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The Economics of Builder Saturation in Digital Markets

Armin Catovic

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.

The Economics of Builder Saturation in Digital Markets

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.
Paper Structure (54 sections, 22 theorems, 45 equations, 1 figure, 6 tables)

This paper contains 54 sections, 22 theorems, 45 equations, 1 figure, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Attention as the Scarce Resource
  4. Information Goods and Near-Zero Marginal Reproduction Cost
  5. Product Proliferation and Monopolistic Competition
  6. Superstar Markets and Convex Reward Structures
  7. Skew Distributions, Preferential Attachment, and Cumulative Advantage
  8. Winner-Take-Most Dynamics and Condensation
  9. Network Effects, Lock-In, and Installed Base Advantages
  10. Congestion and Contest Analogies
  11. Contribution Relative to Existing Literature
  12. Model
  13. Environment
  14. Attention Allocation
  15. Symmetric Benchmark
  16. ...and 39 more sections

Key Result

Proposition 1

When $\alpha = 1$ and $q_i = q$ for all $i$, the stationary distribution of attention shares follows a power law $P(x) \propto x^{-3}$ in the limit $B \to \infty$.

Figures (1)

  • Figure 1: Calibrated simulation of the Builder Saturation model using iOS App Store parameters ($B = 800{,}000$ publishers, $A = 38$ billion downloads). (a) Rank--attention distribution on log--log axes for varying reinforcement strength $\alpha$. Higher $\alpha$ produces a steeper power-law-like region among top-ranked publishers and a sharper collapse in the long tail. (b) Lorenz curves showing the cumulative share of downloads captured by publishers ordered from smallest to largest. At $\alpha = 0.6$--$0.8$, the curve closely resembles the extreme concentration documented in App Store data sensortower2016.

Theorems & Definitions (42)

  • Proposition 1: Power law under homogeneous reinforcement; imported from barabasi1999
  • Proposition 2: Fitness-dependent power law and condensation; imported from bianconi2001competitionbianconi2001bose
  • Proposition 3: Monotone attention dilution
  • proof
  • Proposition 4: Vanishing attention in the limit
  • proof
  • Proposition 5: Elasticity of attention with respect to entry
  • proof
  • Proposition 6: Equilibrium entry
  • proof
  • ...and 32 more