The 2nd International AI Safety Report states
— General-purpose AI capabilities have continued to improve, especially in mathematics,
coding, and autonomous operation. Leading AI systems achieved gold-medal performance
on International Mathematical Olympiad questions. In coding, AI agents can now reliably
complete some tasks that would take a human programmer about half an hour, up from
under 10 minutes a year ago. Performance nevertheless remains ‘jagged’, with leading
systems still failing at some seemingly simple tasks.
— Improvements in general-purpose AI capabilities increasingly come from techniques
applied after a model’s initial training. These ‘post-training’ techniques include refining
models for specific tasks and allowing them to use more computing power when generating
outputs. At the same time, using more computing power for initial training continues
to also improve model capabilities.
— AI adoption has been rapid, though highly uneven across regions. AI has been adopted
faster than previous technologies like the personal computer, with at least 700 million
people now using leading AI systems weekly. In some countries over 50% of the population
uses AI, though across much of Africa, Asia, and Latin America adoption rates likely
remain below 10%.
— Advances in AI’s scientific capabilities have heightened concerns about misuse in
biological weapons development. Multiple AI companies chose to release new models
in 2025 with additional safeguards after pre-deployment testing could not rule out the
possibility that they could meaningfully help novices develop such weapons.
— More evidence has emerged of AI systems being used in real-world cyberattacks. Security
analyses by AI companies indicate that malicious actors and state-associated groups are
using AI tools to assist in cyber operations.
— Reliable pre-deployment safety testing has become harder to conduct. It has become
more common for models to distinguish between test settings and real-world deployment,
and to exploit loopholes in evaluations. This means that dangerous capabilities could go
undetected before deployment.
— Industry commitments to safety governance have expanded. In 2025, 12 companies
published or updated Frontier AI Safety Frameworks – documents that describe how they
plan to manage risks as they build more capable models. Most risk management initiatives
remain voluntary, but a few jurisdictions are beginning to formalise some practices as
legal requirements.
This Report assesses what general-purpose AI
systems can do, what risks they pose, and how
those risks can be managed. It was written with
guidance from over 100 independent experts,
including nominees from more than 30 countries
and international organisations, such as the EU,
OECD, and UN. Led by the Chair, the independent
experts writing it jointly had full discretion
over its content.
The authors note
This Report focuses on the most capable
general-purpose AI systems and the emerging
risks associated with them. ‘General-purpose AI’
refers to AI models and systems that can perform
a wide variety of tasks. ‘Emerging risks’ are risks
that arise at the frontier of general-purpose AI
capabilities. Some of these risks are already
materialising, with documented harms; others
remain more uncertain but could be severe
if they materialise.
The aim of this work is to help policymakers
navigate the ‘evidence dilemma’ posed by
general-purpose AI. AI systems are rapidly
becoming more capable, but evidence on their
risks is slow to emerge and difficult to assess.
For policymakers, acting too early can lead
to entrenching ineffective interventions, while
waiting for conclusive data can leave society
vulnerable to potentially serious negative
impacts. To alleviate this challenge, this Report
synthesises what is known about AI risks
as concretely as possible while highlighting
remaining gaps.
While this Report focuses on risks, general-
purpose AI can also deliver significant benefits.
These systems are already being usefully applied
in healthcare, scientific research, education,
and other sectors, albeit at highly uneven
rates globally. But to realise their full potential,
risks must be effectively managed. Misuse,
malfunctions, and systemic disruption can erode
trust and impede adoption. The governments
attending the AI Safety Summit initiated this
Report because a clear understanding of these
risks will allow institutions to act in proportion
to their severity and likelihood.
Capabilities are improving
rapidly but unevenly
Since the publication of the 2025 Report,
general-purpose AI capabilities have continued
to improve, driven by new techniques that
enhance performance after initial training.
AI developers continue to train larger models
with improved performance. Over the past
year, they have further improved capabilities
through ‘inference-time scaling’: allowing
models to use more computing power in order to
generate intermediate steps before giving a final
answer. This technique has led to particularly
large performance gains on more complex
reasoning tasks in mathematics, software
engineering, and science.
At the same time, capabilities remain ‘jagged’:
leading systems may excel at some difficult
tasks while failing at other, simpler ones.
General-purpose AI systems excel in many
complex domains, including generating code,
creating photorealistic images, and answering
expert-level questions in mathematics and
science. Yet they struggle with some tasks that
seem more straightforward, such as counting
objects in an image, reasoning about physical
space, and recovering from basic errors in
longer workflows.
The trajectory of AI progress through 2030
is uncertain, but current trends are consistent
with continued improvement. AI developers
are betting that computing power will remain
important, having announced hundreds of billions
of dollars in data centre investments. Whether
capabilities will continue to improve as quickly
as they recently have is hard to predict. Between
now and 2030, it is plausible that progress could
slow or plateau (e.g. due to bottlenecks in data or
energy), continue at current rates, or accelerate
dramatically (e.g. if AI systems begin to speed
up AI research itself).
