this post was submitted on 21 Nov 2025
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Although Grok's manipulation is so blatantly obvious, I don't believe that most people will come to realize that those who control LLMs will naturally use this power to pursue their interests.
They will continue to use ChatGPT and so on uncritically and take everything at face value because it's so nice and easy, overlooking or ignoring that their opinions, even their reality, are being manipulated by a few influential people.
Other companies are more subtle about it, but from OpenAI to MS, Google, and Anthropic, all cloud models are specifically designed to control people's opinions—they are not objective, but the majority of users do not question them as they should, and that is what makes them so dangerous.
There's huge risk here but I don't think most are designed to control people's opinions. I think most are chasing the cheapest option and it's expensive to have people upset about racist content so they try to train around that sometimes too much leading to black Nazi images etc.
But yeah, it is a power that will get abused by more than just grok
Yeah I'm on team chaos theory. People can plan and design shit all they want, but the complexity will lead to unexpected behavior, always. How harmful that unwanted behavior is, or how easy it is to control or contain, is often unknown in advance, but invented things tend to develop far, far outside the initial vision of the creators.
Yeah. Strongly agreed for most of the behaviour. I think most amusingly in Grok where obvious efforts have been made to update the output beyond rails and accuracy checks
But the guy here talking about how these will be used control the information diet of people, he's probably right about how this turns out unless there's changes to legislation (and I'm expecting any changes to be in the wrong direction) even if he's possibly misinterpreting some LLM output now
I use various AI models and I repeatedly notice that certain information is withheld or misrepresented, even though it is freely available in abundance and is therefore part of the training data.
I don't think this is a coincidence, especially since the operators of all cloud LLMs are so business-minded.
A bunch of this can be expected failure modes for LLMs. Do you have a list of short examples to get an idea?
Yes, it's clear that some of this may have to do with the fact that even if cloud LLMs have live browsing capabilities, they often still rely on outdated information from their training data. I am simply describing my impressions from somewhat extensive use of cloud LLMs.
I don't have a list of examples, but in my comment above I have mentioned two that I find suspicious.
I simply think that these products should be used with skepticism as a matter of principle. This is simply because none of the companies that offer them are known for ethical behavior - quite the opposite.
In the case of Google, for example, I don't think it will be too long before (public) advertising opportunities are implemented in Gemeni, because Google's business model is essentially the advertising business. The other cloud LLMs are also products of purely profit-oriented companies—and manipulating public opinion is a multi-billion dollar business that they will certainly not want to miss out on. Social media platforms have demonstrated this in the past as has Google and others with their "classic" search engines, targeting and data selling schemes. Whether this raises ethical issues is likely to be of little concern to these companies as their only concern is profit.
The simple fact is that it is completely unclear what logic the providers use to regulate the output. It is equally unclear what criteria are used to select training data (here, too, the output can already be influenced by deliberately omitting certain information).
What I am getting at is that it can be assumed that all providers are interested in maximizing profits—and it is therefore likely that they will allow themselves to be paid to specifically promote certain topics, products, or even worldviews, or to withhold information that is unwelcome to wealthy interest groups.
As a regular user of cloud LLMs, I have the impression that this is already happening. I cannot prove this tho, as it would require systematic, scientific studies to demonstrate whether and to what effects manipulation occurs. Unfortunately, I do not know whether such studies already exist.
However, it is a fact that in the past, all technologies that could have been used to serve humanity have been massively abused for profit. I don't understand why it should be any different with cloud LLMs, which are offered exclusively by some of the world's largest corporations.
What do you find is being suppressed?
For example, objective information about Israel's actions in Gaza. The International Criminal Court issued arrest warrants against leading members of the government a long time ago, and the UN OHCHR classifies the actions of the State of Israel as genocide. However, these facts are by no means presented as clearly as would be appropriate given the importance of these institutions. Instead, when asked whether Israel is committing genocide, one receives vague, meaningless answers. Only when specifically asked whether numerous reputable institutions actually classify Israel's actions as genocide do most LLMs reveal that much, if not all, evidence points to this being the case. In my opinion, this is a deliberate method of obscuring reality, as the vast majority of users will not or cannot ask questions if they are unaware of the UN OHCHR's assessment or do not know that arrest warrants have been issued against leading members of the Israeli government on suspicion of war crimes (many other reputable institutions have come to the same conclusion as the UN OHCHR and the International Criminal Court).
