Executive summary: This post argues that governance should be treated as an outcomes-driven intervention, uniquely capable of both advancing and safeguarding key organisational and community goals in EA, and outlines a Theory of Change for how good governance can produce capable organisations, a healthy movement, and better stewardship of resources and people.

Key points:

1. Governance as intervention: The author frames governance as a Theory of Change, emphasizing it should only be invested in when it directly addresses real risks and produces valuable outcomes.
2. Unique value of governance: Unlike other interventions, governance both contributes to outcomes (e.g. financial discipline) and steps in when things go wrong (e.g. removing ineffective leaders).
3. Capable organisations: Good governance enables clear, purpose-led planning, outcome-aligned execution, accountable leadership, and financial discipline—each linked to common risks seen in EA organisations.
4. Healthy movement: Strong governance ensures responsibility is clearly allocated (so funders can focus on prioritisation rather than compliance) and fosters an empowered community through transparency and external challenge.
5. Cross-cutting outcomes: Governance supports resource stewardship (ensuring organisations continue or close appropriately) and people support (advising, coaching, fair compensation, and mental health safeguards for leaders).
6. Practical orientation: The author intends to refine this public Theory of Change over time, and stresses that governance’s value depends on reliable, scalable implementation that avoids common pitfalls.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: Despite hype, preliminary analysis suggests that generative AI has not yet led Y Combinator startups to grow faster in terms of valuations, though measurement issues, macroeconomic headwinds, and the possibility of delayed effects leave room for uncertainty.

Key points:

1. The author tested Garry Tan’s claim that YC companies are growing faster due to GenAI, but found that post-ChatGPT cohorts (2023+) show lower average valuations and few top performers compared to earlier batches.
2. Only two GenAI companies (Tennr and Legora) appear in the top-20 fastest-growing YC startups by valuation, suggesting GenAI hasn’t broadly transformed YC outcomes yet.
3. Data limitations (sparse valuation data, LLM scraping errors, name duplication) and confounders (interest rates, secular decline in YC quality) mean the results should be interpreted cautiously.
4. Stripe’s revenue data shows faster growth for AI firms, but this may not translate into higher valuations due to poor margins and lower revenue multiples; Carta’s funding data supports the “no acceleration” view.
5. The author argues that YC may not be the right reference class for GenAI success, since most leading AI companies (Anthropic, Cursor, Wiz, etc.) are not YC-backed.
6. Tentative conclusion: GenAI hasn’t yet shortened exit timelines for startups, though future shifts remain possible; YC’s diminished role could even reflect AI making traditional accelerators less necessary.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: This reflective write-up by EA Spain organizers describes how their first national retreat successfully built cross-city cohesion and sparked collaborations, while also identifying lessons for future retreats, including balancing social connection with impact-focused programming and strengthening follow-up structures.

Key points:

1. EA Spain has historically been fragmented, with limited activity outside Madrid and Barcelona; the retreat aimed to create a shared national identity and stronger cross-city collaboration.
2. The organizing team adopted an “unconference” format guided by principles of connection, collaboration, and actionable commitments, drawing 22 participants and funded by CEA.
3. The retreat achieved strong social outcomes (average rating 8.6/10, 100% made at least one “new connection”), catalyzed collaborations like a mentorship program and a national book club, and built enthusiasm for future gatherings.
4. Popular formats included speed-friending, shared cooking, unstructured social time, and grounding check-ins; organizers highlight these as replicable practices for other community builders.
5. Key improvement areas include adding more impact-focused sessions, providing stronger central vision-setting, structuring unconference contributions more deliberately, and ensuring clearer post-retreat pathways.
6. Future plans include a 2026 national summit, cross-cause gatherings, stronger Madrid–Barcelona collaboration, and ongoing communication channels across the Spanish EA ecosystem.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: The author critiques traditional “pivotal act” proposals in AI safety (like destroying GPUs) as inherently suppressive of humanity and instead proposes a non-oppressive alternative: a “gentle foom” in which an aligned ASI demonstrates its power, communicates existential risks, and then switches itself off, leaving humanity to voluntarily choose AI regulation.

