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AI adoption and Solow's productivity paradox
401–410 of 783 posts
Re: AI adoption and Solow's productivity paradox
#402Earlier quoted context omitted.
And if you make someone 3x faster at producing a report that 100 people has to read, but it now takes 10% longer to read and understand, you’ve lost overall value.
You are forgetting that they are now going to use AI to summarize it back.
This reminds me of that "telephone" kids game.
Re: AI adoption and Solow's productivity paradox
#403Earlier quoted context omitted.
At some point the players will need to reach profitability. Even if they're subsidising it with other revenue - they'll only be willing to do that as long as it drives rising inference revenue. Once that happens, whomever is left standing can dial back the training investment to whatever their share of inference can bear.
> Once that happens, whomever is left standing can dial back the training investment to whatever their share of inference can bear. Or, if there's two people left standing, they may compete with each other on price rather than performance and each end up with cloud compute's margins.
Sure, but they will still need to dial it back to a point where they can fund it out of inference at some point. The point is that the fact they can't do that now is irrelevant - it's a game of chicken at the moment, and that might kill some of them, but the game won't last forever.
Re: AI adoption and Solow's productivity paradox
#404Earlier quoted context omitted.
You are forgetting that they are now going to use AI to summarize it back.
This is one of my major concerns about people trying to use these tools for 'efficiency'. The only plausible value in somebody writing a huge report and somebody else reading it is information transfer. LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high, and you will be worse off reading the summary than if you skimmed the first and last pages. In fact, you will be worse off than if you…
> Those questions that it will no longer occur to you to ask (not just of the robot, but of yourself) might be the most pertinent ones!
That is true, but then again also with google. You could see why some people want to go back to the "read the book" era where you didn't have google to query anything and had to make the real questions.
Re: AI adoption and Solow's productivity paradox
#405Earlier quoted context omitted.
LLMs might not save time but they certainly increase quality for at least some office work. I frequently use it to check my work before sending to colleagues or customers and it occasionally catches gaps or errors in my writing.
But that idealized example could also be offset by another employee who doubles their own output by churning out lower-quality unreviewed workslop all day without checking anything, while wasting other people's time.
Something I call the 'Generate First, Review Never' approach, seemingly favoured by my colleagues, and which has the magical quality of increasing the overall amount of work done through an increased amount of time taken by N receivers of low-quality document having to review, understand and fact check said document.
See also: AI-Generated “Workslop” Is Destroying Productivity [1]
[1] https://hbr.org/2025/09/ai-generated-workslop-is-destroying-...
Re: AI adoption and Solow's productivity paradox
#406Earlier quoted context omitted.
This is exactly what I meant, a centrally-planned economy where the state owns everything and people are forced to give everything up is just one terrible (Soviet) model, not some defining feature of socialism. Yugoslavia was extremely successful, with economic growth that matched or exceeded most capitalist European economies post-WW2. In some ways it wasn't as free as western societies are today but it definitely w…
"just one terrible (Soviet) model" It is the model, introduced basically everywhere where socialism was taken seriously. It is like saying that cars with four wheels are just one terrible model, because there were a few cars with three wheels. Yugoslavia was a mixed economy with a lot of economic power remaining in private hands. You cannot point at it and say "hey, successful socialism". Tito was a mortal enemy of S…
You've created a tautology: Socialism is bad because bad models are socialism and better models are not-socialism.
> You cannot point at it and say "hey, successful socialism"
Yes I can because ideological purity doesn't exist in the real world. All of our countries are a mix of capitalist and socialist ideas yet we call them "capitalist" because that's the current predominant organization.
> Tito was a mortal enemy of Stalin, stroke a balanced neither-East-nor-West, but fairly friendly to the West policy already in 1950, and his collectivization efforts were a fraction of what Marxist-Leninist doctrine demands.
You're making my point for me, Yugoslavia was completely different from USSR yet still socialist. Socialism is not synonymous with Marxist-Leninist doctrine. It's a fairly simple core idea that has an infinite number of possible implementations, one of them being market socialism with worker cooperatives.
Aside from that short period post-WW2, no socialist or communist nation has been allowed to exist without interference from the US through oppressive economic sanctions that would cripple and destroy any economy regardless of its economic system, but people love nothing more than to draw conclusions from these obviously-invalid "experiments".
"You" (and I mean the collective you) are essentially hijacking the word "socialism" to simply mean "everything that was bad about the USSR". The system has been teaching and conditioning people to do that for decades, but we should really be more conscious and stop doing that.
Re: AI adoption and Solow's productivity paradox
#407Earlier quoted context omitted.
Our job is not the intellectual exercise you think it is. We're not smarter than anyone else and software development is not automatically more thought-intensive than other jobs. The fact that programming is the first job task to be fully automated says it all.
It’s weird that you equate time spent thinking with intelligence and egotism. Plenty of “normal people” jobs require lots of time spent thinking like art, writing, product and ad design. The only one implying taking time to think equals big brain master race is you
"Normal people"? "Big brain master race"? The only one implying weird things here is you.
Re: AI adoption and Solow's productivity paradox
#408Earlier quoted context omitted.
This is one of my major concerns about people trying to use these tools for 'efficiency'. The only plausible value in somebody writing a huge report and somebody else reading it is information transfer. LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high, and you will be worse off reading the summary than if you skimmed the first and last pages. In fact, you will be worse off than if you…
> LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high I could go either way on the future of this, but if you take the argument that we're still early days, this may not hold. They're notoriously bad at this so far . We could still be in the PC DOS 3.X era in this timeline. Wait until we hit the Windows 3.1, or 95 equivalent. Personally, I have seen shocking improvements in the past 3 mo…
I would like to see the day when the context size is in gigabytes or tens of billions of tokens, not RAG or whatever, actual context.
Re: AI adoption and Solow's productivity paradox
#409Earlier quoted context omitted.
Does it matter if they can't ever stop training though? Like, this argument usually seems to imply that training is a one-off, not an ongoing process. I could save a lot of money if I stopped eating, but it'd be a short lived experiment. I'll be convinced they're actually making money when they stop asking for $30 billion funding rounds. None of that money is free! Whoever is giving them that money wants a return on…
It matters because as long as they are selling inference for less than it costs to serve they have a potential path to profitability. Training costs are fixed at whatever billions of dollars per year. If inference is profitable they might conceivably make a profit if they can build a model that's good enough to sign up vast numbers of paying customers. If they lose even more money on each new customer they don't have…
> If they lose even more money on each new customer they don't have any path to profitability at all.
In theory they can increase prices once the customers will be hocked up. That's how many startups works.
Re: AI adoption and Solow's productivity paradox
#410Earlier quoted context omitted.
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Maybe the take is that those reports that people took a day to write were read by nobody in the first place and now those reports are being written faster and more of them are being produced but still nobody reads them. Thus productivity doesn't change. The solution is to get rid of all the people who write and process reports and empower the people who actually produce stuff to do it better.
> The solution is to get rid of all the people who write and process reports and empower the people who actually produce stuff to do it better.
That’s the solution if you’re the business owner.
That’s definitely not the solution if you’re a manager in charge of this useless activity, in fact, you should increase the amount of reports being written as much as humanly possible. The more underlings under you= more power and prestige.
This is the principal-agent problem writ large. As the comment mentioned above, also see Graeber’s Bullshit Jobs essay and book.