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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

#91

Post by ozgrakkurt »

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…

Thinking is always the hardest part and the bottleneck for me.

It doesn’t capture everyone’s experience when you say thinking is the smaller part of programming.

I don’t even believe a regular person is capable of producing good quality code without thinking 2x the amount they are coding

Re: AI adoption and Solow's productivity paradox

#92

Post by bryanlarsen »

46493168 wrote: post #78
torben-friis wrote: post #42

Earlier quoted context omitted.

The comparison seems flawed in terms of cost. A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer. Larger implementations like automating customer service might be more costly, but…

>I think there are enough short term supposed benefits that something should be showing there. As measured by whom? The same managers who demanded we all return to the office 5 days a week because the only way they can measure productivity is butts in seats?

Productivity is the ratio of outputs to inputs, both measured in dollars.

Re: AI adoption and Solow's productivity paradox

#93

Post by hattmall »

abraxas wrote: post #64
torben-friis wrote: post #42

Earlier quoted context omitted.

The comparison seems flawed in terms of cost. A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer. Larger implementations like automating customer service might be more costly, but…

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.

I find that highly unlikely, coding is the AIs best value use case by far. Right now office workers see marginal benefits but it's not like it's an order of magnitude difference. AI drafts an email, you have to check and edit it, then send it. In many cases it's a toss up if that actually saved time, and then if it did, it's not like the pace of work is break neck anyway, so the benefit is some office workers have a bit more idle time at the desk because you always tap some wall that's out of your control. Maybe AI saves you a Google search or a doc lookup here and there. You still need to check everything and it can cause mistakes that take longer too. Here's an example from today.

Assistant is dispatching a courier to get medical records. AI auto completes to include the address. Normally they wouldn't put the address, the courier knows who we work with, but AI added it so why not. Except it's the wrong address because it's for a different doctor with the same name. At least they knew to verify it, but still mistakes like this happening at scale is making the other time savings pretty close to a wash.

Re: AI adoption and Solow's productivity paradox

#94

Post by ahepp »

I read an article in FT just a couple days ago claiming that increased productivity was becoming visible in economic data

> My own updated analysis suggests a US productivity increase of roughly 2.7 per cent for 2025. This is a near doubling from the sluggish 1.4 per cent annual average that characterised the past decade.

good for 3 clicks: https://giftarticle.ft.com/giftarticle/actions/redeem/97861f...

Re: AI adoption and Solow's productivity paradox

#95

Post by fourthark »

pram wrote: post #16

It’s funny because at work we have paid Codex and Claude but I rarely find a use for it, yet I pay for the $200 Max plan for personal stuff and will use it for hours! So I’m not even in the “it’s useless” camp, but it’s frankly only situationally useful outside of new greenfield stuff. Maybe that is the problem?

Why do you find it useless for legacy code? I find I have to give it plenty of context but it does pretty well on legacy code.

And Ask DeepWiki is a great shortcut for finding the right context… Granted this is open source and DW is free.

Is it the specific nature of your work?

Re: AI adoption and Solow's productivity paradox

#96

Post by danans »

LPisGood wrote: post #31
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…

I’m confused what kind of software engineer jobs there are that don’t involve meeting with people, “aligning expectations”, getting consensus, making slides/decks to communicate that, thinking about market positioning, etc? If you weren’t doing much of that before, I struggled to think of how you were doing much engineering at all, save some more niche extremely technical roles where many of those questions were alre…

> I’m confused what kind of software engineer jobs there are that don’t involve meeting with people, “aligning expectations”, getting consensus, making slides/decks to communicate that, thinking about market positioning, etc?

I'd suspect the kind that's going away.

Re: AI adoption and Solow's productivity paradox

#97

Post by hydrox24 »

rr808 wrote: post #43

BTW the study was from September 2024 to 2025, so its the very earliest of adopters.

This article is mostly based on NBER working paper 34836, which was published this month, and the data was collected from September 2025 to January 2026[0]

[0]: See page 2: https://www.nber.org/system/files/working_papers/w34836/w348...

Re: AI adoption and Solow's productivity paradox

#98

Post by gsf_emergency_6 »

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…

Fwiw fortune had another article this week saying this J-curve of "General Technology" is showing up in the latest BLS data

https://fortune.com/2026/02/15/ai-productivity-liftoff-doubl...

Source of the Stanford-approved opinion: https://www.ft.com/content/4b51d0b4-bbfe-4f05-b50a-1d485d419...

https://www.apolloacademy.com/waiting-for-the-ai-j-curve/

Re: AI adoption and Solow's productivity paradox

#99

Post by MrDarcy »

Herring wrote: post #9

My compsci brain suggests large orgs are a distributed system running on faulty hardware (humans) with high network latency (communication). The individual people (CPUs) are plenty fast, we just waste time in meetings, or waiting for approval, or a lot of tasks can't be parallelized, etc. Before upgrading, you need to know if you're I/O Bound vs CPU Bound.

Interesting analogy to explore a Distributed System as compared to Organizational Dynamics.

Re: AI adoption and Solow's productivity paradox

#100

Post by protocolture »

abraxas wrote: post #64
torben-friis wrote: post #42

Earlier quoted context omitted.

The comparison seems flawed in terms of cost. A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer. Larger implementations like automating customer service might be more costly, but…

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.

Would hardly drag Graeber into this, theres a laundry list of issues with his research.

Most "Bullshit Jobs" can already be automated, but can isnt always should or will. Graeber is a capex thinker in an opex world.

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