The Economics of Builder Saturation in Digital Markets

• URL: http://arxiv.org/abs/2603.23685v2
• Date: Thu, 26 Mar 2026 10:11:07 GMT
• Title: The Economics of Builder Saturation in Digital Markets
• Abstract summary: We introduce the Builder Saturation Effect, formalizing a model in which production scales elastically but human attention remains finite.<n>We show that equilibrium outcomes exhibit declining average payoffs and increasing concentration, consistent with power-law-like distributions.<n>Results suggest that AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success.
• Score: 0.7252027234425333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative AI systems have dramatically reduced the cost of digital production, fueling narratives that widespread participation in software creation will yield a proliferation of viable companies. This paper challenges that assumption. We introduce the Builder Saturation Effect, formalizing a model in which production scales elastically but human attention remains finite. In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer, even as total output expands. Extending the framework to incorporate quality heterogeneity and reinforcement dynamics, we show that equilibrium outcomes exhibit declining average payoffs and increasing concentration, consistent with power-law-like distributions. These results suggest that AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success. Contribution type: This paper is primarily a work of synthesis and applied formalisation. The individual theoretical ingredients - attention scarcity, free-entry dilution, superstar effects, preferential attachment - are well established in their respective literatures. The contribution is to combine them into a unified framework and direct the resulting predictions at a specific contemporary claim about AI-enabled entrepreneurship.

Related papers

• Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems [0.0]
  This paper reconceptualizes creativity as a distributional and process-based property that emerges under shared constraints and competitive incentives.<n>We introduce a quantitative framework for measuring creativity as novelty in synthesis, operationalized through idea generation and idea transformation within embedding space.<n>The findings suggest that in the age of generative AI, distinctiveness rather than fluency becomes the primary signal of human creative capability.
  arXiv  Detail & Related papers  (2026-04-10T12:49:17Z)
• Generative AI as a Non-Convex Supply Shock: Market Bifurcation and Welfare Analysis [4.887749221165767]
  We show how the GenAI cost shock reshapes the market into exit AI, and human segments, generating a -class hollow'' output.<n>We conclude that optimal governance must be toward laissez-faire congestion management.
  arXiv  Detail & Related papers  (2026-01-18T17:00:40Z)
• A Sustainable AI Economy Needs Data Deals That Work for Generators [56.949279542190084]
  We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality.<n>We analyze 73 public data deals and show that the majority of value accrues to aggregators.<n>We propose an Equitable Data-Value Exchange Framework to enable a minimal market that benefits all participants.
  arXiv  Detail & Related papers  (2026-01-15T01:05:48Z)
• The Economics of Information Pollution in the Age of AI: A General Equilibrium Approach to Welfare, Measurement, and Policy [4.887749221165767]
  The advent of Large Language Models (LLMs) represents a fundamental shock to the economics of information production.<n>By asymmetrically collapsing the marginal cost of generating low-quality, synthetic content while leaving high-quality production costly, AI systematically incentivizes information pollution.<n>This paper develops a general equilibrium framework to analyze this challenge.
  arXiv  Detail & Related papers  (2025-09-17T06:31:17Z)
• Exploring Bias in over 100 Text-to-Image Generative Models [49.60774626839712]
  We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms like Hugging Face.<n>We assess bias across three key dimensions: (i) distribution bias, (ii) generative hallucination, and (iii) generative miss-rate.<n>Our findings indicate that artistic and style-transferred models exhibit significant bias, whereas foundation models, benefiting from broader training distributions, are becoming progressively less biased.
  arXiv  Detail & Related papers  (2025-03-11T03:40:44Z)
• Variance reduction in output from generative AI [11.248899695350323]
  We demonstrate that generative AI models are inherently prone to the phenomenon of "regression toward the mean"<n>We discuss potential social implications of this phenomenon across three levels-societal, group, and individual-and two dimensions-material and non-material.
  arXiv  Detail & Related papers  (2025-03-02T21:34:10Z)
• Competition and Diversity in Generative AI [3.1507640991166603]
  We show that competitive markets select for diverse AI models, mitigating monoculture.<n>A generative AI model that performs well in isolation (i.e., according to a benchmark) may fail to provide value in a compet- itive market.
  arXiv  Detail & Related papers  (2024-12-11T18:34:31Z)
• Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
  We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.<n>First, it is not sustainable, as, despite efficiency improvements, its compute demands increase faster than model performance.<n>Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
  arXiv  Detail & Related papers  (2024-09-21T14:43:54Z)
• The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment [19.963531237647103]
  Generative artificial intelligence (AI) exhibits the capability to generate creative content akin to human output with greater efficiency and reduced costs. This paper empirically investigates the impact of generative AI on market equilibrium, in the context of China's leading art outsourcing platform. Our analysis shows that the advent of generative AI led to a 64% reduction in average prices, yet it simultaneously spurred a 121% increase in order volume and a 56% increase in overall revenue.
  arXiv  Detail & Related papers  (2023-11-13T04:31:53Z)
• Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition [99.7047087527422]
  In this work, we demonstrate that competition can fundamentally alter the behavior of machine learning scaling trends. We find many settings where improving data representation quality decreases the overall predictive accuracy across users. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare.
  arXiv  Detail & Related papers  (2023-06-26T13:06:34Z)
• Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima [114.31577038081026]
  We propose an efficient method that tackles the designer's and agents' problems simultaneously in a single loop. Although the designer does not solve the equilibrium problem repeatedly, it can anticipate the overall influence of the incentives on the agents. We prove that the algorithm converges to the global optima at a sublinear rate for a broad class of games.
  arXiv  Detail & Related papers  (2021-10-04T06:53:59Z)
• Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer [72.5190560787569]
  In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets. Our proposal posits a meta-distributional scenario, where the data generating mechanism is invariant across the label-conditional feature distributions. This allows us to leverage a causal data inflation procedure to enlarge the representation of minority classes.
  arXiv  Detail & Related papers  (2020-11-25T00:13:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.