Explore the possible development trajectory of AI by 2030

OECD ARTIFICIALINTELLIGENCE PAPERS Exploring possibleAItrajectoriesthrough Hamish Hobbs, Dexter Docherty,Luis Aranda,KasumiSugimoto,Karine Perset,Rafał Kierzenkowski This OECD Working Paper should not be reported as representing the official views of the OECD or of OECD or GPAI member countries.The opinions expressed and arguments employed are those of the authors. Working Papers describepreliminary results or research inprogress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works.Comments on Note to Delegations: This document is also available on O.N.E Members & Partners under the reference code: DSTI/DPC/GPAI(2025)/13/FINAL This document, as well as any data and map included herein, are without prejudice tothe status of or sovereignty over any territory, to the Cover image: ©Kjpargeter/Shutterstock.com ©OECD2026 Attribution4.0 International (CCBY4.0) This work is made available under the Creative Commons Attribution4.0 International licence. By using this work, you accept to be bound by the terms of this licence Attribution–you must cite the work. Translations–you must cite the original work, identify changes to the original and add the following text:In the event of any discrepancy between the original work and the Adaptations–you must cite the original work and add the following text:This is an adaptation of an original work by the OECD. The opinions expressed and arguments employed Third-party material–the licence does not apply to third-party material in the work. If using such material, you are responsible for obtaining permission from the third party and Abstract Artificialintelligence (AI) has advanced rapidly in recent years, with systemsbecoming increasingly capable. This paper presents expert-and evidence-informed scenarios for how AI couldprogress by 2030.It considers recenttrends in AI and key uncertainties for AI progressthrough 2030.Currentevidence suggests that four different broad scenario classes are allplausiblethrough to2030: progress stalling, progress slowing, progresscontinuing, and progress accelerating.This suggests that AI progress by2030 has a plausible range that includes both a plateau at approximately 4 Acknowledgements This paper wasdrafted by Hamish Hobbsfrom the OECD Strategic Foresight Unit, in close collaborationwithDexter Docherty from the Strategic Foresight Unit andKasumi Sugimoto, Luis Aranda and KarinePerset fromthe OECDDivision on AI and Emerging Digital Technologies.Strategic direction and input The teamgratefully acknowledges the input ofStuart Elliot,Sam Mitchelland ZinaEfcharyregarding theintegration of the OECDbetaAI Capability Indicators.The team also thanksNiamh Higgins-Lavery fromthe Strategic Foresight Unit for operationalsupportandShellie Laffont,Christy Dentler andAndreia The paper benefitted significantly from the oral and written contributions of GPAI delegates as well asexperts from the OECD.AI network of experts. The authors would like to extend their sincere gratitude tothe Delegations of Brazil,Greece,India, Israel,Spain, Saudi Arabia,Slovenia,Türkiye,andthe United This report benefited greatly from discussions and input fromthewriting teamof the International AI SafetyReport, includingCarina Prunkl, Stephen Clare,Maksym Andriushchenko,Patrick KingandHannah Finally, the authors gratefully recognise the substantial contributions fromexternalexperts, includingÁlvaro Soto (Pontificia Universidad Católica de Chile), Friedrik Heintz (Linköping University), GopalRamchurn (University of Southampton), Hiroshi Ishiguro (Osaka University), Nick Jennings (LoughboroughUniversity) Jonas Sandbrink(AI Security Institute), Stuart Russell (University of California, Berkeley), Table of contents 3 Abstract Acknowledgements Table of contents Executive summary 1.Introduction and methodology 1.1. Understanding possible AI trajectories will enable governments to capture the benefits andprepare for potential impacts 2. AI progress trends and uncertainties 12 2.1. AI systems have demonstrated rapid progress on a wide range ofbenchmarks2.2. Key uncertainties about future trends in AI progress2.3. Key uncertainties about future AI inputs 3. Scenarios 3.1. Using the OECD’s AI Capability Indicators (beta) to define AI capability categories3.2. Building on these indicators to explore scenarios for AI progress in 20303.3. Scenario 1: Progress Stalls3.4. Scenario 1: Potential variations3.5.Scenario 2: Progress Slows3.6. Scenario 2: Potential variations3.7. Scenario 3: ProgressContinues3.8. Scenario 3: Potential variations3.9. Scenario 4: Progress Accelerates 6 4. Which futures are plausible? Conclusions41 Annex A. Expert Interviews and Review42 AnnexB. AI Progress Trends and Uncertainties44 Annex C. AI Input Trends and Uncertainties53 Annex D. Trend Extrapolations58 References63 Endnotes73 Figure 1: AI system benchmark scores relative to human scores over time44Figure 2. Perf