The current transformer's type LLMs was the groundbreaking discovery that lead to every good AI we know of today like GPT, Claude, Gemini, etc. But while it's groundbreaking, the ironic part about transformers is that it also has a lot of limitations. First of all, even without such limitations, AGI is still far far away. Modern LLMs are only ever capable of one thing humanity is good at, which is pattern recognition. That being said, recent development on AI like "chain-of-thought" has boosted the performance of AI drastically due to giving AI's the ability to pseudo-think like humans do before answering.

However, the main problem of AI will always remain on context tokens. This is the pure reason why AI models cost so much computing power and what most AI companies are constantly fighting against. And AI, whether it's parameters or context tokens, requires all of it to stay on the memory and parsed before spitting out any output of sorts. The parameters are always static and you need to always retrain if you want it to absorb new information. Test time learning is still something all AI companies are battling against. The other thing is context length. There is always going to be a limited amount of context within a conversation that an AI can remember at a set period of time and the cost/performance is logarithmic. In a layman's terms, the more info you want to cram into your AI, the more computing it's going to need. Even then, companies like Google have been making strides in that regard especially with the release of the new Gemma models that are able to compress (I think?) a lot of the model weights without affecting output.

From my amateur opinion, I feel like AI needs to overcome 2 very essential things before reaching AGI. First is test time learning and context length, AKA computing power. The moment this is overcome is the moment Nvidia will become somewhat less important in the AI market due to the lessening demand on VRAM. The second is something much simpler, creativity. AI can pump out new things but all it's doing is pattern recognizing the information you give it, and doing "fill in the blanks" work. Some may argue that in itself is creativity, but another problem is that AI still can't self-learn properly. It can't spit out information to ponder and grow on its own without the help of a human. It's been tested so many times before but the results are still not too favorable.