The Risks and Rewards of Embodying Artificial Intelligence with Cloud-Based Laboratories

Abstract

Autonomous, cloud-based laboratories (CBLs) are transforming scientific research by democratizing access to advanced instruments that accelerate high-throughput discovery. As artificial intelligences (AIs) become integrated or “embodied” with CBLs and gain independence from human oversight, efforts to identify novel pharmaceuticals, renewable energies, and agricultural biotechnologies will accelerate. AI-driven CBLs can perform tasks more efficiently and accurately than human scientists at lower costs, achieving results in weeks rather than years. However, as AI systems approach or exceed human intelligence, their decision-making abilities could outpace the need for human input, raising ethical, economic, and safety concerns. Aligning AI goals with human values is critical, as unregulated systems could pose existential risks, including global health hazards or the distortion of knowledge-generating systems. AI-driven misinformation in research highlights the need for transparency and data integrity, which may be achieved by aligning incentivizes and engineered fail-safes to promote long-term human flourishing. To mitigate risks, strict compartmentalization of AI systems and CBLs with third-party supervision at fine temporal resolutions will be necessary. While current CBLs are piloted by humans, future AI systems may relegate humans to the role of co-pilot. Anticipating increased AI-CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm.