Will Claude cause the next Covid?

Crossposted from my blog.Biosafety remains a relatively unexplored topic for people within the AI Safety community. This posts aims to briefly summerize current bio-capabilities of available models, as well as mitigation efforts.Current Capabilities Ai-systems could theoretically uplift non-experts to synthesize, acquire, and disseminate biological weapons and could raise the ceiling of harm by creating agents more deadly or more resistant to current medicine.Biological systems are complex however, and involve interplay between many different molecules of varying complexity in systems that we have very varying levels of understanding of.Small molecules for example are a set of, as the name might suggest, small organic molecules that regulate many processes in the body and are a common medicinal target. AI-systems have been incredibly successful at quickly generating small-molecule based drugs. Insilico, a generative AI Software company has developed at least 28 drugs using generative AI tools, with nearly half already at a clinical stage. Currently it can take as little as 12 months to get the drug to preclinical tests (compared to 3–6 years using traditional methods). The target identification step has been drastically reduced to about 30 days. It should, however, be noted these aren’t end-to-end models. To reach the preclinical stage, experimentation has to pass through multiple in-vitro stages including lab validation, animal model validation, several optimization steps and safety checks that still form a bottleneck.Although genetic material is a significantly more complex molecule, the progress in AI-powered Biological Design Tools (BDT) synthesis also significant. While less established than small molecule generation, significant leaps have been made in the generation of mRNA with up to 41-fold increases in protein expression , assembly of DNA, and methods that significantly decrease the cost of manufacturing biomolecules designed by generative systems. Although