A field guide to AI positions

Teach with the AI Atlas

Use this pack for a group discussion or independent reading. Start with the map, compare the labels, then check the evidence behind an explanation.

Prepared with AI assistance. Not independently fact-checked. Check the sources and qualifications before drawing conclusions.

For the discussion leader

Ask participants to distinguish the source wording from their own interpretation. Accept a well-supported disagreement with a placement. Leave uncertainty visible and record what additional evidence would help.

Finish with one claim, its source, one qualification and one open question.

Map for discussion

The PDF includes the full-name map. On a small screen, use the interactive map or the readable list. Map.

AI risk × development pace | 2026-09-16Public catastrophic-risk concern against preferred frontier capability pace. Dots are editorial placements, not risk probabilities or safety ratings. 23 actors are shown. Consult the accompanying actor evidence. AI risk × development pace Frontier AI debate · Evidence cutoff 2026-09-16 · Qualitative, not a ranking v0.22.0 23 plotted / 15 unplaced Individuals Organizations Advocacy groups Provisional / low clarity Each point has an evidence note HIGHER CONCERN · RESTRAINT HIGHER CONCERN · DEVELOPMENT LOWER CONCERN · RESTRAINT LOWER CONCERN · DEVELOPMENT STATED CONCERN ABOUT AI CATASTROPHE Higher Lower No actors placed here in this edition. Restraint can be motivated by jobs, rights or power, without emphasizing AI-extinction risk. An unfilled quadrant is not evidence of an empty viewpoint. Eliezer Yudkowsky · Superintelligence and the frontier leading to it. This is his advocated position, not this project's prediction. Eliezer Yudkowsky PauseAI · Its April 2026 proposal for a global pause on the most powerful general-AI training. A movement has internal variation; this point represents its published proposal. PauseAI Yoshua Bengio · Superintelligence restriction, not a ban on every AI application or safety research. Conditional superintelligence restraint is reported separately from his directly read account of control risks. His research proposal is not a proven safety solution. Yoshua Bengio Dario Amodei · Frontier capability growth, especially unchecked self-improvement. Pacing is not stopping. Coordinated steps remain proposals; no independent operational audit is claimed. Dario Amodei Anthropic · Published company safeguards and its announced embedded-evaluator commitment. Giving outside evaluators access does not commit the company to slow all frontier development. The Responsible Scaling Policy separates company promises from industry recommendations; implementation is not independently audited. Anthropic Demis Hassabis · Public governance proposal and subsequent endorsement. Do not convert a personal endorsement into a verified Google DeepMind-wide slowdown. Demis Hassabis Sam Altman · Reported September 2026 pacing support, with a directly read earlier governance proposal. The original social posts were unavailable to this review; the reporting is linked. Sam Altman OpenAI · Selected frontier research workloads and controls. The company reports selective restrictions, not a full training halt. Concern level remains an editorial inference. OpenAI Elon Musk · Personal statements combining frontier scaling, concern about human control and reported support for pacing. The September endorsement is indirect and brief. Building plans indicate a personal preference, not a general regulatory policy or a company safety rating. The conflicting signals warrant a broad pace range. Elon Musk Google DeepMind · Institutional frontier-safety framework. Its framework supports conditional progress. No whole-lab slowdown is established by this evidence. Google DeepMind Microsoft AI · Microsoft AI model development, not the entire Microsoft group. A human-control code is not a catastrophe probability. The consultation is not a verified slowdown. Microsoft AI Meta · Meta's frontier development and published safeguards. Publishing a risk framework does not reveal a probability belief; nor does openness imply low concern. Meta Yann LeCun · His personal public views, separate from the policies of Meta and AMI Labs. He disputes takeover arguments but also raises concerns about unsafe actions and concentrated control. The map position still relies on his 2024 interview; the 2026 material adds context. Yann LeCun Marc Andreessen · His dated 2023 manifesto, with a 2026 interview-publisher summary for context. The detailed catastrophe argument remains the 2023 manifesto. A newer episode summary supports continued pro-growth advocacy but cannot substitute for a full updated two-axis interview review. Marc Andreessen Donald Trump · His September 2026 opposition to slowing advanced AI, read alongside his June executive order. September posts are verified through reporting, not the original Truth Social pages. This maps expressed catastrophic-risk concern, not private beliefs, technical expertise, or every administration policy. The ranges are editorial judgments. Donald Trump Peter Thiel · His arguments for continued AI development and against concentrated power used to suppress it. Lower catastrophic-risk emphasis is relative to his concern about political control, not a claim that he rules out AI catastrophe. His economic and theological arguments do not specify a detailed frontier-training policy. Peter Thiel Geoffrey Hinton · Conditional controls on exceptionally capable AI in a coauthored policy paper. The proposal is collective and dated. Its control-risk warning is not a probability estimate or a claim that every kind of AI should stop. Geoffrey Hinton Stuart Russell · Conditional limits on dangerous