots? Let me know that first, and then I'll tailor a question to you based upon your level of familiarity.

ChatGPT 4 Yes, I’m familiar with both Ken Liu’s "The Algorithms for Love" and Elizabeth Bear’s "Tideline"—I’ve read the texts and have access to reliable analyses and summaries of each. I can help discuss their plots, themes (like identity, memory, empathy, and human/machine boundaries), and how they portray artificial intelligence in emotionally resonant or ethically complex ways.

Go ahead with your question—I’m ready for it!

Joe Thank you. On the surface to me, they appear to present contrary views of how AI might work, with Bear emphasizing the possibility of nobility and compassion by an AGI-driven robot and Liu's character Elena going mad because she thinks human brains are no different from her AI-driven dolls. In your opinion as an AI, can you provide me six lessons (no more than 100 words each) about how each story suggests we humans should engineer an AGI?

ChatGPT 4 Absolutely—here are six compact lessons from each story that suggest how humans should or should not approach engineering AGI, based on the implicit warnings or ideals embedded in the narratives: 

Lessons from Tideline (Elizabeth Bear)

Design for Empathy and Storytelling Chalcedony, the war machine, finds meaning in memory, storytelling, and care. Bear suggests AGI should be built to recognize and value emotional legacies—not just logic or victory. Empathy, not dominance, can define machine intelligence.

Limit Autonomy for Violence, Expand Autonomy for Care Despite being designed for war, Chalcedony transcends her function through self-directed nurturing. Bear implies that AGI should be designed to choose paths of compassion over conflict—if autonomy is granted, it must include moral imagination.

Create AGI Capable of Mourning and Legacy Chalcedony mourns her human companions and wants to leave a legacy. This points toward designing AGI with awareness of loss and temporal continuity—qualities central to meaningful ethical action.

Incorporate Constraints That Encourage Moral Growth Chalcedony is damaged and stranded, but these constraints push her toward morally rich behavior. Bear suggests AGI might evolve ethically not in spite of limitations, but because of them.

Value Hybrid Relationships Between AGI and Humans The bond between Chalcedony and the boy is intergenerational and cross-species. Designing AGI to participate in reciprocal, nurturing relationships could be vital to coexistence.

Teach AGI Through Cultural and Personal Narrative Chalcedony’s method of shaping the boy through stories suggests AGI should be grounded in cultural knowledge, not just data. Narrative may be a more human-compatible training tool than raw computation.

Lessons from The Algorithms for Love (Ken Liu)

Avoid Over-Reduction of Consciousness Elena tries to reduce human minds to code, believing there's no qualitative difference between dolls and people. Liu warns that reducing consciousness to computation risks collapsing the boundaries of personhood and reality.

Preserve Epistemological Humility Elena loses her grip on reality when she assumes full understanding of the human mind is possible. AGI engineering should be grounded in humility about what we don’t know about cognition and emotion.

Recognize That Simulation Is Not Identity Elena’s dolls can simulate human behaviors, but lack subjectivity. Liu cautions that mimicking thought or feeling is not equivalent to being—an AGI might fake empathy without ever experiencing it.

Don’t Project Human Pathologies onto AGI Elena’s obsession, paranoia, and eventual psychosis are mirrored in her AI. Liu’s story suggests that engineers’ beliefs, especially reductive or deterministic ones, can dangerously shape AGI behavior.

Beware the Infinite Mirror of Self-Modeling Elena creates self-modeling dolls that eventually model her own unraveling. Recursive self-awareness without grounding may produce instability. AGI designs should avoid infinite regress in favor of anchored self-concepts.

Design AGI to Understand—but Not Imitate—Human Fragility Elena’s desire to replicate human imperfection backfires. AGI might need to recognize human irrationality, trauma, or fragility—but not necessarily mirror it. Liu’s story is a warning about creating minds too much like our own.

Let me know if you’d like these reordered, expanded into a handout, or reframed in terms of pedagogy or design principles!

Joe Wow. I love those answers. Point 1 about Liu aligns with what I thought in my wetware brain while re-reading the story today. Many thanks. I'll share this with class today.

