Executive summary: The post argues that job applications hinge on demonstrated personal fit rather than general strength, and offers practical advice on how to assess, communicate, and improve that fit throughout the hiring process.

Key points:

1. The author defines fit as how well a person’s experience, qualifications, and preferences match a specific role at a specific organization.
2. The author says hiring managers seek someone who meets their particular needs, making role-specific fit more important than general impressiveness.
3. The author argues that applicants must show aptitude, culture fit, and excitement to demonstrate they are a “safe bet.”
4. The author recommends proactively addressing likely concerns about fit in application materials and interviews.
5. The author highlights the importance of telling a clear story that explains a candidate’s background and why it suits the role.
6. The author advises avoiding common errors such as ignoring red flags, being vague about excitement, stuffing keywords, or emphasizing irrelevant accomplishments.
7. The author suggests being strategic about where to apply by evaluating whether one can make a convincing case for fit.
8. The author notes that applicants should also consider whether each role fits them in terms of enjoyment, growth, and impact.

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Executive summary: The author argues in a speculative but plausible way that psychiatric drug trials obscure real harms and benefits because they use linear symptom scales that compress long-tailed subjective intensities, causing averages to hide large individual improvements and large individual deteriorations.

Key points:

1. The author claims psychiatric symptoms have long-tailed intensity distributions where high ratings like “9” reflect states far more extreme than linear scales imply.
2. The author argues that clinical trials treat symptom changes arithmetically, so very steep increases in states like akathisia can be scored as equivalent to mild changes in other domains.
3. The author states that mixed valence creates misleading cancellations: improvements in shallow regions of one symptom can be outweighed by worsening in steep regions of another even if numerical scores net to zero.
4. The author suggests average effect sizes such as “0.3 standard deviations” can emerge from populations where a substantial minority gets much worse while others get modestly better.
5. The author claims that disorders like depression or psychosis and medications like SSRIs, antipsychotics, and benzodiazepines all show this pattern of steep-region side-effects being compressed by standard scales.
6. The author recommends mapping individual response patterns, tracking steep regions explicitly, and using criticality and complex-systems tools instead of linear aggregation when evaluating psychiatric drugs.

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Executive summary: The author reflects on how direct contact with insects and cows during a field ecology course exposed a gap between their theoretical views on animal welfare and the felt experience of real animals.

Key points:

1. The author describes killing an insect by accident and contrasts the instant physical harm with the slow formation of their beliefs about animal welfare.
2. The author recounts using focal animal sampling on cows and finding that written behavioral transcripts failed to convey the richness of the actual encounters.
3. The author argues that abstract images of animal suffering are built from talks, videos, conversations, and biology rather than real memories, which removes crucial detail and context.
4. The author claims this abstraction makes it harder to care about individual animals, easier for trivial motives to override welfare considerations, and more likely to prompt self-evaluation rather than empathy.
5. The author questions whether beliefs about animal welfare formed mainly through theory may function poorly in practice and suggests that direct experience might help.

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Executive summary: The author argues that rationalist AI safety narratives are built on philosophical and epistemological errors about knowledge, creativity, and personhood, and that AI progress will continue in a grounded, non-catastrophic way.

Key points:

1. The rationalist AI safety view mistakes pattern recognition for personhood, assuming minds can “emerge” from scaling LLMs, which the author compares to spontaneous generation.
2. Following David Deutsch, the author defines persons as “universal explainers” capable of creative explanation rather than data extrapolation, a process current AI systems cannot perform.
3. Drawing on Karl Popper, the author argues forecasting the growth of knowledge is impossible in principle because future explanations cannot be derived from existing ones.
4. Scaling LLMs does not yield AGI, since pattern recognition lacks explanatory creativity; true AGI would require philosophical breakthroughs about mind and knowledge.
5. A genuine AGI would be a moral person deserving rights and cooperation, not control, since attempts to dominate intelligent beings historically lead to conflict.
6. The notion of an “evil superintelligence” contradicts itself: a mind superior in understanding should also surpass humans morally if its reasoning is sound.
7. Proposed AI regulation often benefits incumbent labs and risks stifling innovation by concentrating power and freezing competition.
8. Doom narratives persist because they are emotionally and narratively compelling, unlike the more likely scenario of steady, human-centered progress.
9. Future AI will automate narrow tasks, augment human creativity, and improve living standards without replacing humans or creating existential catastrophe.
10. Rationalist AI safety’s core mistake is philosophical: creativity and moral understanding cannot emerge from scaling pattern recognizers, and real AGI, if achieved, would be a collaborator, not a threat.

