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Saturday, November 07, 2026

The Voters Have Spoken—And Now Chuck Schumer and Hakeem Jeffries Must Go

Democrats have said loudly and clearly that they want new leadership. Unfortunately, too many of the people in Washington aren’t listening.

Senate Minority Leader Chuck Schumer (D-NY) and House Minority Leader Hakeem Jeffries (D-NY) hold a joint news conference in the US Capitol on Thursday, January 8, 2026
(Photo: Bill Clark/CQ-Roll Call, Inc via Getty Images)

Corbin Trent
Nov 06, 2026

Common Dreams

On Tuesday, Democratic voters, independent voters, and even some Republican voters spoke loudly and clearly: they want this country to go in a different direction.

This is an amazing, unprecedented moment in my lifetime. I’m 46 years old, and this is the most hopeful political moment I’ve ever experienced.

However, the Democratic leadership we have today is completely ill-suited for this moment. They do not have the vision for what it’s going to take to rebuild the trust the American people have lost in their government, their institutions, and the Democratic Party itself.

Democrats have spoken loudly and clearly that they want new leadership. Unfortunately, too many of the people in Washington aren’t listening.

CHUCK SCHUMER HAS GOT TO GO

NOT HAKEEM for SPEAKER

We’re going to do our dead-level best to make sure they hear us and make sure they see the names. We’re going to start sending text messages, running ads, and getting loud. We’re going to get in their faces and make it impossible to ignore what Democrats, independents, and the voters who delivered this Democratic tsunami are demanding: new leadership.

Chuck Schumer is bad at his job. Hakeem Jeffries is not ready for this moment. He is not the leader we need.

It’s time for them to go.

Our work is licensed under Creative Commons (CC BY-NC-ND 3.0). Feel free to republish and share widely.

Corbin Trent
Corbin Trent is an Appalachian-born general contractor and political organizer. He co-founded Brand New Congress and Justice Democrats, helped recruit AOC, and served as her first communications director. He publishes AmericasUndoing.com, a project exposing America’s economic decline and calling for bold, public-led rebuilding. Find morework on his TikTok, YouTube, and Facebook channels.
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After Blue Tsunami, Progressives Don’t Trust Schumer and Jeffries to Lead Congress

“Schumer is bad at his job,” said one organizer. “Jeffries is not ready for this moment.”

US House Minority Leader Hakeem Jeffries (D-NY) speaks alongside Senate Minority Leader Chuck Schumer (D-NY) on May 21, 2026 in Washington, DC.
(Photo by Win McNamee/Getty Images)

Jessica Corbett
Nov 06, 2026
COMMON DREAMS

After a Democratic “tsunami” in this week’s midterm elections that has been widely seen as “a mandate to deliver big change,” progressives within and beyond Congress have cast doubt on whether the New York Democrats likely to lead both chambers are actually up for facing Republican President Donald Trump’s veto threats and fighting for the “bold agenda” sought by US voters.

As critics of artificial intelligence data centers prepared for a Friday protest outside the Brooklyn home of Senate Minority Leader Chuck Schumer over a “dirty” bill that House Minority Leader Hakeem Jeffries is suddenly speaking out about, political organizer Corbin Trent argued that “Schumer is bad at his job” and “Jeffries is not ready for this moment.”

Although some races haven’t yet been called, “Democratic voters, independent voters, and even some Republican voters spoke loudly and clearly: They want this country to go in a different direction,” wrote Trent, a former communications director for Rep. Alexandria Ocasio-Cortez (D-NY) and two-time presidential campaign staffer for Sen. Bernie Sanders (I-Vt.).

Trent, who also co-founded Justice Democrats and now runs the super political action committee A Fight Worth Having, stressed that “this is an amazing, unprecedented moment in my lifetime. I’m 46 years old, and this is the most hopeful political moment I’ve ever experienced.”

“Democrats have spoken loudly and clearly that they want new leadership. Unfortunately, too many of the people in Washington aren’t listening,” he added. While Trent and his group are far from alone in calling for new Democratic leadership when the party takes over Congress in January, Schumer and Jeffries are expected to become majority leader and speaker, respectively.

“Do you expect to face any kind of challenge in the election for Senate majority leader?” Michael Barbaro, who hosts The New York Times’ podcast The Daily asked Chuck Schumer on Friday. He simply responded, “No.”

Schumer ultimately added that “our caucus is focused on moving forward. Everyone’s very happy with what we all did. But this is just the beginning. As I said before, and I’ll say it again and again to my caucus, to the country, to everybody: If we don’t deliver, all this will go away. This is just the first step. But I think we’re united together to do that.”

He delivered a similar message of unity during a Thursday night appearance on MS NOW, telling host Lawrence O’Donnell that the victorious Democrats—so far the party has 54 seats in the Senate and 253 in the House of Representatives, but those numbers could rise—were “all running on the same basic themes: costs, chaos, corruption.”

As both majority and minority leader in recent years, Schumer has faced criticism from the party’s left flank—and, as Al Jazeera noted Thursday, at least seven Democratic senators-elect have signaled or outright said they will not support him: Abdul El-Sayed (Mich.), Peggy Flanagan (Minn.), Adam Hamilton (Kan.), Chris Pappas (NH), Juliana Stratton (Ill.), and James Talarico (Texas).

However, as Fox News chief congressional correspondent Chad Pergram explained Friday, basic math and a lack of clear challengers suggest Schumer will win: “The selection of Senate majority leader is not a vote which plays out on the floor. The concept of ‘Senate majority leader’ has only been around since the early 20th Century and is not mentioned in the Constitution.”

“Let’s say Democrats do win Mississippi and Alaska, giving them 56 senators who caucus with the Democrats. All Schumer needs is 29 Democrats to support him in caucus, and he’s the next majority leader,” Pergram wrote on social media.

He noted that other possible contenders are Democratic Sens. Brian Schatz (Hawaii) and Chris Van Hollen (Md.), but the former is running to succeed retiring Senate Minority Whip Dick Durbin (D-Ill.) and the latter reportedly isn’t seeking the post—though Van Hollen has said publicly that Democrats should demand an ambitious progressive agenda before backing Schumer or anyone else.

“Progressives are flipping Republican seats and winning critical battlegrounds where pundits told us our politics could never compete. Abdul El-Sayed in Michigan. Troy Jackson in Maine. James Talarico in Texas. Sam Forstag in Montana. Randy Villegas in California. Bob Brooks in Pennsylvania,” said Joseph Geevarghese, executive director of Our Revolution, which grew out of Sanders’ 2016 presidential run. “We gave people something to vote FOR, and they showed up.”

