[Ologn]

People have different opinions about this, but I think one problem is there are different questions.

One is - Google, Facebook, OpenAI, Anthropic, Deepseek etc. have put a lot of capital expenditure into train frontier large language models, and are continuing to do so. There is a current bet that growing the size of LLMs, with more or maybe even synthetic data, with some minor breakthroughs (nothing as big as the Alexnet deep learning breakthrough, or transformers), will have a payoff for at least the leading frontier model. Similar to Moore's law for ICs, the bet is that more data and more parameters will yield a more powerful LLM - without that much more innovation needed. So the question for this is whether the capital expenditure for this bet will pay off.

Then there's the question of how useful current LLMs are, whether we expect to see breakthroughs at the level of Alexnet or transformers in the coming decades, whether non-LLM neural networks will become useful - text-to-image, image-to-text, text-to-video, video-to-text, image-to-video, text-to-audio and so on.

So there's the business side question, of whether the bet that spending a lot of capital expenditure training a frontier model will be worth it for the winner in the next few years - with the method being an increase in data, perhaps synthetic data, and increasing the parameter numbers - without much major innovation expected. Then there's every other question around this. All questions may seem important but the first one is what seems important to business, and is connected to a lot of the capital spending being done on all of this.