frontier development and evidence required before release. A coauthored policy proposal and dated testimony support this interpretation. They do not establish a blanket ban or an institutional Berkeley position. Stuart Russell Ilya Sutskever · Personal research ambitions and proposed limits on the most powerful superintelligence. The proposed power cap has no specified method. Safety is a research aim, not an established property of future systems. Ilya Sutskever Mark Zuckerberg · Personal advocacy for frontier model innovation and distributed control of superintelligence. Distributed power is his proposed safeguard. The essay does not establish that competing self-improving systems remain controllable. Mark Zuckerberg Mustafa Suleyman · Personal advocacy for advanced-model research with limits on autonomy and loss of control. Limits on autonomy do not establish a general capability slowdown. He says implementation of the new code begins after consultation. Mustafa Suleyman Arvind Narayanan · Joint public arguments about frontier development, AI control and catastrophic risk, including the September 2026 update. The reviewed arguments are jointly authored. Pauses concern particular experiments; the overall pace preference remains conditional. Coordinates summarize public arguments, not private probabilities. Arvind Narayanan Sayash Kapoor · Joint public arguments about frontier development, AI control and catastrophic risk, including the September 2026 update. The reviewed arguments are jointly authored. Pauses concern particular experiments; the overall pace preference remains conditional. Coordinates summarize public arguments, not private probabilities. Sayash Kapoor An informal label for wanting slower AI development, often used as criticism. Check which limits a person actually supports. How it relates to the map: Relates to calls for slower development. The label alone does not explain what should be limited, why, or for how long.“Decels” ↗ Calls to temporarily halt specified AI development until conditions are met. Scope, international cooperation and enforcement are central challenges, including in PauseAI’s own proposal. How it relates to the map: Toward restraint on the horizontal axis. The scope and conditions for resuming development matter.Pause advocacy ↗ A disputed label for people emphasizing catastrophic AI risks. Concern does not mean believing disaster is inevitable. How it relates to the map: Relates to public concern about severe outcomes. It does not specify a probability or tell us which policies a person supports.“Doomers” ↗ A movement favoring faster technological growth and opposing centralized restraint. Its claim that acceleration leads to better outcomes is a philosophical position, not a demonstrated safety guarantee. How it relates to the map: Points toward faster development. That does not give every participant the same belief about catastrophic risk.e/acc ↗ An outlook emphasizing technology's potential to improve life, which can still include concern about particular risks. How it relates to the map: Often favors development, while allowing very different views about the severity of AI risk.Techno-optimism ↗ SLOW DOWN / PAUSE CONTINUE WITH CONDITIONS BUILD FASTER HOW FAST SHOULD MORE CAPABLE AI BE DEVELOPED? IDEAS & TERMS · Labels do not assign anyone to a group. Select for explanations and sources. RELATED IDEAS A community seeking effective ways to help others. Critics question whose measures of benefit count and how much power donors should have. How it relates to the map: No single location on either axis. A method for prioritizing good does not determine one AI policy.EA ↗ Vitalik Buterin’s proposal to accelerate defensive technologies and spread power. Deciding what counts as defensive and how to prevent concentrated control remains part of the proposal. How it relates to the map: Asks what to accelerate and who gains power. Faster defensive tools can coexist with caution about frontier AI.d/acc ↗ The view that protecting future generations deserves much more attention. Critics dispute predictions about distant effects and how far possible future benefits should outweigh present needs. How it relates to the map: Can motivate catastrophic-risk work, but does not fix a development speed or a particular AI forecast.Longtermism ↗ Research aimed at reducing AI harms and improving reliability and alignment with intended goals. A safety goal or framework is not proof that a system is safe. How it relates to the map: Neither a single actor nor one required pace preference. Research, evaluation and governance can support different development policies.AI safety ↗ Computer programs tell a machine what operations to carry out. Software includes those programs and their associated data. How it relates to the map: Map context: this explains how systems work; it is not a position for or against faster AI development.Computer programs ↗ Narayanan and Kapoor's view that people can shape AI's impacts through institutions, engineering and policy. Its safety claims are contested: in September 2026, they acknowledged underestimating risks during development and called for stronger controls. How it relates to the map: Map interpretation: context across both axes, not a measured point or membership label. Its authors support pausing unsafe experiments where needed, while rejecting claims that catastrophic risks are imminent.Normal technology ↗ READING THE MAP Coordinates reflect stated positions, not p(doom), safety performance or verified implementation. EA is a community. AI safety is a field. Neither is a single dot. People and organizations are separate. Sources and explanations: theaiatlas.org · Positions interpret public statements; they are not rankings

