International AI Safety Report 2026

Key information

• Investments in AI development are expected to grow significantly in coming years. Forecasts suggest that the computational power used to train the largest AI models could grow 125-fold by 2030 without hitting hard limits in energy, chips, or data. Training methods are also projected to use that computing power two to six times more efficiently each year.

• Plausible trajectories for capability improvements range from incremental or even plateauing progress to rapid acceleration. Uncertain factors such as technical limits or energy bottlenecks could constrain capability gains despite large investments, while positive feedback loops – such as AI systems contributing to AI research – could accelerate progress. There is little expert consensus on which trajectory is most likely.

• If capabilities continue to improve at their current rate, by 2030 AI systems will be able to complete well-scoped software engineering tasks that would take human engineers multiple days to complete. Projections for future performance in other domains are scarce, and the extent to which capability improvements will generalise to domains where training data is more limited and performance hard to assess is unclear.

• Since the publication of the last Report (January 2025), key trends suggest that capabilities will continue improving. In expectation of future gains, AI companies have announced unprecedented investments of more than $100 billion in data centre development to support larger training runs and wider deployment.

• Beyond 2030, the trajectory of AI capabilities becomes even harder to forecast. Over time, some experts expect it will be harder to obtain data, chips, capital, and energy at the scale needed for larger training runs. However, researchers may find ways to use these resources more efficiently or discover new approaches that sidestep current bottlenecks. Which considerations will prove most important is highly uncertain.

The key inputs of AI progress – compute, algorithmic improvements, and data – have grown exponentially in recent years, and new inference-time scaling methods are further improving models’ capabilities, even after they are trained. If these trends continue, experts expect AI capabilities to advance substantially by 2030. However, researchers cannot reliably predict when specific capabilities will emerge, and experts disagree about whether exponential increases in inputs will continue. Some expect that current training techniques will plateau, or that bottlenecks in data and energy will limit future progress. Yet others think that progress will accelerate further, since the application of AI systems to AI research itself could create positive feedback loops. To illustrate these divergent trajectories, this section presents four AI capability scenarios for 2030, developed in collaboration with the OECD. Additional technical details on scaling laws, inputs to scaling, and current benchmark performance are provided in the technical supplement.

Drivers of progress: compute, algorithms, and data

Frontier AI progress is driven by three inputs: compute, algorithmic advances, and data.

Compute refers to the computational resources, including hardware, software, and infrastructure, used in AI development and deployment. More compute allows for larger models to be trained on larger datasets (Figure 1.6), leading to better performance across various tasks. Compute can also be used during deployment to improve the quality of an AI system’s outputs.

Algorithmic advances improve how efficiently computational resources translate into model performance, and they can also enable qualitatively new capabilities. One model is more efficient than another if it uses less training or inference compute to reach the same performance. For example, GPT-5 is more efficient than GPT-4.5, because it was likely trained with less compute, but it outperforms 4.5 on a range of benchmarks, such as GPQA Diamond, which features PhD-level science questions.

Data refers to the information used to train models, including text from the internet, images, and artificially generated synthetic data. Both the amount and the quality of data are relevant for progress.

In recent years, all three drivers have grown dramatically. For the most compute-intensive models, training compute has grown about 5x per year. If this trend were to continue until 2030, these models could be trained with roughly 3,000 times more compute than they are today. Algorithmic efficiency, according to a 2024 study, has improved roughly 2-6x per year, reducing the compute needed for equivalent performance. Training datasets have expanded from billions to trillions of data points, with an average 2.5x annual increase. New inference-time scaling methods further improve capabilities once a model is trained, unlike traditional approaches that depend mostly on more training compute and larger datasets. One study finds that AI systems can complete well-specified software engineering tasks that take human experts 30 minutes around 80% of the time, and the duration of these tasks has been doubling every seven months (Figure 1.7). If this trend continues, AI systems could complete tasks lasting several hours by 2027 and tasks lasting several days by 2030.

How will AI capabilities change in the coming years?

