3 ms·

This is the fundamental reality I keep seeing. The people who think an LLM or other type of model can do a job are the people who don't know anything about tha

This is the fundamental reality I keep seeing. The people who think an LLM or other type of model can do a job are the people who don't know anything about that job. Managers who don't know what developers do think you can replace a developer with an LLM. Can an LLM shit out some code? Sure, but that's hardly what (good) developers do. Magazine publishers who don't know what editors do think an LLM can replace an editor. Can an LLM make a bunch of statements about the quality of a piece of writing? Sure, but they may have no basis in reality and will require a real human editor to review them. Or your publication can succumb to being LLM generated slop. Bad coders who don't know what good coders do see that an LLM can do what they've been doing and think developers will be replaced but they don't actually realize what it means to be a developer so they don't see all the things the LLM isn't doing. Tech bros think a model will be able to revolutionize materials development but when actual materials scientists look at the output it turns out it's mostly garbage. And crucially it took actual materials scientists spending a whole lot of time to figure that out. [0] Most of what these models do is waste actual experts' time by forcing them to wade through huge quantities of plausible looking but completely incorrect output. 0: https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00643 https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00643

bee_rider 2y ago
The main thing I see (and it is possible I’m biased by some of these same factors), is that it does seem to make some moderately helpful tools? Like coding assistants, to knock out boilerplate. Maybe if it could make a developer twice as effective, it could halve the developers to project ratio. Jevons paradox, and we get twice as many projects, great. But the management requirements would be different, right? If teams are half as large, wouldn’t expect management to just, like, go away. But the tree might be able to lose some middle “summarize and pass up” levels, right?
- deleted 2y ago
  [deleted]
- orwin 2y ago
  Basically it helps with really generic stuff, but writing generic stuff isn't my job. I have access to windows copilot pro (basically ChatGPT 4 Turbo/ 4o depending of the time of the day) as a tester, and github copilot as a user, i can say without hesitation that ChatGPT is sightly better to write comments, but both are bad to do anything harder than writing HTML forms, at least from scratch. They _are_ a great rubber duck though, today i had a concurrency issue and asked windows copilot for solutions. It was wrong, but gave me an idea, basically saving me at least 45 minutes. Github copilot is a great autocomplete, saving me some time too, but i don't think it can make me twice as effective as a coder. 20%? Maybe 40% if i take into account the fact that it generate really good test cases (that i still have to read)? But coding is like 25 percent of my job, database/object design and software architecture are like 30%, network security another 25%, and the rest is meetings/coordination, so all in all, i don't think you can halve teams because you give them good genAI
spacemadness 2y ago
Every code generator I’ve used makes me think developers jobs are plenty safe at the moment from an AI takeover. You can’t seem to spend your way to a solution of producing good data. Nvidia is certainly enjoying watching people try though. What’s sad is the tech is actually impressive and fascinating but it’s being forced to look more useful than it is by greedy investors, but what else is new. Water is wet and all that.

bee_rider 2y ago

The main thing I see (and it is possible I’m biased by some of these same factors), is that it does seem to make some moderately helpful tools? Like coding assistants, to knock out boilerplate. Maybe if it could make a developer twice as effective, it could halve the developers to project ratio. Jevons paradox, and we get twice as many projects, great. But the management requirements would be different, right? If teams are half as large, wouldn’t expect management to just, like, go away. But the tree might be able to lose some middle “summarize and pass up” levels, right?

deleted 2y ago
[deleted]
orwin 2y ago
Basically it helps with really generic stuff, but writing generic stuff isn't my job. I have access to windows copilot pro (basically ChatGPT 4 Turbo/ 4o depending of the time of the day) as a tester, and github copilot as a user, i can say without hesitation that ChatGPT is sightly better to write comments, but both are bad to do anything harder than writing HTML forms, at least from scratch. They _are_ a great rubber duck though, today i had a concurrency issue and asked windows copilot for solutions. It was wrong, but gave me an idea, basically saving me at least 45 minutes. Github copilot is a great autocomplete, saving me some time too, but i don't think it can make me twice as effective as a coder. 20%? Maybe 40% if i take into account the fact that it generate really good test cases (that i still have to read)? But coding is like 25 percent of my job, database/object design and software architecture are like 30%, network security another 25%, and the rest is meetings/coordination, so all in all, i don't think you can halve teams because you give them good genAI

spacemadness 2y ago

Every code generator I’ve used makes me think developers jobs are plenty safe at the moment from an AI takeover. You can’t seem to spend your way to a solution of producing good data. Nvidia is certainly enjoying watching people try though. What’s sad is the tech is actually impressive and fascinating but it’s being forced to look more useful than it is by greedy investors, but what else is new. Water is wet and all that.