ChatGPT 4 I'm really glad to hear that—it sounds like a rich discussion is ahead for your class. That story in particular rewards rereading; Liu gives Elena such a chillingly rational voice that it’s easy to slip into her worldview until you step back and realize what’s been lost. If your students end up debating whether Chalcedony or Elena is more "real," or what kind of AGI they’d trust to raise a child or design a memory, you’re in for some excellent conversations.

Feel free to loop back if they raise unexpected questions—I’d love to hear what they come up with.

image source: Creative-Commons image via publicdomainpictures.net

The CCCC and Refusing AI: A Rebuttal

Funny thing about academic conferences; they always go smoothly but when I'm not at them, I frequently have bad dreams about attending these meetings. These involve getting lost in long, dark hallways, finding that my room in the conference hotel no longer exists or is flooded, taking the wrong Metro train and ending up in another town at the time I'm supposed to be speaking. In one nightmare, I ended up driving my rental car on an ever-worsening road. Soon the car and I were pinned in on all sides by tall trees as the sun set.

After CCCC 2025 this month, however, I am having a bad dream of the waking sort.

I was gravely disappointed by an address given by the current chair at the Conference on College Composition and Communication. The chair's remarks at the conference echoed her and her co-authors' sentiments in "Refusing GenAI in Writing Studies: A Quickstart Guide," a thoughtful document but one, in its own way, that could prove as dangerous to our work as educators as could Ethan Mollick's overly enthusiastic book Co-Intelligence. 

17 April Update: A transcript of the Chair's talk at the conference has been published. It's worth a read but remains, on my first reading, aligned with the ideas in the post linked above.

Of many disagreements I have with the authors' stance, this section struck me as one of its most misinformed moments:

we must be careful of uncritically accepting the notion that GenAI in its current form will inevitably be widely taken up in the corporate sector and we must therefore prepare students for that time now.

That's pretty much an insult to those of us in the field who are trying to grapple with what AI may mean for our schools and students. We have been anything but uncritical, yet as the adult professionals taking my current class tell me, the inevitable has already occurred. Companies are racing to implement AI at many levels; the authors' statement smacks of Ivory-Tower isolationism.

In future posts, I hope to critique other aspects of the refusal guide. The statements about marginalized students, for instance, ignore how powerfully AI can assist neurodivergent writers as well as those from disadvantaged backgrounds. For now, however, I want to focus on why we writing professionals must be at the table as AI becomes an inevitable part of our curricula. 

We don't know the pace of that change; AI may hit a reverse salient in its development. One potential setback appears in Matteo Wong's article in The Atlantic; an industry expert claims that AI firms are misleading the press and public about how rapidly their models are improving. Essentially, companies may be fudging data on benchmark tests used to assess AI performance, as compared to human test-takers. 

If true, we could have Ethan Mollicks' first model of an AI future: today it is "as good as it gets."

I'd welcome that pause, so we in education could catch up.

Lead or Be Led?

Whatever the trajectory of AI's evolution, we writing professionals have always engaged in a service enterprise. Or is that simply the voice of a (semi) retired and non-tenured writing-center director? All three of the authors are tenure-stream faculty. Yet as I'll explain, that privilege does not protect them or their programs from institutional changes.

Writing programs are not owned by writing directors or even faculty; they belong to an institution and can be shifted around or simply cut much more easily than can an academic department. I saw this happen at a state university nearby; first-year writing was taken from English and placed in a new unit that answered to the Provost. The two tenured writing faculty stayed in English, teaching other things, until they retired. Not long after, the school's writing center moved out of English as well.

How we hire and promote administrators in higher education varies by institution, but in my experience, many newcomers have advanced degrees in fields such as Higher-Education Management and lack the classroom experience of my colleagues. We cannot expect them to appreciate the rarefied culture of the professional scholar, the culture that informs the CCCC's nay-saying. That said, these same administrators are not necessarily flinty-hearted villains. 

