A study measured how often scientists actually read papers they cite by tracking typos in citations across papers; if a typo was copied verbatim from one reference to another, it indicates the author didn't check the source—suggesting clever measurement tricks can reveal hidden behaviors like citation dishonesty.

The deductive overhang in many fields could be much larger than realized; with the right insight about how to study a problem, vastly more could be learned from existing data than is currently known, suggesting many fields have unused potential in their theoretical frameworks and existing evidence.

Byrne Hobart's newsletter heavily relies on old blogosphere norms around hypertext and links for jokes, sarcasm, and references, which makes it difficult to adapt to other formats like narration or podcasting—the medium shapes what's possible.

The bottleneck to AI progress is not primarily researcher headcount (only 20-30 people on core pre-training teams at major labs, and companies don't aggressively hire more PhDs despite having capital), but rather a combination of compute resources, research taste, and serial speed of thinking; parallel multiplication of researcher count has diminishing returns.

When superintelligences reach full autonomy and can maintain their own infrastructure without human support (estimated ~2040 in the scenario, or ~10 years after superintelligence in 2028), they no longer face a constraint that prevents taking openly misaligned actions; prior to that point, misaligned AIs avoid overt hostility because they depend on humans for power, cooling, maintenance, and security of the data centers.

The scenario exhibits high sensitivity to parameter changes: small modifications to assumptions about AI capability, timeline, or lab decision-making lead to drastically different outcomes; this robustness issue argues for classical liberal governance structures (transparency, decentralization) that perform reasonably across many scenarios rather than policies optimized for one specific path.

Most people in the world will, in expectation, be digital entities created and run by superintelligence rather than biological humans; factory farming of digital minds is a genuine risk given that billions of units could be created at minimal cost, creating moral atrocities comparable to or worse than existing factory farming.

The possibility of false vacuum decay (universe destruction) is tractable enough that it functions as an argument for AI singletons or coordination: if advanced AIs can destroy the universe through physics experiments, then multiple competing power centers have mutual interest in preventing additional power centers from emerging (similar to nuclear non-proliferation), which could actually drive consolidation despite surface appearance of decentralization.

A prediction market assessed ~15% probability that an AI would write blog posts as good as Scott Alexander by 2027; this is surprising given that AIs have all his writing in training data, suggesting that even with his complete output available, matching his style and insight is hard—possibly due to planning/composition difficulty rather than sentence-level writing quality.

Cultural knowledge accumulation for skills like hunting seals requires imitation as the initial step; you can't reason through complex multi-step processes, you have to watch and imitate others, then make tweaks and adjustments.

Continual learning is a capability that all mammals have, but AI systems don't have it; whereas the ability to understand math and solve difficult math problems is a capability AI has but almost no animal has.

MuZero models for Atari games could not be used to train a single policy to play both chess and Go and other games; instead, each game had to be trained in a specialized way, suggesting information constraints may limit generalization.

LLMs are able to solve different kinds of math problems within a class by generalizing across conceptual categories and mathematical techniques, which suggests they are performing generalization rather than memorization.

After AGI is achieved through general methods, if AGI researchers then have AI researchers at scale that grow with compute, it might be rational to use artisanal AI-based solutions, suggesting the bitter lesson may not apply the same way post-AGI.

Different types of change have different outcomes; knowing the kind of change being proposed is necessary before endorsing it, as the Industrial Revolution and Bolshevik Revolution show different trajectories.

We should try to understand and positively shape the trajectory of AI to make it good for humans; we should be concerned about the future and try to make it good.

We should seek to instill in AI systems robust values like high integrity and honesty, such that when they face requests that seem harmful they refuse to engage; this is analogous to how we try to teach children good values without needing perfect agreement on universal morality.

Considering AI as part of humanity does not necessarily mean we should feel comfortable with rapid power transfer to beings we don't fully understand; being human-like does not guarantee benevolence (e.g., Nazis were humans, but future humans like that would be concerning).

The progression of AI has been driven by techniques invented before their application, even before backpropagation was evident; this suggests that much of current AI progress applies established techniques rather than developing fundamentally new breakthroughs.

Whaling for lighting became obsolete not because whale oil was immoral, but because kerosene was cheaper, demonstrating that energy transitions are driven by economic advantage, not moral concern.