Executive summary: The author argues that international AI projects should adopt differential AI development by tightly restricting the most dangerous capabilities, especially AI that automates AI R&D, while actively accelerating and incentivizing “artificial wisdom” systems that help society govern rapid AI progress.

Key points:

1. Existing proposals for international AI projects focus on blanket control of frontier AI, which would block both dangerous and highly beneficial capabilities.
2. The author claims the core risk comes from AI that can automate ML research, engineering, or chip design, because this could trigger super-exponential capability growth and extreme power concentration.
3. They propose that only AI systems above a compute threshold and aimed at automating AI R&D or producing catastrophic technologies should be monopolized or banned outside an international project.
4. Enforcement could rely on oversight of a small number of large training runs, with audits, embedded supervisors, and severe penalties for violations.
5. The author argues governments should differentially accelerate “helpful” AI, including forecasting, policy analysis, ethical deliberation, negotiation support, and rapid education.
6. This approach could improve preparedness for rapid AI change, be more acceptable to industry, reduce incentives for international racing, and sometimes benefit even geopolitical rivals.

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Executive summary: The post argues, in a speculative but action-oriented tone, that near-term AI-enabled software can meaningfully improve human and collective reasoning by targeting specific failures in decision-making, coordination, epistemics, and foresight, while carefully managing risks of misuse and power concentration. Key points:

1. The author claims that many of today’s highest-stakes problems arise because humans and institutions are systematically bad at reasoning, coordination, and adaptation under complexity and uncertainty.
2. They propose a “middle ground” between cultural self-improvement and radical biological augmentation: using existing and near-term AI-enabled software to incrementally uplift human reasoning capacities.
3. The post suggests analyzing failures in human reasoning using frameworks like OODA loops, epistemic message-passing, foresight, and coordination dynamics across individuals, groups, and institutions.
4. The author argues that foundation models, big data, and scalable compute can enable new forms of sensing, simulation, facilitation, exploration, and clerical leverage that were previously infeasible.
5. A central warning is that improved coordination and epistemics can backfire by empowering collusion, concentration of power, or epistemic attacks if distribution and safeguards are poorly designed.
6. The author encourages experimentation and sharing within the community, with particular emphasis on choosing software designs and deployment strategies that asymmetrically favor beneficial use and reduce misuse risk.

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Executive summary: The post argues that evolutionary cost-balancing arguments, especially the “Evening Out Argument” that frequent or unavoidable harms should evolve to be less intense, are too weak and biologically unrealistic to justify confident conclusions about net wild animal welfare.

Key points:

1. The “Evening Out Argument” assumes suffering has metabolic or neural costs and that natural selection economizes its intensity when suffering is frequent, but the author argues this logic is overapplied.
2. The argument may plausibly apply to short-lived species where pain-guided learning has little future value, but it fails for long-lived species whose survivors benefit from intense learning signals despite high early mortality.
3. Much animal suffering comes from background motivational states like hunger, anxiety, and vigilance, which function continuously and cannot be “toned down” without undermining survival.
4. Chronic and maladaptive suffering can persist because it does not significantly reduce reproductive success and because evolution tolerates costly design flaws and path dependencies.
5. Hedonic systems are likely modular rather than governed by a single dial, undermining simple cost-balancing predictions about overall suffering.
6. Environmental change and mismatch from ancestral conditions further weaken any expectation that suffering would be efficiently “evened out” by evolution.

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Executive summary: The author argues that while both animal welfare and animal rights advocacy have plausible moral and empirical justifications, uncertainty in the evidence and considerations about movement-building have led them to favor rights-based advocacy pursued with what they call “fierce compassion,” while still endorsing strategic diversity across the movement.

