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.
This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. For the past 100 days or so, my mornings have had a new rhythm.
AI is mass decompiling games. That's both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and producing a boom in game preservation projects to get old games working on new hardware (often in a browser using WebAssembly).
Cerebras CEO Andrew Feldman predicts a future where personal AI assistants become as commonplace as household appliances, running partly on local devices and partly in the cloud.
Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI
Cerebras CEO Andrew Feldman has a message for AI leaders spreading doomsday warnings: Maybe stop talking? At TechCrunch Disrupt, he argued that the industry's focus on AI's risks is drowning out its potential benefits.
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."
Geoffrey Huntley proposes using a pre-commit hook to inspect the git diff, reject commits that call observability modules directly and bypass the diagnostics module, and inject a prompt telling the AI agent to use the diagnostics library instead. He argues that updating AGENTS.md alone still leaves a delta and will drift, whereas this approach made the agent's performance instantly ten out of ten.
MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next.
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.
Voice AI often misses important points for its context layer, and causes the whole pipeline to break.
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.
Microsoft CEO Satya Nadella now calls artificial intelligence "super intelligence" instead of artificial intelligence, falling in line with Trump's preferred language. In his latest blog post, he frames AI models as insider security threats, taking yet another shot at OpenAI and Anthropic.
In a lengthy post on X, Microsoft's CEO laid out his views on the dangers posed by highly advanced AI models and how to confront those risks.
In a Saturday morning post, Microsoft's CEO wrote that it’s time “to step back and assess the trust architecture” of AI.
At this year's OpenAI DevDay, CEO Sam Altman unveiled the company's new AI agent Dots - and told the crowd that the company wants to "set a new standard for privacy in frontier AI." OpenAI would spend the day taking veiled shots at Meta's Muse, its primary competitor, for failing to keep users' data safe.
From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding, Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology.
Moore sees a huge opportunity in consumer AI, particularly if the industry can tap into revenue streams beyond just subscriptions and API charges.
"When we are drawn into even the most primitive exchanges with a relational artifact, we believe it cares for us," Dr. Sherry Turkle writes. "And we are wired to care for it in return."
Cryptographer Matthew Green says there is a 1% chance we live in Minicrypt, a world where public-key encryption is impossible, and a 15% chance we functionally lose confidence in our existing public-key encryption algorithms. He notes that the speed of AI producing surprises and the speed of human beings replacing standards are orders of magnitude different, and that you only recover from such a surprise if you do the preparation in advance.
President Donald Trump has a knack for turning words against his enemies. His first successful presidential run was built on monikers like "Little Marco" and "Crooked Hillary"; he changed "fake news" from a phrase describing scammy media outlets to a derogatory term for the press at large.
Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary.
Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next.
Carson Gross argues that programming is fundamentally about two things: solving problems with computers and learning to control complexity while doing so. He says he has a hard time imagining a future where knowing how to solve problems with computers and how to control the complexity of those solutions is less valuable than it is today, so he thinks it will continue to be a viable career even with the advent of AI tools.
A special Science pod and Engineering pod crossover.. with Forward Deployed Engineering kicker!
Ben Affleck is going viral for his deep knowledge of AI, from neural networks and transformers to open weights. The actor, who sold his AI filmmaking startup to Netflix earlier this year, is proving he's more than just a Hollywood star.
At the New York Film Festival premiere of Artificial, Luca Guadagnino's satirical Sam Altman biopic, the director said onstage that "[when] someone wants to play God, that's very interesting to me."
My Decoder guest today is Hayden Field, The Verge’s senior AI reporter, and we’re discussing the new wave of consumer-friendly AI agents.
A 16-year-old planned his hiking route on Crown Mountain near Vancouver using Anthropic's Claude and had to be airlifted off a steep rock face. The chatbot had sent him down a route that requires climbing gear. Bryce doesn't blame the AI but says he'll never use it for route planning again.
Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments.
The story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live. A robot shaped like a human—white with a black head and torso—has been popping up on video feeds.
Ben Affleck says he has been interested in computers since he was young, and became more interested as film moved from analog to digital, and that he can write "pretty shitty Python scripts." He notes that visual effects workflows have long included machine learning, and that convolutional neural networks, precursors to what the transformer can do, process tensors to perform edge detection and feature extraction, making it easier to take out the green screen image and replace it with something.
Anti-Patterns in Software Blogging Some excellent writing advice from Michael Lynch. Michael warns against "meandering intros", misjudging your reader's existing knowledge, assuming they'll read your previous posts, and excessive formality. He also warns against overreliance on links as an excuse not to explain terminology. This one hurt!
The “father of the iPod” says the first generation of AI gadgets failed to solve real problems — and the next wave will need to earn consumers’ trust.
Commenting on the openai/math discussion on Hacker News, Jake Boggan said he spent 24 years and thousands of hours on Barnette's Conjecture, and last summer briefly thought he had solved it. The conjecture is said to have been proven at problem 180, and he described feeling a far-off sadness on hearing the news, like learning an ex-girlfriend had died suddenly in a car crash.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.
Microsoft published a bearish AI outlook from Nobel economist Daron Acemoglu. He predicts about 1.5 percent GDP growth over ten years and at most five percent of jobs replaced. Bigger models won't move the needle, he argues. What's missing are practical apps that change how work gets done.
With AI hardware, tech companies are pushing the definition of what does and doesn't constitute a recording. For most of gadget history, it'd be reasonable to assume that a device with a microphone or camera is either recording you or it isn't; it's either on or off, without much gray area. Microphones capture sound.
This past year, OpenAI, Anthropic, and other labs have announced breakthroughs on numerous long-standing mathematical problems, in some cases pushing well beyond what researchers expected current systems to be capable of — including resolving one of the famous Millennium Prize problems.
Sam Altman thinks that the benefits of AI will be so great that "the world should accept some bad things happening" along the way.