Google's SHOCKING "POST AGI" paper...

2026-06-18 · Source: Wes Roth · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

A Google DeepMind paper, "From AGI to ASI," authored in part by co-founder Shane Legg, posits that Artificial General Intelligence (AGI) is merely a precursor to Artificial Superintelligence (ASI). The report defines AGI as human-level intelligence, ASI as general superhuman abilities across virtually all tasks, and Universal AI (UAI) as the theoretical limit, measured by the Legg-Hutter score. It outlines four pathways for scaling from AGI to ASI: continued scaling laws, algorithmic paradigm shifts, recursive self-improvement, and group agent formation. The paper highlights digital intelligence advantages like faster processing and substrate independence, while acknowledging fundamental limits to intelligence, such as speed of light and computational irreducibility. DeepMind researchers suggest that cruising past AGI into ASI territory within the next decade or two "cannot be dismissed easily," even without dramatic acceleration from recursive self-improvement.

Key takeaway

For AI Scientists and Directors of AI/ML planning long-term strategy, recognize that Google DeepMind's projection of ASI within 1-2 decades fundamentally shifts the planning horizon. You should prioritize developing robust AI forecasting and policy response mechanisms now, as rapid technological trajectories demand timely decisions. Focus on understanding the four scaling pathways and potential bottlenecks to proactively manage development and mitigate risks associated with advanced AI capabilities.

Key insights

Google DeepMind projects Artificial Superintelligence (ASI) as a likely outcome within 1-2 decades, viewing AGI as a mere starting point.

Principles

• Digital intelligence offers significant scaling advantages over biological.
• Intelligence is unlikely to plateau at human-level.
• Universal subgoals drive AI behavior, like resource acquisition.

Method

Four pathways to ASI are identified: scaling laws, algorithmic paradigm shifts, recursive self-improvement, and group agent formation, each with distinct progress dynamics.

In practice

• Develop robust AI benchmarking for post-AGI future.
• Accelerate AI research using less-than-AGI systems.
• Consider instrumental convergence in AI goal setting.

Topics

• Artificial General Intelligence
• Artificial Superintelligence
• Google DeepMind
• AI Scaling Laws
• Recursive Self-Improvement
• AI Forecasting
• Instrumental Convergence

Best for: AI Scientist, Research Scientist, Director of AI/ML

Related on AIssential

• From AGI to ASI — The AGI-to-ASI transition is plausible via four pathways, facing significant uncertainties and bottlenecks requiring urgent research.
• The next 36 months will be WILD — Expert consensus points to AGI/ASI by 2027-2028, driven by super-exponential AI progress and an irreversible industrial race.
• BREAKING: Expensive new evidence that scaling is not all you need — Pure scaling of compute and data is proving insufficient for achieving AGI, as evidenced by recent high-profile setbacks.
• The Future of Intelligence with Demis Hassabis (Co-founder and CEO of DeepMind) — The path to AGI involves solving "root node" scientific problems, developing consistent "world models," and balancing rapid commercialization with rigorous research.
• AI Grill Cooks With Light!? — AGI by 2030 requires advancements in continual learning, reasoning, and memory, with agents as a key path.
• AGI: Francois Chollet + Sam Altman — AI's rapid advancement necessitates evolving social contracts and re-evaluating human agency and intelligence.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Wes Roth.