2028 Scenarios for Global AI Leadership

This episode explores Anthropic’s “2028: Two Scenarios for Global AI Leadership” as a strategic argument that advanced AI leadership may hinge on control of compute, semiconductor supply chains, and the ability to slow near-frontier imitation through export controls and distillation defenses. It examines the report’s core claim that the United States and its allies could preserve a 12 to 24 month lead over Chinese labs, while questioning whether ideas like “near-frontier” capability or “model intelligence” are defined well enough to support that kind of forecast. The discussion connects those claims to broader work on the AI triad of compute, data, and algorithms, the geopolitical importance of chip bottlenecks, and the risks of reducing national AI strength to a single score. A listener would find it interesting because it links frontier model development to real policy choices about industrial capacity, national security, and who gets to shape the norms around transformative AI.

Sources:
1. 2028 Scenarios for Global AI Leadership
https://www.anthropic.com/research/2028-ai-leadership
2. The AI Triad and What It Means for National Security Strategy — Ben Buchanan, 2020
https://scholar.google.com/scholar?q=The+AI+Triad+and+What+It+Means+for+National+Security+Strategy
3. Artificial Intelligence with American Values and Chinese Characteristics: A Comparative Analysis of American and Chinese Governmental AI Policies — Emmie Hine, Luciano Floridi, 2022
https://scholar.google.com/scholar?q=Artificial+Intelligence+with+American+Values+and+Chinese+Characteristics%3A+A+Comparative+Analysis+of+American+and+Chinese+Governmental+AI+Policies
4. China's Current Capabilities, Policies, and Industrial Ecosystem in AI — Jeff Ding, 2019
https://scholar.google.com/scholar?q=China%27s+Current+Capabilities%2C+Policies%2C+and+Industrial+Ecosystem+in+AI
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https://scholar.google.com/scholar?q=China%27s+Access+to+Foreign+AI+Technology%3A+An+Assessment
6. Maintaining China's Dependence on Democracies for Advanced Computer Chips — Saif M. Khan, Carrick Flynn, 2020
https://scholar.google.com/scholar?q=Maintaining+China%27s+Dependence+on+Democracies+for+Advanced+Computer+Chips
7. Assessing the New Semiconductor Export Controls — Matthew Reynolds, 2022
https://scholar.google.com/scholar?q=Assessing+the+New+Semiconductor+Export+Controls
8. Choking off China's Access to the Future of AI — Gregory C. Allen, 2022
https://scholar.google.com/scholar?q=Choking+off+China%27s+Access+to+the+Future+of+AI
9. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification — Samar Ansari, 2026
https://scholar.google.com/scholar?q=Hardware-Level+Governance+of+AI+Compute%3A+A+Feasibility+Taxonomy+for+Regulatory+Compliance+and+Treaty+Verification
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https://scholar.google.com/scholar?q=Global+AI+governance%3A+barriers+and+pathways+forward
11. International governance of civilian AI: a jurisdictional certification approach — Robert Trager, Ben Harack, Anka Reuel, Allison Carnegie, Lennart Heim, Lewis Ho, Sarah Kreps, Ranjit Lall, Owen Larter, Sean O hEigeartaigh, Simon Staffell, Jose Jaime Villalobos, 2023
https://scholar.google.com/scholar?q=International+governance+of+civilian+AI%3A+a+jurisdictional+certification+approach
12. Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence — Oliver Guest, Kevin Wei, 2025
https://scholar.google.com/scholar?q=Bridging+the+Artificial+Intelligence+Governance+Gap%3A+The+United+States%27+and+China%27s+Divergent+Approaches+to+Governing+General-Purpose+Artificial+Intelligence
13. Concrete Problems in AI Safety — Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mane, 2016
https://scholar.google.com/scholar?q=Concrete+Problems+in+AI+Safety
