The first big AI disaster is yet to happen

Responsibility, Negligence, and Blame-Shifting

• Many argue “AI disasters” will stem less from AI itself and more from humans wiring opaque algorithms into dangerous systems without proper oversight.
• The core failure is permission and governance: who decided the system could touch “meatspace” (infrastructure, weapons, health, legal processes)?
• Historical and current examples of automation used as a scapegoat (rental-car arrest systems, airline chatbots, bureaucratic “computer says no”) are seen as the template: corporations will point at “AI” to shirk liability.
• Several see modern bureaucracy itself as a long-standing “artificial intelligence” that already traumatizes people while diffusing responsibility.

What Counts as the “First Big AI Disaster”?

• Some say it’s already here as “a thousand small cuts”: unsafe reliance in engineering, coding, medicine, hiring, and policy decisions that no one tracks centrally.
• Others reserve “big disaster” for a Therac‑25–style event: an AI-assisted medical, transport, or industrial failure that kills people and becomes global news.
• There’s concern about prompt-injection–driven data breaches and scandals (e.g., executives’ private data leaked via AI tools), though some think current demos overstate real-world impact.
• Several point to AI-guided targeting systems in warfare as already qualifying, while others say these are primarily human/ethical disasters where AI just scales existing brutality.

Comparisons to Other Technologies and Regulation

• Analogies to fire, cooking, stoves, and past computer/internet failures (Morris worm, radiation overdoses, social media destabilization) are used to argue that dangerous-but-useful tech is normal and regulated after blood is shed.
• One camp emphasizes that AI, like past tech, needs liability rules, audits, and safety culture commensurate with its externalities.
• Another worries AI’s benefits accrue mainly to corporations and elites while harms (job precarity, surveillance, epistemic chaos) fall on the broader population.

Catastrophic and Epistemic Risks

• Some fear we may reach artificial general or superintelligence before any contained “warning shot,” making the first true disaster potentially existential. Others dismiss this as unlikely in the near term.
• Beyond physical harm, commenters highlight epistemic disasters: hallucinated citations shaping school or health policy, low-quality but authoritative AI-generated government reports, COVID-era information failures, and deepfakes eroding trust in any evidence.

Labor, Society, and Over-Reliance

• Debate persists over whether AI is actually displacing jobs versus providing cover for broader economic cuts.
• There is concern that over-reliance on AI tools will deskill professionals, entrench complexity, and make quiet, systemic errors more likely—until one of them finally looks like a “disaster” in hindsight.