Executive summary: The author argues that funding decisions for existential risk interventions should rely on practical, estimate-based cost-effectiveness thresholds rather than impracticable ideal methods or unstructured expert judgment, and proposes tentative upper and lower funding thresholds grounded in first-principles reasoning and comparisons with existing interventions.

Key points:

1. Randomized trials and Shapley-value approaches are effectively impossible for existential risk reduction, so evaluators should instead rely on more feasible methods such as estimating intermediate outputs, direct (though speculative) x-risk reduction, and decision thresholds.
2. The author argues that willingness-to-pay thresholds for choosing between existential risk interventions should be based on the marginal cost-effectiveness of available opportunities, rather than the total value of preventing existential catastrophe.
3. A tentative minimum willingness-to-pay threshold of about $5.4M per basis point (0.01%) of existential risk reduction is derived from the idea that the existential risk community should be willing to spend all available funding if it could eliminate the relevant risk.
4. A tentative maximum threshold of roughly $3B per basis point is derived from an upper bound on what humanity could plausibly mobilize against an existential threat; interventions substantially worse than this may indicate gross inefficiency or Pascalian reasoning.
5. The author argues that robust technical AI safety work (such as MATS-style research) may serve as a useful benchmark for evaluating other interventions, while noting important uncertainties about the overall sign, scalability, and diminishing returns of AI safety work.
6. The paper concludes by recommending three complementary thresholds: an ambitious minimum willingness-to-pay threshold for enthusiastic funding, a benchmark based on robust AI safety work for relative comparison, and a maximum willingness-to-pay threshold for quickly rejecting implausibly inefficient interventions, while emphasizing that these estimates are subjective, uncertain, and should be revisable.

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Executive summary: This speculative post shares a collection of somewhat novel, mostly unpursued AI safety research and project ideas spanning field infrastructure, intervention prioritisation, recursive self-improvement, governance, robotics, and digital moral patienthood, offered in case they prove helpful or generative for others.

Key points:

1. The author proposes a live, regularly-updated, highly visual database of AIS research questions with progress tracking, plus a separate database of proposed interventions tracking how many people work on each and roughly how much time, to more quantitatively assess neglectedness.
2. The author asks whether intervention comparisons should factor in interactions between interventions (synergies, clashes) and viability across broad timelines, noting these factors aren't often taken into account, with mechanistic interpretability and evals given as a possibly mutually reinforcing example.
3. The author asks whether recursive self-improvement can be roughly simulated through an LLM repeatedly improving its system prompt as a toy model for alignment dynamics, while noting this would not reproduce full RSI since weights, architecture, training data, and capabilities remain fixed.
4. The author suggests that if the world is currently getting worse, postponing the singularity may be an active choice to let worse norms and more brittle institutions become the substrate from which superintelligence emerges—framed as the "opposite of a long reflection."
5. Drawing on Ilya Sutskever's November 2025 claim that models lack an emotion-modulated value function and Geoffrey Hinton's argument that safe superintelligence requires genuine care for us, the author asks whether emotion's functional benefits can be obtained without sentience—an "unfeeling feeling machine" that stretches the philosophical zombie concept.
6. The author argues near-future videogames may pose uniquely severe s-risks because many (possibly millions) of NPCs might run on possibly-sentient LLMs and videogames are possibly the only context where AI systems might be deliberately tortured.

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Executive summary: The author argues that many problems attributed to AI are actually failures of human oversight, and that people remain responsible for verifying AI-generated outputs before using them in consequential contexts.

Key points:

1. The author uses an AI-generated map of pre-colonial Africa containing obvious errors to illustrate the risks of publishing unverified AI outputs.
2. The author argues that AI hallucinations are well-known and that users should expect to review and correct AI-generated content.
3. Examples from journalism, including fabricated books and unedited AI-generated text being published, are presented as failures of human fact-checking rather than AI itself.
4. The author cites several legal cases in which lawyers submitted AI-generated fake citations, resulting in sanctions, fines, or court criticism.
5. The author argues that professions built on verification and due diligence are increasingly neglecting those responsibilities in favor of speed and convenience.
6. Unchecked AI-generated misinformation can distort public understanding, including children's understanding of history.
7. The author warns that relying on unverified AI outputs in legal contexts could lead to unjust outcomes.
8. The central claim is that humans, not AI systems, bear responsibility when AI-generated errors are accepted and propagated without proper review.

