Don't underestimate Big Corp's resistance to using OpenAI's hosted solutions (even on Azure) for anything that's not marketing fluff.

You can say that about anything, though. BigCorps aren't exactly known for adopting useful tech on a reasonable timeline, let alone at all. I don't think anyone is under the impression that orgs who refuse to migrate off of Java 5 will be looking at OpenAI for anything.

No, this is silly reasoning. A middle manager somewhere has no clue what Java 5 is. But he does know -- or let's say IMAGINES what he knows about ChatGPT. And unlike Java 5-- he just needs to use his departmental budget and instantly mandate that his team now use ChatGPT.

Whatever that means you can argue it.

But ChatGPT is a front line technology and super accessible. Java 5 is super back end and very specialized.

The adoption you say won't happen: it will come from the middle -> up.

Honest question: do you really mean Java 5 when you say Java 5? It sounds a bit 2000s to me.

Parent used "Java 5" as an example. Java 5 somehow in my mind is from like the 200x era.

But no. I practically mean any complicated back end technology that takes corporations months or years to migrate off of because its quite complicated and requires an intense amount of technical savoir-faire.

My point was that ChatGPT bypasses all this and any middle manager can start using it anywhere for a small hit to his departmental budget.

If you care about the security of OpenAI, you care about the EOL of 14 year old Java 5

In 2016 I worked on a project with a client who still mandated that all code was written to the Java 1.1 language specification - no generics, no enums, no annotations, etc., not to even mention all the stuff that's come since 1.5 (or Java 5, or whatever you want to call it). They had Reasons(tm), which after filtering through the nonsense mostly boiled down to the CTO being curmudgeonly and unwilling to approve replacing a hand-written code transformer that he had personally written back in the stone ages and that he 1) considered core to their product, and 2) considered too risky to replace, because obviously there were no tests covering any of the core systems...sigh. At least they ran it all on a modern JVM.

But no, it would not surprise me to find a decent handful of large companies still writing Java 5 code; it would surprise me a bit more to find many still using that JVM, since you can't even get paid support through Oracle anymore, but I'm sure someone out there is doing it. Never underestimate the "don't touch it, you might break it" sentiment at non-tech companies, even big ones with lots of revenue, they routinely understaff their tech departments and the people who built key systems may have retired 20 years ago at this point so it's really risky to do any sort of big system migration. That's why so many lines of COBOL are still running.

> But he does know -- or let's say IMAGINES what he knows about ChatGPT. And unlike Java 5--

Those of us who've been around for a long time know that's pretty much how Java worked as well. All of the non-technical "manager" magazines started running advertorials (no doubt heavily astroturfed by Sun) about how great Java was. Those managers didn't know what Java was either. All they knew (or thought they knew) was that all the "smart managers" were using Java (according to their "smart manager" magazines), and the rest was history.

chatgpt will have an on prem solution eventually. in the mean time players like NVIDIA are working on that as well.

Marketing fluff is what 90% of tech is... it amazes me how many people think otherwise on hacker news. Unless you are building utility systems that run power plants, at the end of the day -- you're doing marketing fluff or the tools for it.

> Unless you are building utility systems that run power plants, at the end of the day -- you're doing marketing fluff or the tools for it.

Even when you are building utility systems for critical infrastructure, you'll still be dealing with a disheartening amount of focus on marketing fluff and sales trickery.

the choice is to live 2 years behind (e.g. integrate the open source stuff and ride that wave of improvement). for businesses in a competitive space, that’s perhaps untenable. but for individuals and anywhere else where this stuff is just a “nice to have”, that’s really just the long-term sustainable approach.

it reminds me of a choice like “do i host my website on a Windows Server, or a Linux box” at a time when both of these things are new.

2 years behind in terms of timeline, but what factor in terms of productivity and quality of life?

Not to mention openai's lead compounds, so 2 years now and 4 years in 2025 may be 10 times the original prod/qol gain.

The gap seems to be shrinking, not growing. The OSS models have reach new capabilities faster than most thought

> it reminds me of a choice like “do i host my website on a Windows Server, or a Linux box” at a time when both of these things are new.

Oof, you reminded me of when I chose to use Flow and then TypeScript won.

Haha this puts me in mind of when I designed a whole deployment strategy for an org based on docker swarm, only to have k8s eat its lunch and swarm to wind up discontinued

A lot of people don't really need to go Full k8s, but I think swarm died in part because for many users there was -some- part of k8s that swarm didn't have, and the 'some' varied wildly between users so k8s was something they could converge on.

(note "died in part" because there's the obvious hype cycle and resume driven development aspects but I think arguably those kicked in -after- the above effect)

For individuals, this is a very short window of time where we have cheap access to an actually useful, and relatively unshackled SOTA model[0]. This is the rare time individuals can empower themselves, become briefly better at whatever it is they're doing, expand their skills, cut through tedium, let their creativity bloom. It's only a matter of time before many a corporation and startup parcel it all between themselves, enshittify the living shit out of AI, disenfranchise individuals again and sell them as services what they just took away.