Another example: if you ask whether it is legally permissible to describe Donald Trump as a rapist, you will be told that this is defamation. However, a judge in the Carroll case has explicitly stated that this description applies to Trump – so it is in fact legally permissible to describe him as such. Again, this information is only available upon explicit request, if at all. This also distorts reality for people who are not yet informed. However, since many people initially seek information from LLMs, this leads to them being misinformed because they lack the background knowledge to ask explicit follow-up questions when given misleading answers.
Given the influence of both Israel and the US president, I cannot help but suspect that there is an intention behind this.
Not to mention the large number of Israelis (often former Mossad/intelligence agents) directly involved in US tech companies.
It's why I trust my random unauditable chinese matrix soup over my random unauditable american matrix soup frankly
Trusting any of that shit is the problem.
There you go. Any of these things is just another datapoint. You need many datapoints to decide if the information you're getting is valuable and valid.
You mean Deepseek on a local device?
Most aren't really running Deepseek locally. What ollama advertises (and basically lies about) is the now-obselete Qwen 2.5 distillations.
...I mean, some are, but it's exclusively lunatics with EPYC homelab servers, heh. And they are not using ollama.
Thx for clarifying.
I once tried a community version from huggingface (distilled), which worked quite well even on modest hardware. But that was a while ago. Unfortunately, I haven't had much time to look into this stuff lately, but I wanted to check that again at some point.
You can run GLM Air on pretty much any gaming desktop with 48GB+ of RAM. Check out ubergarm's ik_llama.cpp quants on Huggingface; that’s state of the art right now.
naw, I mean more that the kind of people who uncritically would take everything a chatbot says a face value are probably better off being in chatGPTs little curated garden anyway. Cause people like that are going to immediately get grifted into whatever comes along first no matter what, and a lot of those are a lot more dangerous to the rest of us that a bot that won't talk great replacement with you.
Ahh, thank you—I had misunderstood that, since Deepseek is (more or less) an open-source LLM from China that can also be used and fine-tuned on your own device using your own hardware.
Do you have a cluster with 10 A100 lying around? Because that's what it gets to run deepseek. It is open source, but it is far from accessible to run on your own hardware.
I run quantized versions on deepseek that are usable enough for chat, and it's on a home set that is so old and slow by today's standards I won't even mention the specs lol. Let's just say the rig is from 2018 and it wasn't near the best even back then.
That's not strictly true.
I have a Ryzen 7800 gaming destkop, RTX 3090, and 128GB DDR5. Nothing that unreasonable. And I can run the full GLM 4.6 with quite acceptable token divergence compared to the unquantized model, see: https://huggingface.co/Downtown-Case/GLM-4.6-128GB-RAM-IK-GGUF
If I had a EPYC/Threadripper homelab, I could run Deepseek the same way.
Yes, that's true. It is resource-intensive, but unlike other capable LLMs, it is somewhat possible—not for most private individuals due to the requirements, but for companies with the necessary budget.
They're overestimating the costs. 4x H100 and 512GB DDR4 will run the full DeepSeek-R1 model, that's about $100k of GPU and $7k of RAM. It's not something you're going to have in your homelab (for a few years at least) but it's well within the budget of a hobbyist group or moderately sized local business.
Since it's an open weights model, people have created quantized versions of the model. The resulting models can have much less parameters and that makes their RAM requirements a lot lower.
You can run quantized versions of DeepSeek-R1 locally. I'm running deepseek-r1-0528-qwen3-8b on a machine with an NVIDIA 3080 12GB and 64GB RAM. Unless you pay for an AI service and are using their flagship models, it's pretty indistinguishable from the full model.
If you're coding or doing other tasks that push AI it'll stumble more often, but for a 'ChatGPT' style interaction you couldn't tell the difference between it and ChatGPT.
You should be running hybrid inference of GLM Air with a setup like that. Qwen 8B is kinda obsolete.
I dunno what kind of speeds you absolutely need, but I bet you could get at least 12 tokens/s.