Executive summary: This exploratory reanalysis uses causal inference principles to reinterpret findings from a longitudinal study on meat reduction, concluding that certain interventions like vegan challenges and plant-based analog consumption appear to reduce animal product consumption, while prior findings suggesting that motivation or outdoor media increase consumption may have stemmed from flawed modeling choices rather than true effects.

Key points:

1. Causal inference requires co-occurrence, temporal precedence, and the elimination of alternative explanations—achievable in longitudinal studies with at least three waves of data, as demonstrated in the case study.
2. The original analysis by Bryant et al. was limited by treating the longitudinal data as cross-sectional, leading to potential post-treatment bias and flawed causal interpretations.
3. The reanalysis applied a modular, wave-separated modeling strategy, using Wave 1 variables as confounders, Wave 2 variables as exposures, and Wave 3 variables as outcomes to improve causal clarity.
4. Motivation to reduce meat consumption was associated with decreased animal product consumption, contradicting the original counterintuitive finding of a positive relationship.
5. Vegan challenge participation and plant-based analog consumption had the strongest associations with reduced consumption and progression toward vegetarianism, though low participation rates limited statistical significance for the former.
6. Some results raised red flags—especially that exposure to activism correlated with increased consumption, prompting calls for further research into the content and perception of activism messages.

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Executive summary: This detailed update from the Nucleic Acid Observatory (NAO) outlines major expansions in wastewater and pooled individual sequencing, air sampling analysis, and data processing capabilities, emphasizing progress toward scalable biosurveillance systems while acknowledging ongoing technical challenges and exploratory efforts.

Key points:

1. Wastewater sequencing has scaled significantly, with over 270 billion read pairs sequenced from thirteen sites—more than all previous years combined—thanks to collaborations with several research labs and support from contracts like ANTI-DOTE.
2. Pooled swab collection from individuals has expanded, with promising Q1 results leading to a decision to scale up; a public report is expected in mid Q2 detailing the findings and rationale.
3. Indoor air sampling work has resulted in a peer-reviewed publication, and the team is actively seeking collaborations with groups already collecting air samples, potentially offering funding for sequencing and processing.
4. Software development continues, with improvements to the main mgs-workflow pipeline and efforts to enhance reference-based growth detection (RBGD) by addressing issues with rare and ambiguous sequences.
5. Reference-free threat detection is being prototyped, including tools for identifying and assembling from short sequences with increasing abundance—efforts recently shared at a scientific conference.
6. Organizationally, the NAO has grown, adding two experienced staff members from Biobot Analytics and securing a $3.4M grant from Open Philanthropy to support wastewater sequencing scale-up, methodological improvements, and rapid-response readiness.

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Executive summary: This post introduces Making God, a planned feature-length documentary aimed at a non-technical audience to raise awareness of the risks associated with the race toward AGI; the filmmakers seek funding to complete high-quality production and hope to catalyze public engagement and political action through wide distribution on streaming platforms.

Key points:

1. Making God is envisioned as a cinematic, accessible documentary in the style of The Social Dilemma or Seaspiracy, aiming to educate a broad audience about recent AI advancements and the existential risks posed by AGI.
2. The project seeks to fill a gap in public discourse by creating a high-production-value film that doesn’t assume prior technical knowledge, targeting streaming platforms and major film festivals to reach tens of millions of viewers.
3. The filmmakers argue that leading AI companies are prioritizing capabilities over safety, international governance is weakening, and technical alignment may not be achieved in time—thus increasing the urgency of public awareness and involvement.
4. The team has already filmed five interviews with legal experts, civil society leaders, forecasters, and union representatives to serve as a “Proof of Concept,” and they are seeking further funding (~$293,000) to expand production and ensure festival/streaming viability.
5. The documentary’s theory of impact is that by informing and emotionally engaging a mass audience, it could generate public pressure and policy support for responsible AI development during a critical window in the coming years.
6. The core team—Director Mike Narouei and Executive Producer Connor Axiotes—bring strong credentials from viral media production, AI safety advocacy, and political communications, and are currently fundraising via Manifund (with matching donations active as of April 14, 2025).

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Executive summary: This personal reflection offers candid advice to the author's past self as a newcomer to Effective Altruism (EA), emphasizing the importance of epistemic humility, clear communication, professionalism, and community engagement, while warning against overconfidence, edgy behavior, and risky schemes.

