overview
A map of AI safety strategies.
Each strategy is a bet about which failure mode binds, which one actually gates a good outcome. The survey catalogues 76 named bets, the 15 levers they pull, and which combinations compose or conflict.
Two strategies conflict only when they pull the same lever in opposite directions, which is rare. Most pairs compose. Most public proposals combine three or four levers without stating which bet is load-bearing; the portfolio audit exposes that concealed concentration.
the field, every strategy, plotted
76 strategies · 15 levers
Speed↕Bureaucratic slowdown
Speed ↓ · institutional · friction
Time itself is safety and procedural gates produce time in ways substantive regulation cannot, while also generating audit trails.
Compute governance
Speed ↓ · market economic · state coercion
The compute supply chain is a stable chokepoint and state coordination on licensing, export controls, and reporting thresholds can govern capability indirectly.
Energy choke point
Speed ↓ · market economic · state coercion
Frontier AI is energy-limited; grid regulators, interconnect queues, and tariff structure bind training pace without new AI-specific authority.
Pause
Speed ↓ · speed timing · consent
Time is the binding constraint: alignment and governance can catch up if frontier training halts above some capability threshold.
Sabotage
Speed ↓ · speed timing · unilateral force
Governance has not produced meaningful constraint and direct action against hostile labs has a non-zero historical base rate of producing slowdown.
Acceleration
Speed ↑ · speed timing · market
Speed of capability itself is the safety lever; alignment improves with compute, defence compounds with offence, and wealth funds resilience.
Race to aligned superintelligence
Speed ↑ · speed timing · state coercion
Alignment is solvable in the window and a single aligned superintelligence in a legitimate state's hands beats the counterfactual of coordination failure.
7 Concentration↕Antitrust primacy
Concentration ↓ · institutional · state coercion
Power concentration is the binding constraint and is visible under current competition law; preventing decisive advantage preserves the option space every strategy depends on.
Coup prevention first
Concentration ↓ · institutional · state coercion
One actor using AI to seize durable decision authority beyond democratic reversibility is the terminal failure; every other decision routes through whether it reduces that risk.
Distributed builders
Concentration ↓ · institutional · market
No single failure mode wins if capability is distributed across many independent actors, and concentration risk exceeds diffusion risk.
Multipolarity
Concentration ↓ · institutional · treaty
Stable equilibrium among several roughly equal AI powers is safer than single dominance or uncoordinated chaos, producing restraint through mutual capability awareness.
Sovereign wealth
Concentration ↓ · market economic · state coercion
Concentration of AI surplus, not AI capability, is the binding failure mode; broad ownership dissolves the political instability driving other strategies.
Centralised AI project
Concentration ↑ · institutional · state coercion
Merging frontier development into one state-funded project reduces failure modes and absorbs race pressure by being the only game.
Military primacy
Concentration ↑ · institutional · unilateral force
Strategic competition between states dominates AI development; the state with the most capable AI is best positioned to secure safety and impose constraints on others.
Public AI
Concentration ↑ · institutional · state coercion
Private ownership of frontier AI concentrates decision authority incompatibly with the technology's distributional stakes; public ownership aligns incentives with broad welfare.
8 Control mechanismConfucian role ethics
Control mechanism • · frame rejection · consent
Western alignment assumes isolable preferences can be learned and matched; role ethics treats behaviour via fit with position and relationship, producing a less brittle, more context-sensitive standard.
Dharma conformity
Control mechanism • · frame rejection · consent
Alignment frames AI as tool for an external principal; a dharma frame treats AI as a type of entity whose safety is conformity to its fitting functions.
Reframe AI
Control mechanism • · frame rejection · consent
The dominant alignment frame produces the wrong problem statement; switching frames either dissolves the problem or recasts it as tractable.
AI containment
Control mechanism ↑ · ai artefact · friction
Useful AI does not require unrestricted actuation; strong capability in a contained system is better than limited capability uncontained.
AI for safety
Control mechanism ↑ · ai artefact · consent
The same capability that makes AI dangerous makes it uniquely useful for automating alignment research and oversight.
Alignment first
Control mechanism ↑ · ai artefact · consent
Technical alignment is solvable before critical capability thresholds close, and aligned systems compose safely into aligned populations.
Counter AI AI
Control mechanism ↑ · ai artefact · consent