Key points:

1. Traditional pivotal acts (e.g., “burn all GPUs”) implicitly require permanently suppressing humanity to prevent future AI development, making them socially and politically untenable.
2. The real nucleus of a pivotal act is not technical (hardware destruction) but social (enforcing human compliance).
3. A superior alternative is a “gentle foom,” where an aligned ASI demonstrates overwhelming capabilities without harming people or breaking laws, then restores the status quo and shuts itself off.
4. The purpose of such a demonstration is communication: making AI existential risks undeniable while showing that safe, global regulation is achievable.
5. Afterward, humanity faces a clear, voluntary choice—regulate AI or risk future catastrophic fooms.
6. The author argues against value alignment approaches (including Coherent Extrapolated Volition), since they would still enforce undemocratic values and risk dystopia, and instead urges alignment researchers to resist suppressive strategies.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: On its 10th anniversary, this post celebrates how EA Global has grown from a small experiment into a professional, high-impact conference series, sharing staff and community stories that illustrate its role in shaping careers, seeding new cause areas, and fostering lasting collaborations and friendships.

Key points:

1. Since 2015, CEA has run 24 EA Global conferences, reaching 18,000+ attendees with consistently high satisfaction (8.6/10) and strong counterfactual value (5–6x more valuable than alternatives).
2. Attendees have reported over 155,000 “meaningful connections,” highlighting the event’s role in catalyzing collaborations, career shifts, and cause area momentum.
3. A standout example is shrimp welfare: an EAG panel in 2022 amplified the Shrimp Welfare Project, helping it gain attention, funding, and traction, eventually influencing global welfare standards.
4. Personal reflections from staff and volunteers illustrate EAG’s evolution from scrappy, shoestring beginnings to a polished, world-class conference—without losing its community warmth and mission-driven focus.
5. Common themes across reflections include: the importance of volunteers, EAG as a career launchpad, the sense of shared purpose and generosity among attendees, and the personal friendships and even life partners found at events.
6. Looking ahead, the team emphasizes balancing growth with integrity (e.g. admissions standards, budget discipline) and invites applications for upcoming events (including EAG NYC 2025) and open staff roles.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: This speculative essay traces the history and imagined future of genetically engineered (GE) livestock, describing how welfare-enhanced animals like Tyson’s “Well Beef” cows—engineered not to feel pain—could represent either a monumental reduction in animal suffering or a deeply uncertain moral gamble, depending on whether bioengineers’ assumptions about neuroscience prove correct.

Key points:

1. The piece is set in 2053, where Tyson unveils “Well Beef,” a GE beef product from pain-free “welfare-enhanced” cows, following earlier successes with Pure Chicken and Ecopig.
2. It recounts the real-world obstacles to GE livestock in the early 21st century: regulatory barriers in the U.S., migration of research abroad, stalled products, and the rise (and limits) of plant-based and cultured meats.
3. A turning point came in the 2030s–40s when zoonotic pandemics and public pressure forced factory farm lobbies to embrace GE as a compromise for both disease resistance and welfare, leading to regulatory reform and a biotech renaissance.
4. Pure Chicken (engineered not to perceive pain or develop complex mental states) became the first welfare-enhanced GE livestock to achieve mass commercial success in the late 2040s, rapidly displacing traditional poultry.
5. Well Beef represents the culmination of these technologies, producing cattle that ostensibly live and die without pain, which some celebrate as a historic reduction in suffering.
6. Critics warn of unresolved uncertainties: the brain’s neuroplasticity might allow pain pathways to reemerge in ways we don’t yet understand, meaning these animals could still experience suffering undetectably.
7. The narrative closes with a mixture of triumph and unease, highlighting both the extraordinary promise and the unresolved ethical risks of designing animals for human consumption.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: The post argues that leading labs now concede their frontier models may have dangerous bio capabilities, but their current “load-bearing” safeguards—API filters and security against weight theft—are uneven, opaque, and often inadequate; the bigger unsolved problems are securing future models at SL5-like levels and preventing misalignment, where plans are thin and credibility low (analytical commentary with a critical, cautious tone).

Key points:

1. New stance from labs: Anthropic, OpenAI, Google DeepMind, and xAI now say top models could materially aid extremists in bioweapons creation, shifting from earlier claims of “no dangerous capabilities”; this makes safeguards—API misuse blocking and model-weight security—central.
2. API safeguards are classifier-centric and mixed: Anthropic (strongest) and OpenAI outline classifier-based defenses with some supportive evidence; DeepMind discloses little beyond using “filters”; xAI’s claims are vague and contradicted by examples, with no published external assessments.
3. Security today is likely below what’s needed: For current models, the post argues SL3-quality security is warranted; Anthropic’s claims may be undercut by a broad insider exception, OpenAI’s posture seems ≈SL2 and non-specific, DeepMind targets SL2 via its framework, and xAI’s assurances are implausible. (See the RAND five-level security chart on p.3 for SL1–SL5 definitions.)
4. Future risk hinges on weight theft prevention: As capabilities rise, stolen weights could proliferate and force unsafe racing; credible protection against state-level actors likely requires SL5-like security. Current roadmaps (e.g., Anthropic’s ASL-4 aspiration; DeepMind only up to SL4; OpenAI vague; xAI silent) look costly, non-binding, and at risk of being abandoned without coordination.
5. Misalignment planning is the weakest link: Anthropic promises an “affirmative case” at an automation threshold but with scant detail; DeepMind’s plan is abstract; OpenAI’s triggers and evidence standards are confused; xAI focuses on honesty/lying metrics that miss scheming risks and better interventions.
6. Bottom line and scorecard: Misuse-via-API matters less than security and misalignment, which are harder and more important; the author’s new scorecard rates labs poorly on these fronts, with most non-frontier firms doing essentially nothing.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: This exploratory post argues that when assessing morally mixed actions—like personal energy use or AI adoption—we should avoid both trivializing small harms and catastrophizing them, instead using tools like Pigouvian taxes, rough cost–benefit heuristics, and carefully framed universalizability tests to distinguish reasonable from wasteful resource use.

