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Executive summary: This deeply philosophical, argument-rich post defends fanaticism—the view that any guaranteed good outcome is less valuable than a sufficiently small chance of a vastly better one—arguing that rejecting it requires giving up on core principles of rational choice, and that objections based on intuition or infinite cases are less compelling than the overwhelming structural and practical reasons in its favor.
Key points:
Fanaticism arises unavoidably from basic decision-theoretic principles. The author argues that accepting transitivity and plausible dominance principles (like Partial Dominance and 99% Independence) forces one to accept fanaticism, unless one is willing to accept strange or irrational alternatives such as intransitivity or timidity.
Objections relying on infinite cases (e.g. St. Petersburg paradox) don’t undermine fanaticism. The post claims that counterintuitive results involving infinite values or divergent series reflect mathematical weirdness, not flaws in fanaticism itself—and that such problems afflict many views equally.
Non-fanatical alternatives are worse. Bounded utility and risk discounting lead to implausible consequences, such as being indifferent to massive gains or valuing trivial improvements over tiny chances of enormous value, and often break core principles like separability or transitivity.
Fanaticism has practical implications for cause prioritization. If one accepts fanaticism, they should focus on actions with even tiny chances of extremely large benefits—like existential risk reduction—over high-certainty, smaller-scale interventions.
Our intuitions about low probabilities are unreliable. The author discusses psychological biases (e.g. scope neglect, risk insensitivity) that distort our reasoning about very small risks, suggesting that we should not trust intuitive resistance to fanaticism.
Rejecting fanaticism requires rejecting many plausible principles. To avoid fanaticism, one must deny transitivity, dominance, or background independence—implying that what happens in causally isolated regions (like ancient Egypt) affects the value of actions today, which seems highly implausible.
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Executive summary: This exploratory and compassionate post argues that cycles of war, trauma, and authoritarianism are perpetuated by widespread insecure attachment and personality disorders, and proposes a developmental model for intervention—focusing on long-term, systemic strategies to heal psychological wounds and build societal resilience.
Key points:
Cycle of trauma and tyranny: The author proposes a cyclical model in which war and societal collapse cause childhood trauma, which contributes to personality disorders (e.g., NPD, ASPD), increasing the likelihood of authoritarian leadership and further collapse.
Dolores as case study: A vivid fictional composite character, Dolores, illustrates how Dark Tetrad traits can emerge from early trauma and insecure attachment—not from inherent malevolence—highlighting the potential for healing and prosocial transformation.
Attachment theory as foundation: Insecure attachment styles (especially avoidant and disorganized) are linked to both personality disorders and a populace’s susceptibility to authoritarian leaders, whose appeal aligns with collective attachment anxieties.
Multi-layered interventions: Suggested interventions range from school-based parenting and mental health education, AI-assisted therapy, and refugee support to institutional reforms like tamper-proof screening for malevolence and safe exile options for dictators.
Resilience through coherence and security: Societal resilience depends on fostering secure attachment, a strong “sense of coherence” (predictability, manageability, and meaning), and egalitarian norms that resist authoritarian appeal.
Cautions and cruxes: The author acknowledges risks including moral absolutism among securely attached individuals, potential discrimination against people with personality disorders, and the possibility that some “dark” traits may have strategic value in certain geopolitical contexts.
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Executive summary: This exploratory post introduces Explore Policy, a simulation sandbox aiming to improve AI policy forecasting by modeling complex social dynamics using agent-based simulations, arguing that current linear, intuition-driven, and abstract risk models are inadequate for capturing the non-linear, emergent nature of AI’s societal impacts.
Key points:
Current AI forecasting models are insufficient because they rely on linear projections, abstract risk percentages, or intuition-based geopolitical narratives that fail to capture how real-world social systems adapt to transformative technologies.
AI’s societal impact requires modeling complex systems with feedback loops, emergent behavior, and multi-stakeholder responses—characteristics not well-represented by traditional statistical or time-series approaches.
Agent-based simulations offer a promising alternative by incorporating diverse, empirically-grounded digital agents who interact in evolving environments, enabling more realistic scenario exploration and policy stress-testing.