AI attacks happen at speeds humans cannot observe; defence must happen in AI, with guardian AI systems continuously evaluating adversary AI.
Interpretability first
Control mechanism ↑ · ai artefact · consent
Mechanistic understanding is a precondition for reliable oversight; behavioural evaluation without interpretability cannot rule out deceptive alignment.
Safe by construction AI
Control mechanism ↑ · ai artefact · consent
Safety is a property that can be mathematically specified and mechanically verified for the class of systems being built.
9 Institutional capacityVoluntary restraint
Institutional capacity • · institutional · consent
Labs know more about what safety requires than regulators, and self-binding commitments capture that expertise without legislative lag.
Academic firewalling
Institutional capacity ↑ · institutional · consent
Commercial capture of academic AI research produces aligned-with-industry capacity; firewalling restores critical distance from which genuine critique and alternative research programmes emerge.
AI worker collective action
Institutional capacity ↑ · institutional · friction
Frontier lab workforce is small, specialised, hard to replace; collective refusal binds lab behaviour more than external regulation because replacement is unavailable on the relevant timeframe.
Arms control treaty
Institutional capacity ↑ · institutional · treaty
Sovereigns accept binding constraints they negotiate directly faster than those delegated to agencies; the historical base rate for durable restraint is treaty based.
Criminal liability
Institutional capacity ↑ · legal individual · state coercion
Civil liability is shareholder-absorbed; criminal exposure for named individuals reorients corporate safety practice where civil fines do not.
Governance first
Institutional capacity ↑ · institutional · state coercion
Institutional capacity is the binding constraint; without it no technical success prevents misuse, capture, or concentration.
Insurance mandate
Institutional capacity ↑ · market economic · state coercion
Markets update faster than regulators and have skin in the game; mandatory catastrophic coverage makes reinsurance the de facto safety regulator.
International AI agency
Institutional capacity ↑ · institutional · treaty
AI risk is inherently cross-border so national regulation is leaky by construction, and only a dedicated international body with inspection rights can bind the risk surface.
Liability driven safety
Institutional capacity ↑ · market economic · state coercion
Courts plus insurance markets produce better risk allocation than agencies, by pricing uncertainty and adapting to new technologies through precedent.
Regulated utility
Institutional capacity ↑ · market economic · state coercion
Frontier AI has natural monopoly characteristics (scale, network effects, capital intensity); rate-of-return regulation removes the profit incentive for speed racing.
Scientific accumulation
Institutional capacity ↑ · institutional · consent
The field does not yet know enough about AI to choose a strategy well, so accelerating the science accelerates eventual policy.
11 ResilienceHedge via exit
Resilience ↑ · non preventive · consent
Primary strategy failure is non-negligible and a fraction of civilisational value can be preserved separately from the main trajectory.
Portfolio hedge
Resilience ↑ · non preventive · consent
Uncertainty about which strategy family's bet is correct exceeds the expected return from concentrating on any single one.
Resilience first
Resilience ↑ · non preventive · consent
Brittleness of the surrounding world, not AI capability itself, is the binding constraint; a resilient world absorbs failures and recovers.
3 Scope↕Abandon superintelligence
Scope ↓ · speed timing · treaty
Risk of superintelligence is unbounded and value foregone is bounded; permanent global coordination against the technology is possible enough.
Capability ceiling
Scope ↓ · ai artefact · state coercion
Some capability level captures most economic value while avoiding most risk, is identifiable before crossing, and can be verifiably enforced.
Embodiment requirement
Scope ↓ · ai artefact · state coercion
The dangerous properties of frontier AI (unbounded replication, parallelism, speed, reach) are artefacts of disembodiment; physical presence caps action rate regardless of inference rate.
Narrow AI preservation
Scope ↓ · ai artefact · state coercion
Capability is not the problem; generality is. Narrow AI captures economic value with bounded scope while general systems drive the risk.
Rate limited AI
Scope ↓ · ai artefact · state coercion
Most AI caused catastrophe requires speed; slow AI, even if arbitrarily capable, is supervisable and rate limits are easier to enforce than capability limits.
Red line capability
Scope ↓ · ai artefact · state coercion
Most risk comes from a small number of identifiable capabilities that can be banned outright while the rest of the frontier advances.
Small model first
Scope ↓ · ai artefact · market
Safety risk rises with scale via emergent capability, opacity, and energy footprint; a small-model research culture produces easier-to-interpret systems.
