I don't think most of the large customers will care about OSS AI.
Over the last decade they've learned (trained themselves?) where to put their money towards (cloud vs. in-house infra for all manner of things, for better or worse) and I think AI tools will follow similar trends.
Businesses will certainly care about cost, but just as important will be:
- Customization and fine-tuning capabilities (also 'white labeling' where appropriate)
- Integrations (with 3rd party and in-house services & data stores)
- SLA & performance concerns
- Safety features
Open Source AI will have a place, but may be more towards personal-use and academic work. And it will certainly drive competition with the major players (OpenAI, Google, etc) and push them to innovate more which is starting to play out now.
Companies that aren't mindful of vendor lock in aren't long for the world.
Though those cloud platforms all have their own proprietary components most users are savvy enough to constrain and compartmentalize their use of them lest they find themselves having all their profits taken by a platform that knows it can set its prices arbitrarily. The cloud vs in-house adoption is what it is in large part because the cloud offerings are a commodity and a big part of them being a commodity is that much of the underlying software is free software.
On the other-hand, companies that fall behind their competitors because they are spending time on adjacent activities rather than leveraging new capabilities on the market will also lose out. It isn't clear at this time that LLMs fall into that categories ... and smart applications of in-house systems can be as much as an advantage as badly done NIH projects can be an albatross.
History is littered with companies that went dead because they focused on things that don't matter (open source, anti-microsoft, pro-linux).
There will be a time when those things matter when it hurts the bottom-line (Dropbox), but to prematurely optimize for that while you are finding product-market-fit is crazy and all companies are finding product-market-fit in the new AI era
Here's a really important reason to care about open source models: prompt engineering is fiddly enough without the risk of your model provider "upgrading" the model you are using in a way that breaks your existing prompts.
OpenAI already upset a lot of (admittedly non-paying academic) users when they shut off access to the old Ada code model with only a few week's notice.
I’m curious about how enterprises will manage model upgrades.
On one hand, as you mention, upgrades could break or degrade prompts in ways that are hard to fix. However, these models will need constant streams of updates for bugs and security fixes just like any other piece of software. Plus the temptation to get better performance.
The decisions around how and whether to upgrade LLMs will be much more complicated than upgrading Postgres versions.
Paying users who need this kind of stability are more likely get access to those models via Azure rather than from OpenAI directly, which comes with the appropriate enterprise support plans and guarantees.
Why would the models themselves need security fixes? The software running the models, sure, but you should be able to upgrade that without changing anything observable about the actual model.
LLMs (at least the ones with read/write memory) can exactly simulate the execution of a universal Turing machine [1]. AFAIK running such models will therefore entails the same fundamental security risks as ordinary software.
Not necessarily. The insecurity from LLMs comes from the fact they’re a black box - what if it turns out that particular version can be easily tricked into giving out terrorism ideas. You could try to add safeguards on top, but they’ve already been bypassed if it has been used for something like that. You might just have to retrain it somehow to make it safe
> I don't think most of the large customers will care about OSS AI.
One would think the same in the 90s but yet, for some reason, Open Source prevailed and took over the world. I don't believe it was about cost, at least not only. In my career I had to evaluate many technical solutions and products and OSS was often objectively superior at several levels without taking account the cost.
The first really successful alternative to "Open"AI will:
* gather many talented developers
* will quickly become a de facto standard solution
* people will rapidly start developing a wide range of integrations for it
* everybody will be using it, including large orgs, because, well, it's open source
Open Source hasn't really taken over the world, if you look at end-solutions.
As a software developer I might use an open source database, but as end-user I'm probably not going to use open-source accounting package - but I will use an accounting SaaS system that happens to be implemented with that OSS DB.
As a software developer I might use an OSS operating system, but as end-user I use a software that has been packaged and maintained by corporation like OSX, or even if OSS in license, has been fully packaged like Android.
Yes, and I believe it will develop in two ways over decades. Just like all major legacy hosting companies such as DigitalOcean, OVH or Hetzner have been offering a kind of public cloud services (at various levels, and after much feet dragging), they - and new AI hosting providers focusing exclusively on this use case - will provide the necessary services.
The other trend is the one we are already seeing right now: more and more mature solutions that you can use even on your laptop with a relatively new GPU. I'm sure we'll see some interesting results in this area, too.