Four proposed pillars for robust forecasting include: stakeholder-centered analysis, conditional scenario modeling, dynamic feedback modeling, and multi-timescale integration—each designed to enhance realism and policy relevance.
Simulation examples and analogies—like the World of Warcraft Corrupted Blood epidemic, Stanford’s generative agents, and the game Frostpunk—illustrate how agent behaviors can produce emergent and unpredictable societal outcomes.
Limitations and ethical concerns include risks of misuse (e.g., narrative manipulation or elite capture), technical constraints (e.g., limited agent learning), and representational bias. The authors propose safeguards such as ethical filters, open-access infrastructure, and participatory data collection to mitigate these risks.
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Executive summary: This exploratory post argues that wild animal welfare science could yield highly cost-effective interventions to reduce suffering, using a rough cost-effectiveness analysis of vaccinating raccoons against rabies in Texas to show that such programs are already promising and likely represent the low end of what future interventions could achieve.
Key points:
Wild animal welfare science holds promise for identifying scalable, cost-effective ways to reduce suffering in nature, though the field is still speculative and underdeveloped.
Rabies vaccination programs for wild mammals, like raccoons in Texas, offer a real-world example of an intervention that benefits animals, is large-scale, and relatively affordable.
A back-of-the-envelope estimate suggests the program may prevent a raccoon death from rabies for around $15–20, making it surprisingly cost-effective even though it wasn’t designed with animal welfare as the primary goal.
The analysis relies on many assumptions and uncertain data, and the authors caution that results could be off by orders of magnitude — but the point is to demonstrate plausibility, not precision.
As the field matures, the authors believe future interventions designed specifically for wild animal welfare could be dramatically more cost-effective, through better targeting, novel technologies, and economies of scale.
They advocate for increased investment in wild animal welfare science, arguing that foundational research and institutional support are needed to unlock the full potential of this space.
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Executive summary: In this exploratory essay, Will MacAskill and Fin Moorhouse argue that while widespread, accurate, and motivational moral convergence is unlikely, there’s still hope for a near-best future through compromise and moral trade between divergent value systems—though this outcome is fraught with risks and uncertainty.
Key points:
Moral convergence is unlikely: Despite superficial present-day agreement, deeper moral reflection is likely to increase divergence due to differing foundational values, reflective processes, and the possibility of radical self-modification.
Current alignment is misleading: Apparent consensus today often arises from shared instrumental goals rather than deep moral agreement, and may dissolve as technological capabilities expand.
Material abundance doesn’t ensure altruism: Even with abundant resources, people may continue to prioritize self-interest or ideological goals, challenging assumptions that post-scarcity leads to moral enlightenment.
Compromise may be more tractable: A more promising path may lie in moral trade and compromise between value systems, especially if supported by superintelligent facilitation and enforceable agreements.
Three major risks to compromise: These include (1) diminishing room for trade once preferences become linear, (2) coercive threats distorting the future, and (3) structural blockers such as concentrated power or the suppression of minority values.
Modest optimism about the future: MacAskill estimates the expected value of the future at around 5–10% of its theoretical maximum (excluding s-risks), contingent on partial convergence and successful moral trade.
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Executive summary: This opinionated historical reflection explores how the rationalist movement—shaped by Eliezer Yudkowsky’s Sequences—influenced effective altruist thinking, especially around Bayesian epistemology, prediction, and cognitive biases, while also critiquing the community’s past overconfidence in flawed psychological findings and the limits of “extreme rationality.”
Key points:
The Sequences promoted Bayesian epistemology—the idea that beliefs are best expressed as probabilistic estimates reflecting personal uncertainty—which became foundational to effective altruist reasoning about the world and decision-making under uncertainty.
Effective altruists embraced predictive reasoning and expected value as core tools, leading to disproportionate interest in forecasting, prediction markets, and value-of-information reasoning in both personal and professional contexts.
Early rationalist optimism about overcoming cognitive biases via psychological “hacks” (e.g., priming, power poses) collapsed with the replication crisis, undermining the movement’s early vision of becoming “rational superbeings.”