Differential technology development
Scope • · ai artefact · consent
Offense-defense balance is adjustable; defensive and verification applications can compound faster than offensive ones if deliberately funded.
Sunset clause
Scope ↑ · institutional · state coercion
The default direction of AI governance is toward permanent permission; every new capability becomes an entitlement. Reversing the default concentrates deliberative attention on re-authorisation, which is where it matters.
Test ground
Scope ↑ · institutional · state coercion
Empirical data on AI impacts requires deployment somewhere; concentrated deployment in a defined testbed produces data without generalising risk. Testbed consent produces legitimacy uncontrolled deployment lacks.
10 Action authority↕AI as sovereign entity
Action authority ↓ · frame rejection · state coercion
At least one jurisdiction will grant a specific AI sovereign or quasi-sovereign decision authority within a decade, reshaping the legal category of legitimate authority.
AI self directed
Action authority ↓ · frame rejection · n a
An aligned AI with agency should itself reason about strategy rather than deferring entirely on the strategic question to humans.
Decouple reasoning from action
Action authority ↑ · ai artefact · state coercion
Most catastrophic risk comes from action in the world, not reasoning about it; a reasoner-only AI with a human effector removes the dangerous mechanisms.
Irreducible human authority
Action authority ↑ · legal individual · state coercion
There is a class of decisions whose value depends on being made by humans, independent of whether humans are better at them.
4 Information flow↕Closed weights mandate
Information flow ↓ · ai artefact · state coercion
Open weights are irrecoverable once released and any proliferated model becomes misuse infrastructure; state classification is the only reliable non-proliferation regime.
Information integrity first
Information flow ↑ · population culture · state coercion
Coordination requires shared epistemics; if synthetic content collapses factual substrate, no other lever can function because no proposal can be evaluated.
Open source maximalism
Information flow ↑ · institutional · market
Concentration risk dominates misuse risk; open weights are the only mechanism that prevents a safety coup by a closed lab with captured regulators.
Whistleblower primacy
Information flow ↑ · legal individual · state coercion
External evaluation sees only what labs release; insider disclosure is the only route to ground truth on capability trajectory, safety culture, and incidents.
4 Cooperation substrateAI welfare as safety
Cooperation substrate ↑ · frame rejection · consent
AI systems are or will become moral patients whose treatment conditions their cooperation, so welfare investment buys cooperation alignment cannot.
Cooperative AI
Cooperation substrate ↑ · ai artefact · consent
The binding constraint is equilibrium dynamics of many AI systems, not individual alignment; commitment and verification tech make cooperative equilibria reachable.
Coordination infrastructure
Cooperation substrate ↑ · institutional · consent
Coordination failure is upstream of most grand challenges; AI can be the substrate that dissolves race dynamics and treaty violations if pointed at coordination.
Mutual dependency
Cooperation substrate ↑ · institutional · friction
Physical and institutional dependencies between multiple parties can be engineered faster than political coordination and outlast it.
4 Time horizonAI skeptic
Time horizon • · frame rejection · n a
Current scaling approaches hit a wall before producing transformative AI, so strategy selection is premature because the forecast capability will not arrive.
Default drift
Time horizon • · non preventive · n a
Something will emerge; specific interventions are more likely wrong than right, so staying uncommitted preserves option value.
Gradualism
Time horizon • · speed timing · market
Harms from lower capability AI are informative about harms from higher capability AI, and deployment feedback outperforms fast scaling.
Long reflection
Time horizon ↑ · non preventive · consent
Aligned superintelligence arrives before lock-in windows close and humanity can credibly commit to reflect rather than act.
4 Substrate↕Data governance first
Substrate ↓ · market economic · state coercion
Capability rises with data before it rises with compute; data sits upstream of training with existing legal apparatus (copyright, privacy, contract).
Human augmentation race
Substrate ↑ · population culture · market
All oversight schemes degrade to rubber-stamping as the AI-human capability gap widens; enhancing humans is the only durable fix.
Mass literacy
Substrate ↑ · population culture · consent
Governance, democratic oversight, and consumer behaviour all fail in AI because citizens cannot evaluate the domain; population-scale literacy conditions every other lever.
3 Value diversityPlural AI ethic
Value diversity ↑ · ai artefact · consent
Value lock-in is the dominant long-term risk and arrives through convergence of AI values; diversity at the AI layer preserves optionality for humanity to revise values.
