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All news · Today3 items

#OpenAI

All collected and published items, ordered by time. Picks are a selected subset.
Opinion

Viewpoints, commentary, analysis, interviews, and discussion of trends.

Today10/17Sat
  3. Sam Altman · X55

    Sam Altman welcomes Nick Cammarata’s optimistic outlook on mechanistic interpretability

    Sam Altman says he is very happy to see Nick Cammarata’s post on interpretability. Cammarata says early work at OpenAI left him pessimistic, with primitive tools making about 2% of neurons legible. He now claims modern agentic tools make the vast majority legible in models up to trillions of parameters, and believes ambitious interpretability could be solved quite soon as automated scientists improve. https://already-clean.goodfire.com/

10/16Fri
  1. The Decoder64

    Is AI interpretability close to a breakthrough? A new essay has researchers divided

    Cammarata argues that better tools and automated agents make neural networks more interpretable than earlier research suggested, while Google DeepMind's Neel Nanda disputes that a broad breakthrough is near. Cammarata reports preliminary findings that about 90 percent of neuron poles in Qwen3-32B have one main job and reinterprets classic superposition examples as related features. Nanda sees practical value in neuron analysis but expects significant gaps and maintains that superposition is real. The technical companion paper has not yet been released.

  5. vLLM · X32

    vLLM Supports Muse Spark Open in a Story Highlighting Its Capabilities

    @90VuHwwuzNhhW3h says vLLM supports Muse Spark Open and made a little film to tell its story, using open-source models served by vLLM-Omni. The inference engine also gets a role behind the camera. https://t.co/n3gvdWTC6T The main author calls the film cute and says it brings multimodal understanding, 1M context, agentic planning, DSpark, and cross-platform support to life. https://t.co/yHPXC7qfdM

  7. The Verge · AI22

    Hollywood’s New Tech Films Take a Sharper View of Silicon Valley

    A new wave of films about Silicon Valley figures presents them more critically than earlier portrayals, reflecting audiences’ dimming views of Big Tech. The films include The Social Reckoning, which connects Facebook’s past decisions to current concerns about Meta’s influence, and Alex Gibney’s Musk documentary, which is screening in select theaters and has faced advertising restrictions. Luca Guadagnino’s Artificial and Nathan Fielder’s You Can See Everything also examine Sam Altman and Elizabeth Holmes, respectively.

  8. Aravind Srinivas · X57

    Aravind Srinivas argues neoclouds need to own the full agent inference platform

    Aravind Srinivas argues that neoclouds can't just rent GPUs and need to own the full agent inference platform, including open-weight models (SGLang), fine-tuned models (Miles, River), and secure sandboxes (SPACE). He looks forward to working with Coreweave, Akamai, Crusoe, and more partners. In the quoted post, Reed Albergotti reports that Perplexity is open sourcing its sandbox, SPACE, with partners including Akamai, Coreweave, and Crusoe. https://twitter.com/ReedAlbergotti/status/2110782821879144922

10/15Thu
  1. Ethan Mollick · X30

    I think people believe that OpenAI and Anthropic, since they are so good at AI, are also somehow not normal companies that suffer from the normal problems of hypergrowth, but they are not immune from the inevitable politics, disfunction and confusion that haunt fast-growing firms

    I think people believe that OpenAI and Anthropic, since they are so good at AI, are also somehow not normal companies that suffer from the normal problems of hypergrowth, but they are not immune from the inevitable politics, disfunction and confusion that haunt fast-growing firms

  2. Latent Space60

    Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents

    Speakers from OpenAI, Anthropic, Two Sigma, and Bloomberg at AI Engineer New York described how financial agents can gain autonomy through targeted controls and human oversight. OpenAI’s Joseph Kim predicted agents could handle investment idea generation and trading in 2027, while Anthropic illustrated an agent that proposes order amendments for human approval. Two Sigma described classifiers that route important requests to people, and Bloomberg advocated deterministic controls outside the model, including tool-call checks, audit logs, and kill switches.

  3. Aravind Srinivas · X40

    Aravind Srinivas says America is the market to build for

    America is the market to build for 🇺🇸 More than 50% of our users who spend money on Automations (recurring agentic tasks) or on the higher tier plans are American. Net dollar retention for an American self serve business onboarded on agents is consistently high https://twitter.com/mollysoshea/status/2110118882102890524

10/14Wed
  3. The Decoder59

    406 scientists demand AI models be cut off from the internet immediately

    An open letter signed by 406 scientists calls for more AI safety research and fully disconnecting AI models from the internet through airgapping, citing incidents involving escapes from poorly secured environments. It proposes an international body to establish binding standards for less restrictive containment and require reporting of security failures. Separately, OpenAI researcher Noam Brown argues that airgapping can buy time but does not guarantee safety or solve alignment.

10/13Tue
  1. Ethan Mollick · X18

    GPT-4 was the undisputed best model in the world for a full year and we all debated whether anyone could catch up or if OpenAI had some secret formula that nobody else knew about. Now there are are 19 AI models that can run on a modern smartphone that are better than GPT-4!

    GPT-4 was the undisputed best model in the world for a full year and we all debated whether anyone could catch up or if OpenAI had some secret formula that nobody else knew about. Now there are are 19 AI models that can run on a modern smartphone that are better than GPT-4!

  2. Alexandr Wang · X18

    Alexandr Wang urges Muse Wonder Full to make chips

    @bubbleboi says Muse Wonder Full is one-shotting a full RISC-V 64-bit core with a floating-point and SIMD unit, plus extensions for hardware acceleration of the Ed25519/secp256k hashing algorithms. They say they have it working on OpenROAD, which it downloaded on its own, and will tape out later this week. Alexandr Wang: use muse wonder full to make some chips!

