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Two hundred seventy-two international AI experts just built an AI risk ranking based on probability instead of guesswork. Most regulators still haven’t managed that in three years.
The study, “Prioritization of Risks From Artificial Intelligence,” comes from MIT FutureTech and the University of Queensland. The paper lists 188 co-authors. Core authorship goes to Peter Slattery, Alexander Saeri, Jess Graham, Michael Noetel and Neil Thompson. They used the Delphi method, a research process that gathers expert judgment over multiple rounds until agreement and disagreement both become visible. The same method shows up in the International AI Safety Report 2026, which cites this same body of work. The underlying data lives in the MIT AI Risk Repository, a running catalog of more than 1,600 documented threats used by policymakers and technologists.
Experts scored 24 risk domains across a five-year horizon, 2025 to 2030, under two scenarios. One scenario assumed business as usual, where organizations and governments keep doing what they’re doing now. The other assumed pragmatic mitigation, where everyone makes cost-effective efforts to reduce harm. Under business as usual, 18 of the 24 domains had at least a 10% probability of catastrophic outcomes. Catastrophic meant more than a million deaths or more than $100 billion in losses, with damage at a comparable civilizational scale in either case. That’s the baseline nobody wanted written down until now. It’s a sharper, numbers-first cut at the risk spectrum this blog already maps.
Why most people are reading this wrong
Most people hear “AI risk” and picture something years out, like rogue models or autonomous weapons in some future conflict. That’s not what this AI risk ranking found. Even under pragmatic mitigation, five domains still cleared 10% probability of catastrophe. Dangerous capabilities and AI-enabled weapons or cyberattacks each sat at 12%. So did environmental harm, a domain most people wouldn’t put anywhere near cybersecurity. Inequality and unemployment came in a point lower at 11%, right alongside power centralization. Two of the five are cybersecurity’s problem, and they didn’t drop much even when everyone tries.
Security work comes down to one job, pushing the attacker’s cost high enough that the attack isn’t worth it anymore. No system stays secure forever, so the alternative just needs to be expensive enough to matter. AI doesn’t invent a new phase of attack. It collapses the cost of the phases that already exist. Reconnaissance gets automated.
Weaponization gets templated, and delivery gets more convincing because a generated phishing email or a cloned voice doesn’t need a skilled operator anymore. It’s the same mechanism behind the AI-enabled cyberattacks already hitting ordinary companies today. As Slattery putand hacking are where AI capability is moving quickest, and that growth shows up on the cost side of the equation more than the probability side.
Competitive pressure works as the mechanism that keeps the other four risks running. When a company or a country believes AI confers an advantage, slowing down for safety just hands that advantage to whoever doesn’t slow down. Nobody wants to be the one who raises their own costs while the competition doesn’t, so the race to the bottom on governance keeps going. It’s the same dynamic that keeps patch cycles too slow and security budgets too small, just running at AI speed instead of IT speed.
Treating this AI risk ranking as a one-time compliance project misreads what the data says. It isn’t something you finish once and file away.
What this means if you’re the one holding the risk
The study also names who’s exposed and who’s responsible, and the two lists don’t overlap. Developers and regulators carry most of the responsibility for addressing these risks. Users and the people affected by AI systems carry most of the exposure. That mismatch is why nobody feels urgency at the right level. The people who could slow the collapse aren’t the ones who’d get hurt by it.
Exposure doesn’t spread evenly. Information absorbs it through misinformation and manipulation, the sort that erodes trust in what people see and read, while national security picks up cyberattacks and weapons development, with surveillance going to whichever hostile actor moves first. Finance isn’t spared either, where fraud and market manipulation get easier and privacy failures ripple into the wider economy on top of that. AI makes doing harm cheaper for anyone who was already capable of it, and possible for people who weren’t.
Slattery framed the findings as a list of what’s worth paying attention to now, drawn from probability rather than certainty. The response belongs in the governance conversation companies already run for cybersecurity and privacy. It needs a place in business continuity planning too, not a separate checkbox with its own deadline.
If you run security for an organization deploying AI, the number that should stick with you about AI risk isn’t 272 experts or 24 categories. It’s that even in the best-case scenario the researchers modeled, cyberattacks and dangerous capabilities didn’t fall out of the top five. Mitigation lowers the odds. It doesn’t remove cybersecurity from the list.