Key points:

1. Communicate claims responsibly: The author regrets repeating EA ideas with undue confidence or without proper context, and urges newcomers to share caveats and signal epistemic uncertainty clearly to avoid echo chamber effects and misrepresentation.
2. Prioritize sensitivity and tone: While humor can be valuable, edgy or insensitive comments—especially online—can alienate people and undermine EA’s goals; newcomers should aim for good-spirited, inclusive communication.
3. Avoid unnecessary jargon: Using plain language helps make EA ideas more accessible and engaging, and many respected EA communicators model this clarity.
4. Steer clear of risky or unethical projects: Though entrepreneurial thinking is encouraged, ideas that could harm EA’s reputation or violate laws are not worth pursuing.
5. Maintain professional boundaries: Especially in social and dating contexts within EA, awareness of power dynamics and gender imbalances is essential to creating a welcoming, respectful environment.
6. Don’t hesitate to ask for help: The author reflects on missed opportunities for deeper involvement due to not reaching out earlier, and encourages newcomers to engage with EA resources, programs, and people to find meaningful ways to contribute.

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Executive summary: This exploratory analysis reviews causal evidence on the relationship between immigration and crime in several European countries, finding little to no effect in the UK and Italy, mixed results in Germany, and limited data for France and Belgium, while suggesting that secure legal status and access to employment significantly reduce immigrant crime rates.

Key points:

1. UK findings: Migrants are underrepresented in UK prisons, and while causal studies show little evidence of either increased or decreased crime due to immigration, the overall effect of large migration waves appears neutral on crime rates.
2. Germany’s mixed evidence: Though immigrants—especially recent Syrian refugees—are overrepresented in prisons, studies diverge on whether immigration has increased crime, with some evidence suggesting any rise in crime is primarily among migrant communities rather than affecting native-born citizens.
3. Italy and legal status: While aggregate effects of immigration on crime are negligible, a key study shows that legalizing undocumented immigrants significantly reduced their crime rates, likely due to improved employment opportunities and greater personal stakes in avoiding criminal charges.
4. France and Belgium: The author found insufficient recent causal evidence to assess the impact of immigration on crime in these countries.
5. General conclusion: Crime among immigrants is closely linked to economic opportunity; policies that provide legal status and integrate migrants into labor markets may effectively reduce criminal behavior.
6. Policy implication: Governments concerned about crime might achieve better outcomes by improving immigrants’ access to lawful employment rather than restricting migration per se.

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Executive summary: In this personal reflection, the author shares how they transitioned from software engineering to an impactful AI policy operations role within just three months, arguing that entry into the field is more accessible than commonly believed—especially for proactive individuals willing to leverage community connections, volunteer experience, and financial flexibility.

Key points:

1. Surprisingly quick career switch: The author expected to need years to break into AI safety but instead secured a job in international AI policy operations within three months of leaving software engineering.
2. Nature of the job: Their role involved logistical and project management work for high-level AI policy events, where AI safety knowledge was primarily useful during initial planning.
3. Path to getting hired: Volunteering at CeSIA led to a personal referral for the role, which was pivotal; being embedded in a local EA/AI safety community also opened up opportunities.
4. Key enabling factors: Unique fit for the role (e.g., fluent in French, available on short notice), financial flexibility, and prior freelance experience made it easier to accept and succeed in the position.
5. Lessons learned: The author emphasizes the difficulty of learning on the job without mentorship and recommends future job-seekers seek structured guidance when entering new domains.
6. Encouragement and offer to help: They invite others interested in AI safety to reach out for career advice and signal openness to future opportunities building on their recent experience.

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Executive summary: This exploratory post argues that while standard expected utility theory recommends fully concentrating charitable donations on the highest-expected-impact opportunity, a pragmatic Bayesian approach—averaging across uncertain models of the world—can justify some degree of diversification, particularly when model uncertainty or moral uncertainty is significant.

Key points:

1. Standard expected utility theory implies full concentration: Under a simple linear model, maximizing expected impact requires allocating all resources to the charity with the highest expected utility, leaving no room for diversification.
2. This approach is fragile under uncertainty: Small updates in beliefs can lead to complete switches in preferred charities, making the strategy non-robust to noise or near-ties in effectiveness estimates.
3. Diversification in finance relies on risk aversion, which is less defensible in charitable giving: Unlike financial investments, diversification in giving can't be easily justified by volatility or utility concavity, as impact should be the sole goal.
4. Introducing model uncertainty enables a form of Bayesian diversification: By treating utility estimates as conditional on uncertain world models (θ), and averaging over these models, one can derive an allocation that reflects the probability of each charity being optimal across possible worldviews.
5. This yields intuitive and flexible allocation rules: Charities get funding proportional to their chance of being the best in some plausible world; clearly suboptimal options get nothing, while similarly promising ones are treated nearly equally.
6. The method is ad hoc but practical: Although the choice of which uncertainties to "pull out" is arbitrary and may resemble hidden risk aversion, the author believes it aligns better with real-world epistemic humility and actual donor behavior than strict maximization.