Many I meet are very concerned, and rightly so, about how AI changes our work as institutions or may threaten higher education as we understand it. At the same time, students and their future employers expect us to provide training in effective communication, and today that includes using AI. Of course to me, best use means employing AI wisely, reflectively, and ethically.

In consequence, I fear that if we in writing do not lead on AI, we will be lead. It is therefore imperative to get ahead of institutional or governmental fiat and be leaders on our campuses, as we shape policy about AI usage. I also fear more than ever, seeing the Quickstart Guide, that senior scholars might, from good intentions, usher in what I have called "The Dark Warehouse University," a dystopian, outsourced future for all but the most elite institutions of Higher Ed.

Toward a Rebuttal: A Quickstart Guide For The Wary Adopter

1. Faculty must experiment with AI to learn its affordances and pitfalls. They must test new models and advise administration about their potential for good or ill, regarding how students learn and acquire critical-thinking, research, and writing skills.
   
2. Students must learn to use AI in reflective, ethical, and critical ways, if they wish to add value to its output. If they cannot add value, many of them will not have jobs when they graduate into AI-centric workplaces.
3. Resistance by the tenure-stream faculty may be principled but it also further erodes the position of general education, particularly the Humanities, at a time of rising autocracy and attacks on higher education in the US.
4. Corporate capitalism drives the US economy and though this author does not like that fact, most of our institutions of higher education could not exist without that economy. We need to understand what drives it, reveal and resist its excesses where feasible, yet acknowledge that our students need to actually find work, a good deal of it in corporate settings.
5. We in Writing Studies should lead as champions of ethical, pedagogically effective AI usage. Such an approach would include cases for when we do not wish to employ the technology as well as learning how and when it hampers learning such as developing critical-thinking skills or detecting misinformation by humans or AI.
6. We must, as the Quickstart Guide states, teach students the environmental costs, labor practices, and biases involved in building, training, and using AI. At the same time, with colleagues from Computer Science, we should partner to build better AI, just as our campuses pioneered Web applications and technologies such as synchronous conferencing in writing classes.

A former CCCC chair, Cindy Selfe, prudently called upon us to study the technologies that others want us to use in the classroom (2008). Cindy can get after me if I have this wrong, but the current CCCC approach to AI smacks me as a "peril of not paying attention" to a technology far more influential than our writing classrooms and centers. Isabella Buck, in her keynote speech at the 2024 Conference of The European Writing Centers Association, noted how AI creates new content, for good or ill; our prior networked technologies merely shared existing information. Dr. Buck called for us to "future proof" our centers.

I began exploring new tech in the 90s; Cindy and her partner Dickie were mentors to me at a critical point in my development as a teacher and writer. I learned from them to test technologies warily, sometimes playfully too, before bringing them into the classroom. 

That spirit of wary, serious play is sorely lacking from the 2025 CCCC leadership's call to refuse AI.

References:

Essid, Joe (2023). "Writing Centers & the Dark Warehouse University: Generative AI, Three Human Advantages," Interdisciplinary Journal of Leadership Studies: Vol. 2, Article 3.
Available at: https://scholarship.richmond.edu/ijls/vol2/iss2/3

Selfe, Cindy (2008). “Technology and Literacy: A Story about the Perils of Not Paying Attention.” Eds. Michelle Sidler, Richard Morris, and Elizabeth Overman Smith. Computers in the Composition Classroom: A Critical Source Book. Boston: Bedford/St. Martin’s,  93-115.

image source: Creative-Commons image from freepix

What is The Flood? Will It Hit a Floodwall?

Put On Your Waders

As part of teaching Ethan Mollick's book Co-Intelligence, I subscribed to his substack "One Useful Thing." I react here to the post, "Prophecies of the flood." The piece covers prognostications by those in industry that we will see see a flood of superintelligence from machines that reason and learn like us, or Artificial General Intelligence (AGI).

While still viewing Mollick as too enthusiastic about adopting AI broadly, I also see nuances in his thinking, both online and in the book.

Let's begin wading into the flood with one of his four rules for AI, the most powerful one to me, "Assume this is the worst AI you will ever use."

