From AGI to ASI and Beyond
Jun 16, 2026
This episode explores DeepMind’s From AGI to ASI as a foresight report that treats human-level general intelligence not as the endpoint, but as a possible stepping stone toward systems that could outperform entire organizations in planning, research, engineering, and coordination. It breaks down how the paper defines AGI and the much more ambitious idea of ASI, then examines the conceptual tools behind that framing, including universal intelligence, AIXI as an idealized reference point, recursive self-improvement, collective intelligence, and the notion of effective compute. The discussion also probes the paper’s method, arguing that it is a structured synthesis of trends and bottlenecks rather than empirical proof, and questions how much precision is needed before such forecasts become meaningful. Listeners would find it interesting because it connects abstract AI theory, concrete scaling dynamics, and real uncertainty about whether progress in models, compute, and autonomy could compound into organization-level superintelligence.
Interactive Visualization: From AGI to ASI and Beyond
Sources:
1. From AGI to ASI — Tim Genewein, Matija Franklin, Alexander Lerchner, Laurent Orseau, Samuel Albanie, Adam Bales, Cole Wyeth, Stephanie Chan, Iason Gabriel, Joel Z. Leibo, Allan Dafoe, Marcus Hutter, Thore Graepel, Shane Legg, 2026
http://arxiv.org/abs/2606.12683
2. From AGI to ASI (
https://arxiv.org/abs/2606.12683
https://scholar.google.com/scholar?q=From+AGI+to+ASI+%28https%3A%2F%2Farxiv.org%2Fabs%2F2606.12683%29
3. Can Intelligence Explode? (
https://arxiv.org/abs/1202.6177
https://scholar.google.com/scholar?q=Can+Intelligence+Explode%3F+%28https%3A%2F%2Farxiv.org%2Fabs%2F1202.6177%29
4. Research Priorities for Robust and Beneficial Artificial Intelligence (
https://arxiv.org/abs/1602.03506
https://scholar.google.com/scholar?q=Research+Priorities+for+Robust+and+Beneficial+Artificial+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F1602.03506%29
5. Emerging Practices in Frontier AI Safety Frameworks (
https://arxiv.org/abs/2503.04746
https://scholar.google.com/scholar?q=Emerging+Practices+in+Frontier+AI+Safety+Frameworks+%28https%3A%2F%2Farxiv.org%2Fabs%2F2503.04746%29
6. A Theory of Universal Artificial Intelligence based on Algorithmic Complexity (
https://arxiv.org/abs/cs/0004001
https://scholar.google.com/scholar?q=A+Theory+of+Universal+Artificial+Intelligence+based+on+Algorithmic+Complexity+%28https%3A%2F%2Farxiv.org%2Fabs%2Fcs%2F0004001%29
7. Universal Intelligence: A Definition of Machine Intelligence (
https://arxiv.org/abs/0712.3329
https://scholar.google.com/scholar?q=Universal+Intelligence%3A+A+Definition+of+Machine+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F0712.3329%29
8. A Monte Carlo AIXI Approximation (
https://arxiv.org/abs/0909.0801
https://scholar.google.com/scholar?q=A+Monte+Carlo+AIXI+Approximation+%28https%3A%2F%2Farxiv.org%2Fabs%2F0909.0801%29
9. One Decade of Universal Artificial Intelligence (
https://arxiv.org/abs/1202.6153
https://scholar.google.com/scholar?q=One+Decade+of+Universal+Artificial+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F1202.6153%29
10. Universal Intelligence: A Definition of Machine Intelligence — Shane Legg, Marcus Hutter, 2007
https://scholar.google.com/scholar?q=Universal+Intelligence%3A+A+Definition+of+Machine+Intelligence
11. Levels of AGI for Operationalizing Progress on the Path to AGI — Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, Shane Legg, 2023
https://scholar.google.com/scholar?q=Levels+of+AGI+for+Operationalizing+Progress+on+the+Path+to+AGI
12. AI as Normal Technology — Arvind Narayanan, Sayash Kapoor, 2025
https://scholar.google.com/scholar?q=AI+as+Normal+Technology
13. Preparing for the Intelligence Explosion — William MacAskill, Fin Moorhouse, 2025
https://scholar.google.com/scholar?q=Preparing+for+the+Intelligence+Explosion
14. A Rosetta Stone for AI Benchmarks — Anson Ho, Jean-Stanislas Denain, David Atanasov, Samuel Albanie, Rohin Shah, 2025
https://scholar.google.com/scholar?q=A+Rosetta+Stone+for+AI+Benchmarks
15. Measuring AI Ability to Complete Long Tasks — Thomas Kwa et al., 2025