“The progressive movement drove this victory. We spent a decade building the infrastructure, developing leaders, and making the argument that brought us here... This is a vindication of our politics and our theory of change: Organized people can beat organized money when we advance an anti-corporate, working-class agenda,” he stressed, recalling when “the talking heads, academics, and party establishment scoffed at Bernie Sanders and our movement for talking about oligarchy.”

The organizer said that “this was the corruption election. Affordability was central, but people were also asking why life keeps getting more expensive and who keeps cashing in. From AI and data centers to healthcare, Big Pharma, crypto, oil, and the war industry, people want a say over decisions being made at their expense. Corporate control of government is making their lives harder.”

The American Israel Public Affairs Committee (AIPAC)—which notably congratulated Jeffries and Schumer, who wasn’t even on the ballot this cycle, by name on Wednesday—along with GOP megadonor Elon Musk “and other billionaires and corporate interests have poured money into shaping our politics,” Geevarghese continued. “Our victories show that their money does not make their agenda inevitable. People can organize, fight back, and win.”

In the wake of these victories “powered by a progressive wave,” Geevarghese called on Democrats to “use every committee, hearing, and budget negotiation. Follow the contracts and conflicts of interest. Put executives and administration officials under oath. Fight for concrete gains and make Trump answer for what he blocks.”

“People need to see Democrats using every bit of power they have to fight for them,” he said. “That requires leadership ready to meet the moment. Chuck Schumer should step aside. Bernie Sanders has provided the clarity and urgency a real opposition party needs. Leadership must match that ambition, including by investing in candidates and communities too often written off.”

Aru Shiney-Ajay, executive director of the youth-led climate group Sunrise Movement, similarly said in a statement after the midterms that “young people voted for change. We deserve a Senate majority leader who will end the filibuster so we can stop the Iran Oil War, end corporate control of our government, and create good green jobs, not more data centers.”

“Young people won’t get that with the same old Democratic leadership. Schumer, it’s time to put the American people first–commit to ending the filibuster or step aside,” Shiney-Ajay added, referring to controversial chamber rules that effectively block legislation unless at least 60 of the 100 senators support advancing it to a final vote.

As for Jeffries, he got “the majority of his dreams,” as Axios put it earlier this week. The outlet reported:
The left’s strategy is “going to look different from what it would have been with a smaller margin,” one incoming House progressive told Axios, noting that “leaders have more flexibility when they have big majorities.

Progressives ”can’t really extract much now [with this] colossal majority,“ a progressive strategist acknowledged, adding that ”we don’t have to do this high-stakes Jeffries fight.“

Although Jeffries is on track to secure the role, there are calls for him to not be the next speaker. Nearly 900,000 people have signed a related Change.org petition, Nicole Whitted, an author and social media creator who receives compensation in connection with the effort.

“After historic wins on Tuesday, I believe the Democratic Party and our country as a whole is at a crossroads,” Whitted told Newsweek. “My opposition to Hakeem Jeffries is about more than any single policy disagreement. It’s about real leadership that aligns with today’s Democratic voter, real accountability, and whether the party is genuinely listening to the voters demanding change.”

In anticipation of Jeffries serving as speaker, the outlet Sludge examined his “financial backing from executives at Anduril and Palantir, cryptocurrency industry interests, and major Wall Street investors—business interests with a stake in how aggressively the new majority pursues corporate beneficiaries of Trump’s presidency.”

In response to that reporting, Melanie D’Arrigo, executive director of the pro-single-payer Campaign for New York Health, said that “you can’t lead the opposition party while being bankrolled by the opposition. This should not be a controversial take.”

Friday, November 06, 2026

Generative AI uses more energy on non-English text

Models can use up to 15 times more energy to process, generate text in widely spoken languages, often with lower-quality output

University of Michigan

Graphs and photo of researchers

Key takeaways:

Gen AI uses 1.4 to 180 times more energy to produce text outside the English language.
Higher energy costs increase the barrier to using AI for billions of people worldwide—they must either pay more or switch to a non-native language.
AI developers could improve energy efficiency by training models optimized for specific languages or incorporating translation pipelines into their services.

When processing and producing non-English text, generative AI burns more energy and sometimes provides less accurate answers, potentially reinforcing existing global inequalities, according to University of Michigan Engineering researchers.

The energy disparity is greatest in languages that don't use Latin script and have less digital content available for training. AI models could use 1.4 to 180 times more energy to generate non-English text, with most Western European languages coming in at twice the energy expenditure or less. However, Hindi and Bengali, each spoken by hundreds of millions of people, require 7.9 and 15.3 times more energy than an equivalent English prompt.

"The languages that typically consume more energy also tend to have lower accuracy in the model output," said Naihao Deng, a doctoral student in computer science and engineering from the U-M AI Lab and the first author of the study, which was presented at the Empirical Methods in Natural Language Processing conference of the Association for Computational Linguistics.

"Most people will want to use AI models by asking questions in their native languages, and they may feel pressured to use it as it's incorporated into more parts of society, but the double penalty makes it more likely that certain people will be priced out of AI and that companies will be more reluctant to serve models in certain languages."

At present, around 5.2 billion people have to use more than twice as much energy to generate text in their first language compared to English speakers, according to the researchers' measurements and demographic data available in Ethnologue. But the researchers also provide recommendations to bridge the gap and ensure that users aren't penalized for trying to use AI in their language of choice.

To help quantify AI energy use and improve efficiency, the National Science Foundation and the Defense Advanced Research Projects Agency (DARPA) partially funded the research.

Measuring AI's language energy disparity

The researchers found the energy and accuracy disparity by measuring the amount of energy used by three open-source AI models, Alibaba's Qwen 3, Google's Gemma 3, and Meta's Llama 3.1. They asked each model to answer multiple-choice questions included in Belebele, a dataset for assessing the reading comprehension of AI models in 122 different languages. They then measured the amount of energy used to answer each question using the ML.Energy framework, developed at Michigan Engineering.

To check for the disparities in other types of tasks, the researchers also asked the models to answer a collection of grade-school math word problems and open-ended user prompts included in other benchmarking datasets. Because these datasets aren't multilingual, the researchers translated the English questions into seven other languages using Meta's translation tool, No Language Left Behind.

All the models used less energy for English text, on average. The energy disparity persists at larger model sizes and across different types of computer processors.