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Executive summary: This evidence-based analysis from the 2024 EA Survey explores which factors most help people have a positive impact and form valuable personal connections in the EA community, finding that personal contact, 80,000 Hours resources, and EA events are consistently influential—though engagement level, gender, and racial/ethnic identity shape which sources matter most.

Key points:

1. Top impact sources: The most influential factors for helping people have an impact were personal contact with other EAs (42.3%), 80,000 Hours content (34.1%), and EA Global/EAGx events (22.9%).
2. New connections: Most new personal connections came from EA Global/EAGx (31.6%), personal contacts (30.8%), and local EA groups (28.2%), though 30.6% selected “None of these,” up from 19% in 2022.
3. Cohort trends: Newer EAs rely more on 80,000 Hours and virtual programs, while older cohorts report more value from personal connections, local groups, and GiveWell.
4. Demographic variation: Women and non-white respondents are more likely to value 80,000 Hours (especially the website and job board), virtual programs, and newsletters; white respondents more often cite personal contact and GiveWell.
5. Engagement differences: Highly engaged EAs benefit more from personal contact, in-person events, and EA Forum discussions, while low-engagement EAs lean on more accessible sources like GiveWell, articles, and Astral Codex Ten—and are much more likely to report no recent new connections.
6. Long-term trends: Despite some changes in question format over the years, the core drivers of impact and connection—especially interpersonal contact and key EA organizations—remain relatively stable across surveys.

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Executive summary: This exploratory post presents a speculative but grounded dystopian scenario in which mediocre, misused AI—rather than superintelligent systems—gradually degrades society through hype-driven deployment, expert displacement, and systemic enshittification, ultimately leading to collapse; while the author does not believe this outcome is likely, they argue it is more plausible than many conventional AI doom scenarios and worth taking seriously.

Key points:

1. The central story (“Slopworld 2035”) imagines a world degraded by widespread deployment of underperforming AI, where systems that sound impressive but lack true competence replace human expertise, leading to infrastructural failure, worsening inequality, and eventually nuclear catastrophe.
2. This scenario draws from numerous real-world trends and examples, including AI benchmark gaming, stealth outsourcing of human labor, critical thinking decline from AI overuse, excessive AI hype, and documented misuses of generative AI in professional contexts (e.g., law, medicine, design).
3. The author highlights the risk of a society that becomes “AI-legible” and hostile to human expertise, as institutions favor cheap, scalable AI output over thoughtful, context-sensitive human judgment, while public trust in experts erodes and AI hype dominates policymaking and investment.
4. Compared to traditional AGI “takeover” scenarios, the author argues this form of AI doom is more likely because it doesn’t require superintelligence or intentional malice—just mediocre tools, widespread overconfidence, and profit-driven incentives overriding quality and caution.
5. Despite its vivid narrative, the author explicitly states that the story is not a forecast, acknowledging uncertainties in public attitudes, AI adoption rates, regulatory backlash, and the plausibility of oligarchic capture—but sees the scenario as a cautionary illustration of current warning signs.
6. The author concludes with a call to defend critical thinking and human intellectual labor, warning that if we fail to recognize AI’s limitations, we risk ceding control to a powerful few who benefit from mass delusion and mediocrity at scale.

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Executive summary: This exploratory proposal advocates for a pilot programme using metagenomic sequencing of wastewater at Auckland Airport to detect novel pathogens entering New Zealand, arguing that early detection could avert the enormous health and economic costs of future pandemics at a relatively low annual investment of NZD 3.6 million.

Key points:

1. Pilot proposal: The author proposes a metagenomic sequencing pilot focused on Auckland Airport—responsible for 77% of international arrivals—using daily wastewater sampling to detect both known and novel pathogens.
2. Cost-benefit analysis: A Monte Carlo simulation suggests that the expected annual pandemic cost to New Zealand is NZD 362.8 million; even partial early detection (e.g., 60% at Auckland) could yield NZD 99–132 million in avoided costs annually, implying a benefit-cost ratio of up to 37:1.
3. Technology readiness: Advances in sequencing technology (e.g., Illumina and Nanopore) have reduced costs and increased sensitivity, making real-time pathogen surveillance more feasible and scalable than ever before.
4. Pandemic risk context: Based on historical data and WHO warnings, the annual probability of a severe pandemic may range from 2–4%, reinforcing the need for proactive surveillance.
5. Expansion potential: The framework could later include additional international and domestic airports, urban wastewater, and even waterways, enhancing both temporal and geographic surveillance coverage.
6. Policy rationale: Current pandemic preparedness spending is relatively low compared to the costs of past pandemics, and the public intuitively supports visible, understandable risks (like fire), underscoring the need to invest in less tangible but equally critical threats like pandemics.

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