Wonder of the goblin stuff is the start of some model collapse.

That is exactly it. Their official explanation avoids the phrase model collapse, but that is exactly what they describe: using the output of one model as training data for another amplified the occurrence of the word goblin (and other creatures), which apparently initially occurred because of their system prompt which was aimed at maximizing the Eliza effect (again they avoid an honest framing, but that is totally what they are doing and it is pretty gross considering all the cases of AI psychosis that have been occuring) by telling the model "You are an unapologetically nerdy, playful and wise AI mentor to a human. "

Widespread financial fraud which was legitimized and in some cases directly backed by EAs! Surely there are no parallels!

Zitron’s analogy is excellent because the bubble is multifactorial and the analogies that we can make are factor-to-factor. Here’s some things that caused the dot-com bubble; people were overly optimistic about:

Ed has also been clear there are a few factors that make this bubble worse (for the economy and the general public) than the dotcom bubble. For one, Ed is strongly convinced that GPU lifecycles are much shorter and worse than fiber optic life cycles. You build fiber optic infrastructure and it will last for decades. Meanwhile, GPUs used constantly at max load have life cycles of 3-5 years. The end result of the internet is also much more useful and less of a double-edged sword than the slop generators which churn out propaganda and spam.

I am a pretty big fan of Ed's work, so I'm going to hold my nose and read Kelsey's work thoroughly enough to do a line by line debunking:

Over the last two years, he has called the top repeatedly:

Well yes, but he has also explicitly said that the bubble peaking and popping would be a multiyear process. I've only kept up with his every article for the past year, but in the past year, his median guess for the bubble pop becoming undeniable was 2027. I guess making timelines with big events in 2027 and hedging on the median number is only for the rationalists? Also, we are already starting to see the narrative fray as Anthropic and OpenAI experiment with price hikes and struggle with getting ready for IPO, which would count as meeting his predictions for the start of the bubble pop.

In 2026, the focus is much more on alleging widespread, Enron- or FTX-tier outright fraud.

This is basically an admission that he can’t make the case in terms of the economics anymore.

??? Ed has been making the case for circular financing and investors being deceived because he thinks there are circular financing deals and investors being deceived. Ed has slightly softened on his position on exactly how useless or not LLMs are, but he is still holding to his economic case that the amount they cost isn't worth the value they provide, extremely blatantly so once consumers start paying the real cost and not the VC-subsidized cost.

By almost every metric, AI progress from 2024 to 2026 has been much faster than AI progress from 2022 to 2024.

And she is quoting a rat-adjacent think-tank for proof that AI improvement has been exponential. Even among the rationalist, the case has been made that the benchmarks are not reflective of real world usage/value and that costs are growing with "capabilities".

It can no longer argue that costs aren’t falling; they are.

Even accepting the premise that real costs have fallen, Kelsey fails to address Ed's case that the costs LLM companies charge is massively subsidized. If real costs are 10x the current subsidized costs (which have already been pushed up as far they can be without losing customers), and model inference prices miraculously drop 5x (which Kelsey would treat as a given, but I think is pretty unlikely barring some radical paradigm shifts), that is still a 2x gap.

It is a straightforward crime to claim $2 billion in monthly revenue if you mean that you are giving away services that would have a $2 billion market value.

Yes, exactly. Technically OpenAI and Anthropic play games with ARR and "gross" revenue (i.e. magically excluding the cost of training the model in the first place), but in a just nation it would straightforwardly be a crime. Why does she find this hard to believe?

Epoch AI has an in-depth analysis of the same financial questions from the same public information

(Looks inside the Epoch AI article):

So what are the profits? One option is to look at gross profits. This only considers the direct cost of running a model

Ed has gone into detail repeatedly about why excluding the cost of training the model is bullshit.

(More details from the article)

But we can still do an illustrative calculation: let’s conservatively assume that OpenAI started R&D on GPT-5 after o3’s release last April. Then there’d still be four months between then and GPT-5’s release in August,22 during which OpenAI spent around $5 billion on R&D.23 But that’s still higher than the $2 billion of gross profits. In other words, OpenAI spent more on R&D in the four months preceding GPT-5, than it made in gross profits during GPT-5’s four-month tenure.24

Oh that is surprising, the Epoch AI article actually acknowledges the point that these models are wildly unprofitable once you account for the training cost! Of course, they throw away their point in the next section by just magically assuming LLMs will prove to massively valuable in the near future! (One of the exact things Ed has complained about!)