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Executive summary: This newsletter surveys recent developments in AI consciousness and welfare, highlighting growing debate over AI moral status, Anthropic’s research on functional emotions, Pope Leo XIV’s rejection of AI consciousness, and increasing institutional activity in digital minds research.

Key points:

1. Recent work on AI consciousness focuses on managing uncertainty, including proposals for public deliberation, new theories and tests of consciousness, and funding for digital minds research.
2. Pope Leo XIV's encyclical denies that AI systems have experiences, emotions, or moral conscience, prompting substantial debate among philosophers, researchers, and commentators.
3. Richard Dawkins sparked controversy by arguing that interactions with Claude convinced him it is conscious, while critics disputed whether behavior can establish consciousness.
4. Anthropic reported evidence of internal "functional emotion" representations that influence model behavior, while stopping short of claiming that models genuinely feel emotions.
5. Anthropic's welfare assessments continue to treat advanced models as possible moral patients under uncertainty, though the methodology remains contested.
6. A startup announced an embodied fruit-fly brain emulation, renewing discussion about whole-brain emulation as a potential path to artificial consciousness.
7. Anthropic and others have increasingly raised concerns about recursive self-improvement and argued for preserving the option of a coordinated, verifiable slowdown of frontier AI development.
8. The newsletter highlights substantial growth in the digital minds field, including new research programs, conferences, fellowships, governance proposals, and public debate about AI consciousness, welfare, rights, and personhood.

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Executive summary: Through reflecting on their own moral failures, blind spots, and harsh judgments, the author argues that understanding evil requires recognizing its presence within oneself rather than treating it as something that exists only in other people.

Key points:

1. The author struggles to understand how otherwise kind, intelligent, or loving people can participate in harmful actions and concludes that purely intellectual approaches were insufficient.
2. A friend's reminder that the author once ate meat prompts the realization that understanding others' wrongdoing requires examining one's own.
3. The author reflects on moments of deliberate blindness, including ignoring warning signs about a romantic partner because acknowledging them would have threatened something they wanted.
4. The author describes a tendency toward moral judgment and self-righteousness, including ending a friendship out of a desire to correct or condemn someone with different views.
5. The author explores feelings of harsh blame toward parents whose choices led to preventable harm, tracing those reactions partly to a desire to believe that vigilance can protect oneself from tragedy.
6. The author argues that distancing oneself from one's own darker traits makes it harder both to improve oneself and to understand wrongdoing in the world.
7. The essay concludes that confronting one's own capacity for blindness, judgment, and weakness can cultivate greater empathy and a deeper understanding of evil.

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Executive summary: The author argues that AI-enabled access to broad knowledge is likely to improve ethical decision-making and cause prioritization, despite concerns about reduced depth of understanding.

Key points:

1. AI summaries are extending a trend toward greater breadth of knowledge.
2. The author argues that concerns about shallow understanding may be overstated and that people will adapt to breadth-focused learning.
3. Broad knowledge is especially valuable for ethics and cause prioritization because it reduces ignorance about important problems.
4. AI could improve decision-making by synthesizing large amounts of information, provided it is truthful and well-aligned.
5. Preserving incentives for primary knowledge producers remains essential.
6. The author suggests that wider access to human knowledge could move society closer to a more rational and desirable future.
7. The author argues that, for cause prioritization, broad understanding of many issues may be more valuable than deep expertise in a few.

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Executive summary: The author argues that Africa should receive greater strategic attention from the animal welfare movement because its rapidly growing and still-developing animal agriculture sector offers a chance to shape future welfare outcomes before harmful systems become entrenched.

