Executive summary: The author argues that identifying and focusing only on bottlenecks—while deliberately not optimizing other parts—can produce disproportionately large gains in real output, even when it feels inefficient.

Key points:

1. The author learned from Goldratt’s The Goal that a system’s output is entirely determined by its slowest component (the bottleneck).
2. Improvements to bottlenecks translate directly into system-wide gains, while improvements to non-bottlenecks have effectively zero impact on output.
3. In the Tanzania M&E team, the author realized they were the bottleneck, producing only 3 reports per year despite much higher data collection capacity.
4. Increasing field team productivity did not increase recommendations, and managing that team actually worsened the bottleneck by consuming the author’s time.
5. The author constrained upstream work (pausing surveys until analysis caught up), which reduced activity but aligned the system with the bottleneck.
6. Despite discomfort and apparent inefficiency (e.g., idle staff), this shift freed time for analysis and increased the team’s actual output of recommendations.
7. Targeted improvements at the bottleneck—hiring one analyst and simplifying reports—produced large gains (roughly 50% more output for ~5% budget increase).
8. In another case, the author argues that spending far more on excess inputs (buying 500 bottles instead of 5) can be rational if it removes a bottleneck that delays high-value outcomes.
9. The author emphasizes that optimizing non-bottlenecks can feel productive but often creates waste or distraction, and may even worsen performance.
10. Correctly identifying the bottleneck is critical, and the author notes uncertainty and error in practice (e.g., later realizing regulatory approval was the true bottleneck in the vaccine example).

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Executive summary: The author argues that people can shift society toward a stable “cooperative equilibrium” by publicly rewarding altruistic actions, even if it requires initial sacrifice, because others will adapt and reinforce the norm over time.

Key points:

1. The author contrasts “Selfishland,” where individually rational selfish behavior leads to worse collective outcomes, with “Altruisticland,” where people reward altruism and achieve higher cumulative utility.
2. In Altruisticland, people financially reward actions that benefit others, creating incentives to act altruistically when benefits exceed personal costs.
3. The current world is between these extremes, with some incentives (markets, laws) but persistent under-rewarding of public goods, knowledge creation, and risk mitigation.
4. The main barrier is equilibrium: if others act selfishly, individuals lack incentive to act altruistically, creating a stable but suboptimal state.
5. The author claims more advanced game theory (e.g., reputation dynamics, Bayesian learning) implies equilibria can shift if enough մարդիկ change strategies and others update in response.
6. Early adopters must bear an “altruistic sacrifice,” but the author argues this can pay off if the cooperative equilibrium is reached and sustained.
7. The expected value of switching increases if there is a non-trivial chance of very long lifespans (e.g., via LEV), since long-term benefits dominate short-term costs.
8. To reduce risk, individuals can gradually increase altruism (e.g., slightly above average), limiting downside if others do not follow.
9. Imperfect observability and attribution can be mitigated with partial knowledge, decentralized funding mechanisms, and potentially future tools like prediction markets.
10. The system should remain decentralized to avoid power concentration, and individuals are encouraged to publicly reward good work, repeat this behavior, and promote the norm to build trust that altruism is rewarded.

Epistemic status: This is a speculative, normative proposal relying on assumptions about behavioral adaptation, future technology, and long-term incentives; key uncertainties include whether coordination dynamics will shift as described and whether sufficient adoption can occur.

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Executive summary: The authors argue that near-term AI-enabled “defense-favoured” coordination technologies could substantially improve collective decision-making and may be important for safely navigating advanced AI, but their impact is highly sensitive to design choices due to significant dual-use risks.

