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OpenAI discloses annualized revenue of about $50 billion to investors
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.
, 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 Octob
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AI data companies are fierce: UniPat reportedly backed by Alibaba and Tencent, valued at $2.5 billion
According to a report by 36Kr's Silicon Star Pro, UniPat, a data company founded less than a year ago, is reportedly in talks to raise about $300 million, led by Alibaba with participation from Tencent and Sequoia China, at a post-money valuation of $2.5 billion. The article says a single piece of data with user behavior trajectories can be quoted at 1,000 to 3,000 yuan, while work-scenario data with a complete operation process can sell for around 10,000 yuan, and Mercor estimates that only 2% to 5% of the data frontier labs need has been completed so far. As model vendors tighten procurement, calculate ROI and run ablations on vendor data, the industry has already seen problems such as blacklisting data vendors and data quality being hard to verify.
According to a report by 36Kr's Silicon Star Pro, UniPat, a data company founded less than a year ago, is reportedly in talks to raise about $300 million, led by Alibaba with participation from Tencent and Sequoia China, at a post-money valuation of $2.5 billion. The article says a single piece of data with user behavior trajectories can be quoted at 1,000 to 3,000 yuan, while work-scenario data with a complete operation process can sell for around 10,000 yuan, and Mercor estimates that only 2%
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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.
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Research reveals why AI agents fail silently, and how to measure such failures
Unite.AI, reviewing multiple studies, points out that when LLM agents make errors they often still return fluent answers and success status codes, making them hard for conventional monitoring to detect. A prompt-format experiment at ICLR 2024 showed that merely changing delimiters, spaces, and capitalization caused LLaMA-2-13B's accuracy on the same task to fluctuate by as much as 76 percentage points; in τ-bench, a GPT-4o agent solved about 61% of retail tasks in a single attempt, but the proportion of 8 consecutive fully correct runs dropped to about 25%. The article recommends putting prompts and tool descriptions under version control, repeating tests multiple times, recording the tools' actual returns in traces, and validating LLM-as-a-judge scores with human annotations.
Unite.AI, reviewing multiple studies, points out that when LLM agents make errors they often still return fluent answers and success status codes, making them hard for conventional monitoring to detect. A prompt-format experiment at ICLR 2024 showed that merely changing delimiters, spaces, and capitalization caused LLaMA-2-13B's accuracy on the same task to fluctuate by as much as 76 percentage points; in τ-bench, a GPT-4o agent solved about 61% of retail tasks in a single attempt, but the propo
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What happens to work when your colleagues are AI agents
The Guardian interviewed several San Francisco startups, describing the actual experience after AI agents took over tasks such as scheduling, coding, marketing, and recruiting as "colleagues." Anything has 15 human employees and about 150 agents; Lindy cut one-third of its staff and used agents to fill the roles; a McKinsey survey shows that about 40% of large organizations used AI agents in some form between May 2025 and June 2026. Employees report spending large amounts of time correcting agents' mistakes, and customers and job applicants are resistant to the way agents communicate.
The Guardian interviewed several San Francisco startups, describing the actual experience after AI agents took over tasks such as scheduling, coding, marketing, and recruiting as "colleagues." Anything has 15 human employees and about 150 agents; Lindy cut one-third of its staff and used agents to fill the roles; a McKinsey survey shows that about 40% of large organizations used AI agents in some form between May 2025 and June 2026. Employees report spending large amounts of time correcting agen
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Partnership on AI research: Agent frameworks generally lack observability records
Madhulika Srikumar and Claire Leibowicz of Partnership on AI write that current agent frameworks cannot reliably generate the key observability records needed for safe deployment. Together with colleagues from academia, civil society, and industry, the authors studied the four most widely used agent frameworks and found that signals such as persistent Agent IDs and human approval records are missing, making it difficult to determine which agent did what and whether it exceeded its authority. The article recommends using OpenTelemetry to fill in the records of "what happened," and calls on framework developers, enterprises adopting agents, and regulators to each bear their respective responsibilities.
Madhulika Srikumar and Claire Leibowicz of Partnership on AI write that current agent frameworks cannot reliably generate the key observability records needed for safe deployment. Together with colleagues from academia, civil society, and industry, the authors studied the four most widely used agent frameworks and found that signals such as persistent Agent IDs and human approval records are missing, making it difficult to determine which agent did what and whether it exceeded its authority. The
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Artificial Analysis v5 and the Benchmarker's Dilemma
Nathan Lambert reviews the newly released Intelligence Index v5 from Artificial Analysis, which weights knowledge work, coding, science, and general each at 25%, and presents new model rankings. He notes that 8 closed-source models in the rankings lead the best open-source model, Kimi K3.1 Pro, and argues that the new index leans more toward measuring where frontier models are heading, potentially underestimating the role of open-source models in the economy. The article also discusses the pros and cons of shifting to private benchmarks, and how the business model of benchmark organizations charging model vendors to verify results affects credibility.
