In a 1938 article, MIT’s president argued that technical progress didn’t mean fewer jobs. He’s still right.
Compton drew a sharp distinction between the consequences of technological progress on “industry as a whole” and the effects, often painful, on individuals.
For “industry as a whole,” he concluded, “technological unemployment is a myth.” That’s because, he argued, technology "has created so many new industries” and has expanded the market for many items by “lowering the cost of production to make a price within reach of large masses of purchasers.” In short, technological advances had created more jobs overall. The argument—and the question of whether it is still true—remains pertinent in the age of AI.
Then Compton abruptly switched perspectives, acknowledging that for some workers and communities, “technological unemployment may be a very serious social problem, as in a town whose mill has had to shut down, or in a craft which has been superseded by a new art.”
Not the parent, but LLMs dont solve anything, they allow more work with less effort expended in some spaces. Just as horse drawn plough didnt solve any problem that couldnt be solved by people tilling the earth by hand.
As an example my partner is an academic, the first step on working on a project is often doing a literature search of existing publications. This can be a long process and even more so if you are moving outside of your typical field into something adjacent (you have to learn what excatly you are looking for). I tried setting up a local hosted LLM powered research tool that you can ask it a question and it goes away, searches arxiv for relevant papers, refines its search query based on the abstracts it got back and iterates. At the end you get summaries of what it thinks is the current SotA for the asked question along with a list of links to papers that it thinks are relevant.
Its not perfect as you'd expect but it turns a minute typing out a well thought question into hours worth of head start into getting into the research surrounding your question (and does it all without sending any data to OpenAI et al). That getting you over the initial hump of not knowing exactly where to start is where I see a lot of the value of LLMs.