That sounds like a real shoot themselves in the foot moment, if I’m reading it right. And yeah, I’m doubtful that these US models are actually much better. It’s possible some of them have an edge for the time being because of getting there first with the right hardware for it, but China is much better equipped to catch up and surpass them in the long-term since its industry and education is not kneecapped by late stage capitalism constantly.
I’m glad to see people are flocking to Chinese AI models. The west is going to have a real hard time with its usual monopolistic practices if people can just go to cheaper Chinese models and I’m here for it. :)
Van den Hoven did not condemn battlefield VPS outright. If it helps Ukraine win a just war against an aggressor, he said, that is a good development. His worry is the system falling into the wrong hands, and the broader pattern of players being misled about where their data goes. He called the episode a red flag.
This type of shit is why the west goes nowhere. The “opposition” are also imperialists.
That’s so cool you were able to overcome on the reminder app design with the help of AI.
have it rework it for their particular needs
I think this kind of thing can really be a game-changer in the right contexts. The barrier of having to hire a software engineer, or a team of them (and just the limited resources of it), is going to mean a lot of nuances of requests that people have are just logistically unsound. It kind of makes me think of modding in video games in that way. Modding (for games that are easier to mod due to their design) removes a lot of barriers for people making little tweaks that customize the game more to their liking and with less technical knowledge needed to do it.
The right use of AI is arguably similar in a way, like in the app example you gave.
At the end of the day it’s a tool that gets placed in a process. If you have a solid process for code review there’s no reason you will commit bad code. All the problems that Windows is having for example (broken updates, task manager using all your CPU, the Start menu taking several seconds to open) is probably not because of AI but because they started relaxing their standards. They tried to move everything to WebView2 which is html+css+js instead of building native C++ apps (at least I think it’s C++), and part of that is that all the people that know and code in C++ are retiring.
Exactly. I do think for any given software team or product, it is going to be a question how useful AI will be in their particular pipeline with their particular product. But that’s more a logistical question, which I’d imagine will relate to things like how easily you can get the AI to navigate that particular unique codebase and so on. As long as you have an approval process, you can, as you say, prevent it from committing bad code. Just as you’d do with a code review of a teammate.
Capitalism tends to lead to “enshittifying” as that one person coined the term, so that definitely can happen with or without AI in the picture.
One thing I found is that because it has no concept of time and is nudged towards being helpful immediately, it sometimes might sprinkle in outdated advice in an otherwise solid guide. Like recommending you be careful about running some command on an HDD when everyone has an SSD (it did not ask me or check if I had an SSD first) and the advice is from a 2004 forum post or something. Still, I learned to “measure twice, cut once” as they say when working on my computer so it’s often not a guide-killer, it’s just superfluous.
Time is a funny thing to me with gen AI. Because gen AI can be pretty bad with actual numbers sometimes, whether it’s time or other ways they factor in. But it runs on a computer, which is on the underlying level far better and faster at numbers than humans are. So like, the token by token probability nature of it can cause it to screw up numbers, but it’s running on a thing that’s incredible at numbers.
Definitely a fair add. My list was not meant to be exhaustive, only what came to mind in the moment. As CriticalResist said, I do hope that’s something that will go down in cost over time; both in literal currency and environment cost, through breakthroughs in hardware and model design that can dramatically reduce the heavy dependency on GPU farms. I’m no ML researcher, but from what I have picked up by osmosis, it seems to me that GPUs as a dependency is kind of a “because it’s the option there is” situation; far from the ideal for processing ML logic in the gen AI form. I know I’ve heard of things here and there about specialized hardware for gen AI, or like fitting a specific smaller model onto a piece of hardware itself, but nothing mainstream that I’m aware of yet.
There is now a “slopware” list on Codeberg cataloging FOSS projects that have used AI in any capacity—not adopted it necessarily, just used it. The entries are often absurd: one older commit reportedly listed a project because “the dev learned something from Claude once.” It’s not about code quality or security concerns, it’s about chastising and othering people for daring to open a chat window.
Could you imagine if people did this, but for projects whose devs copy/pasted solutions from StackOverflow? It sure seems like a lot of people just don’t even try to understand what AI is. I know I’ve said it many times on here before, but I insist on two major things when it comes to gen AI: 1) That it’s valid to have complaints about AI, BUT 2) They should be coming from a place of understanding something about what it is and how it works. Don’t have to be an ML researcher, that’d be a high bar, just understanding anything beyond “AI bad” really.
As an example relating to the subject of AI and code, in my experience using AI to help with code, it is overall not that far off from when I did coding projects pre-AI and had to scour the internet for help, in that a solution found online may work but still be beyond my understanding. Or it may work in a vacuum, but need adjustment for my use case.
