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AI could change the stakes in bioweapons development, but current systems have not been shown to independently produce a deployable weapon. The clearest near-term effect is to reduce time, expertise and coordination costs for parts of biological research. Production, testing, delivery and operational security remain major barriers, while the same technologies can strengthen surveillance and medical countermeasures.
What “changing the stakes” means
AI can search and synthesize large bodies of biological knowledge, propose molecular or protein designs, help plan experiments and coordinate work across software and laboratory services. Combined with gene editing, sequencing, DNA synthesis and cloud laboratories, those capabilities could lower some historical barriers to biological research.
That does not mean a chatbot can simply produce a working bioweapon. A harmful biological agent must be made reliably, tested, delivered, kept under control and used without exposing the people handling it. Those steps require specialized equipment, tacit laboratory knowledge, quality control and secure operations. Current evidence therefore supports a narrower conclusion: AI may amplify existing capabilities and widen access to parts of the process, but it has not demonstrated autonomous creation of a deployable bioweapon.
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What AI could make easier
Finding and combining biological knowledge
Language and scientific models can summarize papers, connect findings across disciplines and help users navigate complex protocols or datasets. This can reduce the time a skilled researcher spends on literature review and planning. It can also make sophisticated information easier for less-experienced users to access, which is why model safeguards and evaluation for biological misuse matter.
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Designing molecules and proteins
AI-assisted design can propose candidate molecules, protein structures or genetic changes for legitimate research. The same design capability could be misused to explore harmful properties. The National Academies’ 2025 consensus study, The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations, explicitly includes transmissible threats with epidemic or pandemic-scale consequences in its scope.
Connecting design to outside services
Sequencing providers, DNA-synthesis companies and cloud laboratories can supply capabilities that were once available only in well-equipped facilities. NTI describes this convergence as a significant misuse risk and recommends controls that extend beyond the AI model itself. An AI system that can suggest a design is materially different from an integrated workflow that can order, test and iterate biological materials, but the latter is the risk policymakers are trying to prevent.
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Why a deployable weapon is still difficult
Biological systems are variable and technically delicate. A design that looks plausible on a screen may fail in real cells, lose its intended behavior, be unstable during production or prove impossible to deliver as planned. Testing can reveal unexpected effects, and handling hazardous material creates risks for the operators themselves. These practical constraints are not removed by better text generation.
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| Stage | Possible AI contribution | Limiting factor identified by current assessments |
|---|---|---|
| Knowledge and design | Search, synthesis, prediction and candidate generation | Outputs still require expert judgment and experimental validation |
| Production | Workflow planning and optimization | Specialized facilities, quality control and biological variability |
| Testing | Data analysis and experiment prioritization | Reliable real-world performance cannot be established by software alone |
| Delivery and control | Planning or coordination assistance | Engineering, logistics, containment and operator safety challenges |
| Operational security | Automation could increase scale or speed | Procurement, personnel and communications create opportunities for detection and failure |
RAND’s 2024 review provides the clearest present-day qualification: current AI systems “do not appear to materially increase risks involving biological weapons development or deployment.” RAND also warns that more advanced future systems could increase biological risks substantially. This is a distinction between demonstrated capability and plausible future capability, not a guarantee that current misuse is harmless.
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Could AI-designed pathogens cause a pandemic?
In principle, a transmissible agent with severe effects could create consequences far beyond a conventional attack. That possibility is serious enough to be included in the National Academies’ study mandate. However, no authoritative, agreed probability of an AI-enabled bioweapon catastrophe has been established. CNAS’s 2024 analysis describes the stakes in stark terms, quoting then–Vice President Kamala Harris’s warning that such weapons “could endanger the very existence of humanity,” but the statement is a warning about potential consequences, not a quantified forecast.
The most defensible risk assessment is conditional. If future models become substantially better at biological reasoning, can operate tools with less supervision and gain reliable access to laboratory services, the barrier could fall further. If model safeguards, synthesis screening and laboratory oversight improve at the same time, the net risk could remain contained. Capabilities and controls will evolve together, so assessments need regular updating rather than a one-time verdict.
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AI’s defensive value
The National Academies also emphasizes that AI-enabled biology can improve prediction, detection, prevention and response. Models may help identify unusual signals in biosurveillance data, forecast how an outbreak could spread, prioritize diagnostic targets and accelerate medical-countermeasure research for both natural outbreaks and intentional threats.
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Controls that address the whole pathway
NTI and the National Academies point to layered measures rather than a single ban or filter:
- Evaluate models before and after release. Test advanced systems for biological misuse capabilities, repeat evaluations as models and tools change, and monitor real-world use.
- Protect sensitive datasets. Apply access controls and security to pathogen, genomic and other high-consequence datasets while preserving legitimate scientific access.
- Screen DNA orders. Use sequence-order screening and metadata that can flag suspicious or AI-generated novel sequences, with procedures for review and escalation.
- Manage access to high-risk systems. Consider identity checks, usage limits, expert review or other managed-access arrangements for models that cross defined capability thresholds.
- Cover design through deployment. Keep controls in place across design, ordering, experimentation, testing, storage and delivery. A model-level safeguard cannot compensate for an unmonitored laboratory workflow.
- Strengthen international transparency. Improve information sharing, confidence-building measures and incentives under the Biological Weapons Convention.
Policy choices and their trade-offs
| Policy axis | Potential benefit | Potential cost or risk |
|---|---|---|
| Open access vs. managed access | Open tools speed legitimate discovery and broaden participation | Unrestricted access may expose powerful capabilities to malicious users |
| Model safeguards vs. end-to-end laboratory controls | Model filters are fast to deploy | They can be bypassed and do not govern ordering or physical experiments |
| Innovation speed vs. assurance | Rapid release can accelerate medical research | Insufficient testing may miss new misuse pathways |
| National measures vs. international coordination | National rules can be enacted quickly | Biological supply chains and threats cross borders |
| Capability benefit vs. misuse exposure | More capable systems may improve diagnostics and countermeasures | The same capabilities may lower barriers to harmful design |
The legal baseline
The Biological Weapons Convention has prohibited biological and toxin weapons since 1975. NTI’s 2024 accounting identifies 44 countries suspected of pursuing bioweapons since 1915 and says 23 likely had a program at some point. Those figures show that state and non-state biological-weapons concerns predate modern AI; AI changes the tools available within an existing security problem rather than creating the problem from nothing.
What to watch next
The most meaningful indicators will be practical, not just impressive demonstrations. Watch whether advanced models can reliably complete multi-step biological tasks, whether they can use laboratory tools with limited supervision, whether DNA-synthesis screening detects novel AI-generated sequences, and whether international rules are adopted consistently. A credible change in the stakes would involve a measurable reduction in the expertise, infrastructure or time required to move from an idea to a controlled, harmful biological capability.
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