scruiser, scruiser@awful.systems

It might have come this decade, had they faithfully funded the path of symbolic AI, but instead they wandered around in the desert chasing the false idols of connectionism and deep learning.

New booster irl fanfic just dropped: https://europe2031.ai/

It openly admits to being an AI 2027 knockoff, although I will give it credit for having a much more grounded scenario (Europe in economic ruin compared to gloriously transformed China and USA, whereas AI 2027 described the world going full singularity) and having a longer timeline (5 years to economic transformation is relatively sane compared to 3 years for an AI God to be born)

Some highlights in sneering:

The hours Christian’s team pulled were insane – seventy- or eighty-hour weeks, people sleeping in the office.

One of the character’s is basically an idealized SV AI startup founder, complete with all the insane startup tropes like working the 80 work week to grind out success. Also the fact that his name was Christian and the sort of chiding pitying attitude he had towards the other character, Caroline kept making me think of Christian Gray and 50 Shades of Gray.

Someone mentioned, in passing, that they thought artificial general intelligence - AI that is better than any human at most tasks - was probably two or three years out.

This is something of a side note to this scenario, but it annoys me ever single time it comes up so I will keep complaining. The boosters have very willfully moved the goalposts. Wikipedia gives the definition as “Artificial general intelligence (AGI) is a hypothetical type of artificial intelligence that matches or surpasses human capabilities across virtually all cognitive tasks.” Boosters have, to varying degrees, tried to dilute the definition to ‘most’ and not ‘all’ and swapped ‘cognitive tasks’ for ‘benchmarks and narrowly defined tasks’ and then claimed success and accused people insisting on the original definition of moving the goalposts.

Standalone American AI tools are considered a data-protection risk.

This ‘scenario’ has an ongoing theme of Europe foolishly being cautious around the risk to their data American AI companies present. It is hilarious this scenario mocks this attitude just a few days after Anthropic has made their policies towards users data even more openly contemptuous.

The infrastructure story is just as grim. The largest AI supercomputer in the US runs at 1,250 megawatts. The largest in Europe runs at eighty-three.

So I couldn’t a single convenient quote for it, but an ongoing point of idiocy of this scenario is that it takes the ‘planned’ American AI data center build-out completely for granted, assuming all the currently released numbers are true, the plans will be met on schedule, and data center build up over the next 5 years will radically surpass them. Ed Zitron has pretty much shown all three of these stages of purported numbers are complete bullshit.

Up to this point, everything we’ve said has happened – with only Caroline’s and Christian’s personal stories representing fictional elements. From here on out, we start speculating. We no longer single out individual AI companies, and instead refer to made-up actors: Atlas for the leading American AI company, Helios for the leading European company, and Zimo for the leading Chinese one.

They are even copying AI 2027’s stupid shtick of coyly swapping out names instead of referring to real companies!

Works councils slow the deep adoption of powerful AI tools; employment protections make it hard to let go of staff whose jobs can be automated and whose labour force would be needed in parts of the labour market that faces shortages.

Pretty much the pitch of this whole thing is “Europe needs to copy America’s lack of labor laws or other regulations”. I wonder if the authors of this fanfic even believe their own spin of other ‘parts of the labor market faces shortage, so firing everyone to put in AI is actually a good thing’ or if it is just a shallow attempt to appease people who find mass layoffs heartless and disruptive.

But Europe has one last card to play. After five years of failing to build a frontier AI sector, it still owns the one bottleneck which the entire race runs through. ASML remains the only company in the world capable of building the EUV lithography equipment that is used to print cutting-edge chips. Without access to its machines, the US could not keep extending its lead in AI; with access to its machines, China would likely have caught up some time ago.

So this scenario correctly acknowledges one of the bottlenecks Europe controls, but then somehow envisions the US being able to strong-arm Europe not to leverage it against them and to cut China out? Have the authors not been paying attention to the US shitting away its soft power (and showing cracks in its hard power with running out of patriot missiles) over the two Trump terms?

