It is not clear if (i) a lot of the surplus will be captured by the AI providers and (ii) that the impact will be anywhere as big as people now guess/want it to be. Making a bet on the future is fine, of course.
You don't need to just take one source. OpenAI authored their own paper [1] on the economic impacts of just LLMs: "Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted."
Goldman Sachs Research just pushlished their own analysis as well. [2] Their conclusions are "As tools using advances in natural language processing work their way into businesses and society, they could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period." and "Analyzing databases detailing the task content of over 900 occupations, our economists estimate that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI. They further estimate that, of those occupations that are exposed, roughly a quarter to as much as half of their workload could be replaced."
From [1]: "In our study, we employ annotators who are familiar with LLM capabilities. However, this group is not occupationally diverse, potentially leading to biased judgments regarding LLMs’ reliability and effectiveness in performing tasks within unfamiliar occupations."
From [2]: "Analyzing databases detailing the task content of over 900 occupations, our economists estimate that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI."
These are people who do not understand the jobs they are claiming AI will do. Ultimately, I think they are not doing much better than guessing.
We’ve got a lot of data scientist talent but I wouldn’t put a lot of stock in this particular estimate. If McK is gonna produce a novel insight it’s usually derived from having the input of many businesses across an industry and experience looking at their problems. It’s hard to imagine this one isn’t more or less made up due to the number of assumptions required.
Likely not much and assuredly wrong, I just wanted to ground my argument with numbers that came from people who presumably did more research than I was willing to do for an HN post.
If anything McKinsey has a lot to gain from exaggerating the numbers so more companies come to them for AI solutions or whatever their next consulting product is.
