Third installment in a series modeling post-AGI economic growth, examining how reoptimizing production recipes for post-AGI factor prices (cheap labor, fast capital reproduction) would accelerate economic doubling beyond what fixed-recipe models suggest. Uses 2017 US input-output tables as baseline.
Category intelligence
Research Briefing — May 23, 2026
11 current items analyzed and ranked.
Executive synthesis
Research Summary
Today's research and commentary cluster around post-AGI economics, AI safety methodology, and timely model evaluations, with limited novel technical contributions.
Transformative AI Economics
- *The AI Industrial Explosion (Part 3)* models post-AGI growth by reoptimizing production recipes for cheap labor and capital-intensive factor prices.
- *Will we really put data centers in space?* offers concrete cost/thermal analysis of orbital data centers, pushing back on speculative Musk-era claims.
AI Safety & Alignment
- *Which technical AI safety fields are automated first?* uses feedback quality and economic incentives to predict which subfields frontier labs will automate.
- *Counting Arguments in AI Safety* critiques goal-space counting arguments by drawing parallels to Bertrand's Paradox.
- *We made a map of the doom debate* releases an interactive probabilistic tree of AI threat pathways from AI Safety Camp.
- *AI is Not Normal Technology* rebuts Narayanan & Kapoor, citing biosecurity as a concrete disanalogy.
Model Reviews & Practice
- Zvi reviews Gemini 3.5 Flash as best-at-speed but inferior to Claude Opus 4.7 and GPT-5.5 for serious work.
- *Notes on Collaborating with Claude Opus* documents prompting patterns showing reasoned instructions improve compliance.
Strategy & Timelines
- The proposed DIAL distribution (decision importance adjusted for leverage) reframes timelines reasoning around decision-relevant probability mass.
- A philosophical defense of strong longtermism rounds out the field.
Key Themes
Primary evidence
Top Ranked Signals
Analysis of the technical and economic feasibility of orbital data centers (ODCs) for AI compute, examining whether claims by Musk and others about space-based AI are realistic. Concludes that cost-competitiveness depends almost entirely on Starship reusability achieving Falcon-like economics (~$250/kg to orbit).
Zvi's review of Google's recently-released Gemini 3.5 Flash, arguing it's the best at its speed point but not preferable to Opus 4.7 or GPT-5.5 for most uses. Covers other Google I/O announcements.
Which technical AI safety fields are going to be automated first?
By Chamod Kalupahana
Analysis of which technical AI safety subfields are most likely to be automated first by frontier labs, using feedback quality and economic incentive as the two key factors. Notes Anthropic's use of Mythos and UKAISI evaluations as early signals.
Examines the structure of 'counting arguments' in AI doom reasoning (vast goal space → most goals are unfriendly), drawing parallels to Bertrand's Paradox to question whether the chosen measure is principled. Engages with prior LessWrong critiques.
AI Safety Camp project produced an interactive tree-structured map of AI threat pathways, allowing users to set probabilities for each branch and identify cruxes with others. Aims to systematize the P(Doom) debate by decomposing assumptions.
Response to Narayanan and Kapoor's 'AI as Normal Technology' essay, arguing AI is fundamentally different and citing biosecurity as the most concrete catastrophic risk. Uses chess history as a parallel.
Practical notes on prompting/collaboration patterns with Claude Opus 4.7, including how reasoning behind instructions improves compliance, the value of labeled response sections, and pitfalls of negatively-framed instructions. Practitioner observations.
Linkpost proposing the 'DIAL distribution' framework (decision importance adjusted for leverage) for reasoning about AI timelines, advocating distributions over point estimates and weighting by decision-relevance.
Philosophical defense of strong longtermism, arguing the vast scale of potential future people means most expected impact of our actions concerns the long-run future. Standard utilitarian/longtermist argument with minor framing updates.
Discussion of rising US insurance premiums, breaking down where premium dollars go and considering causes. Not AI-related.