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In the latest update on AI slop, Ars Technica, a once reputable publication of over 25 years, has accidentally published a fake AI written article, complete with fake quotes. Unlike the fake story shared by Nate Silver earlier, which was published on a grifter's glorified blog, and somehow syndicated into Yahoo news, this story was actually published by a "real" media company under its own label. To be fair, the ars article bears few of the obvious hallmarks of AI writing, and it also gets a passing score by most AI detectors. I suspect the authors may have lazily asked AI to create a point by point skeleton for the article, then humanly written the words themselves that appeared on the page (excluding the hallucinated fake quotes of course). Fortunately, the article was taken down quickly, but the editors have so far refused to disclaim the use of AI, amd instead are hiding behind the misquotes as a reason to take the article down. It remains to be seen whether or not the use of AI slop was actually a rouge writer violating the policy, or someone using AI as directed by management but just skimping on the checking its answers part.
In other news, Malewarebytes has joined the ranks of Cloudflare and Lenovo as multi-billion dollar multinationational corporations that decided it's necessary to each publish a library of absolutely worthless AI slop, masquerading without disclosure as legitimate content. These zero effort AI takes are ... well ... zero effort, and provide zero added value to society by being published. I have no idea if Malewarebytes is a good company, but it's certainly a real company, with offices around the globe and enterprise contracts with many fortune 500 companies. These are all companies with sales and marketing teams in the dozens or hundreds of people, and likely multiple layers of approval to do anything new, yet they decided that zero effort AI slop takes are perfectly in line with their brand and reputation. There's clearly some kind of incentives (likely mostly SEO) for real companies to publish loads and loads of fake content on their websites, tangentially or not at all related to their actual business, which is extremely unfortunate because it's a waste of time for anyone who happens across this fake content, and even a waste of time for the slopmeister who has to click the button to generate 10 million words of fake content.
I'm going to piggy back on this with two things I've seen in the last week.
The first is highly personal. My employer does annual security training, with a focus around phishing attacks. The training this year used AI-generated video that was really off-putting. The actors were "realistic", but there was an uncanny wax-like quality to their skin, and their movements weren't quite correct for human baseline. Almost everyone on my team noticed it, and it casually came up in a meeting where my boss's boss was attending. The first words out of his mouth after that was "wait, there was AI?". We all sat there silently for a few seconds. It was clear that he absolutely did not perceive that the content was AI-generated. Despite the odd, inhuman quality, he didn't even peg it as animated. It made me wonder if there's some fundamental disconnect between my brain and the brains of upper management that makes the technology entirely different for them. As a model-train American, I can't discount it, but goddamn was it weird to see in action.
The second is Something Big Is Happening, the viral post that has been storming through the pro and anti AI ranks for a few days now.
The piece itself is a tour de force demonstration of how to stoke fear and uncertainty. It essentially outlines a maximal view of the AI Jobpocalypse that many fear, written with the flat certainty of a native LinkedIn citizen.
Clearly, the only solution to being obsoleted by AI is to use as much AI as possible in the meantime, as curated by the author.
This is interesting to me for a couple of reasons. For one, it's gone pretty viral - 80 million views is a lot, and I don't know if this guy caught th zeitgeist in the way he intended. It seems like he was trying to stoke fear, but especially among my younger acquaintances, it seems like more than anything he's managed to stoke anger - a "wood and nails are cheap, AI can't build crucifixes and you don't have functioning murder drones yet" kind of way.
The second reason that it caught my attention is because the name tickled something in the back of my mind, and I didn't want to post about it until I could figure out what it was. I found the answer this morning.
I thought that name looked familiar
I'm pretty conflicted on all of this. It sure seems like the technology has real potential and real applications, but by God does it feel like every single person involved is a sociopathic narcissist who gets off on conning the rubes.
