People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.
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People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.
I thought I was using AI in an incredibly positive and healthy way, as a bicycle for my mind and a way to vastly increase my thinking capacity. But LLMs are insidious–using them to explore ideas feels like work, but it’s not real work. Developing a prompt is like scrolling Netflix, and reading the output is like watching a TV show. Intellectual rig... See more
Dustin Curtis • Thoughts on thinking • Dustin Curtis
In this analogy, LLMs become better than humans at writing. But it’s still widely understood that learning to write is good for you (maybe writing is “the best way to think”) so people send their kids to writing camp and humble-brag about writing in their free time. But in the end, most writing was done for the mundane purpose of making text
... See moredynomight • Historical analogies for large language models
My LLM use for writing: I do all the typing on my own. Sometimes if I can’t come up with the right word, I explain what I mean to Claude and get a few suggestions. Even more rarely, I have a sentence which is giving me problems, where I feel like it is complicated but I can’t figure out a simple way of rearranging the information, and then I ask fo... See more
Henrik Karlssonsubstack.comFoundation models like GPT and Claude now serve as the index funds of language. Trained on enormous corpora of human text, they do not try to innovate. Instead, they track the center of linguistic gravity: fluent, plausible, average-case language. They provide efficient, scalable access to verbal coherence, just as index funds offer broad exposure ... See more