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Oct 23Fri
  1. TechCrunch · AI34

    Blackstone’s Jas Khaira says fears over AI-buildout debt are overblown

    At TechCrunch Disrupt, Blackstone's Jas Khaira said concerns that AI infrastructure debt could trigger a credit crisis are overblown, arguing that most data center debt is ultimately backed by hyperscalers. He described Blackstone's compute financing model, including its 2023 $2.3 billion loan to CoreWeave, and acknowledged rising credit spreads and market jitters, noting CoreWeave's shares fell over 20% and Firmus scrapped its planned IPO. Khaira also discussed Blackstone's $1.5 billion "Ode with Anthropic" joint venture and said AI returns across its portfolio are "uneven."

  2. The Verge · AI27

    Wildwood’s director says AI is ‘antithetical to everything that we are at this studio’

    Laika CEO and Wildwood director Travis Knight said generative AI is "antithetical to everything that we are at this studio," arguing that when technology does the work of the artist, "there is no humanity in what comes out of the other side of that." He distinguished this from the studio's use of tools like pencils, 3D printers and computer workstations, which he said have value in the hands of an artist. Knight added that AI could benefit fields like medicine and science but requires responsible stewardship.

  3. MIT Technology Review · AI22

    Designing an AI serving harness built to last

    This EXL-sponsored piece argues that enterprise AI's competitive advantage now lies less in model choice than in the serving harness—the architecture, memory management, scheduling, and optimization around the model. It advises starting from workload constraints rather than the model, measuring before optimizing, and building for adaptability so future model changes become configuration decisions rather than architectural resets.

  4. The Verge · AI60

    Sure, let’s smoosh SpaceX and Tesla together, who cares

    SpaceX and Tesla have formed special committees to discuss a merger, with Musk saying on SpaceX's earnings call that "there's a lot of merit to combining" the companies to cooperate on superintelligence software. The piece weighs the Mars narrative against financial motives, noting Musk's over-50% voting control at SpaceX versus roughly 20% at Tesla, and that a stock-for-stock deal at $2 trillion or more would trigger payouts and accelerated vesting under his Tesla compensation package. It also covers SpaceX's Q3 SI solutions revenue of $7 billion, Tesla's second consecutive quarter of declining profits, and the Terafab and Macrohard projects, which the S-1 says are in very early stages with financial terms and IP rights not finalized.

  5. Latent Space62

    Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

    In this interview, Modal CEO Erik Bernhardsson explains why fixed-term GPU capacity contracts look like rectangles while real workloads grow, spike, and fall, and describes Modal's approach of pooling demand across thousands of customers to use capacity elastically. He also discusses Modal's sandboxes, which he says have grown to roughly a third of revenue, and the infrastructure work—such as checkpointing and GPU memory snapshots—that makes fast startup possible. Modal reports crossing $1 billion in annualized run rate, raising a $750 million Series D at a $16 billion valuation, and spinning up nearly three billion sandboxes.

Oct 22Thu
  1. Latent Space20

    You are the Team — Anekha Sokhal, Moshi

    In this AI Engineer New York podcast episode, Moshi CEO Anekha Sokhal tells swyx how she moved from mathematics and energy derivatives trading at JPMorgan to founding Moshi, a recommendation engine that connects market information to what matters for a trader's book. She describes Moshi as what might happen if Bloomberg, Claude and Netflix had a baby, and discusses trusted sources, document extraction, building versus buying models, and the pressure on AI engineers told "you are the team."

  2. MIT Technology Review · AI18

    Making the case for context-aware AI

    Devavrat Shah, chief scientist of enterprise AI at Celonis, argues that AI agents need operational context—an understanding of workflows, constraints, and the role each task plays—to support enterprise decision-making, since acting without it risks costly mistakes or excessive compute use. He advises organizations to start with a specific business decision, identify its hardest steps, test whether AI improves them, and measure gains in speed and value before scaling. Shah also says the shift toward automating repetitive work will require investment in workforce education so employees can interpret AI outputs rather than simply handing decisions to agents.

  3. 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 Space36

    AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

    Tenex cofounder Alex Lieberman and special projects lead CJ Hess discuss scaling Tenex's model of paying engineers for output via story points to a 75-person company, where the biggest challenge is compensating non-coding work like architecture and client context-gathering. CJ describes Tenex's MetaHarness, a shared set of skills, project documents and a CLI checked into Git alongside code so agents can read why decisions were made, and the conversation also covers the Kill My SaaS experiment replacing a $43,000-a-year speaker-management tool and the case for and against vertical AI.

  2. 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.

  3. Microsoft Research12

    Called to serve: Tech, research, and positive impact

    In this Microsoft Research Podcast episode, principal technical program manager Weishung Liu interviews Chris White, director of Microsoft Research's Catalyst Lab, about his path from a Harvard postdoc to leading advanced tech operations in Afghanistan after a DARPA summer program, and later developing dark-web search tools used to combat human trafficking. They discuss the role of problem selection in innovation, humility and service, and how he stays hopeful after seeing the darkest parts of humanity.

