![[AINews] Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo](https://substackcdn.com/image/fetch/$s_!76lN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F709bf7b6-3173-4a7f-9099-fcabd2ebd438_1954x2078.png)
a quiet day lets us reflect on a great essay
The proliferation of AI models is leading to a necessary distinction between foundational model development and higher-level agentic applications, which is becoming clearer with increasing complexity and accessibility.
Sophisticated readers should care because this refines the investment and strategic focus within the AI landscape, distinguishing between infrastructure providers and application innovators.
The perceived value chain in AI is differentiating, with 'agent labs' potentially emerging as distinct and equally vital entities alongside traditional 'model labs'.
- · AI Agent Developers
- · Open-source AI Community
- · Early Adopters of Agentic Systems
- · Monolithic Model Providers
- · Companies without clear AI application strategies
Increased focus and investment shift from pure model development towards agentic applications leveraging these models.
New competitive landscapes form between companies specializing in foundational models and those specializing in deploying agents.
The definition of 'AI company' broadens significantly, potentially leading to new regulatory and ethical considerations for agent behavior.
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Read at Latent Space