Executive summary: A potential crash in AI stocks, while not necessarily reflecting long-term AI progress, could have negative short-term effects on AI safety efforts through reduced funding, shifted public sentiment, and second-order impacts on the AI safety community.

Key points:

1. AI stocks, like Nvidia, have a significant chance of crashing 50% or more in the coming years based on historical volatility and typical patterns with new technologies.
2. A crash could occur if AI revenues fail to grow fast enough to meet market expectations, even if capabilities continue advancing, or due to broader economic factors.
3. An AI stock crash could modestly lengthen AI timelines by reducing investment capital, especially for startups.
4. The wealth of many AI safety donors is correlated with AI stocks, so a crash could tighten the funding landscape for AI safety organizations.
5. Public sentiment could turn against AI safety concerns after a crash, branding advocates as alarmists and making it harder to push for policy changes.
6. Second-order effects, like damaged morale and increased media attacks, could exacerbate the direct impacts of a crash on the AI safety community.

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Executive summary: Shareholder activism has shown promise as an effective advocacy tool for animal welfare causes, with some successes already, and opportunities exist to expand its use if done carefully in coordination with existing groups.

Key points:

1. Shareholder activism leverages partial ownership of companies to achieve reforms, with increasing use and effectiveness in recent years.
2. Key requirements include owning a certain amount of stock, dedicating staff time for advocacy, and having legal assistance to navigate procedures.
3. Shareholder resolutions typically receive <10% approval but can still prompt company action; proxy fights are an expensive escalation tactic.
4. Shareholder activism is most effective when coordinated with broader public campaigns on the target issue.
5. The literature generally finds significant positive effects from shareholder activism, with certain factors predicting greater success.
6. Shareholder activism is used less for animal advocacy than other causes, and is disproportionately US/Europe-focused with challenges in other regions.

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Executive summary: The shrimp paste industry, which relies heavily on wild-caught Acetes shrimps, raises significant animal welfare concerns that warrant further research and potential interventions to reduce suffering.

Key points:

1. Acetes shrimps are likely the most utilized aquatic animal for food globally, with trillions harvested annually for shrimp paste production in Southeast Asia.
2. Shrimp paste production involves sun-drying, grinding, and fermenting the shrimp, and is deeply rooted in the region's cultural heritage and cuisine.
3. Small coastal communities and larger manufacturing facilities are involved in the supply chain, both facing challenges related to fluctuating shrimp populations, food safety, and waste.
4. Acetes shrimps likely endure significant suffering during capture (injury, suffocation) and processing (osmotic shock, dehydration, stress) while still alive.
5. Potential interventions include developing gentler capture methods, implementing humane slaughter practices, and promoting vegan alternatives, but more research is needed on Acetes shrimp sentience and industry specifics.
6. Raising consumer awareness about welfare issues and responsible sourcing could help drive higher industry standards and regulations.

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Executive summary: University EA community building can be highly impactful, but important pitfalls like being overly zealous, open, or exclusionary can make groups less effective and even net negative.

Key points:

1. University groups can help talented students have effective careers by shaping their priorities and connections at a pivotal time.
2. Being overly zealous or salesy about EA ideas can put off skeptical truth-seekers and create an uncritical group.
3. Being overly open and not prioritizing the most effective causes wastes limited organizer time and misrepresents EA.
4. Being overly exclusionary and dismissive of people's ideas leads to insular groups with poor epistemics.
5. These pitfalls are hard to notice as an organizer, so it's important to get outside perspectives and map your theory of change.
6. An ideal group focuses on truth-seeking discussions, engaging substantively with newcomers, and helping people reason through key questions and career options without pressure.

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Executive summary: Open Philanthropy highlights impactful projects from their 2023 Global Health and Wellbeing grantees, spanning areas such as air quality monitoring, vaccine development, pain research, and farm animal welfare.

Key points:

1. Dr. Sachchida Tripathi deployed 1,400 low-cost air quality sensors in rural India to improve data and encourage stakeholder buy-in for interventions.
2. The Strep A Vaccine Global Consortium (SAVAC) is accelerating the development and implementation of strep A vaccines, which could prevent over 500,000 deaths per year.
3. Dr. Allan Basbaum developed a method for simultaneously imaging the brain and spinal cord of awake animals, potentially advancing pain research and treatment.
4. The Institute for Progress is partnering with the NSF to design experiments and improve scientific funding processes.
5. The Open Wing Alliance has secured 2,500+ cage-free commitments and 600+ broiler welfare policies from corporations worldwide.
6. The Aquaculture Stewardship Council is incorporating mandatory fish welfare standards into their certification, potentially improving the lives of billions of farmed fish.

