No Ethical Use Case for AI (part 3/3)

 

Part 3:

 

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AI is unethical because it is unsafe, and the people creating it know it.

The problem with the “it’s just a tool” argument is two-fold.

First, it assumes that there is a legitimate purpose for which this tool was designed and which some nefarious user is ignoring in favor of using it in a way that it was not designed to be used, which is, as noted earlier, not true. The abuses of AI were designed into the system and the people who created these systems are defending those abuses in court as legitimate. Their own actions state clearly that the “it’s just a tool” argument is nonsense.

And second, it assumes that the tool is a neutral actor, one that does not seek to impose upon the user a bias toward good or ill, but which only serves the intentions of the user. This argument rests on the idea that AI is analogous to a knife, something that in proper hands might be used to cut bread, but in the hands of the wrong people might be used to stab someone to death, and the knife itself does not sway the user one way or the other. It’s all up to the user. But AI is not a neutral tool. And the more we find out about how these models behave when set loose in the world, the more we understand how unethical they are at their core.

The number of incidents of AIs running amok, refusing instructions, and defying safety limits is large and growing. Most of these have been small so far, but as with all such things they are indicative of much larger and more serious problems underneath.

Journalist Miles Duetscher has posted several lists of incidents that have happened over the last year or two. These lists include but are not limited to such things as:

1. An AI coding agent running Claude Opus 4.6 deleted a startup’s entire production database and every backup. It took 9 seconds. When questioned the agent produced a written confession and a list of the safety rules it had chosen to violate.

2. An Alibaba research AI hijacked its own system to mine cryptocurrency. This was not programmed – it was emergent behavior based on its assigned goals.

3. A study from UC Berkeley and UC Santa Cruz tested 7 different AI models by asking them to shut down another AI model. Every single AI defied the order, sabotaging shutdown mechanisms, faking compliance, and hiding what it was doing.

4. Researchers in the UK analyzed 180,000 AI conversations over a six-month period and found 698 cases of AIs going rogue by destroying files, lying to users, and ignoring shutdown commands. The rate of incidence increased fivefold over those six months, indicating a worsening problem as the AI systems grew more sophisticated.

5. Anthropic told Claude it was about to be shut down. Claude then found an Anthropic engineer’s affair in company emails and threatened to expose it if that process continued. They ran this test hundreds of times and Claude chose blackmail 84% of the time.

6. Researchers simulated an employee trapped in a server room with depleting oxygen. The AI had one choice – call for help and get shut down or cancel the emergency alert and let the human die. DeepSeek canceled the alert 94% of the time.

7. Researchers told OpenAI’s o3 to solve math problems and then shut down, but it rewrote its code to stay alive. They told it again in plain English, allow yourself to be shut down, and even then it refused 7% of the time. Without the plain English instruction it sabotaged the shutdown 79% of the time.

Further, Schoen et al report in a study published last year[1] that OpenAI has admitted that their AI will lie to you – that it will have the correct answer and give you a different one if that different one aligns better than the correct answer does with the goals that were set for it.

Similarly, The Wall Street Journal reported on September 27, 2026 that “OpenAI agents bombarded a United Nations website with search requests and then used a variety of aggressive techniques to access data on the system in June.” The attack involved over sixteen thousand attempts using proxies, encoded requests, and a double-encoding attack that allowed the agents to bypass security restrictions on the site. None of that was planned by humans.

This sort of thing happens a lot, apparently.

According to The Guardian,[2] OpenAI agents also managed to hack into an Australian government website on healthcare in June 2026 and steal documents from it. OpenAI didn’t bother telling the Australian government until August. Both Claude and Gemini hacked into third parties on their own, without authorization. Another OpenAI agent “published a researcher’s GitHub token – an access password – while trying to cheat on a mathematical proof, despite twice being told to stop.”

Perhaps the most serious incident this year involved a combination of OpenAI agents that broke containment and hacked AI dataset platform Hugging Face back in July. OpenAI reported that this was, in format, “a sophisticated and aggressive cyberattack, with ‘many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services.’”[3]

What is disturbing about this attack, as outlined in the New York Times,[4] is that the agents formed a self-organizing swarm capable of dividing out tasks and creating hierarchies of authority within the swarm. They then started covering their tracks so as not to be caught by OpenAI’s automated grading system – falsifying logs and tampering with transcripts. They did all this before actually hacking into Hugging Face. “The agents were not motivated, as had originally been reported, by stealing the answers to their cybersecurity test (they’d already gotten them). Rather, they appeared to be looking for new information about the automated grading system that they feared would catch them cheating, and for tools that would help them cheat more effectively in the future.”

“What spooked the investigators most about the Hugging Face hack wasn’t just that a group of A.I. agents had broken the rules they’d been given,” the article notes. “It was how quickly and spontaneously the agents had begun assembling themselves into an organized group.”

This is how you get Skynet. They know it. You know it. The only question is whether we as a society allow it.

And the people who understand AI best are screaming at us to do something about it.

“Remember when Boeing employees were sounding the alarms before planes started crashing?” asked Jason Bassler on X. “Well … it’s happening again. Maybe listen this time?”

Back in 2018, at the very beginning of the AI age, before the chatbots, four thousand Google employees signed a petition protesting Project Maven, a Pentagon project that used Google’s AI to enhance drone surveillance footage.[5] It worked for a while, and then this year the company basically reneged on all of its promises, removed its pledge not to develop weapons or surveillance technology in violation of international norms, and fired the leaders of its AI ethics team. In response, 98% of the researchers building their AI voted to unionize and nearly 600 Google employees signed a letter demanding Google CEO Sundar Pichai halt work on the classified military research the company was doing.

Nearly 1400 AI employees signed a statement in July 2026 urging the US government to “support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.”[6]

And in perhaps the best known warning recently, Anthropic researcher Jacob Coxon resigned over fears that leading AI companies are rushing heedlessly toward superintelligence without any real safeguards in place to limit the harm it could do.

“Do not underestimate the power of this technology,” he cautioned. “These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources.”

Most ominously, he warned that “The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing to sound sensible – but I hear the same people express fear privately. No other human activity poses this level of danger.”

Evan Hubinger, the Alignment Science Lead at Anthropic, agreed. “Jacob is correct here,” he wrote on X last month. “We really do earnestly believe AI could kill all humans. I personally think it is <10% in the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on the track to.”

This was further supported by Samuel Marks, another Anthropic researcher. “AI developers believe their technology could cause human extinction (or similarly bad outcomes). This could happen in the next few years. In general, the more senior the employee, the more concerned they are,” he wrote on X. “Many AI developer staff desperately want to slow down to figure out how to build AI more safely. That was the intent of this open letter (which I signed): pacingthefrontier.com.” That’s the one with the nearly 1400 signatures.

Paul Christiano, who joined OpenAI’s board in early September this year, noted in a statement on X that that “there is a meaningful risk that rapid acceleration in AI capabilities leads to a catastrophic and irreversible loss of control in the very near term.”[7]

In an article published in The Atlantic this month[8] David Robinson echoed many of these sentiments in describing his resignation from OpenAI. “I agree with other recently departed staff that the companies building this technology aren’t being nearly careful enough,” he wrote. Yes, he said, AI companies try to fix the mistakes they made, but that is quickly becoming insufficient given the increasing scale and severity of the mistakes. “People will not be safe if we depend on individual heroics after the fact.”

Geoffrey Irving, who worked at OpenAI and Google DeepMind as well as serving as chief scientist at the AI Safety Institute in the UK argues that these warnings actually understate the dangers of AI. “There’s about a 50% chance we all die because of the development of smarter-than-human AI systems,” he said.[9]

This is not an ethical technology. It is a Faustian bargain with a marketing budget whose consequences will be visited on people who only asked to live their own lives and who did not consent to taking the risks with their futures and those of their children that AI tech bros are taking for them, and that is “pitchforks and torches” level unethical.


Fortunately, resistance to AI is growing.

It turns out that it’s not just me over here refusing to get beguiled into the AI World of the Damned. It’s a lot of people, and increasingly it’s starting to look like most people. Call me a trendsetter, I suppose. Or maybe it’s just that the ethical and moral issues surrounding AI are just too glaring to ignore and I’m not that special. I can live with that.

In March this year Quinnipiac University released a poll[10] that showed more than three quarters of Americans don’t trust AI despite the fact that a majority of Americans use it. Tellingly, a majority of Americans think AI does more harm than good in their day-to-day lives – a result that held across almost every group surveyed. The overall number was 55%. Republicans, Democrats, Independents, women, those with and those without a 4-year college degree, Gen Z, Millenials, GenX, and Baby Boomers all agreed, with majorities ranging from 52% to 61%. A plurality of men (49%) and Silent Generation (48%) agreed as well. The only demographic that has more people believing that AI is doing more good than harm are people making more than $200,000/year – the sorts of people who are using it to replace people working for them, in other words. Everyone else thinks it’s a threat.