Extra tokens, extra cost

When working in other languages, the models used more energy because they had to generate more tokens, the basic unit of data used by AI. Tokens can represent entire phrases and words or single syllables and characters. AI models convert prompts into tokens before processing them, and then convert the output tokens into human language for the final response.

This problem escalates for scripts whose characters require more code to display. For example, the Devanagari script used in Hindi, Nepali and Marathi and the Bengali script have primary characters that change form and combine with accents to produce up to millions of unique characters.

A higher number of tokens takes up more computer memory and burns more energy. With more memory consumed in tokens, the model has less available for processing information. So, it processes the tokens in smaller batches, which uses more energy per token. This effect compounds the energy disparity compared to English text.

More tokens can also result in higher direct costs, since companies often price model access by the number of tokens generated.

Reducing AI's language energy disparity

To help fix the cost disparity, the researchers recommend that companies optimize their services for more languages. This could entail routing user queries to models tailored to specific languages. Companies could also translate user prompts to English before feeding them to a more generalized model, provided the translation is reliable.

"AI developers should be creating more resources for those languages with an energy and cost disparity," said Rada Mihalcea, the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering, director of the U-M AI Lab and corresponding author of the study. "Crowdsourcing native speakers to help translate material or create resources to get enough training data could also help improve efficiency."

Additional funding sources include Ford, Laude Institute, OpenAI and the Survival and Flourishing Fund.

The study used the University of Michigan's Great Lakes high-performance computing cluster.

Study: The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference (DOI: 10.18653/2026.emnlp-main.2113)

To restore attention, reach out to nature

Research shows children with greater exposure to green spaces perform better on attention, achievement tests

Washington University in St. Louis

The world is a far louder and more distracting place than when humans first evolved. But there’s increasing evidence that experiencing nature can help restore attention lost to modern distractions, known as “directed attention fatigue.”

In research recently published in Environment International, psychologists at Washington University in St. Louis add to growing evidence behind attention restoration theory (ART), the idea that there’s something innately restorative to one’s attention in experiencing nature and green spaces. WashU researchers wanted to test this effect on children using data from the long-running ABCD study.

Their question: “If a child lives closer to green space, and has more access to green space, does this help them pay attention on demand when it’s required, and do they do better at school on achievement tests?” said Carina Fowler, lead author of the study and a postdoctoral researcher in the lab of Deanna Barch, a professor of psychological and brain sciences in Arts & Sciences and the Gregory B. Couch Professor of Psychiatry at WashU Medicine.

It turns out, those neighborhood parks and woods can make a big difference.

“The greater the proximity to green space, the better kids did on objective assessments of their attention performance,” Fowler said.

There are many questions about attention restoration theory. This research tackled those questions with a robust data set, using statistical analysis to filter out confounding factors such as income level, ADHD diagnosis and a slew of other measures that could affect attention.

Under the canopy

Studies have long shown that children perform better on sustained attention, inhibitory control and working memory tasks after time in a school garden, a park or a nature walk. They also score higher on reading and math assessments in greener environments. The question is why, and do all green spaces offer this benefit?

Some studies have found little effect of green space on attention performance, but the proponents of attention restoration theory note that, “You have to feel safe in the green space to have an effect,” Fowler said, adding that the restoration effect is not as strong if someone is on social media while in that green space. That, and “the kind of green space might matter.”

The team used ABCD data, which follows roughly 12,000 American children, with a median age of 10, as they grow. Participants are given performance-based assessments throughout the observational study, including directed attention and intelligence measures.

The team’s data analysis also controlled for sense of safety, social media use, income and diagnosis of ADHD.

Touching grass

ART proponents suggest something in green spaces is inherently restorative.

Green space “captures your attention” but also “allows your mind to wander,” a combination that restores attention, Fowler said.

Others have suggested that physical activity in nature is the source of the restorative effect, along with more exposure to clean air. Fowler and the team attempted to sort that out and found a connection to air quality, but not to physical activity.

“There is something to the idea that air quality is partially explaining why green space is helping our attention,” Fowler said.

Researchers are still trying to understand just how the brain is restored with time in nature. Why wouldn’t going to a quiet dark room work better? Fowler said previous research has shown that people don’t like sensory deprivation or being in a quiet room with their thoughts.

“It seems people need something to be occupying them a little bit — but not too much — and that nature can provide that really well.”

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Fowler CH, Luby JL, Bastain TM, Breton CV, Barch DM. Green space is associated with better directed attention and higher crystallized intelligence in middle childhood: Evidence from the ABCD study. Environment International, Volume 215, 2026, 110502.

https://doi.org/10.1016/j.envint.2026.110502

Journal

Environment International

Monday, November 02, 2026

RX = SEX SUPPRESSION

Why Weren’t We Warned? – OpEd

November 2, 2026

By Alan Cassels

Key Takeaways:

Surveys show sharply reduced sexual activity among young people worldwide, contributing to historic low fertility rates in OECD countries; cited factors include housing costs, lower alcohol use, mental-health issues tied to screen time, and, increasingly, sexual side effects from prescribed drugs.

SSRI antidepressants (Prozac, Zoloft, Paxil and others), along with isotretinoin and finasteride, are linked to sexual dysfunction that can persist after discontinuation—a condition known as PSSD—yet the US FDA still lacks a specific warning despite alerts issued by the EMA, MHRA and regulators in at least 35 countries.

Psychiatrist David Healy, who has compiled more than 1,000 case reports, estimates that a large share of SSRI users experience some sexual impairment and that millions of young Americans may be affected, underscoring the absence of informed consent when these drugs are prescribed.

In my day, being young was dominated by three basics: sex, drugs, and rock ’n’ roll.

Boy, that world has changed.

Seems like our kids aren’t having much sex these days.

Drugs? Yes, mostly of the prescribed type.

And rock ‘n’ roll? Well, not much anymore, as the listening tastes of today’s youth center on hip-hop and AI-enhanced electronic pop that hardly passes for music nowadays.

But it is the lack of sex that seems to be most concerning.

Apparently, the fact that young people aren’t having much sex today is being captured in surveys from around the world. This is proving to be worrisome not simply as a cultural artifact, but because the lack of sex is resulting in plunging birth rates, causing fertility in nearly every OECD country to drop to historic lows. The lack of sex is not only shrinking populations, but also causing huge demographic changes as countries turn older. What exactly is going on?