He’s found too many grounds for dismissing all the financial information we have as dishonest or irrelevant to seriously engage with what any of it would imply if it were true.

He has shown in detail how the companies use barely technically not lying obfuscated bullshit metrics like gross profit or ARR to inflate their numbers and if you try un-obfuscate them the numbers look a lot worse.

Kelsey goes on to try to claim how much value LLMs provide

Making them more productive is a big deal, and in 2026, AI makes them more productive.

Zitron can’t really contest this with contemporary data, so he cites 2024 and 2025 studies of much weaker AIs with much weaker productivity impacts.

Two years to... 4 months ago! Such outdated information! In the first place there has been very few rigorous studies of how much of a productivity boost LLM coding agents actually provide, and one of the few studies with even a passing attempt at rigor (while still below good academic standards), was METR's study (and keep in mind they are a rat-adjacent think tank and not proper academics), which showed programmers thought they got a productivity boost but actually got a net productivity decrease!

From this set of beliefs, you could, in fact, defend a delightful bespoke AI bubble take: that AI would have been a catastrophic investment bubble, but the AI companies were saved from their mistakes by the determined NIMBYs of America killing off the excess data center build-out.

But that’s not Zitron’s stance. He seems to account “the build-out is too aggressive” and “the build-out is not happening as planned” as both independent strikes against AI — both things that show it’s bad, and the more of those he finds, the more bad it is.

It could in fact be all 3! The hyped-up build out, such as that indicated by OpenAI's and Oracle's 300 billion dollar detail was completely insanely too aggressive (for it to pay off, Ed calculated LLMs would have to drastically exceed Netflix+Microsoft Office in terms of ubiquity and price point), not achievable given realistic build times for data centers (Ed has also brought the numbers here), and even at the reduced actually rate of build out, still not actually financially viable (is simply because the LLM companies aren't charging enough). So yes, both things are bad, and one type of badness partway mitigates the other, but it is still all bad!

I advise being very cautious about consuming Zitron’s posts

He has got a dramatic and vitriolic style, but as dgerard says, he has also dug through the numbers. I see lots of criticism of Ed's style, but not nearly so substantial criticism about the hard numbers he has come up with. The LLM companies put out contradictory and obfuscated numbers, and taken naively they seem to contradict Ed's numbers, but as Ed has shown, many, many times, when you start trying to un-obfuscate them they start looking really bad for everyone betting on LLMs.

Many coders are using chatbots, but I don’t know of evidence that it makes them more productive

So more and more coders are coming around to "actually AI code is okay"... but as we've seen repeatedly with LLM generated content, it is very easy for people to "Clever Hans" themselves and convince themselves LLMs are contributing more than they actually are, so I am not going to trust anecdotal reports.

This really is the dumbest timeline.

simulating battle scenarios

Regurgitating reddit armchair generals from /r/noncredibledefense

Saw this posted to the Reddit Sneerclub, this essay has some excellent zingers and a good overall understanding of rationalists. A few highlights...

Rationalism is the notion that the universe is a collection of true facts, but since the human brain is an instrument for detecting lions in the undergrowth, almost everyone is helplessly confused about the world, and if you want to believe as many true things and disbelieve as many false things as possible—and of course you do—you must use various special techniques to discipline your brain into functioning more like a computer. (In practice, these techniques mostly consist of calling your prejudices ‘Bayesian priors,’ but that’s not important right now.)

We're all very familiar with this phenoma, but this author has a pithy way of summarizing it.

The story is not a case study in how rationality will help you understand the world, it’s a case study in how rationality will give you power over other people. It might have been overtly signposted as fiction, with all the necessary content warnings in place. That doesn’t mean it’s not believed. Despite being genuinely horrible, this story does have one important use: it makes sense out of the rationalist fixation on the danger of a superhuman AI. According to HPMOR, raw intelligence gives you direct power over other people; a recursively self-improving artificial general intelligence is just our name for the theoretical point where infinite intelligence transforms into infinite power.

