""I think we're in an LLM bubble, and I think the LLM bubble might be bursting next year," explained Delangue. "But 'LLM' is just a subset of AI when it comes to applying AI to biology, chemistry, image, audio, [and] video. I think we're at the beginning of it, and we'll see much more in the next few years," he noted."
""I think all the attention, all the focus, all the money, is concentrated into this idea that you can build one model through a bunch of compute and that is going to solve all problems for all companies and all people," said Delangue. "I think the reality is that you'll see in the next few months, next few years, kind of like a multiplicity of models that are more customized, specialized, that are going to solve different, different problems.""
""You don't need it to tell you about the meaning of life, right? You can use a smaller, more specialized model that is going to be cheaper, that is going to be faster, that maybe you're going to be able to run on your infrastructure as an enterprise, and I think that is the future of AI," Delangue pointed out."
An LLM bubble has formed due to concentrated attention, money, and expectations that one massive model can solve all problems. LLMs power popular chatbots but are only a subset of AI applications spanning biology, chemistry, images, audio, and video. Many use cases do not require large general-purpose models; smaller, specialized models can be cheaper, faster, and runnable on enterprise infrastructure. The market is likely to shift to a multiplicity of customized models solving distinct problems. A burst in LLM hype would not end AI progress; instead innovation will continue across modalities and more targeted models will gain adoption.
Read at TechCrunch
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