AI news · Thursday, November 5, 2026

Voters just hit the brakes on the AI data center boom

Across Tuesday's US midterms, 38 of 44 local ballot measures restricting data centers passed, and candidates who campaigned against them won governor races in Texas, Georgia and Arizona. An NBC exit poll found 76% of voters oppose data centers in their own communities. Texas alone has 13.3 gigawatts of planned capacity now at risk of delay.

Zoom out: This is the first election where AI infrastructure, not AI itself, became a genuine voting issue nationwide.

via The Verge AI

China's open DeepSeek now runs most AI traffic on OpenRouter

DeepSeek alone handled 53% of all 27 trillion tokens processed on OpenRouter, a marketplace that routes requests across many AI models, in a single day. Open-weight models overall, meaning ones anyone can download and run, crossed 75% of that traffic. A year ago closed US models dominated this kind of usage data.

The twist: The 'China is behind' narrative doesn't match what developers are actually choosing to run in production right now.

via r/LocalLLaMA

Google will cut school internet search if they skip its AI

Google says schools can turn off its Gemini chatbot, but if they do, they also lose Google Search entirely, since the company now treats the two as inseparable. Each tool will be off by default starting next year, administrators must opt in. Critics, including former FTC chair Lina Khan, call it using a search monopoly to force AI adoption on kids.

The catch: Schools wanting to protect kids from AI now face a real cost: giving up the basic research tool their lessons depend on.

via Ars Technica AI

OpenAI's AI found 948 hidden bugs in Linux

OpenAI built an automated system that searches, confirms, and writes fixes for security flaws called privilege-escalation bugs, which let an attacker who already has limited access take full control of a computer. It found and patched 948 of these in the Linux kernel, the core software running most servers and phones worldwide, without a human doing the detective work each time.

What's next: If AI can find bugs at this scale, it can likely also be used to find and exploit them before anyone patches.

via OpenAI

Four people used an AI agent to book an Uber last quarter

Despite hype about AI assistants doing our errands, Uber disclosed that third-party AI agents booked just 4 rides and about 500 food orders during its busiest week, out of 320 million total trips and deliveries. Uber's CEO said booking a ride in the app already takes just 50 seconds, which may be why an AI agent saves little time.

The catch: A useful reality check on how far actual AI agent adoption lags behind the industry's own marketing about it.

via The Verge AI

UK's AI growth forecast came almost entirely from Google

British officials spent two years citing a 400 billion pound economic boost from AI, a number that turns out to have come from a 2023 report Google itself paid a consultancy to write. The government's own current estimate is far lower, about 120 billion pounds by 2030. Ministers kept using the bigger figure without disclosing its source for nearly two years.

Follow the money: Government AI policy has leaned on industry-funded math that inflated the case for AI by more than three times.

via Wired AI

A 2-billion-parameter AI now beats big models at reading forms

IBM researchers built a small specialized model that pulls exact fields, like an invoice total or a patient's exam date, out of any scanned document and returns clean, structured data. They say it outperforms every large general-purpose AI model they tested on this specific task, at a fraction of the cost, because it was trained just for this one job rather than everything at once.

The twist: It's a reminder that bigger, general AI isn't always better: a small, focused model can beat it on a narrow job for less money.

via Hugging Face

An open AI coding model was built for one-tenth the usual cost

Startup Ricursive Intelligence says it trained its new Ripple-65B model, which writes and runs code autonomously, for just $32 million, versus roughly $300 million typically spent on a rival open model of similar ability. It still beats much larger US open-weight models, including ones with over ten times more parameters, on a leading coding-agent test.

Follow the money: If smaller labs can match frontier results for far less money, the idea that only giants with huge budgets can compete starts to crack.

via Hacker News (front page)

Anthropic built a way to prove an AI answer is genuine

Anthropic released a cryptographic system that lets an AI company mathematically prove a response really came from the specific model it claims, rather than a cheaper substitute swapped in secretly. On a 400-billion-parameter model, it generates this proof at 110 tokens per second on one computer chip, about 70 to 95 times slower than just running the model normally.

Why it matters: As AI gets used for things like compliance or safety testing, knowing which model actually answered becomes a real trust problem to solve.

via Hacker News (front page)

Hugging Face CEO says real AI risk is secretive labs, not open ones

Clement Delangue, whose company hosts downloadable AI models used by 20 million builders, said at a conference that most future AI danger will come from a handful of big companies building powerful models behind closed doors. He called his own probability of AI causing catastrophe 'close to zero' and argued wider access to open models is actually the safeguard, not the risk.

The catch: It's a direct rebuttal to the argument that keeping AI closed and controlled is the safer path.

via Wired AI

What is a large language model, really?

A large language model, or LLM, is a computer program trained on huge amounts of text, like books, articles and websites, to predict the next word in a sentence. It does this so well, after seeing billions of examples, that it can write essays, answer questions, and hold conversations, even though it has no understanding the way a person does.

For you: Knowing an LLM predicts words rather than 'knows' facts explains both why it's useful and why it sometimes confidently makes things up.

Original explainer

ChatGPT can now make GIFs straight from a prompt

OpenAI's new Images 2.6 update lets ChatGPT generate animated GIFs from a text prompt or an uploaded photo, with options to adjust playback speed and edit individual frames. OpenAI also says users now generate over 4 billion images per week across ChatGPT and its API, up from 3 billion per week in September, about a 33% jump in two months.

What's next: Small, playful features like this are often what actually gets casual users to open an AI app daily, more than big capability upgrades.

via The Verge AI