martialg

Author here - thanks for reading and thoughtfully replying

I’d personally love a world where open weights compete with proprietary ones, but I don’t believe it solves the core concentration issue. In that scenario most value still flows to capital holders, it’s just hardware holders not model weight holders.

I emphatically do not want to put the genie back in the bottle nor do I believe it’s possible. Technology has never been restrained for long (export controls on cryptography textbooks in the 90’s comes to mind here)

I also have already personally benefited a great deal from LLMs. I actually frame the entire essay series from this perspective in my prelude essay here: https://www.wysr.xyz/p/a-consigliere-on-every-desk-and-in

However, I believe we may disagree on the definition of a public good. If you’re referring to the free tiers of private models, then I’d argue that unless there is some legal framework passed that forces the frontier labs to offer that to everybody, it’s a customer acquisition cost laundered as a public good. It could disappear at any time and probably will as cutting edge model margins are reduced via competition.

In general, I believe the best AI policy balances allowing for maximum competitive market dynamics while hedging existential economic disruption risk for the general population.

I’ll go into this deeper over the next few essays. Appreciate the feedback

Author here! It's from a workshop in 2001 for the National Research Council's Board on Science, Technology, and Economic Policy. He gave a talk.

You can ctrl+f for it at this link

https://www.ncbi.nlm.nih.gov/books/NBK208682/

Glad you noticed. My personal preference is a more aggressive pursuit of a true public data commons, but for better or worse I am trying to write the essay series by balancing what I can be implemented sooner/with less pain + heartache and I think that discussion is a much longer debate with much harder policy to define.

Author here. This is a great point! I explored this case extensively and ultimately didn't include it in this essay because I didn't want to get bogged down in east coast riparian rights vs the west coast judgments. It is certainly a tumultuous time for contested public rights.

Author here. Thanks for reading and the kind words. I will talk about distillation and OS in coming essays (the is a multi-part series).

Author here. Appreciate your thoughts and I mostly agree actually.

I'll explain more over the next few essays, but I am designing my proposed regulatory structures to try to accomplish 2 purposes in tension simultaneously like the Fed: 1. Maintain global competitiveness for frontier labs 2. Create a societal hedge against the AI bull case (AKA the economic black hole case)

A % of revenue scales in a way that I think balances the two well while avoiding all the other problems I mentioned in the essay. I’ll get into ratchets, timing, and thresholds in later essays, but I agree the China/competitiveness problem is central.

Author here. Thanks for reading. I agree there are a lot of levers we can pull to work towards something better. I've structured this essay series as a sequence of nested regulatory solutions so in the next few essays I propose additional structures with instruments like this. They're sequenced in a way I believe that can be pragmatically implemented and start showing progress in the next decade or so by the US. So stay tuned!

Thank you kindly

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.

Author here. Really appreciate you taking the time to read and for the kind comment.

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.

Author here. Thanks for taking the time to read.

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?

Author here. Thanks for reading!

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.

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