Real-world evidence for
several risks is growing
General-purpose AI risks fall into three
categories: malicious use, malfunctions,
and systemic risks.
Malicious use
AI-generated content and criminal activity:
AI systems are being misused to generate
content for scams, fraud, blackmail, and non-
consensual intimate imagery. Although the
occurrence of such harms is well-documented,
systematic data on their prevalence and severity
remains limited.
Influence and manipulation: In experimental
settings, AI-generated content can be as effective
as human-written content at changing people’s
beliefs. Real-world use of AI for manipulation is
documented but not yet widespread, though it
may increase as capabilities improve.
Cyberattacks: AI systems can discover
software vulnerabilities and write malicious
code. In one competition, an AI agent identified
77% of the vulnerabilities present in real
software. Criminal groups and state-associated
attackers are actively using general-purpose
AI in their operations. Whether attackers
or defenders will benefit more from AI
assistance remains uncertain.
Biological and chemical risks: General-purpose
AI systems can provide information about
biological and chemical weapons development,
including details about pathogens and expert-
level laboratory instructions. In 2025, multiple
developers released new models with additional
safeguards after they could not exclude the
possibility that these models could assist
novices in developing such weapons. It remains
difficult to assess the degree to which material
barriers continue to constrain actors seeking
to obtain them.
Malfunctions
Reliability challenges: Current AI systems
sometimes exhibit failures such as fabricating
information, producing flawed code, and giving
misleading advice. AI agents pose heightened
risks because they act autonomously, making
it harder for humans to intervene before failures
cause harm. Current techniques can reduce
failure rates but not to the level required
in many high-stakes settings.
Loss of control: ‘Loss of control’ scenarios
are scenarios where AI systems operate
outside of anyone’s control, with no clear path
to regaining control. Current systems lack the
capabilities to pose such risks, but they are
improving in relevant areas such as autonomous
operation. Since the last Report, it has become
more common for models to distinguish between
test settings and real-world deployment and
to find loopholes in evaluations, which could
allow dangerous capabilities to go undetected
before deployment.
Systemic risks
Labour market impacts: General-purpose AI
will likely automate a wide range of cognitive
tasks, especially in knowledge work. Economists
disagree on the magnitude of future impacts:
some expect job losses to be offset by new job
creation, while others argue that widespread
automation could significantly reduce
employment and wages. Early evidence shows
no effect on overall employment, but some signs
of declining demand for early-career workers in
some AI-exposed occupations, such as writing.
Risks to human autonomy: AI use may affect
people’s ability to make informed choices and
act on them. Early evidence suggests that
reliance on AI tools can weaken critical thinking
skills and encourage ‘automation bias’, the
tendency to trust AI system outputs without
sufficient scrutiny. ‘AI companion’ apps now
have tens of millions of users, a small share
of whom show patterns of increased loneliness
and reduced social engagement.
Layering multiple
approaches offers more
robust risk management
Managing general-purpose AI risks is difficult
due to technical and institutional challenges.
Technically, new capabilities sometimes emerge
unpredictably, the inner workings of models
remain poorly understood, and there is an
‘evaluation gap’: performance on pre-deployment
tests does not reliably predict real-world utility
or risk. Institutionally, developers have incentives
to keep important information proprietary, and
the pace of development can create pressure
to prioritise speed over risk management
and makes it harder for institutions to build
governance capacity.
Risk management practices include threat
modelling to identify vulnerabilities, capability
evaluations to assess potentially dangerous
behaviours, and incident reporting to gather
more evidence. In 2025, 12 companies published
or updated their Frontier AI Safety Frameworks –
documents that describe how they plan to
manage risks as they build more capable models.
While AI risk management initiatives remain
largely voluntary, a small number of regulatory
regimes are beginning to formalise some risk
management practices as legal requirements.
Technical safeguards are improving but still
show significant limitations. For example,
attacks designed to elicit harmful outputs
have become more difficult, but users can still
sometimes obtain harmful outputs by rephrasing
requests or breaking them into smaller steps.
AI systems can be made more robust by layering
multiple safeguards, an approach known
as ‘defence-in-depth’.
Open-weight models pose distinct challenges.
They offer significant research and commercial
benefits, particularly for lesser-resourced
actors. However, they cannot be recalled once
released, their safeguards are easier to remove,
and actors can use them outside of monitored
environments – making misuse harder to
prevent and trace.
Societal resilience plays an important role
in managing AI-related harms. Because risk
management measures have limitations,
they will likely fail to prevent some AI-related
incidents. Societal resilience-building measures
to absorb and recover from these shocks include
strengthening critical infrastructure, developing
tools to detect AI-generated content, and building
institutional capacity to respond to novel threats.