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Comment by antupis | Hacker News Reader

Pretty much especially in Europe there is lots of big companies and public sector institutions that would pay serious € if they could run these.

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Spot on! I concur most European business and public sector institutions would be eager to rent this because they are not allowed by law to use US datacenters like AWS or Azure.

Not true for two reasons! Azure data center in europe is fine if thr data stays there and generally there is the EU-US Data Privacy Framework https://en.m.wikipedia.org/wiki/EU%E2%80%93US_Data_Privacy_F...

That's not the only issue. They want a guarantee that the model wasn't trained on copyrighted material.

Now that is a real feature for now. A lot of hesitation in embracing generative AI in large enterprises stems from uncertainty about copyright issue. Anyone who trained an o1-level model from scratch on public/properly licensed data only would be able to provide a very valuable service to those enterprise customers.

However, if both training and operating costs of a DeepSeek-like model are as small as they are, the companies best able to offer this service are... Microsoft, Amazon and Google. And second best are... teams inside the would-be customer enterprises themselves. $6M to train and $6K to run is effectively free for such companies; there is no moat here. The services that enterprise customers would happily buy instead of building are... operations, and assuming legal liability if the model turns out not to be safe from copyright infringement lawsuits. But those are exactly the services those companies are already buying from Microsoft, Amazon and Google.

This would result in some refreshing models, I guess they would be trained mostly on out-of-copyright stuff from 75+ years ago and wouldn't have knowledge of the modern world.

Maybe they could skin the robotic bureucrats in vintage scifi appearance as well to have the whole consistent experience when you go to the building permits bot, there could be small talk about the latest Beatles record etc.

Enforcing copyright on training data to this extent would actually create a temporary moat for the biggest players - they can afford to hire a lot of cheap labor to supplement the training dataset with human-authored original works that skirt IP protections by interpreting, parodying, commenting on or otherwise describing the protected works without actually infringing on them. As long as they keep those datasets private, everyone else is shit out of luck.

(I'm reiterating my prediction wrt. AI and moats - the only mid-term moat there can be is in human labor. Hardware vendors benefit from selling better hardware to more people for less; software and research are cheap to scale, datasets eventually leak or get reproduced. Human labor is the one thing that doesn't scale, and except for an economic crisis, only ever gets more expensive with time. Whatever edge one can get by applying human labor that cannot be substituted by AI - like RLHF and its evolutions - is the one that will last all the way to AGI; past that, moats won't matter anymore.)

One of the many reasons I'm firmly on the side of making the training of large neural models exempt of copyright considerations for everyone.

Isn't the training already exempt from copyright? Copyright is in the core about enabling licenses related to who's allowed to distribute copies of content (not ideas, but the exact same text, etc).

edit: apparently in the EU the situation is complicated by new AI specific legislation in the works: https://www.morganlewis.com/pubs/2024/02/eu-ai-act-how-far-w...

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