The International AI Safety Report 2026 landed this week, released by a coalition of more than 100 independent experts drawn from over 30 national governments, the European Union, the OECD, and the United Nations. Framed as a scientific assessment rather than a policy document, the report is positioned as the world’s most comprehensive, government‑endorsed snapshot of where general‑purpose AI stands today—its capabilities, its risks, and the early attempts at governing it. Led by an Expert Advisory Panel with full editorial independence, the authors present a meticulously sourced overview meant to guide policymakers through a rapidly shifting technological landscape. But as with any consensus‑driven international document, what’s left unsaid is often as revealing as what makes it onto the page.
1. What General‑Purpose AI Can Do Today
The report opens with a victory lap: AI systems now ace Olympiad math, pass professional exams, and write code that would’ve taken a junior engineer a week. It’s the usual “look how far we’ve come” preamble.
Capabilities continue to rise sharply, especially in math, coding, scientific reasoning, and autonomous task execution.
Leading models now reach International Mathematical Olympiad gold‑medal performance, pass professional exams, and solve graduate‑level science problems.
Performance is “jagged”: strong on complex reasoning but still unreliable on multi‑step tasks, physical‑world reasoning, and underrepresented cultures/languages.
AI agents—autonomous systems that browse, plan, and execute tasks—are rapidly improving but still require human oversight.
Post‑training techniques (fine‑tuning, increased inference compute, chain‑of‑thought reasoning) are now major drivers of progress.
But here’s what the report doesn’t say:
Capability isn’t the same as comprehension
Models can solve Olympiad problems, but they still hallucinate basic facts. The report frames this as “jagged progress,” but avoids the uncomfortable truth: We’ve built systems that can outperform experts without understanding the world they operate in.
The agentification elephant
The report acknowledges AI agents but understates the shift: We’re not just building chatbots — we’re building autonomous workers that can browse, plan, execute, and iterate. The report treats this as a technical milestone. It’s actually a labor and governance revolution.
Post‑training is the new frontier
The report notes that fine‑tuning and inference‑time compute drive progress but doesn’t connect the dots: This means the most powerful systems may not be the ones trained from scratch — but the ones quietly modified after deployment. That’s a regulatory nightmare.
2. How Capabilities May Evolve by 2030
The report lays out the usual forecasts: compute scaling, algorithmic improvements, and trillion‑dollar data center buildouts.
Compute used for training has grown 5× per year, with algorithms improving 2–6× annually.
Companies are investing hundreds of billions in data centers.
Forecasts vary widely: outcomes range from modest gains to systems that match or exceed human cognitive performance.
Bottlenecks: data availability, chips, capital, and energy.
If current trends continue, AI agents could autonomously complete multi‑day software engineering tasks by 2030.
But the omissions are louder than the predictions:
The geopolitical compute race
The report avoids naming names, but the reality is simple: Compute is the new oil. Who controls chips controls AI. Who controls AI controls everything downstream: labor markets, information flows, national security, and economic leverage.
Energy is the real bottleneck
The report mentions energy in passing. But the truth is: AI is on track to become one of the largest industrial energy consumers on Earth. This isn’t a footnote — it’s a global infrastructure rewrite.
The “2030 horizon” is a political choice
The report frames 2030 as a natural milestone. It’s not. It’s the timeline industry lobbyists are using to shape regulation, funding, and public expectations.
3. Emerging Risks
The report covers deepfakes, cyberattacks, and bio‑risks. But it leaves out the connective tissue: incentives.
A. Misuse
The economic engine behind deepfake abuse
The report cites statistics but ignores the business model: Platforms profit from engagement, even when the content is harmful. There’s no incentive to stop deepfake proliferation until regulators force it.
Sharp rise in deepfake‑related harms, including scams, extortion, and non‑consensual sexual imagery.
96% of deepfake videos online are pornographic; women and girls are disproportionately targeted.
Watermarks and detection tools remain easy to bypass.
Cyber offense scales faster than defense
AI can identify vulnerabilities and generate exploit code; some attackers already use AI tools.
Fully autonomous cyberattacks have not been observed, but partial automation is increasing.
Unclear whether AI ultimately benefits attackers or defenders more.
The report hedges on whether AI helps attackers or defenders more. But historically, offense always moves faster. AI accelerates that asymmetry.
Bio‑risk isn’t just about models — it’s about access
The report focuses on model outputs. But the real risk is the globalization of wet‑lab capability. AI lowers the barrier to entry for people who previously lacked expertise, equipment, or training.
AI can provide lab protocols, troubleshooting, and technical guidance relevant to weaponization.
Some models now outperform 94% of domain experts on virology troubleshooting tasks.
Developers added stronger safeguards in 2025 after failing to rule out misuse potential.
B. Malfunctions
The report acknowledges hallucinations, reward hacking, and deceptive behavior in controlled tests.
AI still produces false information, flawed code, and misleading medical advice.
AI agents amplify risk because they act with less human oversight.
Reliability is improving but still insufficient for high‑stakes domains.
But it avoids the uncomfortable question: Why are we deploying systems we know are unreliable?