Yep, the author nails the warped view Rationalists have about intelligence.

We’re supposedly dealing with a group of idiosyncratic weirdos, all of them trying to independently reconstruct the entirety of human knowledge from scratch. Their politics run all the way from the furthest fringes of the far right to the furthest fringes of the liberal centre.

That is a concise summary of their warped Overton Window, yeah.

So, I've been spending too much time on subreddits with heavy promptfondler presence, such as /r/singularity, and the reddit algorithm keeps recommending me subreddit with even more unhinged LLM hype. One annoying trend I've noted is that people constantly conflate LLM-hybrid approaches, such as AlphaGeometry or AlphaEvolve (or even approaches that don't involve LLMs at all, such as AlphaFold) with LLMs themselves. From their they act like of course LLMs can [insert things LLMs can't do: invent drugs, optimize networks, reliably solve geometry exercise, etc.].

Like I saw multiple instances of commenters questioning/mocking/criticizing the recent Apple paper using AlphaGeometry as a counter example. AlphaGeometry can actually solve most of the problems without an LLM at all, the LLM component replaces a set of heuristics that make suggestions on proof approaches, the majority of the proof work is done by a symbolic AI working with a rigid formal proof system.

I don't really have anywhere I'm going with this, just something I noted that I don't want to waste the energy repeatedly re-explaining on reddit, so I'm letting a primal scream out here to get it out of my system.

The promptfondlers on places like /r/singularity are trying so hard to spin this paper. "It's still doing reasoning, it just somehow mysteriously fails when you it's reasoning gets too long!" or "LRMs improved with an intermediate number of reasoning tokens" or some other excuse. They are missing the point that short and medium length "reasoning" traces are potentially the result of pattern memorization. If the LLMs are actually reasoning and aren't just pattern memorizing, then extending the number of reasoning tokens proportionately with the task length should let the LLMs maintain performance on the tasks instead of catastrophically failing. Because this isn't the case, apple's paper is evidence for what big names like Gary Marcus, Yann Lecun, and many pundits and analysts have been repeatedly saying: LLMs achieve their results through memorization, not generalization, especially not out-of-distribution generalization.

Of course, part of that wiring will be figuring out how to deal with the the signal to noise ratio of ~1:50 in this case, but that’s something we are already making progress at.

This line annoys me... LLMs excel at making signal-shaped noise, so separating out an absurd number of false positives (and investigating false negatives further) is very difficult. It probably requires that you have some sort of actually reliable verifier, and if you have that, why bother with LLMs in the first place instead of just using that verifier directly?

They are more defensive of the racists in the other blog post on this topic: https://forum.effectivealtruism.org/posts/MHenxzydsNgRzSMHY/my-experience-at-the-controversial-manifest-2024

Maybe its because the HBDers managed to control the framing with the other thread? Or because the other thread systematically refuses to name names, but this thread actually did name them and the conversation shifted out of a framing that could be controlled with tone-policing and freeze peach appeals into actual concrete discussion of specific blatantly racists statements (its hard to argue someone isn't racist and transphobic when they have articles with titles like "Why Do I Hate Pronouns More Than Genocide?").

There's more shit gems in the comments, but I think my summary captures most of the major points. One more comment that stuck out:

Being a republican is equally as compatible with EA as being a Democrat. Lots of people on both sides have incompatible views. I honestly think you just haven't met enough Republicans!

Yes, this is actually true, and it is a bad thing and an indictment of EA.

Edit 1: There is another post clarifying that it wasn't mostly racists (https://forum.effectivealtruism.org/posts/34pz6ni3muwPnenLS/why-so-many-racists-at-manifest ) but 1) this is sneerclub, not careful count of the exact percentage of racists and racist talks to avoid hurting feelings club 2) if you sit down at a table with 3 Neo-Nazis, there are 4 Neo-Nazis sitting down. 3) "Full" is a subjective description, so yes its valid. two major racists would be more than my quota 4) see sidebar on debate