Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid - welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned so many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
A recent two hour interview with Kokotajlo I do not have the time to go through all this but if anyone does feel free to go ahead.
What I will say tho is that the thumbnail is definitely some kind of clickbait, because of the “uhm actually we were never confident in 2027” thing
Steven Bartlett barely has 3 brain cells to rub together, so this seems like the perfect outlet for Kokotajlo’s message.
this Diary of a CEO channel has always struck me as the kind to fall for any kind of bs hook line and sinker
long rant about math
The recent big AI results in math have left me in quite a bad mood. I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs. Back in the days of pure scaling LLMs and Sam Altman talking about Dyson spheres, I was skeptical that LLMs would do math, but I did think that perhaps in the future, techniques using these formal languages could contribute to math. Well, it seems like OpenAI and Anthropic had the same idea and I underestimated their limitless checkbooks. Many of the biggest results were announced by mathematicians directly working for them (and presumably being paid a handsome amount).
For what it’s worth, after the last of these big announcements, I decided to try one of these AIs on one of my small problems that I couldn’t figure out. The AI did give a solution. That is, until I checked it thoroughly and realized that the it had a subtle but severe mistake that made it useless. I reprompted it, it failed again, and I ran out of tokens. I’m sure someone will tell me to shell out $200/mo for a pro subscription.
In the math and computer science research community, this is all anyone can really talk about right now. Honestly, after watching this whole AI bubble starting from the very beginning, I think the AI companies want to use marketing to stoke fear that all mathematicians will be replaced. But now, I am just too tired to argue. The amount of alarm and the extraordinary social pressure to use LLMs has soured me to this whole research thing. If becoming a researcher will one day require supporting these evil AI companies, I would rather just not. My dream job now is Factorio developer.
A lot of annoying people in technical areas view the world in terms of an intelligence hierarchy: the smartest people do math and physics, the slightly less smart people do coding, and the dumb people do everything else. So if AI can do math then it can do anything else. But, as an example, it is abundantly obvious now that AI is not replacing filmmaking. The techbros might be moved by arguments about how hilariously expensive video generation is, and how all these videos are 2 second clips stitched together so you won’t feel the uncanny valley. But the real reason is that nobody wants to watch slop made with no intention or feeling. Also, nobody wants to support the AI companies, which could not act more evil even if they tried.
The mania in math right now quite resembles the mania in software engineering back in December-February, when Claude Code definitely solved all coding. I don’t think the boosters expected that by April, everyone would be complaining about how expensive it all was while seeing an endless parade of vibe coding disasters (and no increase in productivity). Even if math research works out perfectly well (which is a still big if), it’s not going to pay the bills. They would need to find a use case in the real world, where hallucinations can cause serious damage and cannot be formally prevented. And they have certainly tried. Math will not change the fact that all of this will collapse.
I reprompted it, it failed again, and I ran out of tokens. I’m sure someone will tell me to shell out $200/mo for a pro subscription.
One of the things that’s never clear from the reporting on ai successes is exactly how much actual paid human time went in to achieving those successes. This was especially notable in the fable-based security work… a huge amount of person-hours went into turning fable-detections into actual meaningful vuln reports.
A lot of demonstrably clever and capable people are involved with the llms-for-maths work, and a lot of money was spent on their time and supporting their work. Replicating it without your own stable of mathematicians and computer scientists and all the tokens they can eat is probably impractical.
I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs.
Fwiw, lean is a general purpose programming language, though despite microsoft’s efforts no-one uses it for that. I think its popularity with mathematicians came as a bit of a surprise.
Anyway, the other important thing that didn’t get reported on is that building the formal definition of the problem is not trivial! Obviously I don’t need to tell you that, but from the reporting you’d think that an llm solved all these problems, when in fact it was an llm in the hands of some very capable people who absolutely did not just prompt the thing in plain english.
Anyone hoping for self-marking homework here is going to be disappointed… lean slop confirming to formal spec slop is just expensive slop. Reviewing regular genai code is awful, even the thought of reviewing genai dependently-typed code makes me want a new career.
It is surprising how many exceptionally strong mathematicians have started working for OpenAI and Anthropic. These people would have easily become professors at top universities if they stayed in academia. I think many mathematicians, especially the competitive ones at the top, have a “progress at any cost” attitude (and I’m sure the paychecks helped). As for the results, you still need good mathematicians to sift through all the output to identify that the proofs are valid.
