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Why does evidence arrive after deployment?
A model can be tested today, but many of the questions that matter most concern what happens when people use it at scale: how often it causes harm, who is affected, how severe the consequences are, and whether those effects persist or spread. Answering those questions requires observing real use over time, across different settings and populations.
That creates an awkward mismatch. Capabilities and deployment can change quickly; reliable evidence about social impacts accumulates more slowly. By the time a pattern is clear, a technology may already be embedded in workplaces, services, or institutions. Yet intervening early is not automatically safer: a poorly targeted rule or safeguard can fail, create new harms, or become difficult to unwind.
The 2026 report is an assessment of emerging risks from frontier, general-purpose AI, not a comprehensive account of every effect AI may have on society. Its evidence base includes research published before December 2025. The authors describe disagreement over timelines, the severity of risks, and whether safeguards are adequate; the report is not a settled forecast or a prescriptive verdict.
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What is already known, and what remains uncertain?
There is no single answer to “How risky is AI?” The evidence differs by harm and by how directly it can be observed. Some present-day harms have robust empirical evidence behind them. Other concerns depend on capabilities that may emerge later and are assessed using modelling, controlled laboratory studies, or theory. Those are different levels and kinds of evidence, not interchangeable proof.
Capabilities are advancing, but not evenly. The report describes notable progress in mathematics, coding, and science, including gold-medal-level performance on International Mathematical Olympiad problems since the previous report. Such results demonstrate performance on difficult tasks; they do not establish broad reliability. Strong benchmark results can coexist with failures on tasks that appear simple.
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Among the consequential open questions are how systems acquire capabilities and behave, how common and severe particular harms are, whether safeguards remain effective as systems and uses change, and how deployment choices and institutions shape the outcomes. In several areas, available evidence does not yet support a reliable estimate of prevalence or severity.
Why don’t benchmarks and lab tests settle the safety question?
Evaluations are useful snapshots, but they are not the world. The report identifies an evaluation gap: benchmark performance alone does not reliably predict a system’s real-world utility or risk. Actual outcomes depend on users, tasks, surrounding tools, incentives, oversight, and conditions that a controlled test may not reproduce.
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- Performance can be uneven. Success on a demanding test does not guarantee dependable behavior across ordinary tasks or unfamiliar situations.
- Real-world exposure is different. A lab test can isolate a behavior, while deployed systems encounter varied users, workflows, and opportunities for misuse.
- Harm data may be incomplete. Without consistent observation over time, it can be difficult to determine how widespread a problem is or to compare its severity across settings.
- Safeguards have to work in context. A protection that succeeds in a test may not scale to varied uses, remain effective as capabilities change, or be enforceable in practice.
For these reasons, a benchmark score should be treated as one piece of evidence—not as a direct measure of safety, social benefit, or the odds of a future harm.
Which AI risks are researchers trying to assess?
The report groups concerns into three broad families. They differ in whether harms are already documented or depend more heavily on future capabilities, and in the kind of evidence available. The categories should not be collapsed into one risk score: the evidence does not support a like-for-like numerical comparison across them.
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| Risk family | What it covers | What can be said about the evidence |
|---|---|---|
| Malicious use | People using AI systems to enable or amplify harmful activity. | Evidence varies by type of misuse. Some current harms are documented; other concerns depend on capabilities or access that may change. |
| Malfunctions | Failures such as unreliable behavior, and concerns about loss of control. | Reliability can be studied in tests and deployments, but tests do not fully predict behavior in the world. Some more severe concerns rely on modelling, controlled studies, or theory. |
| Systemic effects | Wider impacts such as labour-market disruption and risks to human autonomy. | These effects can depend on adoption patterns and institutional choices, so their prevalence and long-term severity may be hard to establish early. |
The table describes broad areas, not settled rankings. For any particular claim, the relevant questions are whether the harm is observed or prospective, what evidence supports it, how common and severe it is, how much deployment differs from testing, and whether a proposed safeguard is effective and enforceable. In many cases, some of those dimensions remain unquantified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does this mean the future is already decided?
No. The report considers plausible trajectories in which AI progress plateaus or slows, as well as trajectories in which it continues or accelerates. The pace and direction of deployment, the choices made by developers and users, and institutional responses also affect outcomes. Current progress does not establish that the fastest or most harmful scenario will occur.
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What it does establish is a difficult decision under uncertainty. Waiting for stronger evidence can improve the basis for action, but some evidence arrives only after exposure has occurred. Acting sooner may reduce exposure, but an intervention can be ineffective or harmful if it is based on weak assumptions. There is no universal answer that resolves this tension for every risk.
What should readers take from the uncertainty?
The practical lesson is to ask what kind of evidence supports a claim, rather than treating all warnings or reassurances as equally established. A demonstrated present-day harm, a laboratory result, a modelled future scenario, and a theoretical concern should be labelled and weighed differently. Likewise, claims that a safeguard works need evidence about its performance in the settings where people will actually rely on it.
The International AI Safety Report 2026 provides a shared baseline for discussing these questions, not certainty about what comes next. Some answers will become clearer only through real-world observation; the challenge is to make decisions before that observation is complete, without pretending either that the worst outcome is inevitable or that uncertainty means there is nothing to act on.
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