No, it's exactly the individuals who can't afford to live "2 years behind". Benefits are too great, and worst that can happen is... going back to where one is now.

--

[0] - I'm not talking the political bias and using the idea of alignment to give undue weigh to corporate reputation management issues. I'm talking about gutting the functionality to establish revenue channels. Like, imagine ChatGPT telling you it won't help you with your programming question, until you subscribe to Premium Dev Package for $language, or All Seasons Pass for all languages.

> Benefits are too great, and worst that can happen is... going back to where one is now.

true only if there's no form of lock-in. OpenAI is partnered with people who have decades of tech + business experience now: if they're not actively increasing that lock-in as we speak then frankly, they suck at their jobs (and i don't think they suck at their jobs).

That's my point exactly. Right now there is no lock-in for an individual. You'd have to try really, really hard to become dependent on ChatGPT. So right now is the time to use it.

dependencies have a way of sneaking up on a person. if there was a clear demarcation at which you'd say "they're locking us in: i'm leaving now while i still can!", then that's not the route by which you'll be locked in. yet, even those of us who keep an eye out for these things, we all probably observe ourselves to be locked into one or more things in our life right now: how did each of those happen?

> the choice is to live 2 years behind...

That's one world - there is another where the time gap grows a lot more as the compute and training requirements continue to rise.

Microsoft will probably be willing to spend multiple billions in compute to help train GPT5, so it depends how much investment open source projects can get to compete. Seems like it's down to Meta, but it depends if they can continue to justify releasing future models as Open Source considering the investment required, or what licensing looks like.

That's definitely what a lot of people think the choice is but learned helplessness is not the only option. It ignores the fact that for many many use cases small special-purpose models will perform as well as massive models. For most of your business use cases you don't need a model that can tell you a joke, write a poem, recommend a recipe involving a specific list of ingredients and also describe trig identities in the style of Eminem. You need specific performance for a specific set of user stories and a small model could well do that.

These small models are not expensive to train and are (crucially) much cheaper to run on an ongoing basis.

Opensource really is a viable choice.

I suspect small specific purpose models are actually a better idea for quite a lot of use cases.

However you need a bunch more understanding to train and run one.

So I expect OpenAI will continue to be seen as the default for "how to do LLM things" and some people and/or companies who actually know what they're doing will use small models as a competitive advantage.

Or: OpenAI is going to be 'premium mediocre at lots of things but easy to get started with' ... and hopefully that'll be a gateway drug to people who dislike 'throw stuff at an opaque API' doing the learning.

But I don't have -that- much understanding myself, so while this isn't exactly uninformed guesswork, it certainly isn't as well informed as I'd like and people should take my ability to have an opinion only somewhat seriously.

I have a slightly different take. Not all use cases are narrow use cases. OpenAI crushes the broad and/or poorly defined use cases. On those if you tried to train your own inhouse model it would be very expensive and you would produce a significantly inferior model.

I'm not sure how my "quite a lot of use cases" and your "not all use cases are narrow use cases" are meaningfully different (slightly) to you.

This isn't a snipe, mind, it's me being unsure if we even disagree, especially given the latter part of your comment seems entirely correct (so far as my limited understanding goes ;).

That's not the part that's different. The part where I feel we perhaps differ is rather than being "premium mediocre" I think that openAI is really excellent where the problem space is very broad or is poorly specified. Then we both agree there are better choices where it is narrow and well specified.

Aha, I see now.

Thank you for the clarification.

OpenAI is obviously using libgen. Libgen is necessary but not sufficient for a top AI model. I believe that Google's corporate reluctance to use it is what's holding them back.

How do you know? Has someone created a set of test questions that can definitively prove it one way or another?

I don't believe it's even possible to create such a set of test questions. You would need to specifically test for differences between the versions found on Libgen and those that aren't available there. But even then, the evidence would be inconclusive.

I won't say I disagree because only time can tell, but what you wrote sounds a lot like what people said before open source software took off. All these companies spend so much money on software development and they hire the best people available, how can a bunch of unorganized volunteers ever compete? We saw how they could and I hope we will see the same in AI.

Kind of hilarious that the vast capabilities of an LLM are held back by copyright infringement.

The original purpose of copyright was to promote progress, and now it seems to hinder it.

I'd love to see a language model that was only trained on public domain and openly available content. It would probably be way too little data to give it ChatGPT-like generality, but even a GPT-2 scale model would be interesting.

If, hypothetically, libraries in the US - including in particular the Library of Congress - were to scan and OCR every book, newspaper and magazine they have with copyright protection already expired, would that be enough? Is there some estimate for the size of such dataset?

Much of that material is already available at https://archive.org. It might be good enough for some purposes, but limiting it to stuff before 1928 (in the United Sates) isn't going to be very helpful for (e.g.) coding.

Maybe if you added github projects with permissive licenses?

Intellectual property rights will yet turn out to be one of the Great Filters.