> What justifies treating human output differently?
Human time is inherently valuable, computer time is not.
One angle:
The real issue is how this is made possible. Imagine an AI being created by a lone genius or a team of really good programmers and researchers by sitting down and just writing the code. From today's POV, it would be almost unimaginably impressive but that is how most people envisioned AI being created a few decades ago (and maybe as far as 5 years ago). These people would obviously deserve all the credit for their invaluable work and all the income from people using their work. (At least until another team does the same, then it's competition as normal.)
But that's not how AI is being created. What the programmers and researchers really do it create a highly advanced lossy compression algorithm which then takes nearly all publicly available human knowledge (disregarding licenses/consent) and creates a model of it which can reproduce both the first-order data (duh) and the higher-order patterns in it (cool). Do they still deserve all the credit and all the income? What if there's 1k researchers and programmers working on the compression algorithm (= training algorithm) and 1B people whose work ("content") is compressed by it (= used to train it). I will freely admit that the work done to build the algorithm is higher skilled than most of the work done by the 1B people. Maybe even 10x or 100x more expensive. But if you multiply those numbers (1k * 100 vs 1B), you have to come to the conclusion that the 1B people deserve the vast majority of the credit and the vast majority of the income generated by the combined work. (And notice when another team creates a competing model based on the same data, the share by the 1B stays the same and the 1k have to compete for their fraction.)
Another angle:
If you read a book, learn something from it and then apply the knowledge to make money, you currently don't pay a share to the author of the book. But you paid a fixed price for the book, hopefully. We could design a system where books are available for free, we determine how much the book helped you make that money, and you pay a proportional share to the author. This is not as entirely crazy as it might sound. When you cause an injury to someone, a court will determine how much each party involved is liable and there are complex rules (e.g. https://en.wikipedia.org/wiki/Joint_and_several_liability) determining the subsequent exchange of money. We could in theory do the same for material you learn from (though the fractions would probably be smaller than 1%). We don't because it would be prohibitively time consuming, very invasive, and often unprovable unless you (accidentally) praise a specific blog post or say you learned a technique from a book. Instead, we use this thing called market capitalism where the author sets a price and people either buy the book or not (depending on whether they think it's worth it for them), some of them make no money as a result, some make a lot, and we (choose to) believe that in aggregate, the author is fairly compensated.
Even if your blog is available for anyone to read freely, you get compensated in alternative ways by people crediting you and/or by building an audience you can influence to a degree.
With LLMs, there is no way to get the companies training the models to credit you or build you an audience. And even if they pay for the books they use for training, I don't believe they pay enough. The price was determined before the possibility of LLM training was known to the author and the value produced by a sufficiently sophisticated AI, perhaps AGI (which they openly claim to want to create) is effectively unlimited. The only way to compensate authors fairly is to periodically evaluate how much revenue the model attracted and pay a dividend to the authors as long as that model continues to be used.
Best of all, unlike with humans, the inner workings of a computer model, even a very complex one, can be analyzed in their entirety. So it should be possible to track (fractional) attribution throughout the whole process. There's just no incentive for the companies to invest into the tooling.
---
> approximately any software they wish with little more than a Q&A session with an expert AI agent
Making software is not just about writing code, it's about making decisions. Not just understanding problem and designing a solution but also picking tradeoffs and preferences.
I don't think most people are gonna do this just like most people today don't go to a program's settings and tweak every slider/checkbox/dropdown to their liking. They will at most say they want something exactly like another program with a few changes. And then it's clearly based on that original program and all the work performed to find out the users' preferences/likes/dislikes/workflows which remain unchanged.
But even if they genuinely recreate everything, then if it's done by an LLM, it's still based on work of others as per the argument above.
---
> the end result would also probably be a net benefit to humanity.
Possibly. But in the case of software fully written by sufficiently advanced LLMs, that net benefit would be created only by using the work of a hundred million or possibly a billion of people for free and without (quite often against) their consent.
Forced work without compensation is normally called slavery. (The only difference is that our work has already been done and we're "only" forced to not be able to prevent LLM companies from using it despite using licenses which by their intent and by the logic above absolutely should.)
The real question is how to achieve this benefit without exploiting people.
And don't forget such a model will not be offered for free to everyone as a public good. Not even to those people whose data was used to train it. It will be offered as a paid service. And most of the revenue won't even go to the researchers and programmers who worked on the model directly and who made it possible. It will go to the people who contributed the least (often zero) technical work.
---
This comment (and its GP), which contains arguments I have not seen anywhere else, was written over an hour long train ride. I could have instead worked remotely to make more than enough money to pay for the train ride. Instead, I write this training data which will be compressed and some patterns from it reproduced, allowing people I will never know and who will never know me to make an amount of money I have no chance quantifying and get nothing from. Now, I have to work some other hour to pay for the train ride. Make of that what you will.