Topic 01 / Technology & AI
Technology Notes
45 articles · 10/13/2026 updated
Two Worlds of Technology: AI for Productivity in America, AI for Attention in China
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Read articleThe Digital Double and Knowing Yourself: AI as a Cognitive Mirror
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Read articleAI: When You Cannot Afford Louis Vuitton, a Bag with Its Label Will Do
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Read articleWriting a Constitution for AI: Competing Logics and the Reshaping of Power
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Read articleAGI and the End of Human Marginal Productivity?
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Read articleAI and Self-Knowledge: Are We Building Our Own Information Cocoon?
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Read articleA New Anchor for AI Valuations: The Physical Constraints of Compute Costs
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Read articleA New Anchor for AI Valuations: The Physical Constraints of Compute Costs — Illustrated
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Read articleIs SaaS Finished? Remember the Advantages of Carbon-Based Life
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Read articleFrom DAU to TPD: Measuring Value through Compute Consumption
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Read articleAnother Doomsday Forecast: Citrini Gives an Old Story an AI Makeover
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Read articleAnthropic versus the Pentagon: Moral Scruples or Playing God?
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Read articleThe Pentagon Finally Bans Anthropic
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Read articlePower Games: The Pentagon, Anthropic, OpenAI, and xAI
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Read articleHerding Sheep or Chopping Wood? Your Workflow Is Your Pasture
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Read articleDeepSeek V4: DualPath and the Bandwidth Bottleneck
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Read articleA Counterintuitive Survey of OpenClaw at Work
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Read articleAfter a Philosopher Leaves, We Finally Ask What Our Products Are Killing
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Read articleAfter the Subsidies Recede: Revisiting My Compute Valuation Model
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Read articleWhat Is an Ontology, and How Is an Ontology-Based Agent Different From a Workflow Agent?
A comparison of workflow and ontology-based agents, covering concepts, relationships, control flow, business scenarios and operational trade-offs. The original article's scores are illustrative author judgments, not measured benchmarks or evidence that either architecture guarantees accuracy.
Read articleWhy Anthropic Fable Was Taken Down: The Story Spreading on X
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Read articleWhy AI Quant Backtests Shine while Live Trading Loses Money
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Read articleA Weekend Conversation with a CFO: Why Published Experts Cannot Always Be Trusted
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Read articleAI Equality Is a False Promise: Giving Everyone a Sniper Rifle
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Read articleWhen Every AI Shares the Same Memory of You
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Read articleThe Openness Trap in AI Infrastructure
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Read articleLoop: Saving Money by Fooling Yourself, or Paying to Stay Honest
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Read articleA Response to Palantir’s CEO: Does Charging per Token Signal a Lack of Confidence?
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Read articleTwo Meanings of Open Source That Many People Confuse
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Read articleThe Model Arms Race: Kimi K3 and the Marketing of 2.8 Trillion Parameters
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Read articleRed Teams in Practice: One AI Writes a Report, Four AIs Challenge It
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Read articleThe FDE Trap: Are AI Companies Becoming Staffing Contractors?
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Read articleAI Finished the Homework. What Happens to the Apprentice?
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Read articleIf OpenAI Has No Innovation, Where Is Kimi K3’s?
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Read articleWhy the LLM API Business Is Such a Poor Business Model
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Read articleModel Valuations: A Poor Business Model at an Expensive Price
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Read articleAgainst the Kondratiev Cycle: Perhaps the Last Wave of Highly Paid Human Labor
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Read articleAfter the Cycle Turns: Protecting the Price of Your Work
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Read articleAfter the Cycle Turns: How Should Personal Capital Follow AI?
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Read articleModels Treat Statistical Deviations as Noise. We May Need to Be Contrary
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Read articleHidden Knowledge: Notes From Building Enterprise AI Knowledge Bases
An FDE's account of the gap between enterprise documentation and practice. It distinguishes missing records, tacit skill and restricted knowledge; separates theory, illustration and design advice; and explains why expert routing and business actions need real-world validation.
Read articleHoliday Reading I: Finite and Infinite Games
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Read articleHoliday Reading II: The Restless Clock
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Read articleHoliday Reflections I: Disruptive Innovation
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Read articleHoliday Reflections II: The End of the MVP and the Return of Experienced B2B Builders
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