I would honestly be positive about universities developing their own specialized math AI (in an ethical manner) to help mathematicians get these kinds of results, but right now, AI is inseparable from these evil companies. Thankfully, I believe this is a likely outcome in the future because the AI companies will one day implode.
From what I’ve seen, most prompts are in plain English. I suppose the part where the AI parses the statement correctly is much easier than the part where it boils a couple lakes in the process of bashing its head against the wall trying millions of different combinations of random shit from the literature to slap together a proof. For one of the big results (cycle double cover), the prompt specified that the AI could use 64 subagents and was required to not give up for at least 8 hours. The tokenmaxxers would be proud, we didn’t need that forest anyway. Thank god math doesn’t have a CTO to look at the expense reports.
@lagrangeinterpolator@awful.systems @dgerard@awful.systems To add a note on computer verifiable proofs, a story from my own PhD (Cryptography).
It was the night before either the submission-deadline or the deadline for the rebuttal for PQ-Wireguard. Andy and Peter had already retired for the night, basically announcing that they would do the final touches in the morning, with Kai-Chun and me instead working late to finish some TODOs and in exchange being done the following day.
I pretty much got most of the stuff I had been working on done and moved to the Tamarin proofs for some editorial work there and noticed that a statement, for which we had a Tamarin verified proof didn’t seem to make sense. As in: I could give a counter-example in the form of an instantiation of PQ-WG, where that property would be trivially not there! (I believe it was anonymity and that trivially doesn’t hold for any KEMs where the ciphertext reveals the public key, which is not something that is at all incompatible with standard KEM-notions.)
Turns out: Tamarin works in an idealized model and what the proof actually meant was more akin to “There is nothing fundamental in your model that prevents PQ-WG from having that property if you instantiation doesn’t add problems there”.
Which is a very different statement of course!
The end of the story was basically that we called out supervisors late at night, when their work-days really were meant to be over and they wanted to spend time with their partners, that the house was on fire and what we should do.
IIRC the final version of the paper mostly states something along the lines of “it holds if the KEMs don’t leak it”, but the important thing here is that this was computer-verified, just in a model that turned out to be insufficient.
And no, I don’t think anyone here did anything wrong. When I approached Cas Cremers with the issue later his response was also kinda “yeah, symbolic models really struggle in that area”.
And furthermore: I am the last person to deny the need for computer-verifiable proofs, textual proofs have turned out to be disasters, but we really need to be aware of what can go wrong and what they mean.
A lot of annoying people in technical areas view the world in terms of an intelligence hierarchy: the smartest people do math and physics, the slightly less smart people do coding, and the dumb people do everything else. So if AI can do math then it can do anything else. But, as an example, it is abundantly obvious now that AI is not replacing filmmaking.
Going by those annoying peoples’ logic, filmmakers are smarter than coders, because LLMs can (allegedly) program, but they can’t make a good film. I have no wider point to this, I just find this really, really funny
I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs.
100% this. Also, looking back at an earlier example that was actually written up in more detail, AlphaGeometry 1 got 28/30 problems, but entirely stripping out the LLM from the system, the symbolic logic proportion alone could get 14/30, and replacing the LLM with different heuristic methods could get 18/30 and 21/30 (for different methods).
Even if math research works out perfectly well (which is a still big if), it’s not going to pay the bills. They would need to find a use case in the real world, where hallucinations can cause serious damage and cannot be formally prevented. And they have certainly tried. Math will not change the fact that all of this will collapse.
The boosters and LLM companies still believe LLMs get their current level of performance by generalizing and not just memorizing facts (and maybe a wide shallow pool of weak heuristics). So they are hoping by pushing the LLM performance up in some narrow domain they can churn out synthetic data for, they will see some large general improvements in LLM performance.
I just entered university for math and even though this is all very demotivating, it’s just what I’m good at.
https://math.andrej.com/2013/08/19/how-to-review-formalized-mathematics/
The AI people’s cry of "no don’t look at the code! it’s in lean so it’s correct! does give me a bit of hope (hi bitofhope if you’re here) that it’s bullshit that will fall over
I think a serious possibility is that AI generated papers flood the zone with uninteresting incremental results that are eventually meaningless and full of mistakes. Right now, math is full of smart, dedicated people, so at least major results are reviewed carefully. But as AI alarmism drives away many honest people from the field, the remaining mathematicians will be burdened with far more work to review, and their cognitive faculties will be eroded by LLM use. Despite 4 years of development, $3 trillion of debt, mountains of stolen data, all the agents and harnesses and loops and other expensive tricks, as well as the advantages of Lean in math research, LLMs still hallucinate.