Key points:

1. The authors argue that AI could significantly improve coordination by enabling faster information processing, secure sharing of sensitive data, and scalable facilitation across groups.
2. They sketch six near-term coordination technologies—fast facilitation, automated negotiation, AI arbitration, background networking, structured transparency, and confidential monitoring—each with plausible pathways using current or near-term systems.
3. They claim improved coordination could yield large benefits such as higher economic productivity, reduced conflict, better democratic accountability, and safer handling of AI development pressures.
4. They emphasize that coordination technologies are dual-use and could enable harms like collusion, crime, coups, or erosion of prosocial norms, especially when confidentiality is involved.
5. They argue that “defense-favoured” design—carefully selecting implementations that mitigate misuse—is crucial, and that indiscriminate acceleration of coordination tech is risky.
6. They highlight cross-cutting enablers like AI delegates for preference elicitation and “charter tech” for analyzing governance systems, which could shape broader coordination outcomes.
7. They note that major challenges include technical limitations (e.g., alignment, security, reliability), trust and legal integration, privacy trade-offs, and political adoption barriers.
8. They suggest early experimentation, pilots, and evaluation infrastructure as valuable steps, both to improve the technologies and to influence how they are deployed.
9. They state uncertainty about which versions of coordination tech are net-positive, and explicitly call for more analysis of harms, benefits, and design choices.

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Executive summary: The author argues that effective foreign aid advocacy requires understanding that policymakers evaluate aid through geopolitical, value-based, and pragmatic lenses, and that even modest advocacy can influence decisions because the field is under-resourced.

Key points:

1. The author’s experience meeting Japanese and Korean lawmakers suggests policymakers are not indifferent but act as overburdened trustees trying to balance public opinion, judgment, and competing demands.
2. In-person engagement helps build relationships, reinforce local advocacy, and provide international validation despite limited staffing capacity.
3. Policymakers frequently ask how a proposed aid program fits within their country’s existing efforts and how it compares to other donors.
4. They assess geopolitical implications, including alignment with allies, competition with China, and opportunities to strengthen international relationships.
5. They care about domestic benefits, such as involvement of national businesses, universities, and citizens, and procurement from local suppliers.
6. They consider political feasibility, including positions of party leaders, coalition support, and public opinion backed by polling or constituency views.
7. They scrutinize funding justification, including why a specific contribution is needed and thresholds for maintaining influence (e.g., board seats or donor rank).
8. They look for evidence of success, progress toward solving the problem, and narratives of impact or recipient self-sufficiency.
9. Value-driven questions include how aid connects to lawmakers’ personal priorities, national history, current events, or domestic policy benefits.
10. Pragmatic concerns include whether relevant bureaucrats support the program, whether recipient governments request it, and how it fits budget structures.
11. Policymakers prioritize credible evidence and endorsements from trusted institutions, and check for consistency across sources.
12. Aid advocacy is highly underfunded (roughly $1–2 per $1,000 of aid), so even imperfect advocacy can have marginal impact, as illustrated by past successes like GAVI, debt relief campaigns, and sustained US global health funding.

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Executive summary: The author argues that, despite strong contrary intuitions, a sufficiently large number of very mild harms (like dust specks) is worse than a single extreme harm (like torture), and that rejecting this leads to more implausible commitments.

Key points:

1. The author claims critics misrepresent the “torture vs. dust specks” view by ignoring the underlying arguments, noting that several non-utilitarian philosophers also accept the conclusion.
2. The spectrum argument suggests that repeatedly trading a slightly less intense harm for vastly more instances leads, via replacement and transitivity, to the conclusion that many tiny harms can outweigh one severe harm.
3. Rejecting the replacement principle requires implausible commitments, such as that no number of slightly weaker pains can outweigh a stronger one even when scaled massively in number or duration.
4. Rejecting transitivity leads to further problems, including violations of dominance, vulnerability to money pumps, and counterintuitive implications about rational choice.
5. When principles conflict with case intuitions, the author argues we should generally trust broad principles over specific intuitions, since human intuitions are fallible and principles apply across many cases.
6. A risk-based argument (following Huemer) suggests that preventing many small harms is preferable to extremely tiny chances of preventing severe harm, which implies that sufficiently many small harms can outweigh a severe one.
7. A simple argument claims that infinitely many mild pains would be infinitely bad, while intense pain is not, implying that infinite mild pains are worse than one intense pain unless one accepts implausible views about infinite badness.
8. The author argues that opposition to the conclusion is driven by scope neglect, as humans systematically underestimate large quantities and therefore misjudge the cumulative badness of many small harms.

