Okay, let me posit one more question to you. Please define novel ideation in technical terms.

I notice you got real quiet about the math part. I guess realizing you think protein folding is a list of 100 items was too embarrassing to address. Ignoring it doesn’t make you look smarter. And now you are frantically moving the goalposts. You claim it’s only “novel” if it invents the rules from scratch? By that definition, a human author never has a novel idea because they are just using grammar rules taught by a teacher. Also: AlphaGo Move 37. The AI played a move that human masters explicitly said was “wrong” based on human strategy. It defied the logic conventions it was fed and won. That is the literal definition of forming a conclusion independent of, and superior to, human intervention. But please, use more periods between your words. It definitely covers up the fact that you don’t know what you’re talking about.

Start chewing. You literally admitted it in your own comment: “Sure, it’s not something humans had gotten to yet.” That is the definition of a novel discovery. You are arguing that because the AI used logic and existing data to reach the conclusion, it doesn’t count. By that definition, no human scientist has ever had a novel idea either since we all build on existing data and patterns. The AI looked at the same data humans had, saw a pattern humans missed, and created a solution humans didn’t have. That is novelty. But honestly it is hard to take your analysis of these papers seriously when you just argued in the comment above that protein folding involves “10^2 combinations.” You realize 10^2 is just 100 right? You think complex biology is a list shorter than a grocery receipt. If your math is off by about 300 zeros I am not sure you are the best judge of what these models are actually capable of.

​I was almost with you on the whole expert act until the part where you said we feed the model “10^2 combinations of amino acids.” ​You realize 10^2 is literally just 100, right? ​You are writing paragraphs acting like the smartest guy in the room, but you think protein folding gets solved by checking a list shorter than a grocery receipt. That is honestly hilarious. ​It kind of explains your whole point though. No wonder you think it is just a “simple sorting mechanism” if you think the dataset is that small. You might want to check the math before the next lecture because being off by about 300 zeros makes the arrogance look a bit silly.

A decade in the space is impressive. It shows dedication and time invested. That alone deserves recognition.

Still, the points you are repeating are familiar. They are recycled claims from years ago. If the goal is to critique novelty, repeating the same arguments does not advance it.

You say LLMs have zero intentional logic. That is true if by intentional logic you mean human consciousness or goals. It is false if you mean emergent behaviors and the ability to combine information in ways no single source explicitly wrote. Eliminating nuance with absolute terms makes it easy to dismiss valid evidence.

Calling someone an AI fanboy signals preference for labels over analysis. That approach does not strengthen an argument. Specific examples do. Concrete failures, reproducible tests, or papers are what advance discussion.

It is also not accurate to suggest that anyone pitches LLMs as supreme beings. Most people treat them as complex tools that produce surprising results. Their speed, scale, and capacity to identify patterns exceed human ability, but they remain tools. Critiquing them as if they were gods is a strawman.

If you want this discussion to matter, show a single reproducible example where an LLM fails in a way your logic cannot explain. Otherwise, repeating slogans and metaphors only illustrates a resistance to evidence.

I am not here to argue for ideology. I am here to examine claims. That is a choice. It is also a choice to resist slogans and demand specificity. Fun, fun. Another fun day.