Biology is becoming programmable:
Two compounding technological trends are rapidly democratizing dual-use biotechnology: the cost of bioengineering is falling, and AI models are getting better at biology.
Together, they’re lowering the barrier to engineering bioweapons. It now costs <$100k to synthesize a pathogen, AI models are able to outperform experts on benchwork troubleshooting evaluations, and tools like AlphaFold and Evo2 are changing our ability to understand and engineer biology at will.
AI scales offense significantly faster than defense: the physical infrastructure to defend against biological threats does not scale by Moore’s law, but AI lab assistance and the quality of AI biological design tools might.
We are transitioning into a new regime, one where highly-resourced state actors, as well as unsophisticated and under-resourced actors, may be able to synthesize a pathogen far more dangerous than a natural virus.
Our current defenses are inadequate for this paradigm, and engineered bioweapons present a significant threat.
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We break down global biosecurity into a stack of defense layers: Delay, Detect, Withstand, and Combat. We have also included two under-discussed enabling layers: Field-building and Policy & Regulatory.
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Bioweapons are also not a new problem, but we have no effective policy in place to stop them.
For example, the 1972 Biological Weapons Convention (BWC) was meant to end state bioweapons programs. While its overall effect may have been positive, it has no oversight or enforcement mechanism.
The international unit responsible for supporting the Biological Weapons Convention runs on a $2.2M annual budget with 4 staff. For context, the average U.S. McDonald’s restaurant takes in ~$4M/year.
As they signed the BWC, agreeing not to build bioweapons, the Soviets concurrently expanded their bioweapons program. The Biopreparat program ran for two decades after the treaty, employing tens of thousands of scientists and weaponizing anthrax, plague, and smallpox at industrial scale. They also built stockpiles of vaccines for their own population.[…]
In a pandemic scenario, costs compound exponentially with the spread of the pathogen. Every day that passes before countermeasure deployment could mean tens of thousands of lives lost. Detection is the layer that gives every other intervention more runway.
The COVID-19 timeline shows the current system’s failure. The first documented cases occurred in Wuhan in early December 2019, but the sequence was not published until January 11th, 2020. When the WHO declared a public health emergency on January 30th, 2020, the virus was already on four continents. Retrospective wastewater analysis later found SARS-CoV-2 in northern Italian samples from December 18th 2019, nine weeks before community transmission was clinically identified at Codogno (La Rosa et al. 2021). This analysis used PCR designed against a published genome, and was therefore able to provide a sensitivity that current metagenomic surveillance for a novel pathogen would not have. However, it is possible that surveillance technology could be developed to achieve something closer to that level of sensitivity, even for novel pathogens.
If there had been wastewater surveillance approaching that level of sensitivity in Wuhan at the time, we could have added ~1 month of time to develop countermeasures and prepare our critical infrastructure for the outbreak, including distributing personal protective equipment (PPE) to critical care workers and ramping up manufacturing capacity.
If the system is unprepared for a pathogen like SARS-CoV-2, it is completely unprepared for an advanced bioweapon.
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Advanced bioweapons enabled by AI could spread with extreme infection rates, often referred to as the “wildfire“ pandemic scenario, or they could spread silently, often referred to as the “stealth” pandemic scenario.
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Pathogens designed to spread asymptomatically with long incubation times will be invisible to the current system, which detects pathogens through clinical surveillance.
The current system is set into motion by some threshold of patients seeking care or clinicians noticing unusual patterns, meaning one or more transmission generations have likely already elapsed. If a patient tests negative for known pathogens, their samples are not sent for metagenomic (agnostic) sequencing by default. In fact, their samples are not sent until a clinician decides to escalate after a prolonged period of ineffective standard treatments, worsening conditions, or unusual deaths, giving bioweapon-class pathogens weeks to spread, especially if designed to do so quietly.
To detect advanced bioweapons, a pathogen-agnostic surveillance system must be built.