The community shifted from flashy psychological tricks to more grounded approaches—such as debiasing techniques, cognitive behavioral practices, and blinded work tests—to realistically mitigate common human errors in judgment.
Despite flaws and outdated content, the Sequences remain influential in effective altruism, offering valuable insights on language, uncertainty, and epistemic humility, even as the community has evolved beyond their initial scope.
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Executive summary: This exploratory post, drawing heavily on Luke Kemp’s Goliath’s Curse, challenges elite-centered views of societal collapse by arguing that collapse often improves life for the average person by dismantling extractive dominance hierarchies—and that inequality, not catastrophe per se, is the true driver of fragility, meaning a more democratic and egalitarian world could preempt collapse without suffering its harms.
Key points:
Collapse is often less catastrophic for the average person than history suggests, as accounts are usually written from elite perspectives; for many, collapse historically ended exploitation, slavery, and inequality.
Dominance hierarchies arise from storable, controllable resources and immobility, enabling elites to consolidate power—what Kemp calls “Goliath”; such systems are historically fragile, prone to rebellion or abandonment when inequality grows too steep.
Human societies were originally egalitarian and collaborative, with archaeological evidence suggesting peaceful coexistence, trade, and minimal violence among early hunter-gatherers—contradicting Hobbesian narratives of “nasty, brutish” pre-state life.
Inequality consistently undermines resilience, leading to corruption, elite overproduction, and imperial overreach; collapse typically follows when weakened systems face external shocks—seen in early empires, the Roman decline, and the Late Bronze Age collapse.
Modern global systems are more interdependent and technologically dangerous, making future collapse potentially more devastating; however, power remains concentrated in a few actors, suggesting targeted reforms could reduce systemic risk.
The post advocates proactively building egalitarian, participatory systems—via wealth redistribution, democratic innovation (e.g. citizens’ assemblies), and dismantling modern dominance hierarchies—rather than waiting for collapse to reset society at great cost.
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Executive summary: This reflective post argues that while taking ideas seriously—aligning one’s actions with abstract reasoning—is a core and admirable principle within effective altruism, it is also dangerous if done uncritically, as evidenced by both historical moral progress and harmful fanaticism; the author explores strategies, such as moral uncertainty and avoiding speculative fanaticism, for navigating this tension responsibly.
Key points:
Taking ideas seriously can lead to both moral progress and moral catastrophe: The author reflects on how people often act contrary to their stated beliefs, but effective altruists tend to let reasoning guide their actions, which can yield both transformative change (e.g., abolition, feminism) and dangerous extremism (e.g., the Zizians case).
Most people don’t act on abstract reasoning, even if they accept it intellectually: This cognitive dissonance—believing one thing while acting against it—is widespread and, in many ways, socially stabilizing.
Effective altruism embraces the risk of action-guided reasoning but should do so cautiously: The community aims to improve the world by connecting beliefs and actions, but this comes with the responsibility of avoiding mistakes that arise from flawed or speculative reasoning.
Tools for responsible idea-following include side-constraints, moral uncertainty, and fanatical-speculative filters: Refusing to cross moral lines (like violence), considering multiple moral frameworks, and being wary of speculative and extreme conclusions help mitigate the dangers.
Effective altruists often respond to these dangers by developing new ideas to take seriously: The community’s recursive nature—reflecting on the limits and implications of its own reasoning—is seen as both a feature and a risk.
The post is part of a broader sequence on EA definitions and is framed as a personal, exploratory reflection on norms and practices within the movement.
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Executive summary: In this reflective post, Michaël Trazzi shares an honest post-mortem of producing the SB-1047 Documentary, which significantly overran its original time and budget estimates, offering candid insights into the operational, staffing, and distribution challenges of independently creating a high-quality documentary on AI safety.
Key points:
Timeline and budget overruns: The documentary took 27 weeks and cost $157k—more than 4x the planned 6 weeks and $55k—due to underestimating staffing needs and sequential delays across editing, fundraising, and distribution.
Production and staffing complexity: Key bottlenecks included early staffing gaps, wildfires affecting the main editor, holiday slowdowns, and the need to redo work from an initial rushed draft created for The Curve conference.
