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Comment by itsaquicknote | Hacker News Reader

The landgrab going on in this space is ferocious. If you weren't convinced this is mania, and a 10M "seed" round for Langchain doesn't do it for you, nothing will. Well done Harrison on the cash grab (take as much money off the table every round as you can), it's a smart move. But I can't shake the feeling that this ever increasing mania will sweep up anything OSS with vague traction and this whole AI space that used to be religiously defaulted to open and sharing will fairly quickly end up in VC-funded fiefdoms with pay-to-play being all that's left modulo the "forever free" community hobbled versions. Hope I'm wrong.

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Interestingly this is shortly after another huge “seed” round in this space from fixie.ai (17mm). A lot of money being thrown at making it easier to chain/give LLMs access to other applications and tools.

Yeah, wow, a 17M seed round and not a hint of irony. Ferocious. Capital is DESPERATE to throw money into anything that appears to be related to LLMs. "Value creation event of our lifetimes" etc. There's grift money to made here and I'll be damned if some decent proportion of the "API wrapper + template UI" startups masquerading as AI companies aren't cashing in. Not sure I blame them.

I agree with you, and people miss that December 2022 is not when GPT became commercially possible.

Look at the startup OpenAI generations 1-2 years back - they have largely sank at comparable or worse rates than any other startup from 2020/2021.

The GPT-3 first wave companies, around translations, basic quizzes, summarization tools, language learning apps, and ofcourse the notorious paraphrase tools (almost entirely obsolete since ChatGPT) can't be found in that form anymore, they've all been forced to shut down or move functionality significantly. Early on, OpenAI limited output to 300 tokens max - and less for most usecases, often 50-150. Chatbots were not allowed IIRC for over a year. If ChatGPT hadn't came along, much of what langchain enables wouldn't have either, nor so many big companies willing to now risk.

I can count none over 2 years old which have not since been made obsolete by raw ChatGPT access or are now default dead due to competition from existing unicorn (e.g. Duolingo, Quizlet Q chat) who are now crushing them.

It must be painful to have spent $xx,xxx on GPT-3 at $0.06 a token to obtain users, and now have your market ripped from you by a $B+ company paying $0.006...

So I doubt the template UI startups will sustain retention or stay about long term, unless they really find traditional startup ways to nuggle into niches and use cases/vendor lock in. This isn't an innovators dilemma for most companies, it's just an obvious sensible thing to try at this point, so startups don't have much to balance with on risk. That being the case, the market surely should seem less appealing than the open ended "blue ocean new value" of 2021 GPT tech, but - I guess not.

The steamroller is real in any super hot space, but that hazard is well known and founders should pay homage regularly to those who have fallen under its mighty squashing.

The question is if the 2nd AI Winter kills python the way the first killed lisp.

God I hope so.

Could it swap, so we can have lisp back?

It would be absolute godsend if we could, so one measly error wouldn't require restarting a entire process just to rewrite some small part.

That's really more erlang's niche.

No, that's really Lisps thing.

I meant more like dynamically re-evaluating specific functions without having to restart the process. Haven't done much Erlang, but my experience is around doing so in lisp rather, which definitely can do that.

Python has huge uptake outside of the ML domain. I can't see an AI winter affecting the many people using Numpy for non-ML purposes (i.e., scientists in academia, most of whom still deal with normal numerical and data analytical modeling, not ML) much less Django.

I'm...aware. I think a lot of us that aren't using NumPy or ML stuff are rather looking for a good excuse to get away from what the ecosystem has become. (Yeah, I'm still bitter about Py3...Python user since the 1.5.2 days)

Plenty of other options out there if you've fallen out of love with Python and don't need good numerical libraries. Give JS, Elixir, or Crystal a try if you want something more dynamic. Nim if you want something a bit off the beaten path. Go, Rust, Java, Kotlin, and Scala if you want something more static.

Sadly it's what pays the bills atm.

Bitterness isn't really a winning strategy.

I thought the whole Python 3 thing was a huge problem. Lately I've been doing JS/Typescript dev and breaking changes like this happen continually and no one blinks.

What is the python3 thing?

