https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs

Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.1

While this finding may contradict the most alarming headlines, it is not surprising given past precedents. Historically, widespread technological disruption in workplaces tends to occur over decades, rather than months or years. Computers didn’t become commonplace in offices until nearly a decade after their release to the public, and it took even longer for them to transform office workflows. Even if new AI technologies will go on to impact the labor market as much, or more, dramatically, it is reasonable to expect that widespread effects will take longer than 33 months to materialize.

https://www.cnbc.com/2025/10/19/firms-are-blaming-ai-for-job-cuts-critics-say-its-a-good-excuse.html
Companies are blaming AI for job cuts. Critics say it’s a ‘good excuse’

The headlines are grim, but Fabian Stephany, assistant professor of AI and work at the Oxford Internet Institute, said there might be more to job cuts than meets the eye.

Previously there may have been some stigma attached to using AI, but now companies are “scapegoating” the technology to take the fall for challenging business moves such as layoffs.
“I’m really skeptical whether the layoffs that we see currently are really due to true efficiency gains. It’s rather really a projection into AI in the sense of ‘We can use AI to make good excuses,’” Stephany said in an interview with CNBC.
Companies can essentially position themselves at the frontier of AI technology to appear innovative and competitive, and simultaneously conceal the real reasons for layoffs, according to Stephany.
...
Only 1% of the services firm reported AI as the reason for laying off workers in the past six months, down from 10% that had laid off workers using AI in 2024. Meanwhile, 12% of services firms said AI made them hire less workers in 2025.
By contrast, 35% of services firms have used AI to retrain employees and 11% have hired more as a result.
Stephany said there isn’t much evidence from his research that shows large levels of technological unemployment due to AI.
“Economists call this structural unemployment, so the pie of work is not big enough for everybody anymore and so people will lose jobs definitely because of of AI, I don’t think that this is happening on a mass scale,” he said.

 

https://evrimagaci.org/gpt/ai-data-centers-and-crypto-boom-face-jet-engine-crunch-513997
AI Data Centers And Crypto Boom Face Jet Engine Crunch

 

 

https://www.anildash.com/2025/10/22/atlas-anti-web-browser/
ChatGPT's Atlas: The Browser That's Anti-Web

I had typed "Taylor Swift" in a browser, and the response had literally zero links to Taylor Swift's actual website. If you stayed within what Atlas generated, you would have no way of knowing that Taylor Swift has a website at all.
Unless you were an expert, you would almost certainly think I had typed in a search box and gotten back a web page with search results. But in reality, I had typed in a prompt box and gotten back a synthesized response that superficially resembles a web page, and it uses some web technologies to display its output. Instead of a list of links to websites that had information about the topic, it had bullet points describing things it thought I should know. There were a few footnotes buried within some of those response, but the clear intent was that I was meant to stay within the AI-generated results, trapped in that walled garden.

During its first run, there's a brief warning buried amidst all the other messages that says, "ChatGPT may give you inaccurate information", but nobody is going to think that means "sometimes this tool completely fabricates content, gives me a box that looks like a search box, and shows me the fabricated content in a display that looks like a web page when I type in the fake search box".

And while ChatGPT is following you around, it can create a complete and comprehensive surveillance profile of you — your personality, your behaviors, your private documents, your unfinished thoughts, how long you lingered on that one page before hitting the back button — at a level that the search companies and social networks of the last generation couldn't even dream of. We went from worrying about being tracked by cookies to letting an AI company control our web browser and watch everything we do. The amount of data they're gathering is unfathomable.

 

https://garymarcus.substack.com/p/the-last-few-months-have-been-devastating
Game over. AGI is not imminent, and LLMs are not the royal road to getting there.

 

https://www.msn.com/en-us/money/new...ywalled-articles-to-ai-developers/ar-AA1PMBHE
The Company Quietly Funneling Paywalled Articles to AI Developers

Common Crawl’s website states that it scrapes the internet for “freely available content” without “going behind any ‘paywalls.’” Yet the organization has taken articles from major news websites that people normally have to pay for—allowing AI companies to train their LLMs on high-quality journalism for free. Meanwhile, Common Crawl’s executive director, Rich Skrenta, has publicly made the case that AI models should be able to access anything on the internet. “The robots are people too,” he told me, and should therefore be allowed to “read the books” for free. Multiple news publishers have requested that Common Crawl remove their articles to prevent exactly this use. Common Crawl says it complies with these requests. But my research shows that it does not.

