The Screening Breakdown: The world’s primary defense against synthetic bioweapons is sequence matching. When a lab orders DNA, automated software checks the order against a “blocklist” of dangerous pathogens (like Smallpox or Anthrax).

AI is moving beyond text, images, and code. Now it’s learning to read and write — shall we say program — DNA.

In this episode of NEXT, John Koetsier talks with Eric Nguyen, co-founder and CEO of Radical Numerics, about the rapidly emerging world of biological AI.

Nguyen and his team helped create Evo and Evo 2, generative foundation models for DNA, and are now working toward what they call “general biological intelligence”: AI systems capable of understanding biology across DNA, gene expression, methylation, proteins, and other biological signals.

The potential upside is enormous.

These systems could help scientists detect cancer earlier, develop treatments for antibiotic-resistant superbugs, understand disease more deeply, and eventually design countermeasures to emerging biological threats on demand.

But the same capabilities introduce serious risks.

Nguyen explains how AI could potentially generate biological sequences that retain dangerous functions while evading traditional sequence-matching detection systems… essentially creating biological “deepfakes.” He also discusses why AI labs need to develop biodefense capabilities alongside increasingly powerful biological design tools.