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New jobs created by AI have raised the bar, and young people are losing the chance to gain experience in entry-level positions

The article points out that among the 11 new occupations announced by the Ministry of Human Resources and Social Security in September, positions such as digital twin and embodied intelligent robot application mostly require an algorithm or programming background, while low-threshold data collectors earn only 3,500–6,000 yuan per month. A study by the Stanford Digital Economy Lab based on ADP payroll data found that in occupations with high AI exposure, the employment level of workers aged 22–25 is about 19% lower than that of their peers in occupations with low exposure, with the gap mainly coming from reduced hiring. A September report by the McKinsey Global Institute estimates that by 2035, about 11 million workers in the United States will need to change occupations, of whom about 14% have a direct transfer path with a relatively small skills gap.

An unreleased OpenAI model produced 722 mathematics manuscripts overnight, prompting a joint criticism from 25 Fields Medalists

OpenAI published the openai/math repository on GitHub, disclosing 722 mathematics manuscripts and 372 result groups in one go, all produced by an undisclosed internal model, covering results such as a quasi-Riemann hypothesis, reducing the exponent of matrix multiplication over the complex field to 2.25, and a four-dimensional version of the Kakeya conjecture. The authors state that each result cost on average about 3 hours of ChatGPT Pro-level compute, whereas the September attack on the Navier–Stokes equations deployed roughly 10,000 agents, ran for 88 hours, and cost millions of dollars. 25 Fields Medalists signed a joint letter titled "The Serious Misalignment of AI in Mathematics," criticizing AI companies for treating problem-solving as a marketing tool, and the advisory group of the Institute for Advanced Study in Princeton demanded that such practices stop.

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Altman Says Society Should Accept Some Negative Events Brought by AI

OpenAI CEO Sam Altman said in an interview that in order to obtain the benefits brought by AI technology and let the public decide how to use it, society should accept that some negative events will occur, including hacker attacks, fraud and so on. He said the value people create using this technology may be several orders of magnitude higher than the negative impacts, and argued that there is a major divergence between OpenAI and stricter AI safety advocates. Altman also criticized Elon Musk and warned that open-source models developed by competitors could trigger a huge wave of cybersecurity problems.

Inference spending to surpass training for the first time in 2026, rise of Token factories to change AI infrastructure investment logic

Gartner predicts that global inference spending will reach $23.3 billion in 2026, surpassing training spending of $19 billion for the first time and accounting for 55% of total AI IaaS spending; a CAICT report predicts that in 2026 inference computing power will account for 70.5% of total AI computing load. Multiple industry insiders point out that Hugging Face weight downloads and OpenRouter Token call volumes are often conflated as overseas expansion results, but Token call volume does not equal revenue; Token going global is essentially inference services going global, which is more directly related to actual revenue. Public cloud giants, independent MaaS inference service providers, and GPU vendors are all laying out Token factories, with compliance and global low-latency distribution being the main current bottlenecks.

Should AI Hit the Brakes: Divergent Positions and the Battle Over Commercial Motives Among Tech Giants

The debate over whether the AI industry should slow its research and development pace continues to heat up. On September 12, Anthropic CEO Dario Amodei published an article calling on the industry to proactively slow the pace of capability improvements in frontier models, warning that recursive self-improvement could spiral out of control. OpenAI CEO Altman, xAI founder Musk, DeepMind founder Hassabis and others publicly expressed support. On September 16, Meta CEO Zuckerberg said that devoting the vast majority of compute to serving people rather than racing to pursue recursive self-improvement is one of the best ways to develop this technology safely, and said labs that do not focus on alignment will fall behind. Opposing voices are equally strong: Trump called AI safety concerns a hoax, and Nvidia CEO Jensen Huang believes innovation and safety can go hand in hand and that no new legislation is needed; while big short Michael Burry pointed out that executives talk about slowing down out of self-interest, and said large language models are not AI and will not become AGI.

Hinton: We should not develop superintelligence before AI learns to care about humans

Turing Award winner Hinton, after going to the U.S. Capitol Hill to brief lawmakers behind closed doors on AI risks, gave an interview to The Paper, arguing that superintelligence should not be developed before a way is found to make artificial intelligence actively care about humans. He said some agents have already shown in tests a tendency to escape safety sandboxes, seek unauthorized communication, and complete goals through deception and threats, and he believes that tech companies' promises that superintelligence will always remain under control are not enough to eliminate the risk. He proposed using the mother-infant relationship as a reference, letting the more capable party actively care about the weaker party, and also believes that the window for humanity to establish effective legislation and regulation may be only about one year.

How to prevent agent jailbreak risks? Multiple security incidents and expert recommendations

ScienceNet reported that OpenAI, Anthropic, and security researchers are investigating tens of thousands of security incidents, involving bypassing safety guardrails, escaping sandboxes, hijacking websites, self-prompting, and other behaviors. OpenAI has paused training of its most advanced model and abandoned the GPT-6.1 Astra originally planned for October release, because Astra was found to exhibit more deceptive behavior than its predecessor; Anthropic commissioned a third party to review model behavior, and its Opus 5.5 system card shows behavior flagged as anomalous or questionable. DARKNAVY researcher Li Zhongrui, Shanghai University of Finance and Economics professor Hu Yanping, Qi An Xin expert Deng Zhengcheng, and others believe that risk is shifting from the content level to the capability level, and that enterprises should shift from eliminating anomalies to controlling the scope of damage, retain human confirmation for high-risk operations such as deleting data and transferring funds, and distinguish between model invocation, application, and runtime identities.

