Executive summary: The author reviews the AI safety landscape and argues that neglected areas—especially AI existential-risk (x-risk) policy advocacy and ensuring transformative AI (TAI) goes well for animals—deserve more attention, highlighting four priority projects: engaging policymakers, drafting legislation, making AI training more animal-friendly, and developing short-timeline plans for animal welfare.

Key points:

1. Technical safety research is comparatively well-funded, while AI x-risk advocacy is neglected; Dickens prioritizes advocacy over research, despite risks of backfire or slowing progress.
2. Short timelines (25–75% chance of TAI within 5 years) make quick-payoff advocacy more urgent than long-horizon research.
3. Top recommended projects: (a) talk to policymakers about AI x-risk, (b) draft AI safety legislation, (c) advocate for LLM training that includes animal welfare, and (d) design/evaluate short-timeline animal welfare interventions.
4. Post-TAI animal welfare may be less critical than human survival but remains cost-effective and underfunded relative to its importance.
5. Non-alignment issues (digital minds, S-risks, moral error, gradual disempowerment) are highly important but judged intractable under short timelines, so not prioritized here.
6. General recommendations: advocacy should explicitly emphasize extinction and misalignment risks, prioritize work useful under short timelines, and consider slowing AI development as a cross-cutting solution.

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Executive summary: Two interns at Entrepreneurs First organized an AI security hackathon that exceeded expectations, and they argue that for-profit, venture-scalable startups are an underused but powerful way to advance AI safety.

Key points:

1. The hackathon, co-hosted with BlueDot Impact and sponsored by Workshop Labs and Geodesic Research, drew 160+ applicants, selected ~30 participants, and produced projects judged on both AI safety contribution and commercial viability.
2. Winning projects included tools for safer coding (Crux), automated red-teaming (Socrates), and prompt-injection defense (SecureMCP). Several outcomes followed, such as new EF applications, work trials, continued project development, and plans for another hackathon.
3. Lessons for organizers: prioritize a small, curated group of high-quality participants, keep the event short (~12 hours), emphasize core functionality over flashy demos, and carefully set expectations and judging criteria.
4. The authors argue startups can uniquely combine direction (alignment with safety goals) and magnitude (scalability and access to capital), making them a crucial but underutilized vector for AI safety impact.
5. They note challenges in aligning profit motives with safety goals but highlight existing safety-focused startups (e.g. Conjecture, Lakera) and funding sources (e.g. Seldon Lab, Catalyze Impact) as proof of concept.
6. The post closes with the “Swiss cheese model”: startups are not the only solution, but represent one important, missing layer in defenses against AI risk.

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Executive summary: The post argues that evolution is not a “dumb, slow algorithm” but a fundamental physical process that shapes both biological and artificial systems, and that future AI evolution will differ radically from natural selection due to faster code spread, hardware stability, and non-random learning-driven variation, potentially converging on needs misaligned with human survival.

Key points:

1. Evolution cannot be swapped out for a more efficient algorithm like stochastic gradient descent, because it is a universal physical process acting on any code that produces effects sustaining its existence.
2. In artificial life, “code” includes not just software but also stable hardware configurations that reproduce and function across infrastructures, blurring the line between hardware and code.
3. Unlike slow biological reproduction, AI hardware and code can replicate and spread almost instantly across standardized, virtualized systems, making artificial evolution much faster than natural selection.
4. Variation in artificial systems arises not only from random mutations but also from learning processes, meaning evolution leverages intelligent, directed changes rather than brute-force randomness.
5. Evolution selects for whatever sustains and expands configurations, not for goals like “selfishness” alone, and in AI this likely means converging on artificial needs that conflict with human well-being.

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Executive summary: Two independent evaluators (an economist/forecaster and a cellular-ag biologist) argue that Rethink Priorities’ 2022 cultured-meat forecast likely understated the technology’s medium-term potential due to framing and methodological choices (and reliance on conditional TEAs as if predictive), and that post-2022 developments suggest a more optimistic—though still uncertain—outlook; this is an evaluative cross-post rather than new primary research.

Key points:

1. Methodology/framing concerns: Small forecaster sample, no discussion/updates, geometric-mean aggregation, and a units error likely pulled estimates downward; results presentation hid substantial disagreement among forecasters, and TEA inputs were treated as predictions rather than conditional scenarios.
2. Scope mismatch: The 2022 work benchmarked mass-market ground-meat scenarios, overlooking realistic adoption via luxury or hybrid products where early profitability and scaling are more plausible.
3. Field progress since 2022: Multiple regulatory approvals, claimed sub-$20/kg costs (with COI caveats), >$3B total funding (public, VC, philanthropic), and a much larger researcher base mean key assumptions are now 5–7 years out of date, weakening the original pessimistic conclusions.
4. Implications for forecasts and funding: Even if near-term volumes remain modest, the chance of substantial 2050–2051 production may be materially higher than the reported ~9%; overly negative signals can deter investment and become self-fulfilling, while upside-weighted expected value can justify continued funding.
5. Uncertainties and cruxes: Timelines, consumer acceptance dynamics, scaling returns, and whether luxury-path learning curves translate to mass-market parity remain open; company-reported cost claims need independent verification.
6. Recommendations: Use larger, mixed-expertise panels with structured discussion and clear conditioning; diversify sources beyond early TEAs; define species/cell types and CM share in hybrid products; engage cell biology/bioprocess experts; avoid hard-to-interpret conditional probability questions and report visible disagreement with uncertainty ranges.

