# Performance Manipulation: Labor Market Implications in AI-assisted Era

arXiv: https://arxiv.org/abs/2604.22230 · HTML: https://mlsanity.com/paper/2604.22230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Manipulation-Proof Machine Learning](https://mlsanity.com/paper/2004.03865/) (2020)
- [Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis](https://mlsanity.com/paper/2601.20103/) (2026)
- [Mechanism Design under Costly Signaling: the Value of Non-Coordination](https://mlsanity.com/paper/2302.09168/) (2023)

## Built on by (most cited first, 1 of 1)

- [One prompt is not enough: Instruction Sensitivity Undermines Embedding Model Evaluation](https://mlsanity.com/paper/2605.22544/) (2026)

## Abstract (arXiv)

Performance manipulation arises when agents exploit easily measurable, routine tasks to inflate observable outcomes without contributing genuine innovation or expert judgment. We formalize this phenomenon in a game-theoretic model in which agents allocate effort along two margins. Creative effort is non-routine cognitive labor whose return is complementary to the agent's private expertise; it is the scarce input that principals seek. Mechanistic effort is the execution of well-defined, rule-based tasks that raise performance independently of expertise, a commoditized input that AI heavily augments. We establish the existence of a symmetric, monotone pure-strategy equilibrium and show that performance-based screening remains viable so long as evaluations retain a sufficient creative component, but collapses into an uninformative pooling equilibrium once AI capability grows large enough to crowd out creative effort. Comparing contest allocations against a single-agent baseline isolates performance manipulation as the competition-induced over-investment in mechanistic effort, which we show is undertaken systematically by low-type agents but not high-type ones. We further prove that more sharply skewed reward structures mitigate this friction by eliciting greater creative effort across the participant pool. Finally, using a novel, language-model-based methodology to measure both effort types from nearly 1,500 Kaggle competition scripts, we provide robust empirical support for the model's predictions.