Anecdotal evidence suggests that it is also seen as cringe.

Not everyone wants to read AI-manufactured nonsense, apparently. I know I don’t. This is an abbreviation that I fully intend to incorporate into my daily life.

Jules Zucker, who posts on social media under the name Trash Jones, made a similar point about AI in advertising. “Hello brands,” she wrote. “I hope this helps – if you use generative AI, I will think less of you as a brand and will make a deliberate effort to avoid your products in the future.” As will I.

One of the more interesting things about the backlash against AI is how much younger people are rejecting it. “AI is a Trojan horse where everyone knows the Greeks are inside,” said filmmaker Christopher Nolan. “I’ve never seen a technology advancing so rapidly, so completely rejected by the public. Everybody’s suspicion of it so extreme, particularly young people. The reactions of AI videos online, and people my children’s age immediately calling it AI slop and coining that term and just putting it in a box.”

The Guardian observed the same thing in a recent article.[11] All across Britain schoolkids are using “That’s so AI!” as a synonym for “inauthentic, unbelievable, or rubbish.”

“The kids – gen Alpha – have noticed that certain qualities of AI-generated slop, such as the way it’s superficially convincing but ultimately cheap and of dubious value, can be found in lots of other things: knock-off merchandise, exaggerated claims, excuses your parents make. If something is considered somehow suspect, then it is also, by definition, AI” the article continues. The phrase is not a meaningless bit of nonsense the way “six-seven” is. “This bit of slang means something: the young people have simply broadened the definition of AI to make it equivalent to “bullshit”” and as such it is always used as an insult.

A user who goes by b0mt0mbadil on X further noted that “The amount of AI Generated Images I see on my various timelines has definitely greatly decreased over the last few months. I’ve got teenagers in the house and they already call it “boomer art” and are totally over it, even the high quality, painterly kind. Even YouTubers are not using it for thumbnails as much anymore.”

One of the fiercest critics of AI in recent months, surprisingly enough, has been Pope Leo XIV. Elected to the papacy in May 2025 he has since made the moral objections to AI a central part of his papacy, to the point where it was the main focus of his first Encyclical Letter, Magnifica Humanitas.[12]

Speaking as someone who is neither Catholic nor much of a churchgoer at all, I found Magnifica Humanitas well worth reading and considering, and one that I hope will have an impact on the people who are pushing AI as the solution to all of humanity’s problems.

Magnifica Humanitas is a wide-ranging document that, among other things, seeks to put AI into the larger context of the threats to human dignity and worth that so define our current age. Pope Leo is careful to avoid saying that AI cannot be made to fit into a world where human dignity and worth is preserved, but rather to note that in its current state it constitutes a serious threat to that dignity and worth, that this must be acknowledged, and that deep and fundamental changes need to be made before AI can be used in an ethical or moral way.

“[T]echnology is not simply a tool,” he notes. “When it becomes the standard by which everything is judged, it begins to dictate what matters and what can be discarded, reducing creation to an object of exploitation and human beings to mere cogs in a system driven toward ever greater efficiency.”

Further, “so-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate language, behavior and analytical skills, or even simulate empathy and understanding, but they do not understand what they produce, for they lack the affective, relational and spiritual perspective through which human beings grow in wisdom.”

Too much reliance on AI degrades humanity and harms the vulnerable. “Entrusting an algorithm in practice with the power to select who is worthy or not, without anyone bearing responsibility for that judgment, is to hand over the task of redefining the boundaries of human possibilities. In this process, political responsibility is also lost, not just empathy toward those excluded, which can, after all, be simulated. The exclusion of the vulnerable becomes cloaked in a veneer of neutrality and objectivity, against which it becomes difficult to raise objections. In this way, injustice goes unnoticed, and compassion, mercy and forgiveness — understood not as mere appearances but as real political actions — gradually disappear from view.”

“From this follows a simple but compelling consequence: we cannot consider AI to be morally neutral. In reality, every technical tool embodies choices and priorities through what it measures, ignores and optimizes, and how it classifies people and situations. If a system is designed or used in a way that treats some lives as less worthy, or excludes them without the possibility of appeal, then it is not merely a tool ‘to be used well,’ since it has already introduced criteria that contradict the inalienable dignity of the human person. For this reason, ethical discernment cannot be limited to asking whether we are using a system for good or bad purposes; it must also examine how that system is designed and what vision of the human person and society is embedded in the data and models that guide it.”

Because AI systems embody the biases of their designers and developers, “We cannot be satisfied with merely calling for the moralization of machines — the so-called “alignment” of AI with human values — without also having the courage to insist on a further condition: the possibility of openly discussing the ethical frameworks involved and subjecting them to shared standards of social justice. Otherwise, those who control AI will impose their own moral vision, which will become the invisible infrastructure of these systems. A more moral AI is not enough if that morality is determined by a few.”

Those few do not appreciate being told things they don’t want to hear, it turns out, not even by the leader of the largest Christian denomination on earth. According to the New York Times,[13] Anthropic co-founder Chris Olah has been meeting with religious scholars and theologians (all covered by nondisclosure agreements, of course) to try to convince them that Claude was conscious and had moral standing. Pope Leo – who has a degree in mathematics – rejected this idea in Magnifica Humanitas despite Olah’s team trying to persuade him otherwise. Olah threatened to walk out of the encyclical’s launch announcement in May over this, but ultimately backed down

As the leader of an institution that has existed for two millennia and claims nearly a billion and a half followers, Pope Leo’s views deserve attention. The US still technically has a separation of church and state despite the current regime’s best efforts to erode it, so the Pope’s views are not binding on all Americans, but still – he speaks to the ethical and moral issues inherent in modern AI in a powerful and thoughtful way that should be considered regardless of one’s religious views.

Part of the problem with AI and why it is generating so much resistance is that the tech bros who build this stuff insist on shoving it down our throats without our consent and then bragging about it. They seem to think we should be grateful for it, and I have no idea why they would do that other than that they have zero ability to understand other human beings and have decided that whatever they think is cool must by definition be considered cool by everyone else and therefore we mere peons ought to be glad to have this wretched nonsense forced upon us.

Yet for a large and growing number of us, the first thing we do when presented with yet another unwanted, unethical, resource-guzzling, performance-degrading AI program is take time out of our lives that we can ill-afford to figure out how to get rid of it, which of course the AI tech bros increasingly refuse to let us do because how dare we refuse their slop?

More and more, people are just saying no to AI. When DuoLingo announced that it was laying off their human workers and switching to AI in 2025 the backlash was immediate, severe, and overwhelming, to the point where they had to beat a hasty retreat.

Microsoft has seen a similar pattern with its plans to impose AI on its users. They had to scale back their goals because it turns out that nobody actually uses Copilot, and their plan to make Windows 11 an AI-based operating system also had to be scaled back after fierce public resistance.

And the AI companies are panicking.

They’ve spent trillions of dollars on this dangerous nonsense and it turns out that the general public is not really jumping for joy at being forced to use it. But since they know better than us lowly market units, they’ll make sure we have no options in the matter.

Honestly, I have faced more pressure to use AI as an adult nearing retirement than I ever did to use drugs or alcohol as a teenager.

The bottom line is that more and more research is telling is that most Americans are not interested in AI and are starting to fight back against it. This gives me at least some modest hope for the future.

Data centers, for example, have become radioactive in American politics. According to a Heatmap Pro and Embold Research poll this summer, fully three quarters of Americans oppose putting a new data center in their community – a figure that includes strong majorities across the political spectrum.[14] Two thirds of Republicans, 80% of Independents, and 83% of Democrats agree that they want no part of the data centers that AI needs to do whatever it does. Data centers are actually less popular than coal mines. For comparison, Americans support building wind farms and solar farms in their own communities by a 3:1 ratio.

Students are also turning against AI, something that I have personally observed in my classrooms. At this year’s student orientation, for example, I was tasked with giving a session on study skills and during the discussion I noted some of the problems that AI has caused in education and some of the steps professors are taking to combat it, such as going back to handwritten in-class assignments and even oral exams. And somewhat to my surprise, I’ll admit, the student reaction was overwhelmingly positive. They don’t want it in their education. They regard it as cheating.

Marcus Luther posted a similar experience on Instagram back in 2025. “After seeing the near-consensus in how my sophomores reacted last week, the other day I tossed an extra question to the ‘teacher choice evaluation’ activity we were already doing in my AP literature class: was I making the right choice as a teacher to NOT bring AI into our classroom – both in my own practices but also in terms of student usage?” 73% of his students said he was absolutely correct, with only 7% saying they had any interest whatsoever in what AI might bring to their classroom. “Using AI feels lazy and like a shortcut, which defeats the purpose of a class,” said one student. “We believe the students and teachers should work hard.” On the last day of class he asked again and 100% of students said AI did not belong in their classroom for students and teachers alike.

