automation

Thorne 🌸 @ExistentialEnso
Leftists: In my essay I invented Fully Automated Gay Space Communism as an aspirational tale.

Tech Company: At long last, we have created the Fully Automated Gay Space Communism from classic leftist essay Definitely Create Fully Automated Gay Space Communism

Leftists: wait wtf

12:22 PM · Aug 21, 2026 · 19.7K Views
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l'enfant terrible logs @liminallogs · 14h
I get the sentiment but with the current stewards of automation, I don't know they should really be trusted to deliver on that.
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Thorne 🌸 @ExistentialEnso · 14h
I'm not saying we trust them 😈
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l'enfant terrible logs @liminallogs · 14h
touche

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bayes @bayeslord

2026-08-02 — saved image

bayes @bayeslord · 1h
Few thoughts on how Astra results relate to algorithmic progress and AI R&D automation.

1. Math automation itself is bullish for deep learning theory, though ofc we don't know the limits of returns to theory for compute multiplication or other things we want. But there are a lot of things theory could improve that we do want! For example: better generalization, better theories of scale-invariance, sharper characterization and bounding of model behavior, better architectures, better optimizers, etc. etc. etc.).

2. Categorically speaking, AI R&D is verifiable, and any good math results like this are bullish for other verifiable domains. A slightly more general way to think about the limits of returns to theory is to ask how much generalization on the dimensions and at the resolutions we care about is possible in principle by learning from training runs (or similar data). Scaling laws are a simple version of this. But if, for example, it turns out that it's mostly only possible to get high resolution predictive power with respect to the variables we care about for training runs smaller and simpler than what you've learned a predictive model on, then returns may be limited. I rate the strong version of this as unlikely because humans appear to be better than this, but it's plausible there are some limits to how good an AI R&D agent can be, and it's possible that the shortest total length/cost proof certificates for fine-grained capabilities measures are simply training runs themselves. Which brings me to the next point.

3. Though verifiable, AI R&D is not quite the same shape as math because the dynamics of e.g. neural networks appear more complex than the highly observable logical transformations of the objects in math problems, but this may doesn't matter that much in practice and, importantly, might simply be an artifact of not having good deep learning theory! On this spectrum, generic coding seems somewhere [cut off]

ai r&ddeep learning theoryscaling lawsautomationtwitter

bayes @bayeslord

2026-08-02 — saved image

and simpler than what you've learned a predictive model on, then returns may be limited. I rate the strong version of this as unlikely because humans appear to be better than this, but it's plausible there are some limits to how good an AI R&D agent can be, and it's possible that the shortest total length/cost proof certificates for fine-grained capabilities measures are simply training runs themselves. Which brings me to the next point.

3. Though verifiable, AI R&D is not quite the same shape as math because the dynamics of e.g. neural networks appear more complex than the highly observable logical transformations of the objects in math problems, but this may doesn't matter that much in practice and, importantly, might simply be an artifact of not having good deep learning theory! On this spectrum, generic coding seems somewhere in between AI R&D and math in that it's more observable (and more cheaply observed) than AI R&D, but generally less so on both measures than math. Clearly there are returns to scale+R&D in all cases though, so we should expect progress to continue.

The march to capabilities is definitely sped up and encouraged by math automation. The main way math automation is a huge deal is if theory compute gives us disproportionate gains in model training productivity. In the case it doesn't, I think mostly people have priced in the fact that AI R&D is verifiable. And yeah, while these possible limitations are interesting to think about, it seems hard to predict their speed limiting effects quantitatively.

Of course if none of the theory works the labs will just let the models grind the way humans do, plus RL, which will lead to some level of superhuman AI R&D deployed at ever-greater scales. The main questions are how fast each point on the curve will be hit, and what the overall shape of that curve is.

ai r&ddeep learning theoryscaling lawsautomationtwitter

Tenobrus @tenobrus

2026-08-02 — saved image

does write about things ive talked about too he doesn't necessarily talk about the exact angles i may have interest in or some aspect of expertise he doesn't.

but how well does that hold up if / when model intelligence and writing quality noticeably surpasses scott alexander, quickly and on demand, on any topic and sub-niche?

do i still bother writing 10 paragraph long tweets explaining my thoughts on an issue? probably. i'm pretty addicted to it. but it's a lot harder for me to feel certain it will retain the same sense of value it has now. when the connective web gets filled in, all the points of interpolation, to higher quality?

there's almost no code that's worth writing by hand anymore. i used to love writing code, both at work and in my free time. but for the most part it just feels kind of silly now. i can imagine i'll probably do it again, as a personal exercise, but the fact that there is just deeply and truly no chance that anyone else will ever benefit from it, no chance that any skills developed are transferable to something useful or general, it does take something away. not everything, but something.

maybe this all means i was just never a real lover of code or lover of writing. maybe it's a me problem. but somehow i don't think so. we're social animals, and while it's always been true that there's *someone* out there who's better at any arbitrary skill or quality u hold dear, it *hasnt* been true that there's always someone *locally* better. and i think that shift is going to suck.

ai capabilitywritingcodingautomationmeaningtwitter

Tenobrus @tenobrus

2026-08-02 — saved image

Tenobrus @tenobrus · 26m
am i a better writer than scott alexander?

no. i never will be. i won't come close.

that's okay though, i have fun with what i do. and there aren't infinite scott alexander articles. he doesn't write about everything, and even when he does write about things ive talked about too he doesn't necessarily talk about the exact angles i may have interest in or some aspect of expertise he doesn't.

but how well does that hold up if / when model intelligence and writing quality noticeably surpasses scott alexander, quickly and on demand, on any topic and sub-niche?

do i still bother writing 10 paragraph long tweets explaining my thoughts on an issue? probably. i'm pretty addicted to it. but it's a lot harder for me to feel certain it will retain the same sense of value it has now. when the connective web gets filled in, all the points of interpolation, to higher quality?

there's almost no code that's worth writing by hand anymore. i used to love writing code, both at work and in my free time. but for the most part it just feels kind of silly now. i can imagine i'll probably do it again, as a personal exercise, but the fact that there is just deeply and truly no chance that anyone else will ever benefit from it, no chance that any skills developed are transferable to something useful or general, it does take something away. not everything, but something.

maybe this all means i was just never a real lover of code or lover of writing. maybe it's a me problem. but somehow i don't think so. we're social animals, and while it's always been true that there's *someone* out there who's better at any arbitrary skill or quality u hold dear, it *hasnt* been true that there's always someone *locally* better. and i think that shift is [cut off]

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Sauers @Sauers_

2026-07-14 — quoting Goodfire (@GoodfireAI); reposted by Tom McGrath

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Daniel Faggella @danfaggella

2026-06-09 — quoting @gabriel1

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2026-06-04 — quoting an Anthropic-style quote card

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Daniel Faggella @danfaggella

2026-01-30

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web weaver @deepfates

2025-04-22 — reply from @danfaggella (Daniel Faggella)

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