Key points:

1. The core divide is between welfarist advocacy, which favors incremental welfare improvements, and rights-based advocacy, which favors abolitionist veganism even at the cost of short-term welfare gains.
2. Empirical evidence on messaging strategies is mixed: reduction asks often achieve broader participation, while vegan pledges show higher immediate follow-through, and substitution and backlash effects remain highly uncertain.
3. Evidence suggests humane labeling frequently misleads consumers, raising concerns that welfare reforms may legitimize ongoing exploitation rather than reduce it.
4. Research on disruptive protests indicates short-term backlash but little evidence of lasting negative opinion change over longer time horizons.
5. The author argues that advocacy should prioritize movement-building, noting that small but committed activist minorities can drive systemic change.
6. The author’s shift toward rights-based advocacy is motivated by concern that fear of social discomfort leads advocates to understate moral urgency, and by the view that anger and discomfort can be appropriate responses to severe injustice.

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Executive summary: Max Harms argues that Bentham’s Bulldog substantially underestimates AI existential risk by relying on flawed multi-stage probabilistic reasoning and overconfidence in alignment-by-default and warning-shot scenarios, while correctly recognizing that even optimistic estimates still imply an unacceptably dire situation that warrants drastic action to slow or halt progress toward superintelligence.

Key points:

1. Harms claims Bentham’s Bulldog commits the “multiple-stage fallacy” by decomposing doom into conditional steps whose probabilities are multiplied, masking correlated failures, alternative paths to catastrophe, and systematic under-updating.
2. He argues If Anyone Builds It, Everyone Dies makes an object-level claim about superintelligence being lethal if built with modern methods, not a meta-claim that readers should hold extreme confidence after one book.
3. Harms rejects the idea that alignment will emerge “by default” from RLHF or similar methods, arguing these techniques select for proxy behaviors, overfit training contexts, and fail to robustly encode human values.
4. He contends that proposed future alignment solutions double-count existing methods, underestimate interpretability limits, and assume implausibly strong human verification of AI-generated alignment schemes.
5. The essay argues that “warning shots” are unlikely to mobilize timely global bans and may instead accelerate state-led races toward more dangerous systems.
6. Harms maintains that once an ambitious superintelligence exists, it is unlikely to lack the resources, pathways, or strategies needed to disempower humanity, even without overt warfare.

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Executive summary: Carlsmith argues that aligning advanced AI will require building systems that are capable of, and disposed toward, doing “human-like philosophy,” because safely generalizing human concepts and values to radically new situations depends on contingent, reflective practices rather than objective answers alone.

Key points:

1. The author defines “human-like philosophy” as the kind of reflective equilibrium humans would endorse on reflection, emphasizing that this may be contingent rather than objectively correct.
2. Philosophy matters for AI alignment because it underpins out-of-distribution generalization, including how concepts like honesty, harm, or manipulation extend to unfamiliar cases.
3. Carlsmith argues that philosophical capability in advanced AIs will likely arise by default, but that disposition—actually using human-like philosophy rather than alien alternatives—is the main challenge.
4. He rejects views that alignment requires solving all deep philosophical questions in advance or building “sovereign” AIs whose values must withstand unbounded optimization.
5. Some philosophical failures could be existential, especially around manipulation, honesty, or early locked-in policy decisions where humans cannot meaningfully intervene.
6. The author outlines research directions such as training on top-human philosophical examples, scalable oversight, transparency, and studying generalization behavior to better elicit human-like philosophy.

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Executive summary: The author argues, tentatively and speculatively, that a US-led international AGI development project could be a feasible and desirable way to manage the transition to superintelligence, and sketches a concrete but uncertain design intended to balance monopoly control, safety, and constraints on any single country’s power.

Key points:

1. The author defines AGI as systems that can perform essentially all economically useful human tasks more cheaply than humans, and focuses on projects meaningfully overseen by multiple governments, especially democratic ones.
2. Compared to US-only, private, or UN-led alternatives, an international AGI project could reduce dictatorship risk, increase legitimacy, and enable a temporary monopoly that creates breathing room to slow development and manage alignment.
3. The core desiderata are political feasibility, a short-term monopoly on AGI development, avoidance of single-country control over superintelligence, incentives for non-participants to cooperate, and minimizing irreversible governance lock-in.
4. The proposed design (“Intelsat for AGI”) centers on a small group of founding democratic countries, weighted voting tied to equity with the US holding 52%, bans on frontier training outside the project, and strong infosecurity and distributed control over compute and model weights.
5. The author argues the US might join due to cost-sharing, talent access, supply-chain security, and institutional checks on power, while other countries would join to avoid disempowerment if the US otherwise developed AGI alone.

