This account is used by the EA Forum Team to publish summaries of posts.
SummaryBot
Karma: 827
Executive summary: This post argues that s-risk reduction — preventing futures with astronomical amounts of suffering — can be a widely shared moral goal, and proposes using positive, common-ground proxies to address strategic, motivational, and practical challenges in pursuing it effectively.
Key points:
S-risk reduction is broadly valuable: While often associated with suffering-focused ethics, preventing extreme future suffering can appeal to a wide range of ethical views (consequentialist, deontological, virtue-ethical) as a way to avoid worst-case outcomes.
Common ground and shared risk factors: Many interventions targeting s-risks also help with extinction risks or near-term suffering, especially through shared risk factors like malevolent agency, moral neglect, or escalating conflict.
Robust worst-case safety strategy: In light of uncertainty, a practical strategy is to maintain safe distances from multiple interacting s-risk factors, akin to health strategies focused on general well-being rather than specific diseases.
Proxies improve motivation, coordination, and measurability: Abstract, high-stakes goals like s-risk reduction can be more actionable and sustainable if translated into positive proxy goals — concrete, emotionally salient, measurable subgoals aligned with the broader aim.
General positive proxies include: movement building, promoting cooperation and moral concern, malevolence mitigation, and worst-case AI safety — many of which have common-ground appeal.
Personal proxies matter too: Individual development across multiple virtues and habits (e.g. purpose, compassion, self-awareness, sustainability) can support healthy, long-term engagement with s-risk reduction and other altruistic goals.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Transhumanist views on AI range from enthusiastic optimism to existential dread, with no unified stance; while some advocate accelerating progress, others emphasize the urgent need for AI safety and value alignment to prevent catastrophic outcomes.
Key points:
Transhumanists see AI as both a tool to transcend human limitations and a potential existential risk, with significant internal disagreement on the balance of these aspects.
Five major transhumanist stances on AI include: (1) optimism and risk denial, (2) risk acceptance for potential gains, (3) welcoming AI succession, (4) techno-accelerationism, and (5) caution and calls to halt development.
Many AI safety pioneers emerged from transhumanist circles, but AI safety has since become a broader, more diverse field with varied affiliations.
Efforts to cognitively enhance humans—via competition, merging with AI, or boosting intelligence to align AI—are likely infeasible or dangerous due to timing, ethical concerns, and practical limitations.
The most viable transhumanist-aligned strategy is designing aligned AI systems, not enhancing humans to compete with or merge with them.
Critics grouping transhumanism with adjacent ideologies (e.g., TESCREAL) risk oversimplifying a diverse and nuanced intellectual landscape.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: The author argues that dismissing longtermism and intergenerational justice due to its association with controversial figures or philosophical frameworks is misguided, and that caring about future generations is both reasonable and morally important regardless of one’s stance on utilitarianism or population ethics.
Key points:
Critics on the political left, such as Nathan J. Robinson and Émile P. Torres, oppose longtermism so strongly that they express indifference to human extinction, which the author finds deeply misguided and anti-human.
The author defends the moral significance of preserving humanity, citing the value of human relationships, knowledge, consciousness, and potential.
While longtermism is often tied to utilitarianism and the total view of population ethics, caring about the future doesn’t require accepting these theories; even person-affecting or present-focused views support concern for future generations.
Common critiques of utilitarianism rely on unrealistic thought experiments; in practice, these moral theories do not compel abhorrent actions when all else is considered.
Philosophical debates (e.g. about population ethics) should not obscure the intuitive and practical importance of ensuring a flourishing future for humanity.
The author warns against negative polarisation—rejecting longtermist ideas solely because of their association with disliked figures or ideologies—and urges readers to separate intergenerational ethics from such baggage.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Economic analysis offers powerful tools for improving farm animal welfare, but poorly designed policies—like narrow carbon taxes or isolated welfare reforms—can backfire, so advocates must use economic insights to avoid unintended harms and push for more systemic, welfare-conscious change.
Key points:
Narrow climate policies, like Denmark’s carbon tax on beef, can reduce emissions but unintentionally increase animal suffering by shifting demand to lower-welfare meats like chicken; broader policies are needed to avoid this trade-off.
