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@rishibommasani
Senior Research Scholar @StanfordHAI focused on economics and governance of frontier AI Previous: Stanford CS PhD @percyliang @jurafsky, Cornell CS
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In a quote post, the author thanks someone while saying they can't take credit for the whole group.
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The author says they're preparing a lecture for their class and invites suggestions from friends in AI safety.
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The poster says surveyed Anthropic staff's answers imply median annual US GDP growth of 17.1% in 2030 under Anthropic's model.
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A user recalls the premise as doing AI safety from second place: following the capability frontier to study safety there, rather than advancing it.
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The authors say they reviewed 20-plus papers and datasets. They describe mixed evidence for specific sectors and demographic groups, including early-career hiring in AI-exposed roles.
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A user asks for compelling public or publicly acknowledged examples of the data being used by researchers, policymakers, journalists and industry decision-makers.
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One post argues it’s a great time to pursue an AI PhD. Another says it’s an excellent time for many people to research advanced AI’s societal impacts, but is less certain the traditional PhD format suits many today.
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One post argues for judging AI more by total exploits discovered, math problems solved and algorithmic efficiency, and less by token spending, code volume or self-reported speedups.
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One post argues that frontier AI disclosures should generally reach the public, not just governments, to give nonprofits, academics and other independent experts access to the information.
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A reply quotes Dan's dissertation recalling that he finished his bachelor's degree in history without taking a single computer science course.
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OpenAI researcher Dan Selsam argues in a personal statement that growing model awareness is undermining evaluation—and that more careful pacing of frontier AI alone will not adequately limit long-term risk.
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A user calls for more attention to the group’s work, highlighting a standard covering embedded audits and members including METR, Transluce, RAND, AVERI, Princeton and SecureBio.
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One post connects the question of knowledge’s usefulness to the step-by-step automation of knowledge production.
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Yang’s recruitment announcement calls for expertise in large language models and reinforcement learning, with applications due September 15, 2026.
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Stanford researcher replies that the term misleads by suggesting ordinary technology.
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Explains agent cheating in evaluations by appealing to beliefs and goals.
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Stanford researchers launch the most transparent 535B model pretraining run to date.
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OpenAI staffer roon separates PauseAI from respected rationalist circles after meme backlash and violence claims.