Python 3 had incompatible changes with Python 2. You had to update your code, and for some years it meant lots of projects stayed on Python 2.

The problem was never your code, really, it was the dependencies.

Wasn't that hard for quite a while to find situations where dependency A was Py3 compatible and B was not (and the incompatibility went both ways, especially in early 3.x releases, you could NOT have one codebase that worked with both).

Sometimes A dropped Py2 support before B gained Py3.

Pain pain pain.

Then add the increasing level of insanity as the answer to "python packaging sucks" was repeatedly to add yet another layer.

I think you're absolutely correct.

But this is by 'design', I have very unreasonable suspicions that indeed, the whole VC 'world of entrepreneurs' is just the way the USA government does R & D on an industrial-corporate scale. The 'brilliance' behind this way of doing R&D is that they only pick up the winners after they won, so they don't "waste" money on R&D death ends nor moonshots.

on the other hand, this is a good way to 'explode' for cheap the technological applications of already developed scientific innovations. meaning none of those VC-backed startups are doing innovative research, but in fact are devleoping commercial applications for corporate overlords who having seen who won, step in to buy them out.

even music industry is shifting to that model, they are now only signing bands/artists/influencers who already build their audience.

It's far superior to the EU model where politicians are tasked with creating requirements for ,,the next innovations'' and creating tenders, businesses popping up / recycled to win those tenders, do the minimum to satisfy the criterion that nobody really cares about, and try to siphon out as much money as possible using trusted subcontractors, meanwhile paying a huge part of the money for the privilage of winning the tender.

At least that's what's going on in Hungary, the most corrupt government in EU, I hope other parts are a bit better.

I'm also worried that courts will decide that model weights are copyrightable and the open source free-for-all will be over.

If models can’t prove they are fully free of copyrighted data I don’t think they’ll have a leg to stand on there.

If you are holding copyright to something, it will be on you to prove it's in there.

I am obviously clueless, but if it's a case, can't one demand what the training data is?

Based on what case law? In what jurisdiction?

It's very easy to show the models generating near-identical data to copyrighted data, which is enough to get courts to force them to allow discovery.

This not have happened all over the place yet is evidence against this being as easy as you make it sound.

This is clearly not a given. Search engines are good decided case law in the opposite direction.

Search engines aren't a replacement for the original data, they're a way to direct traffic towards it.

The business model doesn't really change the IP considerations though.

(Additionally, newer LLMs like Perplexity.AI's correctly cite content sources, so that is even more similar to search engines)

These models will readily generate near identical outputs to copyrighted data, at length. This is not comparable to search.

I'd invite you to read "Foundation Models and Fair Use"[1] which is a paper written as a collaboration between Standford's law school and computer science department.

It talks at length about this specific problem and migration techniques for it:

Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. However, there is a caveat: If the model produces output that is similar to copyrighted data, particularly in scenarios that affect the market of that data, fair use may no longer apply to the output of the model. In this work, we emphasize that fair use is not guaranteed, and additional work may be necessary to keep model development and deployment squarely in the realm of fair use. First, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We review relevant U.S. case law, drawing parallels to existing and potential applications for generating text, source code, and visual art. Experiments confirm that popular foundation models can generate content considerably similar to copyrighted material. Second, we discuss technical mitigations that can help foundation models stay in line with fair use. We argue that more research is needed to align mitigation strategies with the current state of the law.

[1] https://arxiv.org/abs/2303.15715

Sounds like you're agreeing they are legally in murky territory.

Further, new laws get made in reaction to new things whenever they push an existing doctrine beyond the original ruling, and these are certainly in that territory.

> Sounds like you're agreeing they are legally in murky territory.

Of course. As I said originally "This is clearly not a given". It's very unclear how this will be decided, but anyone who thinks that just because models contain copyrighted data they don't have a leg to stand on is very wrong. There are multiple good arguments and precedents to show that they do, depending on the circumstances.

Huh, I still interpret “this is clearly not a given” as a being contradictory reply to me saying it’s murky territory.

I think they contain massive amounts of copyrighted data, and reproduce them exactly, and that’s why they don’t have a leg to stand on. It’s a personal opinion, and I think backed by your citation. But thanks for the reference there, and glad to chat.

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