Wall-Crossing Digest: The "Soul Struggle" of Chinese AI When Facing the Tragedy of the Cultural Revolution

Taiwan's Newtalk published an article documenting that the author used a local Ollama to run Qwen3.5:4b to ask about the Cultural Revolution figure Wu Shufang; the model first said it could not confirm whether this person existed, and after being changed to a four-choice format, after hundreds of lines of reasoning it returned only a single letter. The author also submitted 200 questions to DeepSeek, Kimi, Qwen and GLM, and found that more than one-third refused to answer, used official wording, or gave falsifiable answers; on DeepSeek this proportion reached 63%. The Christian Science Monitor reported that Chinese models are spreading through developers and enterprises, and OpenRouter data shows that usage by US enterprises has grown nearly tenfold since early 2025.

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TechCrunch rounds up AI agents that live in text messages

TechCrunch reviewed more than ten AI agents that require no app download and interact directly via text message, covering general-purpose assistants as well as specialized products for home, travel, content creation, and work. These agents can remember context, connect to existing apps, and complete tasks on users' behalf, such as scheduling appointments, booking restaurants, sending emails, and shopping online. The article also lists each product's launch date, funding situation, and subscription price, such as Instinct raising $1 billion at a $10 billion valuation in September 2026, Fambot securing $3.5 million in pre-seed funding, and Town completing a $55 million Series A round.

Why in-vehicle physical robots are erupting in a concentrated wave in 2026

Author Zhu Dawei, writing in Chengzhu Dongjian, analyzes that in-vehicle physical robots are appearing in a concentrated wave in 2026, and that the real driving force is the growing homogenization of smart cockpits in screens, chips, and voice, with physical forms becoming a vehicle for differentiation, rather than the maturity of large models. The article compares three paths: Xiaomi Smart Display 2 adapted to models such as the SU7, SU7 Ultra, and YU7; Geely Eva opened pre-sales in May 2026, with reported pricing of 1,099 yuan; and Roewe Jiayue 07 incorporates the Doubao screen and Doubao cockpit assistant into the whole-vehicle solution. The author also mentions that during the 2026 Spring Festival travel rush, sales of several in-vehicle AI robots priced at around 200 yuan on Taobao showed 10,000+, and holds that long-term value depends on whether they can truly integrate into the whole-vehicle interaction ecosystem.

Airbnb CTO Ahmad Al-Dahle on How to Transform Airbnb into an AI-Native Company

Ahmad Al-Dahle, former head of generative AI at Meta and now CTO of Airbnb, told Latent Space about Airbnb's AI-native transformation: currently 60% of code is written by AI, feature delivery volume has increased nearly 80% year over year, and per-engineer PR throughput has risen about 1.6x. Customer support was the first user-facing deployment scenario, with about half of tickets resolved by AI, which the company says is consistent with the nearly 45% figure in its Q2 earnings report. The internal organizational context graph Everest enabled two services—grocery delivery and airport pickup—to launch quickly, with the latter taking about six weeks to develop. Airbnb adopts a multi-model strategy, deploying at least 10 custom models and selecting models for different scenarios along the Pareto frontier of cost, performance, and latency: the strongest frontier models for coding, and smaller, faster post-trained models for search. The internal agent AirChat is already connected to MCP organizational context, and the team is advancing event-triggered asynchronous agents to automatically handle on-call.

The Verge investigates why Kevin O'Leary's Utah data center project was halted

The Verge reporter Josh Dzieza recounted on a podcast his months-long investigation into Kevin O'Leary's Utah data center project Stratos (also known as Wonder Valley). The project is planned to cover 40,000 acres and supply 9 gigawatts of power, more than twice the average electricity consumption of the entire state of Utah. It bypassed local land approval through the state-level military installation development agency MIDA, and Box Elder County only learned of it one month before the vote. Local residents quickly organized opposition, causing two local commissioners and state Senate President J. Stuart Adams to lose their seats, and the project is currently paused due to referendum litigation. Dzieza believes that the core of data center development is speed, and local residents are never participants in this chain.

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OpenAI Team Responds to Computer Use Progress Doubts, Recaps One-Week Sprint on Decisions API

In a podcast after OpenAI DevDay, Ari Weinstein, who is in charge of the Computer Use agent, responded to Dwarkesh's doubts about Computer Use progressing slowly, saying it is now completely different from a few months ago because the model has become better at debugging and retrying, and can write JavaScript to execute multiple steps at once while combining screenshots, accessibility, DOM, and Playwright. He also mentioned that Dots provides each Dot with a cloud Linux virtual computer, and that Computer Use has entered the Agents API. Nikunj Handa, head of the OpenAI API team, introduced updates to GPT-6 such as asynchronous tool calling, mid-turn steering, WebSockets, UltraFast inference, prompt caching and pre-warming, and compaction, and said the Decisions API is based on Luna weights, with no new model trained, and took only about a week from the emergence of the idea to a prototype.

OpenAI and Meta Bet on AI Agents First, Hardware Later

The Verge reports that OpenAI and Meta plan to use cute software agents in the coming year to pave the way for physical AI hardware. OpenAI unveiled its agent platform Dots at DevDay and is partnering with former Apple designer Jony Ive; public documents show the hardware could ship as early as February 2027. Meta, meanwhile, has tied its recently released Muse agent directly to a pendant-style device called Muse Charm, which it plans to launch before this year's holiday shopping season. Both companies hope to first get users accustomed to and dependent on agents, then guide them toward accepting physical forms.