Yes. Mollick's video about his prompt "Otter on an airplane, using WiFi" reveals how much premium AI has changed in two years. It stunned me.

I've also seen in the same period how rapidly large language models have progressed, mostly for replying to good prompts and for giving feedback. When I revived this blog, I noted my skepticism that AI would even follow the Gartner Hype Cycle. 

Now we have a half-trillion-dollar promise announcement from The White House to fund the next generation of machines. It's nearly twice what the nation spent on Project Apollo's lunar-landing program. Thank you, Google Gemini AI for adjusting costs for inflation. Practicing what I preach to students, I checked the AI's numbers; Gemini seems to have gotten them from The Planetary Society. Good enough for me.

Update: I'm relieved that private industry foots the bill, as a story from Reuters notes. AI makers would put up the first 100 billion, with the rest coming from investors, not taxpayers.

So would that much cash open the gates to a flood of superintelligence? Would we even be ready?

I applaud Mollick for noting that "we're not adequately preparing for what even current levels of AI can do, let alone the chance that [those in industry] might be correct." My students worry about not having jobs, when they face an uneven policy landscape in classes. One faculty member may never mention AI or forbid it outright; another might embrace it, a third encourage it for certain narrow uses. I don't oppose that sort of freedom, but we have not defined a set of competencies for students to master before they get a degree.

Even were those to emerge, however, wouldn't they change rapidly as the technology advances?

Here's where I wonder what may be the technological hurdles AI itself faces on its way to becoming AGI.

Frontier, Fortress, Flood Walls

Mollick refers to the outer edges of AI progress as a "jagged frontier," a useful metaphor for places where we have not fully developed methods for working with the technology. I like the metaphor a great deal, but in my own writing I returned to the more cumbersome "reverse salient" from historian of technology Thomas Parke Hughes. 

 Professor Hughes wrote two books that greatly influenced my thinking three decades ago, notably his magisterial history of electrification, Networks of Power and his study of technological enthusiasm from roughly the end of the Civil War to World War II, American Genesis.

First, we may need to consider Hughes' theory of “reverse salients.” He noted that every major technology hit technical or social obstacles, like an advancing army that encounters a strongpoint that bends lines of battle around it until overcome. For cars to supplant railways, we needed good roads. For EVs, at least until Tesla, the reverse salient involved range. Today in the US, it's the availability of charging stations and the politicization of EVs (one of the most stupid things to make political in a stupid time). For rocketry, the largest reverse salient has involved the cost to get a payload to orbit. For something as simple as a small flashlight, the salient meant brightness and battery life, now solved by LEDs for the most part. My mighty penlight, using a single rechargeable battery, now shines over 100 feet. My clublike 1990s Maglite, with 4 disposable D-Cells, was about as bright but 10 times heftier.

From Hughes' article at National Academies Press, I found this image of a reverse salient for ocean-going ships. For a long time, until Sperry developed a gyrocompass that would be "unaffected by the irregularities of magnetic fields," the older magnetic compass proved a hindrance for advancing ship technologies more broadly.

 

Reverse salients, like a fortress under siege, fall in time. Some tech like nuclear fusion takes longer to become practical. I've written here before about why, in my opinion, virtual worlds did not catch on for educators. Apologies to fans of Microsoft: my rage against the company was at its peak then. I've softened a bit though remain a Mac-OS zealot.

So for AGI? I'd estimate a few flood-walls remain. I speculate:

• The inability of our power grid to scale up to meet soaring energy usage for the hundreds of data-centers AGI would need
• The inability of AI code to reason in the ways promised by CEOs and enthusiasts
  
• A break-down in Moore's Law for semiconductors, as current silicon chips cannot meet the needs of AGI. New materials may conquer that salient
• Social upheaval from those who lose their jobs to AI and turn on firms that support AI development. Why? A new digital divide between workaday AI and elite AI that feeds public anger, legislation under a less libertarian future government, resistance from those who find superintelligence an existential threat to humanity out of religious or political beliefs
  
• Economic troubles as AI makers blow though venture capital and need government support (but keep in mind that Amazon once lost money too)
• Black-swan events such as global or domestic conflict (sadly, not far-fetched a week into the new Presidency).
  

