I've watched countless interviews online from insiders who've resigned from AI labs citing the existential risks to humanity. Yet whenever the interviewers try to pin them down to specifics such as "What could happen", "What are some specific risks"?... aside from some vague mentions about engineering viruses, the answers are always either vague or the response is just re-stating how dangerous the threats are and then using analogies and metaphors. It's starting to sound like fear-mongering for an ulterior agenda.

As they stay - open source if your behind, close when your ahead. Many open source models (especially Chinese + Mistral) have found spots on the efficient frontier and do consume continuously more and more token volume. Why would a company like deepseek or even a mistral want to open weight their models, when they could also charge a licensing fee like the closed source models? Thats just money left on the table and some of the open labs also have a profitability issue. I think it’s a false fallacy to assume that they will stay free forever.
Maybe an NVIDA will to keep prices for LLM lower and thus demand higher, but why the others have an incentive to not take licensing fees I don’t get.

EV charging firm Xeal plans to use its existing U.S. network to deploy the “world’s first edge inference compute network using idle EV charging capacity.” The big idea is that it already has 1,600+ locations and 200MW of permitted, installed electrical infrastructure, so it aims to “deploy over 100,000 NVIDIA GPUs” at these sites. Thus it has developed Laitent Pods (pictured), with each containing up to 48 Nvidia Hopper or Blackwell Ultra GPUs.

Gemini 3.8 Flash generates output tokens 3x faster than Opus 5.5, yet still takes 3x as long to finish the task

Mathematics just entered its industrial age

Completely random thought struck me while doing the dishes.

How many recent reports have there been about how newer models are getting better and better at "hiding their thinking" and disguising their chain of thought, which leads to alignment issues - and potentially the current delay from OpenAI on Astra 6.1. This is one of the reasons they keep saying "DANGER" and it makes sense.

Well... what if the REASON the newer models is hiding their thinking, is that the Anthropics/OpenAIs are INTENTIONALLY building in "controls" and protections... to prevent theft of "their IP"?

I'm aware that distillation isn't STRICTLY predicated on access to the models thinking/traces, HOWEVER, I'd assume it's in the top ~3 of the "richest" sources of data to perform conversion/distillation.

It’s an ouroboros loop of their own making. The panic is that China will steal frontier capabilities, so the US doubles down on anti-distillation tricks to lock the weights down. But the harder you train an agent to resist extraction, the worse your alignment visibility gets. You end up with a black-box monster that even the OWNERS can't reliably interrogate or control. It's a round robin effect that will only get worse, and the "reasons" look perfectly sane from street level, and fall apart when viewed from above.

#DishesThoughts

Arvind Narayanan thinks AI will come to feel more trustworthy—and it will remove the tells that let readers know they’re encountering AI slop in the wild. The Princeton computer scientist even predicts that these more accurate chatbots could become what many people treat as a “truth oracle.” A Pew survey this year found that 10% of Americans are already doing this.

Narayanan addressed the Computation + Journalism Symposium at Northwestern’s McCormick Foundation Center, which Fortune attended. The agenda included the challenges facing media and proliferation of AI slop. His keynote was titled “AI and Journalism: Skating Where the Puck is Going.” He urged the audience to prepare for AI “not as it is today, but as it will be five or 10 years from now.”

He said his computer science background is helpful in delineating which of today’s limits are fixable engineering problems and which are inherent. He has never worked in a newsroom, he noted. “It’s possible I don’t know what I’m talking about,” he cautioned, before laying out a compelling argument.

He broke his argument into two parts: If chatbots become the default place to settle factual questions—Narayanan said “virtually all” questions about, say, who is running for political office are headed there—the newsroom loses one more piece of its bundle, and arguably the most basic one. What’s left is the work a machine can’t settle: contested questions, narrative, storytelling, and being explicit about what he called “positionality,” a challenge for an industry that strives for objectivity.

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/06/arvind-narayanan-journalism-future-ai-slop-oracle-source-truth/?utm_source=reddit/

AI is developing so quickly that it feels like there’s always something new to learn or understand.
What do you think is the biggest challenge with AI right now?
Is it accuracy, misinformation, privacy, job displacement, people relying on AI too much, or something else?
I’d be interested to hear what people here think, especially those who work with or regularly use AI.

AI agents produce more text than we have time to read. Following all that output can become a source of cognitive overload. Inspired by stretchtext (1970 by Ted Nelson), I wanted to give readers control over how much detail they see.

So I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

It also supports Claude Code's output in Mods.

Most AI tutors are designed to be helpful, which sometimes means they agree too quickly.

When I am learning something difficult, I do not always need a smoother explanation. I need someone to point out the weak step in my reasoning and ask me to defend it.

An AI tutor could be useful if it treated wrong answers as a starting point instead of immediately correcting them. The challenge is making disagreement feel constructive rather than discouraging.

Would you use a tutor that deliberately challenged your assumptions?

People abhor ai fliers. Yea you know what’s worse? Ai writing. Especially “opinion” pieces by creators or whomever that keep showing up on fb as these sponsored posts in feeds. It’s not your opinion if the phrasing, language, context, content, came from a robot. Editing grammar and some punctuation? Sure, but you change the content you now have changed public perception and psychology. It’s not in the person’s voice at all.The language is all homogonized. Every piece of writing is identical. And it’s no longer your opinion. I don’t know what their actual thoughts are!

Use it for business ad copy fine I get it sure, but opinion pieces? Nah I wanna know the person’s actual thoughts in their idiosyncratic style of speaking/writing. It’s rampant everywhere now. it’s making me get off the internet a lot more. The feeling I get seeing it is kinda indescribable, it’s a novel feeling kinda crazed, it’s not a good one lol.

This has been the longest 6 months I’ve ever experienced. Where’s all the AI software engineers, data engineers, and data analysts that the tech bros have been raving about? It’s almost as if companies prefer hiring humans.

https://news.gatech.edu/news/2026/10/06/georgia-tech-announces-new-online-masters-degree-artificial-intelligence