#glyph

Glyph develops this analogy to describe why LLMs are worse than slot machines. He says that (non-pathological) gamblers set a budget for the amount of money they're prepared to lose to the slots, while a coder who's feeling warmly disposed to an LLM coding assistant may not put any explicit limits on how much time they'll spend futzing with LLM-generated code (I'll add here that part of the seductive joy of coding is that it can put its practitioners into a kind of autohyptnotic fugue state where they don't notice the passing of time, a state that is also a feature of pathological gambling).

Glyph poses a hypothetical: if you have a coding project that you ask a chatbot to write, and the resulting code initially doesn't work, but does work after ten minutes of futzing, that feels amazing and you will remember it forever as the time you saved 3:50 by using a chatbot. But it's possible that you repeated the "well, I'll just futz with this for ten minutes" step to get to that final success so many times that the whole affair took six hours, two hours longer than it would have taken had you just written the program from scratch. It's like winning a $1000 jackpot after "just putting a dollar in," except that that was the one-thousand-and-first dollar that you fed to the machine.

Glyph says that in other business activities, the "let's just try this for 10 minutes more" strategy usually pays off, but that LLMs produce an "an emotionally variable, intermittent reward schedule" that subverts your ability to wisely deploy that tactic.

But that's not the only way in which an LLM coding assistant is like a slot machine. Reg Braithwaite proposed that AI companies' business model is also like a casino's, because they charge every time you re-prompt the AI. He writes:

When you are paying by the "pull of the handle," the vendor's incentive is not to solve your problem with a single pull, but to give the appearance of progress towards solving your problem.

https://social.bau-ha.us/@raganwald/115033262770049100

Jpeck likens the use of an LLM coding assistant to "a dense intern" who has to be walked through each step and then have their work double-checked:

https://universeodon.com/@boscoandpeck/115033787721848290

But there's an important difference between an intern and an LLM. For a senior coder, helping an intern is an investment in nurturing a new generation of talented colleagues. For a reverse-centaur, refining an LLM is either an investment in fixing bugs in a product designed to put you on the breadline (if you believe AI companies' claims that their products will continue to improve until they don't need close supervision), or it's a wasted investment in a "dense intern" who is incapable of improving.

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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Frank Schwichtenberg (modified) https://commons.wikimedia.org/wiki/File:Anonymous_%E2%80%93_CeBIT_2016_00.jpg