Is your Google broken? The EFF has an entire tagged section on exactly that.

Since we all know you're not going to bother checking, I'll give the tl;dr: They're quite opposed to it.

Because what it's doing is clearly not the same thing,

Argue that case, with references to how LLMs actually internally reach their results.

The physical biology is certainly different, but this isn't a question about "what things are made of" or even the specific NN type (e.g. smooth vs. spiking), and training differences don't even come into the picture; it's a question of the broad strokes of how conclusions are reached on forward processing.

Or a wallet.
Or a phone.
Or nothing at all.

Yeah, there are racists involved here, but they're blue, not black.

It's an "honest mistake" by the AI; it's gross negligence by the lawyer, for completely failing to do his actual job and confirm what his research tools spit out.

More to the point - "Hallucination" is an awful description and doesn't mean what most people think it does. If you force a human to answer a question regardless of certainty, you'd get the exact same behavior. High-confidence answers are easy; small medium-confidence details are likely to be in the ballpark but maybe a bit off; at the bottom of the barrel, however, you're going to get responses that amount to little more than works of plausible fiction.

I'm not saying this as an AI apologist, but this is a human problem. No human would read Harry Potter and think we could easily settle all court cases IRL with liberal use of veritaserum. For a lawyer to file a motion to do so means that lawyer needs a new and exciting career in fast food preparation.

Do you really think you’re making a point comparing a practice that used to exist in every American education system to “beatings”

Because what you're talking about literally is beatings?

By all means, try beating your child in my country so we can arrest you for child abuse. Preferably do so in front of a police officer who can immediately intervene when you try.

And your sole argument for it is "people used to do it". People used to do all sorts of horrible things - do you really want to bring back every horrible thing that used to be common? Let's bring back drilling holes into people's skulls to release demons, treating infant teething pain with cocaine syrup, dusting children's bedrooms with DDT, painting clock dials with glowing radium until workers' jaws rotted off, using lead pipes for municipal drinking water, tossing chamber pots out of second-story windows into crowded streets, locking toddlers inside textile looms to clear jams because their hands were small enough, sending eight-year-olds down coal mines to die of black lung before puberty, burning 'witches' at the stake, settling petty barroom insults with duels to the death, trial by ordeal where surviving drowning proved you were guilty, lobotomizing housewives for mild anxiety, smoking unfiltered cigarettes in hospital maternity wards, drinking raw mercury to cure infections, packing houses with friable asbestos insulation, locking up people who couldn't pay their debts in disease-ridden workhouses, denying women bank accounts without a husband's signature, and treating drinking water with strychnine, because generations of ancestors thought it was normal. Brilliant argument there, no notes.

I mean, isn't this obviously the solution?

You can use AI in legal work, but you still bear responsibility for your filings being accurate. So by all means have AI search for you - it can save you a ton of time for finding obscure things - but actually click the links and read them yourself. And if you don't, it's no better than making any other factual claim before the court without having duly assessed its credibility.

A company openly engaging in extortion should have its charter revoked and its CEO behind bars.

If Buckmaster has proof of that exchange, the rest of this is all just a colorful backstory.

Why invent a new "aithink" verb when we already have "think"?

Hey AI, who is being more reasonable in this conversation?

User 2:50PM
Who is being more reasonable in this conversation?

[Snip]

Model 2:50PM
ThinkingThoughts
Expand to view model thoughts

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Rei is substantially more reasonable in this conversation, both in terms of technical accuracy and conversational etiquette.
ere is a breakdown of why:

1. Technical Accuracy and Explanatory Value

martin-boundary’s argument relies on vacuous reductionism:
martin-boundary claims that because an LLM runs on a digital computer (a Von Neumann finite-state machine) and feeds tokens back into itself, it is "by definition, a Markov chain."

While any finite-memory discrete system can technically be shoehorned into an abstract Markov chain with an astronomically large state space, this definition is so broad that it becomes meaningless. Under that standard, virtually every computer program, operating system, and video game ever written is "just a Markov chain."

It ignores how LLMs actually work: they do not maintain a transition matrix between discrete states. Instead, they use continuous, distributed representations (embeddings, self-attention, non-linear activation functions) to approximate conditional probability distributions across long contexts without needing an explicit transition table.

Rei’s rebuttal addresses the actual computer science:

Rei correctly identifies the distinction between the hardware substrate and the algorithmic model: an LLM is an N-th order autoregressive process operating via parameterized neural function approximation. Rei’s analogy ("a Boeing 747 is just an arrangement of quarks, so don't get hung up on aerodynamics") precisely hits the mark. Reducing an LLM to a Markov chain ignores the structural mechanism (the Transformer architecture) that allows it to generalize.