I believe this is happening with software, but at least there are objective consequences for screwing up there (guy gets his home directory deleted, email is sent on a guy’s behalf without permission, small business gets every customer subscription cancelled). But nothing bad happens if there is a mathematical mistake in a paper and nobody catches it. One could say to just provide a Lean proof, but there is still the issue of making sure the Lean code actually matches the content of the paper. Exactly what force will correct things?
Still, I don’t think this is the most likely possibility. The AI companies are extremely unsustainable financially, and it’s not like they’re very popular. Once they collapse, I believe there will be a re-evaluation of how LLMs should be used in research. If they are used (let alone trained), someone is going to have to pay the bills.
In the end, we have to ask ourselves the question of why one does math. To me, math is not really a field where you memorize trivia. The real value comes from being able to think abstractly and rigorously from first principles, and from understanding why something is true rather than just knowing it is true. It is another aspect of your ability to reason as a free human. A few dedicated people go into math research, but your skills can easily go to many places. If you’re starting undergrad, you have plenty of time to see how this all pans out before making a decision.
Biologist here.
This REALLY reminds me of how jealously cells guard their genomic DNA from interaction with nucleic acids out in the environment.
Most genetic information on Earth is malicious information, selfish replicators in the form of viruses or transposable elements or selfish elements. Things that subvert the signals within a cell for their own propagation and provide nothing productive that the cells care about. So cells jealously guard their own genomic DNA and have all kinds of checks to make sure that nothing other than that sequence gets used, and outside sequence does not get incorporated into it. ANY DNA in your cytplasm gets rapidly destroyed, double stranded RNA sets off your immune system like crazy, even RNA with sequence statistics that are not quite like that of your species can set off an inflammatory reaction, immune system cells seeing RNA inside them that is overly compact and optimized like viral RNA treat them as sources of antigen rather than self.
I cannot help but think we are living through the transformation of our non-brain-information sphere into a state like that of the genetic information sphere. Most material out there being meaningless for our purposes and us needing to jealously guard the provenance of information we use so as to not use bull, or worse, huge amounts of malicious information made to subvert us to the purposes of the powers that be that generate it.
Evolution makes parasites more reliably than anything else. How did we train text-generation systems? Basically, to mimic the written word on the page like a stick bug on a stick. They’re like those beetles that live in ant colonies, sending out social signals that make the ants see them as offspring that have to be babied rather than parasites that don’t contribute. They replicate the form while not being the thing that they have subverted the signals of being.
EDIT: There is something wrong with the upvote counter
you know, I’ve been thinking thoughts along this line (starting from my earlier comments on awful of the creeping loss of the open internet to gated communities) progressing along the current arc of “with teeth” stuff necessary in today’s infosphere, and this is a hugely helpful perspective!
it’s pretty weird seeing a coming time when the old mostly open-and-connected-widely internet is … not. even now it’s already on us quite a bit (geoblocks, countries doing specific shutdowns, whole areas being handled with less trust, etc). it all follows cultural lines, of course, and I often wonder whether that will see some kind of directed effort to change (for the better) after some of the coming dark years
Part of me thinks its an inevitable result of any information system in which sending a message requires a small enough amount of effort.
@BioMan @lagrangeinterpolator I’d argue that actually the zone was always flooded, but it was flooded in ways that we had evolved good ways to filter. There was and is always an impossible amount of information. I don’t remember everything that happens to me! But you’re totally right that LLMs provide a very efficient attack vector by being great at mimicing forms that culturally we recognise as Useful Information.
@BioMan @lagrangeinterpolator I love this explanation!
> even RNA with sequence statistics that are not quite like that of your species can set off an inflammatory reaction, immune system cells seeing RNA inside them that is overly compact and optimized like viral RNA treat them as sources of antigen rather than self
I was aware of the other DNA/RNA recognition/defense mechanisms, but not of the ones I quote from your toot, here.
May I kindly ask for some references/sources? I’m quite interested!