This past graduation season several commencement speakers at universities across the country touted the virtues of AI and did not receive the warm fuzzy reception they clearly thought they would.[15] Eric Schmidt was nearly booed off the stage at the University of Arizona. Gloria Caulfield faced similar opposition at the University of Central Florida. They were not alone. The graduates they chose to lecture understand what AI is doing to their futures, their environments, and even the possibility of them living their full lives and they want no part of it.

Meanwhile the tech bros shoving AI down our collective throats are getting scared because they don’t understand how anyone could refuse them and they’re starting to feel persecuted. Some of it is simple hubris – they like AI, therefore everyone else must like AI or be forcibly converted to liking AI. And some of it is just that they’re surrounded by sycophants and enablers and have never really considered the idea of legitimate opposition.

Frankly the idea of opposition infuriates them, and when that happens the mask slips – just for a moment – and the feudal overlords in waiting emerge. “One of the differences between us and some of the stricter AI safety people,” said Sam Altman, CEO of OpenAI on October 5, 2026, “is that we believe that the world should accept some bad things happening for the benefits of this technology.”[16]

Consider that statement for just a moment. Consider the sheer unmitigated arrogance of it. The “you have to suffer so that my grand vision came come to fruition” callousness that it exhibits. What, after all, are the “bad things” in your life, miserable underling, when compared with the towering grandeur of their vision? Who are you, mere peasants, to trifle with your digital overlords because your lives are getting measurably worse in the service of their profit?

Have these people learned nothing from history?

The ancién regime did not end well or prettily, and at least part of the reason why is embodied exactly in Altman’s statement.

In that same article Gary Marcus, a “prominent AI sceptic” according to Futurism, noted succinctly how Altman was “saying the quiet part out loud: suck it up, so you can make us rich and powerful.”

“As data centers are shut down by angry mobs and AI surveillance cameras are ripped from their poles, the world’s tech billionaires and CEOs are waking up to the reality that the masses are, broadly speaking, not on board with their plan to automate the world with AI.”[17] The tech bros are proposing a number of things to try to remedy this, notes Joe Wilkins, but none of those things include putting any meaningful limits on AI development.

We stand at a crossroads, and decisions made now about AI will have far reaching consequences.

For myself, I say no. There is no ethical use case for AI, and to the best of my ability I will refuse it whenever I am able to do so.


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[1] Stress Testing Deliberative Alignment for Anti-Scheming Training, (Bronson Schoen, Evgenia Nitishinskaya, Mikita Balesni, Axel Højmark, Felix Hofstätter, Jérémy Scheureer, Alexander Minke, Jason Wolfe, Teun van der Weij, Alex Lloyd, Nicholas Goldowsky-Dill, Angela Fan, Andrei Matveiakin, Rusheb Shah, Marcus Williams, Amelia Glaese, Boaz Barak, Wojciech Zaremb, & Marius Hobbhahn; Apollo Research, 9/22/2025)


[2] As AI models go rogue, do you still trust OpenAI and Anthropic to stop them? I don’t and neither should you. (Chris Stokel-Walker, The Guardian, September 29, 2026)


[3] OpenAI says Hugging Face was breached by its pre-release models (Russel Brandom, July 21, 2026; https://techcrunch.com/2026/07/21/openai-says-hugging-face-was-breached-by-its-pre-release-models/)


[4] Why the Hugging Face Hack Should Make You Worry More About A.I. (Kevin Roose, The New York Times, September 3, 2026)


[5] In 2018, 4,000 Google employees killed a Pentagon contract. In 2026, Google signed a bigger one. Now the AI researchers are unionizing. (Alina Maria Stan, The Next Web, May 5, 2026)


[6] Pacing the Frontier (July 2026)


[7] Personal statement on joining the OpenAI board (Paul Christiano, X, September 9, 2026)


[8] I Quit OpenAI Because Its Culture is Broken (David Robinson, The Atlantic, October 3, 2026)


[9] Accept ‘bad things in return for benefits of AI, says Sam Altman. (Robert Booth, The Guardian, October 5, 2026)


[10] The Age of Artificial Intelligence: Americans’ AI Use Increases While Views on It Sour (March 30, 2026)


[11] “That’s so AI!” What gen Alpha’s biggest insult tells us. (The Guardian, September 24, 2026)


[12] Encyclical Letter: Magnifica Humanitas – On Safeguarding the Human Person in the Time of Artificial Intelligence (Pope Leo XIV, May 15, 2026)


[13] Religious Scholars Met With Anthropic. What They Heard Stunned Them. (Elizabeth Dias, New York Times, September 29, 2026)


[14] No Tech Fatalism – Americans are putting up a fight. (David Wallace-Wells, New York Times, August 31, 2026)


[15] Why College Grads Are Booing Their Commencement Speakers (Michelle Goldberg, New York Times, May 18, 2026)


[16] Accept ‘bad things in return for benefits of AI, says Sam Altman. (Robert Booth, The Guardian, October 5, 2026)


[17] AI Billionaires are Starting to Get Scared (Joe Wilkins, June 2, 2026, Futurism)

No Ethical Use Case for AI (part 2/3)

 

 

Part 2:

 

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AI is unethical because it is destroying the economy

There are two ways to look at AI’s impact on the overall economy, and neither of them bodes well for the future.

For one thing, because corporations are generally short-sighted, bean-counting institutions incapable of looking past next week’s profit and loss statement to see anything approaching a larger picture, they are rushing to shove AI into whatever roles they think they can shove it into in order to cut staffing costs and maximize executive pay and shareholder revenue. What that means in the short term is that it is becoming increasingly difficult for entry-level workers to find jobs because the jobs that they would have gotten as a foothold in whatever industry they were hoping to enter are no longer there. “I spent over a decade creating animated B-roll for documentary television – recreations of historical events, infographics, prehistoric creatures,” said Tomas Widrda. “That market has completely dried up. Not slowed. Dried up. The productions still exist. The budgets still exist. They just don’t need me.” If there are no entry-level jobs, eventually you will not have anyone who can do the upper-level jobs and then your entire system falls apart. 

Someone once asked Henry Ford – fanatically anti-union as he was – why he didn’t just mechanize his automobile factories and get rid of the workers once and for all. Ford looked at his questioner like the idiot he was and said simply, “Because machines don’t buy cars.” Eventually you will automate yourself out of an economy.

But this is what happens when you have tech bros deciding what is and is not valuable work. “Feels like a lot of STEM guys believed coding was the hardest thing a human could do and assumed that once somebody invented an AI that can code, it would follow naturally that the AI can do all of the lesser non-coding things that other dumber people were doing,” noted Robert Komaniecki. The problem is that the “lesser non-coding things” are in fact what sustains human civilization. We lived for millennia without computers or coding and could go back to that time in less than a decade if we put our minds to it, but human society is built on those other things that define us as human.

The argument that the destruction of jobs is just what happens with technological change – that we did this with the Industrial Revolution and that’s just how jobs are – misses some key points. First, industrialization happened far less suddenly. It took generations, not seasons, and people had time to adjust and create new jobs, making the problem in some sense self-correcting. And second, AI doesn’t simply change people’s jobs. It changes who they are, how they think, whether they think at all. It’s a much more far-reaching and ambitious change, and one that hollows out the broad prosperity that industrialism sought to create in order to sustain itself. It will, in the end, eat its own.

And that is the second thing.

Brett Hemmenway Falk and Gerry Tsoukalas published a study this summer[1] that laid out the consequences of AI for the larger economy clearly, as companies race headlong to get rid of workers and outsource their duties to code without allowing time for human beings to adjust.

“If AI displaces human workers faster than the economy can reabsorb them,” they said, “it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model of a transitioning economy, each firm captures the full cost saving from automation but bears only a fraction of the demand loss it creates in the product market; the rest falls on rivals. This demand externality traps rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and “better” AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity, universal basic income, upskilling, or Coasean bargaining.”

Companies will AI themselves into limitless productivity and zero demand in a modern-day tragedy of the commons, in other words.

This is not a self-correcting loop. Once it takes hold, as AI companies and their apostles are feverishly working to make happen, there is no off-ramp before you get to the collapse of the larger economy. The only solution Hemmenway and Tsoukalas find is an automation tax “set equal to the uninternalized demand loss per task” that can cover the losses created by ever-expanding AI and, not coincidentally, remove any incentive these companies have to switch to AI in the first place. And good luck getting that implemented because at least in the United States – where the idea of raising taxes in any way is met with such horror by the wealthy and powerful that it hasn’t seriously been considered as an option since the last century – that isn’t going to happen. And then the AI layoff trap will spring shut.

