Grey market fast-follow via distillation seems like an inevitable feature of the near to medium future.
I've previously doubted that the N-1 or N-2 open weight models will ever be attractive to end users, especially power users. But it now seems that user preferences will be yet another saturated benchmark, that even the N-2 models will fully satisfy.
Heck, even my own preferences may be getting saturated already. Opus 4.5 was a very legible jump from 4.1. But 4.6? Apparently better, but it hasn't changed my workflows or the types of problems / questions I put to it.
It's poetic - the greatest theft in human history followed by the greatest comeuppance.
No end-user on planet earth will suffer a single qualm at the notion that their bargain-basement Chinese AI provider 'stole' from American big tech.
I have no idea how an LLM company can make any argument that their use of content to train the models is allowed that doesn't equally apply to the distillers using an LLM output.
"The distilled LLM isn't stealing the content from the 'parent' LLM, it is learning from the content just as a human would, surely that can't be illegal!"...
The argument is that converting static text into an LLM is sufficiently transformative to qualify for fair use, while distilling one LLM's output to create another LLM is not. Whether you buy that or not is up to you, but I think that's the fundamental difference.
The whole notion of 'distillation' at a distance is extremely iffy anyway. You're just training on LLM chat logs, but that's nowhere near enough to even loosely copy or replicate the actual model. You need the weights for that.
> The U.S. Court of Appeals for the D.C. Circuit has affirmed a district court ruling that human authorship is a bedrock requirement to register a copyright, and that an artificial intelligence system cannot be deemed the author of a work for copyright purposes
> The court’s decision in Thaler v. Perlmutter,1 on March 18, 2025, supports the position adopted by the United States Copyright Office and is the latest chapter in the long-running saga of an attempt by a computer scientist to challenge that fundamental principle.
I, like many others, believe the only way AI won't immediately get enshittified is by fighting tooth and nail for LLM output to never be copyrightable
Thaler v. Perlmutter is an a weird case because Thaler explicitly disclaimed human authorship and tried to register a machine as the author.
Whereas someone trying to copyright LLM output would likely insist that there is human authorship is via the choice of prompts and careful selection of the best LLM output. I am not sure if claims like that have been tested.
The US copyright office has published a statement that they see AI output analogous to a human contracting the work out to a machine. The machine would hold the copyright, but can't, consequently there is none. Which is imho slightly surprising since your argument about choice of prompt and output seems analogous to the argument that lead to photographs being subject to copyright despite being made by a machine.
On the other hand in a way the opinion of the US copyright office doesn't matter, what matters is what the courts decide
It's a fine line that's been drawn, but this ruling says that AI can't own a copyright itself, not that AI output is inherently ineligible for copyright protection or automatically public domain. A human can still own the output from an LLM.
>I, like many others, believe the only way AI won't immediately get enshittified is by fighting tooth and nail for LLM output to never be copyrightable
If the person who prompted the AI tool to generate something isn't considered the author (and therefore doesn't deserve copyright), then does that mean they aren't liable for the output of the AI either?
Ie if the AI does something illegal, does the prompter get off scot-free?
When you buy, or pirate, a book, you didn't enter into a business relationship with the author specifically forbidding you from using the text to train models. When you get tokens from one of these providers, you sort of did.
I think it's a pretty weak distinction and by separating the concerns, having a company that collects a corpus and then "illegally" sells it for training, you can pretty much exactly reproduce the acquire-books-and-train-on-them scenario, but in the simplest case, the EULA does actually make it slightly different.
Like, if a publisher pays an author to write a book, with the contract specifically saying they're not allowed to train on that text, and then they train on it anyway, that's clearly worse than someone just buying a book and training on it, right?
> When you buy, or pirate, a book, you didn't enter into a business relationship with the author specifically forbidding you from using the text to train models.
Nice phrasing, using "pirate".
Violating the TOS of an LLM is the equivalent of pirating a book.
Contracts can't exclude things that weren't invented when the contracts were written.
Ultimately it's up to legislation to formalize rules, ideally based on principles of fairness. Is it fair in non-legalistic sense for all old books to be trainable-on, but not LLM outputs?
Try Codex / GPT 5.3 instead. Basically superior in all respects, and the codex CLI uses 1/10 the memory and doesn't have stupid bugs. And I can use my subscription in opencode, too.
Yeah, I have been loving GPT 5.2/3 once I figured out how to change to High reasoning in OpenCode.