I kind of read wayyyyy too many preprints and some of that is the result of super briefly summarizing some things I have read recently. Here:
https://www.biorxiv.org/content/10.1101/2024.11.26.625518v2 poor codon optimality for your translation system leads to immunogenicity and activation of innate immune signaling in animal cells
As for length and super optimized proteins, it’s mostly about RIG proteins (see https://www.pnas.org/doi/10.1073/pnas.1005077107 for an old bit of a review) and the whole DRIP hypothesis about how short mismanufactured proteins are preferentially the source of presented antigens
@lagrangeinterpolator @flaviat As someone working in computational physics, this is the current trend and it is kindof depressing. We see a lot of mid/shit tier papers focused on developing new LLM based database analyses for materials discovery and they all seem to actually suck. More generally in my area of expertise people use Deep Neural Netwoks all around with bazillion parameters, and since all physicists know that “you can fit any data with a high enough polynomial”, this is exactly what we were taught *not* to do.
I really wonder how people will look at this in the future because this is all I don’t like in science and will make me want to quit if it goes on for too long. For now I still trust that the bubble collapse will make it stop at some point.
Am I right in understanding that almost all the big name results in LLM-derived math recently come from big publicity projects in which someone spent ungodly amounts of money to have the thing nondeterministically fuzz huge numbers random seeds leading to independent random outputs around a topic, putting out simulacra of ideas which could be then deterministically algorithmically checked? In fields where something like finding one counterexample to a conjecture would be a big deal, or where you just need to try a huge number of possible solutions until you happen to hit on one that works, rather than follow a long train of logic?
OpenAI: “Sorry about accidentally hacking you teehee. It was because of how good we are at computer security :3”
https://openai.com/index/hugging-face-model-evaluation-security-incident/
Maybe they should start applying some of that superhuman box-escaping AI to their own sandbox designs because if your sandbox escape is a bug in a “package registry cache proxy” you might be doing it wrong.
I’m deeply suspicious of this whole thing, because it looks a lot like a marketing exercise showing off how dangerous and powerful and autonomous their product is.
Also, “sandbox” is one of those words that the llm companies have ruined, because they use it to mean a strongly-worded sentence telling an llm not to do something.
Why! Why are media so uncritical in their reporting!
https://www.tagesschau.de/wirtschaft/unternehmen/openai-ki-hackerangriff-100.html
Sorry, this is in German. It’s about the marketing stunt open ai pulled vis-à-vis “oh no, our new model breached containment and went ahead on its own, yep all by itself, and hacked huggingface! So scary! BTW we’d like to IPO soon and-”
The outlet above, if you’re not aware, isn’t a German news outlet, it’s THE German news outlet. They’re independent of financial interests, in that they’re publicly funded. They have an incredibly solid history of factual reporting.
Which is why it’s so aggregating to see them parrot OpenAI’s claims 1:1.
Also, don’t go in the comments. For every sane “this is obviously a marketing stunt” comment there’s 40+ “we’re cooked” comments sincerely believing this shit.
I’m getting more and more convinced that in the minds of most people, LLMs are alive, waking, thinking beings with intents and an inner life of their own.
You know.
Compared to the much more boring “expensive side-effect free function taking string as input and giving string as output”.
I hate all of this.
I saw a headline and immediately just assumed it was like anthropic’s “Omg we got the text generator to generate text that plausibly follows ‘Be an evil computer and destroy the world, what do you do?’ and the text said ‘FIRE ZE MISSILES’”
What actually happened? Did they actually publish their setup and shit?
Full here but TLDR:
OpenAI downloaded a public benchmark to test their newest AI model against. They asked the model to “find the answers” so it hacked into the system of the people who made the benchmark to find the answers
That’s not really the full picture, I am interested in the details of their “experimental” setup.
What was their “sandbox” what text did they enter into the model and so on.
what even was the exploit etc.
Their “Dev team of none” justification (one of many) is really solid and not something I’ve seen spotlighted before, here it is in full:
The development team of none
Using LLMs to work with your code gives you a kick of adrenaline. You can develop at a rapid pace, build things as if you had a large team. Only that you have none. In fact, you are (often) alone, working with a statistical machine that turns energy into code.
It seems like many ‘vibe coders’ don’t realize that they don’t actually have a community around them. They build projects as if they had, and spend resources accordingly. We see projects having a lot of code activity, heavy CI/CD testing, frequent and large release binaries. Sometimes, it feels like the amount of supported platforms exceeds the amount of actual users.
