ChatGPT

LLMs are overhyped and not delivering as much as people claim

I absolutely agree it's overhyped, but that doesn't mean useless. And these systems are getting better everyday. And the money isn't going to be in these massive models. It's going to be in smaller domain specific models. MoE models show better results over models that are 10x larger. It's still 100% early days.

most businesses doing LLM will not exist in 2-5 years because Amazon, Google and Microsoft will offer it all cheaper or free.

I somewhat agree with this, but since the LLM hype train started just over a year ago, smaller open source fine-tuned models have been keeping ahead of the big players that are too big to shift quickly. Google even mentioned in an internal memo that the open source community had accomplished in a few months what they thought was literally impossible and could never happen (to prune and quantize models and fine-tune them to get results very close to larger models).

And there are always more companies that spring up around a new tech than the number that continue to exist after a few years. That's been the case for decades now.

They are great at generating content but honestly most content is crap because it's AI rejuvenating something it's been trained on.

Well, this is actually demonstrably false. There are many thorough examples of how LLMs can generate novel data, even papers written on the subject. But beyond generating new and novel data, the use for LLMs are more than that. They are able to discern patterns, perform analysis, summarize data, problem solve, etc. All of which have various applications.

But ultimately, how is "regurgitating something it's been trained on" any different from how we learn? The reality is that we ourselves can only generate things based on things we've learned. The difference is that we learn basically about everything. And we have a constant stream of input from all our senses as well as ideas/thoughts shared with other people.

Edit: a great example of how we can't "generate" something outside of what we've learned is that we are 100% incapable of visualizing a 4 dimensional object. And I mean visualize in your mind's eye like you can with any other kind of shape or object. You can close your eyes right now and see a cube or sphere, but you are incapable of visualizing a hyper-cube or a hyper-sphere, even though we can describe them mathematically and even render them with software by projecting them onto a 3D virtual environment (like how a photo is a 2D representation of a 3D environment).

/End-Edit

It's not an exaggeration that neural networks are trained the same way biologic neural networks (aka brains) are trained. But there's obviously a huge difference in the inner workings.

They are our next gen spam for the most part.

Maybe the last gen models, definitely not the current gen SOTA models, and the models coming in the next few years will only get better. 10 years from now is going to look wild.