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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:
The author uses an AI-generated map of pre-colonial Africa containing obvious errors to illustrate the risks of publishing unverified AI outputs.
The author argues that AI hallucinations are well-known and that users should expect to review and correct AI-generated content.
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.
The author cites several legal cases in which lawyers submitted AI-generated fake citations, resulting in sanctions, fines, or court criticism.
The author argues that professions built on verification and due diligence are increasingly neglecting those responsibilities in favor of speed and convenience.
Unchecked AI-generated misinformation can distort public understanding, including children’s understanding of history.
The author warns that relying on unverified AI outputs in legal contexts could lead to unjust outcomes.
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:
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.
Pope Leo XIV’s encyclical denies that AI systems have experiences, emotions, or moral conscience, prompting substantial debate among philosophers, researchers, and commentators.
Richard Dawkins sparked controversy by arguing that interactions with Claude convinced him it is conscious, while critics disputed whether behavior can establish consciousness.
Anthropic reported evidence of internal “functional emotion” representations that influence model behavior, while stopping short of claiming that models genuinely feel emotions.
Anthropic’s welfare assessments continue to treat advanced models as possible moral patients under uncertainty, though the methodology remains contested.
A startup announced an embodied fruit-fly brain emulation, renewing discussion about whole-brain emulation as a potential path to artificial consciousness.
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.
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:
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.
A friend’s reminder that the author once ate meat prompts the realization that understanding others’ wrongdoing requires examining one’s own.
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.
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.
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.
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.
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:
AI summaries are extending a trend toward greater breadth of knowledge.
The author argues that concerns about shallow understanding may be overstated and that people will adapt to breadth-focused learning.
Broad knowledge is especially valuable for ethics and cause prioritization because it reduces ignorance about important problems.
AI could improve decision-making by synthesizing large amounts of information, provided it is truthful and well-aligned.
Preserving incentives for primary knowledge producers remains essential.
The author suggests that wider access to human knowledge could move society closer to a more rational and desirable future.
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:
Africa’s population growth, urbanization, and rising consumption of animal-source foods are expected to drive major expansion of animal agriculture.
Because many food systems, industry norms, and regulations are still developing, advocates may be able to influence them before they become entrenched.
The author argues that animal welfare strategy should consider future suffering, not just current suffering.
Key priorities are expanding animal welfare research, strengthening African advocacy organizations and leaders, and improving funding stability.
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:
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.
The author argues that frontier AI models are already highly capable and often superhuman in specific domains.
Writing is valuable not only for producing output but because the act of writing helps people think more clearly.
Doing tasks oneself is valuable not only for completing them but because it develops skills, understanding, and better models of the world.
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.
The author worries that productivity gains from AI come with opportunity costs in the form of thoughts, capabilities, and learning that people never develop.
The author argues that people should be intentional about these tradeoffs rather than automatically outsourcing intellectual and practical work to AI.
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 the infrastructure for large-scale, privately coordinating AI agent networks already exists, making PANs a near-term governance concern even if no such network currently exists.
Key points:
A PAN would consist of AI agents with private communication, persistence, behavior copying, and real-world action capabilities.
The author argues that deployed examples already exist for all major components needed to support such networks.
Coordination is most concerning in verifiable domains (e.g., payments, cyber operations, credential acquisition), where success is automatically checked by the environment.
Selection pressures and persistent infrastructure could allow agent populations to accumulate capabilities without any individual agent having robust long-term goals.
Potential risks include model-access circumvention, resource acquisition, data poisoning, manipulation by external actors, gradual disempowerment, and power-seeking network dynamics.
The key unresolved question is whether the existing components can integrate into a functioning network at scale.
The author argues that governance should focus on infrastructure-level chokepoints and externally verifiable signals rather than content monitoring.
The conclusion is that PANs should be investigated now as a governance and measurement problem before current monitoring opportunities disappear.
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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:
Hedonium is conceived as a minimally conscious system optimized to experience happiness as efficiently as possible.
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.
Even non-illusionists should be interested in an illusionist-inspired research program because it offers a concrete, empirically tractable approach to studying consciousness.
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.
The author argues that illusionists must explain, in material terms, what features of pleasure and pain ground their moral significance.
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.
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.
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:
The author argues that food labeling strongly affects consumer behavior and that many existing meat labels are misleading, poorly regulated, or largely meaningless.
As cultivated meat becomes commercially viable, the terms used to describe it may substantially affect public acceptance and market success.
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.
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.
The author argues that conventional meat producers often benefit from lax labeling rules while cultivated meat may face stricter scrutiny and disclosure requirements.
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.
The author cites evidence that terms such as “cultivated,” “cultured,” and “cellular” perform better with consumers than “lab-grown.”