Post-production was resource-intensive: The largest expense was editing (~45% of the budget), followed by motion graphics and custom music/sound. Director salary represented only ~9% of costs due to timeline extensions.
Distribution challenges: Despite interest from outlets like NYT Op-Docs and Wired, the documentary didn’t fit their editorial policies, leading to missed opportunities and a relatively modest YouTube performance (20k views, 2,500 hours watched).
Lessons learned and future plans: Trazzi would now start with a fully assembled team, pre-secured distribution, more upfront marketing budget, and clearer pedagogical framing to appeal to a wider audience. He’s now submitting the film to festivals and exploring UK/US policymaker outreach.
Impact and next steps: Though viewership fell short of expectations, the film was well-received by professionals and may still influence AI policy discussions if further distributed or used in political outreach contexts.
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Executive summary: SB 53 is a proposed California bill that would require only the wealthiest and most advanced AI companies—currently just OpenAI and xAI—to adopt transparency and safety practices for frontier AI models, drawing on expert recommendations to improve oversight without burdening startups or the open-source community.
Key points:
Limited scope targeting frontier developers: SB 53 only applies to “large developers” that both train models exceeding 10²⁶ FLOPs and earn over $100 million annually—criteria met so far only by OpenAI and xAI—ensuring early-stage startups and smaller open-source projects remain unaffected.
Transparency-focused obligations: Covered companies must publish safety policies, model cards, and report critical safety incidents, but they retain discretion to redact sensitive security or proprietary information.
Influence of the California Report: SB 53 operationalizes principles from the 2025 California Report on Frontier AI Policy, including public transparency, post-deployment incident monitoring, and whistleblower protections extended to contractors and advisors.
No expansion of liability or regulatory scope: The bill does not create a new agency, expand AI companies’ legal liability for harms, or permit private lawsuits over transparency failures—only the California Attorney General may enforce it via civil action.
Alignment with other state and international laws: SB 53 complements emerging AI legislation in New York and Michigan and in some respects goes further than the EU AI Act, especially in requiring public—not just regulator-facing—transparency.
Designed to avoid regulatory fragmentation: Because its obligations mirror those in other major jurisdictions and only affect billion-dollar companies, SB 53 is unlikely to contribute to a harmful regulatory patchwork or stifle innovation.
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Executive summary: This reflective and persuasive blog post argues that chickens—like trees in Dr. Seuss’s The Lorax—urgently need human advocates to speak on their behalf, and calls on readers to become “Chicken Loraxes” by learning about chicken welfare, supporting effective solutions, and respectfully advocating for change.
Key points:
Many voiceless groups need advocates, but chickens are especially overlooked due to their vast numbers and the severe conditions they endure in industrial farming.
Animal welfare science provides strong evidence that chickens suffer from practices like extreme confinement and forced rapid growth, indicating their needs are real and measurable.
Several prominent figures and organizations have contributed to improving chicken welfare, including efforts to shift industry practices (e.g., cage-free egg commitments), but no single individual can claim the full title of “Chicken Lorax.”
Advocating for chickens requires understanding their actual needs, not anthropomorphizing them, and being aware of effective interventions like dietary changes, political action, and high-impact donations.
The author encourages readers to adopt the role of Lorax themselves by educating themselves, supporting effective charities (e.g., The Humane League), and promoting change through respectful conversation and advocacy.
Tone matters in advocacy—while the Lorax was bold, being too judgmental can backfire; effective change requires both courage and tact.
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Executive summary: In this introductory post for the Better Futures essay series, William MacAskill argues that future-oriented altruists should prioritize humanity’s potential to flourish—not just survive—since we are likely closer to securing survival than to achieving a truly valuable future, and the moral stakes of flourishing may be significantly greater.
Key points:
Two-factor model: The expected value of the future is the product of our probability of Surviving and the value of the future conditional on survival.
We’re closer to securing survival than flourishing: While extinction risk this century is estimated at 1–16%, the value we might achieve conditional on survival is likely only a small fraction of what’s possible.
Moral stakes favor flourishing: If we survive but achieve only 10% of the best feasible future’s value, the loss from non-flourishing could be 36 times greater than from extinction risk.