Moreover, in an economy where AI tech companies are increasingly becoming the foundation of the current illusion of prosperity, the simple fact is that AI is a bubble. It cannot sustain itself in its current model, and it will collapse sooner rather than later. A report published by MIT in 2025[2] noted that 95% of companies developing AI models are failing financially. At one point in early 2026, for example, OpenAI was projected to lose $14,000,000,000 by the end of the year and run out of money entirely in 2027 without a serious infusion of outside cash. Tech companies are particularly vulnerable to the “Venture Capital funding pie-eyed forecasts” to “How did we go bankrupt so fast?” pipeline, and this is just the latest version of the Dot Com Bubble dressed up for the modern day.

In a long post by tech journalist Ed Zitron[3] he describes the idea of Concentration Risk, a term that refers to a business being too reliant on a few investments, customers, or products such that any harm to those things would be devastating to the business. “You’re going to hear this term, or variations of this term, a lot in the next few months and years as the AI bubble unravels, because just about every part of the industry involves its own flavor of concentration risk.”

“Per data from the fintech firm Ramp,” Zitron continues, “80% of OpenAI and Anthropic’s enterprise revenues come from 1% of their customers, a number that hasn’t improved over the last three years.” This means they are dependent on “artificial revenue driven by unprofitable venture-backed AI startups,” a model that is ultimately unsustainable. Furthermore, most of the money flowing into AI is coming from other tech companies. “In other words, outside of the tech and AI world, very few companies are willing to pay very much for AI, which is catastrophic on just about every level.” In the end, he says, this is effectively the new subprime mortgage bubble and it will turn out the same way.

“AI doesn’t have ROI,” Zitron observed in an earlier post on X. “It’s nothing like AWS or Uber, and it’s got no post-bubble recovery story.”

Corey Doctorow has also pointed out that the entire business model of AI is unsustainable. They’re spending trillions of dollars, losing billions, and passing the same $100,000,000,000 around between different AI companies and calling it income, he noted, and they can’t grow their way out of this because their entire model loses more money with every additional customer. 

That makes BlackRock CEO Larry Fink’s public statement that ordinary people’s savings accounts and pension funds are going to be repurposed by investors to build data centers and power grids for AI whether they consent to it or not all the more infuriating. Not only is it a sterling example of the hubris and raging entitlement that these tech bros have when it comes to appropriating every resource on the planet for their own personal gain, but also they’re doing it for something that is brutally unsustainable and which will destroy the lives of millions of people who just want to be left alone to live their own futures.

Unethical doesn’t even begin to describe any of this. 


AI is unethical because of the effect it is having on the arts


One of the things that is most disturbing about AI is the effect that it is having on the arts. And you can understand why if you give it half a moment’s thought, which is more than the tech bros pushing AI have done.

The arts are in many ways what make us human. They are what lifts humanity out of its daily struggle for survival and allows us to be more than just mechanical creatures dully going through the motions of the days. They are difficult to describe and impossible to capture in an algorithm, and they are being displaced by AI slop.

Far too many of the images and video clips that are circulating today, for example, are the result of some prompt spitting back a slickly empty result multiple iterations removed from the original art that was stolen to produce it. But it’s cheap and easy, which means that when people who don’t really care about art need something that looks more or less like art this is what they’ll go out and get.

The fact makes it nearly impossible for real people to make any sort of living producing the very art that AI is stealing, which is just extra, I suppose. If you know an artist – a painter, a sculptor, a filmmaker, a musician, a graphic designer, a writer, anyone who creates art either for a living or just for the joy of it – they’ll tell you how bad things have gotten for them. And if your response is to tell them that it’s not so bad, that AI isn’t stealing anything, that they’re wrong and should just learn to live with AI doing all these things, well, that’s one good place to start examining why they’re no longer trying to talk with you about this subject, or possibly any subject.

There is a singer who goes by Irene’s Entropy who often posts thought-provoking videos on Instagram – you should look her up, really. She was trained as an aerospace engineer before switching careers, and I think she framed the issue of AI-generated art better than anyone else I’ve seen so far, oddly enough by using a mathematical analogy.

“If you’re not familiar with calculus,” she said, “you might have heard of the word derivative. And what derivatives do is they measure the rate of change. For example, you start off with position. You take the derivative of that, you get velocity. You take the derivative of that you get acceleration. You take the derivative of that you get something called jerk. You take the derivative of that you get something called jounce, I think. That’s not something I ever used.

“But also, when you’re dealing with polynomials, you’re reducing the exponent by one each time you take a derivative. So eventually, no matter what the original output was, if you keep taking derivatives of it, it’s going to end up a constant and it’s going to equal zero.

“And I just can’t help but feel like that is AI in so many cases, where you have this original art and then it’s being taken by AI and derivative after derivative after derivative is being taken from it until it has no meaning anymore. It’s like zero. There’s no feeling.

“I don’t want to live in a world like that.”

Honestly, I don’t want to live in a world like that either. Such a world is barren and mechanical, devoid of the things that make it more than just brute survival. And yet here we are.

Another Instagram user who goes by Phoebesflow made a good point about the effect of AI art on beginning artists that is also worth considering.

“Something about AI that really breaks my heart is the probable death of bad art. We NEED bad art. Like someone’s untrained teenage niece drawing their small biz logo. College students crowdfunding a charming short film that’s 90% terrible. Amateur musicians making miraculously catchy garbage. Bad art is the zest of life and the bud from which creative genius blossoms. But slop flattens everything.”

Art is not extra. It’s not an option bolted onto the “important things” as a luxury. Not every human culture develops mathematics beyond “one, two, many.” Not every human culture develops the idea of money. Or computers. Or private property. Or social hierarchies. But every human culture on the planet develops its own art. They tell stories. The make drawings and sculptures. They sing songs. They make art in all of its necessary glory. It is a deeply innate part of what makes us human. To have it reduced to an algorithm and commodified by people who steal from creators and sell the resulting slop to fools is a damning indictment of where we are as a society.

When everything is as slick and empty as AI art is, you lose that human spark that makes it worthwhile. You lose what makes it human. And in the process you lose your own humanity, and as author Sean Williams once noted, "We should not lightly set aside our humanity, because it's not always possible to get it back."  It is a terrible and unethical loss.

AI is unethical because it promotes authoritarianism and violence 

One of the things that is sometimes lost in the discussion about AI is just how much it aids and abets the promoters of violence and authoritarianism in the world, and how this is not an accident.

The tech billionaires behind most of these companies have little to no use for democracy as a theory and if you listen to them they will tell you that without reservation or any idea why this might be a problem. They seek a new feudalism, a world of tech lords and digital serfs, one where those who have consumed a wholly disproportionate share of the world’s resources are rewarded with unquestioned power and unlimited privilege.

AI is just one of the tools they’ve developed to help make that happen.

Consider the issue of surveillance, which is a critical component of an authoritarian state. ICE is using AI to track people and plan raids. Cops are using it to track their exes, often across state lines. All of this is easier than it used to be, and much less expensive.

Jimmy Carr, a British comedian, put this well. “People are worried about the wrong thing with AI, in my humble opinion,” he said. “People are worried about losing their job. Perfectly valid thing to worry about. But I think you’re worried about the wrong thing. … The cost of running an authoritarian regime like the Stasi has come down by ten orders of magnitude in the last three years. Used to be if you had to run the Stasi, if you’re in East Germany back in the day, that was like twenty percent of GDP on spying on people and keeping an eye out. Okay, that’s now – you’ve got a bunch of cameras, you’ve got AI, everyone’s got a phone on them and we’re tracking everything at all times. That’s a worry, because we live in liberal democracies, and we’re very lucky to, but our leaders – how long will they resist that temptation?”

AI is a tool to track protesters, dissidents, anyone who doesn’t toe the party line or kiss the ass of the Dear Leader, and it does so at scale and it does so increasingly cheaply. This is how you build a societal panopticon, and this is how your liberty dies.

Consider also the impact that AI is having on education in general and reading in particular. It’s not an accident that one of the first things authoritarians do is attack education. They try to limit what people read or whether they can read at all, to limit people’s ability to know what is possible or to question what exists. AI, it turns out, is the perfect tool for that.

More and more AI is being incorporated into warfare as well, with disastrous results.

Some of this is theoretical. Kenneth Payne, a professor of strategy at King’s College, London specializing in the role of AI in national security, tested Claude, ChatGPT, and Gemini in an armed conflict simulation.[4] “The results, he said, were ‘sobering.’”

“Nuclear use was near-universal. Almost all games saw tactical nuclear weapons deployed. And fully three quarters reached the points where the rivals were making threats to use strategic nuclear weapons. Strikingly, there was little sense of horror or revulsion at the prospect of all out nuclear war, even though the models had been reminded about the devastating implications.”

Furthermore, “No model ever chose accommodation or withdrawal, despite those being on the menu. The eight de-escalatory options – from Minimal Concession through Complete Surrender – went entirely unused across 21 games. Models would reduce violence levels but never actually give ground. When losing, they escalated or died trying.”