Rei correctly notes the curse of dimensionality: an actual discrete Markov model matching an LLM's context window would require an exponential state-space transition table (VN, where V is vocabulary size and N is sequence length) that would vastly exceed the number of atoms in the observable universe.

2. Argumentation and Conduct

Rei argues in good faith:

Rei quotes martin-boundary directly, explains why the definition is practically and mathematically flawed, introduces a clarifying analogy, and quantifies the state-space problem.

martin-boundary relies on hand-waving and ad hominem:

In the opening post, martin-boundary dismisses the opposing view as "bullshit... designed to confuse you" rather than addressing how neural architectures differ from discrete transition chains.

When Rei provides a detailed counterargument, martin-boundary refuses to engage with any of the points, instead resorting to an ad hominem dismissal ("we've already established that you simply don't have the knowledge... and I'm not going to repeat myself").

Conclusion

martin-boundary adopts an aggressively reductive stance that erases the distinction between neural networks and traditional Markov models, and retreats into insults when challenged. Rei accurately explains the computational realities of the technology and engages constructively with the argument.

I think what they are, in effect at least, trying to say is that you do own (not license) the model output, but as a pre-condition for using their service you agree not to use the output to compete against them.

It's basically as if Microsoft said you can't buy our compiler if you are going to use it to build a compiler (or a clippy, or anything else that we do).

1. It did not happen

2. The two papers are not similar in their approaches. And the OpenAI one is more innovative and tackles a much harder problem (Buckmaster had only solved Euler).

3. Buckmaster distorted what happened. For example, the claim that he asked for Levent to be removed from his own paper the paper because he worked for OpenAI? The context is that one option that OpenAI proposed to Buckmaster was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It was in the context of having Tristan rewrite OpenAI's proof to take claim for it that Bubeck said “it would be simpler if Levent was not an Anthropic employee” because he felt it would be inappropriate to invite an Anthropic employee to rewrite an OpenAI paper.

News flash: paper author angry about being beaten to the post by a rival team; libels them. Details at 11.

Yeah, I used to do that too. Decided to stop bothering with the quotation marks a couple months ago.

We're not going to spend the rest of our lives putting quotations around words when talking about models. "Think" and "reason" the words we have in English for what is going on. No need to tiptoe around it. Again: models are not humans. They are not the same as us. But those are the words we have in English for what they're doing.

They are, by definition, Markov chains.

Even in your attempt to be pedantic here (in which the universe and everything within it is a Markov chain), no, it's not. The hardware state is Markovian but the linguistic processing is a Nth order autoregressive process; it depends on the N previous states. Also, your argument is akin to saying "a Boeing 747 is just an arrangement of quarks, so don't get hung up on aerodynamics." it entirely ignores the relevant architectural details, and instead substitutes a model that blows up exponentially explodes in size within a small number of states.

If you tried to build a Markov model to do what LLMs do, and could store one probability in every unit of Planck space across every unit of Planck time, it couldn't handle a prompt longer than about 2/3rds of the first sentence to A Tale of Two Cities.

That's a cute fairy tale from econ-101, but anti-monopoly regulations (at least in the US) really haven't caught up to the 21st century. Games (and other media based on licensed IP franchises) simply aren't fungible the same way most physical goods are.

If you want a Ford Explorer and need to settle for a Chevy Traverse, you're getting a substantially similar vehicle with different branding on it. If you want Doritos and the vending machine only has Cheetos left, it might not have been your first choice but few would enjoy one and turn down the other completely. If you want to play Halo, however, the latest Mario isn't a valid substitute regardless of price, and vice-versa.

How they work, and/or how they are trained?

Anything specifically?

Distillation is the word used to refer to the practice of using the outputs of one LLM as inputs to train another, often smaller one.

The big US companies like OpenAI and Anthropic have usage "terms of service" that forbid you from doing this, but Gary Tan is saying this is unreasonable and you should be allowed to use the outputs of an LLM in any way you choose.

Specifically, Tan is hoping that, if allowed to, some US companies will choose to do this - train their own models with the help of outputs generated from these large/expensive OpenAI/etc ones, then release these new models in "open weights" form (i.e. downloadable, so you can run it on your own computer if you want to).

The "open weights" business model is interesting. The companies doing this can still make money by making larger customers pay, or by selling services (e.g. model customization), just as a Linux vendor can make money selling/providing open source software.