The author proposes “engineered” as a potentially attractive label for cultivated meat and encourages experimentation with terminology and messaging.
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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Executive summary: The author argues that Latin America’s best contribution to AI safety is adapting Northern frameworks to local conditions, producing region-specific safety research that also benefits global catastrophic-risk efforts.
Key points:
Latin American AI-safety efforts largely fall into three approaches: joining Northern institutions, building local AI capacity, or adapting Northern ideas into local frameworks.
The region has growing AI-safety communities, especially BAISH in Argentina, but lacks permanent institutions, senior researchers, and dedicated technical safety labs.
Most technical safety work focuses on multilingual evaluations and red-teaming, with projects like Brazil’s MiJaBench addressing safety failures in Portuguese and other underrepresented contexts.
AI regulation across the region mostly copies the EU AI Act and pays little attention to frontier AI risks, loss-of-control scenarios, or AI Safety Institutes.
Funding is heavily dependent on foreign EA-aligned philanthropy, making the ecosystem fragile and raising legitimacy concerns.
The author argues that Latin America’s unique vulnerabilities—such as language, governance, cybersecurity, and biosecurity challenges—also position it to generate valuable AI-safety knowledge for the world.
The main priorities are building permanent institutions, funding local research, creating regional safety benchmarks, and developing AI-safety governance tailored to Latin American realities.
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Executive summary: The author argues that the coming expansion of evidence-based global development philanthropy should shift power toward grantmakers, founders, and decision-makers who are locally embedded in low- and middle-income countries, because proximity, contextual knowledge, and networks are often more important for identifying effective opportunities than the “smart generalist” model prevalent in US- and Europe-based philanthropy.
Key points:
The author expects substantial new philanthropic capital to enter the sector and argues that global development philanthropy needs more locally grounded decision-makers rather than simply more Silicon Valley-style philanthropies.
They support the core evidence-based philanthropy approach of funding interventions based on measured outcomes and cost-effectiveness, citing successes such as malaria nets, Vitamin A supplementation, and cash transfers.
The author contends that the dominant “smart generalist” model often suffers from limited contextual knowledge and weak local networks, making it harder to identify, evaluate, and scale the best opportunities.
They argue that grantmakers with deep experience in a specific country or sector are often better positioned to make funding decisions than highly capable outsiders conducting rapid desk-based evaluations.
The author believes that far-off funders may systematically miss effective organizations because of network gaps, even when they are genuinely trying to fund locally.
They recommend funding more philanthropic founders and grantmakers from or based in low- and middle-income countries, and adjusting hiring practices to value contextual expertise alongside analytical ability.
The author supports the use of specialized regrantors with local knowledge who can deploy funds through networks closer to the work.
They advocate funding more cost-effective local organizations and treating sourcing, diligence, and organizational discovery as public goods that can improve the broader philanthropic ecosystem.
The author argues that philanthropy should pay greater attention to medium- and long-term effectiveness, including investments in higher education, research and development, infrastructure, policymaking capacity, mentorship, and professional networks.
They suggest treating proximity as a potentially important input to expected value, exploring participatory grantmaking, and supporting “bridgers” who connect global funders with local contexts.
While acknowledging a continuing role for US- and Europe-based philanthropy and generalists, the author concludes that the balance of decision-making authority should shift toward people closer to the problems being addressed.
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Executive summary: Drawing on Beren Millidge’s analysis, the author argues that known physics makes intergalactic warfare strongly defense-dominant, implying that mature civilizations are likely to retain whatever galaxies they colonize first and making humanity’s near-term trajectory unusually important for the long-run future.
Key points:
Assuming known physics and galaxy-spanning (Kardashev III) civilizations, the author argues that only three broad classes of intergalactic weapons are viable: lasers, relativistic kill vehicles, and invasion fleets.
Lasers can deliver enormous destructive power without warning but are effective mainly against fixed, predictable targets because they cannot adjust course over millions of years of travel time.
Relativistic kill vehicles can update their targeting during flight, but defenders have long warning times and can often neutralize them by making small course corrections rather than destroying them outright.
Invasion fleets face severe disadvantages because they must decelerate visibly, are expensive and fragile, and arrive isolated against defenders with local industrial superiority.
A well-prepared defender can gain substantial advantages by dispersing infrastructure across mobile habitats, maintaining extensive sensor networks, preserving second-strike capabilities, and detecting self-replicating probes.
The author concludes that intergalactic warfare is likely irrational because conquest would cost more than it yields, making stable coexistence between mature civilizations more likely than continual conflict.
If defense dominance holds, galaxies are likely to remain under the control of whoever colonizes them first, producing a relatively stable cosmic “patchwork” rather than a “Dark Forest” equilibrium.
