Import AI 475: swarm scaling, Google DeepMind watermarks biological sequences, the economics of AI science

Google DeepMind introduced SynthID Bio, a watermarking method designed specifically for synthetic biology, embedding detectable signals by adjusting amino acid sequences and the atomic coordinates of predicted 3D structures. In wet-lab tests on three target proteins—VEGF-A, the SARS-CoV-2 spike protein RBD, and PD-L1—the watermarked designs matched the unwatermarked versions in hit rate, binding affinity, and natural sequence diversity. Toby Ord's analysis suggests that AI swarms are a new form of inference scaling: a 4-agent swarm achieves equivalent performance with roughly twice the total token count, but half the tokens per agent, theoretically allowing task time to be cut in half. Swarm scaling exhibits diminishing returns similar to an economic "stepping on toes" parameter, with 10x agents yielding only 3 to 5x performance gains. A CSAIP poll (sample size 2498) shows 61% of Americans consider AI companies' voluntary self-regulation commitments "not enough," and 54% of voters believe the government should create and enforce AI rules.