The counter argument is that it's a growing market where any early entrants will be lifted with the tide and can probably yield enough profit from spillover hype for investors to make their investments back.
It's not the software or hardware that will "win" the race, it's who delivers the packaged end user capability (or centralizes and grabs most of the value along the chain).
And end user capability is comprised of hardware + software + connectivity + standardized APIs for building software on top + integration into existing systems.
If I were Nvidia, I'd be smiling. They've been here before.
My dad told me a quip once: "It's amazing how much luckier well prepared people are."
In the GP's scenario, I wouldn't be building either piece of software.
But i think it is underestimated how important it is for the model to be uncensored. ChatGPT is currently not very useful beyond making fluffy posts. As a public model, they won't be able to sell it for e.g. medical applications because it will have to be perfect to pass regulators. It cannot give finance advice. Censorship for once is proving to be a liability for a tech company.
In-house models OTOH can already do that, and they can be retrained with additional corpus or whatever. And it's not even like they require very expensive hardware.
…I mean, “not-bad-at-all” depends on your context. For doing mean real work (ie. not porn or spam) these tiny models suck.
Yup, even the refined ones with the “good training data”. They’re toys. Llama is a toy. The 7B model, specifically.
…and even if it weren’t, these companies can just take any open source model and host it on their APIs. You’ll notice that isn’t happening. That’s because most of the open models are orders of magnitude less useful than the closed source ones.
So, what do want, as an investor?
To be part of some gimp-like open source AI? Or spend millions and bet you can sell it B2B for crazy license fees?
…because, I’m telling you right now; these open source models, do not cut it for B2B use cases, even if you ignore the license issues.
Eventually there will be a good enough model for most personal uses, our personal AI OS. When that happens there is a big chance advertising is going to be in a rough spot - personal agents can filter out anything from ads to spam and malware. Google better find another revenue source soon.
But OpenAI and other high-end LLM providers have a problem - the better these open source models become, the more market they cut underneath them. Everything open source models can do becomes "free". The best example is Dall-E vs Stable Diffusion. By the next year they will only be able to sell GPT4 and 5. AI will become a commodity soon, OpenAI won't be able to gate-keep for too long. Prices will hit rock bottom.
I really don't think you understand just how absurdly high the cost is to train models of this size (which we still don't know for sure anyways). I struggle to see what entity could afford to do this and release it as no cost. That doesn't even touch on the fact that even with unlimited money, OpenAI is still quite far ahead.
You can also run your stack on a single VPS instead of cloud, gimp instead of photoshop, open street maps instead of Google maps, etc.
There will always be companies who can benefit from a technology, but want it as a service. In addition, there will be a lot fine-tuning of LLMs for the the specific use case. It looks like OpenAI is focusing a lot on incorporating feedback into their product. That’s something you won’t get with open-source models.
The application of AI to business problems will be lucrative, but the models are just a tool and the money will come from the domain-specific data (i.e. user and business data), which Microsoft, Google, and even Meta are positioned for. Having a slightly better model but no customer data or domain expertise doesn’t seem like a great recipe.
Then again it’s AI, so there’s more uncertainty than the commodity market. Maybe Anthropic will surprise and I’ll be as wrong about this as I was about OS/2 being the future. But I’m very skeptical.
Think of LLMs as the understanding component in the brain, once you can understand instructions and what actions need to happen from those instruction you’re done.
The rest is integrations, the arms legs and eyes of langchain. Then memory and knowledge from semantic search, vector databases and input token limits.
The LLM is but the core of the entire ecosystem. Just like how MLOps is 99% of the work, choosing an LLM is 1% of the effort in the final product.
R&D heavy markets might have some different characteristics but it's still way too early to say with AI.
The correct term for this is “pyramid scheme”.
But if early investors only profit due to late investors pouring money in, that’s by definition a pyramid scheme.