Exponential growth in key inputs until 2030 is technically feasible

Exponential growth in key inputs to frontier AI – compute, algorithmic techniques, and data – is technically feasible until around 2030. Analyses of constraints such as production capabilities, investment, and technological progress suggest that compute per frontier model could continue growing at current rates without hitting fundamental bottlenecks in chip manufacturing or energy production. To support this scaling, companies are making large investments in compute infrastructure; for example, Meta and OpenAI have announced plans to spend $65 billion and $500 billion respectively. Importantly, these investments also support increases in inference compute and computational resources for research and development (R&D), the latter of which constitutes the bulk of AI company compute spending.

Algorithmic efficiency improvements have historically provided an additional 2–6x performance gain per year. However, experts disagree about how sustainable this growth is, especially beyond 2030. Disagreement centres on whether energy constraints and high-quality data scarcity will force fundamental changes to current development approaches.

Experts expect progress in problem-solving to continue

As discussed in §1.2. Current capabilities, AI models have made rapid advances in mathematical reasoning. Building on these advances, experts forecast major progress in reasoning-based problem-solving by 2027–2028. In a study by the Forecasting Research Institute, experts forecast a 50% chance that AI models will achieve 55% accuracy by 2027 and 75% accuracy by 2030 on undergraduate-level FrontierMath problems. However, experts disagree on whether these capabilities will generalise beyond mathematics and programming. Most evidence on the impact of reasoning techniques remains restricted to these domains. More extensive evaluations and attempts at applying AI systems’ reasoning skills to novel domains, such as legal and scientific reasoning, will be required to determine how far reasoning techniques will generalise.

AI systems have also made rapid gains in autonomous software execution. AI systems that could only complete tasks taking human experts a few seconds in 2019 can now, with an 80% success rate, finish software engineering tasks that take human experts 30 minutes. This metric – the maximum task duration that AI systems can complete with an 80% success rate – has been doubling roughly every seven months for the past six years. If it were to continue, AI systems could autonomously complete hours-long software projects by 2027–2028 and days-long projects by the end of the decade. However, these projections assume an 80% success rate, which likely falls below the standards required for autonomous deployment in many professional settings. Current evidence shows declining performance as tasks get longer, suggesting that achieving a production-ready success rate may require new innovations. Additionally, the benchmark tasks differ systematically from real-world software work in ways that may overstate progress: for example, they do not feature ‘messy’ real-world features such as resource constraints, incomplete information, or multi-agent coordination.

Experts disagree on the scale and timing of advances in specialised domains

General-purpose AI capabilities are expected to improve across many specialised domains by 2028–2030, though experts disagree about the extent and timing of these advances. AI systems have already surpassed graduate-level performance on some scientific benchmarks, such as GPQA Diamond, where leading models now exceed PhD-level experts. Trend extrapolations suggest that models could reach research-level performance across specialised scientific domains in the next few years, although forecasts remain uncertain.

Specific capabilities can emerge unpredictably even as overall performance improves steadily. For example, general-purpose AI models showed a sharp performance jump in adding large numbers once they were prompted to work step-by-step, rather than gradually improving at this as models scaled. Researchers refer to such sudden jumps as ‘emergent capabilities’. These create planning challenges because it is difficult to anticipate when AI systems will suddenly acquire strategically relevant cognitive abilities. Importantly, researchers cannot yet determine whether new prediction methods will make capability emergence more forecastable, and they disagree on how unpredictable these capability leaps truly are.

What bottlenecks might slow down progress?

Economic returns from additional compute may diminish

Resource scaling alone may lead to diminishing economic returns and threaten to slow progress, since ever-larger investments will be required to sustain the same rate of capability improvements. Current frontier AI training runs already cost approximately $500 million in computational resources alone, with next-generation models projected to require $1–10 billion. Meanwhile, consumer trust in AI systems is still low on average, and many enterprises are struggling to adopt AI systems successfully, making large-scale investments of hundreds of billions of dollars a bet on uncertain returns. If such investments fail to generate revenue (Figure 1.8), companies may sharply reduce scaling investments. This would create a potential ceiling on capability progress, since without continued investments, the 5x annual increase in training compute that has been a driver of recent advances would slow substantially. In that case, capability gains would depend more heavily on algorithmic progress rather than physical scaling alone.

