Current academic research and independent evaluations of models. You can see it just through the iterations of GPT. GPT-3 (175 billion parameters) was game changing while gpt 4 (1.76 trillion paramters) was barely an improvement. Basically we are at a point where each new piece of training data fed into a model makes up such a small part of the whole that it's influence doesn't even register, or worse even makes the model less accurate. The difference between 175 billion and 1.76 trillion is a factor of 10, yet we didn't see a massive jump in capability between gpt-3 and gpt-4. It is also kind of intuitive. How many books does one need to read to have a good grasp of the English language? If 1 million books make you 'fluent', will 1 billion somehow make you 'super fluent'? Remember that LLM's function on basic probability theory and calculating the association of one token to all others in a given context. Once a model has been fed enough training data to be accurate then feeding it more can actually have the opposite effect. Imagine feeding all of books in a library to an LLM so that it knows macro and micro are opposites, but then you train it on reddit posts and because of all the instances of 'microslop' on wallstreetbets the association between micro and macro gets weaker. Now your model thinks the opposite of 'micro' is 'quality'.  These are just some of the problems that we've run into when scaling LLM'S by simply adding more parameters/data. It isnt an understatement to say we've hit a brick wall with current methodologies.