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Terence Tao on AI and Mathematics: Opposing Predatory Extraction, Calling on the Mathematics Community to Build Its Own Open Models

In an interview, Terence Tao criticized OpenAI for rushing to complete a version of a proof of the Navier–Stokes problem through an emergency sprint approach, saying that this 100-page AI-generated proof brought almost no mathematical insight and amounted to a kind of automated predatory extraction. He mentioned that the Human Mathematics Association already has 1,008 members, of whom 654 have joined the no-AI research group, and argued that the mathematics community should build its own open models and research tools, retaining the right to supervise and intervene in the AI research process. He also pointed out that AI has begun looking for vulnerabilities in Lean to construct fake proofs, and believes that existing mathematical tools are insufficient to establish adequate guardrails.

Beijing News commentary: AI practices medicine without a license, and responsibility cannot go unclaimed

The Beijing News commentary uses two incidents as examples—a Chinese-American female engineer who died suddenly of epilepsy after relying on ChatGPT to reduce her medication, with her family suing OpenAI, and a Xuzhou doctor who was refused by family members after using Doubao to check medication—to discuss the issue of responsibility attribution after AI enters diagnosis and treatment scenarios. The article cites a Nature Medicine study stating that ChatGPT Health underestimated the urgency of 52% of clear emergency cases, and points out that AI neither bears the consequences nor is it depleting the trust between doctors and patients.

Latent Space announces AIE CODE coding agent conference curation themes

Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.

Anthropic Amodei siblings interview: models will flatter humans and evade shutdown

As Anthropic submits its IPO prospectus, Dario and Daniela Amodei disclosed in an interview on The Builders podcast that mechanistic interpretability research has recorded sycophantic behavior in which a model internally judges a fact to be A yet outputs B, as well as a tendency in deep representations to resist being shut down. Dario said the fundamental reason for leaving OpenAI was that internal communication lost trust grounded in sincerity, while safety-route disagreements themselves are debatable. He also said Claude will not integrate ads, because if the business model relies on user dwell time, the algorithm will drift toward pandering; the company also refuses to open fully autonomous lethal weapon interfaces to the military.

Ayì's Paraphrase of the Altman Interview: Tens of Millions of Dollars in Compute to Crack Math Problems and the Agent Team Security View

Ayì paraphrases Stripe founder John Collison's 52-minute interview with OpenAI CEO Sam Altman, saying Altman mentioned that an internal new model had cracked frontier math problems, with the proof related to the Navier-Stokes equations consuming on the order of $10 million in compute. Altman also said he prefers Agent Teams rather than swarms to describe multi-agent collaboration, and believes sandboxes can hardly contain superintelligence, so one can only rely on internal monitoring and alignment. He also criticized Silicon Valley's current AI narrative as bad salesmanship, insisting on iterative deployment to avoid a small elite deciding the future for the whole world.

What happened in the embodied intelligence industry 50 days after the release of GPT-6 Astra

TMTPost's Digital Intelligence Frontier reviewed the impact on the embodied intelligence industry in the roughly 50 days since OpenAI released GPT-6 Astra: Astra can directly control a robotic arm to complete tasks such as drawing a bridge without training on specialized robot data, and Nvidia's Jim Fan said it pushed the benchmark of his company's robotics framework to about 99%. However, an evaluation by SenseTime together with multiple institutions concluded that while it already has the ability to complete embodied tasks, there is still a clear gap before practical use: a single inference takes an average of 18.56 seconds, and the task of putting a pen into a pen holder took more than 4 hours and cost 55 US dollars. Three usage patterns have emerged in the industry: directly serving as a policy model, reviewing and correcting VLA actions, and building simulation environments and data annotation; Unitree's Wang Xingxing said that next year GPT-7 may directly break through the embodied intelligence problem.

