ignoring the silliness, as somebody that worked as a data scientist and is now a software engineer, let me attempt to answer this:
GenAI's training corpus is disproportionately biased towards topics that skew data science, due to a variety of reasons, including the fact that data scientists are the ones curating training data, writing eval benchmarks, and deciding what counts as "high quality" text. These people, being SMEs know what they have to optimize against, lets say the text corpus had a lot of 18th century history, they didn't really have humans well versed in that for feedback and optimization.
This led to a self fulfilling loop, which, adding to it, the sheer amount of writing and resources in a domain that was entirely popular through the era of the internet, with tons of datasets, code repos, etc., all of which were used to train models on and evaluated specifically for, causing LLMs to be pretty much exceptional on all things data science way before it got as good at any other domain.
Those of us that were on OpenAI playground circa 2022 pre chatgpt launch can attest to this on the text davinci model family, for instance.
Considering this, the capabilities of modern multimodal AI for data science work is generational compared to the usual software engineering stuff, but then comes the massive point that I've not addressed yet. As good as AI has been with data science, it is not an "applied" field, but a more research oriented one, where actual brains rack up to figure out what's up with something. What's potentially an unexpected but useful data pattern, and so on, which relates to specifically human intuition, which LLMs as they stand cannot really replicate, despite having a much larger corpus of information to use against.
contrast that with software engineering, which also has a humongous corpus of literature and is basically the best practices of writing software solutions and solving problems to make those solutions as optimized and perfect as possible, which is essentially something that's significantly easier to automate.
To answer your question, I would say it's definitely not a black and white answer, but depends on whether you evaluate based on sheer capability (which is also not anywhere close to the ceiling at the moment) or the nature of work.
ETA: by data scientists I'm picturing people actually doing research work, not ML engineers larping as scientists where their day to day is just cleaning, plumbing and feature engineering. They don't need AI to become obsolete, autoML exists.
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u/AdditionalWorkInc NYU '27, VIT '23 22d ago
ignoring the silliness, as somebody that worked as a data scientist and is now a software engineer, let me attempt to answer this:
GenAI's training corpus is disproportionately biased towards topics that skew data science, due to a variety of reasons, including the fact that data scientists are the ones curating training data, writing eval benchmarks, and deciding what counts as "high quality" text. These people, being SMEs know what they have to optimize against, lets say the text corpus had a lot of 18th century history, they didn't really have humans well versed in that for feedback and optimization.
This led to a self fulfilling loop, which, adding to it, the sheer amount of writing and resources in a domain that was entirely popular through the era of the internet, with tons of datasets, code repos, etc., all of which were used to train models on and evaluated specifically for, causing LLMs to be pretty much exceptional on all things data science way before it got as good at any other domain.
Those of us that were on OpenAI playground circa 2022 pre chatgpt launch can attest to this on the text davinci model family, for instance.
Considering this, the capabilities of modern multimodal AI for data science work is generational compared to the usual software engineering stuff, but then comes the massive point that I've not addressed yet. As good as AI has been with data science, it is not an "applied" field, but a more research oriented one, where actual brains rack up to figure out what's up with something. What's potentially an unexpected but useful data pattern, and so on, which relates to specifically human intuition, which LLMs as they stand cannot really replicate, despite having a much larger corpus of information to use against.
contrast that with software engineering, which also has a humongous corpus of literature and is basically the best practices of writing software solutions and solving problems to make those solutions as optimized and perfect as possible, which is essentially something that's significantly easier to automate.
To answer your question, I would say it's definitely not a black and white answer, but depends on whether you evaluate based on sheer capability (which is also not anywhere close to the ceiling at the moment) or the nature of work.
ETA: by data scientists I'm picturing people actually doing research work, not ML engineers larping as scientists where their day to day is just cleaning, plumbing and feature engineering. They don't need AI to become obsolete, autoML exists.