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.