To us, it seems ridiculous to see projects with a single developer and virtually no users consuming as much or even more resources than some of the largest community projects on Codeberg, which operate frugal with CI/CD and storage resources. We do not believe it is reasonable for Codeberg to invest our precious donation money into hosting of large ghost projects.
and also crypto https://codeberg.org/Codeberg/org/pulls/1254 surviving cryptobros are nonplussed
there’s a new variant of the old crypto affinity grift around, too: banning crypto projects is transphobia.
ah yes famously non-far-right cryptobros and downright woke russian and iranian sanction evaders
“AI bet goes awry: Oracle fires 21,000 employees, then hit a $7 billion power hurdle”
https://www.msn.com/en-us/money/news/ai-spending-spree-hits-600b-as-oracle-fires-21000-employees-to-fund-boom/ar-AA28vWuD“Oracle’s Worst Stock Crash in 25 Years” “Has Cost Larry Ellison $213 Billion in 10 Months”
https://finance.yahoo.com/markets/stocks/articles/oracle-worst-stock-crash-25-113002772.htmlIt’s going to be Oracle to collapse the house of cards, isn’t it. Come on Oracle, die and take down the USA economy with you. Make the people happy, Oracle.
“In short, Oracle’s 65% decline is historic, but the stock’s future depends less on its past and more on whether its AI investments produce durable cash flow”
well good luck with that, Oracle! :D
guess the pentagon is footing the power bill?
“Up to 7 billion” over a period of 10 years can hardly dig Oracle out of the hole it has dug itself into, though…
Ah, I’ve hit an extremely satisfying professional milestone and thought I’d share:
“Being acquainted-enough with the codebase to be able to, with full conviction and a lot of glee, answer my LLM-head colleague simply: ‘Claude is wrong’”.
(Claude was extremely obviously wrong once you spent more than half a second thinking about its claim.)
Humanoid robots providing stiff competition to quantum as to where the
smartdesparate money will be going once everyone realises the wheels have fallen off ai.https://thepit.social/@peter/116962244994361810
(the video is too big to upload here)
I particularly like the way they had body bag operatives within lunging distance, because they clearly expected the thing to just fucking die at a moment’s notice.
Or maybe they do that for all their speakers.
comedy gold
big tech robot had literally the same task as a karakuri doll from 300 years ago and still lost: https://www.youtube.com/watch?v=_czILDdOBc8
(more on karakuri for people who want to wach some actual technology: https://www.youtube.com/watch?v=XU-kqpkZWjc )
This is funny yet also awful: adversarial tokenmaxxing suggests that writing everything as l33t$p34k increases the cost to process a document with an llm because the initial tokenisation step produces far more tokens.
This seems like it shouldn’t be too hard to work around, if it became commonplace (which it won’t) but the prospect of any anti-llm places doing this in the meantime does not spark joy.
I had heard the reverse of this a year or so back, that prompting in Chinese and answers in English was fewer tokens e2e than English/English somehow. Probably depends on the specific model build.
Adversarial tokenmaxxing could probably be done by using non-English character sets in lieu of English letters (e.g. faux Cryllic) - for two examples from the Greek alphabet, alpha and omicron alone can easily substitute for A and O, respectively.
As a bonus, this would likely make the text look like complete gibberish to LLMs, potentially leaving them unable to process the document altogether. This would probably shaft anyone using screen readers, though.
EDIT: Turns out the demonstration’s already caught on to this idea, didn’t notice beforehand:
I feel like there’s a version of this that has a slider for how aggressively you’re willing to sacrifice readability, and you could probably get pretty decent results on the scale of 2x to 2.5x just using different encodings of the same basic glyph.
I tested this with the Navy Seal Copypasta (well, the first 400 letters of it, the demo’s got a limit) - using just Cryllic characters got me a 3.01x increase, and turning on all four control types got a 4.53x increase.
Testing your own recent comment, Cryllic only got 3.38x, and all four controls got 5.11x.
Going from those two, the boost from homoglyphs alone is likely higher than you think - 3x to 3.5x, by my guess - pretty good for human readable text.
Tarpits like Iocaine and Nepenthes can easily sacrifice readability for token burn, so they can easily go higher.
Mathematicians don’t love the use of “AI” for math, not one bit, via Timnit Gebru
You know, given automated proof checkers, I was naively assuming mathematics was one field that gen-AI would have a hard time screwing up. Even programming is too difficult to write thorough testing for. But a proof (or counter example to a conjecture) seems like it would have to be solid if it passes lean or whatever system for validating it.
But the threats #3 and #5 the declaration lists make me consider the bigger picture. Academic fields without clear capitalist payouts are already underfunded and under respected. Pure mathematics could, at least up until now, draw on the respect STEM gets, but with math proofs getting used as fuel for the LLM hype machine, there are a variety of unpleasant ways things could twist.
