A core flaw in virus hunting is moving pathogens from isolated natural environments to labs in dense population centers. Despite security ratings, all categories of labs have a history of leaks. The lack of a uniform reporting system means we don't know the failure rate, making labs a riskier container than nature.

The rationale for "virus hunting" is to create advance vaccines. However, you cannot safely test a vaccine for a novel, deadly pathogen on healthy humans. This makes the knowledge unactionable for prevention, while creating immense risk by bringing dangerous pathogens into leaky labs and publicizing their existence.

Instead of trying to control open-source AI models, which is intractable, the proposed strategy is to control the small, expensive-to-produce functional datasets they train on. This preserves the beneficial open-source ecosystem while preventing the dissemination of dangerous capabilities like viral design.

Current biosecurity screens for threats by matching DNA sequences to known pathogens. However, AI can design novel proteins that perform a harmful function without any sequence similarity to existing threats. This necessitates new security tools that can predict a protein's function, a concept termed "defensive acceleration."

Deep Vision's plan to publish the genomes of deadly viruses would effectively give the "killing power of a nuclear arsenal" to an estimated 30,000 unvetted individuals with synthetic biology skills. In the bio-age, openly publishing certain information can be a greater security threat than physical weapons.

A biosecurity data-level (BDL) framework, modeled after biosafety levels for labs, would keep 99% of biological data open-access. Only the top 1% of data—that which links pathogen sequences to dangerous properties like transmissibility—would face restrictions like requiring use-approval.

In a significant shift, leading AI developers began publicly reporting that their models crossed thresholds where they could provide 'uplift' to novice users, enabling them to automate cyberattacks or create biological weapons. This marks a new era of acknowledged, widespread dual-use risk from general-purpose AI.

While 80% of DNA synthesis companies voluntarily screen orders for dangerous pathogen sequences, the system is not mandatory. This creates a glaring loophole, as a malicious actor can simply place their order with the 20% of companies that do not perform this critical safety check.

A crucial legal distinction in the US fuels investment in embryo editing. While creating babies from edited embryos is illegal, conducting research on them with private funds is not. This loophole allows startups to advance controversial science without immediate legal repercussions, attracting Silicon Valley capital.