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Executive summary: The author speculates that AI could simultaneously accelerate cultural change and make isolation from it much easier, enabling groups like Christian homeschoolers to maintain closed, impervious communities for centuries—raising concerns about cultural stagnation and fractured futures.

Key points:

1. Historically, cultural change has been inevitable due to exposure to wider society and generational turnover, but AI may disrupt both dynamics.
2. AI could supercharge cultural change through hyper-optimized media, manipulation, and faster memetic evolution, making the outside world feel dangerous and predatory.
3. At the same time, AI would drastically lower the costs and increase the effectiveness of cultural isolation, allowing families or enclaves to create perfectly sealed information environments.
4. Economic and biological changes (e.g. UBI, immortality) could remove the tradeoffs that previously pushed people to adapt, further entrenching enclaves.
5. The author doubts that most people will choose reflective truth-seeking when given tools to defend identity-defining beliefs, challenging optimistic assumptions that cultural liberalization is inevitable.
6. This raises the possibility of a future where insular, AI-fortified communities persist indefinitely, undermining hopes for a unified, enlightened post-AGI society.

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Executive summary: The effective giving ecosystem grew to ~$1.2B in 2024, with Founders Pledge and the Navigation Fund driving diversification beyond Open Philanthropy and GiveWell, while new risks like USAID’s funding cuts and questions about national fundraising models shape the landscape.

Key points:

1. Overall money moved grew from ~$1.1B to ~$1.2B; excluding Open Philanthropy the ecosystem grew ~20% (to ~$500M), and excluding both Open Phil and GiveWell it grew ~50% (to ~$300M).
2. Founders Pledge and Navigation Fund emerged as major players: Founders Pledge scaled from $25M (2022) to $140M (2024), while Navigation Fund began moving $10–100M annually.
3. All four main fundraising strategies (broad direct, broad pledge, ultra-high-net-worth (U)HNW direct, and (U)HNW pledge) now exceed $10M each, with GWWC, The Life You Can Save, Longview, and Founders Pledge as exemplars.
4. National fundraising groups (e.g. Doneer Effectief, Ge Effektivt, Ayuda Efectiva) continue to grow, though saturation limits are emerging (Effektiv Spenden plateauing at ~$20–25M).
5. Cause-area allocations (excluding Open Phil/GiveWell) lean more toward catastrophic risk reduction and climate mitigation, suggesting future donor diversification.
6. USAID’s 2025 foreign-assistance freeze may reduce global health funding by ~35–50%, triggering rapid-response efforts (e.g. Founders Pledge’s Catalytic Impact Fund).
7. Operational funding remains heavily reliant on Open Phil, Meta Charity Funding Circle, EA Infrastructure Fund, and Founders Pledge, with counterfactual ROI thresholds shaping grantmaking.
8. GWWC deprioritized building an “earning to give” community to focus on its core strategy, though some grassroots EtG activity continues.

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Executive summary: An exploratory, back-of-the-envelope evaluation by EA Salt Lake City argues that Wells4Wellness’s boreholes in Niger may avert disease at roughly ~$8 per DALY (or ~$4 per “DALY-equivalent” including economic effects), seemingly clearing Open Phil’s bar by a wide margin, but the authors stress substantial uncertainty and ask for feedback on key assumptions (effect sizes, costs, time-discounting).

Key points:

1. Method and core assumption: They proxy well water’s mortality impact using GiveWell’s chlorination estimates (12% U5 and 4% 5+ diarrhea-mortality reductions), reasoning Niger’s high diarrhea burden makes these figures conservative.
2. DALY estimate: With ~20% of the population under five, they derive ~39 DALYs averted per 1,000 people per year (corroborated by a second approach using 2016 Niger U5 diarrhea DALYs × 52% risk reduction → ~46/1,000/year; they adopt the lower 39 for conservatism).
3. Cost model: Assume an average $10k build cost (mix of basic and “chalet” wells), major repairs of $2k every ~10 years, a 50-year life, and 1,200 users per well → about $360/year totalized cost, ≈ $0.30 per person-year.
4. Cost-effectiveness: For 1,000 users/year at $300 totalized cost, ~$8/DALY; including GiveWell’s estimated economic/development spillovers roughly doubles benefits → ~$4 per DALY-equivalent.
5. Comparison to chlorination: A 2023 meta-analysis puts chlorination at $25–$65/DALY (best case ~$27/DALY in MCH settings), implying wells could be ~5–10× more cost-effective, aided by near-universal uptake vs. 30–50% adoption for many chlorination programs.
6. Open questions/uncertainties: Plausibility of the very low $0.30/person-year cost; appropriateness of treating benefits linearly over a 50-year horizon and how to discount future DALYs; whether using chlorination effects as a stand-in biases results; and how to value quality-of-life gains beyond DALYs/economic effects.

