For thinking machines, they provide super simple finetuning APIs.
if it is their model, they can have more lower level integrations for that.
Thinking machines might be the only large lab in the US to have business interest aligned with open sourcing strong models that are customizable.
Just serving the model over API seems like a natural fit and is what many of them are doing. So simply being the cloud provider for your own open weight model can be a source of revenue
What is the moat? The time it takes for AI to rewrite an efficient inference stack for a new model? Considering most LLMs follow a similar architecture, adapting to a new model shouldn't take that much time.
There is no moat. At the moment, all of these companies are burning money to gain mindshare and market share. That's what Thinking Machines is doing; they're not looking for a business model.
I don't know why people keep saying there's no moat. There's no moat. Having a FUCK ton of money to train these gigantic fucking models and retain the brains to make it happen is a moat.
You're not going to train one using a VPS from LowEndBox.
But so can everyone else. What’s the moat for spending all those billions. I understand the Chinese angle, they need to undermine American models as a matter of statecraft, but what is the business model here? It just seems like VC charity.
Open source low cost models will dominate most enterprise tasks as cost curves will dictate usage. TM is trying to replicate that especially as the US and China gets more defensive with their tech
Similar to companies working on FOSS codebases, hosting (sometimes with the license restricting third-parties in some way), providing tailored models and services to customer's and getting bought for your team if your model happens to be competitive enough.
For thinking machines, they provide super simple finetuning APIs.
if it is their model, they can have more lower level integrations for that. Thinking machines might be the only large lab in the US to have business interest aligned with open sourcing strong models that are customizable.
Just serving the model over API seems like a natural fit and is what many of them are doing. So simply being the cloud provider for your own open weight model can be a source of revenue
What is the moat? The time it takes for AI to rewrite an efficient inference stack for a new model? Considering most LLMs follow a similar architecture, adapting to a new model shouldn't take that much time.
There is no moat. At the moment, all of these companies are burning money to gain mindshare and market share. That's what Thinking Machines is doing; they're not looking for a business model.
Nobody in the LLM world has a moat, or even an actual business model
I don't know why people keep saying there's no moat. There's no moat. Having a FUCK ton of money to train these gigantic fucking models and retain the brains to make it happen is a moat.
You're not going to train one using a VPS from LowEndBox.
But if people can download it for free, that is a drawbridge across the moat competitors can use to get the same model.
But so can everyone else. What’s the moat for spending all those billions. I understand the Chinese angle, they need to undermine American models as a matter of statecraft, but what is the business model here? It just seems like VC charity.
use open models to gain marketing/users/attention and then go closed? maybe
There are no moats. LLM's are a commodity. The point in spending all of the billions is to have strong domestic open-weight models.
One of the worst case scenarios regarding LLM's is monopoly control, so these billionaires know they need to invest in competition.
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Mira Murati's success isn't because she's a woman.
Maybe the thesis is that
Open source low cost models will dominate most enterprise tasks as cost curves will dictate usage. TM is trying to replicate that especially as the US and China gets more defensive with their tech
Similar to companies working on FOSS codebases, hosting (sometimes with the license restricting third-parties in some way), providing tailored models and services to customer's and getting bought for your team if your model happens to be competitive enough.
- inference
- RLaaS (Tinker, or the more involved FDE motion a la Reflection / Applied Compute)