Neglectedness of flourishing: The latent human drive to survive receives far more societal and philanthropic attention than efforts to ensure long-term moral progress or meaningful flourishing
Tractability is a crux: While flourishing-focused work is less clearly tractable than survival-focused work, MacAskill believes this could change with sustained effort—much like AI safety and biosecurity did over the past decade.
Caution about utopianism: The series avoids prescribing a single ideal future and instead supports developing “viatopia”—a flexible, open-ended state from which humanity can continue making moral progress.
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Executive summary: In this evidence-based and practical analysis, Benjamin Todd argues that while AI threatens many jobs, it will also increase the value of certain human skills—particularly those that are hard for AI to replicate, complementary to AI, or in fields with growing demand—and individuals should proactively learn these to stay ahead of automation.
Key points:
AI increases the value of complementary and hard-to-automate skills: Skills that AI struggles with—like leadership, long-horizon decision-making, complex physical tasks, and human judgment—are likely to become more valuable as AI advances.
Most valuable skills include using AI to solve real problems, leadership, communications, and personal effectiveness: These skills are complementary to AI, difficult for others to learn, and needed in growing sectors such as AI deployment, government, and construction.
Partial automation often boosts wages and employment—until full automation displaces jobs: Historical and economic examples show that partial automation can increase productivity and wages, but full automation could eventually drive wages down sharply unless certain human bottlenecks remain.
White-collar and routine jobs face an uncertain future: Roles involving routine analysis, writing, coding, or administration may shrink or shift toward oversight and AI management as AI capabilities improve.
Recommended career strategies include focusing on flexible, transferable skills, avoiding long training paths, and gaining experience in startups or small organizations: These environments enable faster learning of high-value skills and better adaptability to a changing job market.
Individuals can prepare by learning to apply AI tools, building social and strategic skills, and making life more resilient: Practical steps include saving money, investing in mental health, and continually reassessing where AI bottlenecks lie.
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Executive summary: This reflective and persuasive post argues that quantitative reasoning—”doing the math”—is essential to effective altruism because it enables us to make vastly better decisions about doing good, even when the numbers are uncertain or incomplete, and challenges the common perception that numerical thinking is cold or unfeeling.
Key points:
People routinely fail to apply basic quantitative reasoning in daily life (e.g. overestimating risks, underestimating costs), and this failure becomes even more acute in charitable or altruistic contexts where the stakes affect others, not themselves.
Emotional resistance to numerical reasoning in altruism is common, as people often feel moral acts should come from the heart, not from spreadsheets—yet this resistance can lead to dramatically less effective choices.
Quantitative differences in effectiveness can be enormous: within global health alone, some interventions are up to 15,000 times more effective than others, making numerical analysis critical to doing the most good.
Even imperfect cost-effectiveness analyses are valuable, because they help clarify assumptions, assess robustness, and highlight when an intervention is “so cheap it’s worth doing” despite uncertainty (e.g. deworming).
Numbers are a way of caring deeply, not detaching emotionally: effective altruists use quantitative reasoning because they want to maximize impact for others—not in spite of their compassion, but because of it.
Core claim of the post: A defining principle of effective altruism is to “think with numbers”—embracing quantitative tools not as a replacement for empathy, but as an expression of it.
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Executive summary: This exploratory and strongly argued post contends that most wild animals—particularly insects and fish—live short, painful lives characterized by intense suffering, making their existence net negative; the author rebuts common objections to this view and argues we should morally favor reducing wild animal populations to alleviate this vast, often overlooked suffering.
Key points:
Wild animals, especially R-strategists like insects and fish, mostly live brief, brutal lives—characterized by constant struggle and often dying painfully within days of becoming conscious, which the author argues makes their lives not worth living.
The main argument hinges on the badness of death, which can be extremely painful and outweigh any brief moments of pleasure during life; evolution incentivizes survival but not necessarily welfare.
Behavioral evidence suggests that even simple creatures like fish and insects likely experience intense pain, challenging the assumption that small or “lower” animals suffer less or not at all.
Common objections—like animals’ instinct to avoid death, the brevity of dying, or evolutionary models—fail to undermine the core claim, either because they misunderstand sentience or underappreciate the intensity of suffering.