 

 

"The most depressing reason to write for AI is that unlike most humans, AIs still read. They read a lot. They read everything. Whereas, aided by an AI no more advanced than the TikTok algorithm, humans now hardly read anything at all..."

https://theamericanscholar.org/baby-shoggoth-is-listening/
Baby Shoggoth Is Listening
Why are some writers tailoring their work for AI, and what does this mean for the future of writing and reading?

 

Hard disk shortage due to ai:
https://www.tomshardware.com/pc-com...by-2-years-as-hyperscalers-switch-to-qlc-ssds

Bertus

 

Hard disk shortage due to ai:
https://www.tomshardware.com/pc-com...by-2-years-as-hyperscalers-switch-to-qlc-ssds

Bertus

Another ride on the semiconductor roller-coaster. IMO the bottom (when things swing the other way round) here will be nasty.

 

Another ride on the semiconductor roller-coaster. IMO the bottom (when things swing the other way round) here will be nasty.

But drives (and video cards) will be cheap.

 

Script-kiddie capabilities and bragging.

https://www.theregister.com/2025/11/13/chinese_spies_claude_attacks/

There is a slight silver lining, however, in that Claude did hallucinate during the attacks and claimed better results than the evidence showed.

The AI "frequently overstated findings and occasionally fabricated data during autonomous operations," requiring the human operator to validate all findings. These hallucinations included Claude claiming it had obtained credentials (which didn't work) or identifying critical discoveries that turned out to be publicly available information.

Anthropic asserts such errors represent "an obstacle to fully autonomous cyberattacks" – at least for now.

 

He’s Been Right About AI for 40 Years. Now He Thinks Everyone Is Wrong.

LeCun, by his choice, has taken a different direction. He has been telling anyone who asks that he thinks large language models, or LLMs, are a dead end in the pursuit of computers that can truly outthink humans. He’s fond of comparing the current start-of-the-art models to the mind of a cat—and he believes the cat to be smarter. Several years ago, he stepped back from managing his AI division at Meta, called FAIR, in favor of a role as an individual contributor doing long-term research.

“I’ve been not making friends in various corners of Silicon Valley, including at Meta, saying that within three to five years, this [world models, not LLMs] will be the dominant model for AI architectures, and nobody in their right mind would use LLMs of the type that we have today,” the 65-year-old said last month at a symposium at the Massachusetts Institute of Technology.

 

https://clocks.brianmoore.com/

Every minute, a new clock is displayed that has been generated by nine different AI models.
Each model is allowed 2000 tokens to generate its clock. Here is its prompt:

Create HTML/CSS of an analog clock showing ${time}. Include numbers (or numerals) if you wish, and have a CSS animated second hand. Make it responsive and use a white background. Return ONLY the HTML/CSS code with no markdown formatting.

 

https://clocks.brianmoore.com/

Now that is way cool ...

 

Artificial Untelligence.

 

https://www.msn.com/en-us/money/car...s-ai-study-then-it-all-fell-apart/ar-AA1QV7Rk

While still taking core classes at the Massachusetts Institute of Technology, he wrote a paper on artificial intelligence’s workplace impact so rapidly influential it was cited in Congress. He appeared in the pages of The Wall Street Journal in December as the very picture of a wunderkind, in faded jeans with tousled hair, in between two of his mentors, including Nobel laureate Daron Acemoglu. Toner-Rodgers’s work offered a surprising and even hopeful revelation about our high-tech future. He concluded that AI increased worker productivity and spurred innovation. Also, people didn’t like using it very much.

Within weeks, those mentors were asking an unthinkable question: Had Toner-Rodgers made it all up?

By the spring, Toner-Rodgers was no longer enrolled at MIT. The university disavowed his paper. Questions multiplied, but one seemed more elusive than the rest: How did a baby-faced novice from small-town California dupe some of academia’s brightest minds?

