Economic productivity is an interesting perspective. I think you are right. Current AI spends too many resources but gets too few values (real values).
Too much hype in the process of AI development.
If analyzing the basic theory of LLM or AI (second hand data, data conversion, cognitive bias, etc.), this will make it more persuasive.
Over the last year I have seen an astonishing spike in poor-quality output within multiple employers. The problem with AI coding, ticket filing, etc is that you must review it afterward, but review would take nearly as long as just doing it yourself. The 10x productivity gain becomes 1.05x.
The adopted solution to this, to meet internal targets, is to accept reduced quality. Flood the ticketing system with bogus reports, each with 5 paragraphs of technobabble. In response, you get similarly faulty AI-driven triage tools to deal with the slop. But the triage tools have the same problem: they just don't work that well. Plenty of garbage gets through, and plenty of actual issues get filtered out.
The first movers get some eye-popping numbers to show management, but the aggregate productivity of the corporation is unchanged at best.
So is the productivity increase an illusion? Depends how you look at it. Is the productivity boost for the corporation an illusion? Yes. But, most ICs are measured by tickets closed, commits pushed, lines of code written, and tokens used. Like it or not, that is the definition of "productivity" that has been given to ICs. They're just doing what they're told.
AI productivity only works if you were the only person in the world using it and no-one else was aware of it. You will look like a wizard, passing off slop to others who are too amazed and ignorant to know any better.
The fact that that is exactly what is happening (everyone passing off slop to everyone else and pretending either they didn't use AI or it isn't slop actually) is telling.
The mistake was giving it to everyone too early too fast. "If everyone is super, no one will be"
Economic productivity is an interesting perspective. I think you are right. Current AI spends too many resources but gets too few values (real values).
Too much hype in the process of AI development.
If analyzing the basic theory of LLM or AI (second hand data, data conversion, cognitive bias, etc.), this will make it more persuasive.
This particular phrase captured my attention:
I read something similar in the book The Goal from Eliyahu Goldratt more than 20 year ago.You can be highly productive and yet produce nothing of value.
Over the last year I have seen an astonishing spike in poor-quality output within multiple employers. The problem with AI coding, ticket filing, etc is that you must review it afterward, but review would take nearly as long as just doing it yourself. The 10x productivity gain becomes 1.05x.
The adopted solution to this, to meet internal targets, is to accept reduced quality. Flood the ticketing system with bogus reports, each with 5 paragraphs of technobabble. In response, you get similarly faulty AI-driven triage tools to deal with the slop. But the triage tools have the same problem: they just don't work that well. Plenty of garbage gets through, and plenty of actual issues get filtered out.
The first movers get some eye-popping numbers to show management, but the aggregate productivity of the corporation is unchanged at best.
So is the productivity increase an illusion? Depends how you look at it. Is the productivity boost for the corporation an illusion? Yes. But, most ICs are measured by tickets closed, commits pushed, lines of code written, and tokens used. Like it or not, that is the definition of "productivity" that has been given to ICs. They're just doing what they're told.
AI productivity only works if you were the only person in the world using it and no-one else was aware of it. You will look like a wizard, passing off slop to others who are too amazed and ignorant to know any better.
The fact that that is exactly what is happening (everyone passing off slop to everyone else and pretending either they didn't use AI or it isn't slop actually) is telling.
The mistake was giving it to everyone too early too fast. "If everyone is super, no one will be"
bike shedding with 28 agents lets you do nothing at scale