this post was submitted on 25 Aug 2026
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China has 500 data centers, America has 5,000, there is no AI race.
I understand that you’re impresssed that it can sometimes slop together functional code. That does not make it intelligent or dangerous.
A year ago, that was my experience coding with AI as well. Sometime this spring that changed, especially when using coding agents, and lately (as I’ve stated elsewhere) the quality is on average pretty good. And contrary to what you’re implying, I’m not that easy to impress…
If there’s anything I hope you take from this exchange, it’s that the capabilities of AI shouldn’t be a part of your arguments against the current SV mania. The concentration of power, the disregard for communities and the environment, the stated goals of replacing human labour, all of that (and a lot more!) is enough, but it is what surrounds the technology itself. That technology is advancing, maybe feeding on itself, so an attack based on what it can do now can become outdated (and I’d argue that some of yours already are).
Fair point
I think the reason that my focus is on LLMs is because they keep showing up in annoying ways in my life, and I know I’m not the only one.
All of the evil actions you’re referencing were undertaken to prop up chat bots who cannot do any of the jobs (outside of some coding) that they are advertised as being able to perform.
To me the strength of the argument I’m trying to make is that they’re doing all of this damage in order to have something that’s effectively worthless. Perhaps I’m not doing a good job communicating what I’m attempting to communicate.
… also, I probably need to stop responding to all of these dumbasses and/or trolls who are constantly contradicting themselves in the first sentence of their assertions, lol
Good points as well. I guess my own view is coloured by having access to models that I find actually useful in my work. If my experience was only grating Claude prose and soulless AI "art" I'm not sure the tech itself would appeal all that much.
Probably that also blinds me a bit to what you argue, but I agree that the reasoning is sound from a point of view where all AI is useless. I'm just not sure that other areas won't have the same OMG moment that coding had earlier this year.
Coding is like insanely well suited to LLMs by nature tho, that’s why it can actually do that.
Code is a language with stricter definitions and less verbosity than a spoken human language. Things only have one definition in code, one spelling, one syntax, etc.
There’s an element of artistry to human coding, but it’s more of a science than an art (compared to spoken human language anyhow). It’s easier to predict.
There are other promising “ai” technologies, like radiological image recognition, aren’t the same technology as LLMs, but the companies who make the LLMs constantly conflate their technology with that of more useful technologies in order to obfuscate their relative uselessness.
Even the coding models are being localized. There’s absolutely no reason to have a massive AI datacenter unless you’re making a shit LLM, and even then, the only reason to have that is to commit financial fraud essentially.
EDIT - I learned a bit more about “agentic” AI since you had mentioned it earlier, and what’s actually going on behind the scenes is wild.
So basically, an “agent” is a computer program that you give a task to. The agent then attempts to accomplish the task by engaging in a loop until the task is accomplished.
The structure of the loop is 3 steps. 1) Prompt an LLM - The agent asks an LLM what the first step in the task you asked it to accomplish would be. 2) The LLM provides a plausible answer, and the agent then attempts to carry out these instructions. 3) If the given instructions don’t work, the LLM appends the prompt to say “I tried step #1, it didn’t work, what should I try next?”. If the instructions work, the agent appends the prompt to say “I took this step and it worked, what’s the next step?”. Then the loop repeats.
So, this would work exceptionally well for code because the computer can Test and verify the code before continuing… but that’s just about the only use case for the technology
A good way to visualize this is to imagine yourself as an agent. Let’s say you need to do your laundry, that’s your task. So you ask the LLM what the first step is, and you do what it says no matter what. Then you go back, append the prompt, and prompt the AI again. This cycle repeats until either 1) you finish the laundry, or 2) you destroy the laundry, or burn down the house, or injure yourself blindly following the LLMs directions (this second outcome is the statistically probable one).
Would you do that? No, probably not. You can think, so you would know which instructions to attempt, and which to ignore. The Agent cannot think. The LLM cannot think. The agent strictly follows directions like any other computer program. The LLM is attempting to construct an answer that appears plausible, token by token. It doesn’t matter if it is plausible, or possible even, the LLM isn’t trying to give you the correct answer, it’s trying to give you an answer that appears correct.
While superficially correct, your description of agentic work overemphasises the error bit of the trial and error. The newest models have been trained on this loop and the tool use involved, the failure rate of tool calls in a given run is low, and the corrections precise. The final outcome is valid and useful in the vast majority of cases.
This article outlines the co-development of harnesses and models, if you’re at all interested: https://www.latent.space/p/attention-interface
I should clarify - I do think there are use cases for smaller local models performing very specific tasks - the technology I am specifically referring to as “useless” are the massive data center LLMs.
AI is such a blanket term (intentionally conflated with useful technology by the people poised to profit from said LLMs) that it’s a bit difficult to discuss it as a singular technology. I have no problem with machine learning, or with neural networks in general.
I am, however, having difficulty reading this as a good-faith article
“Lukasz Kaiser, one of the people who invented the Transformer, said on “Unsupervised Learning” in June:”
That man (Lukasz Kaiser) is an employee of OpenAI, which is a massive conflict of interest imo, and disqualifies him from making impartial claims about the technology.
What I find particularly sketchy is the vague use of the word transformer in this context. The transformer was invented in 1886, there are no living persons who helped to invent it. To present this man as the inventor of “the transformer” without clarification/disambiguation is disingenuous. It smells fishy to me.
Kaiser is one of the coauthors of Attention is all you need, the paper that introduced the Transformer architecture, the basis for all major LLMs.
3b1b has a writeup: https://www.3blue1brown.com/lessons/attention/
Attention is the mechanism that lets an LLM use the (correct parts of the) entire context to predict the next word.
Oh yeah, I was able to figure out why the article said that with a couple search queries, but I still think that the phrasing in the article is misleading (and likely intentionally so).
Edit: Also, I think part of what’s confusing people is that they keep using terms like “attention” and “inference” to describe computer processes that may or may not have some kind of underlying similarity to the corresponding human capabilities. I also believe this to be deliberate obfuscation.