Executive summary: The post argues, through metaphor and personal reflection, that individuals and institutions should invest in early-stage potential rather than select solely for proven performance, because nurturing undeveloped talent creates long-term value that harvesting only finished “gems” cannot.

Key points:

1. The author uses the Pien Ho parable to illustrate how valuable potential can be mistaken for an “ordinary stone” when judged only by immediate surface qualities.
2. The author argues that optimizing exclusively for proven talent leads to widespread underinvestment in developing people who could become highly valuable with support.
3. The author claims early-career programs should prioritize promise, drive, and character traits like kindness and responsibility over fully demonstrated performance.
4. The author notes that mentors and institutions often wish to support emerging talent but face resource constraints.
5. The author encourages prospective mentees to seek mentors who are caring, responsive, and growth-oriented rather than simply prestigious.
6. The author concludes that investing in latent potential benefits both individuals and the broader world, illustrated by the story of a friend whose promise was eventually recognized.

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Executive summary: The post outlines Giving Green’s updated research approach, its 2025–2026 philanthropic priorities and Top Climate Nonprofits, recent regranting decisions totaling $26 million, and plans to expand climate and biodiversity work as an independent organization.

Key points:

1. Giving Green states that systems change through policy, technology, and market-shaping is the most leveraged route for climate philanthropy, and cost-effectiveness analyses serve as supporting inputs rather than decisive metrics.
2. The organization argues that several high-impact climate areas remain neglected, citing aviation’s projected rise to over 20% of global CO₂ emissions by 2050 and noting that less than $15 million per year goes to mitigating aviation’s non-CO₂ effects.
3. Its 2025–2026 high-leverage giving strategies include clean energy in the U.S., aviation, maritime shipping, heavy industry, food systems, LMIC energy transitions, carbon dioxide removal demand, and solar radiation management governance.
4. For Q4 2025, the Giving Green Fund recommended $26 million to 29 nonprofits aligned with these strategies.
5. Planned 2026 work includes about $30 million in new grants and research on livelihood-improving climate interventions, catastrophic risks, overshoot, heavy industry, LMIC energy transitions, and food systems.
6. Giving Green is developing Top Biodiversity Nonprofits for 2026, focusing on preventing land use change and reducing ecosystem damage from fishing.
7. The organization became an independent nonprofit in late 2025, now hosts its own fund, and reports influencing over $56 million in climate donations since 2019 at an estimated 20x impact multiplier.

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Executive summary: The author argues that early-career people should prioritize building rare, valuable skills and becoming legible to others, rather than trying to immediately secure an “EA job,” and presents strategies for skill identification, testing fit, deliberate practice, and sustainable long-term growth.

Key points:

1. The post claims people should prioritize identifying an important problem, improving relevant skills, and becoming legible to others instead of treating “getting a job” as the milestone.
2. It argues many young applicants implicitly frame success as landing an EA role fast, which creates pressure and leads to distorted decisions.
3. It states that talent and impact are extremely right-tailed but malleable, and that deliberate practice and tight feedback loops accelerate growth.
4. It recommends studying top performers, reading job postings, having informational chats, and running small side projects to discover which skills matter most.
5. It describes “testing fit” through empirical exploration such as short projects, fellowships, internships, and conversations to gather signals about aptitude and motivation.
6. It emphasizes working in public, seeking criticism, and producing concrete artifacts (writing, GitHub projects, events) to improve faster and increase visibility.
7. It discusses burnout and imposter syndrome, noting the value of sustainable habits, calibrated comparisons, and roles that offer real skill-building.
8. It advises leaving roles with weak growth prospects or harmful work and expanding one’s “luck surface area” by building relationships and showing work publicly.
9. It concludes that long-term impact comes from getting good and being known, not from early job titles.

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Executive summary: The author argues that, given the moral weight of conscious experience and the role of luck in determining life circumstances, a voluntary simplicity pledge tied to the world’s average income lets them meet their ethical duties while still maintaining a balanced and meaningful life.

