AI predictions

Capability Scaling

• 2020-09: Ajeya Cotra: Draft report on AI timelines
• 2023-05: Richard Ngo: Clarifying and predicting AGI
• 2025-03: Measuring AI Ability to Complete Long Tasks Measuring AI Ability to Complete Long Tasks

• The Road to AGI (timeline visualization)

AGI Achievable

• Yoshua Bengio: Managing extreme AI risks amid rapid progress
• Leopold Aschenbrenner: Situational Awareness: Counting the OOMs
• Richard Ngo: Visualizing the deep learning revolution
• Katja Grace: Survey of 2,778 AI authors: six parts in pictures
• Epoch AI: Machine Learning Trends
• AI Digest: How fast is AI improving?

AGI Definition

• 2023-11: Allan Dafoe, Shane Legg, et al.: Levels of AGI for Operationalizing Progress on the Path to AGI
• 2024-04: Bowen Xu: What is Meant by AGI? On the Definition of Artificial General Intelligence

Economic and Political

• 2023-12: My techno-optimism: "defensive acceleration" (Vitalik Buterin)
• 2024-03: Noah Smith: Plentiful, high-paying jobs in the age of AI: Comparative advantage is very subtle, but incredibly powerful. (video)
• 2024-03: Scenarios for the Transition to AGI (AGI leads to wage collapse)
• 2024-06: Situational Awareness (Leopold Aschenbrenner) - select quotes, podcast, text summary of podcast
• 2024-06: AI and Growth: Where Do We Stand?
• 2024-09: OpenAI Infrastructure is Destiny: Economic Returns on US Investment in Democratic AI
• 2024-12: By default, capital will matter more than ever after AGI (L Rudolf L)
• 2025-01: The Intelligence Curse: With AGI, powerful actors will lose their incentives to invest in people
• 2025-01: Microsoft: The Golden Opportunity for American AI
• 2025-01: AGI Will Not Make Labor Worthless
• 2025-01: AI in America: OpenAI's Economic Blueprint (blog)
• 2025-01: How much economic growth from AI should we expect, how soon?
• 2025-02: Morgan Stanley: The Humanoid 100: Mapping the Humanoid Robot Value Chain
• 2025-02: The Anthropic Economic Index: Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations
• 2025-02: Strategic Wealth Accumulation Under Transformative AI Expectations
• 2025-02: Tyler Cowen: Why I think AI take-off is relatively slow
• 2025-03: Epoch AI: Most AI value will come from broad automation, not from R&D
  • The primary economic impact of AI will be its ability to broadly automate labor
  • Automating AI R&D alone likely won’t dramatically accelerate AI progress
  • Fully automating R&D requires a very broad set of abilities
  • AI takeoff will likely be diffuse and salient
• 2025-03: Anthropic Economic Index: Insights from Claude 3.7 Sonnet

Job Loss

• 2023-03: GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
  • 2023-03: These jobs are most at risk due to ChatGPT, as per OpenAI study
• 2023-08: The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market
  • Freelancer sector shrinking

• 2023-09: What drives UK firms to adopt AI and robotics, and what are the consequences for jobs?
  • 78% of Companies Say AI Created More Jobs
• 2023-11: New Analysis Shows Over 20% of US Jobs Significantly Exposed to AI Automation In the Near Future
• 2024-01: Duolingo cuts 10% of its contractor workforce as the company embraces AI
• 2024-02: Gen AI is a tool for growth, not just efficiency: Tech CEOs are investing to build their workforce and capitalise on new opportunities from generative AI. That’s a sharp contrast to how their peers view it.
• 2024-04: AI is Poised to Replace the Entry-Level Grunt Work of a Wall Street Career
• 2024-07: AI Is Already Taking Jobs in the Video Game Industry: A WIRED investigation finds that major players like Activision Blizzard, which recently laid off scores of workers, are using generative AI for game development
• 2024-08: Klarna: AI lets us cut thousands of jobs - but pay more
• 2025-01: AI and Freelancers: Has the Inflection Point Arrived?
• 2025-01: Yes, you're going to be replaced: So much cope about AI
• 2025-03: Will AI Automate Away Your Job? The time-horizon model explains the future of the technology

Near-term

• 2021-08: Daniel Kokotajlo: What 2026 looks like
• 2025-02: Sam Altman: Three Observations
  1. The intelligence of an AI model roughly equals the log of the resources used to train and run it.
  2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use
  3. The socioeconomic value of linearly increasing intelligence is super-exponential in nature
• 2025-03: Glimpses of AI Progress: Mental models for fast times
• 2025-04: Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, Romeo Dean: AI 2027 (pdf)

Overall

• 2025-03: Kevin Roose (New York Times): Powerful A.I. Is Coming. We’re Not Ready. Three arguments for taking progress toward artificial general intelligence, or A.G.I., more seriously — whether you’re an optimist or a pessimist.
• 2025-03: Nicholas Carlini: My Thoughts on the Future of "AI": "I have very wide error bars on the potential future of large language models, and I think you should too."