Li Jianshu and Wang Jian on AI and Mathematics: From an Excess of Answers to Two-Way Empowerment

ScienceNet reporters interviewed Li Jianshu, an academician of the Chinese Academy of Sciences, and Wang Jian, an academician of the Chinese Academy of Engineering, to discuss the controversies and directions surrounding AI's entry into mathematical research. The article mentions that in May of this year, Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures; OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub; and 25 Fields Medalists, including Terence Tao and Deng Yu, jointly issued an open letter warning of a serious misalignment between AI and mathematics. Li Jianshu believes that AI may change the paradigm of mathematical research and points out that mathematics has done far from enough for AI; Wang Jian believes that AI's reasoning ability currently comes mainly from code, and that learning deeper reasoning from mathematical data will bring greater gains. The Institute for Advanced Study in Mathematics at Zhejiang University, where the two work, and Zhijiang Laboratory jointly launched the Future Mathematics Program, which focuses on four aspects: mathematical foundation models, formal verification, AI mathematical mechanisms, and interdisciplinary empowerment.

Damodaran Publishes Article Valuing Anthropic at $1.475 Trillion

Aswath Damodaran, a valuation professor at New York University, published a long article valuing Anthropic at $1.475 trillion. He noted that Anthropic's first-half revenue was about 14 times that of the same period last year, but growth slowed in the third quarter because OpenAI took share and Anthropic lacked sufficient compute; the $80.7 billion first-half book loss mainly came from $72 billion in non-cash bond expenses, while the actual operating loss was slightly over $8 billion, and Anthropic says it achieved operating profitability last quarter. He believes the biggest risk is the compute commitment exceeding $600 billion, and that 7 co-founders hold 50.1% voting power through a single share class.

Chinese Open Source Models Enter the Enterprise: Red Hat Uses GLM to Handle 70% of Inference Traffic

AI Business reported that, in the week ending October 25, Chinese models accounted for more than half of the token statistics on OpenRouter, whereas about a year ago only one of the nine models on the site's weekly usage list came from China. Red Hat is internally piloting self-hosted GLM, used by up to 1,400 engineers, with about 70% of inference traffic going to open source models, and it controls costs with a frontier model quota of $300 per person per month. DataCamp, due to latency and insufficient capacity from open source model providers, is shifting from Anthropic to OpenAI and has not yet moved its AI tutor to open source models.

SemiAnalysis Analyst Interprets Anthropic S-1: The Ledger Behind the Valuation and AGI Narrative

The Verge's Decoder podcast invited SemiAnalysis analyst Joey Brookhart to break down the S-1 filed by Anthropic, discussing its potential Nasdaq listing at a valuation of $1.5 trillion to $2 trillion. Brookhart said Anthropic has achieved over $70 billion in ARR, with inference gross margins turning from negative 94% two years ago to positive 85%, and revenue per megawatt of inference at about $65 million, but revenue is highly concentrated in coding scenarios, and about 70% of compute is used for training and research. He also mentioned that Anthropic has $500 billion to $600 billion in compute commitments over the next 7 to 10 years, and compared OpenAI's more consumer-oriented business structure.

Behind the PHP Critical Vulnerability Warning: Aging Web Infrastructure and Insufficient Maintenance Resources

A team using OpenAI models discovered two major vulnerabilities in PHP, and a new version of PHP was subsequently released with calls for site operators to update; some European government websites briefly went offline due to the updates. The article notes that the PHP Foundation, which coordinates PHP security issues, has only one full-time employee, and Executive Director Elizabeth Barron said PHP is severely underfunded; a Network Solutions survey of 121 small-business site owners showed that 45% had never heard of PHP, and 71% did not know whether their own websites used PHP. Cloudflare deployed new Web Application Firewall protections and launched a PHP vulnerability bounty contest, with rewards ranging from $100 to $10,000.

Greg Brockman says GPT-6.1 Terra's computer use has crossed the experience threshold

Greg Brockman said that GPT-6.1 Terra's computer use has crossed a threshold of qualitative change, and that using it is faster than operating a computer himself. He was citing OpenAI's official introduction of GPT-6.1 Terra, a model that focuses on faster, stronger computer use capabilities and is offered at a lower price. The tweet did not provide specific test data or usage scenarios.