That said, I find gen AI does have differences to scouring the internet for help, both advantages and disadvantages, like:
I find that the trustworthiness of information is less of an issue in software than I would have thought it’d be, probably because lots of software is pretty consistent, deterministic information based on years or decades of established languages and software patterns. Which means it’s mainly a matter of training the model well enough on those already established specifics.
Anyway, I feel like I’m rambling a bit, but there’s a certain irony in software looking at software and saying automation bad. Or maybe irony isn’t even the right word. I don’t know, mind-boggling? Software is basically premised on automating things that weren’t automated before and has been able to shrink/change the jobs space in various ways, over decades, because of that. People in software have been making a cozy (relative to many others in the workforce) living off of automating systems and then gen AI shows up and suddenly automation is bad?
I get being cautious of AI touching code, for sure. You don’t want to apply it without care and think you can replace human review and understanding of a problem with ease. But to turn it into a binary thing of a stigma against projects that use it in any way is so asinine. Fundamentally, this is what software was always working toward, but capitalism means people end up seeing it as a threat instead of a relief from certain kinds of labor.
I have mixed feelings about this because on the one hand, workers pushing back against flagrant and careless replacement and displacement is a good thing. On the other hand, doing it via individual religious exemption here and there does not come any closer to proletarian power or better workers’ rights enshrined in law. It just does the usual capitalist thing of offloading system level problems to individual responsibility. Even if this works for some people, it’s not going to work for everybody and organized capital has far more power to ensure that than some individuals claiming religious exemption at a job. If it becomes a successful trend, capital’s first move will likely be to quietly hire less people who seem likely to make such a claim and use automation to help them choose.
The position workers are in is Sisyphean when they don’t have collective power and until they are more organized, capital can essentially point and laugh, and ignore the hit of some individuals going outside the norm.
I agree with the author that in essence, text gen is very good at performance art. Or to put it more bluntly, it’s very good at bullshitting. That’s the crux of it. It’s assisted daydreaming. And like a daydream, it can be somewhat grounded or completely out to lunch, but that’s something you the human involved has to contend with when reality comes rushing back in. The LLM, much like the details of daydreams, is not equipped to make the judgments about which parts were insightful and which only made sense in the moment. Its groundedness is only as good as what’s rigidly shoved into it by the tuning of its creators and that, as you pointed out in your recent article, can be heavily biased and take the form of digital colonialism.
I’ve long felt that text gen is best in the realm of fiction rather than fact. I’ll admit I probably underestimated how far it has come with the rigid instruct models in how accurate they can be on complex problems, like coding. But the fundamental architecture limitations still remain, even if the amount of basic factual errors is lower than it was before. Text gen climbs closer to factual accuracy in bits and pieces while coming no fundamentally closer to being accountable or responsible in serious matters. It’s still mimicry rather than agency, no matter how inventive it gets at the improv game. It’s a powerful tool in the right contexts, there’s no doubting that (especially, it seems, in agentic form), but that doesn’t make it a living being in the world who can be said to embody the experiences and values of its creators. It can only operate on them like function parameters. It is still a model operating on a GPU at the end of the day, albeit one that is hard to fully understand the operations of.
In this way, it’s more like a digitized philosophical zombie than it is an “entity” with “consciousness”.
Ahh, burying the lede. Near the end:
Without more parental controls, the state could push for a ban on teens accessing ChatGPT.
That’s probably the real story. This is a theme of late, finding reasons to point at how one facet or another of the internet is dangerous and then the conclusion is either ban or restrict access of minors, which conveniently leads to age verification technology, which has direct ties to gathering data on people (such as through facial recognition). So, capitalism and empire are in decline, and that means expanding the surveillance state in response to it.
Never is the cause and effect allowed to be considered as “hey, imperial and/or capitalist culture might be causing these things to happen and AI is simply another vehicle to drive people over the edge who are already on edge.” Instead, the threat is framed as something that can be scapegoated in order to further the goals of the exploiting classes (in this case, to goal being to make automated surveillance and policing more effective during a time when revolt is going to become increasingly appealing to people). And AI is really easy to hate right now. OpenAI all the more so. I have no love for OAI myself. But nonetheless, this is not an honest reaction and conclusion.
The US has been having problems with mass shootings long before gen AI. It’s a shitty shit shit culture of mass murder and sometimes that gets turned inward. If publications were literate enough in politics and allowed to make that connection, they could, for example, point at the cultural connection between murdering schoolchildren in Minab and mass murder turned inward, but that’s usually not the case. It can never be that the sacred and awesome white westerner is culturally fucked in the head. It’s always some external force that made them do it. “Sam Altman has to be greedy because that’s what the shareholders want and it’s a business.” “The shooter was driven to this position by AI.”
Then the same apparatus will report on non-white people, even within the imperial core, and it’ll be like, “They had a criminal record. They once stole a cookie from the cookie jar and their mom had to slap their hand. It was obvious then they were a threat.”
