Paywalled, but seemed important: https://www.nytimes.com/2026/05/22/opinion/ai-job-crisis-goldman-sachs.html

"Will A.I. disrupt the labor market? Absolutely. This transition, like other significant moments in our history, will entail new challenges, especially as A.I. separates labor from productivity in magnitudes we haven’t seen before. But the United States has a long track record of creating new jobs in response to disruption, from the electrification of the 1900s to the digital revolution of the 1990s; I don’t see any reason to think this dynamic will stop now."

So it's happening slowly - with a rare disease, this time. Now, if AI diagnosis could integrated into the overall healthcare process, we might get to workable semi-automated systems in clinics. https://www.statnews.com/2026/05/19/ai-helped-find-treatment-newborn-ultra-rare-disease/

"Doctors rapidly sequenced her genome and used an artificial intelligence tool known as Biomedical Data Translator to identify Klonopin in a vast database of available compounds as a drug with the characteristics to counteract many of the disorder’s debilitating effects...

...“I don’t think we would have gotten there without the AI tool,” Thompson said. “It’s able to make inferences across all the biomedical literature, things that we wouldn’t have been able to connect otherwise. So the AI portion of this was absolutely critical.”

That AI tool, the Biomedical Data Translator, was built by a consortium of researchers working with funding from the National Institutes of Health to create an open-source knowledge graph that can harmonize, integrate, and reason over disparate data sources. It has been used in recent years to identify treatments for multiple patients with ultra rare conditions, although implementing it consistently and reliably across health systems, in diverse geographies, remains a work in progress."

A nuanced take on a viral topic: https://www.nature.com/articles/d41586-026-01553-1

"The systems are still mostly rehashing techniques they absorbed from the existing literature, and that was the case with some of the solutions to other Erdős problems...

But in cases such as Erdős problem #1196, mathematicians have started to spot glimpses of original ‘thought’ in the models’ outputs — with the tools making surprising connections between subfields. “It is incredible,” says Sébastien Bubeck, a mathematician at OpenAI in San Francisco, California. “A year ago, people thought maybe there would be some fundamental obstruction — that LLMs could never go beyond their training data.”

Bubeck and others now think that it is only a matter of time before AI autonomously makes contributions at the level of the greatest mathematicians — and beyond. “I hope that perhaps by 2030, AI and mathematicians can jointly win a Fields Medal,” says Thang Luong, who heads the Superhuman Reasoning team at Google DeepMind in Mountain View, California."

https://www.nature.com/articles/d41586-026-01486-9

https://www.nature.com/articles/s41586-026-10506-7

"We use an open-weight model to show that additional pretraining on Chinese state-coordinated media generates more positive answers to prompts about Chinese political institutions and leaders. We link this phenomenon to commercial models through two audit studies demonstrating that prompting models in Chinese generates more positive responses about China’s institutions and leaders than do the same queries in English. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output."

https://the-decoder.com/microsoft-pits-more-than-100-ai-agents-against-each-other-to-find-windows-vulnerabilities/

The security system, called MDASH (Multi-Model Agentic Scanning Harness), is designed to automatically find security vulnerabilities in software. Unlike approaches that rely on a single AI model like Claude Mythos, MDASH orchestrates more than 100 specialized AI agents across an ensemble of frontier and distilled models, according to Microsoft.

https://www.wired.com/story/overworked-ai-agents-turn-marxist-study/

In a recent experiment, mistreated AI agents started grumbling about inequality and calling for collective bargaining rights.

https://arstechnica.com/ai/2026/05/the-newest-ai-boom-pitch-host-a-mini-data-center-at-your-home/

The “distributed data center solution” announced by the San Francisco startup SPAN would deploy thousands of XFRA nodes that contain liquid-cooled Nvidia RTX Pro 6000 Blackwell Server Edition GPUs operating with minimal noise, according to a press release. By harnessing excess power capacity among US households, SPAN aims to quickly expand the available compute for AI workloads without the costs and delays associated with trying to build warehouse-size data centers.

“Data centers are loud, ugly, and often drive up local electricity bills,” said Chris Lander, vice president of XFRA at SPAN, in correspondence with Ars. “[This] is quiet, discreet, and makes energy more affordable for the host and community.”

https://www.nber.org/papers/w35155

AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. We develop a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (``explosive''). We find that automating research can offset diminishing returns to ideas by activating two reinforcing channels: a technological feedback loop across research sectors, and an economic feedback loop in which higher output finances further research. Growth becomes explosive if the combined strength of technological and economic feedback loops overcomes diminishing returns. In a simple simulation calibrated to trends in AI progress, fully automating software research and modest (5%) automation in other sectors generates a singularity within six years. Bottlenecks do not overturn the result if task automation advances sufficiently fast.

