Archive: https://archive.md/5eZWq

The startup is Advanced Machine Intelligence Labs: https://amilabs.xyz/

As someone in the tech twitter sphere this is yann and his ideas performing a suplex on LLM based companies. It is completely unfathomable to start an ai research company… Only sell off 20% and have 1 billion for screwing around for a few years.

Why world model? To emulate how we became sentient?

A "world" is just senses. In a way the context is one sense. A digital only world is still a world.

I think more success is in a model having high level needs and aspirations that are borne from lower level needs. Model architecture also needs to shift to multiple autonomous systems that interact, in the same ways our brains work - there's a lot under the surface inside our heads, it's not just "us" in there.

We only interact with our environment because of our low level needs, which are primarily: food, water. Secondary: mating. Tertiary: social/tribal credit (which can enable food, water and mating).

What use is it to understand the physical world if all investments are misallocated to the virtual world? Perhaps the AI will detect that there is a housing shortage and politicians will finally believe it because AI said so?

Or is it to accelerate Skynet?

Selfless plug here... Some collaborators and I just released a first version of a benchmark we think highlights a critical gap in recent models in understanding causality in the real-world, beyond a physics focus.

Everyday environments are rich in tangible control interfaces (TCIs), like, light switches, appliance panels, and embedded GUIs, that are designed for humans and demand commonsense and physics reasoning, but also causal prediction and outcome verification in time and space (e.g., delayed heating, remote lights).

SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios (https://huggingface.co/papers/2511.17649)

Feedback, suggestions, and collaborators are very welcome!

Wasn't there some recent argument that world models won't achieve AGI either due to overlooking the normative framework, fundamental symmetries of the world purely from data and collapse in multi-step reasoning? JEPA is sacrificing fidelity for abstract representation yet how does that help in the real world where fidelity is the most important point? It's like relying on differential equations yet soon finding out they only cover minuscule amount of real world problems and almost all interesting problems are unsolvable by them.

Does anyone have a sense of how funding like this is typically allocated? how much tends to go toward compute/training versus researchers, infrastructure, and general operations?

A fair amount of negative comments here, but Yann might very well be the person who brings the Bell Labs culture back to life. It’s been badly missing, and not just in Europe.

Following in the foot steps of miss Fei Fei Li's World Lab?

They are currently estimated to be at a 5bn valuation.

Refreshing to see some competition to the US AI scene. It's been the same three models trying to one up each other by copying and tweaking rather than pushing true innovation