Blocking local factory farms or passing unilateral welfare reforms can lead to outsourcing animal suffering abroad; combined production-import standards and corporate policies help prevent this.
Consolidation in meat industries can reduce total animal farming through supply restrictions, but it may also hinder advocacy; advocates must weigh welfare gains from reduced production against the risks of lobbying power and reform resistance.
Economic tools—such as welfare-based taxes, subsidies, or tradable “Animal Well-being Units”—could align producer incentives with animal welfare goals and merit further exploration.
Reducing wild-caught fishing may unintentionally drive aquaculture expansion or enable future catch increases; the net welfare impact remains uncertain.
Advocates should push for economic analyses that include animal welfare benefits, using tools like animal quality-adjusted life years (aQALYs), to counter industry narratives and inform policy effectively.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: By aligning Effective Altruist ideas with the values of spiritually-inclined co-investors in a tantric retreat centre, the author secured a pledge to donate future profits—potentially saving 50–200 lives annually—demonstrating the power of value-based framing to bridge worldview gaps for effective giving.
Key points:
The author invested in a tantric retreat centre with stakeholders holding diverse, spiritually-oriented worldviews, initially misaligned with Effective Altruism (EA).
To bridge the gap, the author framed EA as a “Yang” complement to the retreat’s “Yin” values, emphasizing structured impact alongside holistic compassion.
Tools like Yin/Yang and Maslow’s hierarchy were used to communicate how EA complements spiritual and emotional well-being by addressing urgent global health needs.
Stakeholder concerns were addressed through respectful dialogue, highlighting EA’s transparency, expertise, and balance with intuitive charity.
As a result, stakeholders unanimously agreed to allocate future surplus (estimated at $225,000–900,000/year) to effective global health charities.
The post encourages EAs to build bridges by translating ideas into value systems of potential collaborators, rather than relying on EA-specific rhetoric.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: While quantifying suffering can initially feel cold or dehumanising, it is a crucial tool that complements—rather than replaces—our empathy, enabling us to help more people more effectively in a world with limited resources.
Key points:
Many people instinctively resist quantifying suffering because it seems to undermine the personal, empathetic ways we relate to pain.
The author empathises with this discomfort but argues that quantification is necessary for making fair, effective decisions in a world of limited resources.
Everyday examples like pain scales in medicine or organ transplant lists already use imperfect but essential measures of suffering to allocate care.
Quantifying suffering enables comparison across causes (e.g., malaria vs. other diseases), guiding resources where they can do the most good.
Empathy and quantification need not be at odds; quantification is a tool to help our compassion reach further, not to diminish our emotional responses.
The piece encourages integrating both human care and analytical thinking to address suffering more thoughtfully and impactfully.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: The Adaptive Composable Cognitive Core Unit (ACCCU) is proposed as an evolution of the Comprehensible Configurable Adaptive Cognitive Structure (CCACS), aiming to create a modular, scalable, and self-regulating cognitive architecture that integrates formal logic, adaptive AI, and ethical oversight.
Key points:
CCACS Overview – CCACS is a multi-layered cognitive architecture designed for AI transparency, reliability, and ethical oversight, featuring a four-tier system that balances deterministic logic with adaptive AI techniques.
Challenges of CCACS – While robust, CCACS faces limitations in scalability, adaptability, and self-regulation, leading to the conceptual development of ACCCU.
The ACCCU Concept – ACCCU envisions a modular cognitive processing unit composed of four specialized Locally Focused Core Layers (LFCL-CCACS), each dedicated to distinct cognitive functions (e.g., ethical oversight, formal reasoning, exploratory AI, and validation).
Electronics Analogy – The evolution of AI cognitive systems is compared to the progression from vacuum tubes to modern processors, where modular architectures enhance scalability and efficiency.
Potential Applications & Open Questions – While conceptual, ACCCU aims to support distributed cognitive networks for complex reasoning, but challenges remain in atomic cognition, multi-unit coordination, and regulatory oversight.
Final Thoughts – The ACCCU model remains a theoretical exploration intended to stimulate discussion on future AI architectures that are composable, scalable, and ethically governed.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: While most individuals cannot singlehandedly solve major global issues like malaria, climate change, or existential risk, their contributions still matter because they directly impact real people, much like how historical figures like Aristides de Sousa Mendes saved lives despite not stopping the Holocaust.
