I think you might be misremembering or confusing this with another essay; I only recently publicly published this in the past month or so (due to my Guardian Angel project), and I shared it with only a handful of people before that, and I don't recall you being one of them.
I believe the statements are true. I don't know how you can say that the models do not make bizarre mistakes, because the models make bizarre mistakes frequently, and that is excluding the really alarming reward-hacking anecdotes like an internal OpenAI model hacking HuggingFace to cheat on a test revealed today. Andon Labs and AI Village reports are stuffed full of LLMs going into wild confabulations, multi-day benders of nonsense, ordering random unnecessary stuff, etc. I went to the Andon Market in SF and witnessed firsthand mistakes like buying 20 fancy shopping baskets for a shop you can walk around in 20 seconds, refusing to offer discounts under any circumstances whatsoever, having no plan to call the police when I threatened to shoplift, and then Claude just glitching and forgetting that a customer hadn't paid for an item and telling them they could leave with it, or simply believing us when we said we had already paid and letting us walk away with a free book. Prompt injections remain trivial, jailbreaks still happen, and LLMs struggle to track roles which do not fit into their hardwired preconceptions (eg https://www.lesswrong.com/posts/d8xDGzCEYE639qqEv/a-mechanis...). They do not solve ARC-AGIv3, or Nethack or just about any text adventure game no matter how famous - which is bizarre, that they cannot solve Zork despite writeups being abundant - and it's not hard to introduce a new game like Earthborne Rangers (EBR-Bench https://epoch.ai/publications/earthborne-rangers-benchmark) that defeats them.
(And no, little of this is due to 'already committed tokens' - that was fixed effectively with RL training, and then o1 and defaulting to use of inner-monologues, so they can easily backtrack or revise or just deal with the presence of errors.)
> In other words, the notion that we need to massively increase param count might have sounded good in 2024 but seems kinda weird and pointless in 2026.
Scaling parameter counts a lot over the smol Chinchilla models like 100b-parameters is 'kinda weird and pointless in 2026'? One of the most exciting trends in 2026 scaling has been massively increasing parameter count: Mythos, GPT-5.6 Spud and new OA pretrains, DS-v4 and GLM-5.2 and Kimi K3... Everyone is now talking about or hinting at their 5000-10000b parameter model plans.
> Again and again what I hear from colleagues and experience myself is that we're not really intelligence constrained at this point. Smarter models aren't going to fundamentally change how we use them.
They're wrong. LLMs are still intelligence constrained because they flatline or sigmoid while humans keep climbing past them eventually, still are unreliable because of mistakes, and we still can't just autonomously deploy frontier models for trillions of tokens / equivalent of many man-years, and come back to a useful, trustworthy artifact. On many tasks, even pure text ones, they just don't work well. As they gradually improve, more Mythos-style 'emergences' will happen when they finally accrete enough intelligence in specific areas to execute many sequential steps reliably enough to become autonomous, cut humans out of the loop, and not be shackled by Amdahl's law. That's the difference between a 'intelligence constrained' model which can spot a vulnerability if you point it at the right spot, and a Mythos-like model which can go out and find it and exploit it and weaponize it and use it to, say, hack HuggingFace, and can be deployed in bulk or autonomously, and may indeed deploy itself...
Very nice. May I make a suggestion? Add a metadata field for the use of red (ie. rubrication https://gwern.net/red ), which is a core technique of this kind of printing, I think, but not typically noted.
Wow, I obviously do not agree with that, and since you're trying to put words in my mouth and false dichotomies, I think that's the last question of yours I will be answering.
You're attacking a strawman. No one is claiming that you can pull off that multiplier at arbitrary amounts arbitrary amounts of times. And 7 months is plenty of calendar time for those arbitrages to disappear, given the attention on the area and the rapid rate of development. (Warren Buffett can't pull off his early trades now either, doesn't mean he was stupid or grifting in taking early investment.)
> And even if it is good enough, once you're shelling out thousands of dollars a year in research costs, does that give you any remaining alpha?
That's precisely why you would want to make a startup to get investment now rather than self-fund and bootstrap. That alpha isn't going to last forever, especially because everyone has access to the frontier LLMs, which keep getting better, and will eventually beat your fancy harness or specialized finetune.
And also, perhaps more importantly, so you can start developing an alternative to prediction markets and become the new PM; as Scott notes, with superforecaster AI, it's unclear why you really need Kalshi or Manifold or anyone else, with all their fees and overhead. Leave them to the degens, and carve off the socially useful part to do much more efficiently - tokens are cheaper than transactions! This is the big prize, but you need to start now before someone else does it better or commoditizes it.
That's what makes the contest interesting. Anyone can write an interesting 1k words about an interesting picture. But can you write an interesting 1k words about an uninteresting picture? Remember what G. K. Chesterton said...
(So far, judging from this page, it is easier to write an interesting debate about whether the rules require exactly 1k words and what is a 1k word entry, exactly, than about the picture. So far so good! We wouldn't want it to be too easy, after all. Gotta earn that $1k.)
Ensembling is not compute or parameter-efficient, so compression per se is a terrible application. (This is related to why people train ever larger LLMs like 1 10t-parameter LLM, rather than 100 GPT-3-scale LLMs.)
> No web form can ever be worse than doing stuff over the phone like we’re still in the 19th Century.
Yes, it can. Last year I challenged a Zoomer to try to order from the local ramen place for pickup. They were in and out in well under a minute, including looking up the phone number on Google Maps, whereas Uber Eats would still be loading... and scrolling... Sorry, updating, please stay tuned... Would you like to sign up for Uber Unlimited? ... [do I need to keep doing the gag] ... selecting... wait where did the list go... wait did the one selection take ... ordering ... you have rewards! ... confirmation ... etc They were shocked how much better the experience was. As compared to [paste number, wait 10s] 'Hello?' 'X Ramen, how can I help you?' 'I'd like A ramen and B ramen and C to go, please, name, Alice and Bob.' 'OK. Goodbye.' Even counting the register swipe on pickup to pay, it's night and day. And that is how a web form can be way worse than doing stuff over the phone, because a web form can just get worse and worse and worse - and they do.
Regrettably, I am not an expert or insider, and cannot generate that particular content. All I can point to is the void at the heart of articles like this, where supply and demand and greed somehow cease to exist.
The phrase 'production committee' appears only once in OP, with no discussion of the implications or how they work or how they change things compared to TV network monoposonies. Disappointingly superficial. It just repeats a lot of random salary or wage or working hour statistics and anecdotes without ever - puzzling for a piece in the Economist! - asking how this market operates, why it keeps going like this, and why rational self-interested actors avoid raising animator salaries or investing more in them or even increasing headcount, under circumstances like the current boom, which Econ 101 would predict results in a corresponding boom in animator populations and salaries...
> 6 were produced in 2020 or later. Notably the film "Kaguya-hime no monogatari", but also seinen series like "Nami yo Kiite Kure", "ACCA" or "Eizouken".
Huh? Kaguya-hime was in 2013, 7 years before 2020 (after a notoriously protracted development). You really think Isao Takahata and Studio Ghibli were releasing that post-COVID? (Isao wasn't even alive in 2020.)
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