The Passion Economy (Guest essay by Li Jin)
Argues new platforms let individuals monetize their individuality rather than commoditizing labor, reshaping marketplace design.
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Argues new platforms let individuals monetize their individuality rather than commoditizing labor, reshaping marketplace design.
Explains how marketplaces move up the hierarchy by increasing ters and defensibility once initial liquidity is achieved.
Introduces a layered framework for building enduring marketplaces, starting with focus on a single high-value use case.
Introduces the concept of the passion economy, detailing how new digital platforms allow individuals to monetize their unique skills and personalities, fostering a new creative middle class.
An essay arguing that the direction of machine learning research is heavily constrained and guided by which ideas happen to run efficiently on current hardware architectures.
Social networks succeed by operating as social capital games where humans act as status-seeking monkeys optimizing for proof-of-work and utility.
Develops a novel methodology to link AI breakthroughs to specific occupational abilities, providing empirical measures of labor market exposure.
Details supply-versus-demand sequencing tactics used by top marketplaces to bootstrap a two-sided network.
Traces the evolution from listings to managed marketplaces and argues the next wave will digitize regulated service industries.
Provides a structured overview of the economic questions surrounding AI, framing it as a shift in prediction costs and outlining research agendas on productivity, jobs, and policy.
Comprehensive taxonomy arguing network effects account for 70% of tech value creation, cataloging 13+ distinct types.
An exhaustive taxonomy detailing different types of network effects, illustrating how marketplaces can build defensive moats around supply-side, demand-side, and local network dynamics.
Establishes a task-based framework to analyze how automation competes with the creation of new complex tasks to determine labor demand.
Introduces the idea of hidden growth ceilings that companies must anticipate, using Amazon's shipping-fee asymptote as the case study.
A study on how DAOs and blockchain systems can support self-organisation and peer production, analyzing the governance models of decentralized collaborative organizations.
Explains how network effects strengthen or weaken over a company's lifecycle and how to measure and nurture them.
An analysis of the real-world governance structures of major cryptocurrencies, investigating power distribution, decision-making dynamics, and centralization trends.
A landmark analysis showing that the amount of compute used in the largest AI training runs has been doubling every 3.4 months, far outstripping Moore's Law.
Examines whether macroeconomics and aggregate demand matter when analyzing the impacts of AI and automation on jobs, productivity, and inequality.
Decentralized networks use crypto-economic incentives to stay open and aligned with users and creators, avoiding the inevitable attraction-to-extraction pivot of centralized platforms.
Integrates AI into long-run economic growth models, exploring automation of invention processes and potential new growth regimes.
A foundational essay that categorizes blockchain decentralization into three distinct dimensions: architectural, political, and logical.
An analysis of AI as a prediction technology that lowers the cost of prediction, transforming business decision-making and economic forecasting.
Analyzes the phenomenon of toy unboxing channels on YouTube to map the intersections of child labor, media commercialization, and platform capitalism.