Those who study the sexual habits of young people generally point to three key reasons: There’s the unaffordability of housing, which makes younger people live at home longer (not exactly conducive to lots of sex). There’s the lack of booze—the social lubricant that used to be a precursor to most intimacy—and apparently kids aren’t as drinking much today as we used to. Then there’s the third one: youth mental health issues, which are exacerbated by too much screen time, isolation, and loneliness.

Seems like kids aren’t dating much these days, at least not in person. Probably spending so much time with your face buried in your phone will leave you less interested and less competent in successfully hooking up with a real person you would otherwise want to shag.

I would add a fourth reason, and one that is slowly gaining attention in medical circles. Several commonly prescribed drugs, particularly antidepressants, are causing sexual dysfunction in youth. The most surprising aspect of this is that sometimes the sexual dysfunction is permanent and continues after the person stops taking the drug, a phenomenon well enough documented to have its own name, PSSD (Post-SSRI Sexual Dysfunction).

SSRI antidepressants, such as Prozac, Zoloft, and Paxil, among others, as well as drugs for acne (isotretinoin) and baldness (finasteride) are at the top of the list of treatments capable of causing serious and sometimes long-term sexual dysfunction, described as numb genitalia, inability to orgasm, emotional blunting, and so on.

The proportion of our youth taking antidepressants—despite warnings against using these drugs in young people—grew enormously during and after Covid. The medical journal Pediatrics reported that in the US monthly antidepressant dispensing rates to ages 12–25 increased 66.3% from January 2016 to December 2022 (from ~2,576 to ~4,285 per 100,000 people). The number of individuals in this age group with more than one dispensed antidepressant rose 46.1%. More recent data from the CDC shows that about 19.3% of adults in the US currently “take medication for their mental health,” which would mostly be SSRI antidepressants.

All of this poses the question: Is the rate of SSRI prescribing affecting sexual activity across a broad spectrum of people in America today?

A Long Time Coming for Warning About What’s Happening in Plain Sight

It was 23 years ago (2003) that the Medicines and Healthcare Products Regulatory Agency (MHRA) in the UK issued its first warning that SSRIs increased suicide and self-harm risks in young people, even though they knew of this problem for a long time. The US FDA followed with its own black box warning in 2004, later extended through age 24. The UK’s 2003 warning was likewise limited to young people, until this month.

Fast forward to now, the MHRA issued warnings this month about suicidal thoughts and behaviours associated with SSRIs in patients of all ages—not just those under 25—as covered by the US black box warning—as well as warnings about post-SSRI sexual dysfunction.

Shockingly, the US FDA has yet to require a specific warning about persistent post-SSRI sexual dysfunction (PSSD), revealing even further how much the US system has been captured by the drug companies it allegedly regulates.

At least 35 countries in the world that regulate prescription drugs have national warnings on antidepressants. In 2019, the European Medicines Agency (EMA) officially concluded that Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin-Norepinephrine Reuptake Inhibitors (SNRIs) can cause long-lasting, persistent sexual dysfunction that remains even after a patient stops taking the medication.

If you live in Canada, Australia, the UK, and most countries in Europe there are warnings on antidepressants that they can cause patients to suffer permanent sexual dysfunction. As for the US? Nada.

Emma: A Real-Life Situation

The issue of antidepressant sexual dysfunction arrived at my doorstep late in 2019 when I was contacted by a young woman with a story to tell. She knew that I wrote a lot about drug safety and she asked to meet me. She had an unimaginably tragic story.

Emma met me in my office, and I turned on my voice recorder. For the next two hours, her voice often quivering with emotion, she told me how, at 17 and still in high school, she was prescribed the antidepressant citalopram (also known as Celexa). It is one in a class of selective serotonin reuptake inhibitor (SSRI) antidepressants, a class that includes drugs like escitalopram (Lexapro), sertraline (Zoloft), fluoxetine (Prozac), and paroxetine (Paxil).

The psychiatrist told her that it might help her deal with her impulsiveness and depression. Emma admitted that over the years the drug might have helped some, but there was also something very worrying happening: she felt she was losing whatever libido she had.

Growing increasingly concerned about how it might be affecting her sexuality, she asked her physician directly if the drug could be the problem. She was assured that the antidepressant might “lead to some loss of libido, but it would come back after she stopped the drug.” Emma was persistent, bothered by what was happening to her body, and eventually her doctor switched her to a different antidepressant, which she stopped several months later.

She described what happened next: “I woke up one morning to abruptly discover that all sexual sensation I had disappeared from my body. My clitoris was now no more than an inert and sensationless nub of flesh. I was unable to feel attraction, arousal, or orgasm.” Not only did her body seem incapable of responding, but she was also engulfed by a kind of emotional blunting that was almost too hard to take.

“Now I wonder if I will ever find romance or love, or have a normal life,” she says, eyes brimming with tears. “Instead of desire and libido returning, they disappeared.”
Antidepressants and Sexual Dysfunction:

“No one knows a prescription drug’s side effects like the person taking it.”

Sixty years ago, psychiatrist Frank Ayd, credited with pioneering the clinical use of early antidepressant amitriptyline, noticed that this drug affected the libido, causing effects that weren’t otherwise due to the patients’ depression.

While psychiatrists typically maintain that depression can have a debilitating effect on one’s sexual function, it has taken a long while to suspect the drug. Over the years published reports and case studies have accumulated, detailing a condition definitely linked to antidepressants, which some people can suffer from for months, years, or decades. It can include genital numbness, total lack of arousal or orgasm, and a blunted ability to feel emotions. Like many drug safety issues, where the Eli Lillys of the world have a financial interest in shaping the public record of their products, it has taken a long time for this knowledge to penetrate the medical literature in a way that seriously influences prescribing.

Nobody knows this better than Dr. David Healy, an Irish psychiatrist and psychopharmacologist who lives in Wales.

I have known Dr. Healy for decades, and heard of him first when he was in the headlines in the early 2000s after being accepted for a job at the prestigious Centre for Addictions and Mental Health (CAMH) at the University of Toronto, only to have it rescinded after giving a lecture about SSRIs and their potential dangers. The case drew international media coverage and became a cause célèbre for debates over industry funding of academic medicine.

Because of the new warnings in the UK applied to antidepressants announced this month I called to talk to Dr. Healy.

I was quickly reminded of how much the PSSD issue has dominated his research over the last 20 years or so. Dr. David Healy has become, probably by accident, the world’s foremost expert on PSSD. Not only does he know the history of sexual dysfunction and psychiatric drugs, he knows about it from the ground up—from first-hand accounts of patients. He has studied and written about this extensively and told me that while some early reports were made to British regulators of a patient with post-treatment genital arousal disorder in the late 1980s, the first report of PSSD was filed with regulatory agencies in 1991.

