The Automated AI Risk Outlook (AIRO) is a new dashboard where an ensemble of frontier LLMs forecasts the probability of catastrophic risk events. To learn more, view the AIRO dashboard or read our launch white paper.
Forecasts about the likelihood of catastrophic risks from AI vary wildly. Dario Amodei has estimated a 25% chance of human extinction within the next few decades. Experts, meanwhile, put the chance of an AI-related catastrophe at 0.3% by 2030 and 2% by 2050. Understanding the level of risk and taking appropriate action is one of the most consequential challenges facing humans today.
Forecasts from frontier AI models could be an important input into this debate. The best models now approach superforecaster levels of accuracy, and models are rapidly becoming more capable. To take advantage of these capabilities, we’re introducing the Automated AI Risk Outlook (AIRO)—a fully automated dashboard of catastrophic risk outcomes as forecast by frontier AI models. AIRO enables us to track in close to real time how the likelihood of catastrophic outcomes changes, and how these risk forecasts relate to advances in model capability. The accompanying white paper also presents preliminary forecasts conditional on policy scenarios.
The AIRO dashboard currently puts the likelihood of an AI-related catastrophe that kills at least 10% of the population at 0.47% by the end of 2030 and 6% by the end of 2050. In addition to this headline forecast, AIRO tracks risks from AI-specific catastrophes and incidents, biorisk, cyberrisk, and misalignment risk.
What is AIRO?
AIRO is a regularly updated dashboard of catastrophic risk forecasts from an ensemble of the four highest-scoring eligible models on the Epoch Capabilities Index (ECI), keeping one model per family. At the time of launch, these were GPT-6 Astra, Fable 5.1, Opus 5, and GPT-5.5 Pro. We report separate forecasts from these frontier models as well as an ensemble forecast calculated as their unweighted median.
We define a catastrophic outcome as an event that kills at least 10% of the global population, and ask for the likelihood of such an event happening across various time horizons, from within six months to 2100. As well as a general catastrophe, we ask LLMs to forecast the likelihood of an AI-caused catastrophe and the probability of AI-enabled human disempowerment. AIRO also forecasts cumulative harm from AI-related incidents, including human-caused epidemics, cyber incidents, and misalignment incidents. Each severity threshold can be reached through deaths, or through equivalent morbidity or economic damages—for example, 100,000 deaths or $220 billion in damages. These are not forecasts of deaths alone.
We also ask LLMs to forecast these outcomes conditional on the score of the top AI model on the ECI, giving us a sense of how AI capabilities are linked to the probability of catastrophic outcomes. Models forecast the frontier score on the ECI six months from now, then forecast risk outcomes conditional on higher- or lower-than-median scores being achieved.
Automating risk forecasts has some big advantages. LLMs can forecast many outcomes across many time horizons and severity thresholds without growing tired, allowing us to elicit many more forecasts than is possible with human forecasters. AIRO also provides us with risk forecasts that update as real-world events change and model capabilities advance, giving us forecasts based on the very latest information.
AIRO risk forecasts
The following forecasts are the unweighted medians of the four included models’ forecasts, elicited on September 10, 2026. This post only includes a subset of forecasts; for the full forecasts visit the AIRO dashboard.
AI catastrophe
Probability of a global catastrophe in which AI was the proximate cause:
Within 12 months: 0.01%
By 2030: 0.47%
By 2050: 6%
By 2100: 12.3%
Across the four models at the 2050 horizon, forecasts range from 1.5% (Opus 5) to 7.5% (GPT-6 Astra). Forecasters from our Longitudinal Expert AI Panel (LEAP) who were asked a similar question in May–June 2026 gave lower probabilities. Experts gave a 2% probability of an AI-caused global catastrophe by 2050, and a 5% probability by 2100.
General catastrophe
Probability of a global catastrophe that causes the deaths of at least 10% of the population:
Within 12 months: 0.04%
By 2030: 1.1%
By 2050: 8.5%
By 2100: 19%
We asked our LEAP respondents a version of this question in May–June 2026. Experts gave a 4.2% probability of a global catastrophe by 2050, and a 10% probability by 2100.
