Read my comment again. keyword here is "recent". The second link also expands on why it's relevant. It's best to read the whole article, but here's a paragraph that captures the argument:
>The shift in recent economic research supports his observation. Although early studies suggested that lower performers could benefit simply by copying AI outputs, newer studies look at more complex tasks, such as scientific research, running a business and investing money. In these contexts, high performers benefit far more than their lower-performing peers. In some cases, less productive workers see no improvement, or even lose ground.
All of the studies were done 2023-2024 and are not listed in order that they were conducted. The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points, whereas the ones showing increased equality are broader and more commonly applicable, like writing, customer interaction, and coding.
>All of the studies were done 2023-2024 and are not listed in order that they were conducted
Right, the reason why I pointed out "recent" is that it's new evidence that people might not be aware of, given that there were also earlier studies showing AI had the opposite effect on inequality. The "recent" studies also had varied methodology compared to the earlier studies.
>The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points
"Debating points" is uncommon? Maybe not everyone was in the high school debate club, but "debating points" is something that anyone in a leadership position does on a daily basis. You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize. You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints. This seems exactly like the type of cookie cutter tasks that the article describes would become like cashiers and have their wages stagnate. Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
> The graphic has four studies that show increased inequality
Three, since Toner-Rodgers 2024 currently seems to be a total fabrication.
https://archive.is/Ql1lQ
Read my comment again. keyword here is "recent". The second link also expands on why it's relevant. It's best to read the whole article, but here's a paragraph that captures the argument:
>The shift in recent economic research supports his observation. Although early studies suggested that lower performers could benefit simply by copying AI outputs, newer studies look at more complex tasks, such as scientific research, running a business and investing money. In these contexts, high performers benefit far more than their lower-performing peers. In some cases, less productive workers see no improvement, or even lose ground.
All of the studies were done 2023-2024 and are not listed in order that they were conducted. The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points, whereas the ones showing increased equality are broader and more commonly applicable, like writing, customer interaction, and coding.
>All of the studies were done 2023-2024 and are not listed in order that they were conducted
Right, the reason why I pointed out "recent" is that it's new evidence that people might not be aware of, given that there were also earlier studies showing AI had the opposite effect on inequality. The "recent" studies also had varied methodology compared to the earlier studies.
>The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points
"Debating points" is uncommon? Maybe not everyone was in the high school debate club, but "debating points" is something that anyone in a leadership position does on a daily basis. You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize. You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints. This seems exactly like the type of cookie cutter tasks that the article describes would become like cashiers and have their wages stagnate. Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
1 reply →