It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
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.
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.
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.
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.
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.
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.
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.
Oof, you reminded me of when I chose to use Flow and then TypeScript won.
(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)
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.
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[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.
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 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.
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.
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.
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 ;).
Thank you for the clarification.
Maybe if you added github projects with permissive licenses?
My experience is that SLA "guarantees" don't actually guarantee anything.
Your provider might be really generous and rebate a whole month's fees if they have a really, really, really bad month (perhaps they achieved less than 95% uptime, which is a day and half of downtime). It might not even be that much.
How many of them will cover you for the business you lost and/or the reputational damage incurred while their service was down?
But that's not something you get "off the shelf", our lawyers negotiate that. You also don't spend that much effort on small contracts, so there's a floor with most vendors for even considering it.
For general cloud, avoiding screwing might mean multi cloud. But for LLM, there’s only one option at the highest level of quality for now.
People tend to over focus on resilience (minimizing probability of breaking) and neglect the plan for recovery when things do break.
I can’t tell you how weirdly foreign this is to many people, how many meetings I’ve been in where I ask what the plan is when it fails, and someone starts explaining RAID6 or BGP or something, with no actual plan, other than “it’s really unlikely to fail”, which old dogs know isn’t true.
I guess the point is, for now, we’re all de facto plug-in authors.
There's always only one at the highest level of quality at a fine-grained enough resolution.
Whether there's only one at sufficient quality for use, and if it is possible to switch between them in realtime without problems caused by the switch (e.g., data locked up in the provider that is down) is the relevant question, and whether the cost of building the multi-provider switching capability is worth it given the cost vs. risk of outage. All those are complicated questions that are application specific, not ones that have an easy answer on a global, uniform basis.
Of course, but right now, there highest quality level option is an outlier, far ahead of everyone else, so if you need this level of quality (and I struggle to imagine user-facing products where you wouldn't!), there is only one option in the foreseeable future.
As more models are released, it becomes possible to integrate directly in some stacks (such as Elixir) without "direct" third-party reliance (except you still depend on a model, of course).
For instance, see:
- https://www.youtube.com/watch?v=HK38-HIK6NA (in "LiveBook", but the same code would go inside an app, in a way that is quite easy to adapt)
- https://news.livebook.dev/speech-to-text-with-whisper-timest... for the companion blog post
I have already seen more than a few people running SaaS app on twitter complaining about AI-downtime :-)
Of course, it will also come with a (maintenance) cost (but like external dependencies), as I described here:
https://twitter.com/thibaut_barrere/status/17221729157334307...
It can be easy to lose sight of that.
I'm hoping for more progress in the performance of vectorized computing so that both model training and usage can become cheaper. If that happens, I am hopeful we are going to see a lot of open source models that can embedded into the applications.
We might see SETI-like distributed training networks and specific permutations of open source licensing (for code and content) intended to address dystopian AI scenarios.
It's only been a few years since we as a society learned that LLMs can be useful in this way, and OpenAI is managing to stay in the lead for now, though one could see in his facial countenance that Satya wants to fully own it so I think we can expect a MS acquisition to close within the next year and will be the most Microsoft has ever paid to acquire a company.
MS could justify tremendous capital expenditure to get a clear lead over Google both in terms of product and IP related concerns.
Also, from the standpoint of LLMs, Microsoft has far, far more proprietary data that would be valuable for training than any other company in the world.
Granted the internet and big tech was young then, and maybe we won’t make the same mistakes twice, but I wouldn’t bet the farm on it
Now that's an idea. One bottleneck might be a limit on just how much you can parallelize training, though.
Moving from 900GB/sec GPU memory bandwidth with infiniband interconnects between nodes to 0.01-0.1GB/sec over the internet is brutal (1000x to 10000x slower...) This works for simple image classifiers, but I've never seen anything like a large language model be trained in a meaningful amount of time this way.
Gonna be similar (or worse) to what happens when Github goes down. It amazes me how quickly people have come to rely on "AI" to do their work for them.
where as if openai goes down i can no longer use ai to generate a lame cover letter or whatever i was avoiding actually doing anyway, thats all
i guess my pedantic point is GH itself is central to many organizations, detached from git itself of course. I can only hope the same is NOT true for OpenAI but maybe there are novel workflows.
just to be clear i do not like github lol
I think thats why OpenAI is trying to move up the value chain with integration.
But...are we? There's a reason that many enterprises that need reliability aren't doing that, but instead...
> It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
...to the extent that they are building dependencies on hosted AI services, doing it with traditional cloud providers hosted solutions, not first party hosting by AI development firms that aren't general enterprise cloud providers (e.g., for OpenAI models, using Azure OpenAI rather than OpenAI directly, for a bunch of others, AWS Bedrock.)
In the future they may allow on premise model but I don’t how they will secure the weights
Right now everyone is scrambling to just get some basic products out using LLMs but as people have more breathing room I can't image most teams not having a non-OpenAI LLM that they are using to run experiments on.
At the end of the day, OpenAI is just an API, so it's not an incredibly difficult piece of infrastructure to have a back up for.
Self-hosting though is useful internally if for no other reason having some amount of fall back architecture.
Binding directly only to one API is one oversight that can become a architectural debt issue. I"m spending some time fun time learning about API Proxies and Gateways.
The API is easy to reproduce, the functionality of the engines behind it less so.
Yes, you can compatibly implement the APIs presented by OpenAI woth open source models hosted elsewhere (including some from OpenAI). And for some applications that can produce tolerable results. But LLMs (and multimodal toolchains centered on an LLM) haven't been commoditized to the point of being easy and mostly functionally-acceptable substitutes to the degree that, say, RDBMS engines are.
Some of the tips in this discussion threads are invaluable and feel good for where I might already be thinking about some things and other new things to think about.
Commenting separately on those below.
You said it so well!
Cloud =! OpenAI
Clouds store and process shareable information that multiple participants can access. Otherwise AI agents == new applications. OpenAI is the wrong evolution for the future of AI agents