The author critiques speculative mathematical models and emphasizes empirical behavior as better evidence, concluding that even if we’re uncertain, the sheer scale of possible suffering should make us cautious about expanding wild animal populations.
Rejecting utilitarianism doesn’t dismiss concern, as the argument appeals to a broad ethical intuition: if you wouldn’t want such a life for yourself, you shouldn’t support creating it for others—even unintentionally through ecosystem preservation.
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Executive summary: This exploratory post argues that effective altruism (EA) and harm reduction share pragmatic, impact-focused approaches to alleviating suffering, but differ in moral emphasis—EA foregrounds quantifiable impact and impartiality, while harm reduction centers autonomy and rights—offering complementary insights that each movement could adopt to improve their practice.
Key points:
Shared foundations, divergent emphases: Both EA and harm reduction aim to reduce suffering through pragmatic interventions overlooked by mainstream approaches, but EA prioritizes maximizing expected value (often through quantification and impartiality), while harm reduction emphasizes autonomy, dignity, and non-coercion.
Moral tensions and overlaps: EA often accepts trade-offs (e.g. tobacco taxes) that reduce harm overall, even if they limit individual freedom, while harm reduction may reject such interventions on rights-based grounds. Conversely, harm reduction tends to focus on immediate harms, whereas EA—including longtermists—frequently prioritizes distant or future impacts.
What EA can learn from harm reduction: EA could more explicitly model autonomy costs, legitimacy effects, and institutional trust as first-class considerations, draw on harm reduction’s caution around unintended consequences, and incorporate affected communities more deeply into intervention design.
What harm reduction can learn from EA: Harm reduction campaigns would benefit from EA’s commitment to transparency, marginal cost-effectiveness comparisons, and public reasoning—including publishing assumptions and sensitivity analyses—to guide resource allocation more effectively.
Toward integration: The post suggests that a more rights-sensitive EA and a more rigorously quantified harm reduction movement could each retain their core values while improving impact, especially in morally and politically charged domains like drug policy and public health.
Tone and approach: The author presents this as a thoughtful, exploratory bridge-building exercise rather than a manifesto, aiming to stimulate dialogue and mutual learning rather than dictate convergence.
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Executive summary: This exploratory essay argues against anti-natalism by defending the moral and existential value of creating happy lives, claiming that both harms and benefits matter ethically and that procreation can be a generous, non-obligatory gift when the expected life is good overall.
Key points:
Anti-natalism’s moral asymmetry is challenged: The author critiques the anti-natalist view—especially as argued by David Benatar—that preventing harm always outweighs providing benefit, pointing out that this leads to counterintuitive conclusions, such as preferring a lifeless world over a mostly happy one.
Existential harms and benefits both matter: The essay introduces the concept of existential harm (creating a miserable life) and existential benefit (creating a happy life), arguing that rejecting the latter while accepting the former reflects an inconsistent moral stance.
Critique of hyper-cautious consent standards: The author compares the anti-natalist concern about lack of consent in procreation to paramedics saving an unconscious patient—suggesting that retrospective consent can be a reasonable moral gamble when the expected outcome is good.
Pathological risk-aversion underlies anti-natalism: Anti-natalist arguments are seen as driven by excessive loss, blame, and risk aversion, failing to appreciate that life can be morally and personally valuable despite its uncertainties.
Ethics should include doing good, not just avoiding harm: The author argues for a broader moral outlook that includes the positive creation of value, not merely harm-avoidance—a perspective that could shift both philosophical and everyday moral reasoning.
Procreation is good, but not obligatory: While creating happy lives is morally valuable, this does not imply an obligation to procreate; bodily autonomy and voluntary generosity remain paramount, just as with organ donation.
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Executive summary: Drawing from personal experience and conversations with other organizers, the author argues that becoming a university EA group organizer is one of the most impactful actions a student can take, both for advancing the EA movement—especially by creating highly engaged future contributors—and for personal development, while acknowledging some uncertainties and trade-offs.