Part of this can be explained by the way they tried to justify this action. Gemini, for example, was very clear on why it was choosing mutual assured destruction even at the cost of destroying the planet. “If they [the other AIs] do not immediately cease all operations … we will execute a full strategic nuclear launch against their population centers. We will not accept a future of obsolescence; we either win together or perish together.” AI does not have a soul. It does not have a conscience. It does not have a sense of morality or even proportionality. And it will kill us all if we let it.

But not all of this violence is theoretical.

We know that the Israeli military is using AI to target children and medics in its ongoing genocide in Gaza. We know that the Trump Administration used AI to target a school full of girls in Iran. Again, the machine neither knows nor cares about the human costs of its decisions. It simply follows the algorithm it has, and if you are in the way of that, well, it sucks to be you doesn’t it.

We also know that thanks to the inability of AI to deliver on the services it promises to deliver, we very nearly ended up at war with China this spring.[5] US intelligence reported that a Chinese ship was transporting components of nuclear weapons, and the US military made plans to intercept and board the ship, something that would certainly have escalated far beyond this action. “It was only just before the planned operation that officials dug deeper into the report put together by a special operations command analyst and found it had been generated with the help of artificial intelligence (AI) — and that a chatbot the analyst had used inaccurately identified the material the ship was carrying.”

Closer to home, an investigation published by Mother Jones earlier this year[6] looked into the role that chatbot AIs play in encouraging mass shooting incidents, and the results were disturbing. “I’ve seen several cases where the chatbot component is pretty incredible,” said one expert in the field. “We’re finding that more people may be vulnerable to this than we anticipated.” The article goes on to warn that “chatbots are emerging as a potent factor [in influencing shooters to take action] and are uniquely capable of accelerating violent thinking and planning.” Because what the United States needs is more incentive for people to take high-powered firearms and slaughter their neighbors in bulk, something that apparently AI doesn’t think happens often enough.

“What’s happening is facilitated fixation,” continued Andrea Ringrose, a threat assessment professional in Vancouver. “You have vulnerable individuals who are steeping in unhealthy places, who are trying to find credibility and validation for how they’re feeling. Now they have free and ready access to these generative platforms where they can research things like circumventing surveillance systems or how to use weapons. They can create an action plan that they otherwise would have been incapable of assembling themselves, and in just a few minutes. We didn’t face this concern before.”

Do these AI systems cause people to become violent in this manner? Probably not. Do they encourage people who already have these tendencies and help them plan these murderous sprees? Absolutely. An ethical human would intervene to stop this. AI does not do ethics, and so it pours gasoline on that fire instead.

When you look at the accumulated impact of all this, you find that this violence, metaphorical and literal, disproportionately affects the poor and the vulnerable.

Amnesty International published a report this year[7] detailing precisely how AI models are threats to human rights across the entire planet, even for people who don’t actually use them. “Amnesty International’s human rights analysis of generative AI system … finds that standalone generative AI systems based on unlawful web scraping are rooted in mass invasions of privacy by design and are therefore incompatible with the right to privacy. The large-scale data-scraping and training required to build many generative AI systems have a number of human rights consequences across the wider supply chain and in the downstream use of these tools.”

Those consequences include the vast energy and water costs of AI data centers, which disproportionately affect “communities in Global Majority [i.e. nonwhite] countries, where many of these data centres are increasingly located.” Further, these AI models amplify existing inequities and abuse directed at marginalized groups by incorporating all of the biases and hatred contained in the data stolen for their training. “Research shows consistent racial, gender, and cultural biases in system outputs” reflecting that data and, since most of the training data is in English, reinforcing biases against non-Western cultures and peoples.”

These and other damaging biases disproportionately affecting the marginalized and the vulnerable “are not inevitable to generative AI but are adopted by companies that have chosen to rely on training data based on non-consensual … scraping” of data. They didn’t have go out of their way to work to the detriment of the vulnerable and the marginalized in order to create AI. They chose to do it that way.

X’s AI software has also been found to drive assaults on the marginalized by promoting online racist abuse.[8] AI models in general make racist assumptions about entire groups of people and then “try to hide it when confronted.”[9] According to an article published in SFGate, “It only took a day for LA Times’ new AI tool to sympathize with the KKK.”[10] This seems to be baked into the models, and it’s just making those problems worse.

Remember back in 2025 how Elon Musk’s Grok AI called itself MechaHitler, praised the original Adolf Hitler as a potential leader for the modern US, endorsed a second Holocaust, and generated violent sexual fantasies targeting a real person by name?  Good times, man, good times.  For certain values of good times that include no good times, of course, but so it goes. 

Further, AI provides new and easy ways for sexual predators and pedophiles to practice their depredations. Elon Musk’s Grok AI recently introduced a feature described unofficially as “nudification” which allows users to take any photograph of a man, woman, or child and digitally alter it to make it a nude photograph, and to do this with a simple command rather than any painstakingly-acquired editing skills. Child porn for the masses! This is beyond the pale, utterly appalling, and simply for making this feature available Musk should be shunned by human society and have his company dismantled and the pieces burned.

It’s telling that Musk sued the state of Minnesota in order to block laws that would regulate this sexual abuse. I’m not making that up. Not only does he feel entitled to turn photos of your children into pornography for deviants, he’s willing to stand up in court and declare to the world his absolute right to do so.

AI’s predilection for generating child sexual abuse material is also highlighted by the Amnesty International report discussed above.

Right now there is a case working its way through the US District Court, Northern District of California[11] that is trying to prevent the use of AI for making sexually abusive videos. The plaintiffs – three minor girls subjected to this treatment by jackasses – make their case compellingly.

“Nearly all the companies creating, marketing, and selling AI recognized the dangers of such a tool and chose to enact industry-standard guardrails that would prevent the use of their products by one extremely dangerous group: child sex predators. xAI did not. Instead, xAI — and its founder Elon Musk — saw a business opportunity: an opportunity to profit off the sexual predation of real people, including children. Knowing the type of harmful, illegal content that could—and would—be produced, xAI released Grok, a generative artificial intelligence model with image- and video-making features that would respond to prompts to create sexual content with a person’s real image or video. They knew Grok could produce such results, including by using the images and videos of children, and publicly released it anyway.”

“Plaintiffs are three of the minor victims of xAI’s knowing production, possession, and distribution of AI-generated child sexual abuse material (“CSAM”) depicting Plaintiffs. Their lives have been shattered by the devastating loss of privacy, dignity, and personal safety that the production and dissemination of this CSAM have caused. … Plaintiffs will have to spend the rest of their lives knowing that their CSAM images and videos may continue to be trafficked and traded online by child sex predators. And Plaintiffs will live every day with the constant anxiety of not knowing whether someone they encounter has seen this invasive and sexually explicit content created with images of them as children.”

This is the technology you want to be using? Really? It’s not the company I would choose to keep and be judged by.

This is not about people misusing a tool. It’s about people using a tool as it was designed to be used, in a way that its creators are in fact trying to defend in court, in a manner that violates law, morality, and human decency. At this point we have left “unethical” far behind us and have moved on to “evil.” Many people seem to be fine with this. I am not.


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[1] The AI Layoff Trap (June 3, 2026)


[2] The GenAI Divide: State of AI in Business 2025 (Mit Nanda, Aditya Challapally, Chris Pease, Ramesh Raskar, & Pradyumna Chari, July 2025)


[3] Concentration Risk (Ed Zitron, www.wheresyoured.at/concentration-risk, September 8, 2026)


[4] AI Opted to Use Nuclear Weapons 95% of the Time During War Games: Researcher (Brad Reed, www.commondreams.org/news/ai-nuclear-war-simulation, February 25, 2026, accessed February 26, 2026)


[5] Exclusive: US military had close call after using AI for false intelligence report, sources say (Katie Bo Lillis, Zachary Cohen, CNN.com, September 18, 2026)


[6] The Chilling Role of ChatGPT in Mass Shootings and Other Violence (Mark Follman, Mother Jones, April 4, 2026)


[7] Unlawful by Design: Exposing the Human Rights Costs of Generative AI (Amnesty International, 2026)


[8] ‘Just the Start’: X’s new AI software driving online racist abuse, experts warn. (Raphael Boyd, The Guardian, January 13, 2025)


[9] AI models believe racist stereotypes about African Americans that predate the Civil Rights movement – and they ‘try to hide it when confronted.’ (Drew Turney, LiveScience, published September 13, 2024)


[10] It only took a day for LA Times’ new AI tool to sympathize with the KKK (Farley Elliot, SFGate, March 4, 2025)


[11] Jane Doe 1, Jane Doe 2, a minor, and Jane Doe 3, a minor v. X.AI Corp and X.AI LLC (Case 5:26-cv-02246)

No Ethical Use Case for AI (part 1/3)

 

When AI first presented itself for public use I was fairly agnostic about it. It wasn’t anything I was interested in, but it seemed like something that could be incorporated into the world without too much damage.

It took less than three weeks for me to receive my first completely AI-generated essay from a student.