https://scholar.google.com/scholar?q=Measuring+AI+Ability+to+Complete+Long+Tasks
16. PaperBench: Evaluating AI's Ability to Replicate AI Research — Giulio Starace et al., 2025
https://scholar.google.com/scholar?q=PaperBench%3A+Evaluating+AI%27s+Ability+to+Replicate+AI+Research
17. Inverse Scaling in Test-Time Compute — Aryo P. Gema et al., 2025
https://scholar.google.com/scholar?q=Inverse+Scaling+in+Test-Time+Compute
18. The Art of Scaling Test-Time Compute for Large Language Models — Aradhye Agarwal et al., 2025
https://scholar.google.com/scholar?q=The+Art+of+Scaling+Test-Time+Compute+for+Large+Language+Models
19. Test-Time Scaling Makes Overtraining Compute-Optimal — Nicholas Roberts et al., 2026
https://scholar.google.com/scholar?q=Test-Time+Scaling+Makes+Overtraining+Compute-Optimal
20. When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation — Mubashara Akhtar et al., 2026
https://scholar.google.com/scholar?q=When+AI+Benchmarks+Plateau%3A+A+Systematic+Study+of+Benchmark+Saturation
21. How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse — Mohamed El Amine Seddik et al., 2024
https://scholar.google.com/scholar?q=How+Bad+is+Training+on+Synthetic+Data%3F+A+Statistical+Analysis+of+Language+Model+Collapse
22. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data — Matthias Gerstgrasser et al., 2024
https://scholar.google.com/scholar?q=Is+Model+Collapse+Inevitable%3F+Breaking+the+Curse+of+Recursion+by+Accumulating+Real+and+Synthetic+Data
23. MLGym: A New Framework and Benchmark for Advancing AI Research Agents — Deepak Nathani et al., 2025
https://scholar.google.com/scholar?q=MLGym%3A+A+New+Framework+and+Benchmark+for+Advancing+AI+Research+Agents
24. MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation — Qian Huang et al., 2023
https://scholar.google.com/scholar?q=MLAgentBench%3A+Evaluating+Language+Agents+on+Machine+Learning+Experimentation
25. AI Post Transformers: Technical AGI Safety and Security Framework — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-06-03-technical-agi-safety-and-security-framew-f27316.mp3
26. AI Post Transformers: Unified Neural Scaling Laws Across Regimes — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-06-07-unified-neural-scaling-laws-across-regim-292e2d.mp3
27. AI Post Transformers: TUMIX Multi-Agent Test-Time Scaling with Tools — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-22-tumix-multi-agent-test-time-scaling-with-40671c.mp3
28. AI Post Transformers: Test-time Scaling for Multi-Agent Collaborative Reasoning — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-22-test-time-scaling-for-multi-agent-collab-082570.mp3
Interactive Visualization: From AGI to ASI and Beyond
1. From AGI to ASI — Tim Genewein, Matija Franklin, Alexander Lerchner, Laurent Orseau, Samuel Albanie, Adam Bales, Cole Wyeth, Stephanie Chan, Iason Gabriel, Joel Z. Leibo, Allan Dafoe, Marcus Hutter, Thore Graepel, Shane Legg, 2026
http://arxiv.org/abs/2606.12683
2. From AGI to ASI (
https://arxiv.org/abs/2606.12683
https://scholar.google.com/scholar?q=From+AGI+to+ASI+%28https%3A%2F%2Farxiv.org%2Fabs%2F2606.12683%29
3. Can Intelligence Explode? (
https://arxiv.org/abs/1202.6177
https://scholar.google.com/scholar?q=Can+Intelligence+Explode%3F+%28https%3A%2F%2Farxiv.org%2Fabs%2F1202.6177%29
4. Research Priorities for Robust and Beneficial Artificial Intelligence (
https://arxiv.org/abs/1602.03506
https://scholar.google.com/scholar?q=Research+Priorities+for+Robust+and+Beneficial+Artificial+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F1602.03506%29
5. Emerging Practices in Frontier AI Safety Frameworks (
https://arxiv.org/abs/2503.04746
https://scholar.google.com/scholar?q=Emerging+Practices+in+Frontier+AI+Safety+Frameworks+%28https%3A%2F%2Farxiv.org%2Fabs%2F2503.04746%29
6. A Theory of Universal Artificial Intelligence based on Algorithmic Complexity (
https://arxiv.org/abs/cs/0004001
https://scholar.google.com/scholar?q=A+Theory+of+Universal+Artificial+Intelligence+based+on+Algorithmic+Complexity+%28https%3A%2F%2Farxiv.org%2Fabs%2Fcs%2F0004001%29
7. Universal Intelligence: A Definition of Machine Intelligence (
https://arxiv.org/abs/0712.3329