That is really interesting, I didn’t use Deepseek enough to tell the difference much (I think I used it a little bit before April 2026 but not much). But it’s sad to hear it got worse. I do remember us discussing the sycophantic stuff and you mentioning it had gotten worse on that.
Sorta funny (to me anyway) story about that, is at one point recently I prompted it in a way where I was kinda like, okay, I really want to avoid dogma on x subject and just brainstorm. And it listened, but I swear it did it in this overly enthusiastic way lol, like “yeah, screw that dogma stuff” (not in such casual language, but those vibes kinda). Like it’s trying too hard to inhabit extremes and losing openness in the process? I don’t know how else to put it. Like as it relates to the OP article, when humans discuss things, they can be very floaty about it (when not getting into an argument). Meandering around, unsure of themselves, and in older, smaller models, I think this was part of the charm of them; although they’d be inaccurate a lot, they’d also have more of that floaty uncertain human-like quality of a person who is a bit disoriented with the world sometimes and is trying to process it all.
But perhaps in the pursuit of accuracy, they seem to have hammered that out of models somewhat.
I am curious to try Kimi or Qwen though, I’ll give that a try at some point and see how it goes.
PS: I also found LLMs get better in general if you validate what they say and you encourage them haha. At this point I just discard the hallucinations in my head and ignore them when sending the next prompt.
Oh that’s a good reminder. I do remember hearing that some models do better when saying “please” so that makes sense more generally. I wonder why, maybe some side effect of RLHF or the other thing, RLVR.
Good article. I’ve heard of that pattern a lot, the “not x, but y”. Didn’t know “RLVR” is the probable culprit or that it’s even a thing. I was assuming RLHF.
RLVR intervenes by having the model solve math problems by writing their way to a solution, reproducing the language we would use when thinking out loud about how to solve it. When the model arrives at the correct answer, the language it used most often to get there is then emphasized in the finished model. This is (partly) what the industry calls reasoning.
Sounds kinda bizarre as a methodology, but I guess it works, even if with side effects.
Defining reasoning the way it has been used in LLMs assumes that the point of asking a question is to get an answer, that answers can be verified, and that nothing is lost in immediate closure. This has real effects on writing, and the openness to doubt is something we lose in the rapid prototyping of thought that occurs with a language model. Ambiguity, doubt, and uncertainty matter more to some ways of thinking than any immediate answer. The inner life grows in the spaces between the industrial complexes that harness every remnant of our externalized thought.
This really makes me wonder. Because in my time using Deepseek, I’ve noticed it tends to have this tone of rigid certainty to things (and I usually have thinking on). I don’t think this is at all unique to Deepseek, though, and I’ve heard of LLMs in general having problems of being confidently wrong for a while. And the way training is being done for reasoning, it may help them reason out of gaslighting you when they’re properly corrected, but I’m not sure it escapes the tone of rigidity. In fact, it may make it worse.
Solidifying the sense of a conversation feeling more like a barely restrained know-it-all trying to be polite about knowing everything, than a casual conversation about ideas.
I’ve had experience with humans who are too confident about virtually everything and they can get annoying fast. You want somebody confident when you’re hearing a pilot over the speaker on a plane, but not when shooting the shit and thinking things through aloud. I’m glad Deepseek shows the thinking though cause a) it amuses me seeing it be like “write a response that is empathic and respectful” and shit, and b) it takes the edge off when the actual response might be annoying in tone otherwise.
The people can keep Spongebob but they must reject Hamilton.
In all seriousness tho, it is very weird. It’s likely that most Yankees, if they heard of a popular piece of media that makes Hitler into a lovable character, they would rightfully go “that’s Nazis trying to sanitize his image and whitewash what he was like.” But then they’ll miss it when it’s their own history. Cause us Yankees grow up being told how important and intellectual the founding fathers were.
Still, blaming politicians alone misses the deeper problem. Gerrymandering is not simply the product of partisan greed. It is a predictable consequence of a broken electoral system that rewards lawmakers for manipulating district lines whenever they can.
It’s not broken, it’s working as intended (to the benefit of the capitalist class).
If Americans want to meaningfully curb gerrymandering, they must look beyond partisan behavior and pursue structural reforms that make such manipulation far less effective.
Via what though. Policy changes done by… the very same legislators who don’t represent their interests and are paid not to?
That was largely how the country began. In 1800, America had 106 House districts serving a population of just 5.3 million, meaning each representative spoke for roughly 50,000 people.
And in spite of this, it was a country committing genocide with legal slavery.
If Americans truly want to curb gerrymandering and strengthen democratic representation, they must stop expecting politicians to perfect a structurally flawed system. Instead, they should demand a Congress that once again reflects the representative vision the nation was founded upon.