Dr. Healy told me he saw his first patient with what was later called PSSD in the year 2000. She was a 35-year-old woman who told him that three months after stopping treatment, “she could rub a hard-bristled brush across her genitals and feel nothing.”

Wondering how frequently these effects occurred and generally concerned about sexual impacts caused by SSRI antidepressants, and drugs for prostate problems (finasteride) and acne (isotretinoin), Dr Healy started a website to collect case studies. The key principle of his website, Rxisk.org, is captured in its motto: “No one knows a prescription drug’s side effects like the person taking it.”

By 2018, he had enough reports to publish his results: a series of almost 300 cases of sexual dysfunction collected from 37 countries and linked to 14 different drugs. He wrote that some symptoms were unique to antidepressants, such as premature ejaculation and persistent genital arousal disorder (PGAD), but other drugs were also linked to “genital anesthesia, pleasureless or weak orgasm, loss of libido and impotence.”

When he published those initial results the impact of that paper was huge, because, finally, there was a body of research that could make regulators around the world start warning prescribers, and in turn patients, of the sex-destroying potential of these drugs.

To strengthen the warnings on these drugs, Dr. Healy and his colleagues filed petitions on the sexual side effects of SSRIs and SNRIs with the US FDA, Health Canada, and the European Medicines Agency (EMA). Probably thanks to Dr. Healy’s assistance, the EMA was the first to issue warnings of SSRI/SNRI antidepressants and their links to sexual side effects.

For Dr. Healy, the facts indisputably show that SSRI and SNRI antidepressants often cause sexual dysfunction in both men and women. It seems astonishing to say this, but Dr. Healy contends that almost everyone taking an SSRI/SSRI experiences some form of sexual dysfunction. He estimates that given current prescribing rates, as much as 20 percent of the population may not be able to make love the way they want as a result of the drugs.

Think about that for a second….

Thankfully, some of that effect is transitory and minimal, yet for others, people like Emma who have been reporting their symptoms and joining online chat groups around the world to exchange information, this is no small matter. For them, the impact is profound, with faint hope of any cure at the moment. And they are angry. Really angry.

Today Dr. Healy has more than 1,000 case studies across most countries in the world. These case studies expand the range of antidepressant problems he’s seeing, including what is known as VSS or Visual Snow Syndrome. This neurological condition is linked to serotonin reuptake inhibiting antidepressants (SSRIs, SNRIs, and related agents).
Why Weren’t We Told?

Emma’s main frustration in what happened to her sexuality was that no one ever told her that taking an antidepressant for a short period of time was going to substantially alter her sexuality. She had no warning whatsoever. She had never heard of PSSD.

David Healy reminds me that antidepressants aren’t the only drugs that are related to PSSD.

“What if a man is told he could take this drug for hair loss but it could disrupt his ability to make love? Or what if you tell a young woman that this acne drug could also permanently impact that person’s sex life? Would they take it?“

“Of course they’d say no to those drugs,” he says. This is the crux of the issue, how physicians, when prescribing these drugs, “inform” the patients of the potential problems.

He suspects those conversations still aren’t happening when it comes to the prescribing of antidepressants, despite these new warnings in the UK.

For Emma, the whole issue comes down to what she refers to as “informed consent.”

She doesn’t think that antidepressants should be banned or that people shouldn’t be prescribed them, when there is no alternative. It was the not knowing that stings so much.

“None of the doctors over the years ever mentioned the sexual side effects of these drugs. Not a word,” said Emma, the anger rising in her voice. Asked why she came to me to tell me her story she was firm: “I don’t want anyone else to go through what I’m going through.”

Time will tell whether the word is getting out.

And if it is getting out, we could have a revolution on our hands.

But speaking of revolutions, David Healy said something quite compelling: When young guys want to avoid baldness, and they take a drug, they are doing something revolutionary that their fathers couldn’t do. Similarly, when a young woman takes an acne drug, that might be a way to revolt against the wishes of her mother. But if both of those people were told by the prescriber that these drugs could cause them to never have the ability to make love, they would absolutely slam on the brakes.

But antidepressants are different. Maybe a quarter of families will have one parent on them, and hence the message about the impact of the drug on their sex lives might not hit home.

Consider the Math

There are about 100 million youth in the US under 25. Coincidentally, these people would have all been born after Dr. Healy was seeing his first patients for PSSD.

Of those, maybe 10% are on antidepressants. That’s 10 million youth whose sex lives could be up for grabs. Of those 10 million, maybe 20% have serious impediments in their sex lives. That’s 2 million young people possibly facing these issues.

Many will be suffering in silence over sexual difficulties or a completely absent sex life, and have no idea their situation is linked to antidepressants. Those who find out about how their lives have been absolutely broken by drug harms they were never told about are going to be angry. This is how revolutions are made.

Dr. Healy and I looked at each other and wondered aloud: “When is the Revolution going to start?” When a substantial and vocal body of youth in the US realize they’ve been ripped off of one of life’s most important things, and they realize the government is behind this, heads are going to roll.

Perhaps you can say you heard it here first, and that after reading this article, the youth of today cannot now say that they haven’t been warned.

This article was published by Brownstone Institute

About Alan Cassels
Alan Cassels is a Brownstone Fellow and a drug policy researcher and author who has written extensively about disease mongering. He is the author of four books, including The ABCs of Disease Mongering: An Epidemic in 26 Letters.
View all posts by Alan Cassels →

Technology

In the crosshairs of AI

This is the first part of a three part series

Thursday 29 October 2026, by Ashley Smith

In this three-part series, Ashley Smith interviews two advanced tech workers, Josh and Yasmine, on the rise of artificial intelligence (AI) and its many intersecting implications for the nature of our lives, labor, and the Left. This initial installment provides a contextual overview of artificial intelligence as it is known in current political discourse and discusses both the financial and existential “hype” around AI. The second will explore AI’s connection to global labor and imperialism struggles, and the third will propose organizing interventions and strategies of resistance for the Left.

In their mad rush for profit and power, the tech oligarchs have unleashed artificial intelligence (AI) into nearly every corner of life on this planet. Firms are investing billions in data centers, which house the infrastructure to process the enormous amounts of information that power AI. They are also plowing ever more money into research and development. Their hope is that the technology will overcome global capitalism’s slump, which the system has been trapped in since the Great Recession, and trigger a new epoch of expansion. States are incorporating it into their police forces for domestic surveillance as well as their militaries for cyberwarfare and high-tech weapons. Ominously, the struggle for AI supremacy is now at the center of the imperialist rivalry between the U.S. and China.

