Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry

TL;DR

This paper explores how multi-objective learning, balancing safety and performance, can lower market entry barriers for new ML companies by reducing data requirements compared to incumbents, supported by theoretical analysis and scaling laws.

Contribution

It introduces a multi-objective high-dimensional regression framework to analyze market entry barriers and demonstrates how such considerations can significantly reduce data needs for new entrants.

Findings

Multi-objective learning reduces data requirements for new entrants.

Reputational considerations influence market dynamics.

Scaling laws for multi-objective high-dimensional regression are established.

Abstract

Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this work, we study this issue from both an economic and an algorithmic point of view, focusing on a phenomenon that reduces barriers to entry. Specifically, an incumbent company risks reputational damage unless its model is sufficiently aligned with safety objectives, whereas a new company can more easily avoid reputational damage. To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry -- the required number of data points can be significantly smaller than the incumbent company's dataset size. En route to proving these results, we develop scaling laws for high-dimensional linear regression in multi-objective environments, showing that the scaling rate becomes slower when the dataset size is large, which could be of independent interest.