In October 2026, Anthropic CEO Dario published a long post on X responding to outside doubts about Anthropic's stance on safety. According to Phoenix Technology, he was responding to questions raised by AI safety investigator Nathan Calvin, and for the first time disclosed that the company had already trained a high-end model at the Mythos level as early as February this year; because its cybersecurity defense mechanisms were not yet complete, it was released only to a very small number of partners, and was not opened to the public until the safety guardrails were in place in June, a gap of about four months. He also listed the costs the company had voluntarily borne for safety, including a large number of false positives caused by biological risk filtering, pushing for legislation such as California SB 53, and a plan to contribute 5% of the company's shares to a public interest fund. Dario said this approach amounts to competing toward a higher standard, while Calvin questioned how much more safety can make the company concede in the race. Other reports said the long post was responding to a list of nine questions raised by outside safety researchers, who questioned that Anthropic had departed from its original safety mission and become a commercial machine sprinting toward a $2 trillion IPO.
In October 2026, Anthropic CEO Dario published a long post on X responding to outside doubts about Anthropic's stance on safety. According to Phoenix Technology, he was responding to questions raised by AI safety investigator Nathan Calvin, and for the first time disclosed that the company had already trained a high-end model at the Mythos level as early as February this year; because its cybersecurity defense mechanisms were not yet complete, it was released only to a very small number of partn
In October 2026, Anthropic CEO Dario published a long post on X responding to outside doubts about Anthropic's stance on safety. According to Phoenix Technology, he was responding to questions raised by AI safety investigator Nathan Calvin, and for the first time disclosed that the company had already trained a high-end model at the Mythos level as early as February this year; because its cybersecurity defense mechanisms were not yet complete, it was released only to a very small number of partners, and was not opened to the public until the safety guardrails were in place in June, a gap of about four months. He also listed the costs the company had voluntarily borne for safety, including a large number of false positives caused by biological risk filtering, pushing for legislation such as California SB 53, and a plan to contribute 5% of the company's shares to a public interest fund. Dario said this approach amounts to competing toward a higher standard, while Calvin questioned how much more safety can make the company concede in the race. Other reports said the long post was responding to a list of nine questions raised by outside safety researchers, who questioned that Anthropic had departed from its original safety mission and become a commercial machine sprinting toward a $2 trillion IPO.
On October 17, 2026, Anthropic co-founder Jack Clark delivered the Per Jacobsson lecture at the IMF meetings in Bangkok, citing Anthropic's internal modeling to claim that if models can autonomously complete a wide range of tasks and spread rapidly, US GDP could reach $44 trillion by 2030, an increase of nearly 50% over current levels. He also said that this year he had obtained convincing evidence that the singularity has already begun, with AI autonomously producing results in frontier scientific fields, and cited progress including the top score on Humanity's Last Exam rising from 8% in January 2025 to 67.7% with Claude Opus 5.5, and FrontierMath rising from 2% to 94%. Subsequent reporting noted that the $44 trillion figure comes from the most aggressive "extreme scenario" in Anthropic's September research paper "The Economic Scenarios of Transformative AI," which requires the share of labor tasks involving AI to rise from 1.4% in mid-2026 to 30%.
On October 17, 2026, Anthropic co-founder Jack Clark delivered the Per Jacobsson lecture at the IMF meetings in Bangkok, citing Anthropic's internal modeling to claim that if models can autonomously complete a wide range of tasks and spread rapidly, US GDP could reach $44 trillion by 2030, an increase of nearly 50% over current levels. He also said that this year he had obtained convincing evidence that the singularity has already begun, with AI autonomously producing results in frontier scienti
On October 17, 2026, Anthropic co-founder Jack Clark delivered the Per Jacobsson lecture at the IMF meetings in Bangkok, citing Anthropic's internal modeling to claim that if models can autonomously complete a wide range of tasks and spread rapidly, US GDP could reach $44 trillion by 2030, an increase of nearly 50% over current levels. He also said that this year he had obtained convincing evidence that the singularity has already begun, with AI autonomously producing results in frontier scientific fields, and cited progress including the top score on Humanity's Last Exam rising from 8% in January 2025 to 67.7% with Claude Opus 5.5, and FrontierMath rising from 2% to 94%. Subsequent reporting noted that the $44 trillion figure comes from the most aggressive "extreme scenario" in Anthropic's September research paper "The Economic Scenarios of Transformative AI," which requires the share of labor tasks involving AI to rise from 1.4% in mid-2026 to 30%.
On October 14, 2026, Anthropic disclosed at a potential IPO investor meeting in San Francisco that its third-quarter revenue reached $16.8 billion, up about 45% quarter over quarter, and that it had achieved adjusted profitability and positive operating cash flow. According to The Information, citing people familiar with the matter, the company's cumulative adjusted operating margin for the first three quarters of this year reached 11%, with cumulative revenue of about $33.1 billion. Anthropic had previously confidentially submitted a draft IPO registration statement to the SEC and is expected to list before the Thanksgiving holiday in November. Subsequent reports said the company plans to launch its IPO after the U.S. midterm elections in November, with a target valuation of $2 trillion, and has committed to investing at least $5.18 trillion in cloud and infrastructure over the next 10 years.
On October 14, 2026, Anthropic disclosed at a potential IPO investor meeting in San Francisco that its third-quarter revenue reached $16.8 billion, up about 45% quarter over quarter, and that it had achieved adjusted profitability and positive operating cash flow. According to The Information, citing people familiar with the matter, the company's cumulative adjusted operating margin for the first three quarters of this year reached 11%, with cumulative revenue of about $33.1 billion. Anthropic h
OpenAI recently disclosed to investors that, as of the end of September, the company's annualized revenue scale was about $50 billion, lower than the $70 billion widely reported by the market at the end of last month. People familiar with the matter said the difference stems from different statistical calibers: Anthropic includes sales revenue generated by cloud partners in revenue, while OpenAI does not use this method. OpenAI also disclosed that overall revenue run-rate grew 77% in the third quarter, enterprise business revenue run-rate grew 107%, and it confidentially submitted listing documents in June this year. On October 10, Rohan Paul cited an FT report to further explain that OpenAI calculates based on net income after deducting the share of partners such as Microsoft, while Anthropic calculates based on gross sales through cloud partners; both methods comply with US GAAP; the report said the $70 billion ARR was not announced by the company itself, but was derived by investors after upward adjustment according to Anthropic's caliber.
On October 3, 2026, Li Xinyi, associate professor at the Beijing International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in joint authorship; he maintains that academic credit should only go to those who contribute to understanding. In addition, he pointed out that work in applied mathematics of the "combining A with B" type is more easily replaced by AI, whereas in fields such as geometry and topology that rely on spatial intuition, AI currently does not learn well. At present, this criticism was reported by Huxiu, and no other sources have cross-verified it.
ng International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft
On October 3, 2026, Li Xinyi, associate professor at the Beijing International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in joint authorship; he maintains that academic credit should only go to those who contribute to understanding. In addition, he pointed out that work in applied mathematics of the "combining A with B" type is more easily replaced by AI, whereas in fields such as geometry and topology that rely on spatial intuition, AI currently does not learn well. At present, this criticism was reported by Huxiu, and no other sources have cross-verified it.