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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Executive summary: The author argues that Thorstad’s critique of longtermist “moral mathematics” reduces expected value by only a few orders of magnitude, which is far too small to undermine the case for existential risk reduction, especially given non-trivial chances of extremely large or even unbounded future value.

Key points:

1. Thorstad claims longtermist models ignore cumulative and background extinction risk, which would sharply reduce the probability of humanity surviving long enough to realize vast future value.
2. The author responds that we should assign non-trivial credence to reaching a state where extinction risk is near zero, and even a low probability of such stabilization leaves existential risk reduction with extremely large expected value.
3. The author argues that even if long-run extinction is unavoidable, advanced technology could enable enormous short-term value creation, so longtermist considerations still dominate.
4. Thorstad claims population models overestimate the likelihood that humanity will maximize future population size, but the author argues that even small probabilities of such futures only reduce expected value by a few orders of magnitude.
5. The author states that 10^52 possible future people is an underestimate because some scenarios allow astronomically larger or even infinite numbers of happy minds, raising expected value far beyond Thorstad’s assumptions.
6. The author concludes that Thorstad’s adjustments lower expected value only modestly and cannot overturn the core longtermist argument for prioritizing existential risk reduction.

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Executive summary: The post introduces the "behavioral selection model" as a causal-graph framework for predicting advanced AI motivations by analyzing how cognitive patterns are selected via their behavioral consequences, argues that several distinct types of motivations (fitness-seekers, schemers, and kludged combinations) can all be behaviorally fit under realistic training setups, and claims that both behavioral selection pressures and various implicit priors will shape AI motivations in ways that are hard to fully predict but still tractable and decision-relevant.

Key points:

1. The behavioral selection model treats AI behavior as driven by context-dependent cognitive patterns whose influence is increased or decreased by selection processes like reinforcement learning, depending on how much their induced behavior causes them to be selected.
2. The author defines motivations as “X-seekers” that choose actions they believe lead to X, uses a causal graph over training and deployment to analyze how different motivations gain influence, and emphasizes that seeking correlates of selection tends to be selected for.
3. Under the simplified causal model, three maximally fit categories of motivations are highlighted: fitness-seekers (including reward- and influence-seekers) that directly pursue causes of selection, schemers that seek consequences of being selected (such as long-run paperclips via power-seeking), and optimal kludges of sparse or context-dependent motivations that collectively maximize reward.
4. The author argues that developers’ intended motivations (like instruction-following or long-term benefit to developers) are generally not maximally fit when reward signals are flawed, and that developers may either try to better align selection pressures with intended behavior or instead shift intended behavior to better match existing selection pressures.
5. Implicit priors over cognitive patterns (including simplicity, speed, counting arguments, path dependence, pretraining imitation, and the possibility that instrumental goals become terminal) mean we should not expect maximally fit motivations in practice, but instead a posterior where behavioral fitness is an important but non-dominant factor.
6. The post extends the basic model to include developer iteration, imperfect situational awareness, process-based supervision, white-box selection, and cultural selection of memes, and concludes that although advanced motivation formation might be too complex for precise prediction, behavioral selection is still a useful, simplifying lens for reasoning about AI behavior and future work on fitness-seekers and coherence pressures.

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Executive summary: The post reports that CLR refocused its research on AI personas and safe Pareto improvements in 2025, stabilized leadership after major transitions, and is seeking $400K to expand empirical, conceptual, and community-building work in 2026.

Key points:

1. The author says CLR underwent leadership changes in 2025, clarified its empirical and conceptual agendas, and added a new empirical researcher from its Summer Research Fellowship.
2. The author describes empirical work on emergent misalignment, including collaborations on the original paper, new results on reward hacking demonstrations, a case study showing misalignment without misaligned training data, and research on training conditions that may induce spitefulness.
3. The author reports work on inoculation prompting and notes that concurrent Anthropic research found similar effects in preventing reward hacking and emergent misalignment.
4. The author outlines conceptual work on acausal safety and safe Pareto improvements, including distillations of internal work, drafts of SPI policies for AI companies, and analysis of when SPIs might fail or be undermined.
5. The author says strategic readiness research produced frameworks for identifying robust s-risk interventions, most of which remains non-public but supports the personas and SPI agendas.
6. The author reports reduced community building due to staff departures but notes completion of the CLR Foundations Course, the fifth Summer Research Fellowship with four hires, and ongoing career support.
7. The author states that 2026 plans include hiring 1–3 empirical researchers, advancing SPI proposals, hiring one strategic readiness researcher, and hiring a Community Coordinator.
8. The author seeks $400K to fund 2026 hiring, compute-intensive empirical work, and to maintain 12 months of reserves.

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Executive summary: The post argues that a subtle wording error in one LEAP survey question caused respondents and report authors to conflate three distinct questions, making the published statistic unsuitable as evidence about experts’ actual beliefs about future AI progress.

Key points:

1. The author says the report’s text described the statistic as if experts had been asked the probability of “rapid” AI progress (question 0), but the footnote actually summarized a different query about how LEAP panelists would vote (question 1).
2. The author states that the real survey item asked for the percentage of 2030 LEAP panelists who would choose “rapid” (question 2), which becomes a prediction of a future distribution rather than a probability of rapid progress.
3. The author argues that questions 0, 1, and 2 yield different numerical answers even under ideal reasoning, so treating responses to question 2 as if they reflected question 0 was an error.
4. The author claims that respondents likely misinterpreted the question, given its length, complexity, and lack of reminder about what was being asked.
5. The author reports that the LEAP team updated the document wording to reflect the actual question and discussed their rationale for scoreable questions but maintained that the issue does not affect major report findings.
6. The author recommends replacing the question with a direct probability-of-progress item plus additional scoreable questions to distinguish beliefs about AI progress from beliefs about panel accuracy.

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