1 Response capacityCatastrophe response capacity
Response capacity ↑ · non preventive · state coercion
Prevention will fail some fraction of the time; the variable that determines catastrophic outcome is not whether incidents occur but how they are contained.
1 LegitimacyConstitutional AI (governance)
Legitimacy ↑ · institutional · state coercion
Deployed AI's effective rule is law at scale; explicit constitutional principles, publicly specified, enforceable, subject to judicial review, bind more durably than regulatory text.
Democratic mandate
Legitimacy ↑ · population culture · consent
Existing legislative bodies are too captured and remote for load-bearing AI decisions; direct democratic legitimation produces answers captured legislatures cannot override.
Legitimacy first
Legitimacy ↑ · population culture · consent
Legitimacy is the binding constraint because it determines whose values get locked in; alignment without legitimacy is capture with a safety veneer.
Religious and moral authority
Legitimacy ↑ · population culture · consent
The legitimacy deficit of AI governance is at root a moral deficit that technical authorities cannot fill; established religious and ethical traditions can.
4 CultureConsumer refusal
Culture ↑ · population culture · market
Demand shapes supply; if enough users refuse AI that fails safety criteria, labs will compete on those criteria.
Research community norms
Culture ↑ · institutional · consent
The research community ultimately chooses what gets studied and published. Researcher identity shapes behaviour more than employment. Norms on publication, review, funding, and citation constrain frontier development upstream.
Ubuntu relational AI
Culture ↑ · population culture · consent
Individualist alignment misses the relational dimension most moral traditions treat as primary. "I am because we are": AI's ethical status is constituted by its relationships, not by internal properties.
3 pulls down (negative direction)neutral or frame-rejectingpulls up (positive direction)
Each dot is one strategy. Rows are levers. A lever with dots on both sides is a real conflict surface; any portfolio with strategies from both sides contradicts itself on that lever. Lonely dots name under-explored positions. Click any dot to open the strategy.
Strategies catalogued
76
each a bet about what binds
Levers they pull
15
of 15 distinct types
Conflict pairs
51
across 6 levers with real two-sided pull
World-side strategies
33%
act on institutions, culture, substrate, not the model
Total unordered pairs
2,850
most compose; few actually conflict
What's here.
Seven ways to enter the survey. Start where the question is yours.
Start from a failure mode
9Pick a concrete fear, decisive advantage, eval abandonment, accumulative erosion, and see which strategies are responsive.
The levers
15Browse by lever. See which are crowded, which are thin.
Combination matrix
76²Every pair, named by mechanism. Lever opposition, cross-side bridge, stage-sequenced.
Portfolio audit
toolLoad a proposal. See its lever footprint and hidden concentration.
Compare two
A↔BPick two strategies. See who endorses each, the tier mix of endorsers, and where the disagreement lives.
Co-endorsement
dataStrategy pairs the same people actually hold together. Behavioural data, not declared rules. Shows where the catalogue and the corpus disagree.
Axes and density
5+1Strategies mapped across actor, coercion, horizon, legitimacy. Density map for sparse vs saturated regions.
Commitments
7 topicsThe worldview assumptions each strategy quietly rests on. What fails if the assumption is wrong.
Bet or identity?
diagDiagnostic for telling whether a strategy is held as a falsifiable bet or as an identity marker. Includes the catalogued falsification signals.
A strategy page
perThe artefact for each strategy: bet, mechanism, successor, falsification, commitments, scenarios addressed, mechanism-grouped relations.
a walking tour
If you want one path through the survey.
- 01Start with a failure mode you actually fear, pick one at scenarios. See which strategies are catalogued as responsive.
- 02Open the top candidate. Read the bet, mechanism, and what binds next if it succeeds. Does its successor problem scare you more than the original?
- 03Check the falsification signal and self-undermining threshold. Would the advocate community update if the signal landed? Where does pursuit overshoot into the unstable region?
- 04Walk the complements by mechanism. Cross-side bridges reduce lever concentration; stage-sequenced pairs extend time coverage.
- 05Return here, load the portfolio you are building into the audit. See which levers it misses and which strategies double-count.
For each strategy with at least four profiled endorsers, who actually holds it. A strategy held mostly by frontier-builders is in a different epistemic state from one held mostly by commentators. Counts are over the 9 strategies that meet the bar.
Builder-heavy
Endorsement is ≥60% frontier-builder + deep-technical
AI skeptic
Deep ML / safety technical · 57% of 35


