Australian AI Scholar Toby Walsh: The Risk Is Not Machines Getting Smarter, but Human Institutions Getting Fragile
Toby Walsh, a professor at the University of New South Wales in Australia, argues in a written interview that the central challenge of AI safety is not predicting the ceiling of machine intelligence but diagnosing the resilience of human institutions. He uses the aviation industry as an analogy to show that safety and growth are not opposites, and warns that the prevailing human-in-the-loop mechanism carries a risk of degrading into a rubber stamp under time pressure.
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Toby Walsh is a professor at the University of New South Wales (UNSW) in Australia and an AI scholar who has long advocated for an international ban on autonomous lethal weapons. His book God AI: Boom or Doom?, published in September 2026, addresses superintelligence, the singularity, existential risk, and labor markets simultaneously, yet the book's central question is not when machines will surpass humans but whether our institutions are prepared for the growth of capability.
In a written interview with Electronic Times (ETNews), Walsh compressed his position into a single sentence: 'What worries me most is not how intelligent machines become, but how fragile our institutions are. The challenge is power, not intelligence.' He elaborated that while how powerful AI becomes matters, the more urgent question is who owns that capability, who controls it, and who is held responsible when it goes wrong.
His argumentative mechanism rests on a concrete analogy: the aviation industry. Rather than treating accidents as an inevitable cost and leaving them unaddressed, the industry built a cycle of accident investigation, design improvement, and operational rule refinement, establishing public trust through safety, and that trust in turn expanded the market. Walsh argues AI should follow the same path—safety is not an obstacle to growth but a precondition for it.
Walsh also identified a specific failure mode in current institutional design. He criticized the widespread adoption of 'human-in-the-loop': if a machine generates a target list and a human has only seconds to approve it, that human review carries the risk of becoming a rubber stamp. His criterion is that humans must have actual time and authority to review information and must be able to reject AI's judgment when necessary—merely being 'in the process' does not mean being 'in control.'