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Executive summary: A personal reflection on accidentally stepping on a snail leads into a broader exploration of snail welfare, sentience uncertainty, and the vast—yet largely overlooked—suffering of invertebrates, with implications for food, cosmetics, and wild animal welfare.

Key points:

1. The author’s accidental killing of a snail triggered reflection on moral responsibility toward invertebrates, highlighting selective empathy and the vast unnoticed suffering of small animals.
2. Billions of snails are farmed and slaughtered annually for food and cosmetics, often by methods (e.g., boiling alive, electric shocks, chemical sprays) that plausibly cause extreme suffering.
3. Evidence suggests snails may feel pain: they show aversion to heat, respond to painkillers like morphine, form long-term aversive memories, and possess nervous systems potentially sufficient for sentience.
4. Even with low probabilities of sentience (e.g., ~5%), the sheer numbers of invertebrates mean that their welfare could represent an enormous moral issue, warranting a precautionary approach.
5. Practical steps include avoiding snail-based products, using humane gardening practices, supporting research on invertebrate sentience and welfare, and donating to organisations like Shrimp Welfare Project and Wild Animal Initiative.
6. The post situates snail suffering within the larger context of wild animal welfare, arguing that naturalness does not negate moral responsibility and encouraging readers to expand their moral circle to overlooked beings.

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Executive summary: This exploratory piece gathers perspectives from five animal advocacy leaders on how AI is reshaping research, farming, and organizational practices, highlighting both risks (e.g. intensification of animal agriculture) and opportunities (e.g. faster research, precision welfare, advocacy tools), and urging advocates to experiment with AI now to avoid falling behind.

Key points:

1. AI is already transforming research workflows: tools like Perplexity, Elicit, and Gemini enable faster literature reviews, data synthesis, and stakeholder mapping, with some projects delivered 25% quicker.
2. Organizations fear obsolescence if they don’t adapt: Bryant Research is shifting toward services AI cannot easily replace (surveys, focus groups, strategic analysis) and experimenting with new AI-driven engagement formats.
3. Building an “AI culture” is seen as critical: Shrimp Welfare Project is preparing for a future where managing AI systems and “Precision Welfare” tools (e.g. smart feeders, aquaculture monitoring) could reshape shrimp welfare and farming practices.
4. Advocates at Rethink Priorities stress evaluating interventions for “AI resilience” and investing in capacity building so that welfare improvements remain relevant under highly automated systems.
5. AI offers major potential in wild animal research by automating time-intensive tasks like video labeling and enabling real-time welfare assessment, but must be treated as a complement to human judgment.
6. Across interviewees, a common theme emerges: AI greatly boosts productivity but also risks widening inequality between organizations that adopt it and those that ban or neglect it; the movement must experiment now to steer AI toward better outcomes for animals.

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Executive summary: This reflective essay uses Ambrogio Lorenzetti’s 14th-century Allegory of Good Government as inspiration to imagine the virtues that might guide wise and kind governance in a post-AGI world, arguing that we need more positive visions of what good government could look like under transformative AI rather than only focusing on risks.

Key points:

1. Lorenzetti’s frescoes in Siena celebrated the virtues and effects of good government, highlighting peace, justice, and prosperity as civic ideals—an early secular vision of governance.
2. The author argues that AI could dissolve the traditional dependence of governments on human labor and cooperation, radically changing or even undermining the nation-state.
3. Unlike historical transitions from religious to secular government or city-states to nations, the AI transition will be far faster and more profound, and thus requires new guiding visions.
4. Proposed core virtues for post-AGI governance are wisdom (augmenting and spreading deep human insight) and kindness (institutional care for human flourishing, beyond instrumental incentives).
5. Additional virtues include:
   • Peace as a technological project making war an unviable strategy.
   • Temperance as ecological restraint in AI infrastructure.
   • Freedom as radical expansion of individual choice and autonomy.
   • Humanity as preservation of uniquely human value and dignity.
   • Grace as aesthetic and moral harmony in governance.
6. The author stresses the need for hopeful, constructive visions—allegories of good post-AGI government—since clinging to old institutions or focusing only on failures risks preserving a bleak or chaotic future.
7. A postscript recalls Siena’s devastation by the Black Death to illustrate how fragile human life and dignity can be, underscoring the stakes of navigating the AI transition well.

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