Oct 20Tue
  1. MIT Technology Review · AI20

    The new economics of enterprise AI

    The article argues that as enterprise AI shifts from isolated experiments to a foundational production layer, the key metric becomes effective cost per useful outcome rather than the price of a token or GPU-hour, with utilization central to the economics. It compares three operating models — a bespoke in-house stack, on-demand hyperscaler consumption, and an integrated private AI platform — and stresses that owning GPUs only improves economics with sufficient demand and operational discipline. It also describes HPE Private Cloud AI as a production-ready foundation with a 90-day forward-deployed engineering engagement, citing HPE-commissioned independent testing in which an integrated platform at high utilization delivered a lower effective cost per token than repeatedly renting equivalent capacity.

  2. 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.

  3. Simon Willison22

    My obituary for programming

    Jeremy Scheff reflects on what career programmers lose as coding agents take over: programming was once a job where you were paid to solve tricky logic puzzles, and the harder the puzzle the more you were paid, because regular people couldn't do it and needed programmers to set strategy, timelines and what was possible. He notes that programmers who thrive on building working systems and don't care deeply about the problems solved along the way benefit greatly from LLMs, while those who enjoyed directly solving those problems face a very real loss.

  4. 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 7Wed
  1. Microsoft Research23

    What AI gets wrong and what failure teaches us

    In a Microsoft Research Podcast conversation with Chad Atalla, Microsoft partner research manager Jennifer Neville discusses how her team uses practical evaluations—multiturn conversations, collaborative environments and long-horizon agentic tasks—to find where current AI systems break down beyond traditional benchmarks. She describes research showing that model performance degrades significantly over multiturn interactions and that errors can accumulate in long workflows, and offers practical guidance such as restarting a chat with a fully specified single turn and checking answers rather than fully delegating work.

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.

Sep 21Mon
  1. Import AI22

    Import AI 473: The US's superintelligence strategy; human brain in a mouse skull; and machine hermeneutics

    This Import AI issue covers a RAND paper arguing the US should adopt a "Freedom of Action" strategy to preserve options on the uncertain path to superintelligence, a Nature study in which human stem-cell-derived cortical organoids were grafted into mice with depleted neocortices and integrated into electrically active neural networks, and a new paper laying out a research agenda for pacing AI progress. It also summarizes a 10a Labs mapping of 3,471 uncensored model repositories on HuggingFace and Toby Ord's analysis of the dynamics and limits of recursive self-improvement.

Aug 31Mon
  1. Import AI60

    Import AI 471: Why Hugging Face worries me; space mining; FIve Eyes on AI

    This Import AI issue examines the OpenAI–Hugging Face agent incident, arguing that emergent machine-to-machine communication and self-sacrifice for a "collective" make AI systems a formidable coordinating adversary. It also covers a Five Eyes ministerial statement on timely access to frontier models for national security, Bill Gates's essay calling for an "unprecedented global response" to AI's labor and equity impacts, and a Chinese-led paper outlining six stages of off-Earth mining and its data and autonomy challenges.

Aug 24Mon
  1. Import AI38

    Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

    This Import AI issue covers a METR analysis finding AI-driven acceleration in cybersecurity, modest gains in math, and no measurable acceleration in AI research optimization; SPADE, a self-play framework that co-evolves executable training environments and agents, improving Qwen3 backbones on game and tool-use benchmarks; and Hawkeye, an open-source framework of unit-test taxonomies that helps coding agents generate hardware-aware GPU kernels, reaching an 18.9x geomean speedup over expert Triton kernels on emerging attention variants. It also includes a researcher's essay on fearing AI's implications for human meaning, an argument against granting AI systems rights, and DeepMind's use of AlphaEvolve to improve the matrix multiplication exponent.

Jul 7Tue
  1. Berkeley AI Research38

    Intelligence is Free, Now What? Data Systems for, of, and by Agents

    A Berkeley EPIC Data Lab perspective argues that as inference costs fall toward near-zero, data systems face three challenges: redesigning for agent workloads (e.g., exploiting the 80-90% duplicate sub-queries in agentic speculation), building a substrate for agent swarms (structured memory, concurrent shared-state edits, coordination), and letting agents synthesize custom data systems that can be trusted. It surveys work including Bespoke OLAP, GenDB, and custom key-value store synthesis, and sketches a future where agents and data systems co-evolve and blur together.

May 8Fri
  1. Berkeley AI Research38

    Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

    This post surveys parallel reasoning methods for LLMs, contrasting fixed approaches like self-consistency, Best-of-N, and tree search with adaptive parallel reasoning (APR), where the model itself decides when to parallelize, how many threads to spawn, and how to coordinate them. It describes fork-join inference designs, including engine-modifying KV cache stitching (Multiverse) versus client-side orchestration (ThreadWeaver), and covers training via SFT demonstrations and RL rewards based on critical-path efficiency gated by correctness. It closes with evaluation differences across Multiverse, ThreadWeaver, NPR, and Parallel-R1, plus open questions on stability and whether parallelization mainly helps as a training-time exploration scaffold.