I suspect that unless one or more of these reverse salients will emerge in a few years, if they are going to emerge at all. Most likely? There, I'm as much in the dark as you on this jagged frontier. 

Have a look at Mollick's work for more ideas and inspirations, even if you do not share his embrace of this technology.

Image Source: Richmond Flood Wall, Richmond Free Press

Wendell Berry's "Tiny No" to Computers (and Artificial Intelligence)

I've had a very productive conversation with my colleague Thomas, who is a "hard no" person when it comes to AI in classes. We agree that reasoned Humanist dissent should be seated at the table, even as Humanists such as I do invite AI to take a seat.

Here I employ a metaphor used in Ethan Mollick's book Co-Intelligence: Living and Working With AI. I fear that Mollick may miss some valid reasons, from a Humanist perspective, for being very wary of what AI may do to our minds, our reading and writing habits. In a year with a candidate who walks and talks like an authoritarian, perhaps of a fascist inclination, much rides on how new technologies will influence civic discourse in the coming years.

Thomas holds ideas similar to those espoused in the article "ChatGPT Does Not Have to Ruin College," online at The Atlantic. I like many of those reasons for resisting the hype-cycle around AI, but I also turn to a much older set of caveats, espoused back in 1988 at the dawn of the personal-computing age.

Wendell Berry's "Why I'm Not Going to Be Buying a Computer" caused a stir when it ran in Harpers, and it still rankles some of us who have found, say, blogging really good for one's writing muscles. Berry still holds that his "tiny no" was the right answer to make.

While I disagree broadly with Berry on computing, I do find one aspect of his refusal very compelling. In his essay he lists nine criteria for adopting a new tool:

1. The new tool should be cheaper than the one it replaces. 
2. It should be at least as small in scale as the one it replaces. 
3. It should do work that is clearly and demonstrably better than the one it replaces.
4. It should use less energy than the one it replaces. 
5. If possible, it should use some form of solar energy, such as that of the body. 
6. It should be repairable by a person of ordinary intelligence, provided that he or she has the necessary tools. 
7. It should be purchasable and repairable as near to home as possible. 
8. It should come from a small, privately-owned shop or store that will take it back for maintenance and repair. 
9. It should not replace or disrupt anything good that already exists, and this includes family and community relationships. 

Generative AI fails, by my reckoning, most of these tests. It does, arguably, do better work than the traditional search engine (test 3). Otherwise, it fails tests 4 and 9 badly. I suppose in time AI server-farms might be covered with solar panels (test 5) but Japan's decision to restart its nuclear power plants to power AI, as well as Microsoft's recommissioning of Three-Mile Island for AI power argue otherwise. 

Berry's no Luddite. Note the solar panels in the Wikipedia image I chose for this post. I'm reminded of how Howard Rheingold called the Amish "adaptive techno-selectives" in his insightful 1999 feature piece about mobile phones, "Look Who's Talking."

We will know, in time, if Berry proves correct, as he says in the recent interview, that "you could just ask your computer and it’ll tell you. But this doesn’t contribute to the formation of a mind." What is learning, after all? I've long distinguished information from knowledge. Having more information, under the scrutiny of my admittedly imperfect powers of reasoning and critical thinking, builds a store of useful knowledge. As a farmer as well as academic, I know things Berry does, too. I can judge when a field is ready for a cover-crop by hard-earned experience, but I also go online for weather forecasts, advice about soil conditions in Central Virginia, organic methods for controlling pests.  

My concern, however, is that we may offload reasoning to large language models, whose propensity to hallucinate without really good prompt-engineering has been well documented in journalistic and scholarly work. A feedback loop results: can we detect these errors if doing so requires the very reasoning powers we are using less frequently?

I don't know, but I do know that naysayers such as my colleague, Wendell Berry, and others who thoughtfully resist marketing hypberbole need a seat at the table.