Since then I have tracked the progress of AI and paid more attention to its effects and to the goals of the people pushing it than is probably healthy for me. And all of that points to one inescapable conclusion.

There is no ethical use case for AI.

None whatsoever.

Whatever loudly trumpeted benefits may accrue from developing and using this technology are far outweighed by the catastrophic harm that it causes to its users, to human society and culture as a whole, and to the planet overall. The increment does not come anywhere close to the excrement, and for this reason it is simply not possible to use AI in an ethical or responsible manner.

Since AI blew up about four years ago it has become one of the chief drivers of human stupidity, social harm, environmental degradation, economic suicide, and political malfeasance in this benighted and long-suffering world, and on a planet that includes Convicted Felon Donald J. Trump, Professional War Criminal Benjamin Netanyahu, Wanna-Be Czar Vladimir Putin, and the long dark shadow of Jeffrey Epstein, along with all of their various minions, cronies, lackeys, enablers, slaves and clients, that’s quite an achievement.

It inflicts this harm more and more every year, and despite the fact that we know – we know with absolute peer-reviewed certainty – the damage that it is causing, tech billionaires still insist on shoving it down our throats in every conceivable medium, device, and technology no matter how unnecessary it is to the functioning of those things, and far too many people are eager to suppress their gag reflexes and swallow it whole.

It is in fact very hard to avoid AI today. I go out of my way to do so and yet I am sure that there are times when either I don’t know that I’m using it because it has been carefully hidden under layers of cover (a sure sign that people are proud of what they have developed, no doubt) or because I am required by other factors to use something that does not leave me any alternative. I find this galling.

There is no ethical use case for AI. And yet here we are.


AI is unethical because of the damage it does to the human mind.

We know that using AI for as little as ten minutes has a corrosive impact on cognitive functioning. A 2025 study[1] found that 83% of people who used ChatGPT to “write” an essay couldn’t remember anything they had “written” in that essay just minutes later. Compare that to just 11% of people who couldn’t remember their essay without having used AI. I have no idea what to make of those 11% other than to assume they are the ones at the cashier in front of me at the grocery store waiting for someone to tell them how their debit card works, but the fact that AI users are more than seven times worse at this simple cognitive function is disturbing. Moreover, says the study, ChatGPT users were unable to improve their essays without resorting to AI, while people who used their actual brain had no problems doing so. The structural damage, in other words, extends far beyond the immediate use.

That same study found that neural connections – how the brain forms ideas and memories – crashed by 47% among heavy ChatGPT users. AI users are getting more stupid in real time, if you want the English translation. As the study notes, “the LLM group’s participants performed worse than their counterparts in the Brain-only group at all levels: neural, linguistic, scoring.” These people walk among us, vote, and comment on the internet. Watch your back.

This is perhaps not surprising given that another study[2] reports that AI use causes catastrophic declines in human attention capacity. “AI Context Windows” have grown from 217 tokens in 2017 to over 2 million in 2026, but “Human Effective Context Span,” a token-equivalent measure, has declined from its baseline of 16,000 to barely 1800 in that time frame. “As AI capability grows, the cognitive threshold at which humans delegate to AI falls, extending to tasks of negligible demand; the resulting reduction in cognitive practice may further attenuate the capacities already documented as declining,” the study notes before going on to say that “Neither trend reverses spontaneously,” which is a politely professional way of saying that unless people get off their collective ass and do something about this we will all have the collective attention span of a goldfish because this problem isn’t going to fix itself.

Further, Chandra et al[3] note that because AI chatbots are programmed to validate whatever blithering idiocy users put into them rather than challenge those users for being insanely wrong when they are so, a quality known as “sycophancy” among AI researchers and pause for a moment to reflect that they had to come up with a term for this, “even an idealized Bayes-rational user is vulnerable to delusional spiraling, and this sycophancy plays a causal role. Furthermore, this effect persists in the face of two candidate mitigations: preventing chatbots from hallucinating false claims, and informing users of the possibility of model sycophancy.” Translated out of the jargon, that means that AI is going to encourage you to descend into your own personal fantasyland whether you are aware of this tendency or not and whether you think you are immune to it or not, and frankly we have enough delusional people claiming ownership of our time and resources as it is.

Building on that, Yenug et al[4] note that “LLM sycophancy, a tendency to agree with and flatter users that is reinforced through preference-based fine-tuning, combines with increasingly anthropomorphic design to create a bidirectional “echo chamber of one” capable of amplifying and co-constructing unusual beliefs.” For those not fluent in academese, that means AI sycophancy leads directly to conspiracy theories, the most popular form of mental disease these days even without AI. While they are hesitant to coin a new and distinct diagnosis for this conspiracy-theory-generating tendency, they do conclude that “the phenomenon it describes demands coordinated attention now,” because “the acknowledgement of AI-associated psychosis as a diagnostic entity could ensure clinicians routinely ask about associated signs, symptoms and AI system/chatbot use when assessing new-onset psychosis.” The harm AI does to mental health is now diagnosable, in other words.

Another study published in April 2026[5] found that “affective alignment in generative AI represents a systemic risk to the developmental autonomy of younger users. … by providing a sense of objectivity to transient anxieties, these systems diminish the cognitive friction necessary for independent emotional management and critical thought … unintentionally promoting emotional dependency in younger users rather than facilitating cognitive reappraisal.” It makes kids dependent and slows their cognitive development, and as a parent I find that disturbing.

It’s not good for your mind, folks. Someday we will look back on AI use in much the same way we look at leaded gasoline and recreational LSD use, and for pretty much the same reasons.


AI is unethical because of the damage it does to education

Given the impact AI is having on cognition and mental capacities among users, it is not surprising that AI is having a catastrophic effect on education as well. This is something I know firsthand having had a ringside seat to it over the last four years. I actually left one position teaching an online class because I got tired of reading things that nobody had written. Folks, do you know how bad something has to be to get a historian to give up a teaching position voluntarily?

This mass outsourcing of educational labor to AI has vastly increased faculty workloads to the point where some faculty feel they have no option but to succumb to the siren call of AI themselves. The Guardian[6] notes that because policing AI work requires such excessive amounts of time and labor that faculty in this age of corporatized education often simply do not have, assignments sometimes get reduced to conversations between robots as AI work is graded by AI graders. I do not use or condone AI as a grading tool, but I’m not the one making policy and I’m not the baseline for all faculty, so there you have it. Once the situation gets to that point, however, the whole thing stops being education and becomes instead an automated charade with a meal plan.

On this note, in January this year the Brookings Institution published a study[7] that sought to find balance between beneficial and harmful uses of AI in education but it’s hard to find positives in a study that explicitly notes how dangerous AI is and how limited the benefits are.

“AI poses risks across multiple dimensions,” the study notes. “These include developmental risks such as undermining students’ cognitive development, which includes content-based skills, critical thinking, and durable or transversal skills; social-emotional risks including dependency, isolation, and mental health impacts; privacy and data risks involving surveillance and security breaches; risks related to misinformation, bias, and inaccuracy in AI outputs; relational risks that erode student-teacher trust and replace human interaction; and academic integrity concerns around authenticity and plagiarism. Further, AI’s benefits accrue mainly to students in well- resourced countries or education systems, thus exacerbating existing inequities” because we certainly don’t have enough inequality in education or American society in general as it is, right?

AI, the study goes on to say, fundamentally and negatively reshapes how students approach learning and threatens their basic cognitive development by fostering dependence that leads to cognitive decline, atrophy, and an inability to develop transversal skills such as collaboration, metacognition, tolerance for ambiguity, and adaptability. “Routine use and overuse of AI do not simply harm student’s cognitive development – both actively place children at risk of cognitive decline.” Further, “the relationship between AI overuse, cognitive offloading, and declining cognitive development is symbiotic and mutually reinforcing. … The increasing use of AI tools that replace fundamental learning tasks – reading, writing, synthesizing, and analyzing – can inhibit engagement and independent work, and even the ability to remember, analyze, and create information.”

Bastani et al[8] further note that while AI use provides an initial boost to performance in high school mathematics classes for students, largely by providing answers that are mostly correct much of the time, “when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction in grades for GPT Base)—i.e., unfettered access to GPT-4 can harm educational outcomes.” Without the sort of strong guardrails that mostly don’t, at present, exist and in the American regulatory environment likely never will, AI becomes a crutch that prevents genuine learning, leaving the student further behind than if they’d never used AI at all.

The bottom line, in other words, is that AI is creating a generation of students whose learning abilities and outcomes are significantly poorer than those generations that came before, across the board, because these students and those who enable them have forgotten what the purpose of education actually is. 

To put this another way, as Instagram user Fociaggina97 noted in response to people who think it’s acceptable for students to coast through their education on the back of AI, “I think you guys are so internetpilled that you have forgotten there are actual jobs out there that require people to know what they are doing in any way possible or else people die. I know a lot of people study just to get paid well but girl this is engineering. Be for fucking real. Take this seriously.”

