“Claude, change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program
@emollick
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AI Story Generation: Exploring What Machines Writing Means
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This is an interesting debate about AI text between an OpenAI researcher who thinks about AI writing and one of the great short story masters Now that we have machines that can write stories, occasionally very good or moving stories, we need to think more about what that means
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AGI Portfolio Hedging Through AI Infrastructure Spending
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I wonder how much of the spend on AI infrastructure is because it is otherwise very hard to get market exposure to the possibility of transformative AI. There are only a few companies in that AI race, so if you want “AGI” hedges in your portfolio, it is data centers or nothing?
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Vibe Coding: Unusual Approach to AI-Assisted Programming
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This is from a unusual sample (people who posted about vibe coding online): https://
arxiv.org/pdf/2510.00328
v1
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Vibe Coding: Process Issues Rather Than AI Problems
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Maybe some of the big problems with vibe coding are process problems, not AI problems…
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Science’s Selection Crisis: Managing Flood of Findings
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A lot of people are worried about a flood of trivial but true findings, but we should be just as concerned about how to handle a flood of interesting and potentially true findings. The selection & canonization process in science has been collapsing already, with no good solution
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Building Systems for AI-Driven Scientific Discovery Integration
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Science isn't just a thing that happens. We can have novel discoveries flowing from AI-human collaboration every day (and soon, AI-led science), and we really have not built the system to absorb those results and translate them into streams of inquiry and translations to practice
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AI Acceleration in Science: Overcoming Systemic Bottlenecks
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Very soon, the blocker to using AI to accelerate science is not going to be the ability of AI, but rather the systems of science itself, as creaky as they are. The scientific process is already breaking under a flood of human-created knowledge. How do we incorporate AI usefully?
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LLM Capabilities Fragmented: Vision, Tools, and Heavy Thinking Models
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The state of LLMs is messy: Some AI features (like vision) lag others (like tool use) while others have blind spots (imagegen and clocks). And the expensive "heavy thinking" models are now very far ahead of all the other AIs that most people use. None of this is well-documented.
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Debating Core Analogies: Understanding AI’s Impact and Nature
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I guess I would be remiss for not including other analogies that get debated here: the eschaton or the home computer? The atom bomb or crypto? A child or a plagiarism machine?