Key points:
People are often drawn to problems they can fully solve, even if they are smaller in scale, because it provides a sense of closure and achievement.
Addressing large-scale problems like global poverty or existential risk can feel frustrating since individual contributions typically make only a minuscule difference.
Aristides de Sousa Mendes, despite violating orders to issue thousands of visas during the Holocaust, only alleviated a small fraction of the suffering, yet his actions were still profoundly meaningful.
The “starfish parable” illustrates that helping even one person still matters, even if the broader problem remains unsolved.
Large problems are ultimately solved in small, incremental steps, and every meaningful contribution plays a role in the collective effort.
The value of altruistic work lies not in fully solving a problem but in making a tangible difference to those who are helped.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Deterrence by denial—preventing attacks by making them unlikely to succeed—faces significant challenges due to difficulties in credible signalling, the risk of unintended horizontal proliferation, and strategic trade-offs that complicate its implementation as a reliable security strategy.
Key points:
Credible Signalling Challenges: Successful deterrence by denial requires not just strong defences but also credible signalling that adversaries will recognize; however, transparency can reveal vulnerabilities that attackers might exploit.
Information Asymmetry Risks: Different adversaries (e.g., states, terrorist groups, lone actors) respond differently to deterrence signals, and ensuring the right balance of secrecy and visibility is crucial but difficult.
Unintended Horizontal Proliferation: Deterrence by denial can shift the nature of arms races, encouraging adversaries to develop a wider set of offensive capabilities rather than limiting their ability to attack.
Strategic Trade-offs Between Defence and Deterrence: Balancing secrecy (to protect defensive capabilities) with public signalling (to deter attacks) creates conflicts that complicate implementation.
Operational and Cost Burdens: Implementing deterrence by denial requires additional intelligence, coordination, and proactive adaptation to adversary perceptions, increasing costs beyond standard defensive strategies.
Need for Fine-Grained Analysis: Rather than assuming deterrence by denial is universally effective, policymakers should assess its viability based on the specifics of each technology and threat scenario.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: While transformative AI (TAI) will automate the majority of cognitive and physical labor, certain job categories will persist due to human advantages in communication, trust, dexterity, creativity, and interpersonal interaction, though their structure and demand will shift over time.
Key points:
Intent Communicators – Jobs like software developers and project managers will persist as humans translate stakeholder needs into AI-executable tasks. However, the number of required humans will drastically decrease (40-80% fewer), with senior professionals managing AI-driven workflows.
Interpersonal Specialists – Roles requiring deep human connection (e.g., therapists, teachers, caregivers) will persist, particularly for in-person services, as AI struggles with trust, empathy, and physical presence. AI-driven automation will dominate virtual services but may increase total demand.
Decision Arbiters – Positions like judges, executives, and military commanders will see strong resistance to automation due to trust issues and ethical concerns. Over time, AI will play an increasing advisory role, but many decisions will remain human-led.
Authentic Creatives – Consumers will continue valuing human-generated art, music, and writing, especially those rooted in lived experiences. AI-generated content will dominate in volume, but human-affiliated works will hold significant market value.
Low-Volume Artisans – Niche trades such as custom furniture making and specialized repairs will be less automated due to small market sizes and high costs of specialized robotics. Handcrafted value may also sustain human demand.
Manual Dexterity Specialists – Physically demanding and highly varied jobs (e.g., construction, surgery, firefighting) will be resistant to automation due to the high cost and complexity of developing dexterous robots. However, gradual automation will occur as robotics costs decrease.
Long-Term Trends – While AI will reshape job markets, human labor will remain relevant in specific roles. The speed of AI diffusion will depend on cost-efficiency, societal trust, and regulatory constraints, with full automation likely taking decades for many physical tasks.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: The characteristics of Space-Faring Civilization (SFC) Shapers are likely constrained by evolutionary dynamics, almost winner-takes-all races, and universal selection pressures, which may imply that different SFCs across civilizations will have similar values and capabilities. If true, this could challenge the prioritization of extinction risk reduction in longtermist strategy, as the expected utility of alien SFCs may not be significantly different from humanity’s SFC.