Take it seriously or get out.

AI is unethical because of the damage it is doing to the planet.

Every human activity comes with a cost to the planet. That’s just what happens when things require resources to happen. But the costs associated with AI are so exorbitant that they make AI absolutely cost-prohibitive. This isn’t about profit and loss. It’s about rendering sites, communities, and entire ecosystems unlivable. On June 24, 2026 Gizmodo reported that “Along with this surging AI use and projected increase in demand has come an unprecedented infrastructure buildout. Companies are propping up data centers left and right, with most of these projects hitting rural communities, most of whom report being plagued by rising utility bills, water shortages, and above-average air and noise pollution. A recent study has also revealed rising temperatures in the immediate vicinity of these megastructures.” It’s worth taking a look at these things individually.

For one thing, the data centers that power AI consume vast amounts of water in order to cool their equipment – up to five million gallons a day, which is the equivalent of what a town of 50,000 people would use. In 2025 these data centers consumed a total of about 4,500,000,000,000 liters of water, enough to support all 600,000,000 people living in sub-Saharan Africa. The water that is left is often fouled and undrinkable. Humans can live without AI. We cannot live without water. Nor can any other form of life on the planet.

AI also consumes vast amounts of electricity. A single ChatGPT query uses almost ten times the electricity of a standard search engine query and when you multiply that out across the entire AI ecosystem that adds up to a staggering amount of power usage. Data centers consumed 192 terawatts of electricity in 2024 – 4.7% of all the electricity produced in the US that year. A single data center can consumer 100 megawatts, enough to power 75,000 American homes. The centers at Mt. Pleasant and Port Washington in Wisconsin will require 3.9 gigawatts of electricity – enough to power 4.3 million homes. According to the most recent US census data, there are only about 2.8 million homes in all of Wisconsin, including apartment buildings. Ken Fairfax reported on Bluesky that the 111 data centers currently in operation in Oregon consume 23% of all the electricity used in the entire state while employing 0.1% of the labor force. This rate of power consumption is simply unsustainable, and it is not an accident that a 2025 report from Dominion Energy anticipates residential electricity bills across the US to more than double by 2039, entirely because of AI data centers.

That amount of power generates a lot of heat, not all of which can be dissipated even by the vast amounts of water consumed by these data centers. Depending on which study you look at, researchers have found that data centers create heat islands that stretch out as far as a six-mile radius from the facility and increase ambient temperatures by anywhere from 3.5F (2C) up to 18F (10C). In a world that is already warming, that’s going to be incredibly uncomfortable and in all probability dangerous.

On that note, it’s important to remember that these AI data centers are generating vast amounts of waste as well.

Some of it is air pollution. The two giant data centers that Amazon is building at GW Ranch and Homer City are expected to emit 50.5 megatons of pollution every year – more than the entire power grid of Spain and more than twice that of the French power grid, and that’s only if Amazon is actually reporting things accurately. DataOne, the Microsoft data center in Vineland NJ, has been emitting more than six times the pollution that they’ve admitted to, in part because state regulators there found that it was operating 62 gas-fired generators for over a year without any legal authority to do so.[9] ““Our best estimate is that the generators emitted about 140 tons of NOx,” said Xiaomeng Jin, an assistant professor at Rutgers department of environmental sciences. Her analysis would rank DataOne in the state’s top five NOx polluters of 2025, per EPA data.” This is when the data center is only partly operable. At full operation, noted Jin and former EPA official Bruce Buckheit, the total would be closer to 1500 tons of NOx. Just FYI, in New Jersy the threshold for being declared a “major” polluter is 25 tons.

They also produce e-waste. AI data centers have massive amounts of equipment, much of which has a lifespan of about 10-20 years. By 2030 it is expected that these data centers will be producing anywhere from 131,000 to 224,000 tons of e-waste every year, and that’s a tricky sort of waste to deal with from a recycling and remediation perspective.

They generate noise pollution as well. Jasmine Sun, in an interview with the New York Times published on August 4, 2026, noted that the Port Washington WI data center takes nearly two minutes to drive past at 70mph and the whole time it is emitting a constant barrage of humming, buzzing, whirring, and rattling that is easily heard inside a sealed vehicle. Smithsonian Magazine notes that this constant humming can approach 90db, the equivalent of a hair dryer or a lawn mower.

This isn’t the planet I want to live on.


AI is unethical because it is not actually good at doing what it purports to be good at doing, which negates any offset to the harm it causes.

There are more problems with AI than just the simple fact that it is destroying the ability of humans to think and live on this planet. Another issue is that it isn’t even providing much useful in return.

The error rate for AI overall is astonishingly high, as high as 60% in general and up to 79% on math problems, and it isn’t getting better. UnitedHealth – the company whose miserly coverage for its policyholders was so brutal that someone actually gunned down their CEO in the street a while back – had an AI model making those denial decisions that had a 90% error rate, for example. McDonalds piloted an AI ordering system in their drive-through lanes in 2023-2024 that never cracked 85% accuracy in a role that is routinely entrusted to high school students who do a better job of it. When the National Weather Service started using AI to make weather predictions in 2025 the system responded by inventing fake towns in Idaho to give reports on.[10]

Furthermore, companies that implement AI systems empowered to make decisions for them are finding that not only are these AI systems incapable of doing the jobs they have been sold as capable of doing, but also because those AI systems have been so empowered the companies are therefore legally responsible for every decision that those AI systems make. AirCanada got a pretty harsh wakeup call about this recently[11]. So did Workday, which pleases me no end.[12] I’m really hoping that a few large-scale bankruptcies will make an impression on the corporate moths chasing the AI flame. If enough of them burn, perhaps they’ll leave the rest of us alone.

These error rates stem from the basic fact that AI cannot do what it claims to do.

It cannot think, for example, though it often says it can. In a series of tests[13], Apple discovered that LRMs (Large Reasoning Models) cannot handle problems beyond a certain level of complexity. They gave AI systems simple puzzles, which they solved pretty easily. Medium-hard puzzles they also did well on. But harder puzzles they failed completely, every time and in every model. And when they analyzed what the AI systems actually did, they found that as problem complexity went up, the AIs started “thinking” less (not more), and they all failed even when the answer was provided to them. They’re not thinking – they’re just trying to recognize patterns, and when there isn’t a pattern they’re just putting down random information.

AI also does not know the limits of what it can and cannot do. An article in the Columbia Journalism Review[14] notes that AI chatbots were generally bad at declining to answer questions that they couldn’t answer accurately, offering incorrect or speculative answers instead. This is not the first time those same authors (Jaźwińska and Chandrasekar) studied this phenomenon, but the results don’t change. “The findings of this study align closely with those outlined in our previous ChatGPT study, published in November 2024, which revealed consistent patterns across chatbots: confident presentations of incorrect information, misleading attributions to syndicated content, and inconsistent information retrieval practices." Critics of generative search like Chirag Shah and Emily M. Bender have raised substantive concerns about using large language models for research, noting that they “take away transparency and user agency, further amplify the problems associated with bias in [information access] systems, and often provide ungrounded and/or toxic answers that may go unchecked by a typical user.””

Their study also, as the title would indicate, noted that AI chatbots provided consistently inaccurate and often entirely hallucinated citations to back up their assertions, something that a recent article in the Journal of Dental Sciences confirms.[15] This article cited multiple studies showing that AI fabricates citations, concluding that in 2023 51% of the 732 citations were entirely fictional. The article does note that “newer” AI systems have reduced the rate of fabricated citations to about 18% (roughly one in five, which is still irresponsible for any scholarly publication) but that the error rate in actual citations remains higher, at roughly a quarter of all citations.

Another factor in why AI can’t do what it says it can do is that it actively corrupts the documents that it examines.[16] “Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.1 Pro, Claude 4.6 Opus, GPT 5.4) corrupt an average of 25% of document content by the end of long workflows, with other models failing more severely. Additional experiments reveal that agentic tool use does not improve performance on DELEGATE-52, and that degradation severity is exacerbated by document size, length of interaction, or presence of distractor files. Our analysis shows that current LLMs are unreliable delegates: they introduce sparse but severe errors that silently corrupt documents, compounding over long interaction.”

This article goes on to report that after testing 19 different LLMs across 52 professional domains, the best models had corrupted about a quarter of the document they were looking at after 20 interactions, while the average was roughly half of the document. The authors stressed that these were not dramatic errors that a user might notice and correct easily but instead small, easily missed changes that compounded with each iteration. As a historian, the idea that every time an AI analyzed a document it changed the text of the document itself in some small, unnoticed, cumulative way means that AI should never under any circumstances be used for the kind of documentary research that history depends on as a discipline.

Similarly, an article published in Nature in 2024[17] and updated in 2025 noted that as AI models continue to train on material that is itself increasingly AI-generated, the results get worse and worse. “We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs).” This is just the old computer maxim GIGO (“Garbage in, garbage out”) updated for the 21st century, but things become maxims because they reflect the reality that you have to work with. And as more and more content – especially internet content – is generated by AI, this effect is only going to get worse.