>I don't think most of the large customers will care about OSS AI
Problem again, is centralization of LLMs by either the governments (and they always act in your best interest, amirite?) and corporation, which Non-FOSS LLMs prevent.
Democratization of the models is the only way to actually prevent bad actors from doing bad things.
"But they'll then have access to it too" you say. Yes, they will, but given how many more people who will also have access to open LLMs we'd have tools to prevent actually malicious acts.
Ah yes, the only other alternative is to give companies and the State sole access to LLMs, and guns, and drones, and militarized police. Because they'll surely only think of YOUR benefits, right?
A lot of B2B startups can technically the cloud API to provide value added applications to Enterprises, but often the banks and healthcare companies will not want their data running through startups pipes to OpenAI pipes.
We provide a low code data transformation product (prophecy.io), and we’ll never close sales at any volume, if we have a to get an MSA that approves this. Might get easier if we become large :)
> I don't think most of the large customers will care about OSS AI.
OSS AI will open up more diverse and useful services than the first-party offerings from relatively risk averse major vendors, which customers *will" care about.
The thing to remember when selling to businesses is that a business is just a stack of people in a trench coat. This might sound a tad evil but you don’t have to offer something that benefits the business as a whole, just something that benefits the person who holds the purse strings.
This is why cloud services are so popular. They’re easy and they don’t cost the decision makers personally.
Yes, but I think for most companies this has more to do with cost. They're not going to pay for the OSS model, and if they can use an OSS model + fine tuning, they'll choose to save the money.
When linux was first released in 1991 a 386 to run it would cost about $2000.
We've already seen big advancements in tools to run them on lesser hardware. It wouldn't surprise me if we see some big advancements in the hardware to run them over the next few years, currently they are mostly being run of graphics processors that aren't optimised for the task.
> is it that important to open source models that can only run on hardware worth tens of thousand of dollars?
Yes, because as we've seen with other open source AI models, it's often possible for people to fork code and modify it in such a way that it runs on consumer grade hardware.
Even a small startup, a researcher or a tinkerer can get a cloud instance with a beefy GPU. Also of note, Apple's M1 Max/Ultra should be be able to run it on their GPUs given their 64/128GB of memory, right? That's an order of magnitude cheaper.
I am confused. Those amounts are ram, not gpu ram, aren‘t they? Macs cpus are impressive, but not for ml. A most realistic one for a consumer is a 4090 rtx 24 GB. A lot of models do not fit in that, so A6000 48GB and over for some professional cards. That might be around 9000€ already.
$30000 is less than price of average car that Americans buy (and most families have two of them) - that's definitely in the realm of something that affluent family can buy if it provides enough value. I also expect price to go down and at $10k it's less than mid-range bathroom update. The question is only if it provides enough value or using in the cloud better option for almost all families.
"It only benefits bad people" is a pretty shitty argument at this point tbf. You can apply this logic to any expensive thing at this point.
I can for example, afford the hardware worth tens of thousands of dollars. I don't want to, but I can if I needed to. Does that automagically make me their competitor or a bad actor?
I agree utility of open source for personal usecase is overblown.
But for commercial usecases, open source is very relevant for privacy reasons as many enterprises have strict policy not to share data with third party. Also it could be a lot cheaper for bulk inference or to have a small model for particular task.
However, the same thing could be achieved with closed source models. There's nothing to stop an LLM being made available to run on prem under a restrictive license. It would really be no different to ye olde desktop software - keeping ownership over bits shipped to a customer is solved with the law rather than technical means.
That said, I really hope open source models can succeed, it would be far better for the industry if we had a Linux of LLMs.
Yes, because it can always be down ported by people with more constraints than the original authors. We’ve see a lot of this in the LLM space, and a lot of other OSS efforts.
They don't only run on high end systems. Good models can run on a desktop you have at home. If you don't have a desktop... I'm not sure what you're doing on HN.
I would disagree that 30B is not good enough. It heavily depends on which model, and what you're trying to use it for.
30B is plenty if you have a local DB of all of your files and wiki/stackechange/other important databases places in a embedding vectordb.
This is typically what is done when people make these models for their home, and it works quite well while saving a ton of money.
While llama-7B systems on their own may not be able to construct a novel ML algorithm to discover a new analytical expression via symbolic regression for many-body physics, you can still get a great linguistic interface with them to a world of data.
You're not thinking like a real software engineer here - there are a lot of great ways to use this semantic compression tool.
I'd add open source to the list, which neither "open"AI or this is.