Prepared with AI assistance. Not independently fact-checked. Check the sources and qualifications before drawing conclusions.

The map summarizes selected public statements. Left to right describes the preferred pace of advanced AI development. Bottom to top describes expressed concern about catastrophic harm. A point is our interpretation, not a probability or a membership label. An unplaced person has no dot because the reviewed evidence does not establish both axes. How to read the map and its limits.

Eight ideas and labels

These short explanations include qualifications. A movement, a field of research and a label used by critics are different kinds of thing.

Artificial intelligence (AI)

AI is a field of computing. Its systems use computer programs and hardware to recognize patterns, generate content or make predictions. [23] [22] [14]

Keep in mind: AI and artificial general intelligence (AGI) are different terms. A system can perform a particular task well without having broad human-level abilities. [10]

Language models & large language models (LLMs)

An LLM is a model trained to process language. Computer software runs it and, for text generation, produces an answer one piece at a time. [16] [17] [15]

Keep in mind: A model can produce a convincing sentence without checking it against an external source. Search or other tools must be supplied by the surrounding application. [13] [18]

e/acc: Effective accelerationism

A movement favoring faster technological growth and opposing centralized restraint. Its claim that acceleration leads to better outcomes is a philosophical position, not a demonstrated safety guarantee. [3] [4]

Keep in mind: Broader techno-optimism does not imply e/acc membership. Its thermodynamic arguments do not establish that AI is safe. Buterin offers a counterpoint: profit alone does not automatically select beneficial directions for technology. [4] [6]

Decel: Deceleration

An informal label for wanting slower AI development, often used as criticism. Check which limits a person actually supports. [4]

Keep in mind: A safety rule, a pause on the most powerful AI and opposition to all technology are different positions. PauseAI says its proposed treaty would usually leave narrow applications unaffected, but also proposes considering longer-term research and hardware restrictions. [8]

More ideas and labels

AI doomer

A disputed label for people emphasizing catastrophic AI risks. Concern does not mean believing disaster is inevitable. [5] [9]

Keep in mind: The CAIS statement calls for reducing extinction risk; it does not say extinction is certain or give a shared probability. The Atlas does not automatically label anyone a doomer. [9]

EA: Effective altruism

A community seeking effective ways to help others. Critics question whose measures of benefit count and how much power donors should have. [2] [19] [20]

Keep in mind: Alice Crary argues that measures of benefit can miss political causes of harm. Emma Saunders-Hastings warns about donors’ power over people receiving help. EA’s own FAQ replies that institutional change belongs in its scope and that EA need not be utilitarian. These disputes concern how help is defined and governed. EA, longtermism and AI safety are not interchangeable. [19] [20] [21] [7]

AI safety & alignment

Research aimed at reducing AI harms and improving reliability and alignment with intended goals. A safety goal or framework is not proof that a system is safe. [12] [11] [1]

Keep in mind: A safety framework records stated safeguards, not a guarantee of safe implementation or a measured catastrophe probability. [1]

p(doom): Probability of doom

Someone's estimated chance of an AI disaster. Check what outcome, time period and assumptions the number refers to. [5] [24]

Keep in mind: These are judgments, not measured disaster rates. Grace and colleagues asked about different outcomes, loss of control and time limits. They warn that AI expertise does not guarantee forecasting skill and who responds can affect results. They still consider researchers' estimates useful. [24]

Three discussion questions

1. What does the map tell you?

Choose two people. What does each say about the pace of development, and what does each say about catastrophic risk?

