I'm also confused with that as well as the moral of the story as the whole. I get the sentiment but what is the lesson here, leave production capacity keep going as well as a "hiring pipeline" and just stock pile the output forever? Also, given article's take on the current situation of AI assisted coding, it seems to suggest that we need to apply that same logic to other industries too, so just don't let any industry/practice die and keep it alive? I would appreciate some inputs into some sort of actual solution or at least ideas of the solution but that is not present in the article.
Anthropic will kick and scream as those are often distilled from their latest models and is cutting into their margin. Though it is not like their hands are clean neither, it is just a different type of stealing, an approved one :-)
Any publicly available evidence to back that up? There have been post-exit blog posts from OpenAI employees on HN before and it did sound like the only black magic they use there is that many employees work 16 hrs a day during launch of new features. I know that some current Claude Code devs are doing interviews where they claim that they use Claude Code extensively but they clearly have a conflict of interest while they are still employed at Anthropic, so it would be like asking a barber if you need a haircut.
I wonder if this ends up like Tesla - China copies it and makes it cheaper -
GG if not protected by huge tariffs/bans. It seems like US these days is just a testing ground for new tech that later scaled further and optimized in China. Are there any hard moats protecting SpaceX from that?
Tesla ended up here because Musk got bored with it and was distracted by his shiny new toy: Twitter. And running a large car manufacturer requires skill, competence and careful attention, all of which Musk lacks.
Yeap, I recently came to realization that is useful to think about LLMs as assumption engines. They have trillions of those and fill the gaps when they see the need. As I understand, assumptions are supposedly based on industry standards, If those deviate from what you are trying to build then you might start having problems, like when you try to implement a solution which is not "googlable", LLM will try to assume some standard way to do it and will keep pushing it, then you have to provide more context, but if you have to spend too much time on providing the context, then you might not save that much time in the end.
Quality of that verification matters, people who might use AI tend to cut corners. This does not completely solve problem with AI slop imo and solution quality. You ask Claude Code to go and implement a new feature in a complex code base, it will, the code might even work, but implementation might have subtle issues and might be missing the broader vision of the repo.
I was trying to find some more context on this but all I could find is that Rob Pike seems to care a lot about efficiency of software/hardware and against bloat which is expressed in his work on Golang and in related talks about it.
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