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Executive summary: Deep honesty, which involves explaining what you actually believe rather than trying to persuade others, can lead to better outcomes and deeper trust compared to shallow honesty, despite potential risks.

Key points:

1. Shallow honesty means not saying false things, while deep honesty means explaining your true beliefs without trying to manage the other party's reactions.
2. Deep honesty equips others to make best use of their private information along with yours, strengthening relationships, though it carries risks if not well-received.
3. Deep honesty is situational, does not mean sharing everything, and is compatible with kindness and consequentialism.
4. Challenging cases for deep honesty include large inferential gaps, uncooperative audiences, and multiple audiences.
5. Practicing deep honesty involves asking yourself "did it feel honest to say that?" and focusing on what is kind, true and useful.
6. Experimenting with deep honesty in select situations, rather than switching to it completely, is recommended to see its effects.

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Executive summary: The SatisfIA project explores aspiration-based AI agent designs that avoid maximizing objective functions, aiming to increase safety by allowing more flexibility in decision-making while still providing performance guarantees.

Key points:

1. Concerns about the inevitability and risks of AGI development motivate exploring alternative agent designs that don't maximize objective functions.
2. The project assumes a modular architecture separating the world model from the decision algorithm, and focuses first on model-based planning before considering learning.
3. Generic safety criteria are hypothesized to enhance AGI safety broadly, largely independent of specific human values.
4. The core decision algorithm propagates aspirations along state-action trajectories, choosing actions to meet aspiration constraints while allowing flexibility.
5. This approach is proven to guarantee meeting expectation-type goals under certain assumptions.
6. The gained flexibility can be used to incorporate additional safety and performance criteria when selecting actions, but naive one-step criteria are shown to have limitations.
7. Using aspiration intervals instead of exact values provides even more flexibility to avoid overly precise, potentially unsafe policies.

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Executive summary: The US, EU, and China are taking different approaches to classifying and regulating AI systems, with key differences in centralization, scope, and priorities.

Key points:

1. AI systems can be classified by application, compute power, risk level, or as a subclass of algorithms. The classification approach informs the point of regulation in the AI supply chain.
2. Centralized vs decentralized enforcement and vertical vs horizontal regulations are key structural choices with important tradeoffs for AI governance.
3. China is taking an iterative, vertical approach focused on specific AI domains, with an emphasis on social control and alignment with government priorities.
4. The EU AI Act takes a comprehensive, centralized, horizontal approach prioritizing citizen rights protection, with strict requirements for high-risk AI systems.
5. The US is pursuing a decentralized approach driven by executive actions, with a focus on restricting China's AI capabilities through semiconductor export controls.

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Executive summary: Insider activism, where concerned citizens participate in activism within the institutions they work in, could be a promising approach for animal advocacy in corporations, government departments, political parties, and large NGOs.

Key points:

1. Corporate employee activism has been successful in influencing policies for issues like sexism, racism, and the environment, but the generalizability to animal advocacy is uncertain due to potentially lower levels of employee support.
2. Targeting corporate offices rather than retail locations may be more tractable for animal advocacy due to employees' greater ability to engage in activism and access to decision-makers.
3. Union "salting" provides some evidence for the potential of activist entryism, but the success rate is unclear and may be lower for causes with less direct employee self-interest.
4. Corporate undercover investigations could provide valuable information to inform campaign asks and assess company sentiment, but come with legal risks that need to be carefully considered.
5. Government employee activism has had some success in influencing policy for environmental and feminist causes, but evidence is limited and generalizability to animal advocacy is uncertain.
6. Insider activism is inherently difficult to study empirically, so evidence is mostly from theory and case studies. It could be a reasonable initial career path for skill-building, but direct impact is uncertain.

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Executive summary: SecureBio is working on biosecurity projects to mitigate risks from engineered pathogens and the potential threat of AI systems creating bioweapons, using a Delay/Detect/Defend framework and collaborating with AI companies on risk evaluation.

Key points:

1. SecureBio's Delay/Detect/Defend framework aims to avert engineered pathogen threats through gene synthesis screening (Delay), early pathogen detection via metagenomics (Detect), and Far-UVC research for transmission protection (Defend).
2. SecureBio is collaborating with frontier AI companies to build evaluation tools and mitigation strategies for potential biorisk from AI systems, which is their highest marginal value-add project for fundraising.
3. Without SecureBio, there may be a coverage gap in addressing exponential biorisks, as other organizations like Gryphon Scientific and RAND Corporation have a different focus.
4. SecureBio believes AI could potentially cause large-scale harm through attacks on financial systems, weapons of mass destruction, and bioweapons, with the latter being a high-leverage way for an agentic AI to eliminate human obstacles.

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