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.
Figures & tables
Parameter
Value
Interpretation
M
10,000
Number of consumers
a
1
Attention budget per consumer
A=M⋅a
10,000
Total attention
B
1,000
Number of builders
z
100
Outside-option weight
qi
∼N(0,1)
Quality draws (i.i.d.)
Table 1: Simulation parameters.
α=0
α=0.5
α=1.0
Share held by top 1%
4.8%
18.3%
62.7%
Share held by top 10%
21.1%
54.6%
91.4%
Gini coefficient
0.31
0.58
0.87
Median / Mean ratio
0.78
0.42
0.04
Table 2: Concentration metrics after T=500 reallocation steps for varying reinforcement strength α ( B=1,000 , A=10,000 , β=1 , δ=0.1 ). Higher α produces sharply more concentrated outcomes.
B
sˉ(B)
πˉ(B)=sˉ(B)−k
100
50.0
49.0
500
16.7
15.7
1,000
9.09
8.09
5,000
1.96
0.96
9,900
1.00
0.00
50,000
0.20
−0.80
Table 3: Average attention and profit per builder as B increases ( A=10,000 , z=100 , p=1 , k=1 ). The zero-profit equilibrium obtains at B∗=9,900 .
Table 5: Calibrated simulation results vs. empirical targets (iOS App Store). The α=0.6 parameterisation produces concentration metrics broadly consistent with observed data.
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 α . Higher α 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 α=0.6 – 0.8 , the curve closely resembles the extreme concentration documented in App Store data [ 9 ] .