Lukasz Kaiser

One of the clearest people to study if you want the throughline from early neural sequence models to transformers, efficient long-context variants, and modern reasoning systems.

A foundational figure in modern sequence modeling whose work on the Transformer changed the technical direction of language and multimodal systems.

A foundational transformer researcher whose work still matters because it connects the original architecture shift to later efforts on efficiency, scaling, and sequence modeling infrastructure.

One of the most important architecture-level thinkers in modern AI, with influence spanning Transformers, efficient scaling, and mixture-of-experts systems.

A useful person to follow for the evaluation layer of open models, especially where benchmark infrastructure and RLHF tooling become reusable community assets rather than one-off lab code.

A foundational transformer co-author who is now worth following for a very different reason: he is one of the few people trying to build a serious frontier lab around alternatives to the default scaling path.