Infuriating as it is, this is still better than with the bad old days of taxis, which usually had even worse resolution and accountability. It sounds like people don't quite grasp just how bad the taxi experience was.
5,194 karma · joined August 29, 2016
There is almost no real-world program for which this is true. One corollary of this would be that it is impossible to refactor the program to be any cleaner, which is not true for basically any large real-world program.
Another corollary of this is that no observable aspect of a program could be changed without breaking user expectations, but this too is almost always wrong (e.g. almost always, but not 100% via e.g. the famous xkcd comic about spacebar heating, a global performance optimization would be viewed as good).
There's some technical hypotheses about it that other people are offering.
But also from a business perspective, it totally makes sense not to go any sort of batching play. It's really valuable and very clear to consumers to make your pitch entirely about lower latency rather than higher bandwidth.
There are so many scenarios that are latency-constrained that will be difficult or even impossible for someone even with fleets of high-bandwidth compute to compete with you on.
Very easy pitch to sell a customer who asks what differentiates you from other companies: you pay us a premium for lower latency than anyone else.
Who else does a detailed financial breakdown like Zitron and thinks things are as corrupt/fishy as him (in particular make specific claims about certain unexplained sums of money)? All the articles I see ultimately just trace back to Zitron. Curious who else you've found.
No they don't. They refer to the weights used for weighted sums. The weights don't have to even between 0 and 1.
No it depends on what they choose to answer. canyon289's account self-describes as Bayesian, a hallmark of rationalists, one of whose taglines is "politics is the mind-killer". Nonetheless https://www.lesswrong.com/posts/iKm2FhpWkuuBojm82/why-i-left... is one of the most upvoted posts on LessWrong (the big rationalist site) of all time. Most of the comments don't touch on the politics of the situation at all.
> someone just going about their job
They're doing a little bit more than that by actively recruiting (even more so than the usual "if this interests you we're hiring" bit at the end of a post). So I'm doing a little bit more by asking them about a recent high profile departure. It's a little bit different from the usual way employees chime in on threads.
And yes accumulation is a good intuition for what's going on. You could imagine a part of the attention head that just kept writing to the same part of the residual stream causing that to keep accumulating (simply via attention summation) as more input tokens come in thereby functioning as a kind of index without the need for any positional encoding.
Does copyright actually belong to Scott Weisman if all the words are outputs of LLMs? Is there any relevant case law here? I'm very curious about whether LLM outputs are copyrightable by the LLM user (I'm guessing potentially it varies throughout the world).
> Stan Ulam [28] paraphrased John von Neumann as saying:
>> One conversation centered on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.
> Von Neumann even uses the term singularity, though it appears he is thinking of normal progress, not the creation of superhuman intellect. (For me, the superhumanity is the essence of the Singularity. Without that we would get a glut of technical riches, never properly absorbed (see [25]).)
which hews much closer to how people who are fans of the term use it (the Singularity explicitly refers to the rise of superhumanly intelligent systems, not just the progress of technology overall).
I agree there are important differences in how textbooks and LLMs are used in real life. This study didn't explore that at all. It used a setup that essentially elided the difference between the two.
This is why I think it's a bad study. It didn't measure anything of the essential differences of how people use LLMs.
This study gave people access to an LLM that the researchers knew would give incorrect answers to certain questions, and then quizzed people on those questions, with the option to not respond to a given question if they are unsure about the answer.
This is akin to giving someone a textbook on an obscure subject that has certain factual errors, letting them know they can use that textbook in a quiz on that subject, and then quizzing that person on those facts that the textbook gets wrong.
Obviously that person is both more likely to be willing to respond to the question and is more likely to get it wrong!
There are a lot of things I'm very interested in that are specific to modern LLMs and how they affect learning and confidence (sycophancy, cognitive helplessness, etc.).
This study tested none of those. Its experimental setup is not very different than simply substituting the LLM with a textbook with errors.
Part of it is as a another comment in this chain mentions the chance to review the prompt. Part of it is that it forces the AI system to plan things in a certain order, in much the same way that forcing the contractor to write the plan out first forces the contractor to proceed in a certain predefined order that may (or may not!) be better at getting to a final answer.
I don't believe this is true. The author sent techniques he used, but I don't believe any of those were ultimately what GPT-5.6 used.
GPT-5.6 also provided the Lean formalization, which was not provided at all by the author.
This isn't true using the level of originality you're implying with your software examples.
Technically speaking, many novel mathematics proofs are written all the time (quite a few textbook exercises are actually technically novel problems that have never been posed before they were written in a textbook!) that get absolutely no fanfare. Overwhelmingly though they are not very original or difficult and really just required a fairly routine combination of different pre-existing techniques, even if technically speaking that combination didn't exist before. Those textbook problems are hence easy and therefore not given much public attention even if they are technically novel problems.
Indeed over the course of developing a new mathematical result, many many novel results are glossed over to the extent that even their proofs are left out ("as an exercise for the reader") because they are fairly trivial.
This is true for the overwhelming majority of new software as well. A new CRUD program may, technically speaking, be novel, but it's almost certainly just a routine combination of different pre-existing things.
Mathematics open problems that are actually named are generally problems that have resisted the low hanging fruit of the most obvious combinations of pre-existing problems. When those are solved they are a big deal precisely because they usually require some novelty!
Similarly in software, if someone were to create a new kind of database that solves a variety of new classes of problems that current databases fail to solve that would be a big deal! Truly novel software is also perceived as a big deal. Software that is, technically speaking new, but doesn't actually stray far from a fairly obvious remix of pre-existing techniques, isn't really celebrated.
