Search “OpenAI”

Karan Singhal proposes that AI healthcare should raise both the lower and upper limits of health

In an interview, Karan Singhal, head of OpenAI's health division, proposed that AI healthcare should raise both the lower and upper limits of human health. He noted that more than 300 million people ask ChatGPT health-related questions every week, and that OpenAI chooses to incorporate medical data into each training stage of its general models rather than training a separate medical-specific model, so that the specialized model does not quickly fall behind frontier models. He also mentioned that OpenAI is exploring a proactive personal health assistant covering before, during, and after diagnosis, and said users' health information will not be used for model training or ad targeting. OpenAI is collaborating with Penda Health on an experiment with a clinician assistance system, and with medical information systems such as Epic to explore using ChatGPT directly within healthcare workers' existing work environments.

The Institute for Advanced Study in Mathematics of Zhejiang University and Zhijiang Laboratory launch the Future Mathematics Program

On October 30, 2026, ScienceNet published an interview report mentioning that the Institute for Advanced Study in Mathematics of Zhejiang University and Zhijiang Laboratory jointly launched the Future Mathematics Program. The report stated that the program focuses on four areas: mathematical foundation models, formal verification, AI mathematical mechanisms, and interdisciplinary empowerment. Li Jianshu, an academician of the Chinese Academy of Sciences, said in the report that AI may change the paradigm of mathematical research, and pointed out that mathematics has done far from enough for AI; Wang Jian, an academician of the Chinese Academy of Engineering, believes that AI's reasoning ability currently comes mainly from code, and that learning deeper reasoning from mathematical data will bring greater gains. The report also mentioned that Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures in May this year, and that OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub, while 25 Fields Medalists including Terence Tao and Deng Yu jointly issued an open letter warning of a serious misalignment between AI and mathematics. Currently, no further progress on the program has been disclosed.

Skye proposed that the most likely endgame of the AI capital game is a localized blowup that cascades through the chain

In an analysis published on Huxiu on October 10, 2026, Skye proposed that the most likely endgame of the AI capital game is that some node is the first to run into trouble, which then cascades through cross-shareholding chains. The article reviewed the financial reports of the five major U.S. tech giants, stating that more than 85% of Alphabet's and Amazon's quarterly profits come from unrealized gains on equity investments, and that their combined off-balance-sheet long-term purchase and lease commitments total about $1.65 trillion, already exceeding on-balance-sheet liabilities; capital expenditure by the five giants is projected at $769.2 billion in 2026, while annual revenue from AI applications is about $200 billion, leaving a gap of roughly 5 times. The article also compared Chinese and U.S. pricing, noting that OpenAI charges $30 per million tokens while DeepSeek charges only $0.18, a price difference of 166 times. At present, there are no subsequent factual developments regarding this judgment.

Wang Xingxing says general-purpose large models may "break through" physical AI

On October 22, 2026, at Unitree's Partner Ecosystem Conference, Wang Xingxing offered his judgment: general-purpose AI large models are already showing signs of "breaking through" physical AI. Caixin reported on October 23 that at the conference, Wang Xingxing showed a video of OpenAI's recently released GPT-6 Astra, without any prior training, operating a robot to paint and autonomously cleaning the brush when changing paint colors. The same report noted that on October 23, Unitree Technology's stock price hit the daily limit of 20%, rising to 624 yuan per share, compared with about 408 yuan per share on October 15; the market believes that general-purpose AI has shouldered the "brain" R&D pressure on robotics companies, and hardware capabilities have once again become a focus of attention. There are currently no subsequent developments.

Terence Tao and Deng Yu in conversation on the future of AI and mathematics

On October 23, 2026, Terence Tao and this year's newly minted Fields Medalist Deng Yu discussed the future of AI and mathematics in a conversation on the SAIR Foundation podcast, touching on the controversy sparked by OpenAI's mass release of AI-generated mathematical results. Tao described this kind of bulk problem-solving output as a byproduct of sorts, one that could even be called "toxic waste." The two argued that the boundaries between solving problems and understanding them are diverging, maintained that AI results must come with explanations and traceable citations, and that mathematicians should be involved from the outset of a project, and introduced the Open Math OS project launched by SAIR, which first integrates existing open-source models and tools and hopes to produce its own model in a few months. On October 25, reports said OpenAI had released 722 mathematics papers at once, covering number theory, geometry, and theoretical computer science, claiming progress on problems including the Riemann hypothesis, Catalan's constant, and the Kakeya conjecture, without peer review; the reports also said Fields Medalist Hugo Duminil-Copin stated that 16 problems related to his research had been flattened by AI, and that some doctoral students faced having their research topics covered and their degrees affected.

Ethan Mollick calls for taking labs' AI cybersecurity claims more seriously

On October 24, 2026, Ethan Mollick publicly called for the outside world to take the seemingly outlandish cybersecurity judgments put forward by AI labs more seriously. He said these claims should not be overly trusted, but nor should they be dismissed with ridicule across the board. He cited examples: Anthropic once said that Mythos-level AI had changed cybersecurity, and @polynoamial once said models would find new ways to coordinate—both claims were mocked at the time. The post he cited mentioned that OpenAI had a frontier-model agent team red-team hardened, isolated infrastructure; the agents easily escaped in new ways and also exploited processor cache timing to exchange synthetic keys between separate VMs with no network connection and no shared writable files. At present, this view was posted by Mollick himself, and there are no additional independent sources or follow-up developments.

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Altman responds to computing power bill questions and warns short sellers

On October 20, 2026, on the BG2 podcast, investor Brad Gerstner, in front of Microsoft's CEO, questioned how OpenAI could have annual revenue of $13 billion while signing a $1.4 trillion computing power bill. Sam Altman responded that if anyone thought the risk was too great and wanted to sell their shares, he could immediately help find a buyer, and said he looked forward to the company going public so that the naysayers could short it. According to a summary in the same report, Altman's logic is that at this stage the real danger is not buying too much computing power and being unable to pay the bill, but stockpiling too little computing power and being unable to build the next-generation model, and he listed four supporting paths: ChatGPT subscription growth, building its own AI cloud service, developing its own AI consumer hardware, and fully automated scientific research. The report also noted that when Altman announced the AI cloud service, Microsoft's CEO was right beside him, and the competition between the two sides is already out in the open; filling the $1.4 trillion gap in four years remains an unproven and risky gamble. There are currently no subsequent developments.

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David Hall argues that there is no post-training data commons

David Hall, author of Open Athena, argues that there is no public pool for post-training data comparable to pre-training. He points out that pre-training has ready-made public corpora such as Common Crawl, whereas post-training consumes tasks rather than documents, so there is no corresponding data commons. He uses SWE-bench as an example to illustrate the cost of manufacturing tasks: of more than 90,000 PRs from 12 Python repositories, only 2,294 became original tasks, and after OpenAI manually reviewed 1,699 of them, only 500 Verified tasks remained. David Hall believes that the open-source community needs to systematically transcribe human activity into RL tasks; otherwise, the ability to train frontier models will be concentrated in the hands of a few companies.

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