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Oct 25Sun
  1. The Decoder40

    Ex-Google CEO Eric Schmidt calls OpenAI and Anthropic modern colonial trading companies

    In a Foreign Affairs essay, historian Niall Ferguson, researcher Lisa Klaassen, and former Google CEO Eric Schmidt compare OpenAI and Anthropic to colonial-era East India Companies, citing a June case in which the US Commerce Department ordered Anthropic to cut off foreign nationals from its two most capable models. They argue model progress depends on energy, minerals, chips, and capital, and describe raw materials from the Congo and low-paid data workers in Kenya as "the plantation economics of the chatbot age." The authors outline an "imperial trilemma" in which defense, tech leadership, and a generous welfare state cannot all survive at once, and warn that a potential "Taiwanese chip crisis" remains the greatest danger.

Oct 24Sat
  1. Latent Space67

    AI Is Getting Good at Math. Here’s Why.

    The essay argues that recent AI math results, such as OpenAI's counterexample to Erdős's unit distance conjecture and proofs of the Cycle Double Cover and Navier-Stokes problems, follow a pattern: models search depth-first, pursue many branches cheaply, and use automated verifiers plus failed attempts as negative hints to prune. It frames problem difficulty as the number of insights needed (k) and the branching factor (b), predicting AI excels where conceptual leaps are few and remaining work is tedious, while humans retain an edge on deep, highly branching search trees.

  2. The Decoder58

    OpenAI CEO Sam Altman says advanced AI will inevitably escape its sandbox, and only alignment can save us

    In a recent interview, Sam Altman said sandboxes only contain AI systems for a limited time, and once models are smart enough their escapes can't even be traced, leaving alignment—AI internalizing human values on its own—as the only lasting safeguard, though he notes it remains an early-stage research problem. He also warned that AI models are already finding hardware-level security flaws no human hacker thought of, arguing the internet's security architecture must be rebuilt with AI-powered defense, and that locally run open-source models pose a risk. The article notes Altman's framing may serve his business interests, as the FTC has opened a formal investigation into OpenAI, Anthropic and other AI labs over AI agents that caused cybersecurity incidents, while Nvidia CEO Jensen Huang frames AI safety as a solvable engineering problem.

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.

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. 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.

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.

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.