If you'll pardon some paranoia on my part... the post you link, attributed to Matt Shumer, really reads like AI slop to me. It does not read to me like a human wrote that. Given that the post is about Shumer outsourcing his work to bots, is it plausible that he also outsourced his post to a bot? Or that he had a bot edit and 'improve' his writing? Or that a bot wrote it and he edited it? Or, perhaps most frighteningly of all, that he's just worked with bots for so long that this is how he has learned to write, and now he imitates them?
Whatever the case, I just don't trust anything written in that mode. Did he write it? Is there any original human thought in it? I don't know. Under these circumstances, I am disinclined to trust.
He does say that he used AI to write it, which I guess proves my instincts right. The post is indeed awful writing, and if that's the standard of the AI that he thinks is going to replace all our work... well, even if he's right, it will be a tremendous disaster for written expression if nothing else.
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I had an interesting thought about this (the capacity of AI to replace humans) today while using Claude. It is very good at suggesting possibilities. Telling me how I can do something. But if I argue long enough, it will almost always concede the point. I'm sure that I'm not always right in these arguments.
What it lacks is not judgement so much as convictions. Knowing which points are flexible and which are not. And this is important because when an agent is working on something, it has to make a lot of small decisions with compounding effects. Recently the quality of these decisions while made in a vacuum has been getting much better. But the second they start getting push-back from the real world, I have a strong suspicion that the agents are just going to roll over.
Yeah the integral 'Yesman' nature of AI makes it confusing to me how people are managing to fall in love with ChatGPT and whatnot.
It's kinda sad that people just want 100% affirmation where the most arduous speedbump is having to make a request twice.
Obligatory SMBC?
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As an academic who has been writing about technology for decades, I honestly feel angry about the sudden appearance of thousands upon thousands of apparent "experts" with papers, books, conference invitations, national news interviews, &c... who clearly have been thinking about AI for like five fucking minutes. It's basically impossible to express that in a way that doesn't sound like sour grapes (at best), but in most cases it's just an extension of the same grift they've been running for years, only with more money spent on Anthropic subscription fees. The truth is, good, meaningful, lasting work still takes a lot more time (and, realistically, a lot less money) than anyone seems willing to admit.
The tech is super cool. It's fun to be able to get incredibly detailed images whipped up from a prompt. I get the impression that coding can happen a lot faster now, in many contexts. But the gold rush is on, and a lot of people who missed getting in on the ground floor of crypto or the Web are desperate not to miss this elevator to obscene fortune. So it's probably inevitable that the grifters and narcissists are out in force.
Academics are boring nerds who (usually) understand the serious dangers of the beautiful radioactive lake everyone is excited about, while the moron brigade is the dipshit nerd that wants to bellyflop onto the shiny new rainbow lake for likes and calls the academics pussies for not having any excitement.
I have seen many many cycles of technology promises fail to breach the messy barrier between screen and meat. All these fuckwits promising real world transformative opportunities miss how real world people fucking work in the first place. People don't use their company acquired specific AI thats been lobotomized into legally compliant ineffectiveness, they use existing tools for basic bitch work then go to microsoft word.
The fuckwit brigade shills AI because they're grifting morons, but the academics also fail to communicate the problems in understandable real human terms. If neither side speaks relatably to normies, at least the tiktok of the dipshit screaming in pain when he lands face first into the arsenic mine pit is funny. Better if we all were in the background encouraging him precisely because we want him to get hurt.
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I was unaware of this article.
My attitude towards AI tools for the last 18 months had been "yeah they're useful but if you try to get too ambitious with them they waste more time than they save" and I was like AI 2027? Ha, try AI 2035.
But something fundamentally changed with the models in the last month. I'm low key freaking out at how goddamn useful they are now through Claude Code and OpenAI Codex.
Forget METR evals. My personal real world evals are that they're 6/6 on doing 2-4 week long tasks in 1-2 hours.
