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  3. Hacker News Popular

    Open Athena author David Hall analyzes why there is no public pool for post-training data

    Open Athena author David Hall points out that pretraining has ready-made public corpora such as Common Crawl, whereas post-training consumes tasks rather than documents, so there is no corresponding public data pool. He uses SWE-bench as an example to illustrate the cost of task creation: among 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. He believes 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.

    , so there is no corresponding public data pool. He uses SWE-bench as an example to illustrate the cost of task creation: among 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. He believes 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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    #hot-signal#English text is being prepared.#English text is being prepared.#Open-source ecosystem

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  2. Huxiu

    $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.

    ities. 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.

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    #hot-signal#English text is being prepared.#Industry news#OpenAI

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