> You've made a few mistakes, they are all rooted in not understanding that Bing Chat is not GPT, nor is it ChatGPT. You're stuck on LLMs while the world is moving on to LLMs as a component, not the whole solution.

It is a GPT (Using OpenAI's one) and fundamentally it is a black box neural network that when asked to explain it's own decisions, it still cannot transparently do so. This have been an unsolved problem for decades and still overlooked by every AI hype cycle to date.

> And even in LLMs, it sounds like you're still seeing training/retraining as single monolithic events which are super expensive. Training is super expensive! New techniques (google LoRA; there are others) are changing that.

Perhaps that is why only Big Tech companies are the ones doing it for training LLMs and the so-called 'AI companies' aren't doing it themselves and still sit on their AI APIs hence why you could only name Google who can afford to foot the bill for training their LLM.

Everyone else has to sit on their APIs just like before.

> I hope you get into what has changed in the past few years...

I think you have struggled to address the other short-comings that both LLMs and fundamentally neural networks still face that I have already outlined.

Until I see a open-source LLM or some other open source solution that matches or surpasses OpenAI's GPT offerings in parameters, explainability, and is small enough in model size to fit on a smartphone then we can discuss about how this AI cycle is worth the hype. Having yet another AI API product with even larger and expensive models sitting behind a SaaS is an indication of another AI bubble.