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AI adoption and Solow's productivity paradox

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Re: AI adoption and Solow's productivity paradox

#291

Post by matwood »

aurareturn wrote: post #232
tabs_or_spaces wrote: post #218

My experience has been * If I don't know how to do something, llms can get me started really fast. Basically it distills the time taken to research something to a small amount. * if I know something well, I find myself trying to guide the llm to make the best decisions. I haven't reached the state of completely letting go and trusting the llm yet, because the llm doesn't make good long term decisions * when working a…

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…

> smaller companies

And large companies. The first half of my career was spent writing internal software for large companies. I believe it's still the case that the majority of software written is for internal software. AI will be a boon for these use cases as it will make it easier for every company big and small to have custom software for its exact use case(s).

Re: AI adoption and Solow's productivity paradox

#292

Post by gzread »

emp17344 wrote: post #90
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.

The thesis of Bullshit Jobs is almost universally rejected by economists, FYI. There’s not much of value to obtain from the book.

Why should I believe "economists" over "David Grabber"?

Re: AI adoption and Solow's productivity paradox

#293

Post by gzread »

aurareturn wrote: post #288
xienze wrote: post #284

Earlier quoted context omitted.

> The seniors today who have got to senior status by writing code manually will be different than seniors of tomorrow, who got to senior status using AI tools. That’s putting it mildly. I think it’s going to be interesting to see what happens when an entire generation of software developers who’ve only ever known “just ask the LLM to do it” are unleashed on the world. I think these people will have close to no unders…

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.

Re: AI adoption and Solow's productivity paradox

#294

Post by itake »

alternatex wrote: post #268
itake wrote: post #244

Earlier quoted context omitted.

yeah, I agree. When Engineering Budget Managers see their AI bills rising, they will fire the bottom 5-10% every 6-12 months and increase the AI assistant budget for the high performers, giving them even more leverage.

Now if only companies knew how to correctly assess actual impact and not perceived impact.

I don't think this is an AI problem. Even before AI, FANGA companies famously optimize promotions on perceived impact.

During the promo review, people will look how many projects were done and the impact of those projects.

Re: AI adoption and Solow's productivity paradox

#295

Post by bandrami »

d--b wrote: post #283

CEOs have no clue what's going on at the IC level. I bet many CEO PA are using AI for many tasks. It's typically a role where AI is very useful. Answering emails, moving meetings around, booking and buying a bunch of crap.

I ask this as a genuine question: who needs help "answering emails" and what part of it do they need help with?

Re: AI adoption and Solow's productivity paradox

#296

Post by vjk800 »

My experience has been that AI is much more useful on my own systems than on company systems. For AI to (currently) be useful, I need to choose my own tooling and LLM models to support AI centered workflow. At work, I have to use whatever (usually Microsoft) tools my company has chosen to purchase and approve for my corporate computer, and usually nothing works as well as on my own machine where I get to set it up as I want.

Re: AI adoption and Solow's productivity paradox

#297

Post by coffeebeqn »

lqstuart wrote: post #184

I was in the “AI is grossly overhyped” camp because I work on large distributed deep learning training jobs and AI is indeed worthless for those, and will likely always be worthless since the APIs change constantly and the iteration loop is too cumbersome to constantly resubmit broken jobs to a training cluster. Then I started working on some basic grpc/fullstack crap that I absolutely do not care about, at all, but…

Ah I see what my goal for this year is then. I have a large Steam backlog to work through. Unfortunately we currently code in short bursts and mostly are trying to figure out how these integrations are supposed to happen and why the different teams tell us different things

Re: AI adoption and Solow's productivity paradox

#298

Post by Kiro »

overgard wrote: post #196
sebmellen wrote: post #12

The thing with a lot of white collar work is that the thinking/talking is often the majority of the work… unlike coding, where thinking is (or, used to be, pre-agent) a smaller percentage of the time consumed. Writing the software, which is essentially working through how to implement the thought, used to take a much larger percentage of the overall time consumed from thought to completion. Other white collar busines…

> The thing with a lot of white collar work is that the thinking/talking is often the majority of the work… unlike coding, where thinking is (or, used to be, pre-agent) a smaller percentage of the time consumed. WHOAH WHOAH WHOAH WHOAH STOP. No coder I've ever met has thought that thinking was anything other than the BIGGEST allocation of time when coding. Nobody is putting their typing words-per-minute on their resu…

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.

Re: AI adoption and Solow's productivity paradox

#299

Post by vjk800 »

aurareturn wrote: post #232
tabs_or_spaces wrote: post #218

My experience has been * If I don't know how to do something, llms can get me started really fast. Basically it distills the time taken to research something to a small amount. * if I know something well, I find myself trying to guide the llm to make the best decisions. I haven't reached the state of completely letting go and trusting the llm yet, because the llm doesn't make good long term decisions * when working a…

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.

Re: AI adoption and Solow's productivity paradox

#300

Post by coffeebeqn »

bazmattaz wrote: post #245
bubblewand wrote: post #7

My company’s behind the curve, just got nudged today that I should make sure my AI use numbers aren’t low enough to stand out or I may have a bad time. Reckon we’re minimum six months from “oh whoops that was a waste of money”, maybe even a year. (Unless the AI market very publicly crashes first)

My manager mentioned that his manager (an executive) is not happy because the org we are in are not using as much tokens as other orgs in the company. Pretty wild

Just have Claude code churn out some Harry Potter fan fiction for an hour a day and you’ll meet your KPI easily
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