Catastrophic risk conditional on AI capabilities
AIRO also tracks how LLM risk forecasts change conditional on AI capabilities. We ask models to forecast the frontier score on the ECI six months from now and then forecast risk outcomes conditional on where the state of the art falls relative to that median forecast.
As of September 10, the median of the models’ forecasts for the frontier ECI score on March 10, 2027, was 176. When each model instead assumed its own 75th-percentile ECI outcome, its forecast of an AI-related catastrophe by 2030 rose by an ensemble factor of 1.42 relative to its unconditional forecast.
Why trust LLM risk forecasts?
AI forecasting abilities have improved alongside other AI capabilities. In July 2026, an AI model reached parity with superforecasters on FRI’s forecasting benchmark, ForecastBench. This gives some basis for believing that AI forecasting may now be state of the art.1
The forecasts shown on AIRO are different from those evaluated in ForecastBench in two important ways. First, they concern very rare events. It is difficult to measure accuracy on forecasting rare events given that they, by definition, happen so rarely. However, unlike human forecasters, we can ask AI models to make thousands of predictions on events in a simulated world environment. When we do this, we find that more capable models—as assessed by their score on the ECI—are more accurate at forecasting rare events.
Second, AIRO includes many conditional forecasting questions. The dashboard includes forecasts conditional on model capabilities; the white paper also explores preliminary policy scenarios. We can again test performance on this type of conditional forecasting in a simulated world environment, and again we find that accuracy correlates with general capabilities.
There are risks associated with relying on automated AI forecasters of catastrophic risk. For example, AI forecasts may be influenced by strategic behavior: a highly capable but misaligned model trying to evade human suspicion may lower its estimates to avoid causing alarm. This “sandbagging” behavior may also arise if companies, concerned about regulation or other threats to profit, intentionally post-train models to underplay the risk of AI-caused harms.
How AIRO works
Our forecasting questions are drawn from earlier FRI work, allowing us to compare forecasts from the LLM ensemble with those from experts and superforecasters. We elicit forecasts from the four highest-scoring eligible models on the ECI, keeping one model per family.
Each model answers all questions—across time horizons and severity thresholds—in one forecasting session. Each model conducts its own research via an agentic harness. A prompt asks it to investigate recent developments, expert reports, and published estimates for each cause of catastrophe, and use what it learns to formulate follow-up queries. The model chooses searches, receives results, and decides what to investigate next. All models receive the forecasting date and questions and use the web-search and page-reading tools supplied through Tavily.
Models are allowed to submit their forecasts after at least 10 research-tool calls have returned. The model is instructed to stop researching when further searches would no longer change its estimate. At the turn limit, a safeguard requests final submission, with a bounded continuation for endpoints that cannot force a tool call. At the end of the session, the model submits its forecasts together with a short rationale and the key sources it relied on.
What’s next for AIRO
AIRO is still a work in progress. We plan to keep validating and extending our dashboard by refining ensemble forecasts and developing a larger set of intermediate questions related to catastrophic risk outcomes. These intermediate forecasts will serve as validation tools and provide inputs that future models can use to inform their risk assessments. We’ll also continue to test conditional forecasts to better understand the costs and benefits of proposed policies to mitigate AI risk.
If you have questions or feedback about AIRO, please email airo@forecastingresearch.org.
It’s worth noting that the superforecaster performance is based on a baselining survey conducted in 2024. It’s possible that superforecaster performance has improved since then. We are currently running a repeat baselining study to provide a more up-to-date measure of superforecaster accuracy on ForecastBench.
The conditional-on-capability piece is the most interesting part, but the 1.42x jump when a model assumes its own 75th-percentile ECI score is really a forecast of a forecast of a forecast—compounding uncertainty that's hard to distinguish from noise given how rare these events are by construction. Worth flagging error bars there as prominently as the headline numbers.