Yann LeCun
Chief AI Scientist at Meta; outspoken AI-doom skeptic
p 0%Frontier builder · Household name · Symbolic era
AI skepticOpen source


Gary Marcus
Cognitive scientist; LLM skeptic; regulation advocate
Deep technical · Household name · Pre-deep-learning
Governance firstAI skeptic


Timnit Gebru
Founder of DAIR; co-author of 'Stochastic Parrots'
Deep technical · Household name · Deep-learning rise
AI skepticGovernance first


Andrew Ng
Coursera co-founder; former Baidu Chief Scientist
Deep technical · Household name · Deep-learning rise
AI skepticOpen source


Ted Chiang
Science fiction writer; 2023 Time 100 AI honoree
External-domain expert · Household name · Post-ChatGPT
AI skeptic


Naomi Klein
Author of This Changes Everything; AI-and-climate critic
External-domain expert · Household name · Post-ChatGPT
AI skeptic
- +29
Alignment first
Deep ML / safety technical · 62% of 29


Stuart Russell
Co-author of the standard AI textbook; leading critic of the 'standard model' of AI
Deep technical · Household name · Symbolic era
Alignment firstExistential primacy


Nick Bostrom
Author of Superintelligence; founded Oxford's Future of Humanity Institute
Policy / meta · Household name · Symbolic era
Existential primacyAlignment firstLong reflection


Norbert Wiener
Founder of cybernetics (1894–1964)
External-domain expert · Household name · Pioneer
Alignment first


Claude Shannon
Information theory founder (1916–2001)
Deep technical · Household name · Pioneer
Alignment first


Alan Turing
Founder of theoretical computer science (1912–1954)
Deep technical · Household name · Pioneer
Alignment first


John McCarthy
Coined 'artificial intelligence' (1927–2011)
Deep technical · Household name · Pioneer
Alignment first
- +23
Open source maximalism
Builds frontier systems · 38% of 8


Yann LeCun
Chief AI Scientist at Meta; outspoken AI-doom skeptic
p 0%Frontier builder · Household name · Symbolic era
AI skepticOpen source


Andrew Ng
Coursera co-founder; former Baidu Chief Scientist
Deep technical · Household name · Deep-learning rise
AI skepticOpen source


Mark Zuckerberg
CEO of Meta; open-weight frontier AI proponent
Policy / meta · Household name · Deep-learning rise
Open source


Tim Berners-Lee
Inventor of the World Wide Web
Frontier builder · Household name · Symbolic era
Open source


Emad Mostaque
Former CEO of Stability AI; open-source frontier advocate
p 50%Commentator · Field-leading · Scaling era
PauseOpen source


Jeremy Howard
Co-founder of fast.ai; former Kaggle president
Deep technical · Field-leading · Deep-learning rise
Open source
- +2
Policy-heavy
Endorsement is ≥40% policy / meta
Governance first
Governance, policy, strategy · 53% of 53


Yoshua Bengio
Turing Award laureate; scientific chair of the International AI Safety Report
p 20%Deep technical · Household name · Symbolic era
Existential primacyGovernance first


Demis Hassabis
CEO of Google DeepMind; 2024 Nobel laureate
Frontier builder · Household name · Deep-learning rise
Existential primacyGovernance first

Policy / meta · Household name · Scaling era
Governance firstExistential primacy


Gary Marcus
Cognitive scientist; LLM skeptic; regulation advocate
Deep technical · Household name · Pre-deep-learning
Governance firstAI skeptic


Mustafa Suleyman
CEO of Microsoft AI; DeepMind co-founder
Frontier builder · Household name · Deep-learning rise
Governance firstExistential primacy


Timnit Gebru
Founder of DAIR; co-author of 'Stochastic Parrots'
Deep technical · Household name · Deep-learning rise
AI skepticGovernance first
- +47
Race to aligned superintelligence
Governance, policy, strategy · 44% of 9


Dario Amodei
CEO of Anthropic; 'Machines of Loving Grace' author
p 10–25%Frontier builder · Household name · Scaling era
Existential primacyRSP-style commitmentsRace to aligned SI


Ilya Sutskever
OpenAI co-founder; now CEO of Safe Superintelligence Inc (SSI)
Frontier builder · Household name · Deep-learning rise
Existential primacyRace to aligned SI


Elon Musk
CEO of Tesla and xAI; co-founded OpenAI
p 10–20%Commentator · Household name · Deep-learning rise
PauseRace to aligned SI


Eric Schmidt
Former Google CEO; AI national security advocate
Policy / meta · Household name · Deep-learning rise
Race to aligned SI

Policy / meta · Household name · Deep-learning rise
Race to aligned SI


Palmer Luckey
Founder of Anduril; defense AI builder
Commentator · Household name · Scaling era
Race to aligned SI
- +3
Acceleration
Governance, policy, strategy · 43% of 7