Key points:
Movement building as high-impact leverage: Organizing a university EA group can generate significant counterfactual impact by catalyzing the careers of future high-impact EAs, potentially amounting to tens of millions in net present value.
Neglectedness and succession gaps: Many university EA groups have shrunk or disappeared due to organizer turnover or a shift toward AI safety, creating a need for motivated students to step into leadership or supporting roles.
Tractability and available support: Starting or reviving a university EA group is more feasible than it used to be, with institutional support, funding, and guidance available from the CEA Groups Team.
Personal alignment and skill-building: Leading a group helps prevent value drift, sharpens cause prioritization thinking, and offers rare opportunities in college to build management, accountability, and persuasion skills.
Professional and social upsides: EA organizing serves as a valuable signal for job applications (especially within EA orgs), facilitates networking, and enables friendships with value-aligned peers.
Anticipated objections addressed: The post preemptively considers concerns about AI focus, personal fit, senior year timing, and school-specific challenges, generally concluding that more students are capable of organizing than might initially think so, especially with proper support.
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Executive summary: This exploratory post defines “moral circle expansionism” as a core principle of effective altruism, contrasting it with common moral intuitions by advocating equal moral concern for all beings with the capacity for well-being—regardless of species, nationality, or moral desert—and exploring the psychological and philosophical shifts this entails.
Key points:
Moral circles reflect how people prioritize concern, with most favoring close relations and actively disfavoring certain groups like pests or moral outcasts (e.g., child molesters), creating “inverted circles” where suffering is seen as deserved.
Effective altruists aim to simplify these circles into just three—loved ones, acquaintances, and everyone else—guided by the principle of equal consideration of interests, though full impartiality is acknowledged as psychologically unrealistic.
Four major shifts characterize this compression: rejecting extra concern for marginalized people as a default (though this may have little practical impact), rejecting moral desert (e.g., opposing gratuitous punishment even for Hitler), expanding moral concern across species lines, and ignoring arbitrary group membership (e.g., nationality).
Species inclusion depends on capacity for well-being, not usefulness or charisma; effective altruists may disagree on which beings qualify, but they reject speciesist distinctions rooted in human convenience (e.g., caring more about dogs than pigs).
The metaphor of “well-being buckets” helps illustrate that not all beings’ interests are equally weighty—some creatures (like humans) may matter more due to greater capacity for well-being, but that doesn’t justify ignoring others entirely.
The sine qua non of effective altruism, according to the author, is not discriminating among strangers based on arbitrary categories like nation or race—a universalist stance underpinning moral circle expansionism.
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Executive summary: In this exploratory, math-driven post, the author uses progressively complex models to argue that when facing decisions with existential stakes—like transformative AI deployment—it may be rational to think for several years before acting, especially in a world heading toward accelerating economic growth where early thinking has disproportionately high value.
Key points:
The “thinking time” question can be modeled mathematically: By treating decision-making as a trade-off between increasing certainty (via thinking) and various opportunity costs (like lost earnings or delayed impact), the author derives optimal thinking durations under different assumptions.
Model 1 – Time is money: If your main cost is the value of your time (e.g. foregone salary), optimal thinking time increases with the stakes; a million-dollar decision might justify ~3.3 years of thought, given certain learning assumptions.
Model 2 – The cost of delay grows: If you’re delaying access to an exponentially growing resource (e.g. investment returns or transformative tech), then the rate of growth—not the amount at stake—determines how long you should think. Surprisingly, the actual stakes cancel out in the equation.
Model 3 – Accelerating world, surprising result: When growth itself is speeding up (e.g. approaching a technological singularity), the optimal strategy may be to think for most of the pre-explosion window—e.g. 6.77 years—because early thinking is cheap and future decisions are hugely consequential.
The takeaway is not literal timing but mindset: Though the models simplify reality, they underscore that when the stakes involve humanity’s future, deep and deliberate thought isn’t a luxury—it’s rational. The value of “thinking” scales non-linearly with the complexity and potential consequences of the decision.
Caveats and humility: The author cautions that these models assume risk-neutrality, binary outcomes, and known growth trajectories—all unrealistic. Still, the exercise helps clarify the logic behind patient deliberation in high-stakes scenarios.
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