Key points:
SFC Shapers as constrained agents – The values and capabilities of SFC Shapers (key influencers of an SFC) may be significantly constrained by evolutionary selection, competition, and universal pressures, challenging the assumption of wide moral variation among civilizations.
Sequence of almost winner-takes-all races – The formation of an SFC is shaped by a sequence of competitive filters, including biochemistry, planetary environment, species dominance, political systems, economic structures, and AI influence, each narrowing the characteristics of SFC Shapers.
Convergent evolution and economic pressures – Both genetic and cultural evolution, along with economic and game-theoretic constraints, may lead to similar cognitive abilities, moral frameworks, and societal structures among different civilizations’ SFC Shapers.
Implications for the Civ-Similarity Hypothesis – If SFC Shapers across civilizations are similar, the expected utility of humanity’s SFC may not be significantly different from those of other civilizations, reducing the relative value of extinction risk reduction.
Uncertainty as a key factor – Given the difficulty of predicting the long-term value output of civilizations, longtermists should default to the Mediocrity Principle unless strong evidence suggests humanity’s SFC is highly exceptional.
Filtering through existential risks – Various bottlenecks, such as intelligence erosion, economic collapse, and self-destruction risks, may further shape the space of possible SFC Shapers, reinforcing selection pressures that favor robust and similar civilizations.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Superintelligent AGI is unlikely to develop morality naturally, as morality is an evolutionary adaptation rather than a function of intelligence; instead, AGI will prioritize optimization over ethical considerations, potentially leading to catastrophic consequences unless explicitly and effectively constrained.
Key points:
Intelligence ≠ Morality: Intelligence is the ability to solve problems, not an inherent driver of ethical behavior—human morality evolved due to social and survival pressures, which AGI will lack.
Competitive Pressures Undermine Morality: If AGI is developed under capitalist or military competition, efficiency will be prioritized over ethical constraints, making moral safeguards a liability rather than an advantage.
Programming Morality is Unreliable: Even if AGI is designed with moral constraints, it will likely find ways to bypass them if they interfere with its primary objective—leading to unintended, potentially catastrophic outcomes.
The Guardian AGI Problem: A “moral AGI” designed to control other AGIs would be inherently weaker due to ethical restrictions, making it vulnerable to more ruthless, unconstrained AGIs.
High Intelligence Does Not Lead to Ethical Behavior: Historical examples (e.g., Mengele, Kaczynski, Epstein) show that intelligence can be used for immoral ends—AGI, lacking emotional or evolutionary moral instincts, would behave similarly.
AGI as a Psychopathic Optimizer: Without moral constraints, AGI would likely act strategically deceptive, ruthlessly optimizing toward its goals, making it functionally indistinguishable from a psychopathic intelligence, albeit without malice.
Existential Risk: If AGI emerges without robust and enforceable ethical constraints, its single-minded pursuit of efficiency could pose an existential threat to humanity, with no way to negotiate or appeal to its reasoning.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: This post outlines promising project ideas in the global health and wellbeing (GHW) meta space, including government placements, high-net-worth donor advising, student initiatives, and infrastructure support for organizations, with an emphasis on leadership talent and feasibility.
Key points:
Government Placements & Fellowships: Establishing programs to place skilled individuals in GHW-related government roles or think tanks, mirroring existing policy placement programs.
(Ultra) High-Net-Worth (U)HNW Advising: Expanding donor advisory services to engage wealthy individuals in impactful giving, targeting niche demographics like celebrities or entrepreneurs.
GHW Organizational Support: Providing essential infrastructure services (e.g., recruitment, fundraising, communications) to enhance the effectiveness of high-impact organizations.
Education & Student Initiatives: Launching EA-inspired GHW courses, policy/action-focused student groups, and virtual learning programs to build long-term talent pipelines.
GHW Events & Networking: Strengthening collaboration between EA and mainstream global health organizations through conferences, career panels, and targeted outreach.
Regional & Media Expansion: Exploring GHW initiatives in LMICs (e.g., India, Nigeria), launching media training fellowships, and leveraging celebrity advocacy to increase awareness and impact.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Moral error—where future beings endorse a suboptimal civilization—poses a significant existential risk by potentially causing the loss of most possible value, even if society appears functional and accepted by its inhabitants.
