A.I. Learns to Write in DNA
Scientists prompted A.I. to design and create new viruses for the first time, a milestone for medicine that also raises new biosecurity concerns.
A widely upvoted thread on Reddit's r/singularity asks why DeepMind, Fei-Fei Li's World Labs, and a cohort of well-funded startups are all building world models, yet none has released one aimed at general reasoning rather than narrow game-playing or robotic control. The post argues that pairing world models with symbolic reasoning and agentic scaffolding is a more direct path to reliable AGI than scaling transformers alone.
The distinction is not academic. World models build internal representations of how environments work, letting systems simulate consequences before acting — something large language models do not do natively. The gap between "predicts the next token" and "understands causality" is where most serious AGI timelines live or die.
Responses ranged from "we're already there and just calling it something else" to careful distinctions between game-specific models like Genie and DreamerV3 versus the more ambitious general-purpose variant the poster had in mind. No consensus emerged, which is itself informative about where the field actually stands.
Expect this question to resurface the moment World Labs or a competitor publishes something public-facing. It has legs.
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Scientists prompted A.I. to design and create new viruses for the first time, a milestone for medicine that also raises new biosecurity concerns.
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