  • Find a dated source for each axis. Separate a personal statement from an organization’s policy.
  • Compare the source wording with the map interpretation. What could justify a different placement?

2. Who chose the label?

Compare e/acc, decel and doomer. Which labels appear in supporters’ descriptions, and which appear in criticism?

  • Open one cited source for each label. Identify who is speaking and what they are arguing.
  • Explain what a label leaves out. What evidence would you need before applying it to a person?

3. Where could a system go wrong?

Imagine an application that drafts email replies. Compare a draft that a person checks with a reply the application sends automatically.

  • Use the drawing to mark the model, the surrounding software and the decision to send.
  • Which permissions, checks and records would you want? Explain which question each measure helps answer and what remains uncertain.
An example to discuss: drafting an email
  1. Input message
  2. Language model
  3. Draft reply
  4. Person checks
  5. Application sends

Your notes and source links

Sources for the idea sheet

Numbers beside the explanations lead to the cited publications below. The full reading notes and retrieval limits are linked from each idea. A citation is not proof that its author is right.

  1. Strengthening our Frontier Safety Framework

    https://deepmind.google/blog/strengthening-our-frontier-safety-framework/

  2. What is effective altruism?

    https://www.effectivealtruism.org/articles/introduction-to-effective-altruism

  3. what the f* is e/acc

    https://effectiveaccelerationism.substack.com/p/what-the-f-is-eacc

  4. Notes on e/acc principles and tenets

    https://effectiveaccelerationism.substack.com/p/repost-notes-on-eacc-principles-and

  5. Guillaume Verdon: e/acc, AI doomers and effective altruism (transcript #407)

    https://lexfridman.com/guillaume-verdon-transcript/

  6. My techno-optimism

    https://vitalik.eth.limo/general/2023/11/27/techno_optimism.html

  7. Longtermism

    https://www.williammacaskill.com/longtermism

  8. PauseAI Proposal (April 2026 version)

    https://pauseai.org/proposal

  9. Statement on AI Extinction Risk

    https://aistatement.com/work/statement-on-ai-extinction-risk

  10. Levels of AGI for Operationalizing Progress on the Path to AGI

    https://arxiv.org/abs/2311.02462

  11. Risks from Learned Optimization in Advanced Machine Learning Systems

    https://arxiv.org/abs/1906.01820

  12. Concrete Problems in AI Safety

    https://arxiv.org/abs/1606.06565

  13. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

    https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

  14. Machine Learning Glossary

    https://developers.google.com/machine-learning/glossary

  15. LLMs: What's a large language model?

    https://developers.google.com/machine-learning/crash-course/llm/transformers

  16. Models

    https://huggingface.co/learn/llm-course/en/chapter2/3

  17. Text generation

    https://huggingface.co/docs/transformers/en/llm_tutorial

  18. Tool use

    https://huggingface.co/docs/transformers/en/chat_extras

  19. Against ‘Effective Altruism’

    https://www.radicalphilosophy.com/article/against-effective-altruism

  20. Response to Effective Altruism

    https://www.bostonreview.net/forum/peter-singer-logic-effective-altruism/response-emma-saunders-hastings/

  21. Frequently asked questions and common objections

    https://www.effectivealtruism.org/faqs

  22. software

    https://csrc.nist.gov/glossary/term/software

  23. Explanatory memorandum on the updated OECD definition of an AI system

    https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/03/explanatory-memorandum-on-the-updated-oecd-definition-of-an-ai-system_3c815e51/623da898-en.pdf

  24. Thousands of AI Authors on the Future of AI (version 3)

    https://arxiv.org/html/2401.02843v3

Prepared with AI assistance. Not independently fact-checked. Check the sources and qualifications before drawing conclusions.

This is the dataset cutoff, not the date every source was read. The saved dataset preserves the exact evidence used for this pack.

Check the latest explanations and published changes before reusing an older pack. Content changes.

Created by Timo Fahlenbock · Legal notice (Impressum)