Key points:

1. The author claims conscious moments have intrinsic importance and that ignoring others’ suffering amounts to endorsing harmful systems.
2. The author argues most advantages and disadvantages in life stem from luck, so they do not view their own wealth as morally deserved.
3. The author states that effective donations can do large amounts of good, citing estimates of $3,000 to $5,500 per life saved and 126,000 cage-free years for chickens per equivalent spending.
4. The author describes voluntary simplicity research, citing Hook et al. (2021) as finding a consistent positive relationship between voluntary simplicity and well-being.
5. The author explains they set their salary to roughly the world’s average income adjusted for London (£26,400 in 2025) and donate earnings above that.
6. The author reports that living this way feels non-sacrificial, supports long-term financial security, and aligns their actions with their values while recognizing others’ differing circumstances.

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Executive summary: The author argues in an exploratory and uncertain way that alternative proteins may create large but fragile near-term gains for animals because they bypass moral circle expansion, and suggests longtermists should invest more in durable forms of moral advocacy alongside technical progress.

Key points:

1. The author claims alternative proteins can reduce animal suffering in the short term and may even end animal farming in the best case.
2. The author argues that consumers choose food mainly based on taste and price, so shifts toward alternative proteins need not reflect any change in values toward animals.
3. The author suggests that progress driven by incentives is vulnerable to economic or social reversals over decades or centuries.
4. The author argues that longtermist reasoning implies concern for trillions of future animals and that fragile gains from alternative proteins may not endure.
5. The author claims moral circle expansion is slow and difficult but more durable because it changes how people think about animals.
6. The author concludes that work on alternative proteins should continue but that moral advocacy may be underinvested in and deserves renewed attention.

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Executive summary: The post argues, in a reflective and deflationary way, that there are no deep facts about consciousness to uncover, that realist ambitions for a scientific theory of consciousness are confused, and that a non-realist or illusionist framework better explains our intuitions and leaves a more workable path for thinking about AI welfare.

Key points:

1. The author sketches a “realist research agenda” for identifying conscious systems and measuring valence, but argues this plan presumes an untenable realist view of consciousness.
2. They claim “physicalist realism” is unstable because no plausible physical analysis captures the supposed deep, intrinsic properties of conscious experience.
3. The author defends illusionism via “debunking” arguments, suggesting our realist intuitions about consciousness can be fully explained without positing deep phenomenal facts.
4. They argue that many consciousness claims are debunkable while ordinary talk about smelling, pain, or perception is not, because realist interpretations add unjustified metaphysical commitments.
5. The piece develops an analogy to life sciences: just as “life” is not a deep natural kind, “consciousness” may dissolve into a cluster of superficial, scientifically tractable phenomena.
6. The author says giving up realism complicates grounding ethics in intrinsic valence, but maintains that ethical concern can be redirected toward preferences, endorsement, or other practical criteria.
7. They argue that AI consciousness research should avoid realist assumptions, focus on the meta-problem, study when systems generate consciousness-talk, and design AI to avoid ethically ambiguous cases.

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Executive summary: The author uses basic category theory to argue, in a reflective and somewhat speculative way, that once we model biological systems, brain states, and moral evaluations as categories, functors, and a natural transformation, it becomes structurally clear that shrimp’s pain is morally relevant and that donating to shrimp welfare is a highly cost-effective way to reduce suffering.

Key points:

1. The author introduces categories, functors, and natural transformations as very general mathematical tools that can formalize relationships and arguments outside of pure mathematics, including in ethics and philosophy of mind.
2. They define a category BioSys whose objects are biological systems (including humans and shrimp) and whose morphisms are qualia-preserving mappings between causal graphs of conscious systems, assuming at least a basic physicalist functionalist view.
3. They introduce two functors from BioSys to the category Meas of measurable spaces: a brain-state functor that represents biological systems as measurable brain states, and a moral evaluation functor that maps systems to measurable spaces of morally relevant mental states.
4. They argue there is a natural transformation between these two functors, given by measurable maps that “forget” non-morally-relevant properties, and that this captures two ways of evaluating shrimp’s moral worth: comparing shrimp’s morally relevant states directly to humans’ or first embedding shrimp’s full mental state space into that of other animals or humans and only then forgetting irrelevant details.
5. The author claims that people often underweight shrimp’s moral value because they focus on morally relevant properties only after seeing them as “shrimp properties,” whereas comparing shrimp’s full pain system to that of humans, fish, or lobsters and then evaluating moral worth more naturally reveals that shrimp have significant morally relevant properties.
6. They suggest that, under any reasonable moral evaluation consistent with this framework, cheap interventions that prevent intense shrimp suffering (such as donating to shrimp welfare organizations) rank very highly among possible moral interventions, and they sketch further category-theoretic directions (e.g. adjunctions, limits, and a category of interventions) for future investigation.

