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I'm in Australia. I've contributed my share of dirt to the delta. Why do I not get a share of this?
I get that the frontier companies are (for the moment) US companies. But that's just corporate ownership, it's not what we're talking about. We're talking about compensating the people who wrote the training data for their contribution. That contribution came from all over the world, so the Corpus Fund needs to be paid all over the world.
Set it up in the UN, get the UN to provide the training data sets as a common good, and have the UN collect the money from all AI companies using the training data sets. And the UN should distribute the money in the most equitable manner globally (so most of it going to alleviate poverty, probably).
I'd happily trade my collected years of shitposts to help folks get out of poverty.
A lot of these posts seem subtextually premised on the idea that it's possible to put the genie back into the bottle; that if frontier labs in America didn't sign on to this tolling scheme, our recourse would be to halt the progression of AI completely. But that option does not exist, unless we're going to fight a world war to create it.
The current administration is also playing strange games about export controls (can we run Fable yet? Kinda. Maybe). I think if they keep this up they'll just be shooting the US AI industry in the foot and the Chinese models will take over as the frontier models.
Maybe the UN can levy the USA for this, and leave the USA to collect that levy from its AI companies.
I have additional essays coming out that will address this exact issue and other issues I know that people will raise.
I’m building the essays series around arguing for practical policy I believe can get implemented and am sequencing it as thoughtfully as I can. I just can’t fit every argument into every essay.
I am personally coming to the conclusion that having these vast repositories of knowledge that can actually talk to us is actually great. We have some issues to solve, but the end-state of having a global repository of all knowledge that can talk to us and answer questions is actually an amazing outcome.
We just need to solve those problems first; mostly getting past the AI bubble and the massive over-investment, and then solving the hallucination problems. I don't believe either of them are insoluble.
I do worry about how future generations move on from this, though. In the same way that 90's music is still effectively the zeitgeist, and we will never move on from that, because of the way that streaming services work. It's a rare new band that can compete with (e.g.) Nirvana when appealing to that segment of audience, a competition that Nirvana themselves didn't have. So we are effectively locking in Nirvana as The Disaffected Youth Grunge Band for the rest of eternity. So similarly, we are in danger of locking in the current state of the world to the training data, and never being able to move on from that, because any new zeitgeist has to compete with this one on unequal footing.
In other words, you'd happily do nothing to help folks get out of poverty?
Why don't we capture Meta and Google as they allegedly take advantage of more publicly available information for profit? Let alone the truly valuable knowledge, like mathematics, has nothing to do with the majority of garbage posts that an average person would "contribute" on social media.
If we really want to tax or nationalize some economic activity, then, in my opinion, the target should be what it takes from society, not what it produces for society. By this logic, we should tax all labs, including those lagging ones, that utilize the public knowledge.
However, if everyone can access the public knowledge without rendering it less useful or reducing its available quantity, there should be no reason to tax it.
I agree we’re in an interesting era where frontier research has shifted from mostly publicly funded to mostly private and it creates challenging incentive structures especially regarding externalized costs of research.
Did you have any thoughts on my argument of how public knowledge does get damaged by the proliferation of AI over time?
Though the income of the individuals and businesses that rely on the expertise of the knowledge would be damaged. Is that what you meant?
But the more consequential one may be that few are motivated to contribute more training data to make Dario or Sam richer. This is already playing out in open source. People write open-source so humans can use it, in that human way that humans do, not to make Dario richer because his models will emit statistically convoluted copies of that open-source. What is my incentive to open-source something that I could commercialise today, compared to what it was before the LLM age?
(Many will say there's not much point in commercialising it, either, but to the extent that software still has commercial value, the appeal of the alternative path has greatly diminished.)
I googled for the quote but all I got is useless web spam and meme style graphics about quotes from writers. But AI told me it was David Hume and provided the full quote.
The real question is when the day will come that AI become the fertile muck that a new thing grows from and clings to and the legal system needs to adjust to. I hope it’s a good thing.
> In every system of morality which I have hitherto met with [...] the author proceeds for some time in the ordinary way of reasoning [...] when of a sudden I am surprised to find that instead of the usual copulations of propositions is and is not I meet with no proposition that is not connected with an ought or an ought not.
I'm not sure it'll get a lot of reception in the technocracy here on HN, whether of the AI booster or AI nihilist sort. However, I think it's a very comprehensive digestion of the questions that will swirl around the idea of LLMs as a public good in the near to medium future.
Opposition to AI here is more than understandable; it takes much of the joy out of programming as a craft, while leaving one with so much much more of what is hateable about it as a day-job.
I think the tension between these ethical questions and the practical realities (both the good and the bad) of AI is likely the defining issues for technology and perhaps society in this decade.
It’s important we’re thorough and rigorous with how we think and act here so I really appreciate you engaging with the topic.
My immediate, from-the-hip thought is that we are slowly lumbering toward the idea that LLMs ("AI") should be a public utility. It may take us quite a while to get there yet, as an unprecedented concentration of wealth and power is arrayed precisely against this outcome, but I think that will be the eventual effect, in that, "in the long run, we're all dead" kind of way.
I’m working through thoughts on this as well and agree with your read on the incentives.
There is an interesting set of conditions that happens if/when models get so competent that they’re effectively indistinguishable from each other and inference becomes a true commodity. IRL impact will lag this ofc but it’s such a wild time to be alive.
The foundational case for Mono Lake as a public trust resource is National Audubon Society v. Superior Court (1983) [1]. The California Supreme Court evaluated appropriative water rights against the public trust doctrine, took both arguments to their logical extremes, and decided that neither was acceptable in itself.
In a pretty jaw-dropping passage, the Court summarized the Los Angeles Department of Water and Power’s position in relation to appropriative use of water diverted from a unique ecosystem hundreds of thousands of years old:
> Defendant DWP, on the other hand, argues that the public trust doctrine as to stream waters has been "subsumed" into the appropriative water rights system and, absorbed by that body of law, quietly disappeared; according to DWP, the recipient of a board license enjoys a vested right in perpetuity to take water without concern for the consequences to the trust.
The decision in Audubon rejected LADWP’s argument, but it remains a stark example of the beneficiary of a public resource recasting a conditional public license as a permanent private entitlement, apparently free from consequence of accountability for harm inflicted on the public trust.
I think this appropriative-use vs. public-trust/public-benefit discussion is going to define the coming decades. The landscape remains unsettled as it applies to water (especially in a changing climate), much less to data in a period of rapidly evolving technology.
With respect to data, progress could be made by formally establishing a public corpus as an accessible commons, with clear expectations and rights around individual contributions made to third-party platforms. Publicly funded research is still often locked behind paywalls. The contents of the Library of Congress, special collections, municipal libraries, university archives, and museums are publicly owned or publicly supported, yet remain largely inaccessible to the general public.
I expect the “leader” in LLM performance to keep changing, but the accumulated genius of public knowledge to remain far more durable, with periodic and incremental additions. Fighting over small reparations for every scraped post seems less transformative than building a public knowledge commons that anyone can use, converse with, search, train on, and learn from.
reCAPTCHA began as a tool that simultaneously authenticated users while helping verify OCR for the backlog of The New York Times and Project Gutenberg. Maybe it is time for a similar public project to digitize and make accessible the body of public knowledge without surreptitious and ethically dubious appropriation of copyrighted works. Authors, writers, and shitposters could opt in as desired.
I would take a public resource like that well ahead of a few bucks of compensation for my decades of shitposting, just as I'd take a thriving Mono Lake well ahead of compensation for it being relegated into lifeless alkali flat via appropriative water rights.