Do you have a dumb question that you're kind of embarrassed to ask in the main thread? Is there something you're just not sure about?
This is your opportunity to ask questions. No question too simple or too silly.
Culture war topics are accepted, and proposals for a better intro post are appreciated.
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Notes -
I'm a data scientist/engineer and I use Gpt a lot. Like probably 95% of the code I wrote in the last 6 months was drafted by Gpt.
It's made a lot of things that would have been a 6-month-long NLP project into 2-3 day projects (the dev ops timeline is also much shorter because we just use the Openai API). And the results are far far superior. NLP projects are a breeze now and some things that were just straight impossible a few months back are easily done in a week or two (RLHF helps). It's actually amazing. I can't be spilling trade secrets but I can assure you with a bit of creativity you can get a lot more done than just making a chatbot or text summarizer (we use it in production for 5 different tasks that used to be done by people, our CEO is kind enough (can afford to) to keep them around for now, albeit with different responsibilities).
I do feel the need to defend the honor of my profession here given you have spoken about how we might just get the boot because Gpt can automate us away.. Color me this. Who do you think people are turning to to make things with GPT? I might even put "Prompt Engineer" in my LinkedIn bio soon.
Also, for NOW, it's not as simple as;
It's more like.
When you have to deal with real-world data and not stuff you download from Kaggle. There are database connections to make (and no tech lead in his right mind is going to let you connect to the database with an external API that you post to somewhere in the code !!), queries to be batched because the replica db times out, json parsers to be written because the field you want is within 15 nested jsons, etc; It's an impediment if you let it take the wheel.
I really wanted Code Interpreter to be useful for things past the simplest of Exploratory Analysis, but I don't think good EDA code that asks the right questions (the answers to which make money), is open-sourced. . Once again, it's alright. But its analysis skills are that of an undergrad who took 2 stats courses and uses techniques from the 1980s. I don't expect Gpt to become competent at statistics anytime soon given most of its training data is written by amateurs ( Good Data Science code is usually not open-sourced for obvious reasons).
And seriously, the above cannot be understated. In an ideal world, Data Scientists don't exist for writing
import pandasand wrangling the shit out of that Data Frame. They exist for the same reason plumbers do...Gpt 5 might know where to strike. But Im not going to hold my breath because for a lack of training data.
I mostly agree with you, but I could also see software engineers going the way of the draftsmen: There used to be a huge sector that focused entirely on taking an engineer's ideas, and turning them into drawings on paper so they could be handed out to the clients. Then Computer Aided Drafting (CAD) software came about, productivity skyrocketed, and the demand for drawings rose modestly. Now there's a small sector focused on drafting, and some fraction engineers do their own drafting in the design stage.
Let's use your example of 40-60x increases in productivity, and imagine that competition keeps salaries at their current level. What if demand for software products only increases by 1000% due to the reduced job prices? The field could shrink to one fifth the size and still meet demand.
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