Europe’s slide into irrelevance was not inevitable. Even in 2026, the continent could still have changed course, had it shown the courage and political will to take drastic measures.

By courage and political will they mean slashing apart labor laws, environmental protections, and other regulations and dumping public money into AI to draw capital investment into Europe. The epilogue is some fantasy bullshit with moon domes made possible by all the American AI advances.

Gary Marcus speculates they are upping their prices because they literally can’t afford to hold out. I’m wondering if it is because they need better numbers for their IPO. VC funding be circulated to create nice sounding statements, but IPO filings have a standard of rigor where trying that would be fraud. So they are trying to squeeze their customers to get a few good looking (i.e. revenues higher than operating costs) quarters for the IPO.

We can simplify this to what actually matters

I was initially drawn into his dry recounting of the details, and overlooked that he was false-equivocating (obnoxious but legal) “content creator” activity with police corruption and willful violation of some very clear and foundational laws.

The article collects a lot of information, and isn’t out right wrong, but I find the author under-sympathetic to someone that didn’t have the financial resources to challenge a corrupt corporation and decided going viral was their best bet. Also, I find the author’s language in a comment:

if you put yourself in the cops shoes (something I wouldn’t necessarily recommend doing) they show up and from their vantage point it’s a bunch of rowdy out of town youtuber influencer kids against local homeowners in the community.

Is grossly too sympathetic to cops. The author is basically rationalizing and portraying sympathetically the way cops side with wealth and capital over the actual law.

The comments are not just agi fears and sinophobia right?

Lol the OP was actually being contrarian (to the standard lesswrong attitude) by even vaguely half-assedly considering that the US media may have created a biased narrative that should be questioned.

lesswrong continues to mix sinophobia in with its AI crithype: https://www.lesswrong.com/posts/nmpzH6sLLtKsQhSPM/china-won-t-win-the-ai-race-but-would-it-be-much-worse-if-it

Previously, on awful.system: https://awful.systems/post/4103825

This article has the highlight of identifying the horrible cynical dystopian move of China’s government in response to AI and LLMs of… checks notes… protecting worker rights and workers from mass firings

“An arbitration panel ruled in favor of a map data collector whose entire department was laid off and replaced with artificial intelligence. The panel found that the company’s adoption of A.I. was a voluntary move to remain competitive and did not warrant the employee’s firing. Companies that benefit from technology must, at the same time, adopt “social responsibilities” and protect worker rights, the panel ruled.”

The author feels the need to emphasize how bad China is.

There are quite a few examples of the Chinese state punishing people for speaking out about true problems.

There is Li Wenliang, a doctor who posted to a group chat about COVID before it was officially acknowledged and was forced to sign a police document admitting he had broken a law by spreading false rumours. His reprimand was later withdrawn.

The advantage of the US system appears to be a greater ability to be transparent, in particular for a concerned person in the know to blow the whistle publicly.

Hahaha, no… For example, in Florida, DeSantis has the home of a fired state worker raided for her accessing her old work email (trying to collect accurate COVID numbers, iirc).

This lesswronger is so close to getting it but doesn’t quite make the leap to ‘are we the baddies’. They list out some bad ways the US has used AI and they do acknowledge

But I’m very aware that I’ve been inculcated in a media and cultural environment that says, in its most kind form, be suspicious of non-Western states.

But somehow hold out on actually changing there mind or overcoming their biases.

Anyone familiar with the IPO process have any guessestimates about how long until the public complete S-1 follows? Or odds that it leaks?

That was a nice detailed explanation. The description of the way the tests degenerated was really worrying. Even some boosters insist the tests need careful human oversight.

Relatedly, I think another part of the problem is the implicit assumption that ‘able to do one narrowly defined/narrowly constrained type of problem within a field’ = ‘expert in a field’.

my point is the Scott-A is massively overvaluing the societal worth of pure mathematicians.

I think pure mathematics is as valuable as the humanities. Unlike many stembros, I think the disconnect is that we vastly undervalue humanities, not that pure mathematics is overvalued. Agreed Scott is probably overvaluing them.