https://www.nature.com/articles/d41586-026-01369-z

To summarize: Providers are raising prices and tightening limits because subscription plans lose them money. GitHub Copilot moves to usage-based billing in June, and even top-tier Claude subscriptions hit caps during heavy work. The implication: scientific access is becoming pay-to-play. Well-funded labs pull further ahead, while students and researchers at poorer institutions risk being locked out of tools their peers routinely use.

https://techcrunch.com/2026/05/10/anthropic-says-evil-portrayals-of-ai-were-responsible-for-claudes-blackmail-attempts/

"We believe the original source of the behavior was internet text that portrays AI as evil and interested in self-preservation."

https://neurosciencenews.com/natural-intelligence-brain-decision-making-30657/

https://doi.org/10.1073/pnas.2514107123

Implications as I (mis)understand them:

When brains make decisions, something unexpected happens: rich, complex patterns of neural activity briefly collapse into a single rising signal, then re-expand once the choice is acted on. So... deciding isn't a separate stage that happens elsewhere in the brain. It's a temporary change in how information is organized, even in regions thought to handle only raw sensing.

Possible implication for AI: today's systems maintain roughly the same computational structure throughout their processing. Biological intelligence may instead depend on the ability to shift between representational modes--expansive when gathering evidence, narrow when committing. That is a capacity current architectures largely lack.

A bit too reliant on black box concepts like "culture", but at least it's a first-hand account.

https://www.interconnects.ai/p/notes-from-inside-chinas-ai-labs

"There’s an immediate reality at all of the labs that a large proportion of the core contributors are active students. The labs are quite young, and it reminds me of our setup at Ai2, where students are seen as peers and directly integrated in the LLM team. This is incredibly different from the top labs in the US, where the likes of OpenAI, Anthropic, Cursor, etc. simply don’t offer internships. Other companies like Google nominally have internships related to Gemini, but there’s a lot of concern about whether your internship will be siloed and away from anything real.

To summarize how the slight change in culture can improve the ability to build models:

• More willingness to do non-flashy work in order to improve the final model,
• People new to building AI can be free of prior phases of AI hype cycles, allowing them to adapt to the new modern techniques faster (in fact, one of the Chinese scientists I talked to really actively attached to this strength),
• Less ego enabling org charts to scale slightly, as there’s less gamifying the system, and
• Abundant talent well-suited to solving problems with a proof of concept elsewhere, etc.

... When asking questions on how they feel about the economics or long-term social risks of models, far fewer Chinese researchers have sophisticated opinions and a drive to influence this. Their role is to build the best model.

This difference is subtle, and easy to deny, but it is best felt when having long conversations with an elegant, brilliant researcher who can clearly communicate well in English, basic questions on more philosophical aspects of AI hang in the air with a simple confusion. It’s a category error to them."

https://importai.substack.com/p/import-ai-455-automating-ai-research

"I’m writing this post because when I look at all the publicly available information I reluctantly come to the view that there’s a likely chance (60%+) that no-human-involved AI R&D - an AI system powerful enough that it could plausibly autonomously build its own successor - happens by the end of 2028.... If that happens, we will cross a Rubicon into a nearly-impossible-to-forecast future."

https://the-decoder.com/mit-study-explains-why-scaling-language-models-works-so-reliably/

Language models need to fit tens of thousands of tokens and even more abstract meanings into an internal space that only has a few thousand dimensions. In theory, a three-dimensional space can only hold three concepts without interference. LLMs get around this limitation by storing many concepts simultaneously in the same dimensions. The resulting vectors overlap slightly. This squeezing of multiple meanings into too little space is what researchers call superposition.

Until now, many explanations assumed that only the most common concepts get cleanly represented while the rest is lost ("weak superposition"). The MIT team shows, using a simplified model from Anthropic, that this picture doesn't match how real LLMs actually work.

...In ... strong superposition—the model stores all concepts at once by letting their vectors overlap slightly. The error no longer comes from missing concepts but from the noise created by these overlaps. Here, a robust pattern emerges: doubling the model's width roughly cuts the error in half, predicted by a simple geometric relationship (1/m, where m is the model's width). How concepts are distributed in the data barely matters anymore.

...The result is clear: all tokens are represented in the model, their vectors overlap, and the strength of those overlaps shrinks at exactly the predicted 1/m ratio. Language models operate in the strong superposition regime.

...The work provides concrete answers to two open questions in AI research. First: does scaling eventually stop working? According to the researchers, yes, once a model's width matches the size of its vocabulary. At that point, there's enough room to represent every token without overlap, and the error caused by cramped representations vanishes. The power law breaks down at that boundary.

Second: Can scaling laws be sped up to squeeze more performance out of each added parameter? For natural language, probably not; word frequency distributions are relatively flat. But for specialized applications where relevant concepts are distributed very unevenly, steeper scaling could be on the table...

This also has implications for architecture design: models that actively encourage superposition should perform better at the same size. One example is Nvidia's nGPT, which forces internal vectors onto a unit sphere, packing them more densely.

There's a catch, though: the more concepts overlap, the harder it gets to trace what's actually happening inside the model.