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Jevons paradox strikes again. When work becomes more productive often the business finds so much more to do that they end up using even more resources. Why do more with less when you can do even more with more?
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> I would just expect with this AI narrative they'd be "doing more with less" as this thing improves every year.

That wouldn't really be an expected outcome for a company like Anthropic. They need to do the maximum they can with the funding they have, or risk losing out to competitors who don't take their foot off the gas.

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I mean, just what I did this morning with Claude would have taken me 2 weeks on my own. So 20x is pretty reasonable for certain types of engineering.

But it's also true that when I'm working on 2 things at once, the bottleneck is me. I'm spending all my time giving Claude direction that it can't give itself.

So you basically have to separate out the tasks where AI can produce a 20x productivity boost, and the stuff where it can't help at all.

There's still so much stuff where it can't help at all, you need real human employees for that. That's why Anthropic is continuing to hire at breakneck pace.

And that's what I'm saying -- knowledge workers aren't being replaced. They're just accomplishing tons more work, and companies aren't running out of work to do.

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Sorry, I know this is the story, but am I expected to believe that Anthropic would need 20,000+ engineers in their org without their use of AI? I do not believe that is close to true.
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No, one thing that would happen is that many projects would never be undertaken - basically, there's latent demand for software that would simply go unmet, because of resource constraints.

I was recently involved in a case like this - we had a critical part of our system based on a dependency that's no longer maintained. Previously, we would have just carried on for a year or more, living with the limitations until it was much closer to being entirely untenable, because rewriting the code in question would have been a multi-month project involving multiple developers, QA, and management time. But now, the code in question was migrated in a few days using AI, along with a suite of new tests in addition to our existing ones, that gave us a lot of confidence in the results. The productivity improvement here is at least on the order of 25x, possibly higher.

But most developers aren't sitting at their desks operating at a 25x pace all day, every day, so average productivity improvement is going to be lower. And of course, some use of AI will be suboptimal or problematic: waste due to e.g. duplicate development which happens when development is so easy, or costs for cleanup of mistakes.

We also now have a slew of AI-generated internal tools, most of which would never have been developed before. Many of them are very useful, the kind of thing that's on your eternal wishlist but never gets done. Some are kinda useless, so represent wasted resources. It's difficult to measure the productivity benefit of all this, because you'd have to compare it to the non-existent company without those tools.

Ultimately, measuring all this meaningfully is not really that feasible, except perhaps over the long term. Managers operate on vibes with this kind of thing because in many cases, that's all you can really do.

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