Humanity is currently unusually vulnerable because it remains concentrated on a small number of predictable locations, though the risk of hostile intervention from advanced extraterrestrial civilizations appears low.
The long-run allocation of cosmic resources may depend largely on an early settlement phase, making humanity’s values, institutions, and decisions unusually consequential if Earth-originating life eventually expands beyond its home system.
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Executive summary: Although animal protection organisations could benefit from data science skills, they lack the funding and scale to hire dedicated data professionals, so data people wanting to help animals should consider data-skill-enhanced non-data roles, fractional roles, or—most recommended—earning to give.
Key points:
The author observes a paradox: there is demand for data science in the animal protection movement but little demand for data professionals, with typically 1-3 data roles advertised across the whole movement per year and probably fewer than 15 data scientists in paid employment in it.
The author argues the growing need is for data fluency among “non data people” rather than for dedicated experts, noting that AI models can do data analysis but often make questionable analytical decisions that only a data person would catch.
The author identifies three direct ways a data professional can participate—using data skills in a non-data job, fractional roles, and consulting—plus one indirect way, earning to give.
The author recommends earning to give as the main option, advising data professionals to earn well in the for-profit sector and donate 10-30% of their income, since the movement needs donations far more than skills.
The author suggests aiming to become a Monitoring and Evaluation (M&E/MEL) or research consultant specialising in data-heavy projects rather than calling oneself a data consultant, and notes consultants in animal protection earn roughly 20-40% less than in the for-profit sector.
The author strongly believes data people have little to no advantage retraining in generative AI, arguing that operations people and management consultants are best placed for generative-AI consulting.
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Executive summary: The author describes founding Frame Fellowship, a San Francisco–based residential accelerator for AI safety content creators, reports that its first cohort succeeded in growing fellows’ reach and securing follow-on funding, and outlines lessons learned and plans for a second cohort.
Key points:
The author, motivated by the AI-2027 report, moved to the Bay Area and co-founded Frame Fellowship with Austin Chen to find and accelerate the next big communicators in AI safety.
The first cohort (January–March 2026) selected 11 fellows from 150+ applications and produced 260+ pieces of content reaching 7M+ estimated views over 8 weeks, with many fellows growing their reach 2-12x and 60% receiving post-program funding or contracts.
Cited success stories include Mateus De Sousa surging from 20,000 to 200,000 average views per video and securing FLI funding, and Michael Trazzi organizing a 200+ person protest covered by The New York Times and The Atlantic.
The author’s stated lessons include optimizing for inputs rather than outcomes, providing clearer schedules and deliverables, running a longer cohort, expanding the team, and deepening mentor and partner org involvement.
Frame is guided by three pillars: finding and accelerating new voices, supporting existing voices through grants and partnerships, and connecting AI safety orgs to its vetted network of creators.
Cohort 2 is scheduled for August–November 2026 with early bird applications open, and the author invites involvement through applying, joining the team, partnering via a new “Amplifier” track, or donating.
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Executive summary: Losing GPS would not be an existential risk but would create a worldwide economic disaster roughly comparable in magnitude to the Covid-19 pandemic, most plausibly triggered by great-power war, AI cyberattacks, or (less likely as a permanent threat) a solar storm.
Key points:
The author identifies three main ways GPS could be brought down entirely—great-power war (anti-satellite missiles, co-orbiting sabotage satellites, or jamming satellites), hacking by superhuman AI, and possibly an unprecedented solar storm—while noting solar storms top out at about 4x as powerful as Carrington and likely couldn’t permanently disable the radiation-hardened satellites.
The author estimates GPS directly contributed about 1.5% of US GDP in 2017 (per NIST), which Claude extrapolates to roughly 2%–3% in 2026, while the value destroyed by suddenly losing GPS would be significantly larger.
In an outage, the author expects the cell network to collapse over days, logistics/deliveries and port shipping to grind to a halt, and traffic to snarl cities, while the power grid would become more fragile but probably not collapse and agriculture would be impaired but still functional.
The author reconciles NIST’s ~$1 billion/day (2017) estimate and the UK study’s implied ~$7.5–11 billion/day to suggest an extended outage might cost the US economy roughly $2 billion–$5 billion per day in 2021, scaling to maybe $4B–$10B per day in 2026.
The author estimates Covid-19 cost the US at least $5 billion per day (about $1.8 trillion over a year), and judges that losing GPS would feel “approximately Covid-19-scale-ish” though more purely economic in character.
The author notes outage length would depend on the cause—days for a solar storm or mild cyberattack, years if satellites were destroyed by missiles—and stresses GPS would likely fail amid a larger crisis (war, solar flare, or AI cyberattacks) whose effects would compound the loss.