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Executive summary: The author argues that animal advocates should redirect their anger from blaming individuals to targeting systemic forces, because this “system failure” framing better supports coalition-building and effective change.

Key points:

1. The author claims anger is a natural and motivating response to animal suffering but has social and personal downsides if sustained or misdirected.
2. Suppressing or compartmentalizing anger limits authenticity, weakens internal discourse, and prevents using anger constructively.
3. Emotions like anger are shaped by underlying “stories,” which determine who or what we blame and how we act.
4. The “Story of Moral Failure” frames meat consumption as individual wrongdoing, casting vegans as moral actors and non-vegans as blameworthy.
5. The author argues this framing creates conflict with loved ones, triggers defensiveness, and discourages people from adopting veganism due to shame and identity costs.
6. This story also reinforces in-group/out-group dynamics, making collaboration and bridge-building harder.
7. It leads to a strategy focused on individual conversion, which the author suggests is unlikely to scale globally.
8. The author proposes an alternative “Story of System Failure,” which explains meat consumption as a product of entrenched cultural and institutional systems rather than individual moral failure.
9. This framing allows anger to be directed at abstract systems instead of individuals, making it easier for non-vegans to engage without immediate self-condemnation.
10. It supports coalition-building by uniting people around shared opposition to systemic harms rather than dividing them into moral camps.
11. The author argues this approach shifts activism toward policy change and systemic leverage points rather than mass personal conversion.
12. The author maintains that both stories contain truth, but choosing more constructive narratives can shape behavior, relationships, and movement effectiveness.

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Executive summary: The authors argue that AI systems should sometimes act as “good citizens” by proactively taking uncontroversial, context-sensitive prosocial actions beyond user instructions, and that this can yield large societal benefits without significantly increasing takeover risk if carefully designed.

Key points:

1. The authors argue that AI should not be purely corrigible or instruction-following but should sometimes proactively take actions that benefit people beyond the user.
2. They define “proactive prosocial drives” as behaviors that help others (not just the user) and involve active intervention rather than merely refusing harmful requests.
3. They claim the cumulative societal impact of such drives could be large as AI becomes more autonomous and embedded in economic and political systems.
4. They argue that refusals alone are insufficient, since positive impacts often come from proactively identifying and acting on opportunities to improve outcomes.
5. They suggest additional (weaker) benefits: reducing the risk of a “sociopathic” AI persona and potentially improving performance on alignment research tasks.
6. They acknowledge the concern that prosocial drives could let companies impose values, and propose limiting drives to uncontroversial actions and ensuring transparency about them.
7. They argue that prosocial drives need not increase takeover risk if implemented as virtues, rules, or heuristics rather than explicit outcome-optimizing goals.
8. They propose making these drives context-dependent so they activate only in relevant situations, reducing incentives for coordinated power-seeking.
9. They recommend making prosocial drives low-priority and subordinate to constraints like corrigibility, non-deception, and legality.
10. They suggest reducing long-horizon optimization for prosocial drives and optionally implementing them via system prompts for greater transparency and control.
11. They note a tradeoff: these safety mitigations may reduce the benefits of prosocial behavior, especially in novel situations.
12. They argue that prosocial drives can make it harder to interpret suspicious behavior as clear evidence of egregious misalignment, but this can be mitigated with narrow heuristics and strong prohibitions.
13. They propose a “best of both worlds” approach: use mostly corrigible AI internally (where misalignment risk is highest) and prosocial AI externally (where benefits are greatest).
14. They suggest an alternative strategy of initially deploying non-prosocial AI and later adding prosocial drives once alignment risks are lower, though they are not confident this is preferable.
15. They compare current policies, claiming Anthropic’s constitution allows limited prosocial behavior while OpenAI’s model spec is more restrictive and avoids treating societal benefit as an independent goal.

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Executive summary: The author argues that under deep AI timeline uncertainty, you should choose career strategies by expected value across scenarios—often favoring paths with higher upside in longer timelines—while balancing learning, limited deference to experts, and acting despite uncertainty.

