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Comment by abathologist | Hacker News Reader

But actually 40% of the S&P 500 are tech companies, and they are saying only the mag 7 are showing clear revenue increase. Would would suggest that even in most tech we are not seeming measurable revenue growth, right?

One plausible explanations that has occurred to me is this: While programmers etc. can produce way more code with LLMs, the declines in quality and coherence of the work, and a widening gap with a coherent strategy and aim for value means the increased activity is mostly not valuable. I suspect this may just be an intensification of https://en.wikipedia.org/wiki/Productivity_paradox#Little_pr...

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The mag 7 are getting direct or nearly direct revenue from AI inference. New money is pouring into that - it is not zero sum. Everyone else is getting internal productivity lifts that are matched by competition, and when they are deploying inference features they are usually ones their customers didn’t ask for and don’t value, OR that they expect everyone to have and are not a differentiator.

I think you are also correct that a lot of value is being wasted with projects that are developed impressively fast but that provide little value. It could be those are simply surplus personnel trying to add value or make people think they do.

> Everyone else is getting internal productivity lifts that are matched by competition

The main metric analyzed in the note is

> Revenue per worker

This can increase regardless of competition, but I guess would require either expansion into new domains or layoffs. So maybe we will see this raise as a lagging indicator if the LLMs are actually delivering the goods.

> when they are deploying inference features they are usually ones their customers didn’t ask for and don’t value, OR that they expect everyone to have and are not a differentiator

I am definitely seeing lots of this happening. Tons of energy going into side quests that aren't critical for anything. Meanwhile, strangely, the central features we actually need to ship, but that require deep redesigns and careful refactors in legacy code, are not moving quickly at all.

Increased productivity is caused by mass layoffs, not by AI adoption.

In case you are not being sarcastic, layoffs obviously don't "cause" productivity increase. [Edit: but they would help the main metric (Revenue per worker) analyzed in the note. so perhaps that is what we will see.]

Not sarcastic, it's how productivity is usually measured when you see any kind of stats. Not features, not lines of code. Revenue (or GDP if we talk about the country) per worker

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