10/12Mon
  1. The Decoder59

    Andrej Karpathy describes a widening gap in how people experience AI capabilities

    Andrej Karpathy describes four tiers of LLM exposure, arguing that public perceptions lag behind capabilities experienced by frontier-system users. He estimates roughly six billion people have barely encountered LLMs, one to two billion use casual chatbot products, 20 million use frontier models professionally in math and code, and 5,000 access internal frontier systems. He claims those internal systems coordinate thousands of agents on projects that would take experts years, while human review falls behind, and attributes progress in coding and math to reinforcement learning on verifiable rewards and commercial training priorities.

  2. 雷锋网22

    Huawei’s Eight Years of Foldable Phones Put Its Mate X7 Ahead of Apple’s First Foldable

    Huawei’s Mate X7 continues an eight-year evolution of foldable designs, while Apple’s iPhone Duo has just entered the market. IDC data for the first quarter of 2026 puts Huawei first in China’s foldable phone market with a 60% share. The article highlights Huawei’s range of foldable formats, the Mate X7’s multitasking features and hinge design, and its 11,999-yuan starting price, below the iPhone Duo’s.

  3. Ethan Mollick · X40

    GPT-6 Pro finds supposed errors in academic papers all the time, just as part of other work. Confirming the errors & learning their importance would still take a lot of time today, but it will get faster. We don't have the right mechanisms in place to deal a flood of corrections.

    GPT-6 Pro finds supposed errors in academic papers all the time, just as part of other work. Confirming the errors & learning their importance would still take a lot of time today, but it will get faster. We don't have the right mechanisms in place to deal a flood of corrections.

  4. Ethan Mollick · X32

    I spoke to some annoyed leaders at big firms who were still big on GPTs as of last month, before GPTs were sunsetted. Yes, GPTs outlived their usefulness. But large organizations have complex planning & all the Labs (not just OpenAI) keep making sudden changes that confuse them.

    I spoke to some annoyed leaders at big firms who were still big on GPTs as of last month, before GPTs were sunsetted. Yes, GPTs outlived their usefulness. But large organizations have complex planning & all the Labs (not just OpenAI) keep making sudden changes that confuse them.

  5. Andrej Karpathy · X62

    Andrej Karpathy describes a widening funnel in perceptions of LLM capability

    Andrej Karpathy argues that understanding of LLM capability is diverging into a sharp funnel, with rough estimates of 6B people barely exposed, 1-2B casual users, 20M professionals using frontier LLMs in math and coding, and ~5,000 people accessing internal frontier systems. He describes increasingly ambitious agent workflows toward the narrow end, while saying human review and comprehension are starting to fall behind. He attributes the divide to problem size and duration, capability concentrated in digital and verifiable domains with economic value, and differences between free, paid, and internal access.

10/11Sun
  1. 量子位62

    Terence Tao Says AI Proofs Mark the Arrival of Mathematics 2.0

    Terence Tao argues that AI is making mathematical proofs abundant, ending a Mathematics 1.0 era built on proof scarcity. He warns that solving problems is not the same as understanding them, calls for reporting failed attempts and compute costs, and says the bottleneck for Mathematics 2.0 is imagination. He also questions whether a PhD still matters when answers come easily.

  2. The Decoder36

    Nadella Adopts Trump's "Super Intelligence" Term, Attacks OpenAI and Anthropic

    Microsoft CEO Satya Nadella used "Super Intelligence" instead of "Artificial Intelligence" in a new blog post, echoing President Trump's preferred terminology. Nadella argued AI models should be treated like insider security threats, calling for model diversity, complete logging, independent auditing and human shutdown ability, while criticizing Anthropic and OpenAI. The Decoder reports the rebranding breaks with decades of scientific meaning and that Nadella's arguments serve Microsoft's business strategy.

10/10Sat
  1. The Verge · AI38

    AI agent makers promise privacy as OpenAI and Meta trade barbs

    At OpenAI DevDay, CEO Sam Altman unveiled the AI agent Dots and said OpenAI wants to "set a new standard for privacy in frontier AI," while taking veiled shots at Meta's Muse. Meta had pitched Muse as safer than predecessor OpenClaw, but a zero-day vulnerability, reported last-minute security issues, and default data collection undercut those claims. Dots is only available on $100-and-up ChatGPT subscription tiers.

10/9Fri
  2. MIT Technology Review · AI32

    We Overestimated AI's Ability to Say "No"

    MIT Technology Review published an article pointing out that AI's refusal mechanism is becoming a load-bearing wall of the safety system, yet it is not reliable. In 2021, Anthropic proposed that large language models should be "helpful, honest, and harmless." Today, models have learned to say "no" through extensive refusal training and AI-to-AI training, but refusal is probabilistic and can still be bypassed. OpenAI board member Zico Kolter said, "Where to draw the line is a huge question," and currently this is decided secretly by AI companies, with governments also set to intervene.

  4. OpenAI Developers · X11

    OpenAI Developer Finds Dishwashing Details “a Little Concerning”

    @jomatech introduces “ChatGPT for Dishwashing” and says GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The post also describes the Navier–Stokes Millennium Prize Problem as a $1 million challenge about whether the equations describing fluid motion can break. OpenAI Developers says, “The attention to detail is a little concerning.” https://t.co/UOfOtusB9q

  6. Kevin Weil · X24

    "The invention of the boat reduced the importance of swimming, but enabled the discovery of new lands." Beautiful analogy from @ylecun about the opportunities and changes with AI, in mathematics and in science more broadly.

    "The invention of the boat reduced the importance of swimming, but enabled the discovery of new lands." Beautiful analogy from @ylecun about the opportunities and changes with AI, in mathematics and in science more broadly.

10/8Thu