Key points:

1. Two common mistakes in thinking about collective harms are the rounding to zero fallacy (ignoring small contributions) and the total cost fallacy (treating all contributions as equally catastrophic).
2. The ideal solution is to internalize externalities through policies like carbon taxes, which would make tradeoffs transparent and remove the moral burden from individuals.
3. In the absence of such policies, individuals should estimate the expected net value of their actions, focusing on cost-effective reductions (e.g., gasoline use over electricity) and remembering that donations to highly effective charities typically outweigh lifestyle sacrifices.
4. Universalizability reasoning (“what if everyone did that?”) can help but must be applied carefully: one should abstract to decision procedures, respect others’ preferences, and distinguish between subcategories of resource use to avoid absurd or overly broad conclusions.
5. Boycotts of technologies like AI, when motivated by indiscriminate universalization, risk suppressing good uses without affecting bad ones; a more sensible approach is to encourage and model responsible use.
6. Shifting social norms through moral stands is possible, but its effectiveness is empirical; activists should assess probabilities and stakes rather than acting on hope alone.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: This exploratory post argues that effective animal advocacy must treat farmers as potential allies rather than adversaries, since their decisions are driven less by attitudes toward animal welfare and more by economic, social, and cultural factors, and collaboration with them could be pivotal for advancing meat reduction and farm transitions.

Key points:

1. Farmers often acknowledge animal sentience but frame welfare in practical, productivity-centered terms; intensified production pressures reinforce this instrumental view.
2. Many farmers experience emotional strain and mental health challenges around slaughter but suppress or normalize these feelings due to social norms.
3. Attitudes toward welfare rarely translate into practice change — economic viability is the strongest driver of farmers’ decisions about herd sizes, welfare upgrades, or transitions.
4. Transition initiatives (like Transfarmation) succeed when they ease financial risks, and government funding (e.g. Dutch transition programs) can make these shifts more feasible.
5. Beyond money, identity, land suitability, and cultural ties to animal farming are major barriers; climate change awareness may offer a promising entry point for coalition-building.
6. Advocates should invest in supporting farmer transitions, lobbying for farmer-inclusive policies, and conducting more rigorous research (especially quantitative and messaging-focused) to strengthen outreach and collaboration.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Executive summary: This post highlights key findings and personal reflections from Futures with Digital Minds: Expert Forecasts in 2025, which surveyed experts on the plausibility, timelines, welfare, and political implications of creating digital minds—computer-based systems with subjective experience—emphasizing both surprising probabilities (e.g. ~5% chance of creation before 2026) and underexplored research directions, while noting the authors’ disagreements with some survey consensus.

Key points:

1. Experts broadly agree digital minds are possible (median 90%) and likely to be created (73%), with roughly a coin flip chance before 2050, and a surprising 4.5% chance before 2026.
2. The first digital minds are expected to trigger rapid scaling due to compute overhang, with welfare capacity potentially surpassing humanity’s within a decade if early machine learning–based digital minds emerge.
3. There is deep uncertainty about whether digital mind welfare will be positive or negative, whether rights will be recognized, and how AI welfare and AI safety will interact.
4. Underappreciated insights include: risks of delaying creation (higher stakes later), the decoupling of cognition and consciousness, and the moral relevance of the order in which cognitive capacities develop.
5. Investigation priorities include clarifying whether “goalpost movement” affects recognition of current AI as digital minds, exploring super-beneficiaries and non-experiential welfare, and assessing interventions before and after AGI.
6. The authors diverge from the median on several points: Bradford is more skeptical about the in-principle possibility (60% vs. 90%), both are more open to digital mind super-beneficiaries, and Bradford also allows for welfare without subjective experience.

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.