i.e., the availability of a free and/or genius labor doesn't change much, at least first-order
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Rob Dearborn
351 posts
There are many (most?) jobs where demand elasticity with respect to cost and/or intelligence is ~0.
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QR codes but for having your AI explain why you need something
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I suppose almost all RL is the latter. No one’s training pirate personas to be agentic.
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To the extent models are fundamentally simulation operating systems, it seems we should be able to limit them to running only the persona processes we want? Or at least optimize them for running these processes
Anthropic@AnthropicAI
To create Claude, Anthropic first makes something else: a highly sophisticated autocomplete engine. This autocomplete AI is not like a human, but it can generate stories about humans and other psychologically realistic characters.
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this is genuinely good. like quietly devastating good.
how did anthropic beat chatgpt to this?
Claude@claudeai
Introducing Cowork: Claude Code for the rest of your work. Cowork lets you complete non-technical tasks much like how developers use Claude Code.
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G3F lacks Claude's soul, but its combo of smart+fast+cheap is really cognitively unburdening. You can just pepper it iteratively without thinking anything through.
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@TheZvi Slightly smarter outputs than Opus but less token efficient and prone to overthinking, so better for oneshotting tasks (if you can wait) and worse for pairing
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ZXX
Earlier this year a consensus formed that AI needs better continual learning to make the METR chart -> GDPval -> prosperity go up. I’ve come to believe that something even more foundational is missing: better executive function. Effective agents require it. Current models don’t have it. Attention is all you need, but the AIs have ADHD.
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Rob Dearborn nag-retweet
LLMs memorize a lot of training data, but memorization is poorly understood.
Where does it live inside models? How is it stored? How much is it involved in different tasks?
@jack_merullo_ & @srihita_raju's new paper examines all of these questions using loss curvature! (1/7)
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It's really something that it's preferable to train on simulations of games: arxiv.org/abs/2509.24527…
Rob Dearborn@RobDearborn
I feel like extending R1 into a bunch of AlphaGo/AlphaZero-esque gameplay environments would for sure yield some interesting skillz
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Rob Dearborn nag-retweet
Between this and the Brynjolfsson paper on AI and call centers, at what point should macroeconomists be tracking AI effects in the Philippines?
Ethan Mollick@emollick
AI in HR: in an experiment with 70,000 applicants in the Philippines, an LLM voice recruiter beat humans in hiring customer service reps, with 12% more offers & 18% more starts. Also better matches (17% higher 1-month retention), less gender discrimination & equal satisfaction.
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Inducing some combo of investor/allocator uncertainty and demand for greater operational efficiency
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On the other hand I do wonder if AI uncertainty's been a larger driver of funding's drying up and tech's broader crunch than is widely attributed
Doug Campbell@TradeandMoney
Lol at this research. Circumstantial at best, the changes in question are very unlikely to be driven by large language models. The tell is it was produced by Stanford university economists. The department that gave the world Nicholas Bloom is unlikely to produce a lot of serious research.
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IMO the correct way to value vertical agent cos long term is fundamentally as customer support + insurance + indemnification providers, and through this lens largely expect them to endure.
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