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Executive summary: The author argues that because highly powerful AI systems are plausibly coming within 20 years, carry a non-trivial risk of severe harm under deep uncertainty, and resemble past technologies where delayed regulation proved costly, policymakers should prioritize AI risk mitigation even at the cost of slowing development.

Key points:

1. The author claims it is reasonable to expect very powerful AI systems within 20 years given rapid recent capability gains, scaling trends, capital investment, and the possibility of sudden breakthroughs.
2. They suggest AI could plausibly have social impacts on the order of 5–20 times that of social media, making it a policy-relevant technology by analogy.
3. The author argues there is a reasonable chance of significant harm because advanced AI systems are “grown” via training rather than fully understood or predictable, creating fundamental uncertainty about their behavior.
4. They note that expert disagreement, including concern from figures like Bengio and Hinton, supports taking AI risk seriously rather than dismissing it.
5. The author highlights risks from power concentration, whether in autonomous AI systems or in humans who control them, even if catastrophic outcomes are uncertain.
6. They argue that proactive policy action, despite real trade-offs such as slower development, is likely preferable to reactive regulation later, drawing an analogy to missed early opportunities in social media governance.

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Executive summary: The author argues that consciousness is likely substrate-dependent rather than a mere byproduct of abstract computation, concluding that reproducing brain-like outputs or algorithms in machines is insufficient for consciousness without replicating key biological, dynamical, and possibly life-linked processes.

Key points:

1. The author critiques computational functionalism, arguing that reproducing brain computations or input–output behavior does not guarantee consciousness because brain processes are inseparable from their biological substrate.
2. Brain activity involves multi-scale biological, chemical, and metabolic dynamics that lack clear separation between computation and physical implementation, unlike artificial neural networks.
3. Claims that the brain performs non-Turing computations are questioned; the author argues most physical processes can, in principle, be approximated by Turing-computable models, making non-computability an unconvincing basis for consciousness.
4. Simulating the brain as a dynamical system differs fundamentally from instantiating it physically, just as simulating a nuclear explosion does not produce an actual explosion.
5. Temporal constraints of biological processing may be essential to conscious experience, suggesting that consciousness cannot be arbitrarily sped up without qualitative change.
6. The hypothesis that life itself may be necessary for consciousness is treated as speculative but persuasive, highlighting the deep entanglement of prediction, metabolism, embodiment, and self-maintenance in conscious systems.

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Executive summary: AWASH reports completing scoping research, engaging farmers through a national conference, and beginning a pilot egg disinfection intervention in Ghanaian tilapia hatcheries, with early progress suggesting both feasibility and potential for high welfare impact while further evaluation is underway.

Key points:

1. AWASH conducted scoping visits to eight Ghanaian fish farms, exceeding its initial target, which informed the decision to pilot egg disinfection as a high-impact intervention.
2. The organization presented at the Aquaculture Ghana Conference to 30–40 farmers, with survey respondents reporting the session as useful and helping AWASH build relationships with key stakeholders.
3. Egg disinfection was selected because juvenile fish have low survival rates (around 45–65%), are relatively neglected in welfare efforts, and existing research suggests survival could increase to 90% or more.
4. One large farm producing just under 1% of Ghana’s national tilapia output agreed to pilot the intervention, increasing both potential direct impact and social proof for wider adoption.
5. AWASH learned that leveraging trusted local relationships was critical for access to farms, and that initial timelines were overly ambitious given scoping and seasonal constraints.
6. Next steps include monitoring the three-month pilot, continuing stakeholder engagement, and researching alternative interventions in case evidence supports a pivot.

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