Also AI-Linkedin-Bullshit likes to use "just" additionally and it's mostly along the lines of Y being something much more impactful then X.

That said I think we will see more efforts also on the business side to have models that can help you build a knowledge base in some kind of standardized way that the model is trained to read. Or synthesize some sort on instructions how to navigate your knowledge base.

Currently e.g. Copilot tries to navigate a hot mess of a MS knowledge graph that is very different for each company. And due to its amnesia it has to repeat the discovery in every session. No wonder that does not work. We have to either standardize or store somewhere (model, instructions) how to find information efficiently.

Regardless of what you think about phytooestrogens (which has very little evidence to have negative effects in normal quantities)

You could imagine that it is possible to learn certain algorithms/ heuristics that "intelligence" is comprised of. No matter what you output. Training for optimal compression of tasks /taking actions -> could lead to intelligence being the best solution.

This is far from a formal argument but so is the stubborn reiteration off "it's just probabilities" or "it's just compression". Because this "just" thing is getting more an more capable of solving tasks that are surely not in the training data exactly like this.

However I would say that the cited studies are somewhat outdated already compared e.g. with GPT-5-Thinking doing 2mins of reasoning/search about a medical question. As far as I know Deepseeks search capabilities are not comparable and non of the models in the study spend a comparable amount of compute answering your specific question.

Because in theory I would say that knowledge is something that does not have to be baked in the model but could be added using reference images if the model is capable enough to reason about them.

One was two screenshots of a phone screen with chats that are timestamped and it had to take the nth letter of the mth word based on the timestamp. While the type of riddle could be in the training data the ability to OCR this that well and understand the spatial relation to each object perfectly is something I have not seen from other models yet.

Personally I don't hope thats the future.

I have tried some off-the-shelfe solutions and they currently do not seem to cut it, or are too complex for my use case.

Sure in some cases a model might do some astounding things that always shine through, but I guess the jury still out on these questions.

(But I just looked that up too because this concept is mostly used/assumes in statistical physics)