651–660 of 1001 posts
I'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself. I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter. They literally post screenshots of ChatGPT as their thinki…
Earlier quoted context omitted.
I thinking that it’s quite a different experience going all Jackson Pollock with AI in your own studio on your own terms, compared to the sorry state of affairs of having 100s of Pollocks throwing paint around wildly within a corp to meet a paint quota.
Can we combine this with the infinite monkey theorem? If we have an infinite number of Pollocks throwing paint at an infinitely large canvas surely they are going to create any piece of art we can imagine...
There's also an online version of the Library of Babel, I just found out that full pages of my own books are in it[0], https://libraryofbabel.info/bookmark.cgi?379:17
I'd like to chime in and mention that its really obvious how to RL a coding agent to get the human addicted asap. and its also clear that there's a ton of $$$ to be made by doing this. therefore its done. the only LLMs I use are the ones I run locally because i know they aren't RL'ed for that metric (no incentive for the company that made them to make their open weights models addictive)
Interesting angle, didn't think of this. How do you think/find that current tools are optimized for being addictive?
- Nuggesting improvements to the code after finishing the task you gave it, very irritating when the improvements were obvious and the ai didn't implement them on its own
- Not trying very hard when implementing something, leading to bugs, which leads to more tokens used (this behavior can be incentivized and learned with RL)
Since its a known fact if a user continues a session after the LLM says something, its not hard to train against this. The least efficient way to do this would be to GPRO directly against the user base and try to get as many people talking to the AI, and with OAI having a billion monthly active users the least efficient method would work really well for them.
Rewriting in rust does makes things faster but if an algorithm is O(n²), the improvement won't take us much farther.
Similarly with AI, if complexity is not structurally adressed, the velocity gains are but temporary.
I'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself. I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter. They literally post screenshots of ChatGPT as their thinki…
Garry Tan has been the primary crusader for AI driven decision making. I'm sure his position is more nuanced, but his twitter driven communication makes him appear like a caricature of a man in AI psychosis.
When the head of YC champions AI driven decision making, companies will inevitably be influenced into doing exactly that. It's unfortunate, because AI is generational technology and the hyperbole distracts from the real sea change occuring in labor markets everywhere.
I'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself. I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter. They literally post screenshots of ChatGPT as their thinki…
Correct. I use AI a ton and I'm having more fun every day than I ever did before thanks to it (on average, highs are higher, lows are lower). Your characterization is all very accurate. Thank you. Here's some other topics I've written on it: - https://mitchellh.com/writing/my-ai-adoption-journey - https://mitchellh.com/writing/building-block-economy - https://mitchellh.com/writing/simdutf-no-libcxx (complex change th…
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To me the big thing I see in blog posts is this implication that “all software engineering best practices are out the window” And to me, AI should best be used to add rocket fuel to existing practices. Better tests, better observability, more atomic changes instead of big changes, automatic rollback etc.
Earlier quoted context omitted.
I’ve had to do a ton of SQL stuff lately, which I haven’t really worked with since the late 90s. ChatGPT has been a godsend, not just for me, but for our only coworker who knows SQL well, whom I’d probably be bugging several times a day at my wits’ end. But no one cares about those kinds of productivity gains. Just the ones that will completely replace us.
I find SQL and data(bases) in general to be LLM’s Achilles’ heel. Databases are rarely under version control, so the training data only has one half of the knowledge. My comments are more in the context of OLAP queries and other non-normalised data often queried via SQL. I train non-LLM transformer models on (older and rarer) datasets, and automating the ingestion of sprawling datasets with hundreds of columns, often…