Key points:

1. The author feels that radically uncertain AI timelines make long-term career planning feel incoherent, but inaction still guarantees zero impact.
2. They propose modeling career choices as expected value across different timeline scenarios, weighted by both probability and impact magnitude.
3. In their example, a slower, investment-heavy path outperforms a sprint approach because it yields much higher impact in medium timelines, even if short timelines are equally or more likely.
4. They argue that maximizing asymmetric upside (high-impact scenarios where you have leverage) can matter more than choosing the most probable future.
5. The author questions strict reliance on “personal fit,” suggesting many skills are more learnable and malleable than commonly assumed.
6. They cite evidence and examples (e.g., deliberate practice, career pivots) to argue that the space of skills one could acquire is large and flexible.
7. However, they note that believing everything is learnable can make the decision space overwhelming and paralyzing.
8. Timeline views can help constrain choices, with short timelines favoring immediately deployable skills and medium timelines favoring foundational investments.
9. Rather than committing to one timeline, individuals can diversify their skill sets across plausible futures.
10. The author argues that deferring entirely to experts on timelines is a false binary; one should understand expert reasoning while forming their own object-level views.
11. Developing independent understanding is instrumentally useful for research taste, decision-making, and impactful work.
12. They recommend increasing “surface area for luck,” revisiting assumptions, and combining calculation with action.
13. The author concludes that acting on an imperfect but robust plan across plausible futures is better than delaying action to seek certainty.

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Executive summary: The author, who previously expected aligned ASI to be good for all sentient beings through coherent extrapolated volition, now expresses uncertainty about whether current alignment approaches would achieve this, though estimates a 70% probability that aligned ASI would be good for animals.

Key points:

1. The author previously believed coherent extrapolated volition would lead aligned ASI to recognize and address animal suffering, but current alignment research has abandoned this approach.
2. Current alignment work using constitutions and RLHF locks in values like "virtues" rather than achieving coherent extrapolation, and it remains unclear how virtue ethics could be formalized into a coherent decision theory for ASI.
3. Claude's Constitution treats animal welfare as one value among many to weigh, leaving unclear whether an ASI following such a constitution would take action on issues like factory farming.
4. The author identifies a positive correlation between alignment techniques that actually work and those good for animals, suggesting barbell outcomes: either good for all sentient beings or bad for all.
5. The field prioritizes alignment techniques unlikely to work well long-term, and if these "streetlight effect" techniques somehow succeed, they would likely benefit humans but not animals.
6. The author estimates that aligned ASI has a 70% probability of being good for animals, derived from a 30% probability of "deep" solutions (80% animal-friendly) and a 15% probability of popular techniques (50% animal-friendly).

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Executive summary: CEA is restructuring the Community Building Grants program in 2026 by moving grant evaluation to EA Funds and phasing out non-monetary support while continuing to fund groups, in order to prioritize more scalable initiatives aligned with its strategic goal of reaching and raising EA's ceiling.

Key points:

1. CBG grant evaluation is moving from CEA's Groups team to EA Funds (which became part of CEA in summer 2025) and will be managed alongside but remain distinct from the EA Infrastructure Fund.
2. Non-monetary support is being phased out or transitioned; grantees have taken ownership of coordination calls and the Slack space, while regular check-ins, new CBG-specific resources, and the grantee retreat in its current form are being wound down.
3. The restructuring reflects CEA's strategic shift toward scalable products, as the CBG program's structure—dependent on diverse group approaches and leadership quality—cannot be replicated across locations.
4. The authors believe most CBG impact comes through grantmaking and can be preserved by phasing out programmatic support, which has required substantial team resources.
5. Funding for CBG groups continues with no expected changes to the funding bar; however, grantees will have less regular interaction with grantmakers and less insight into funding decisions.
6. The authors acknowledge trade-offs including potential loss of valued support for some grantees, possible difficulty recruiting and retaining community builders, reduced cross-group learning opportunities, and increased frustration from less transparent funding decisions.

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