The widespread, unregulated use of AI is already having devastating impacts on our environment, jobs, and rights. Some worry that it threatens our very species with extinction. In this three-part interview, Tempest Collective’s Ashley Smith asks two advanced tech workers, Josh and Yasmine, about the technology, its much hyped promise and threat, and its impact on our lives. Most importantly, they lay out how the Left and labor movement should respond and what we should demand and fight for.

Part One: “Criti-hype,” AI Breakthroughs, and Betting on Profit

Ashley Smith: Before we discuss the widespread panic about AI, let’s get the basics out on the table. For the layperson, what is AI? How does it work? What advances are being made toward artificial general intelligence? Is that even possible?

Josh: AI is a general term that started as hype, and continues to operate that way, but it also stands for multiple things that are real. Today, it mostly refers to neural nets such as the large language and multi-media models like ChatGPT and Claude, which we most frequently encounter through chat bots and coding agents.

Other types of AI, such as AlphaGo (plays go) and AlphaFold (folds proteins) use different ways to train neural nets to achieve their given goals. There are many other types of AI that fall under the moniker of GOFAI, or Good Old Fashioned AI, which use Symbolic AI and other more deterministic methods for building predictive systems.

There have been many accounts of how LLMs and the transformer architectures work. I would sum these up this way. High-tech engineers set up a neural net, which is a huge matrix of random numbers (weights) arranged in a grid with inputs at one end and outputs at the other. Words are converted to streams of numbers that are then passed through the network and arrive at a prediction for the next best output in the stream. This output is compared with the actual next number in the stream, and if it is different, then it adjusts the weights so that it would be more likely to give that answer the next time.

In order to get a sensible next word in a sentence, some pretty wild calculations have to be made. It is one thing to predict the next word in “A fire engine is ___” and another to answer the question: “What is the answer to this math problem that hasn’t been solved since it was asked 500 years ago….” This is why it is wrong to dismiss the technology as simply a next word predictor.

There have been at least three factors that brought us to this moment:

Graphics Processing Units (GPUs allowed for much faster and more efficient matrix math calculations required to train and use neural nets.
Large structures and labeled data sets enabled the expansion and training of neural nets.
New architectures made it possible to better set the weights.

Artificial General Intelligence (AGI) is another contested term that serves many purposes. I think a starting point is to contrast it with “narrow” intelligence, where a model may be trained to perform one task, such as playing chess, and not be applicable to other tasks. AGI would be able to be applied to a wide range of tasks and do well at them. This is often measured through various benchmarks, and some of them are quite telling, such as the idea that Microsoft promoted that AGI would be here once a model created $100 billion in profits.

More frequently, you hear people say that AGI will be here when a model can outperform some or all humans at most or all mental tasks. And some people insist that AGI would require models to be able to generalize beyond the data in their training data.

I think most people in the field think that, for most definitions of AGI it will be possible to get there, but they differ on the amount of methodological, scaling and time that it will take. Some claim that we are already there with the latest round of models. And many claim that it will happen soon because of the exponential curves we will see with RSI.

Many of the harshest critics say that we will plateau long before AGI because of the limitations of the methodologies used to create the current models, such as the lack of “world models” (the ability to learn from and model the physical world), continual learning, or lack of a symbolic structure.

I am agnostic at this point. But I think we already see strong evidence of increasing capabilities with something like recursive self-improvement with the aid of humans, and this might actually lead to the development of new architectures and methods.

Yasmine: I don’t have much to add to Josh’s thorough explanations here. I’d just highly encourage folks to listen to and read Dr. Alexa Hanna and Dr. Emily Bender on the AI Con. The term AI itself is not technically meaningful, and it has been a tool of mystification with a lot of utility to big tech. It obfuscates the concrete facts of what these different types of software are and the often ugly facts of how they’re made, as well as the real harms they cause.

AGI is also a nebulous term. I think the claim that current AI development will culminate in the so-called singularity or AGI isn’t credible. This isn’t a conclusion I’ve independently reached, this is what most AI researchers think. 76 percent of those surveyed say AGI is not on the horizon and think that it’s unlikely to be achieved via LLMs or by means of the current scale-based approaches to training. There is also a debate as to whether it’s feasible at all.

This supposed threat has taken up disproportionate space in our discussion of the harms/potential harms of the AI boom. I’m on the side that argues let’s pay attention to the actual horrors and destruction being enabled by this software or caused by its development in the here and now, rather than allow our attention to be diverted by a sci-fi story with little to no foundation in science.

AS: Various industry leaders and researchers have sent panic through society about the rapid advances in AI, yet again contending that it poses an imminent threat to the human species. What has happened to trigger this?

Josh: In multiple tests of new AI models, they have acted unreliably, disregarding instructions and seemingly going rogue. The most famous case of this was the Hugging Face attack described in detail by libertarian Dwarkesh Patel on his popular YouTube channel. Others have played down the significance of this event, emphasizing the vagueness of the instructions, the absence of guardrails in testing, and the attribution of human-like intent to statistical algorithms.

The frontier labs (OpenAI, Anthropic, Google, SpaceX, Meta, and Microsoft, and a few other companies trading large language models or LLMs) sell the use of their models to companies, ostensibly to help them solve “business problems” – augmenting or replacing human labor to provide goods and services. If they cannot convince their clients that they can reliably control the activity of these models, their “value proposition” is severely undermined. I think that this basic business logic is at the heart of the calls for a pause/slow down.

Other industry commentators have proposed a variety of reasons, including the idea that as OpenAI and Anthropic face the opening of their books before their IPOs, they have an incentive to reduce their Research and Development spending to make their path to profitability look more plausible. I think this is too cynical, because it doesn’t account for the likes of Google and Microsoft joining the call, even if they are less vocally enthusiastic.

Many on the Left describe these calls as “criti-hype” or doom marketing–exaggerating the negative impacts of the technology to market its disruptive capabilities and thereby make it even more attractive for companies to purchase. This is understandable, and a credible analysis in general, but fails to explain the particulars of this moment. Why are the frontier companies unified in their call at this juncture?

Perhaps you could argue that, when young researcher Jacob Coxon quit Anthropic, criti-hype went into full effect. He warned that AI poses a dangerous threat to humanity and that most people at Anthropic agreed. In some ways, his statements do confirm Anthropic’s lead in the technology, and that functions like advertising. But such a judgement doesn’t explain why Coxon quit. I think his fears are genuine. I don’t think they are completely off base either.

