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AI adoption and Solow's productivity paradox

fortune.com

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Re: AI adoption and Solow's productivity paradox

#311

Post by aurareturn »

vjk800 wrote: post #299
aurareturn wrote: post #232

Earlier quoted context omitted.

The future of work is fewer human team members and way more AI assistants. I think companies will need fewer engineers but there will be more companies. Now: 100 companies who employ 1,000 engineers each What we are transitioning to: 1000 companies who employ 10 engineers each What will happen in the future: 10,000 companies who employ 1 engineer each Same number of engineers. We are about to enter an era of explosiv…

> I think companies will need fewer engineers but there will be more companies. This would be strange, because all other technology development in history has taken things the exact opposite direction; larger companies that can do things on scale and outcompete smaller ones.

  This would be strange, because all other technology development in history has taken things the exact opposite direction; larger companies that can do things on scale and outcompete smaller ones.
I don't think this has always been true.

Youtube allowed many more small media production companies - sometimes just one person in their garage.

Shopify allowed many more small retailers.

Steam & cheap game engines allowed many more indie game developers instead of just a few big studios.

It likely depends on the stage of the tech development. I can see Youtube channels consolidating into a few very large channels. But today, there are far more media production companies than 30 years ago.

Re: AI adoption and Solow's productivity paradox

#312

Post by fragmede »

lolive wrote: post #261
abraxas wrote: post #64

Earlier quoted context omitted.

What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.

We made an under-the-radar optimization in a data flow in my company. A given task is now much more freshData-assisted that it used to. Was a LLM used during that optimization? Yes. Who will correlate the sudden productivity improvement with our optimization of the data flow with the availability of a LLM to do such optimizations fast enough that no project+consultants+management is needed ? No one. Just like no one…

CONEXPO, World of Concrete, and NAHB IBS is where vendors go to show off their new ladders and the attendees totally evaluate the value of those ladders vs their competitors.

Re: AI adoption and Solow's productivity paradox

#313

Post by vrighter »

simonw wrote: post #159
Esophagus4 wrote: post #68

Earlier quoted context omitted.

I always assumed that with inference being so cheap, my subscription fees were paying for training costs, not inference.

Anthropic and OpenAI are both well documented as losing billions of dollars a year because their revenue doesn't cover their R&D and training costs, but that doesn't mean their revenue doesn't cover their inference costs.

Models are fixed. They do not learn post training.

Which means that training needs to be ongoing. So the revenue covers the inference? So what? All that means is that it doesn't cover your costs and you're operating at a loss. Because it doesn't cover the training that you can't stop doing either.

Re: AI adoption and Solow's productivity paradox

#314

Post by aurareturn »

gzread wrote: post #293
aurareturn wrote: post #288

Earlier quoted context omitted.

I barely know how assembly, CPUs, GPUs, compilers, networking work. Yet, software that I've designed and written have been used by hundreds of millions of people. Sure, maybe you would have caught the bug if you wrote assembly instead of C. But the C programmer still released much better software than you faster. By the time you shipped v1 in assembly, the C program has already iterated 100 times and found product ma…

Casey Muratori says that every programmer should understand how computers work and if you don't understand how computers work you can't be a good programmer.

I might not be a good programmer but I've been a very productive one.

Someone who is good at writing code isn't always good at making money.

Re: AI adoption and Solow's productivity paradox

#315

Post by globular-toast »

crazygringo wrote: post #21

Just to be clear, the article is NOT criticizing this. To the contrary, it's presenting it as expected , thanks to Solow's productivity paradox [1]. Which is that information technology similarly (and seemingly shockingly) didn't produce any net economic gains in the 1970's or 1980's despite all the computerization. It wasn't until the mid-to-late 1990's that information technology finally started to show clear benef…

If things like computer-aided design and improved supply chain management, for example, make manufactured goods last longer and cause less waste, I would expect IT to cause productivity to go down. I drive a 15 year old car and use a 12 year old PC. It's a good thing that productivity goes down, or stays the same.

Re: AI adoption and Solow's productivity paradox

#317

Post by EliRivers »

Aurornis wrote: post #132
abraxas wrote: post #64

Earlier quoted context omitted.

What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.

> but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book. That book was very different than what I expected from all of the internet comment takes about it. The premise was really thin and did't actually support the idea that the jobs don't generate value. It was comparing to a hypothetical world where everything is perfectly organized, everyon…

"They exist because the company gets more benefit out of the job existing than it costs to employ those people."

Sure, but there's no such thing as "the company." That's shorthand - a convenient metaphor for a particular bunch of people doing some things. So those jobs can exist if some people - even one person - gets more benefit out of the job existing than it costs that person to employ them. For example, a senior manager padding his department with non-jobs to increase headcount, because it gives him increased prestige and power, and the cost to him of employing that person is zero. Will those jobs get cut "eventually"? Maybe, but I've seen them go on for decades.

Re: AI adoption and Solow's productivity paradox

#318

Post by ben_w »

vidarh wrote: post #282
overgard wrote: post #187

Earlier 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…

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.

Re: AI adoption and Solow's productivity paradox

#319

Post by marcyb5st »

xienze wrote: post #279
brandensilva wrote: post #130

Earlier quoted context omitted.

Yeah it's the caching that's doing the work for them though honestly. So many cached queries saving the GPUs from hard hits.

How is caching implemented in this scenario? I find it unlikely that two developers are going to ask the same exact question, so at a minimum some work has to be done to figure out “someone’s asked this before, fetch the response out of the cache.” But then the problem is that most questions are peppered with specific context that has to be represented in the response, so there’s really no way to cache that.

From my understanding (which is poor at best), the cache is about the separate parts of the input context. Once the LLM read a file the content of that file is cached (i.e. some representation that the LLM creates for that specific file, but I really have no idea how that works). So the next time you bring either directly or indirectly that file into the context the LLM doesn't have to do a full pass, but pull its understanding/representation from the cache and uses that to answer your question/perform the task.

Re: AI adoption and Solow's productivity paradox

#320

Post by ben_w »

generic92034 wrote: post #302
krainboltgreene wrote: post #239

Earlier quoted context omitted.

There's an argument to be made that a "return on investment by way of eliminating all workers" is a reasonable result for the capitalists.

At least until they are running out of customers. And/or societies with mass-unemployment destabilize to a degree that is not conducive for capitalists' operations.

That's a problem above most CEOs' pay grade.
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