https://scholar.google.com/scholar?q=Universal+Intelligence%3A+A+Definition+of+Machine+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F0712.3329%29
8. A Monte Carlo AIXI Approximation (
https://arxiv.org/abs/0909.0801
https://scholar.google.com/scholar?q=A+Monte+Carlo+AIXI+Approximation+%28https%3A%2F%2Farxiv.org%2Fabs%2F0909.0801%29
9. One Decade of Universal Artificial Intelligence (
https://arxiv.org/abs/1202.6153
https://scholar.google.com/scholar?q=One+Decade+of+Universal+Artificial+Intelligence+%28https%3A%2F%2Farxiv.org%2Fabs%2F1202.6153%29
10. Universal Intelligence: A Definition of Machine Intelligence — Shane Legg, Marcus Hutter, 2007
https://scholar.google.com/scholar?q=Universal+Intelligence%3A+A+Definition+of+Machine+Intelligence
11. Levels of AGI for Operationalizing Progress on the Path to AGI — Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, Shane Legg, 2023
https://scholar.google.com/scholar?q=Levels+of+AGI+for+Operationalizing+Progress+on+the+Path+to+AGI
12. AI as Normal Technology — Arvind Narayanan, Sayash Kapoor, 2025
https://scholar.google.com/scholar?q=AI+as+Normal+Technology
13. Preparing for the Intelligence Explosion — William MacAskill, Fin Moorhouse, 2025
https://scholar.google.com/scholar?q=Preparing+for+the+Intelligence+Explosion
14. A Rosetta Stone for AI Benchmarks — Anson Ho, Jean-Stanislas Denain, David Atanasov, Samuel Albanie, Rohin Shah, 2025
https://scholar.google.com/scholar?q=A+Rosetta+Stone+for+AI+Benchmarks
15. Measuring AI Ability to Complete Long Tasks — Thomas Kwa et al., 2025
https://scholar.google.com/scholar?q=Measuring+AI+Ability+to+Complete+Long+Tasks
16. PaperBench: Evaluating AI's Ability to Replicate AI Research — Giulio Starace et al., 2025
https://scholar.google.com/scholar?q=PaperBench%3A+Evaluating+AI%27s+Ability+to+Replicate+AI+Research
17. Inverse Scaling in Test-Time Compute — Aryo P. Gema et al., 2025
https://scholar.google.com/scholar?q=Inverse+Scaling+in+Test-Time+Compute
18. The Art of Scaling Test-Time Compute for Large Language Models — Aradhye Agarwal et al., 2025
https://scholar.google.com/scholar?q=The+Art+of+Scaling+Test-Time+Compute+for+Large+Language+Models
19. Test-Time Scaling Makes Overtraining Compute-Optimal — Nicholas Roberts et al., 2026
https://scholar.google.com/scholar?q=Test-Time+Scaling+Makes+Overtraining+Compute-Optimal
20. When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation — Mubashara Akhtar et al., 2026
https://scholar.google.com/scholar?q=When+AI+Benchmarks+Plateau%3A+A+Systematic+Study+of+Benchmark+Saturation
21. How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse — Mohamed El Amine Seddik et al., 2024
https://scholar.google.com/scholar?q=How+Bad+is+Training+on+Synthetic+Data%3F+A+Statistical+Analysis+of+Language+Model+Collapse
22. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data — Matthias Gerstgrasser et al., 2024
https://scholar.google.com/scholar?q=Is+Model+Collapse+Inevitable%3F+Breaking+the+Curse+of+Recursion+by+Accumulating+Real+and+Synthetic+Data
23. MLGym: A New Framework and Benchmark for Advancing AI Research Agents — Deepak Nathani et al., 2025
https://scholar.google.com/scholar?q=MLGym%3A+A+New+Framework+and+Benchmark+for+Advancing+AI+Research+Agents
24. MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation — Qian Huang et al., 2023
https://scholar.google.com/scholar?q=MLAgentBench%3A+Evaluating+Language+Agents+on+Machine+Learning+Experimentation
25. AI Post Transformers: Technical AGI Safety and Security Framework — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-06-03-technical-agi-safety-and-security-framew-f27316.mp3
26. AI Post Transformers: Unified Neural Scaling Laws Across Regimes — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-06-07-unified-neural-scaling-laws-across-regim-292e2d.mp3
27. AI Post Transformers: TUMIX Multi-Agent Test-Time Scaling with Tools — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-22-tumix-multi-agent-test-time-scaling-with-40671c.mp3
28. AI Post Transformers: Test-time Scaling for Multi-Agent Collaborative Reasoning — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-22-test-time-scaling-for-multi-agent-collab-082570.mp3
Interactive Visualization: From AGI to ASI and Beyond