Comment by eigenspace

And for a lot of non-frontier purposes these days, you can bootstrap via LLM-as-judge so your hyperspecific wakeword model or whatever can be trained with little to no human input, that aspect of it is fully terrific.

The frontier models are a replicator that can give you another replicator which specifically produces tea, earl grey, hot, when you push the single button, and does nothing else.

I guess it’s more like asking the paid genie to give you a new cheaper genie.

They'd still have strong models without distillation, but strong enough to challenge frontier models and to claim the meaningful market share that they have? Probably not.

Im sure that's well on its way.

Yes, but 10 or 15 years ago, I would have thought that there'd be more to it than just a slight modification on the ideas behind a CNN to get this level of AI.

There were somewhat good reasons to think it needed more than just this data-driven ML approach.

Been using a lot of Kimi K3 lately and the answers have been… „load-bearing“ to the point of hilariousness. It‘s obvious from where they distilled, even if sceptics rightly point out it can‘t have been the only source of their secret sauce, as it‘s been better than the current Opus 4.x at the time of release.

Distillation a pretty well documented technique that actually pre-dates LLMs https://arxiv.org/pdf/1503.02531

Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit

It turns out you can train a 1b model at almost 1000 tokens/s on a m5 max laptop. As a personal experiment, I've been asking Sol for synthetic training data and synthetic agentic training data (model distillation in it's purest form), plus modified opencode, codex transcripts etc for training data, and nobody's even paying me to do it. If I'm doing it has a hobby, you can bet industrial users are doing it.

Why does Kimi insist its name is Claude?

The evidence is Anthropic's own reporting [1]. You may doubt that they're telling the truth, but that's what they're reporting.

[1] https://www.anthropic.com/news/detecting-and-preventing-dist...

Musk confirmed in federal court that xAI does it: https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...

It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.

Here's an example: https://github.com/microsoft/Build25-LAB329

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there is no evidence. it shortcuts post training by a huge margin this is true. but that is all.

Funny you mention Chinese Room and LLMs in the same response, I would say LLMs proved Searle wrong, agents now make cutting edge discoveries and meaningful problem solving. They not lookup tables though and you need to pay for inference, so the intuition of syntax doing the work of semantics without understanding was wrong.

> Hmm, don't people think that if the frontier labs really put enough engineering effort into preventing distillation that they would be able to do that, or at least diminish it significantly?

Distillation was big news a year or even 6 months ago, but as far as we can tell it's not really a moat anymore. Now that multiple players have trillion+ parameter models and the capacity to post-train them, there's no putting the genie back in the lamp.

The frontier labs have competing goals in mind. They want high growth (which means little friction for account creation), API access (because enterprise money is the best money to have), and distillation protection.

Besides, identity verification that actually works at scale is a much harder problem than identity verification which is good enough to satisfy your compliance people and regulators. Especially if the fraudsters have a major world government standing behind them, and if their aim is to be identified as a real customer, not one customer in particular.

The nature of LLMs is that you give them input, they give you output. That allows for distillation. They do try to make it harder by hiding the chain of thought stuff, but fundamentally, if your model is publicly available, its distillable.

At the end of the day, while you can do your best to obfuscate your reasoning tokens, it's a losing battle to hide actual user-visible output tokens. The very nature of API offerings is that you can't do KYC on where that API's output is going - there's a rich secondary market that's not going away.

And with the sheer volume of data created from that, coupled with benign-seeming prompts like "plan out your reasoning in a document before implementing" that could never be patched without breaking existing customer workflows... there's more than enough for someone to distill on. Even if that only gets them to not-quite-frontier, if you're pushing the frontier every few months, they're only ever a few months behind you.

Even if it were possible it wouldn't change the outcome. China is capable of training frontier models even without distillation. Distillation is only an accelerant.

The primary resource you need to train LLMs is money and China has plenty of that.

Only the Chinese authorities can stop Chinese labs from distilling from western labs. And they won’t do that, for obvious reasons.

i trained another AI on all my codex logs... it's pretty good actually

Difference is that it was free to google/bing search. Ai prompting costs money.

If I run out of tokens on ChatGPT of course I will try Claude. I never ran out of Google searches so no reason to try Bing

Google also spent heavily setting up deals with other platform owners, invested in Chrome and Android to establish Google search as the default option which most users accepted. If integrations of good enough AI features are made within existing platforms most users will probably accept using those and not think too much about whose model is powering it.

> OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name.

ChatGPT is AI for the average non-techie the world over, but the average non-techie isn't eager to pay for it. The more progress that's made, the less incentive to pay - most people are happy with the total garbage spewed by google AI overview. They'd be happy with google's 30b MoE gemma, whose performance will likely be squeezed down to something that can run on a phone in 2-3 years. Why would they pay $20 a month?

It's why OpenAI is pushing a variety of things such as ads and offer a more polished ui/ux than the competition, I think. The models are already good enough for people who just want to know how much sugar to add to their cake or when's the next basketball match their team plays - it's OpenAI's game to lose those people, by annoying UX and whatnot. If they can make a few bucks off of every one of their non-paying users it'll stretch their runway immensely. Those users will never go to Antrophic or some cheap Chinese model, but they might defect to Google because a popup on Android / in Chrome told them to.

Mass adoption isn’t where the money is, though. It doesn’t matter if ChatGPT has 1 billion users if they won’t pay for it.

> If OpenAI and Anthropic are smart, they will maintain similar pricing rather than aggressively undercutting each other, allowing the market to resemble Home Depot and Lowe’s, or cloud computing, where AWS, Google Cloud, and Azure coexist as highly profitable competitors.

Wouldn't that just be price fixing? If they arrive at their prices independently and they all happen to be similar, fine. But if they're all "smart" and coordinate so none of them undercuts the other, that's probably illegal.

Never in my life have I bought any of the household names when it comes to household equipments. Of course, I use the household names’ names when I speak about wipes, washing powder, glue, etc., but never ever would I pay the 2x to 5x premium. Same will be true for most people with regard to “intelligence”, as long as it’s suffices their needs. Thats the crucial part. Cloud computing, on the other hand, has a massive lock-in. But more importantly, their ideosyncratic ways of doing things leads to tribalism. It’s so much harder to move running processes from one provider to another without interruption than just changing an API key or the URL for your chatbot.

Their current strategy is "make the product affordable in tiny little bursts here and there." That is not a great way to build a moat.

That's just the first mover moat, OP was worried about intelligence itself being monopolized.

I hope it's a good bit cheaper than that, I pay close to $400/mo for electricity and I'm in no way interested in paying anything like that for AI.

Altman of *Open* AI? No idea why I would trust him without very convincing proof.

He also wanted to do a non-profit.

He even raised money on that premise.

He is a pathological liar, so is Dario. Don’t rely on the benevolence or truthfulness of these people.

They will say whatever is beneficial to say in the moment.

Yeah, he sure does lie about a variety of things! He doesn't have the name Scam Altman for nothing.

Lol, of course what he left out is this will happen by inflating the cost of electricity rather than driving down the cost of AI.

"Who knows" is the right answer, I think.

If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.

But as you said, who knows.

I'd take a market crash over a monopoly in the hands of a ghoul like Altman.

The economy he and his ilk want to build is infinitely worse.

This is funny because the stock Market has been ahistorically high. My portfolio went up over 20 percent in the last 12 months.

A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.

The market (s&p500) crashing 40% puts us at levels we haven't seen since 2024, well into the creation of LLMs. Probably a worthwhile trade if it was either/or!