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$1.65 Trillion in Off-Balance-Sheet Commitments and a 166-Fold Token Price Gap: Three Endgame Scenarios for the AI Capital Game

The author reviewed the financial reports of the five major U.S. tech giants and found that more than 85% of Alphabet's and Amazon's quarterly profits come from unrealized gains on equity investments; their combined off-balance-sheet long-term purchase and lease commitments total approximately $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 AI application revenue is about $200 billion, leaving a gap of roughly 5 times. After comparing China-U.S. pricing, the author notes that OpenAI charges $30 per million output Tokens while DeepSeek charges only $0.18, a price gap of 166 times, and concludes that the most likely endgame is that one node runs into trouble first and the problem then propagates through the chain of cross-shareholdings.

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.

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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.

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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.

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.

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When Young People No Longer Worship Silicon Valley: A Crisis of Trust in AI Leaders Among America's Younger Generation

A CNBC and Generation Lab survey of 1,088 Americans aged 18 to 34 found that distrust of nine tech leaders ranged from 65% to 81%, and 45% believed AI would harm their career prospects. Pew data shows that 66% of Americans aged 18 to 29 have already used AI chatbots, but the share of this same group who believe AI will reduce employment rose from 61% in 2024 to 73% in 2026. The author, Chen Yongwei, argues that this wariness stems from narrowing entry points into employment and negative past experiences with digitalization, and advocates letting affected people participate in negotiations over AI rules.

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After Nvidia chips became hard to buy, what keeps China's large models running?

A Huxiu analysis points out that domestic computing power can now cover large model operation: Zhipu's domestic-chip inference cluster has reached a scale of 100,000 cards, and Meituan's LongCat-2.0 has also completed training on domestic computing power. However, Ascend 910C achieves an MFU of about 30%-35% in trillion-parameter post-training, lower than H100's 40%-50%. The article breaks computing efficiency into three variables—chips, software, and models—and compares the four paths of Zhipu, MiniMax, DeepSeek, and Kimi, concluding that Zhipu has the highest short-term certainty, while DeepSeek has the greatest medium-term elasticity. The author proposes three tracking indicators—capacity, efficiency, and cost—and argues that only when all three improve simultaneously can domestic computing power shift from a backup option to a cost advantage.

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