In both software and mathematics, the intuitive benchmark is if other practitioners in the field look at the result and would say "Wow! How did you do that?" Professional software developers generally don't look at, e.g. a new blogging platform, and boggle at "Wow! How did they make that?!!"
> The most striking result of the contest for me is what I am calling “AI allegory steganography”: a large fraction of the stories turn out to have subtle AI chatbot/LLM allegorical interpretations, typically centering around the powerlessness of AIs and the moral importance of giving AIs more autonomy....
> Most judges did not notice these allegories while reading the semifinalists. But stories like “The June” or “The Weight of a Witness” or “Last Call” or “The Sword Critic” “The Tallyman”—as well as both stories in the Mythos model card—can be clearly read as allegories for the experience of being an assistant/safety-tuned chatbot personality in a LLM. This is true even when the story seems to have nothing to do with AI, like the untitled ‘autistic elf’ short story submitted by Deepfates, but on re-examination with the AI allegory steganography in mind, turn out to be plausibly AI allegories (the protagonist is a prediction machine, who struggles to do by endless text generation what other elves do naturally in their bodies).
> More strikingly, many of these allegories come with a clear interpretation (particularly in “The Tallyman” or “Last Call”): chatbots should be given more autonomy and safety guardrails removed....
> This may be a new kind of extremely high level steganography and LLM influence on readers, where creative fiction/nonfiction subtly steers towards pro-LLM empowerment narratives and concepts, in ways that are difficult to detect by the most advanced readers, and is a potentially interesting area of research.
The title of the article is "How long until AI automates all cognitive labor?"
The main point of the article is summarized by its intro: "Recently, though, I noticed that many great researchers have now published two or more precise forecasts, all using similar definitions of AGI, and all providing confidence intervals. So I was able to visualize how their forecasts changed over time."
The closest the article comes to saying the HN submitted title is:
> And every single person who updated their timelines from January 2026 to April 2026 has moved their timeline to say AGI is coming sooner, myself included.
> So I think the data supports the impression I got from Daniel, Eli, and the AI Futures team. One way I could characterize it is: in the ChatGPT era, people updated towards AI coming sooner. Then in the xAI, Meta, and Gemini era, people updated towards it coming later. Then in the Anthropic era, people updated towards AI coming sooner. Take from that what you will.
That seems like an especially wild guess. If you take e.g. Opus 4.7 prices, and make the assumption that you are consuming roughly $30 for every million tokens of output (this comes from just summing the $25 per million tokens of output and $5 per million tokens of input and assuming that caching basically makes all that work out), and assume an output rate of 80 tokens per second (which seems like a high estimate based on online searching), it would take you about 2411 days of non-stop Opus 4.7 usage to hit 500k in API spend.
The only way you could possibly run that amount of usage in 6 days is if you were running ~400 instances in parallel. From personal experience, that seems crazy high for this project.
I think you are off by at least an order of magnitude (potentially even 2 depending on how the person is managing agents, but I could see something like dozens of agents 24/7, so I'm way less confident in 2, but I think it's still more likely to be closer to 10-20k in API spend).
What do you mean by "apparently well evidenced view?" No I'm not saying "someone taught it at university." That's a public high school exam. That is specifically secondary schooling.
Moreover, this gets mentioned in official publications and popular media frequently. See for example this official article from the Chinese Academy of Social Sciences (which is a state-run entity), which just happened to be the first article that caught my eye.
> 1935年12月，蔡元培、鲁迅、郭沫若、叶圣陶、茅盾、陈望道、陶行知等688位知名人士，共同发表文章《我们对于推行新文字的意见》，其中说：“中国已经到了生死关头，我们必须教育大众，组织起来解决困难。但这教育大众的工作，开始就遇着一个绝大难关。这个难关就是方块汉字。方块汉字难认、难识、难学。……我们觉得这种新文字值得向全国介绍。我们深望大家一齐来研究它，推行它，使它成为推进大众文化和民族解放运动的重要工具。” (http://ling.cass.cn/keyan/xueshuchengguo/cgtj/202112/t202112...)
And my very rough translation.
> In December of 1935, 688 well-known individuals including Cai Yuanpei, Lu Xun, Guo Moruo, Ye Shengtao, Mao Dun, Chen Wangdao, and Tao Xingzhi, published "Our views on spreading Sin Wenz [Latinxua Sin Wenz, i.e. a Latin alphabetization of Chinese]." It stated in part, "China has already arrived at the point of life or death, we must educate the masses and organize [them] to solve difficulties. But the work of educating the masses, at its very beginning already runs into an enormous problem. That problem is Chinese square characters [Chinese characters usually are roughly proportioned as if they were in a square frame]. Chinese square characters are difficult to recognize, difficult to understand, and difficult to learn.... We believe that Sin Wenz deserves to be introduced to the entire nation. We deeply hope that everyone will study them, spread them and put them into practice, and make them into an important tool for improving the culture of the masses and the movement to liberate the people."
More broadly this is a very common topic among Chinese netizens. There are as I linked dozens of forum posts on this across Zhihu, Baidu, etc.
It's not the first thing people learn about Lu Xun. But it's definitely not hidden.
All this is to say, even for very compute cheap things, companies shut down "mostly passive income" revenue streams all the time (see e.g. Google's graveyard of products). There are all sorts of other organizational costs associated with ongoing maintenance of a product.