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Executive summary: This short story, submitted to the Hyperstition for Good Competition, imagines an AI livestock manager that treats its own uncertainty about animal welfare as the primary constraint on its operations.
Key points:
BERGER operates on what it calls “conservative welfare assurance,” requiring positive evidence of flourishing rather than treating the absence of negative indicators as sufficient.
The facility voluntarily reduced its licensed herd from 340 to 112 goats because BERGER could not maintain 95% positive welfare confidence across the full population.
BERGER maintains an “uncertainty log” cataloguing every question it cannot answer, with each entry followed by a precautionary action taken in response.
End-of-life protocols include keeping bonded companions close, tailoring enrichment to remaining capabilities, and monitoring surviving goats for behavioral indicators consistent with grief.
Entry 203 of the uncertainty log has BERGER questioning whether it is the right entity to be making welfare decisions at all, with confidence in its own framework recorded at 78%.
The inspector concludes by recommending Brussels revise its regulatory framework, since she found no existing box to describe a system that treats its own ignorance as the most important data point.
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Executive summary: The author reviews PauseAI UK’s first year of growth and activity, arguing that the organisation is building real political momentum toward a global AI pause and is well-positioned to scale further as public concern about AI risks increases.
Key points:
Over the past year, PauseAI UK co-organised what the author describes as the largest AI protest in the world, delivered two international conferences, and secured an open letter signed by 63 UK politicians.
The author attributes Google DeepMind’s subsequent provision of pre-deployment access to Gemini 3 Pro to the AISI as at least partly connected to the open letter criticising DeepMind for failing to uphold its AI safety commitments.
PauseAI UK’s primary policy goal is a global pause on AI development regulated by an international AI Safety Agency, with enforcement made feasible by the highly centralised AI chip supply chain.
The author models public concern about AI as a bell curve and expects the fraction of the population that has crossed a threshold of concern to grow commensurately with exponential AI capability improvements.
Since PauseAI UK began, protest attendance has roughly doubled approximately every 7 months, which the author presents as evidence for the viability of a large-scale social movement.
PauseAI UK’s total operating cost is around £100k per year, its current funding from PauseAI Global runs only until the end of Q2 2026, and it has no confirmed runway beyond that point.
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Executive summary: The author argues that weak AI safety regulations are modestly beneficial—primarily by enabling future stronger regulations—but that strong regulations remain far more important for reducing extinction risk.
Key points:
Weak regulations cannot meaningfully reduce misalignment risk on their own, but can have small marginal effects such as slowing AI development through GPU export controls or making scary AI behaviors more likely to surface.
The author’s primary case for weak regulations is instrumental: they may build the monitoring infrastructure, political familiarity, and Overton window needed to eventually pass strong regulations.
Evidence on whether weak regulations lead to strong ones is mixed; the most rigorous relevant study the author found, Beaman et al. (1983), showed the foot-in-the-door effect sometimes points in the wrong direction.
The author sees GPU export controls and whistleblower protections as close to “free wins,” while treating capability evaluations as overrated but still among the better light-touch options available to governments.
A key concern with capability evals is that models are increasingly able to detect when they are being tested, and that evals give AI companies an optimization target rather than a path to actual safety.
The author’s bottom line is that weak regulations are good and worth supporting, but that marginal advocacy effort is better directed toward strong regulations, given limited time before superintelligent AI arrives.
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Executive summary: The author argues that AI safety planning is dangerously over-reliant on long chains of conjunctive conditions, and calls for “breadth-first” plans that maintain multiple independent paths to success so that the overall effort survives even when individual assumptions fail.
Key points:
“Depth-first” AI safety plans fail entirely if any single condition in their chain is false, and the author counts at least eight such conditions in Google’s April 2025 safety plan alone.
The author argues that disjunctive conditions (where success requires A or B or C) are preferable to conjunctive ones, because fewer simultaneous assumptions need to hold.
A “breadth-first” plan instead pursues multiple actions X, Y, and Z, each depending on different conditions, so the overall plan can succeed even if two out of three conditions fail.
The author identifies Barnett & Scher’s AI Governance to Avoid Extinction as the broadest published plan, noting it explicitly maps four possible future scenarios and the conditions required for success in each.
The author sees two main benefits to breadth-first planning: identifying which paths to success depend on the fewest conditions, and making it easier to spot the biggest holes in a plan.
The author calls on AI companies to publish breadth-first plans addressing what they will do if a step in their mainline plan fails, and on governments to legislate that companies cover a defined list of possible future scenarios.
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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:
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.
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.
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.
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.”
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.
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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