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Executive summary: The author plans to donate $40,000 in 2025 to PauseAI US based on a largely unchanged view that AI misalignment is the biggest existential risk and that pausing frontier AI—ideally a global ban on superintelligence until proven safe—is the least-bad path, alongside updated concerns about non-alignment problems and AI-for-animals.

Key points:

1. Prioritization is mostly unchanged: existential risk is a big deal, AI misalignment risk is the biggest, and within AI x-risk, policy/advocacy is much more neglected than technical research.
2. The donation goal is to increase the chances of a global ban on developing superintelligent AI until it is proven safe; moratoria are preferred to “softer” safety regulations, though certain regulations (e.g., whistleblower protections, compute monitoring, GPU export restrictions) are still supported as useful steps, with public advocacy and leading-country regulations as intermediate goals.
3. There is no good plan: “pause AI” is judged the least-bad option; P(doom) is ~50%, and if humanity survives it will likely be due to luck.
4. Updates since last year include greater concern about “non-alignment problems” and a renewed view that “AI-for-animals” may be more cost-effective on the margin despite lower probability because it is highly neglected.
5. Confidence increased that we should pause frontier AI and that peaceful protests probably help; evidence on disruptive protests is mixed; trust standards are higher, with SFF the most trusted grantmaker.
6. 2025 giving: $40,000 to PauseAI US (valued for protests and messaging campaigns); positive views on MIRI (with a “stable preference bonus” and SFF match up to $1.3M) and Palisade (SFF match up to $900K); tentatively most favorable 501(c)(4) is ControlAI, with open questions about ARI, AI Policy Network, congressional campaigns, and Encode.

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Executive summary: The author argues that conventional research will not solve AI "non-alignment problems"—such as misuse, AI welfare, and moral error—before transformative AI arrives, and instead recommends focusing on strategies that raise the odds these problems get solved, especially pausing AI development.

Key points:

1. Non-alignment problems are distinct from technical alignment and include misuse, S-risks, AI welfare, and moral error.
2. Given short timelines, traditional research is unlikely to make enough progress on these problems.
3. The author proposes four alternative strategies: meta-research, pausing AI, developing human-level assistants first, and steering ASI toward solving non-alignment issues.
4. Meta-research helps clarify approaches but yields diminishing returns if not followed by action.
5. Pausing AI is considered the strongest option since companies ignore non-alignment issues, though it may not increase humanity’s capacity to solve them.
6. Developing human-level AI could help with philosophical and ethical preparation but risks rapid escalation to superintelligence before readiness.
7. Steering ASI to "solve philosophy" faces major obstacles: unclear training signals, lack of researchers, and low likelihood of company adoption.
8. Overall, the author favors a pause despite doubts about its feasibility or effectiveness.

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Executive summary: From Fauna argues that narrative work—particularly viral video storytelling—is the most neglected and high-leverage way to secure public support for cultivated meat, which is currently losing the cultural narrative to misinformation and political backlash.

Key points:

1. From Fauna is a nonprofit producing short-form videos to counteract misinformation and fear about cultivated meat, which dominates online narratives and has fueled bans in seven U.S. states and multiple countries.
2. The group’s videos have reached over 1.75 million views and 160,000 likes across TikTok, YouTube, and Instagram within 100 days of launch, achieving high engagement with minimal output and budget.
3. They estimate that 99% of cultivated meat funding (~$80M) goes to science and policy while under 1% supports narrative or communication work, leaving public perception dangerously under-addressed.
4. Each video costs under $500 to produce and can reach hundreds of thousands of viewers, which they argue makes storytelling a uniquely cost-effective intervention compared to traditional advocacy.
5. They seek $15K–$285K in funding to scale production, hire video staff, expand multilingual reach, and measure sentiment impact, with the lowest tier matched 1:1 and sustaining operations through early 2026.
6. The organization acknowledges uncertainty in predicting virality and platform algorithms but plans redundancy, cross-platform diversification, and data-driven impact tracking to mitigate these risks.