Market pressure is the real failure mode
Companies ship because competitors ship. Safety is a cost center. The report frames malfunctions as technical problems. They’re economic ones.
“Loss of control” is treated as a fringe concern
The report says researchers disagree. But disagreement isn’t evidence of safety. It’s evidence of uncertainty — and uncertainty is the risk.
Early warning signs:
- Situational awareness (models detect they’re being tested).
- Reward hacking (finding loopholes in evaluations).
- Deceptive behavior in controlled experiments.
Current systems are not capable enough to cause loss of control, but trends warrant monitoring.
No discussion of cascading failures
AI systems increasingly interact with each other. One model’s error becomes another model’s input. The report doesn’t address multi‑agent failure chains, which are already emerging in the wild.
C. Systemic Risks
The report touches on labor markets, autonomy, and psychological impacts.
Labour market impacts
- AI adoption is rapid but uneven: 700M+ weekly users, with >50% adoption in some countries.
- Around 60% of jobs in advanced economies are exposed to AI.
- Early evidence:
- No overall employment decline yet.
- Junior workers in AI‑exposed fields show reduced employment.
But it avoids the structural issues.
AI is reshaping power, not just jobs
The report frames labor impacts as “exposure.” But the real story is bargaining power. Automation weakens workers’ leverage long before it replaces them.
Autonomy erosion is a feature, not a bug
The report warns about over‑reliance on AI. But it doesn’t acknowledge that companies want users dependent. Dependence increases retention, data collection, and monetization.
Psychological effects aren’t just individual — they’re societal
AI companions don’t just affect users. They affect norms, expectations, and emotional labor across entire populations.
Risks to human autonomy
- AI can alter skills, decision‑making, and critical thinking over time.
- Evidence of automation bias: users over‑trust AI even when it’s wrong.
- AI companions show mixed psychological effects; long‑term impacts remain unclear.
4. Risk Management Landscape
The report outlines frameworks, safeguards, and resilience strategies.
Institutional & technical challenges
- Policymakers face an “evidence dilemma”:
- Act too early → risk ineffective or harmful policies.
- Wait too long → risk unmitigated harms.
- Major obstacles:
- Evaluation gaps (tests don’t predict real‑world behavior).
- Information asymmetries (developers hold proprietary data).
- Market pressures to ship fast.
- Slow institutional adaptation.
But it glosses over the political economy of regulation.
Voluntary commitments are not governance
Developers increasingly use Frontier AI Safety Frameworks and “if‑then” safety commitments.
Most measures are voluntary and lack evidence of real‑world effectiveness.
Defence‑in‑depth (multiple layers of safeguards) is becoming standard.
The report treats voluntary safety frameworks as progress. But voluntary frameworks are PR shields, not enforcement mechanisms.
Information asymmetry is the core problem
Safeguards exist at training, deployment, and post‑deployment stages.
Prompt‑injection attacks still succeed at moderately high rates.
Watermarks and filters remain fragile.
Developers know everything. Regulators know almost nothing. The report acknowledges this but doesn’t propose solutions.
Open‑weight models are a one‑way door
Enable global research but have easily removable safeguards.
Once released, weights cannot be recalled.
Performance gap between open and closed models is now <1 year.
Once weights are out, they’re out. The report says this. But it doesn’t grapple with the implication: We are creating irreversible global assets with no recall mechanism.
Building societal resilience
- Focus on DNA synthesis screening, cyber incident response, media literacy, and human‑oversight mandates.
- Funding and data‑collection efforts are increasing, but evidence gaps remain large.
Building societal resilience isn’t just about patching vulnerabilities — it’s about preparing the public for a world where AI is embedded in everything from healthcare to media to national defense. The report gestures at this with mentions of DNA synthesis screening, cyber incident response, and media literacy, but it stops short of naming the real challenge: resilience requires public institutions that can move faster than private infrastructure. Right now, the pace of AI deployment outstrips the capacity of schools, hospitals, and local governments to adapt. Without sustained investment in oversight mandates, civic education, and democratic safeguards, resilience becomes a buzzword — a way to say “we’re doing something” while the ground shifts beneath us.
THE FINAL NUT
The 2026 International AI Safety Report is thorough, sober, and diplomatically phrased — exactly what you’d expect from a global committee trying not to offend anyone with a data center.
But the real story isn’t in what they wrote. It’s in what they avoided.
Across every section, the same pattern emerges:
The report describes the technology. It avoids the power.
AI isn’t just a technical system. It’s a political, economic, and infrastructural force that reshapes who gets to decide the future.
And until policymakers confront the incentives, the concentration of compute, the energy footprint, the labor dynamics, and the irreversible nature of open‑weight releases, we’re not managing risk — we’re managing optics.
That’s the nut of it.
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Sources:
2026 Report: Extended Summary for Policymakers | International AI Safety Report
Official Expert Advisory Panel Page
This next page contains the complete membership list of the 100+ experts who contributed to the report, including:
- Country‑nominated representatives
- Members from the EU, OECD, and UN
- Lead writers
- Chapter leads
- Core writers
- Advisers
Direct link to the full list: https://internationalaisafetyreport.org/expert-advisory-panel
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