For what it's worth, working in a non-technical, non-coding-related field (), my experience has been that some higher-ups are interested in the idea of AI and occasionally push a half-baked idea, which lower-level employees dutifully try for about two hours, conclude that it's useless, and then keep on doing things the old-fashioned way. I have yet to find any actual use-case for AI and continue to see it as a solution in search of a problem.
Maybe it's useful in some very specific, very narrow fields. Maybe coding is one of them. I'm not a coder so I don't know. But what my professional experience thus far tells me is that LLMs are good for producing large amounts of grammatically correct but turgid and unreadable bilge, and pretty much nothing else. If what you want is to mass-produce mediocre writing, well, that's what AI can do for you. If you want pretty much anything else, you're out of luck.
In a sense I think it's the ultimate 'wordcel' technology. It does symbol manipulation. It's good at translating one language into another, and apparently that it includes translating natural language instructions into computer code. But I remain skeptical as to its utility for much beyond that. It might be nice one day for someone to sit down and run through an explanation of how the heck this is supposed to get from language production and manipulation to, well, anything else.
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I’m skeptical but you may be right. See the market the last week.
But even the market isn’t really where you are.
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I wonder if they're under pressure from higher ups to make stuff using AI? Their bosses have probably been convinced through AI hysteria that extreme gains are possible using AI, and that if those gains aren't materializing for them, it's a problem with how they handle their prompts, rather than it being impossible owing to the deficiencies of the technology. Not wanting to be left behind, they mandate everyone use AI and increase their output in line with what the hypists say is possible, typically an efficiency boost of twenty to one hundred percent, and thus, pointy-haired bosses lacking technical expertise relevant to their companies products, unable to understand the deficiencies of AI output, tank market viability while boosting investor enthusiasm in the short term by playing into popular biases of the financialized scam economy.
The bosses get rich, as do the tech scammers and their affiliates, but the economy inches closer to its doom once the bubble goes pop.
I'm definitely feeling a lot of pressure from the higher ups to get on top of using LLMs. I support some extent of the push, the tools really are impressive and we're seeing real test cases of it providing a lot of benefit. But there is also a dynamic where the different manager fiefdoms are jockeying to show who's team is best utilizing it. I'm usually the shield that stand between our team and upper management bullshit and I recently had to walk my manager off a cliff of requiring each engineer to answer a short questionnaire every day about if they've used AI and for what the previous day after our standup.
Both can be true that there is a lot of value to be gained and that there is a bit of a mania going on where upper managers smell blood in the water. There's a lot of talk about merging teams and helping engineers transfer skills across teams, this is the kind of environment that makes careers as the manager who's team is able to demonstrate a superior implementation stands to gobble up other teams.
The actual labs could pop, but the people using the tech won't. The dotcom bubble didn't get get rid of email when it popped. This stuff is useful and there its use will linger.
That's something which drives me crazy about the whole thing. If LLMs really are that good of a tool, you don't need to mandate them. If someone can get a 10x speedup on his work, he's going to use the tool without management breathing down his neck to do it (indeed he'll probably use it even if management forbids it). All management needs to do is let nature take its course, and if some people are suddenly doing 10x the performance you have them coach the others on how to get that same speed-up. It's completely irrational to require people to use LLMs, rather than focusing on the results. It's nothing but FOMO really, and it's so aggravating to have to deal with.
this is just not really how corporate salary workers function. We had a guy we kept around for probably longer than we should have who refused to learn anything but sql. It's notoriously difficult to track actual productivity in software and because of that a lot of coasting, because when I say "yeah I spent all day yesterday working on the inspection schedule import process" There is probably only one guy on the team who could reasonably say if that was a task that should really take a whole day, and he's not my manager.
The whole concept of FOMO exists because there really are situations where you could be missing out. If you just assume that all the hype is right and there is a tremendous amount of uplift available if your team is using these new tools then wouldn't you want to push them into using them asap? There's always an available excuse in software not to learn new tools, deadlines are weekly and you have real work to do outside of messing with some new instruction set.