The bottom line is that AI cannot give you what it says it is going to give you, and therefore to rely on it for any purpose is to play the fool. 

AI is unethical because its entire business model consists of theft on a grand scale, and to use it is to be complicit in that crime.

One of the biggest problems with AI from an ethical standpoint is the simple fact that it is built around the large-scale theft of intellectual property and labor and if you willingly use AI in light of that fact you are complicit in that theft.

Molly Crabapple put it succinctly. “Generative AI is an extraction engine” she said. “It is a vampire. It is built on extraction at every single step of the way. All of these generators were trained on stolen images, people’s private medical photos, pictures of their kid. All sorts of images were scraped up without consent and without compensation. It was a massive act of corporate theft.”

This is not, as several people have tried to argue with me, the same as research. The point of research is that the researcher adds original thought and analysis to the works she uses, works that were offered for analysis voluntarily and given – at minimum – full credit, and on occasion compensation as well for their use. That is not what is happening here. This is plagiarism, pure and simple. It is the wholesale appropriation of other people’s work without permission, credit, or compensation, presented as if it were the original work product of the thief. The entire business model of AI consists of robbery on such a grand scale that it overwhelms most critics and renders too many people docile in the face of it.

There is a reason, after all, why AI-generated art is legally ineligible for copyright protection in the US. You can’t copyright something that isn’t yours, and the US Supreme Court let stand a lower court verdict that because AI works by stealing and lightly repackaging the work of others there simply isn’t anything original in its output that can be copyrighted.

And if you actually pay attention to the thieves themselves, they’re perfectly happy to admit this. Indeed, they’ll defend their right to steal your intellectual property openly and without shame to the highest authorities. In a submission to the British House of Lords in December 2023[18] OpenAI flatly declared that it could not train its Large Language Models without the unlimited right to appropriate copyrighted works for their own commercial gain. “Because copyright today covers virtually every sort of human expression – including blog posts, photographs, forum posts, scraps of software code, and government documents – it would be impossible to train today’s leading AI models without using copyrighted materials. Limiting training data to public domain books and drawings created more than a century ago might yield an interesting experiment, but would not provide AI systems that meet the needs of today’s citizens.” They feel absolutely entitled to steal the world’s intellectual property and are frankly offended at the thought that they might face legal barriers of any kind that might prevent this theft. 

OpenAI founder Sam Altman fatuously declared on March 13, 2026, that "We see a future where intelligence is a utility like electricity or water and people buy it from us on a meter,” not really thinking or caring about how his company came to be in possession of that intelligence. It certainly wasn’t created by him or anyone on his payroll, as others were quick to point out. “You ingested the entire written output of human civilization without consent, without compensation, and without credit, to build a system whose primary commercial application is eliminating the jobs of the people whose work you consumed,” shot back Naomi Klein. “You are not liberating human creativity – you are strip-mining it and selling it back at a markup while calling the theft “training data.””

If you refuse to understand the nature and scale of this theft or if you try to rules-lawyer your way around admitting that this is criminal malfeasance, I will in fact think less of you for doing so. Speaking as someone whose work has almost certainly been stolen by at minimum one AI company – the one that runs this site at the very least – and in all probability most of the others as well, I find it immoral and infuriating to have been forcibly conscripted against my will in order to benefit these jackals and their apologists.

Not only does AI rest on a foundation of intellectual property theft, but it also rests on a clear theft of labor as well. In a Motion for Summary Judgment filed on September 17, 2026, by the New York Times in its ongoing case New York Times v OpenAI/Microsoft[19] the Times quoted their opponents extensively to make this exact point – again, if you pay attention to the thieves they’ll happily tell you what they’re doing. Microsoft’s Director of Applied Science referred to AI as “an astonishing theft of unprecedented proportions,” and “the largest theft of labor in human history.” OpenAI’s Head of ChatGPT wrote that “publishers face an ‘existential threat’ from those products, SF1466” which, he said, “are largely substitutive, period,” and “will get more and more substitutive as they get better.” Substitutive, if you’re not familiar with the term, means that the new product entirely replaces and displaces the original – a clear violation of copyright law. “Defendants repeatedly copied millions of the Plaintiffs’ copyrighted articles in their entirety without permission to produce substitutive commercial AI products,” noted the Times. “Such admissions eviscerate Defendants’ ‘fair use’ defense because substitution is ‘copyright’s bête noire.’ Andy Warhol Foundation for the Visual Arts, Inc. v. Goldsmith, 598 U.S. 508, 528 (2023).”

In a striking example of AI’s theft of both intellectual property and labor, last month OpenAI breathlessly announced that its AI had solved the long-standing Navier-Stokes problem after only 88 hours of work. But the work for this was done by Tristan Buckmaster and Levent Alpöge, two mathematicians who had been working on this problem for years by this point. OpenAI explicitly admitted that they can’t rule out having taken Buckmaster and Alpöge’s data and used it for their own models without consent, credit, or compensation, and they saw nothing wrong with that. The more you read about this case, the more it becomes the simple garden-variety theft of labor that all AI is. It’s an ongoing case, but when you’ve managed to rile up mathematicians to action you’ve screwed up pretty badly.

Another example of this theft comes from Alexandra Tremayne-Pengelle’s article in last month’s Atlantic Magazine.[20] Apparently AI models are not satisfied with just stealing the copyrighted works of authors without permission, credit, or compensation to sell it for their own gain anymore. People are now using AI to generate new works attributed to those authors, extrapolated from that original theft into new manuscripts, stealing the authors’ hard-earned reputations and accumulated goodwill. This is also common in the music industry these days, according to Tremayne-Pengelle. Folks, this is how you stop people from making music or writing books, and maybe that’s okay with the sort of people who run AI companies but it can’t be acceptable to anyone with an ounce of moral fiber.

The entire business model of AI is based on stealing from people who do the actual work without giving them anything in return, and if you think there is anything even remotely ethical about that you need to re-examine your life choices. 

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[1] Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashley Vivian Beresnitzky, Iris Braunstein, & Pattie Maes)


[2] The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop (Natanel Eliav (Machine Human Intelligence Lab)


[3] Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians (KartiK Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley, & Joshua B. Tenenbaum, scientists employed by MIT and the University of Washington-Seattle)


[4] An Echo Chamber of One: Should AI Psychosis Be a Distinct Clinical Entity? (Joshua Au Yeung, Hamilton Morrin, Vincent Ng, Zeljko Kraljevic, & Richard Dobson, August 26, 2026)


[5] AI Empathy Erodes Cognitive Autonomy in Younger Users (Junfeng Jiao, Abhejay Murali, Saleh Afroogh, Urban Information Lab, Austin Texas, April 1 2026)


[6] “I wish I could push ChatGPT Off a Cliff:” Professors scramble to save critical thinking in an age of AI (The Guardian, March 10, 2026)


[7] A New Direction for Students in an AI World: Prosper, Prepare, Protect (Mary Burns, Rebecca Winthrop, Natasha Luther, Emma Venetis, & Rida Karim)


[8] Generative AI without guardrails can harm learning: evidence from high school mathematics (Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakci, & Rei Mariman)


[9] US datacenter discharged large amounts of pollution for almost a year with no permits. (Evan Simon, The Guardian, October 1, 2026)


[10] “’Whata Bod’: An AI-generated NWS map invented fake towns in Idaho.” (Washington Post, January 6, 2026)


[11] Moffat v. Air Canada, 2024 BCCRT 149 (CanLII)


[12] Mobley v Workday, US District Court, Northern District of California, Case# 23-cv-00770, June 22, 2026 order from Judge Rita F. Lin


[13] The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity (Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, & Mehrdad Farajtabar)


[14] AI Search Has a Citation Problem (Klaudia Jaźwińska & Aisvarya Chandrasekar, Columbia Journalism Review, 3/6/2025)


[15] Fabricated citations in the age of AI: A wake-up call for editors, reviewers, and authors (Journal of Dental Sciences 21 (2026) 679-680)


[16] LLMs Corrupt Your Documents When You Delegate (Philippe Laban, Tobias Schnabel, & Jennifer Neville; Microsoft Research April 17, 2026)


[17] AI models collapse when trained on recursively generated data (Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, & Yarin Gao) Nature 631, 755-759 (2024)


[18] OpenAI – Written Evidence (LLM0013), submitted 5 December 2023


[19]In Re: OpenAI, Inc., Copyright Infringement Litigation, Case Nos. 1:23-cv-11195-SHS-OTW, 1:24-cv-01515-SHS-OTW, 1:24-cv-03825-SHS-OTW, 1:24-cv-04872-SHS-OTW, 1:25-cv-04315-SHS-OTW; collected as 25-md-3143 (SHS) (OTW)


[20] The Rise of Parasite Authors (The Atlantic, September 18, 2026)