全部 AI 动态

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link: https://institute.deepmind.com/essays/the-economics-of-intelligence/

Granola now does voice typing. Unlimited words, for free. Granola already writes your meeting notes. Now it can do the typing for you, in any app.

No.1 💎

We’ve trained Claude to flag potential misuse in conversations, a technique we call self-assessment. Right after it responds, Claude gives a preliminary safety assessment of the conversation. Since we began using self-assessment in June, it has surfaced misuse that our other safeguards weren’t designed to catch. Because Claude has already read the conversation, self-assessment is very efficient. And unlike classifiers, which target a discrete set of harms, it draws on Claude’s own judgment, so it gets more accurate as our models improve. Read more on our new Safeguards blog: https://safeguards.anthropic.com/2026/self-assessment/

The computing power of AI chips sold each quarter has grown 2.5× per year since the start of 2024. That’s down from roughly 6× in 2023, led by Nvidia’s then-new H100 GPU. Some evidence suggests growth slowed further. But quarterly sales are noisy, and our data ends just as Nvidia begins shipping its next-generation chips, a move that has historically spiked sales. This could be a lull between generations. See our website for more details on the analysis: https://epoch.ai/data-insights/ai-computing-capacity-additions-doubling This is from our AI chip sales dataset, which uses financial reports, company disclosures, and more to estimate compute, power usage, and spending for a wide variety of chips: https://epoch.ai/data/ai-chip-sales

Introducing RL with Neural Feedback (RLNF). We have found reliable ways to train AI models to respond to human preferences purely from brain signals. The example below is a picture of a face that self-transforms into a more attractive version for the subject as they are looking at it.

Your coding agents are fast now. The problem? They can move just as fast in the wrong direction. Miss one update, and they can spend days building the wrong thing - costing you tokens and engineering time. That's why devs are still glued to their monitors. Meet Serro: the Agentic TPM. It keeps your people and coding agents working from the same evolving program. 👇 @serroai

Tobi Lütke on the AI agent revolution and future of Shopify @tobi on where agent commerce is headed, sci-fi becoming reality, and his GPU wine cellar. Why did Amazon block Meta's Muse but Shopify welcomed it? I sit down with Tobi in Toronto to discuss why the companies are taking different approaches to agents and how shopping changes when AI starts buying things on your behalf. He explains the early AI shopping behavior he’s seeing. We also get into why he thinks AI makes starting a business easier, the backlash against the technology, and why everyone will eventually have to embrace it to get ahead in their careers. We get into how AI agents will make money, who pays when they handle a purchase, and why advertising gets complicated when your assistant is supposed to work for you. We discuss his experience on Meta’s board and working with Mark Zuckerberg. Lütke also shares why he turned his wine cellar into a GPU server rack and how he uses his personal AI agent that runs entirely from his house. This is one of my favorite pods I've ever done. Timestamps: 00:00 Amazon blocks Muse 02:40 Shopify's approach to AI agents 07:00 How AI agents will change shopping 11:05 Sci-fi and tech optimism 17:57 Zero-sum thinking vs. abundance 21:20 AI backlash and the founder advantage 45:28 Building an AI-first Shopify 52:18 Who profits from agentic commerce? 01:03:22 How Shopify stays ahead 01:10:39 Advertising and discovery in the agent era 01:16:02 Rack, Cellar, and Claw Links: Spotify: https://open.spotify.com/episode/71xOycjYFjWEVSqhTpYPv8?si=rtVJne2kSr6L6QnAZWRGYw Substack: https://sourcesnews.substack.com/p/tobi-lutke-shopify-ai-agents YouTube: https://youtu.be/r9SHzCW9zxo Apple Podcasts: https://sources.news/p/tobi-lutke-shopify-ai-agents This episode's presenting sponsor is @hyperagentapp Thanks to the show’s premiere sponsors: @Atlassian, @meetgranola, and @mercury

认识 Pika H3 Flex，今天首次亮相，成为 DesignArena 的 t2v 榜单上排名第一的开放权重模型。 它为专业迭代而打造，支持可导出的音频分轨、最高 4k 的分辨率，以及快于实时的生成速度。它能在短短 7 秒内生成一段带配乐的 15 秒片段。 所以，没错。这有点像是在炫技 💅

We raised a $150M Series C at a $3B valuation, led by @sequoia. Our third round in 12 months, bringing our total funding to $277M. Our team has grown 10x in the last 12 months. We now serve Fox Corporation, WPP, Live Nation, Lucid Motors, and Ramp, and many of them are fully replacing legacy platforms with @getserval. AI coding tools have transformed how engineers work. Serval brings that same power to every team, so anyone can build governed automations across the systems they already use. We’re hiring 500+ people over the next year and expanding in San Francisco, NYC, London, Austin, and beyond. If you’re down to take on legacy enterprise systems and transform how teams work, my cofounder @AlexAtServal and I would love to meet you. It’s Serval now.

😳 This could be the scariest Halloween film you’ll watch today! Here’s the exact process we used to make it: But first: a big THANK YOU to @Kling_ai for sponsoring this video and making it possible! And if you want to learn more about my workflows, make sure to follow me! We at promptr teamed up with @NOMOREorg to create a film with AI that’s relevant to many people right now. We wanted to use our skills to create something with impact, something that normally isn’t visible but should be. We wanted to use AI the way we think AI should be used today. Everything in this film is AI-generated. After writing the script, we started by building the world: the monsters, the location, the general style. We iterated a lot with different models to create the start frames. That process took us two weeks. Then we generated the interviews with the monsters, and this is where some magic happened. Honestly, I was completely blown away by the realism that came out of Kling 4.0. The monsters look very lifelike, and the reason is that we didn’t force Kling to just repeat a written line. Instead, a prompt looked something like this: Prompt: Talking points – NOT a script! These are only the ideas Morris should get across. He answers completely in his own words, searches for them, improvises freely, and never reads them out word for word. I directed this whole piece more like I would direct real people on set, and I think that’s the way to go! Not micromanaging every little detail, but letting AI do its magic with guidance. Kling Flash was also VERY fast to iterate with, so we were able to create lots of takes and really choose the best ones. Share this film, send it to your friends, family and colleagues to make sure everyone in every country in the world knows nomoredirctory What do you think of the concept? What would you have done differently? I’d love to hear your thoughts. Made by PROMPTR AI Director: Simon Meyer AI Lead Artist: Lubbertus Mueller-Frick AI Artist: Ciro Kavouras Producer: Benjamin L. Schiller Sound Design & Mix: Krane & Rabe GmbH In collaboration with NO MORE Thomas Schmitt Arne Kreutzfeldt Dr. Georg Ramme Tim Sproten Ben Chimoy Elham Maddah Paul Alexander Probst Alysha Jeske Manuela C. Palencia Florian Boesel Kerstin von Kieseritzky

Drop 1/15: Introducing Joy, a dual-channel conversational dataset. Real, unscripted conversations between two people, each on their own track. Built to teach voice models how people actually talk: when to listen, when to jump in, when to say “hmm”. 100,000 hours. 50,000 speakers. 10 languages.

The UNBROKER skill by @SHL0MS, now in Computer's skills marketplace, finds a person's listings on data-broker sites and drafts opt-out requests. Each request waits for approval before sending. Tasks that use /unbroker are free through November 6, up to 5,000 credits per user.