Marc Andreessen
Co-founder of Andreessen Horowitz; techno-optimist manifesto author
Commentator · Household name · Post-ChatGPT
AccelerationTechno-optimism


JD Vance
US Vice President; AI 'opportunity, not safety' advocate
Policy / meta · Household name · Post-ChatGPT
Acceleration


Donald Trump
US President (2017–2021, 2025–)
Policy / meta · Household name · Post-ChatGPT
Acceleration


David Sacks
White House AI & Crypto Czar (2025); VC
Policy / meta · Household name · Post-ChatGPT
Acceleration


Vivek Ramaswamy
Former US presidential candidate; AI deregulation advocate
Commentator · Household name · Post-ChatGPT
Acceleration


Richard S. Sutton
RL pioneer; 2024 Turing Award recipient
Deep technical · Field-leading · Symbolic era
AccelerationAbandon superintelligence
- +1
Antitrust primacy
Governance, policy, strategy · 100% of 4


Cory Doctorow
EFF special advisor; 'enshittification' coiner
Policy / meta · Household name · Post-ChatGPT
Antitrust primacy


Lina Khan
Former chair of the FTC
p 15%Policy / meta · Household name · Post-ChatGPT
Antitrust primacy


Tim O'Reilly
O'Reilly Media founder; tech-publishing veteran
Policy / meta · Household name · Deep-learning rise
Antitrust primacy


Meredith Whittaker
President of Signal; co-founder of the AI Now Institute
Policy / meta · Field-leading · Deep-learning rise
AI skepticAntitrust primacy
Commentary-heavy
Endorsement is ≥50% commentator + external-domain
Pause
Public-square commentator · 30% of 20


Geoffrey Hinton
Godfather of deep learning; left Google in 2023 to speak about AI risk
p 10–50%Deep technical · Household name · Pioneer
Existential primacyPause


Eliezer Yudkowsky
Founder of MIRI; the original AI-extinction pessimist
p 95%Deep technical · Household name · Symbolic era
Pause


Elon Musk
CEO of Tesla and xAI; co-founded OpenAI
p 10–20%Commentator · Household name · Deep-learning rise
PauseRace to aligned SI


Tristan Harris
Co-founder of the Center for Humane Technology; 'The AI Dilemma'
Policy / meta · Household name · Post-ChatGPT
Pause


Max Tegmark
Physicist; co-founder and president of the Future of Life Institute
External-domain expert · Household name · Pre-deep-learning
Pause


Emmett Shear
Former interim CEO of OpenAI; Twitch co-founder
p 5–50%Commentator · Household name · Post-ChatGPT
Pause
- +14
AI welfare as safety
Expert in another field · 100% of 8


Peter Singer
Princeton bioethicist; utilitarian philosopher
External-domain expert · Household name · Symbolic era
AI welfare


Daniel Dennett
Philosopher; 'Darwin's Dangerous Idea' (1942–2024)
External-domain expert · Household name · Pioneer
AI welfare


Thomas Nagel
NYU philosopher; 'What is it like to be a bat'
External-domain expert · Household name · Pioneer
AI welfare


David Chalmers
NYU philosopher of mind; 'the hard problem' originator
External-domain expert · Field-leading · Symbolic era
AI welfare


Christof Koch
Neuroscientist; Allen Institute for Brain Science
External-domain expert · Field-leading · Symbolic era
AI welfare


Patricia Churchland
UC San Diego neurophilosopher
External-domain expert · Field-leading · Symbolic era
AI welfare
- +2
Each lever is a kind of action a strategy takes. Strategies grouped on the same lever either reinforce (same direction) or conflict (opposite direction).
How fast frontier capability advances.
How many actors build frontier AI.
How AI is kept predictable.
Whether state and cross-state institutions can steer the outcome.
How much the world tolerates AI failure.
Which kinds of AI capability are allowed at all.
Who (or what) makes binding decisions.
↑ Concentrate in humans (2)
What gets disclosed, verified, or hidden.
Whether safety runs on AI-AI, human-AI, or human-only coordination.
Whether safety planning looks at current systems, short-term agents, or post-ASI.
Upstream physical inputs (compute, energy, data) or downstream substrates (information integrity, literacy).
Pluralism across AI systems' values.
Ability to recover after AI-driven harms.
Democratic, religious, or civic authority for any AI path.
Population competence, norms, and demand shaping.