AI news · Tuesday, November 3, 2026

MIT finds AI can read and rewrite its own weights

MIT had a frontier AI directly hand-edit a smaller AI's weights (just 0.016% of them) to fix a math weakness, no training loop needed, raising its math score from 7.9% to 48.1%. It also built game-playing networks from scratch that beat ones trained for 100 million steps.

Zoom out: If AI can directly edit AI brains by reasoning about them, the slow, expensive training process that built today's models may not stay the only way forward.

via MIT CSAIL

Wired finds 400 apps hiding abusive deepfake tools

Wired found a network of over 400 apps and websites, disguised as things like pet-photo or language apps, that secretly let users generate sexually abusive AI images and videos, including scenes depicting violence. The apps, linked to a Hong Kong entity, were promoted through tens of thousands of ads on Meta's platforms and distributed via Apple and Google's app stores.

The catch: The apps passed app store review by hiding their real function, showing that current moderation checks the surface, not what software does after approval.

via Wired

NYT sues Brave browser over AI content scraping

The New York Times and several other publishers sued the Brave web browser, alleging it built an index of over 30 billion web pages and sold access to AI companies including Anthropic, Perplexity, Mistral, xAI and Cohere, while also letting users bypass news paywalls. The suit seeks an injunction and damages, calling Brave both a "burglar" and a "fence" for stolen content.

Follow the money: This targets the pipeline feeding AI models rather than the AI companies themselves, a new front in the fight over who gets paid for training data.

via The Verge

Anthropic says it is nearing AI that improves itself

In filings ahead of its IPO, Anthropic said its internal estimates suggest it may soon hit its own threshold for AI that dramatically speeds up its own development, a milestone it calls recursive self-improvement. The company says no current model has crossed that line yet, and it plans to keep humans supervising the process even once it becomes technically possible.

The catch: This claim appears in a stock-sale document, where companies have an incentive to sound both groundbreaking and safely managed at the same time.

via TechCrunch

Qwen's boss promises future models stay open source

Amid rumors that Qwen4 would be the last open-weight release, Alibaba's Qwen head Liu Dayi Heng said Qwen 4.0 and Qwen 5.0 will remain open source, arguing AI's value should not belong to only a few companies. Qwen models have passed 3 billion cumulative downloads, with over 460 open-source models and 300,000 derivative models built on them.

Zoom out: While US labs lean toward closed, paid models, China's biggest open-weight project is doubling down on giving its models away free to use and modify.

via r/LocalLLaMA

Argentina pitches itself as a data center free-for-all

Argentina's president Javier Milei is courting OpenAI, Amazon, Google and Oracle to build AI data centers in Patagonia, promising essentially no AI regulation and tax cuts from 25% to 15% for projects over $1 billion. The region offers cold weather for cooling and vast open land, but Milei's plan does not require companies to disclose energy or water use, or reserve computing power for Argentines.

The catch: As the US and EU add rules on data centers, this shows AI infrastructure simply moving to wherever oversight is thinnest, not disappearing.

via Fast Company

OpenAI pitches data centers as power plants for your grid

OpenAI announced a pilot in Effingham County, Georgia, where it says its AI data center could free up to 1,000 megawatts of power during extreme weather, enough for about 700,000 homes' air conditioners. The move comes as several states have passed laws blocking AI companies from pushing energy costs onto residents, amid growing local opposition to data center construction.

Follow the money: Recasting a power-hungry data center as a neighborhood's emergency backup generator is a clever reframe of a facility local communities have been fighting.

via The Verge

Humanist wins religious exemption from using AI at work

David Durman, a University of Kentucky research administrator who identifies as a humanist, got a religious accommodation excusing him from using AI tools like Copilot and ChatGPT, arguing it would diminish human judgment and moral responsibility. The university granted the request in September, saying it would not burden his department. He is not alone: a coalition of humanist groups has adopted formal principles opposing AI replacing human judgment.

The twist: Federal religious-accommodation law was not written with AI in mind, but this case shows it may become a legal tool for workers who want to opt out.

via Fast Company

Grok Bot usage limits slashed over runaway costs

Some Grok Bot users found their weekly usage limits maxed out over Halloween weekend, even without overnight use. Elon Musk confirmed the cutback was deliberate, saying serving Grok Bot had become "unsustainably high" relative to revenue. The tool had been a growth story for newly public SpaceX, which said AI makes up 52% of its income, with the unit turning a $431 million profit.

The catch: Free or cheap AI tools running at a loss is common across the industry; this is a rare public admission of what happens when a company stops absorbing it.

via TechCrunch

When to trust AI search, and when to just Google it

Wired tested AI chatbot search versus Google. For common, well-documented questions, AI search works fine. But for obscure queries, like a minor actor on an old show, it often confidently gives a wrong answer due to fewer sources. For an exact quote, statistic, or spec, classic search still gives more control over which source you trust.

For you: The riskiest AI searches are the ones that feel most convincing: polished, confident answers to questions where good sources barely exist online.

via Wired

What is training data, and why does it matter?

Training data is the huge pile of text, images or examples an AI learns from before answering questions. Like a student studying millions of books, it learns statistical patterns, then predicts what word or pixel comes next. An AI is only as good and as current as its training data. Example: a medical chatbot trained mostly on US health forums may give worse advice for conditions more common elsewhere.

For you: Understanding training data explains why chatbots confidently state wrong facts, miss recent news, and why lawsuits over scraped content keep piling up.

Original explainer

Garry Tan spends up to $200K a year on AI tokens

Y Combinator CEO Garry Tan told a podcast he spends $100,000 to $200,000 a year running AI agents that manage his email, calendar, and even 20 years of therapy notes. He calls using agents a "crazy privilege" most people can't afford, and says the technology won't go mainstream until the cost of running it drops sharply.

For you: A top VC openly spending six figures a year on AI agents is a preview of a cost gap between power users and everyone else that today's cheap chatbots don't show.

via TechCrunch