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    This study identifies near-term limits for artificial intelligence-enabled biological design (AIxBio). It clarifies which AI and biological constraints may prevent misuse, aiding biosecurity policy.

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    Area of Science:

    • Biosecurity
    • Artificial Intelligence
    • Synthetic Biology

    Background:

    • Growing concerns exist regarding artificial intelligence (AI) potentially enabling novel pathogen design.
    • The specific risks and timelines associated with AI-driven biological design remain unclear, necessitating focused research.

    Purpose of the Study:

    • To assess the near-term limitations of AI-enabled biological design (AIxBio).
    • To identify biological and AI-related constraints that could act as barriers to misuse.
    • To inform biosecurity policy and risk analysis by defining plausible near-future capabilities.

    Main Methods:

    • Conducted a Delphi study involving experts in biology and artificial intelligence.
    • Engaged experts in two parallel elicitations to evaluate proposed constraints.
    • Assessed the validity and applicability of constraints for the near-term future (2025-2027).

    Main Results:

    • Evaluated biological trade-offs (e.g., transmissibility, environmental stability) as potential constraints.
    • Examined technical AI challenges (e.g., data availability, model generalization) limiting capabilities.
    • Identified specific constraints that may serve as hard or persistent barriers to AIxBio misuse.

    Conclusions:

    • Findings help distinguish between plausible near-future AIxBio capabilities and those unlikely to emerge.
    • Clarifies the scope of biosecurity planning by focusing on what might be impossible or improbable.
    • Aims to improve the signal-to-noise ratio in discussions surrounding AI and biological design risks.