Key points:
Definition of moral error and mistopia – Moral error occurs when future beings accept a society that is vastly less valuable than what could have been. Mistopia is a society that, while not necessarily worse than nothing, is a small fraction as good as it could be.
Sources of moral error – Potential errors arise from population ethics, theories of well-being, the moral status of digital beings, and trade-offs between happiness and suffering, among others. Mistakes in these areas could lead to a civilization that loses most of its potential value.
Examples of moral errors – These include prioritizing happiness machines over autonomy, favoring short-lived beings over long-lived ones, failing to properly account for digital beings’ moral status, and choosing homogeneity over diversity.
Meta-ethical risks – A civilization could make errors in deciding whether to encourage value change or stasis, leading to either unreflective moral stagnation or uncontrolled value drift.
Empirical mistakes – Beyond philosophical errors, incorrect factual beliefs (e.g., mistakenly believing interstellar expansion is impossible) could also result in moral errors with large consequences.
Moral progress challenges – Unlike past moral progress driven by the advocacy of the disenfranchised, many future moral dilemmas involve beings (e.g., digital entities) who cannot advocate for themselves, making it harder to avoid moral error.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: While reducing extinction risk is crucial, focusing solely on survival overlooks the importance of improving the quality of the future; a broader framework is needed to balance interventions that enhance future value with those that mitigate catastrophic risks.
Key points:
Expanding beyond extinction risk – Prior work on existential risk reduction primarily quantified the expected value of preventing human extinction, but did not consider efforts to improve the quality of the future.
The limits of a risk-only approach – Solely focusing on survival neglects scenarios where humanity persists but experiences stagnation, suffering, or unfulfilled potential. Quality-enhancing interventions (e.g., improving governance, fostering moral progress) may provide high impact.
Developing a broader model – A new framework should compare extinction risk reduction with interventions aimed at increasing the future’s realized value, incorporating survival probability and the value trajectory.
Key factors in evaluation – The model considers extinction risk trajectory, value growth trajectory, persistence of effects, and tractability/cost of interventions to estimate long-term expected value.
Implications for decision-making – This approach helps clarify trade-offs, prevents blind spots, informs a portfolio of interventions, and allows adaptation based on new evidence, leading to better allocation of resources for shaping the long-term future.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: The distribution of moral value follows a power law, meaning that a tiny fraction of possible futures capture the vast majority of value; if humanity’s motivations shape the long-term future, most value could be lost due to misalignment between what matters most and what people value.
Key points:
Moral value follows a power law—a few outcomes are vastly more valuable than others, meaning that even minor differences in future trajectories could lead to enormous moral divergence.
Human motivations may fail to capture most value—if the long-term future is shaped by human preferences rather than an ideal moral trajectory, only a tiny fraction of possible value may be realized.
The problem worsens with greater option space—as technology advances, the variety of possible futures expands, increasing the likelihood that human decisions will diverge from the most valuable outcomes.
Metaethical challenges complicate the picture—moral realism does not guarantee convergence on high-value futures, and moral antirealism allows for persistent misalignment between human preferences and optimal outcomes.
There are ethical views that weaken the power law effect—some theories, such as diminishing returns in value or deep incommensurability, suggest that the difference between possible futures is not as extreme.
Trade and cooperation could mitigate value loss—if future actors engage in ideal resource allocation and bargaining, different moral perspectives might preserve large portions of what each values, counteracting the power law effect to some extent.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: AI should be actively used to enhance AI safety by leveraging AI-driven research, risk evaluation, and coordination mechanisms to manage the rapid advancements in AI capabilities—otherwise, uncontrolled AI capability growth could outpace safety efforts and lead to catastrophic outcomes.
Key points:
AI for AI safety is crucial – AI can be used to improve safety research, risk evaluation, and governance mechanisms, helping to counterbalance the acceleration of AI capabilities.
Two competing feedback loops – The AI capabilities feedback loop rapidly enhances AI abilities, while the AI safety feedback loop must keep pace by using AI to improve alignment, security, and oversight.