Nathan Lambert reviews the newly released Intelligence Index v5 from Artificial Analysis, which weights knowledge work, coding, science, and general each at 25%, and presents new model rankings. He notes that 8 closed-source models in the rankings lead the best open-source model, Kimi K3.1 Pro, and argues that the new index leans more toward measuring where frontier models are heading, potentially underestimating the role of open-source models in the economy. The article also discusses the pros
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Day 130 of Xiaowei's gray release: WeChat breaks mini programs into parts and hands them to AI
It has been 130 days since WeChat's native AI assistant "Xiaowei" launched. Its entry points have expanded from the 16 officially disclosed to 28 measured by third parties, covering chat, search, official accounts, Channels, and PC. Through the developer integration guide released on June 8, WeChat requires developers to break mini programs into atomic interfaces and atomic components and write SKILL documentation; mini programs that have not integrated will be unable to be recommended and invoked by WeChat AI. The article also mentions that the development model must pass platform evaluation (80 points or above in the three items of service delivery, interaction experience, and performance quality, and 60 points or above in scenario coverage) to be eligible for invocation, while the invocation ranking rules have not yet been made public.
It has been 130 days since WeChat's native AI assistant "Xiaowei" launched. Its entry points have expanded from the 16 officially disclosed to 28 measured by third parties, covering chat, search, official accounts, Channels, and PC. Through the developer integration guide released on June 8, WeChat requires developers to break mini programs into atomic interfaces and atomic components and write SKILL documentation; mini programs that have not integrated will be unable to be recommended and invok
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Behind the Kimi K3.1 Pro quota controversy: the stronger the model, the tighter the compute
Moonshot AI released Kimi K3.1 Pro on October 24, scoring 65.7% on Terminal-Bench 4.0, but users of the 199-yuan and 699-yuan plans complain that quotas are insufficient, and some developers estimate that the API value of the old plans is about 1.94 times that of the new plans. K3.1 Pro's per-million-Token input and output prices dropped from K3's 20 yuan and 100 yuan to 13 yuan and 68 yuan, while complex Agent tasks can consume millions of Tokens. A CAICT report shows that in June 2026, domestic daily average Tokens were about 500 trillion, nearly four times the figure in December 2025; the author believes that the compute shortage will still constrain independent model companies over the long term.
Moonshot AI released Kimi K3.1 Pro on October 24, scoring 65.7% on Terminal-Bench 4.0, but users of the 199-yuan and 699-yuan plans complain that quotas are insufficient, and some developers estimate that the API value of the old plans is about 1.94 times that of the new plans. K3.1 Pro's per-million-Token input and output prices dropped from K3's 20 yuan and 100 yuan to 13 yuan and 68 yuan, while complex Agent tasks can consume millions of Tokens. A CAICT report shows that in June 2026, domesti
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Amazon CEO Jassy on $220 Billion in Capital Expenditure: Compute Is Still in Short Supply, Inference Costs Need to Fall by Another Order of Magnitude
Amazon CEO Andy Jassy said in an interview on the a16z podcast that the company's capital expenditure is expected to reach $220 billion in 2026, but compute capacity is already sold out for 2026, essentially fully booked for 2027, and a large share of 2028 quotas has been locked in. He described enterprise AI demand as dumbbell-shaped: a large number of production systems in the middle have yet to be deeply integrated because inference costs are too high, and costs need to fall by another order of magnitude before an explosion in adoption occurs. Jassy also noted that 99% of AWS's top 1,000 customers use Graviton, which offers about 40% better price-performance than x86, while Trainium has a 30% to 40% advantage over comparable hardware.
Amazon CEO Andy Jassy said in an interview on the a16z podcast that the company's capital expenditure is expected to reach $220 billion in 2026, but compute capacity is already sold out for 2026, essentially fully booked for 2027, and a large share of 2028 quotas has been locked in. He described enterprise AI demand as dumbbell-shaped: a large number of production systems in the middle have yet to be deeply integrated because inference costs are too high, and costs need to fall by another order
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AI-driven cyberattacks erupt intensively; industry insiders call October a "Black October" for cybersecurity
Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgium, France and the Netherlands have proactively gone offline. Shane Fry, chief technology officer of security firm RunSafe Security, said AI can weaponize a vulnerability into attack code within hours, whereas in the past this took weeks or even months.
Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgiu
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