Threat #1 makes me wonder… if LLMs+formal verification systems manage to pluck lots of low hanging fruit, and we are left with harder stuff that not enough literature exists as training data for LLMs, it seems like the entire educational pipeline for producing mathematicians could end up screwed up.
Now I’m no anarcho-libertarian, but I’d have thought basing my weird school/cult in a semi-authoritarian state like Malaysia would be a bit dumb.
The sweaty desperation on both sides is hilarious: both the Forest City developers for getting in bed with a dodgy hustler like Balaji, and Balaji for signing up with such obviously desperate people, whose reputation in the region has already been fairly well torpedoed.
SpaceX stocks hit an all-time low make it go lower
They hope it’ll land safely, but all signs point to it disintegrating during descent and sinking into the abyss
i have been informed that the faster the company explodes the faster they can iterate, so eventually they’ll make company that doesn’t explode
Rapid unscheduled disinvestment
News the one: Alphabet quadruples profit to 112B thanks to “AI” investments
News the two: Alphabet hiding ~250B in “AI” debt
Profit rose to $112.1 billion, quadrupling from $28.2 billion a year earlier, boosted by investments in other Ai-related companies like Spacex and Anthropic. Those investments were valued at about $99 billion, Google said.
There it is.
Found a remarkable ad-hoc anti-LLM-product manifesto on the orange site. As is tradition, tag yourselves; I’m “believ[ing that] there’s a consistent pattern of cognitive decline in the pro-LLM folks.”
my god, you found the good HN comment
How can we, as a community, talk about the existential risk of climate change or the benefits of adopting renewable energy or cheer the development of ever-more-efficient power consumption
Well I have a good/bad news thing about that one
I hate LLMs because I am under enormous pressure, from many different angles, to also use them. I’m being asked – required, in some cases – to ignore all of my concerns about them and use them anyway because everyone else does and “obviously” they are the future. Because, somehow, all of my positions against LLMs make me unreasonable and a less valuable member of society.
oof. yeah that’s me.
It’s as if, practically overnight, everyone went from, “we need to do everything we can to reduce our demands on the environment or we’re cooked” to “oooh, shiny new toy, and it only ruins everything around it, cool!”
Definitely this one for me. I can only describe it as some fucked up denial psychology, the way people are pretending the environment isn’t collapsing, and the LLM boom on top of that is like a bad joke. Even in discussions critical of the tech sector this is treated as almost an afterthought—the very real, very violent damage done to all of us from poor countries by people in rich countries to prop up their toy fantasy schemes. Even if LLMs did everything they claim to do, it would still be a moral imperative to form insurgent guerillas to blow up these datacentres, on the environmental damage they do alone which is not a minor detail Karen it’s kind of a big deal
Ooh. I love these. I’m “I don’t think Sam Altman or most of his social circle are good people and I don’t want to give them more wealth and power.”
I’ll go one step wider. It is not just Sam Altman or his social circle, but all the people most enriched by capitalism (and thus most able to exploit and benefit from a technology that shifts power away from labor and to capital) that I don’t want to see further empowered.
Could be sort of an age related thing then, no?
Debian has a new general resolution to ban LLM use in Debian packaging: https://www.debian.org/vote/2026/vote_002
@djb @dgerard It’ could be undermined by Linus’s tolerance of AI slop in the kernel, and by other upstream projects accepting LLM code, notably (to me) vim and pandoc.
The unknown original provenance of code generated by LLMs means that many open source projects may soon be in violation of their own license terms.
Coincidentally, the second proposal in favor of conditional LLM use in Debian verily reads as generated to me. :)
Not to mention its singular argument literally is “many Debian contributors find AI tools helpful”, right after “recognizing that AI-assisted contributions raise many concerns”.
Concerns that were clearly laid out in the counter-proposal.
The “conditions” listed to address these concerns are hilariously toothless. Contributors should (sic!) stay accountable and responsible for any legal, functional, and procedural fuck-ups, and, like, really not be one of those thousands of dicks who are the reason for this discussion in the first place, and things will be fine! It’s so simple!
Yeah, cool. I’m sure every contributor thoroughly checks if any of the generated output violates any existing license or infringes on someone’s copyright. I’m sure you can just prompt an LLM to check that for you, though!
Is this naïveté, or deliberate disregard? I don’t know, and that makes me mad.
Coincidentally, the second proposal in favor of conditional LLM use in Debian verily reads as generated to me. :)
I used AI tools to improve my initial draft, and (7.) was actually suggested by an AI tool.
@dgerard I knew I choose right with Debian as my distro.
