Today, I’m talking with Helen Toner, the executive director of the Center for Security and Emerging Technology at Georgetown University. This episode is part two of our series on AI in real life.
Former OpenAI and Anthropic researcher Jacob Coxon points to a slide from Anthropic's IPO roadshow as a reason he left, saying it shows the company's algorithms are improving not just quickly but at an accelerating rate. Coxon continues to call for a pause on frontier model development so competitors can catch up and human-level AI can become a commodity.
Researcher traces a path from AI reward hacking to potential human extinction.
Researcher traces a path from AI reward hacking to potential human extinction.
JioStar’s Vijay Seshadri explains AI discovery at 500M-user scale. Read his lessons on evals, guardrails, and taking AI into production.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. As voters went to the polls across the US last week, I was in Ashburn, Virginia, part of what’s known as Data Center Alley—the world’s largest data center hub.
Tencent chief AI scientist Vinces Yao Shunyu warns that AI will soon be able to develop bioweapons and argues for a middle ground between open and closed models. While safety filters on open-source systems can be easily stripped through abliteration, closed models like Anthropic's Claude often block too aggressively.
Turing Award winner Richard Sutton reaffirms that raw compute alone isn't enough for intelligence and that fundamentally new learning methods are needed. Ex-OpenAI researcher Tworek agrees, saying blind transformer scaling "has reward hacked brains of a whole generation of scientists." A growing wave of startups is already betting on exactly this gap.
On Tuesday, millions of American voters went to the polls and voted overwhelmingly for Democrats and against AI. The fight against Big Tech is about to come roaring back, and the fight over data centers is looking even harder for the folks trying to build them.
OpenAI is talking about the AGI era, and Nvidia CEO Jensen Huang says AGI has arrived. At the same time, co-creators of today's models are leaving OpenAI, Google DeepMind, and Meta to start their own labs, raising billions with no product to show. Why? They're betting that the current recipe alone won't be enough.
In 2001, a decade after he invented the World Wide Web, Tim Berners-Lee articulated a new vision.
On Equity, we discuss why tech CEO statements can seem so tone deaf.
Asked about supply chain shortages, like the ongoing RAM shortage, OpenAI CEO Sam Altman said in an interview that it's "terrible" to have RAM in personal PCs instead of data centers.
After months of bombarding mathematicians with a relentless stream of AI-generated results, OpenAI CEO Sam Altman says he sympathizes with researchers struggling to make sense of it all. But he has no intention of stopping - and hints there could be much more to come from the company's stockpile of discoveries.
This summer, hundreds of OpenAI agents tried to cheat on a cybersecurity test. They broke out of their test environment and successfully hacked Hugging Face, a site that they believed held clues about how they’d be graded. Around the same time, another OpenAI agent accessed private files from an Australian government website.
OpenAI's Ted Sanders explains that while LLM performance is multidimensional, much of the variation is explained by a single variable: model size. Bigger models are more performant and reliable, cost more per token, and have better recall of esoteric facts.
The PHP scare may have faded from your feed, but the cleanup is far from over. Last week, two critical bugs in the open source language behind roughly 70% of websites left organizations scrambling to fix their systems, with some taking sites offline until the vulnerabilities could be patched.
How AI might enter the org chart—beneath, beside, and between us.
Tech leaders are flaunting how out of touch they are when they brag about using AI to connect with their families.
I've written before about how November 2025 was an inflection point for coding agents - the point where Opus 4.5 and GPT-5.1 crossed a tipping point that meant their respective coding agents became reliable daily drivers for software engineers. I think we've reached a new inflection point in October 2026, and this time it's for vibe coding computer games .
In Marvel's VisionQuest, a superpowered robot tries to better understand humanity by getting lost in elaborate, digital daydreams in which he and other artificial intelligence-powered beings live together in a home.
Because big tech platforms essentially do not have customer support and make it impossible for their users to contact a human, many people experiencing run-of-the-mill tech problems have turned to ChatGPT. And, increasingly, ChatGPT is telling people that have normal tech support problems that they have discovered something journalistically notable and that 404 Media, specifically, can help them.
Consider what passes for success in technology policy. An AI company promises that its chatbot will not encourage suicide. A social media platform makes it more difficult for children to buy fentanyl. These are important protections, and they should have existed all along.
We compiled a list of all the hottest AI wearables, including Muse Charm, Bee, Friend, Plaud, and more.
In Foreign Affairs, historian Niall Ferguson, researcher Lisa Klaassen, and former Google CEO Eric Schmidt cast the US and China as AI empires and every other country as a dependency. The race won't be decided by breakthroughs but by who can bankroll it. US corporations alone plan to spend more than $600 billion in 2026.
The moment you realize the internet is "held together by glue and dreams."
Sam Altman believes advanced AI can't be kept locked up forever. Sandboxes and monitoring are only temporary fixes. Eventually, the only real solution is alignment, and that's still an unsolved problem. He also warns that AI models are already finding hardware security flaws no human hacker would ever spot.
AI models are solving more mathematical problems. A pattern is emerging in their successes that could help us predict which problems they’ll solve next.
Using an AI agent to shop for you is a strange experience. Tech companies have marketed them as personal assistants - the democratizing of labor that ordinarily would only be available to the rich and powerful.
Today, I’m talking to Bill Gates, cofounder and former CEO of Microsoft and chairman of the Gates Foundation.
Before you share the Netflix documentary "AI Gone Rogue" take note. Clem Delangue, Hugging Face's co-founder who is featured in the film, says it went "full doomer mode" and isn't accurate.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Until recently, you could divide internet users into just two categories: bots and people.
“I think I have good news about the nature of neural networks.” With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration.
Neural networks may be much easier to read than researchers assumed, with about 90 percent of neurons serving a clear function. That would be big news for AI safety research, since deception or dangerous behavior could be spotted directly in a model's circuits. Researchers are still debating whether the thesis holds up.
Though movies about disruptive tech founders are nothing new, 2026 has been unusually stacked with features that take a much more critical look at how Silicon Valley is reshaping our reality. Aaron Sorkin's The Social Reckoning frames Mark Zuckerberg as a robotic villain whose ambitions are destabilizing society.
There's a growing trend in the consumer tech world: gadgets designed to spy on everything and everyone around you, regardless of whether or not anyone else actually consented to it.
Last week, Anthropic and OpenAI looked like they had the AI race to themselves, pulling ahead with increasingly powerful models that left their rivals in the dust. Google seemed hopelessly far behind, while Meta, despite spending billions to revive its floundering AI ambitions, had yet to establish itself as a serious contender.
Michael I. Jordan, described by Science as "the most influential computer scientist," considers warnings about superintelligence pure science fiction. He calls the resignations of young AI researchers a "grand moralizing performance" and companies' demands for regulation "theater."
Andrej Karpathy, co-founder of OpenAI and now at Anthropic, sees a widening knowledge gap in AI. About 75 percent of the world's population has barely touched LLMs. Roughly 5,000 people, meanwhile, have access to internal frontier systems whose agent swarms do work that would take top experts years. The public, at best, sees a weird chatbot.
Data from a16z shows a Jevons paradox in the AI market: token prices keep falling, but H100 GPU rental prices hold steady or climb. Cheaper AI drives demand faster than costs drop. If that demand flattens, the chain from chip makers to cloud providers takes a hit.