Surveys of Opinions/Predictions

• 2025-02: Why do Experts Disagree on Existential Risk and P(doom)? A Survey of AI Experts
• 2025-02: Nicholas Carlini: AI forecasting retrospective: you're (probably) over-confident

Bad Outcomes

• 2019-03: What failure looks like
• 2025-01: Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development (web version)
  • 2025-02: The Risk of Gradual Disempowerment from AI

Intelligence Explosion

• 2025-02: Three Types of Intelligence Explosion
• 2025-03: Future of Life Institute: Are we close to an intelligence explosion? AIs are inching ever-closer to a critical threshold. Beyond this threshold lie great risks—but crossing it is not inevitable.

Psychology

• 2025-01: A defence of slowness at the end of the world

Science & Technology Improvements

• 2024-09: Sam Altman: The Intelligence Age
• 2024-10: Dario Amodei: Machines of Loving Grace
• 2024-11: Google DeepMind: A new golden age of discovery
• 2025-03: Fin Moorhouse, Will MacAskill: Preparing for the Intelligence Explosion

Plans

• A Narrow Path: How to Secure our Future
• Marius Hobbhahn: What’s the short timeline plan?
• Building CERN for AI: An institutional blueprint
• AGI, Governments, and Free Societies
• Control AI: The Direct Institutional Plan

Philosophy

• Dan Faggella:
  • 2018-07: Moral Singularity – Unpredictable Values Bodes Poorly for Humanity
  • 2025-02: There is No Pause – We Must Bend the Posthuman Trajectory
• Joe Carlsmith: 2024: Otherness and control in the age of AGI
  1. Gentleness and the artificial Other
  2. Deep atheism and AI risk
  3. When “yang” goes wrong
  4. Does AI risk “other” the AIs?
  5. An even deeper atheism
  6. Being nicer than Clippy
  7. On the abolition of man
  8. On green
  9. On attunement
  10. Loving a world you don’t trust
• Anthony Aguirre:
  • 2025-03: Keep The Future Human

Alignment

• What are human values, and how do we align AI to them? (blog)
• Joe Carlsmith: 2025: How do we solve the alignment problem? Introduction to an essay series on paths to safe, useful superintelligence
  1. What is it to solve the alignment problem? Also: to avoid it? Handle it? Solve it forever? Solve it completely? (audio version)
  2. When should we worry about AI power-seeking? (audio version)
  3. Paths and waystations in AI safety (audio version)
  4. AI for AI safety (audio version)

Strategic/Technical

• 2025-03: AI Dominance Requires Interpretability and Standards for Transparency and Security

Strategic/Policy

• Amanda Askell, Miles Brundage, Gillian Hadfield: The Role of Cooperation in Responsible AI Development
• Dan Hendrycks, Eric Schmidt, Alexandr Wang: Superintelligence Strategy
  • Executive Summary
  • Introduction
  • AI Is Pivotal for National Security
  • Deterrence with Mutual Assured AI Malfunction (MAIM)
  • Nonproliferation
  • Competitiveness
  • Conclusion
  • Appendix FAQs
• Anthony Aguirre: Keep The Future Human (essay)
  • The 4 Rules That Could Stop AI Before It’s Too Late (video) (2025)
    1. Oversight: Registration required for training >10²⁵ FLOP and inference >10¹⁹ FLOP/s (~1,000 B200 GPUs @ $25M). Build cryptographic licensing into hardware.
    2. Computation Limits: Ban on training models >10²⁷ FLOP or inference >10²⁰ FLOP/s.
    3. Strict Liability: Hold AI companies responsible for outcomes.
    4. Tiered Regulation: Low regulation on tool-AI, strictest regulation on AGI (general, capable, autonomous systems).

See Also

• AI safety