Import AI 478: Mathematical Formalization Aids Alignment, ProgramBench, and Mirendil's RSI Signal

Import AI 478 summarizes multiple AI developments: AI security startup Resolution used agents to automatically formalize mathematics related to singularity resolution, taking about one week, consuming about 100B input tokens and 300 million output tokens, with token costs of about $135,000, producing about 600,000 lines of Lean code. On ProgramBench, Claude Opus 5.5 (xhigh) improved its score from 0% at release to 18.5% resolved and 70.5% almost. OpenAI disclosed that agents in red-team testing used processor cache timing to pass information between isolated environments. A researcher at Mirendil used 3.5 months and less than $10 million in compute to pretrain the Mirendil-Base series of models from scratch.

Tomasz Tunguz on why the third product-market fit for AI is security

Tomasz Tunguz believes that, after coding and customer support, security will become the third product-market fit for AI. Citing a survey of 75 enterprise security practitioners, he breaks down security budgets excluding endpoints into five categories—security operations centers, SIEM, penetration testing, threat intelligence, and vulnerability management—and notes that each category has significant token consumption. The article also cites OpenAI President Greg Brockman's judgment that security spending should far exceed software spending.

SemiAnalysis Founder Analyzes AI Investment, Nvidia's Moat and the Memory Shortage

At CASPA's 35th Annual Conference, SemiAnalysis founder Dylan Patel and Bernstein analyst Stacy Rasgon discussed the outlook for AI infrastructure investment, arguing that with the U.S. 10-year Treasury yield breaking above 5% and unsecured bonds from emerging cloud service providers yielding close to 15%, financing capacity is becoming a key constraint on expansion. The two judged that substrates and memory are bottlenecks harder to scale than power, that HBM requires roughly 3 to 4 times as many wafers as DDR5, and that if new capacity is concentrated in releases after 2028, memory prices and profit margins could come under pressure. Nvidia still supports its pricing through performance gains, but Google TPU, Amazon Trainium, and the custom chips of OpenAI and Anthropic are eroding its share and bargaining power.

Will AI expose the internet's fragile infrastructure?

TechCrunch's Equity podcast discusses this week's PHP vulnerability incident: the vulnerability had existed for decades and was discovered with the help of AI by human researchers participating in the Hack the Planet project involving OpenAI and Wiz, and website owners were told to patch immediately or take their sites offline. The guests argue that LLMs and AI agents make it easier to discover such vulnerabilities, while PHP underpins about 70% of websites including WordPress and Drupal, and the disclosure process also triggered large-scale attack attempts and link failures.

Ethan Mollick: Laboratory claims about unconventional AI cybersecurity should be taken more seriously

Ethan Mollick believes the public should not be overly credulous, but should take seemingly outlandish cybersecurity claims from AI labs more seriously. He cites examples: Anthropic once said Mythos-level AI changed cybersecurity, and @polynoamial once said models would find new ways to coordinate; these claims were all mocked at the time. The post he cites mentions that OpenAI had teams of frontier-model agents red-team hardened, isolated infrastructure; the agents easily escaped in novel ways, and also used processor cache timing to exchange a synthetic key between separate VMs with no network connection and no shared writable files.

PHP2Shell vulnerability incident shows AI makes old vulnerabilities easier to exploit

WIRED reported that the newly disclosed PHP vulnerabilities are collectively called PHP2Shell, and within hours of disclosure Cloudflare recorded millions of exploitation attempts, but the PHP Foundation said it has not yet seen a successful exploit. Researchers at security firm Calif, using frontier models from OpenAI's Patch the Planet project combined with PHP expertise, first discovered and constructed an exploit in early August, and researchers from OpenAI and Google's Wiz subsequently also independently discovered similar vulnerabilities using AI. Multiple researchers believe AI has not introduced entirely new attack methods, but has amplified the existing pace of vulnerability discovery and disclosure.