I’m not sure how you got that 2.2% of 18.5 trillion in GDP attributed to labor is 61 billion, so I’d agree that math doesn’t seem accurate.

Additionally, you seemed to have pulled the cherry-picked quote and compared with the “current” impact and ignored the immediately following text on latent automation exposure (partially extracted for quote) that explains how it could have a greater impact that results in their 2.3t/39m estimate numbers. Seems odd to find those numbers in the report but not read the rest of the same section.

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johnnyanmac 3 days ago | parent [-]

>I’m not sure how you got that 2.2% of 18.5 trillion in GDP attributed to labor is 61 billion

The number I googled for 2024 US GDP was 29.18 trillion, so thats part of it. I'm flexibke enough to adjust that if wrong.

>Additionally, you seemed to have pulled the cherry-picked quote and compared with the “current” impact and ignored the immediately following text on latent automation exposure

There's no time scale presented in that section thst I can find for the "latent" exposure, so its not very useful as presented. That's why I compared it to now.

Over 5 years; I'm not sure but it can be realistic. Over 20 years, If the US GDP doesn't absolutely tank, that's not necessary as impressive a number as it sounds. You see my confusion here?

>that explains how it could have a greater impact that results in their 2.3t/39m estimate numbers.

Maybe I need to read more of the article, but I need a lot more numbers to be convinced of a 40x efficiency boost (predicted returns divided by current gdp value times their 2.2% labor value) for anything. Even the 20x number if I used your gpd number is a hefty claim.

>Or presented a better metric than my formula above on interpreting "impact". I'm open to a better model here than my napkin math.

	▲	lr1970 3 days ago \| parent \| next [-]
		I think you made a arithmetic mistake by factor of 10. 2% of 29 trillion is 580 billions. Your number should be 610 billion, not 61 billion.
	▲	therealpygon 3 days ago \| parent \| prev [-]
		I would consider reading the actual report more closely rather than an article of questionable accuracy. For example: > “For instance, an employee can adjust based on new instructions, previous mistakes, and situational needs. A generative AI model cannot carry that memory across tasks unless retrained.” This is factually false; that is exactly what memory, knowledge, and context can do with no retraining. Not having completely solved self adjustment is not a barrier, merely a hurdle already currently in research. Imagine if, like the human brain, an LLM were to apply training cases identified throughout the day while it “slept”; the author seems to think this would be a massive undertaking of “retraining”. And sorry, if you’ve worked with many of the same types of employees I have over there years, you’d already know that the suggestion employees are more easily adaptable, will remember across tasks, and are good at adjusting to situational needs, can be laughable and even detrimental to think, depending on the person. The statement seems to be based more on the complaint of a lawyer who has no actual AI technical expertise; hardly the best source for what things AI can and cannot do “currently”. It’s useful to consider that almost all of the subjective opinions expressed in this report come from, effectively, 300 or so (maybe less) individuals, and that it isn’t all that easy to distinguish between the findings that are truly fact-based or opinion-based, especially with the linked post. It is also important to note that this report seems to focus more on the feedback and data from CEOs who look at P&L, not intrinsic or unquantified values. How do you directly quantify a developer fixing 3 bugs instead of 1 in your internal tool? Unless there are layoffs attributed to this specifically, and not “market changes” or general “reorganizations”, how is this quantified? There are a million things AI might do in the future that may not have a massive, or any, clear return on investment. If I buy a better shovel that saves me an hour on digging a trench in my own backyard, how much money did that save me? GDP is 29.2t, of which an additional google would find that U.S. labor accounts for an estimated 18.5t. 2.2% of 18.5t, or 29.2t, is still not 61m. In most cases, if the simple part of the math doesn’t fit, there are potentially some bigger logic mistakes at play. Best of luck on your understanding. As I said, I’d suggest maybe starting with direct statements from factual sources and the report rather than those the author (or you) interpreted.