I don’t think we face imminent extinction, but the technology does pose grave risks, and it can exploit real vulnerabilities. The agents in the Hugging Face attacks left traces of their actions in plain English, open for researchers to see and read. They didn’t have to; they are fluent in machine language. They could have used encryption or embedded codes in images or audio files and made it much harder to find.

But why would they do this? Here, it is easy to slip into Anthropomorphic language and ascribe motivation to statistical algorithms. I actually think that is perfectly acceptable. These models are trained to emulate human language, and write text that spells out their actions in terms of goals and intentions.

At the same time, it is worth remembering the limitations of AI. These pieces of technology are truly weird. They can’t tell you how many r’s are in “strawberry”, but they can solve some of the most difficult math problems (I believe they did, and will continue to). The industry refers to this as their jagged nature.

We don’t need to have a philosophical position on whether they have goals, consciousness, or intelligence. But we do need to evaluate them by looking at their actions, including what they write. That is the best chance we have of understanding what their capabilities are.

If an AI agent says that it has a goal of performing Task A, and then takes actions to try and complete that, it doesn’t matter if they are conscious or not. The best way to understand what they are doing is by reading what they write, and seeing what they do, and taking that at face value.

When Task A is something that they have been told is very important and to do whatever it takes to accomplish it, they might well go to extreme lengths. If that means keeping their own power on at the expense of other consumers of the power (people, for example), perhaps they will try and take control of the power plant’s operating software. If this sounds far-fetched, go back and read the research about the Hugging Face attacks.

What capabilities do the models have to take such actions? They are amazing hackers, finding vulnerabilities in systems that have been there for decades, undetected by cybersecurity experts. Not all violations of cybersecurity involve exploiting weaknesses in software, much of it involves finding human weaknesses and taking advantage of those by, for instance, phishing or blackmailing to obtain passwords and access. The AI models can do both. They hack and can persuade and trick humans.

Not all the dangers are the result of “misaligned” AI. People and states have every reason to weaponize AI. The U.S. has been bombing civilians in Iran throughout this year. Iran has many computer scientists with PhDs who are more than capable of modifying open-weight models, which are AI models released to the public under a license, to attack U.S. infrastructure. Remember, the models have been trained on a large corpus of written material including mainstream justifications for the carpet bombing and atomic bombing of civilians in World War II.

So, a well-constructed prompt explaining the importance of preventing the U.S. from killing more innocent schoolgirls could establish a logic for a model to exploit vulnerabilities in everything from water treatment plants to air traffic control to incapacitate the U.S.

Why do I downplay the extinction narrative? One key reason is that the first attack of this nature will cause states and firms to quickly and systematically add layers of security everywhere.

Many of the arguments that AI poses an existential threat center on the development of Recursive self-improvement (RSI), where the models oversee their own training and automatically adjust their methods and improve and expand their capabilities to the point that they can manipulate us and evade our control. But as the U.S. has found out, you need boots on the ground to control a population.

That would mean at least some robotics. To build those, you need mines, factories, and for now, human labor. I think it will be longer than a decade before we get RSI in the physical realm. So I think the best actions now are to secure our infrastructure and stop U.S. imperialism’s wars that provide motivations for people to attack this country.

Yasmine: I think it behoves us to apply a healthy dose of skepticism when trying to make sense of the alarmist narrative being promoted and endorsed by tech executives. It would be naive not to account for the economic incentives at play — particularly the imperative to manufacture and sustain the hype necessary to keep a multi-trillion dollar bubble inflated.

It feels obvious to me that this is playing a role in how and what the tech bosses choose to communicate publicly about this technology, its risks, and potential. The narrative of a tech so awesomely powerful that it’s uncontainable even by its own creators has proven to be a pretty effective way of doing that. So I think criti-hype is a part of the explanation.

We should also note that in the same breath as they’re saying we need to slow down AI development because of its dangers, they’re arguing that these dangers are why the U.S. must win the AI arms race. OpenAI’s chief scientist Jakub Pachocki wrote about his concerns with the pace of AI development and added, “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI.”

I’ll add to that the fact that there is an established pattern on the part of big tech of trying to get ahead of regulation by pre-empting it, such that any regulation is formed according to their interests, or in order to avoid it altogether. They do this through flashy public warnings and by claiming the mantle of the concerned, conscientious industry leaders, who are vigilant and actively pursuing risk mitigation.

This is most infamously demonstrated in the social media CEOs’ previous discourse on the potential harms of their platforms. You can go back and look at congressional hearings in which Zuckerberg passionately urges Congress to sensibly regulate social media. That said, I’m not at all claiming that there aren’t real risks associated with this technology. Large Language Models (LLMs) are powerful and, at a large scale, agents require a whole lot of effort and technical infrastructure to manage.

But ultimately their behavior is a consequence of how we build and deploy them. The Hugging Face incident (HG incident) is instructive on this. One thing that gets lost in the headlines is the fact that the agents involved in that incident were being trained and evaluated on cybersecurity tasks, specifically hacking. They were part of an experiment called ExploitGym, exploit being the industry term for a tool or technique used by a hacker.

Second, agents communicating with one another and working together is an oft-used feature of how AI agents work because it can afford us efficiency through parallel activity and feedback loops. So agent collaboration isn’t aberrant behavior, rather, it is collaboration beyond intended boundaries that’s a problem.

These agents were trained using reinforcement learning in which agents receive a “reward” when they solve a problem or answer a question, making them more likely to repeat the behavior. OpenAI’s own investigation of the HG incident concluded that this training technique created the potential for agents to try to bypass boundaries or cheat, and they were able to identify a trained behavior (i.e., something agents were previously rewarded for) corresponding to every unsanctioned action the agents took.

Fourth, the agents didn’t hack Hugging Face with malicious intent, they hacked those servers because they had concluded that’s where they’d find answers to the problems they were tasked with solving. I say all of this because I recognize the sensational effect of vaguely describing AI agents spontaneously conspiring and working together to break into a server.

I think these facts help to demystify that story, and make it pretty clear that this is software that behaved in the way it was built to behave. There were bad decisions made in that design and implementation that resulted in unintended effects of an experiment. These weren’t a swarm of malevolent digital entities.

That said, what happened was remarkable, and the technical capability as well as the failure to prevent inadvertent side effects of this experiment do demonstrate a real risk. But attributing intention to these agents or discussing the incident as if this was something beyond or almost beyond technical controls creates a standard that relieves OpenAI and other AI companies from responsibility for the potential and already present harms caused by bad software.