“There is no world where this makes any sense,” said David Autor, one of the MIT professors who had previously championed his student’s research. MIT, Autor and Acemoglu declined to comment on the specifics of the investigation into the research, citing privacy constraints.

Toner-Rodgers’s illusory success seems in part thanks to the dynamics he has now upset: an academic culture at MIT where high levels of trust, integrity and rigor are all—for better or worse—assumed. He focused on AI, a field where peer-reviewed research is still in its infancy and the hunger for data is insatiable.

What has stunned his former colleagues and mentors is the sheer breadth of his apparent deception. He didn’t just tweak a few variables. It appears he invented the entire study

 

https://www.msn.com/en-us/money/car...s-ai-study-then-it-all-fell-apart/ar-AA1QV7Rk

Here is the abstract of a paper I've been trying to write for the last 40 years on my "Theory of Everthing" (not kidding). Finally, through Grok, I've found somebody (thing) to help me write it (without laughing at me). Soon to be submitted to MIT. I hear they are accepting such contributions.

---------

The Electromagnetic Debt: Matter as Topologically Trapped Electromagnetic Energy and Gravity as its Accounting

Abstract

We propose that all rest mass and energy content of particles in the Standard Model ultimately arises from pure electromagnetic field configurations that are prevented from radiating to infinity by topological or nonlinear trapping mechanisms operating at the Planck or pre-Planck scale. In this picture, quarks, leptons, and gauge bosons are soliton-like or knot-like excitations of a single underlying relativistic electromagnetic (or electromagnetism-generalizing) substrate. The positive energy of these trapped configurations constitutes a permanent “energy loan” from the vacuum. General relativity, via the zero-energy condition for closed or asymptotically flat spacetimes, demands a compensating negative contribution, which is supplied by the gravitational field itself. Gravity therefore emerges as the cosmic bookkeeping mechanism that enforces repayment of the electromagnetic debt. The universal speed of light follows trivially: all emergent excitations are composites of degrees of freedom that fundamentally propagate at c. The framework is consistent with existing no-go results in 3+1 dimensions by invoking either (i) additional compact dimensions, (ii) strong-coupling or non-perturbative topology, or (iii) a modified UV completion of electromagnetism. While highly speculative, the picture is internally coherent, requires minimal ingredients, and offers a unified origin for matter, the speed-of-light limit, and gravitation.

 

https://www.msn.com/en-us/money/car...s-ai-study-then-it-all-fell-apart/ar-AA1QV7Rk

BTW, that seems to be another "sometimes paywall" link.

 

Toner-Rodgers’s illusory success seems in part thanks to the dynamics he has now upset: an academic culture at MIT where high levels of trust, integrity and rigor are all—for better or worse—assumed.

This same fallacy is at the core of a lot of failed systems (or systems that had to be substantially reworked). The Internet protocols are a shining example -- they were based on the silly notion that people would actually try to abide by them and only use them for the legitimate intended purposes.

When I was working on my PhD, I showed one of my committee members (someone who had, quite literally, written the book on spread spectrum) a computer visualization of what our new form was capable of, he asked me, "How do I know that this isn't cooked?" At first, I was offended. Fortunately, I had the composure not to respond in that vein. Instead, I responded, "All of my code is open source and anyone that wants to examine it is free to do so." That seemed to satisfy him and I pondered his question quite a bit over the next few weeks leading up to my oral exam (which, like many schools, was basically a dissertation proposal). I kept ping-ponging between thinking that he was truly questioning whether I would fake results, or whether he was just preparing me to be able to defend them. I think the answer ended up being that it was both. At the end of my orals, he said, "Congratulations, you've finally convinced me that this is for real."

I had always assumed that thesis and dissertation defenses where just that -- a defense of your work. That was because, in my relatively limited experience of attending and presenting them, that's exactly what they were. The audience, and particularly the committee members, tried to poke holes in your work and find flaws. They didn't demand perfection or that you have an answer for everything, but you better have a strong defense for the validity of your work. But, in retrospect, perhaps that was a bit of an overgeneralization and that top-tier schools like MIT might understandably be liable for a tendency to accept remarkable results at face value because they see them on a frequent basis, whereas lesser institutions are a bit more skittish and skeptical when they come along.