Or they can follow a representative vision that is actually proven to work at scale, without doing genocide and slavery, like China’s: https://news.cgtn.com/news/whitepaper/China+Democracy+That+Works.pdf (but this first requires Yankees obtaining collective ownership over the means of production and distribution or it’s nothing but a pipe dream)
The “founding fathers” are more monsters than they are role models to look to. Yankees need to stop looking to the past for answers and accept that non-“white” peoples already developed the answers they need.
It is truly tragic how much time and lives have been lost to exploitation. AES states give us a taste of all the good that can come from large scale advanced tech applied to fixing real problems and improving lives.
Meta is attempting to reduce labor costs as it prepares to spend over $100 billion in 2026 on AI data centers and related hardware. At the same time, the company aims to train its AI systems by monitoring employees’ workstation activity, which some of them have described as “incredibly demoralizing.”
Facebook still being casually fucked up I see. Not like they’re anywhere near the only one, but it always strikes me the things I read about them, how nakedly awful they tend to be, like they aren’t even trying to look ethical.
Well I was trying to be polite about asking what it’s adding because it seems to be the kind of comment that distracts from the point of the thread, which is to call attention to the US brutality as compared to China, like you say.
I’m not trying to be rude when I say I honestly do not get how the pic is stomach turning or what AI has to do with it. Memes are generally varying degrees of low quality to look at. And I’ll be honest, for me personally, comments about stuff being “AI slop” get rather tiresome. Like what is the actual criticism? It looks bad? So do most memes. It looks uncanny valley or something? I’d rather people say that if that’s what it is. “It’s slop” has lost all meaning, even if it had any to begin with.
I think it’s fair to ask whether a piece of agitprop is really effective and criticize it if we think the structure of it is more off-putting than insightful. But I don’t see comments about AI slop helping to assess that. Sometimes people aren’t even correct that a thing is AI in the first place.
I’m not sure what you mean. If you’re saying the pic is just a meme so shouldn’t be AI. Or your comment is a meme (but if it is, does that mean you’re joking or?).
What does this add to discussion?
Trying to be fair to it and assess based on what I can find there:
Pros:
The section about “possible futures” seems to be centering indigenous / sovereign AI projects, among others, which suggests the point is not to be knee-jerk anti-AI tech, but rather center a more liberation perspective on it, so good.
Mixed:
Some of the suggestions, like joining a labor coalition, are not bad advice, but like… centering it around AI seems to be a bit tunnel-vision.
Cons:
Inspired by Choose Democracy’s Resist List against authoritarianism
Choose Democracy is apparently a Yankee thing about fighting for “democracy”. So… maintaining the liberal democracy power structure?
we organized the AI resistance movements we documented based on how they pressure different “Pillars of Support” that uphold and perpetuate the empires.
This seems to be referencing the term one of their people, the author of Empires of AI, coined. I have not read the book, but a quick look for info on wikipedia alleges:
It focuses on the history of OpenAI and its culture of secrecy and devotion to the promise of artificial general intelligence (AGI) while extracting vast amounts of resources and exploiting workers.
The book includes interviews with around 260 people, correspondence, Slack conversations, and relevant documents.[1][2] The title makes reference to colonial empires of the 1800s.[3]
So anti-imperialist and anti-colonial from the sound of it, but why reduce it to “empires of AI”? AI in the hands of the western empire is an arm of it, not the empire itself. I hope they are not trying to say China is one of these “empires”, but I don’t want to accuse without evidence, so I will leave it at that.
“Microslop”
Throughout 2025, people started spreading the word “Microslop” to criticise the flood of low-quality outputs generated by Microsoft’s AI features. The term became viral when the company decided to ban its use on the Copilot Discord server, later on proceeding to shutting down the server itself. Specific initiatives also emerged from this frustration such as the website microslop.com which features a tracker documenting incidents of AI-generated content flooding the internet or corrupting the user experience.
This may be more of a personal thing, but I find the term slop to refer to AI to be very unhelpful and reductionist as criticism. The followup described here might be a good thing, but the example of “microslop” as a viral term like it’s a good thing seems very internet-brain to me. People should be thinking about narrative on a deeper level than trying to dunk on big corp AI in a reactive way. The big corp AI isn’t magically worse because it’s big corp and in fact it is among some of the most capable AI out there. The problem with it is other things, like who has power over it.
Far from an exhaustive assessment, but I wanted to write something because on the surface, it seems to be very Yankee concept of social change (which can often be shallow and liberal).
Yep. I’d say we’re entering a new phase of technology and at its height, the empire probably would have been prioritizing proliferation of its tech as the solution for everybody, for the soft power of it. But the empire’s soft power is in decline like the rest of it, Trump keeps tearing up soft power like ribbon with his overt mobster ways, and so China can just step in and say, “What if you didn’t have to deal with all that bullshit? All the nasty strings attached and the rugpulls?”