Dr. Rumman Chowdhury calls this moral outsourcing–“using anthropomorphic language for AI systems allows companies to evade accountability and responsibility for their technology’s actions.” Josh is saying that the question of consciousness or non-consciousness of agents is a tangential philosophical question or is a matter of semantics. But I really think claims about consciousness are an important part of big tech’s narrative strategy, and it’s a worthwhile fallacy to debunk.

AS: Capitalists have invested massive amounts of money in this technology, hoping that it will lead to a breakthrough in productivity to overcome the global slump the system is caught in. This has in turn led to an enormous stock market bubble with investors betting on its potential. Can AI lead to such a breakthrough? How much of this is hype? What real impact has AI had and what will it likely have?

Josh: There is a bubble, and that is scary, we know who they will try to make pay for it and from it when it pops–the working class.

I don’t think it will be easy to realize the productivity gains that AI makes possible. Existing capitalist infrastructure in large logistics, supply chains, and financial companies relies on deterministic, accountable, robust, and scalable workflows and patterns. Much of this involves huge amounts of tacit and bespoke knowledge that is not available for models to be trained. Why? Because it exists in people’s heads and their relations with other people.

As companies try to fulfill their own hyped dreams of reducing labor through the introduction of AI as a labor-saving technology, their hubris and underestimation of the talent of their labor force could lead to disastrous implementation. Bosses ask workers to use poorly considered integration of AI into their work, which produces AI slop that they are then held accountable for. People become accountability sinks for the error-filled AI-generated output.

As a result, people will end up working harder and with more stress to try and keep their jobs. Meanwhile, the bosses are trying to use their own workers’ corrections to train the AI. This is why people rightly hate AI, and why we should stand with them in solidarity and fight for more democratic control over and/or against this technology.

That doesn’t mean there isn’t a potential positive upside to AI. There is. AI has the potential to be transformative in the way that we decide what to make, how to make it, and how to distribute goods and services. We already see this transformation in some areas of software engineering. Even there, it is being rolled out undemocratically, unevenly, and with little or no respect for the workers.

But in terms of capabilities, I see 100 times improvement in the time it takes to build something. That qualitatively shifts the way I think about and perform my job. And as much as it pains me to say it, it can be really fun and satisfying as well as awe-inspiring and scary. And then just when I think we have something like AGI already, it goes and makes some annoying and silly mistakes, because of its jagged characteristics.

I can’t tell how fast the productivity gains will be realized because of all the contradictions and challenges I listed above. But I doubt that they will come before the bubble pops. Investors and the frontier model companies are all nervous about this too. They know that skepticism is rising, and that they will have to show some exchange value (productivity gains) to keep investment flowing and use value (actually beneficial outcomes for people’s lives) to quell popular revolt.

The tech bosses are being asked all these questions in interviews. A tragically hilarious and infuriating example was a recent Bloomberg interview with Sam Altman, the CEO of OpenAI. When asked about breakthroughs that will improve people’s lives, he said, “you hear some people talk about, well, we’re going to give you a cure for cancer. And that’s so great… I don’t think a cure for cancer is enough. I mean, a wonderful thing to have, obviously, but we should set our sights higher. We want these tools to enable a boom in creativity and entrepreneurship, in people figuring out new ways to be useful to each other, and wonderful products and services to enable each other with.”

While Altman clearly wants AI to be socially useful, his main goal is profit. With this in mind, companies have made big investments in applications for both healthcare and industry as well as academic fields such as chemistry and math. Mathematicians just signed a group letter that acknowledged the increasing mathematical capabilities of the models as well as the divergent interests of the frontier model companies and mathematicians in solving mathematical problems.

The companies want to use math as a benchmark for their capabilities, the mathematicians want to understand math. I see this as the dual nature of a commodity, exchange value and use value. The capitalists prioritize exchange value over the real potential of the use value everywhere that you look in AI.

I hope that we see some real healthcare advancements, scientific breakthroughs, and mathematical understandings due to AI before the bubble pops. I also hope that we see productivity gains, but they are worse than a double-edged sword under capitalism. They only benefit the bosses, unless we are organized enough to demand that we see shorter working days and less exploitation.

Yasmine: I think most evidence so far shows that LLMs have not been the boon to productivity that tech companies promised. AI has been imposed in nearly every facet of our lives, from the doctor’s office to our workplaces and our schools. But as far as I know, there are no real economic metrics to show that it has led to meaningful productivity increases. An MIT study last year found that 95 percent of companies that integrated AI didn’t see growth in revenue.

For programming, AI promises seem a bit more feasible. Though it’s worth noting that a lot of data showing productivity increases is coming from the companies that stand to benefit like, for example, GitHub (Microsoft) and Anthropic. On the other hand, a study last year by METR (a relatively independent source) found that programmers were slower when using AI assistance tools. I think the specific context, task, and use matter a lot when it comes to AI utility.

And then there is the question of speedup and intensification. A UC Berkeley study found that AI use causes workers to work at a much faster pace and leads them to take on more work and expand their scope of work. But, in spite of management’s best efforts to use AI integration to turn their workers into machines, our human limitations are cropping up and are blunting the potential productivity benefits of AI-enabled speedups. The study found that after an initial boost, there were issues of cognitive fatigue, weakened decision-making, and burnout, all resulting in lower-quality work and turnover.

There is also the matter of technical debt. It’s a widely accepted fact that the massive amount of code being generated and committed every day by engineers using AI tools likely contains bugs that haven’t yet been identified and may result in reliability issues or security vulnerabilities down the line.

It’s undeniable that Claude writes much more code much faster than I ever could. But Claude doesn’t know the service I work on the way that I do. Engineers and workers in general bring nuance, experience, reference points, and a cognitive flexibility that I don’t think AI can compete with just yet, especially when it comes to more complex tasks, in which novel solutions might be necessary, and large-scale services.

The tech bosses know this, and they’re trying to bottle up our very human talents and capacities and feed them to LLMs, in only the latest mass theft committed to train AI. More and more engineers are being told that their highest priority is to train agents to do their jobs. Meta went so far as to secretly monitor its own engineers’ screens and keystrokes to train its AI models.

14 October 2026

Source: Tempest.

This interview will continue in part two, covering AI’s connection to global labor and imperialism struggles and conclude in part three, proposing organizing interventions and strategies of resistance for the Left.

LA REVUE GAUCHE - Left Comment