Was at Nvidia's GTC conference recently and honestly, it was one of the most eye-opening events I've attended in a while. There was a lot to unpack, but my single biggest takeaway was this: world modelling is the actual GOAT of AI right now, and I don't think people outside the research community fully appreciate what's coming.
A year ago, when I was doing the conference circuit, world models were still this niche, almost academic concept. You'd bring it up and get blank stares or polite nods. Now? Every serious conversation at GTC was circling back to it. The shift in recognition has been dramatic. It feels like the moment in 2021 when everyone suddenly "got" transformers.
For those unfamiliar: world models are AI systems that don't just predict the next token. They build an internal representation of how the world works. They can simulate environments, plan ahead, reason about cause and effect, and operate across long time horizons. This is fundamentally different from what LLMs do, which is essentially very sophisticated pattern matching on text.
Jensen Huang made it very clear at GTC that the next frontier isn't just bigger language models, rather it's AI that can understand and simulate reality aka world models.
That said, I do have one major gripe, that almost every application of world modelling I've seen is in robotics (physical AI, autonomous vehicles, robotic manipulation). That's where all the energy seems to be going. Don’t get me wrong, it is still exciting but I can't help but feel like we're leaving enormous value on the table in non-physical domains.
Think about it, world models applied in business management, drug discovery, finance and many more. The potential is massive, but the research and commercial applications outside of robotics feel underdeveloped right now.
So I'm curious: who else is doing interesting work here? Are there companies or research labs pushing world models into non-physical domains that I should be watching? Drop them below.
[link] [comments]
