or that 'AI is going to solve all of physics' or that 'AI is going to clone his brain by 2027' .

PG famously called him 'Michael jordan of listening' , i would say he is 'Michael jordan of bullshitting'

I don’t think he ever said or even implied any percentage of ‘creative work of the world is worthless’?

A lot less valuable then what artists may have desired or aspired to at the time of creation, sure, but definitely with some value.

Well, there you go. These companies are always closer than they seem at first glance, and my preference for Anthropic may just be patting myself on the back.

do you feel the less censorship yourself from their instruction tuned model, or is there some public reference to showcase? (i haven't used mistral model before). It's interesting if a major llm player adopt a different safety / alignment goal.

I actually agree with your principle, but don't think it applies to Anthropic, because I interpret the name to mean that they are making machines that are "human-like".

More cynically, I would say that AI is about making software that we can anthropomorphize.

> Anthropic means "human," and if a company must remind me it is made of/by/for humans

Why do you think that that's their intended reading? I had assumed the name was implying "we're going to be an AGI company eventually; we want to make AI that acts like a human."

> if there's any "making" of the training content, Anthropic didn't do it

This is incorrect. First-gen LLM base models were made largely of raw Internet text corpus, but since then all the improvements have been from:

• careful training data curation, using data-science tools (or LLMs!) to scan the training-data corpus for various kinds of noise or bias, and prune it out — this is "making" in the sense of "making a cut of a movie";

• synthesis of training data using existing LLMs, with careful prompting, and non-ML pre/post-processing steps — this is "making" in the sense of "making a song on a synthesizer";

• Reinforcement Learning from Human Feedback (RLHF) — this is "making" in the sense of "noticing when the model is being dumb in practice" [from explicit feedback UX, async sentiment analysis of user responses in chat conversations, etc] and then converting those into weights on existing training data + additional synthesized "don't do this" training data.