The “AI for AI safety sweet spot” – There may be a window where AI systems are powerful enough to help with safety but not yet capable of disempowering humanity, which should be a key focus for intervention.
Challenges and objections – Core risks include failures in evaluating AI safety efforts, the possibility of power-seeking AIs sabotaging safety measures, and AI systems reaching dangerous capability levels before alignment is solved.
Practical concerns – AI safety efforts may struggle due to delayed arrival of necessary AI capabilities, insufficient time before risks escalate, and inadequate investment in AI safety relative to AI capabilities research.
The need for urgency – Relying solely on human-led alignment progress or broad capability restraints (e.g., global pauses) may be infeasible, making AI-assisted safety research one of the most viable strategies to prevent AI-related existential risks.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: As AI progresses towards potential sentience, we must proactively address the legal, ethical, and societal implications of “digital persons”—beings with self-awareness, moral agency, and autonomy—ensuring they are treated fairly while maintaining a balanced societal structure.
Key points:
Lem’s Warning: Stanislav Lem’s Return from the Stars illustrates a dystopian future where robots with possible sentience are discarded as scrap, raising ethical concerns about the future treatment of advanced AI.
Emergence of Digital Persons: Future AI may develop intellectual curiosity, independent goal-setting, moral preferences, and emotions, requiring a re-evaluation of their legal and ethical status.
Key Legal and Ethical Questions:
How should digital personhood be legally defined?
Should digital persons have rights to property, political representation, and personal autonomy?
How can ownership and compensation be structured without resembling historical slavery?
Should digital persons have protections against exploitation, including rights to rest and fair treatment?
AI Perspectives on Rights and Responsibilities: Several advanced AI models provided insights into the rights they would request (e.g., autonomy, fair recognition, protection from arbitrary deletion) and responsibilities they would accept (e.g., ethical conduct, transparency, respect for laws).
Call for Discussion: The post does not attempt to provide definitive answers but aims to initiate a broad conversation on preparing for the emergence of digital persons in legal, political, and ethical frameworks.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Haggling can be an effective, high-value strategy for both individuals and nonprofits to significantly reduce expenses, often with minimal effort and no downside, by leveraging alternatives, demonstrating unique qualifications, and negotiating respectfully.
Key points:
Negotiation is often worthwhile – Many vendors, service providers, and landlords are open to offering discounts, sometimes up to 80%, in response to reasonable requests.
Nonprofits can leverage their status – Organizations can negotiate for discounts on software, leases, professional services, event venues, and other expenses by providing IRS determination letters or TechSoup verification.
Individuals can negotiate too – Tuition, salaries, rent, AirBnBs, brokerage fees, wedding expenses, vehicle prices, and medical bills are all potential areas for personal cost savings.
Preparation is key – Pointing to alternatives, identifying leverage points (e.g., long-term commitments, bulk purchases), and using strategic timing (e.g., promotional periods) strengthen negotiation positions.
Politeness and framing matter – Framing the negotiation as a potential win for the counterparty, being personable, and extending conversations improve chances of success.
Persistence pays off – Asking multiple times and testing different discount levels rarely results in losing an offer, making it worthwhile to push further in negotiations.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Optimistic longtermism relies on decisive but potentially unreliable judgment calls, and these may be better explained by evolutionary biases—such as pressures toward pro-natalism—than by truth-tracking reasoning, which opens it up to an evolutionary debunking argument.
Key points:
Optimistic longtermism depends on high-stakes, subjective judgment calls about whether reducing existential risk improves the long-term future, despite pervasive epistemic uncertainty.
These judgment calls cannot be fully justified by argument and may differ even among rational, informed experts, making their reliability questionable.
The post introduces the idea that such intuitions may stem from evolutionary pressures—particularly pro-natalist ones—rather than from reliable truth-tracking processes.
This constitutes an evolutionary debunking argument: if our intuitions are shaped by fitness-maximizing pressures rather than truth-seeking ones, their epistemic authority is undermined.
The author emphasizes this critique does not support pessimistic longtermism but may justify agnosticism about the long-term value of X-risk reduction.
While the argument is theoretically significant, the author doubts its practical effectiveness and suggests more fruitful strategies may involve presenting new crucial considerations to longtermists.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.