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Executive summary: The author argues that AI 2027 repeatedly misrepresents its cited scientific sources, using an example involving iterated distillation and amplification to claim that the book extrapolates far beyond what the underlying research supports.

Key points:

1. The author says AI 2027 cites a 2017 report on iterated amplification to suggest “self-improvement for general intelligence,” despite the report describing only narrow algorithmic tasks.
2. The author quotes the report stating that it provides no evidence of applicability to “complex real-world tasks” or “messy real-world decompositions.”
3. The author notes that the report’s experiments involve five toy algorithmic tasks such as finding distances in a graph, with no claims about broader cognitive abilities.
4. The author states that AI 2027 extrapolates from math and coding tasks with clear answers to predictions about verifying subjective tasks, without supplying evidence for this extrapolation.
5. The author argues that the referenced materials repeatedly disclaim any relevance to general intelligence, so AI 2027’s claims are unsupported.
6. The author says this is one of many instances where AI 2027 uses sources that do not substantiate its predictions, and promises a fuller review.

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Executive summary: The author argues that ongoing moral catastrophes are probably happening now, drawing on Evan Williams’s inductive and disjunctive arguments that nearly all societies have committed uncontroversial evils and ours is unlikely to be the lone exception.

Key points:

1. The author says they already believe an ongoing moral catastrophe exists, citing factory farming as an example, and uses Williams’s paper to argue that everyone should think such catastrophes are likely.
2. Williams’s inductive argument is that almost every past society committed clear atrocities such as slavery, conquest, repression, and torture while believing themselves moral, so we should expect similar blind spots today.
3. Williams’s disjunctive argument is that because there are many possible ways to commit immense wrongdoing, even a high probability of avoiding any single one yields a low probability of avoiding all.
4. The author lists potential present-day catastrophes, including factory farming, wild animal suffering, neglect for foreigners and future generations, abortion, mass incarceration, natural mass fetus death, declining birth rates, animal slaughter, secularism causing damnation, destruction of nature, and child-bearing.
5. The author concludes that society should actively reflect on possible atrocities, expand the moral circle, take precautionary reasoning seriously, and reflect before taking high-stakes actions such as creating digital minds or allocating space resources.
6. The author argues that taking these possibilities seriously should change how we see our own era and reduce the chance of committing vast moral wrongs.

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Executive summary: The author reflects on moving from a confident teenage commitment to Marxism toward a stance they call evidence-based do-goodism and explains why Effective Altruism, understood as a broad philosophical project rather than a political ideology, better matches their values and their current view that improving the world requires empirics rather than revolutionary theory.

Key points:

1. The author describes being a committed Marxist from ages 15–19, endorsing views like the labor theory of value and defending historical socialist leaders while resisting mainstream economics.
2. They explain realizing they were “totally, utterly, completely wrong” about most of these beliefs, while retaining underlying values about global injustice and unfairness toward disadvantaged groups.
3. They argue that violent or rapid revolutionary change cannot shift economic equilibria and has historically produced brutality, leading them to leave both revolutionary and reformist socialism.
4. They say they now identify with “Evidence-Based Do-Goodism,” making political judgments by weighing empirical evidence rather than adhering to a totalizing ideology.
5. They present Effective Altruism as a motivating, nonpolitical framework focused on reducing suffering for humans, animals, and future generations through evidence-supported actions.
6. They emphasize that people of many ideologies can participate in Effective Altruism and encourage readers to explore local groups, meetups, and concrete actions such as supporting foreign aid, AI risk reduction, or reducing animal product consumption.

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