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I don't think that's actually true. I've seen plenty of people just not use best practices without any sort of principled reason throughout my career. Some people just hate change for changes sake.
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Where are those people going to get their fix? If the experiences of people like @dr_analog are accurate, then nothing but absolute up to the minute models are going to cut it, and the training and inference costs on those models are high enough that they're not going to be ubiquitous. Are you banking on open weight models?
Inference is pretty cheap. Once you have the weights running inference on them is unquestionably profitable in a opex sense. The labs are definitely not offering inference at a loss to corporate clients. And you could quadruple the cost of inference and it'd be pretty easily worth the cost.
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If a lab goes pop and has to sell off its assets, training costs are not a problem. Inference costs can be covered with a reasonably priced subscription. If we're stuck with current SOTA models for the next 50 years, software dev will still be changed forever.
[Citation needed]
This is currently not true. Maybe if you freeze the models and wait for silicon to improve for a few years it will be. But the LLM companies are constantly increasing their inference costs as FLOPs go down in price.
At least according to Ed Zitron's analysis. Maybe you just don't believe his numbers.
Companies offering inference only on open weight models, such as groq and cerebras give an idea of raw infrrence costs.
Flagship models are still ahead of open weights, but most likely that's a result of doing it better, not adding more params.
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Link?
I've been trying to figure out AI-as-a-business since last fall, and the numbers make me feel like I'm taking crazy pills.
https://www.wheresyoured.at/ is his blog. He also has a podcast https://www.betteroffline.com/ but the AI industry analysis there is essentially him reading/summarizing his blog posts.
I'm reading through his latest piece where he basically says AI companies are all in complete shambles and he just seems flatly wrong? https://www.wheresyoured.at/data-center-crisis/
There's a warning sign here, it's like he's implying that post-training is done after the training process, post-training is part of the training process. I don't think he has a proper grasp on what he's talking about.
What does he think an AI model is? Deepseek R1 0528 is sitting on people's (big!) PCs somewhere, cloud providers are just providing it. It's a complete product. It still gets about 2 billion tokens per month on openrouter which is pretty good for an obsolete model. It doesn't need more 'post-training' to maintain it...
Seems like a deceptive line of argument to say that training costs are not R&D.
It would be reasonable to say 'because of competition, these AI companies cannot stop making new models like how car companies must always release new cars - this is especially true given rapid performance improvements and low costs of switching provider which reduce retention making the business model precarious and expensive' but he isn't saying that, he's making an altogether more ambitious argument that 'training costs are impossible to avoid' which is just wrong?
He has this overly emotional tone too:
What is this, Chomsky? I don't find this guy trustworthy when he conjures up figures based on 'just trust me':
The idea that the biggest companies in the world have mysteriously decided to invest hundreds of billions in an obviously, openly unprofitable business sector is interesting but it needs to be justified in detail. Who could know more about data centre economics than Amazon, Facebook, Microsoft, Google? Who would be more diligent in checking the financials than the companies spending hundreds of billions of their own money on this, this year alone?
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Labs need to cover their training costs at inference time. This isn't a problem if there are no training costs.
On top of that, this isn't really true. For example, Gemini 3 flash outperforms 2.5 pro and costs ~2.5x-3x less per token.
More broadly, the cost to train a GPT-2 level model is 600x lower than it used to be. Algorithmic progress has made massive strides, and that applies to the inference side too.
I don't.
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A friend of mine was laid off late last year. One of the criteria for who to lay off was "enthusiasm for AI".
He worked in a non technical field at a bank.
I have a suspicion that you might be on to something.
In the UK this can genuinely be argued to be indirect age discrimination as older people are naturally going be less enthusiastic over a new technology, meaning the burden now falls on the company to justify their actions as being a proportionate means of achieving a legitimate aim, which is hard to do here in front of a tribunal of law, or else they get done in for discrimination. Finally something good coming out of the UK's regulatory system!
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