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That’s true. My point was that the article is claiming that since the share of GDP which is consumer spending decreased, total consumer spending also decreased. But since GDP per capita increased at the same time, the actual total consumer spending per person increased (the 7 percentage point decrease does not outweigh the doubling of real GDP per capita). This could be misleading in its own right, with the richest spending more and the median spending less even in total numbers, but the article doesn’t claim that. It claims that total spending has gone down, which is just not true.

This reads like a lazily written article to me. The em dashes don’t increase my enthusiasm. Just in the opening I noticed:

Consumer spending as a share of US GDP moved from roughly 61% in 1980 to about 68% today. technology is not meaningfully expanding the total amount humans consume

Of course, real GDP per capita more than doubled in this time period which means consumer spending also doubled (more since it increased by 7pp). Is most of this billionaire yachts? I have no clue, but if you want to convince me you should try to not claim total amounts when you mean relative amounts.

A physical bookstore in 2000 took in $100 from a book sale and distributed it roughly like this: about 60% went to labor (store staff, publisher employees, authors), 30% went to capital (owner profit, rent), and 10% covered other costs. The money circulated locally through wages.

Amazon today takes in that same $100. The distribution looks fundamentally different: warehouse and tech labor receives roughly 25%, Amazon’s infrastructure and profit captures around 55%, and the remainder flows to publishers and authors. Labor’s share of that transaction dropped by more than half.

… unless you count the publisher and authors like you did for the 2000s data, in which case it decreased from 60% to 45%. And that’s persumably not counting the manufacturing of server farms, refinement of minerals, purchase of the actual reading tablet. Amazon has high margins but not 55% margins.

The labor share of US GDP fell from approximately 64% in 1980 to around 58% today — a 6-percentage-point shift. Applied to a $28 trillion economy, that gap represents roughly $1.7 trillion per year that once flowed to workers but now flows to capital.

Once again, since the GDP per capita has doubled the labor dollars per person has actually increased. The label for the $1.7 trillion is similarly misleading, those dollars never “once flowed to workers”, they just would have if the economy had grown without any changes to its composition.

If I were the author of the article, perhaps I would say that since 1980, real median wages have only grown by about 20% which seems very slight given the technological improvements made in that time. But how much of that 20% increase would have been possible without technological improvement, and how much has the quality of the things people spend their money on grown in that time? No clue, that’s beyond the thinking budget I have for this article.

Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q3 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

I use LLMs for the following, you can decide for yourself if they are major enough:

  • Generating example solutions to maths and physics problems I encounter in my coursework, so I can learn how to solve similar problems in the future instead of getting stuck. The generated solutions, if they come up with the right answer, are almost always correct and if I wonder about something I simply ask.
  • Writing really quick solutions to random problems I have in python or bash scripts, like “convert this csv file to this random format my personal finance application uses for import”.
  • Helping me when coding, in a general way I think genuinely increases my productivity while I really understand what I push to main. I don’t send anything I could not have written on my own (yes, I see the limitations in my judgement here).
  • Asking things where multiple duckduckgo searches might be needed. E.g. “Whats the history of EU+US sanctions on Iran, when and why were they imposed/tightened and how did that correlate with Iranian GDP per capita?”

What does this cost me? I don’t pay any money for the tech, but LLM providers learn the following about me:

  • What I study (not very personal to me)
  • Generally what kinds of problems I want to solve with code (I try to keep my requests pretty general; not very personal)
  • The code I write and work on (already open source so I don’t care)
  • Random searches (I’m still thinking about the impact of this tbh, I think I feel the things I ask to search for are general enough that I don’t care)

There’s also an impact on energy and water use. These are quite serious overall. Based on what I’ve read, I think that my marginal impact on these are quite small in comparison to other marginal impacts on the climate and water use in other countries I have. Of course there are around a trillion other negative impacts of LLMs, I just once again don’t know how my marginal usage with no payment involved lead to a sufficient increase in their severity to outweigh their usefulness to me.

The pizza place looks like it exists: www.burattinopizza.ca

The slogan “Crunch baby crunch” is also correct. Also the text seems far too consistent especially when the cardboard is bent to be AI generated, even if a reference image was given. And if you were generating an AI video, why would you use this unknown pizza place with two locations and a complex logo and not a chain which the model has seen millions of boxes of?

If this was an AI generated video, I would expect the hand to rip through the cardboard without there being any damage to it before. But at the start of the video you can see a hole punched through to make it possible to get a grip and tear. There’s also a piece of pepperoni stuck which is a detail I’d expect AI to miss but makes sense if scissors were stuck down and rotated to enlargen the hole. This pepperoni is then preserved even after it is covered and flies away a second later. I dont have access to video generators to check but I think this might be possible although difficult with current SOTA. All of this leads me to conclude the video is real.

The voiceover certainly sounds AI generated, but theres really no good way to tell anymore especially from such a short clip.

OP is mocking you

What??? They saw a video online and reposted it to a shitposting community on lemmy. The point of the video is that it’s a parody of the stupid life hacks you don’t like. If they wanted to mock people there are much better ways to do so.

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Burattino Brick Oven Pizza Inc. Take out and delivery in Toronto.

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Chad rule - sh.itjust.works

Lemmy