It is unclear how much AI-assisted research automation will accelerate AI R&D

Experts disagree about whether AI-assisted research automation could dramatically accelerate AI progress in the coming decade. In a pilot study, forecasting experts were asked about the probability that progress in the next few years could compress six years of advancement (2018–2024) into just two years. AI forecasting experts gave a median 20% probability, while superforecasters (skilled generalist forecasters) estimated only 8%. However, forecasters’ estimates increased to 18% in scenarios where AI systems perform better than human researchers on month-long research projects. In such scenarios, AI research could become fully automated much sooner, which some have hypothesised could greatly accelerate AI progress.

Current empirical evidence on AI-assisted research automation is mixed. On a benchmark measuring AI research engineering capabilities, AI agents perform better than humans at two-hour tasks but have lower success rates at eight-hour tasks. While suggestive, this evidence does not account for real-world bottlenecks in AI R&D, such as the fact that researchers must manage ambiguous goals, and that it takes a long time to learn whether an algorithmic improvement actually improved model performance. This uncertainty creates extreme planning challenges for policymakers and institutions: if each AI advancement that accelerates the pace of AI R&D also facilitates the next advancement, decades of progress could happen in years.

Commercial deployment often lags behind capability improvements

Current AI systems demonstrate advanced capabilities in controlled settings, but their adoption occurs at different speeds across sectors. AI coding assistants achieved widespread adoption among software developers within one to four years of release. In contrast, many sectors face substantial obstacles to deploying AI systems. Healthcare AI systems that achieve human-level diagnostic accuracy in research settings often require three to five additional years for regulatory approval, clinical integration, and physician training before widespread deployment. Experts forecast that deployment of autonomous vehicle technology will still be limited in 2030, citing barriers including cultural resistance, infrastructure requirements, and regulatory pushback. Small and medium enterprises, which employ 60% of workers globally, face particular deployment challenges including limited technical expertise, insufficient computational infrastructure, and prohibitive integration costs that can delay AI adoption. Geopolitical factors, including export controls on advanced semiconductors and divergent regulatory frameworks across jurisdictions, could create additional barriers that affect both the development and deployment of AI capabilities.

That said, experts disagree about whether deployment gaps will narrow quickly or persist as a long-term constraint. On the one hand, the rapid uptake of AI tools across particular sectors suggests that deployment will accelerate if organisations observe concrete productivity gains and competitive advantages. Other researchers contend that organisational and regulatory adaptation inherently takes years, regardless of technical progress. This disagreement has implications for policy timing. Policies designed for rapidly deployed AI capabilities may be premature, while those assuming slow adoption may be insufficient to properly manage the risks.

What could progress through 2030 look like: OECD progress scenarios

Considering current trends and uncertainties, including those detailed above, the OECD has developed expert- and evidence-informed scenarios for how AI could advance – or slow down – by 2030. The OECD collaborated with the International AI Safety Report to integrate these scenarios into the Report. The analysis suggests that four broad classes of scenarios are all plausible by 2030:

Scenario 1: Progress stalls

A scenario in which AI capabilities remain largely unchanged. Rapid gains observed over recent years halt, and progress plateaus.

• Scenario: In 2030, AI systems can quickly undertake a range of tasks that would take humans hours to perform, but issues of robustness and hallucinations impact reliability. AI systems typically rely upon substantial support from humans to complete tasks, such as detailed prompting, review, and provision of context. They lack robust abilities to learn new skills or form memories, maintain coherence over longer complex tasks, or engage with dynamic physical or social environments.

• Pathway: After 2025, gains within existing approaches for developing frontier AI models hit fundamental limits. This could occur if AI progress slows due to: diminishing returns from larger training runs and more powerful reasoning systems; limitations in accessing computing resources or other critical inputs; a significant drop in AI investment; or the absence of major algorithmic breakthroughs.