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Oct 23Fri
  1. The Verge · AI42

    Retro gaming is being overrun with AI

    AI coding tools have sharply accelerated decompilation and recomposition projects that port retro games to new platforms, but the surge has strained preservation communities, with one tracking site shutting down over hostile forks and AI-assisted "finishing" of others' work. The article weighs the boom in interest and accessibility against preservationists' concerns that AI-driven speed sacrifices understanding of how games were built, while noting Xbox has set up an Xbox Labs division to explore the space.

  2. TechCrunch · AI38

    Spotify’s current AI bet is about being the music service that agents choose

    Spotify reported a strong third quarter with 788 million monthly active users and nearly €5 billion in revenue, and on its earnings call Co-CEO Gustav Söderström argued the company is well-suited to the agentic age because entertainment is not a chore people want agents to replace. Spotify, which was the first music service to integrate with Meta’s Muse assistant and also integrates with ChatGPT and Claude, aims to be the media service people use from any agent, with the agent requesting content and Spotify using its own systems to reason about the request rather than sending listener data to partners. Söderström described Spotify as an intelligent media service encapsulated as an agent that users can talk to and instruct, citing examples like building a running playlist from listening history back to 2008 with beat-stretching and sorting by key and BPM.

  3. The Decoder52

    Only 13 percent of companies see real returns from AI, Gartner survey finds

    A Gartner survey of 2,002 CIOs and technology leaders, presented at the Gartner IT Symposium in Orlando, found that only 13 percent of respondents report major or transformative returns from AI. While 58 percent of CIOs are tasked with delivering cost savings through AI, 51 percent expect AI to raise total operating costs for their tech stack, and 44 percent of CEOs struggle to demonstrate AI-driven financial returns to stakeholders. Gartner analyst Alicia Mullery sees a growing gap between the "innovation race" vendors are running and the "outcomes race" companies need to win, while Bloomberg CEO Vlad Kliatchko says it is too early to measure AI's economic payoff.

Oct 22Thu
  1. MIT Technology Review · AI30

    AI’s hidden storage bill: What inference leaves behind

    This WD-sponsored article argues that AI infrastructure planning overlooks data storage costs, since inference runs continuously and leaves back-end data behind—citing an internal measurement where an 8-second 720p video clip returned as a 3.97MB file left more than 500MB of associated data. It cites McKinsey's projection that inference demand will grow at a 35% CAGR through 2030 and a WD-sponsored IDC survey of 763 IT and business decision makers in which nearly 95% reported data volume increases from generative AI over the past 12 months. It recommends planning storage around the full AI data life cycle, with HDDs as a cost-effective high-capacity foundation for retained data.

Oct 21Wed
  1. Latent Space40

    The Programmable Data Center — Evan Conrad, SF Compute

    SF Compute CEO Evan Conrad explains how the company moved from subleasing a GPU cluster to building much of a cloud's operating capacity so it can physically settle the compute traded on its market, delivering usable compute rather than a cash payment against an index. Rather than waiting for GPU clusters to become interchangeable, SF Compute matches orders with differing constraints—interruptibility, storage recovery speed, GPU generation, networking and software configuration—which makes trading possible while technology changes but spreads liquidity across more variants. Conrad also discusses spot and forward pricing, batch versus live inference leases, why an index can drift from the real cost of buying compute, and why he argues a neutral market operator cannot be run by companies with large hardware or inference businesses.

Oct 20Tue
  1. MIT Technology Review · AI22

    AI adoption by industry shows what other sectors can learn

    TripleTen survey data shows tech leads AI adoption, with finance second (48% of workers asked to use AI chatbots), while healthcare (18%) and education (25%) lag due to compliance-heavy and human-facing operations. Across industries, 79% of workers report being asked to learn at least one new skill, and 88% of employee-led AI innovations became standard company practice when managers learned them from an employee. Out-of-pocket training spending varies widely, from $1,758 on average in tech to $883 in education, with nearly half of healthcare and education workers spending nothing.

  2. MIT Technology Review · AI58

    Making the internet better for AI agents might make it worse for everyone else

    Personal AI agents like OpenAI's Dots and Meta's Muse form a third category of web visitor—not human, not quite bot—and many sites, including Amazon and Yelp, are blocking them. As agentic traffic grows, platforms may redesign the web for agents rather than people, potentially degrading interfaces, verification, and pricing transparency for humans. The rules governing how agents interact with websites are still being written and will determine whether the web works for or against consumers.

Oct 19Mon
  1. Import AI30

    Import AI 477: In which I tell the IMF the Singularity has begun

    In this Import AI issue, Jack Clark publishes the Per Jacobsson lecture he gave at the IMF meeting in Bangkok, arguing that AI systems operating at the frontier of human science show the singularity has begun. He cites AI contributions to open math problems, AI-designed viruses and drug candidates, and benchmark jumps such as Humanity's Last Exam (8% to 67.7%) and FrontierMath (2% to 94%), plus a theoretical physics test where a model scored 98% partly by finding errors in expert answers. He urges building compute, power, lab and robotics capacity to share the benefits, while exercising restraint and oversight, especially in biological and AI research.

Oct 5Mon
  1. Import AI30

    Import AI 475: Swarm scaling; Google DeepMind watermarks biology; and the AI science economy

    This Import AI issue covers Toby Ord's analysis of AI swarms as a form of inference-scaling that trades more tokens for less wall-clock time, with diminishing returns as agent counts grow; CSAIP polling finding 61% of Americans consider voluntary corporate AI self-governance "not enough"; Google DeepMind's SynthID Bio watermarking methods for synthetic biology; C5R Corp's SciUniverse benchmark of 92 lab tasks where Claude Fable 5.1 leads at 45.3% pass rate; and a DeepMind paper proposing an Automated Scientific Economy with proof of ideation, ex-ante evaluation, brokerage and validation payouts.

Sep 28Mon
  1. Import AI58

    Import AI 474: Platonic mindspace; TPUs in space; Zhipu starts an outer RSI loop

    This Import AI issue covers Michael Levin's paper proposing that minds are non-physical patterns from a Platonic space that embed themselves in bodies and machines, a Stanford/Physical Intelligence piece arguing robotics needs a universal post-training recipe like LLMs had, Google's Project Suncatcher preparing to send radiation-tested TPUs to space on a SpaceX rideshare mission, Zhipu AI using its GLM-5.3-powered Infra Agent to help optimize GLM-5.3-Flash to production readiness in under two weeks with tripled throughput, and Periodic Labs' Periodic Neon model trained with RL on physical lab data. It also notes the Old Models Foundation site for running historical image models and includes a short tech tale.