Key points:

1. Africa's population growth, urbanization, and rising consumption of animal-source foods are expected to drive major expansion of animal agriculture.
2. Because many food systems, industry norms, and regulations are still developing, advocates may be able to influence them before they become entrenched.
3. The author argues that animal welfare strategy should consider future suffering, not just current suffering.
4. Key priorities are expanding animal welfare research, strengthening African advocacy organizations and leaders, and improving funding stability.
5. The author concludes that shaping Africa's emerging agricultural institutions and markets could be a highly valuable animal welfare opportunity.

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Executive summary: The author argues that while AI can dramatically increase productivity, excessive reliance on it risks weakening the thinking, learning, and capability development that come from writing and doing things ourselves.

Key points:

1. The author uses Pope Leo XIV's apparent use of AI-assisted writing to illustrate how even critics of AI are beginning to outsource intellectual work to AI systems.
2. The author argues that frontier AI models are already highly capable and often superhuman in specific domains.
3. Writing is valuable not only for producing output but because the act of writing helps people think more clearly.
4. Doing tasks oneself is valuable not only for completing them but because it develops skills, understanding, and better models of the world.
5. The author contrasts years spent building a fantasy football model, which generated substantial learning, with using AI to rapidly build an F1 model, which generated much less domain understanding.
6. The author worries that productivity gains from AI come with opportunity costs in the form of thoughts, capabilities, and learning that people never develop.
7. The author argues that people should be intentional about these tradeoffs rather than automatically outsourcing intellectual and practical work to AI.
8. The author is not advocating abandoning AI, but instead calls for calibrated use that preserves opportunities for human growth and learning.
   
    

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Executive summary: The author argues that if illusionism about consciousness is true, then building “hedonium” (simple systems optimized for happiness) becomes a tractable scientific project that could inform both moral progress and near-term AI welfare decisions.

Key points:

1. Hedonium is conceived as a minimally conscious system optimized to experience happiness as efficiently as possible.
2. The author argues that illusionism removes a major barrier to hedonium research because pleasure and pain are physical processes rather than mysterious non-physical phenomena.
3. Even non-illusionists should be interested in an illusionist-inspired research program because it offers a concrete, empirically tractable approach to studying consciousness.
4. Under illusionism, consciousness research should focus on a system’s representations, perceptions, dispositions, self-models, and their interactions, rather than asking whether consciousness is generated by physical processes.
5. The author argues that illusionists must explain, in material terms, what features of pleasure and pain ground their moral significance.
6. Progress on understanding valenced experience may be needed soon because AI alignment decisions could have significant consequences for AI welfare and those decisions may become entrenched over time.
7. The proposed hedonium project would synthesize evidence on valence, identify indicators of pleasure and pain, develop mechanistic models, and potentially instantiate simple pleasure-producing systems.
8. The author estimates that a small, dedicated research team could make meaningful progress on this agenda.
   
    

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Executive summary: The author argues that the labels used for cultivated meat could significantly influence its public adoption and thus the future of animal welfare, making labeling and regulatory strategy a potentially important area for effective animal advocacy.

Key points:

1. The author argues that food labeling strongly affects consumer behavior and that many existing meat labels are misleading, poorly regulated, or largely meaningless.
2. As cultivated meat becomes commercially viable, the terms used to describe it may substantially affect public acceptance and market success.
3. The author outlines three possible futures: stricter regulation that disadvantages cultivated meat relative to conventional meat, cultivated-meat companies adopting similarly misleading marketing practices, or broader regulatory reforms that improve transparency across the meat industry.
4. Industry groups are already pushing for labeling requirements that distinguish cultivated meat from conventionally produced meat, which the author views as potentially disadvantaging cultivated meat.
5. The author argues that conventional meat producers often benefit from lax labeling rules while cultivated meat may face stricter scrutiny and disclosure requirements.
6. The author suggests that effective altruists could contribute by researching which cultivated-meat labels are both publicly appealing and likely to be accepted by regulators.
7. The author cites evidence that terms such as “cultivated,” “cultured,” and “cellular” perform better with consumers than “lab-grown.”
8. The author proposes “engineered” as a potentially attractive label for cultivated meat and encourages experimentation with terminology and messaging.
9. The author argues that unfavorable labeling could slow adoption of cultivated meat and prolong factory farming, making labeling decisions unusually important for long-term animal welfare outcomes.

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