It has been crushing every request that would have gone to Opus at a fraction of the cost considering the massively increased quota of the cheap Codex plan with official OpenCode support.
I just roll my eyes now whenever I see HN comments defending Anthropic and suggesting OpenCode users are being petulant TOS-violating children asking for the moon.
Like, why would I be voluntarily subjected to worse, more expensive and locked down plan from Anthropic that has become more enshittified every month since I originally subscribed given Codex exists and is just as good?
It won't last forever I'm sure but for now Codex is ridiculously good value without OpenAI crudely trying to enforce vendor lock-in. I hate so much about this absurd AI/VC era in tech but aggressive competition is still a big bright spot.
I like using Codex inside OpenCode, but frankly most times I just use it inside Codex itself because O.Ai has clearly made major improvements to it in the last 3 months -- performance and stability -- instead of mucking around trying to vibe code a buggy "game loop" in React on a VT100 terminal.
I had been using Codex for a couple weeks after dropping Claude Code to evaluate as a baseline vs OpenCode and agreed, it is a very solid CLI that has improved a lot since it was originally released.
I mainly use OC just because I had refined my workflow and like reducing lock-in in general, but Codex CLI is definitely much more pleasant to use than CC.
I have started using Gemini Flash on high for general cli questions as I can't tell the difference for those "what's the command again" type questions and it's cheap/fast/accurate.
> But 4.6? Apparently better, but it hasn't changed my workflows or the types of problems / questions I put to it.
The incremental steps are now more domain-specific. For example, Codex 5.3 is supposedly improved at agentic use (tools, skills). Opus 4.6 is markedly better at frontend UI design than 4.5. I'm sure at some point we'll see across-the-board noticeable improvement again, but that would probably be a major version rather than minor.
If that's what they're tuning for, that's just not what I want. So I'm glad I switched off of Anthropic.
What teams of programmers need, when AI tooling is thrown into the mix, is more interaction with the codebase, not less. To build reliable systems the humans involved need to know what was built and how.
I'm not looking for full automation, I'm looking for intelligence and augmentation, and I'll give my money and my recommendation as team lead / eng manager to whatever product offers that best.
Now I use claude with agent orchestration and beads.
Well actually, I’m currently using openclaw to spin up multiple claudes with the above skills.
If I need to drop down to claude, I do.
If I need to edit something (usually writing I hate), I do.
I haven’t needed to line edit something in a while - it’s just faster to be like “this is a bad architecture, throw it away, do this instead, write additional red-green tests first, and make sure X. Then write a step by step tutorial document (I like simonw’s new showboat a lot for this), and fix any bugs / API holes you see.”
But I guess I could line edit something if I had to. The above takes a minute, though.
That sounds like wishful thinking. Every client I work for wants to reduce the rate at which humans need to intervene. You might not want that, but odds are your CEO does. And babysitting intermediate stages is not productive use of developer time.
Well, I want to reduce the rate at which I have to intervene in the work my agents do as well. I spend more time improving how long agents can work without my input than I spend writing actual code these days.
Full automation is also possible by putting your coding agent into a loop. The point is that an LLM that can solve a small task is more valuable for quality output, than an LLM that can solve a larger task autonomously.
"the greatest theft in human history" what a nonsense. I was curious, how the AI haters will cope, now that the tides here have changed. We have built systems that can look at any output and replicate it. That is progress. If you think some particular sequence of numbers belongs to you, you are wrong. Current intellectual property laws are crooked. You are stuck in a crooked system.
As one of those authors (3 books in this case) I'll just point out:
Most authors don't own any interesting rights to their books because they are works for hire.
Maybe I would have gotten something, maybe not. Depends on the contract. One of my books that was used is from 1996. That contract did not say a lot about the internet, and I was also 16 at the time ;)
In practice they stole from a relatively small number of publishers. The rest is PR.
The settlement goes to authors in part because anything else would generate immensely bad PR.
I've previously doubted that the N-1 or N-2 open weight models will ever be attractive to end users, especially power users. But it now seems that user preferences will be yet another saturated benchmark, that even the N-2 models will fully satisfy.
Heck, even my own preferences may be getting saturated already. Opus 4.5 was a very legible jump from 4.1. But 4.6? Apparently better, but it hasn't changed my workflows or the types of problems / questions I put to it.
It's poetic - the greatest theft in human history followed by the greatest comeuppance.
No end-user on planet earth will suffer a single qualm at the notion that their bargain-basement Chinese AI provider 'stole' from American big tech.