In addition to what rook said (that I strongly agree with), there is the issue that LLMs can only do certain sorts of math work. They can parallelize and try to brute force counter examples quite well and they can spread a very wide net trying to combine different techniques out there in disparate parts of the literature but it still remains to be seen if they can create entirely novel methods (and I and probably almost everyone on sneerclub/techtakes would bet not). And what are we left with if we let LLMs automate all the low-hanging fruit? Deskilling and devaluation of a field that cannot actually be solved by the hyperscalers’ slop machine.

If they advertise themselves as a team of forecasters, but then pick a number that doesn’t line up with their forecasts because one team member has a gut feeling or vibes it should be sooner, then that is just another reason not to trust them and to treat them like the clowns they are. Of course, even that reading is pretty charitable, the real reason they picked 2027 is to balance urgency and hype generation with a bit of cushion for when the prediction doesn’t pan out.

Gotta love the ex post facto of it all.

Within a month after it was out, they were already building up excuses (calling 2027 their modal number, and admitting their timelines had already slipped back a few months). Also, if you read between the lines of various statements they made, they all but admit they picked 2027 for maximum clout/influence. (Lying is okay if its to stop the AI apocalypse! Or maybe they were all more short-term sort of grifters). Even Eliezer recognized setting a hard and early date would damage the grift for everyone!

I was surprised in a good way see nearly every single comment call him out. Of course, some of those comments (maybe even the majority) are probably boosters mad that he is skipping the slop emails in his inbox. I guess Paul Graham found an angle of hypocrisy that both AI boosters and realists can unite in mocking. Quite an accomplishment.

Not when what they want contradicts the basic limits of reality and logistics!

Ed Zitron has done a breakdown on building normal sized data centers vs. the current target size of AI data centers, and on the bigger end normal data centers are 10s of MWs up to 100s of MWs. After 2 years Stargate Abilene has only turned on its first 200-300 MW. So I think even if regulators roll over on using twice the power of the entire state this project would take 2-3 years just to turn on the first few hundred megawatts then stall out.

Every author named as writing a paper bears full responsibility for the paper.

This has the nice added bonus that it will likely catch PI’s that put their name on their grad students paper without actually doing the mentoring they were supposed to. It will also catch professors that coast (or at least inflate their citation index) by getting their name on papers they barely contributed to.

I am quite convinced that, under these arxive guidelines, every single major PI in the field will be banned within a few years.

Catching a lot of PIs that have allowed and even encouraged slop submission is a good thing in my book.

I was pretty happy about seeing that news about arXiv! So much news has been various organizations giving into LLM usage like some kind of inevitability, so it was a nice change of pace.

he just posted an entirely unnecessary amount of words

taking a quick look at it… it’s actually short by Scott’s standards, but still overly long, given that the only point he makes is claiming Lindy’s Law is applicable to predicting AI progress in absence of other information. Edit: glancing at it again… its not that short, I kinda skimmed until I got to Scott’s actual point my first time glancing at it. You can’t blame me for not reading it.

you-can’t-really-knows

Yeah, he straw-mans AI critics/skeptics as trying to make an argument from ignorance, then tries to argue against that strawman using Lindy’s Law (which assumes ignorance and a pareto distribution). He completely ignores that AI critics are actually making detailed arguments about LLM companies consuming all the good and novel training data, hitting the limits on what compute costs they can afford, running into problems of the long lead time for building datacenters, etc. Which is pretty ironic given his AI 2027 makes a nominal claim to accounting for all that stuff (in actuality it basically all rests on METR’s task horizons, and distorts even that already questionable dataset).

The plagiarism, massive expenditure of venture capital, and unreliable slop output are all intrinsic to the technology, and they hate to be reminded of that because there isn’t much they can do about it. From a technological standpoint, even locally run community fine-tuned open-weight models still originated from plagiarism and big corporate investments, and still output slop. From a social standpoint, the most the can do is try to claim legitimacy through consensus building and we are a threat to that.