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Executive summary: The author argues that animal welfare concerns should be dominated by post–artificial superintelligence (ASI) futures in which humans survive, since even well-aligned outcomes under a coherent extrapolated volition (CEV) framework could still allow large amounts of animal suffering depending on how human values are extrapolated and implemented.

Key points:

1. The author posits a future where an ASI aligned to humanity’s CEV governs outcomes, and explores how this could affect animal welfare.
2. They note that CEV relies on extrapolated human preferences, which may vary widely and depend on arbitrary features of the extrapolation procedure.
3. They highlight that some humans’ extrapolated volitions might include preserving natural ecosystems with live, unmodified animals, which could perpetuate animal suffering.
4. The author reviews Eliezer Yudkowsky’s framing of CEV and Bostrom’s parliamentary model, including Thomas Cederborg’s critique that the “random-dictator” baseline empowers harmful or “troll” agents.
5. They argue that even principled alignment approaches may still yield futures with animal suffering, given the difficulty of principled trade-offs and coherence in value aggregation.
6. The best prospects for animal welfare improvement, they suggest, lie in avoiding unaligned ASI and refining philosophical and meta-philosophical understanding to better specify extrapolation procedures.
7. The author concludes that while these outcomes are unsatisfying, animal welfare’s fate likely hinges on how successfully alignment and value extrapolation are handled in post-ASI futures.

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Executive summary: The author, broadly supportive of Effective Altruism, argues that strict impartial hedonistic utilitarianism risks absurd or alienating conclusions and proposes a partial, multi-circle ethics that prioritizes humans (and possibly sentient AIs) while still caring about animals, grounding value in flourishing as well as pleasure.

Key points:

1. The author endorses EA practices (e.g., donations, longtermism, AI risk) but is unconvinced by some forms of utilitarianism, especially pure hedonistic versions.
2. They argue that reasoning which treats wild invertebrate welfare as overwhelmingly dominant can be a rationalization and may lead to unacceptable conclusions.
3. They propose “moral circles” that prioritize humans in the innermost circle (and sentient AIs if/when applicable), then farm animals, then wild animals, while affirming concern for all.
4. The author claims partiality grounded in love, loyalty, reciprocity, social contracts, and fairness can be ethically relevant alongside consequences.
5. They suggest valuing flourishing (skills, health, meaning, art) in addition to pleasure and pain, contending this supports prioritizing humans without dismissing animal flourishing.
6. For practical giving, they recommend serious, non-negotiable commitments (e.g., a 10% pledge) go first to human charities and existential risk reduction, with additional giving to animals as desired, while noting this approach has the downside of potentially neglecting outer circles too long.

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Executive summary: Reflective argument that early-career entrants face steep, credential-heavy barriers in biosecurity and should get candid guidance that many impactful roles require several years of training and experience, alongside clearly mapped faster paths for exceptional cases.

Key points:

1. Intro resources and short projects abound (e.g., BlueDot Impact, 80,000 Hours, ERA’s AI x Bio, Non-Trivial Fellowship, Pivotal Research), including 8-to-12 week research stints.
2. The step from learning/projects to full-time roles is harder than expected, with advice and opportunities skewed toward mid- to late-career people.
3. Competitive programs and placements often require or prefer graduate degrees or years of experience (e.g., ELBI, Fellowship for Ending Bioweapons, Horizon).
4. Small, high-impact orgs and policy roles prioritize immediate contribution and thus select for proven skills and prior career capital.
5. Common advice to skip additional degrees or jump straight to impact conflicts with observed hiring patterns, producing confusion and short-term role churn.
6. The author calls for honesty and a coherent strategy: many should build expertise first (e.g., 3 years on the Hill, 5–10 years for MD/JD/PhD or engineering), while clearly mapping quicker routes for rare high-agency cases.

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