14. Concrete Problems in AI Safety, Revisited — Inioluwa Deborah Raji, Roel Dobbe, 2024
https://scholar.google.com/scholar?q=Concrete+Problems+in+AI+Safety%2C+Revisited
15. An Overview of Catastrophic AI Risks — Dan Hendrycks, Mantas Mazeika, Thomas Woodside, 2023
https://scholar.google.com/scholar?q=An+Overview+of+Catastrophic+AI+Risks
16. US-China perspectives on extreme AI risks and global governance — Akash Wasil, Tim Durgin, 2024
https://scholar.google.com/scholar?q=US-China+perspectives+on+extreme+AI+risks+and+global+governance
17. Machines of Loving Grace — Dario Amodei and Anthropic, 2024
https://scholar.google.com/scholar?q=Machines+of+Loving+Grace
18. The Adolescence of Technology — Anthropic, 2025
https://scholar.google.com/scholar?q=The+Adolescence+of+Technology
19. Chip War: The Fight for the World's Most Critical Technology — Chris Miller, 2022
https://scholar.google.com/scholar?q=Chip+War%3A+The+Fight+for+the+World%27s+Most+Critical+Technology
20. The Export Control and China AI Debate — Gregory C. Allen, 2024
https://scholar.google.com/scholar?q=The+Export+Control+and+China+AI+Debate
21. Tracking AI Compute Trends and the Hardware Bottleneck Literature — Epoch AI and related empirical compute-trend researchers, 2023-2026
https://scholar.google.com/scholar?q=Tracking+AI+Compute+Trends+and+the+Hardware+Bottleneck+Literature
22. Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities — Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock, 2023
https://scholar.google.com/scholar?q=Compute-Efficient+Deep+Learning%3A+Algorithmic+Trends+and+Opportunities
23. On Efficient Training of Large-Scale Deep Learning Models: A Literature Review — Li Shen, Yan Sun, Zhiyuan Yu, Liang Ding, Xinmei Tian, Dacheng Tao, 2023
https://scholar.google.com/scholar?q=On+Efficient+Training+of+Large-Scale+Deep+Learning+Models%3A+A+Literature+Review
24. Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions — Luyang Fang, Xiaowei Yu, Jiazhang Cai, Yongkai Chen, et al., 2025
https://scholar.google.com/scholar?q=Knowledge+Distillation+and+Dataset+Distillation+of+Large+Language+Models%3A+Emerging+Trends%2C+Challenges%2C+and+Future+Directions
25. PanDa: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation — Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao, 2022
https://scholar.google.com/scholar?q=PanDa%3A+Prompt+Transfer+Meets+Knowledge+Distillation+for+Efficient+Model+Adaptation
26. Tulu 3: Pushing Frontiers in Open Language Model Post-Training — Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Hamish Ivison, et al., 2024
https://scholar.google.com/scholar?q=Tulu+3%3A+Pushing+Frontiers+in+Open+Language+Model+Post-Training
27. AI Diffusion to Low- and Middle Income Countries; A Blessing or a Curse? — Rafael Andersson Lipcsey, 2024
https://scholar.google.com/scholar?q=AI+Diffusion+to+Low-+and+Middle+Income+Countries%3B+A+Blessing+or+a+Curse%3F
28. Exporting the Surveillance State via Trade in AI — Martin Beraja, Andrew Kao, David Y. Yang, Noam Yuchtman, 2023
https://scholar.google.com/scholar?q=Exporting+the+Surveillance+State+via+Trade+in+AI
29. AI Post Transformers: Simple Self-Distillation for Better Code Generation — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-02-simple-self-distillation-for-better-code-cc88e0.mp3
30. AI Post Transformers: Nemotron 3 Super Hybrid Mamba-Transformer MoE — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-19-nemotron-3-super-hybrid-mamba-transforme-31ac75.mp3
31. AI Post Transformers: DeepSeek-V4 and Practical Million-Token Context — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-25-deepseek-v4-and-practical-million-token-6f4de1.mp3
32. AI Post Transformers: Why LLM Serving Needs Mathematical Optimization — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-why-llm-serving-needs-mathematical-optim-647fc6.mp3