# The Hitchhiker's Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semantics

arXiv: https://arxiv.org/abs/2607.13077 · HTML: https://mlsanity.com/paper/2607.13077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 54 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 26 of 26)

- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)](https://mlsanity.com/paper/2510.22954/) (2025)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision](https://mlsanity.com/paper/2109.08203/) (2021)
- [Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study](https://mlsanity.com/paper/2310.02059/) (2023)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [LLMs Meet Library Evolution: Evaluating Deprecated API Usage in LLM-based Code Completion](https://mlsanity.com/paper/2406.09834/) (2024)
- [All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds](https://mlsanity.com/paper/2411.18810/) (2024)
- [Generative Monoculture in Large Language Models](https://mlsanity.com/paper/2407.02209/) (2024)
- [Practices and Challenges of Using GitHub Copilot: An Empirical Study](https://mlsanity.com/paper/2303.08733/) (2023)
- [The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot](https://mlsanity.com/paper/2409.08379/) (2024)
- [Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes](https://mlsanity.com/paper/2307.05862/) (2023)
- [Assessing the Macro and Micro Effects of Random Seeds on Fine-Tuning Large Language Models](https://mlsanity.com/paper/2503.07329/) (2025)
- [Identifying Inaccurate Descriptions in LLM-generated Code Comments via Test Execution](https://mlsanity.com/paper/2406.14836/) (2024)
- [Prompt Adaptation as a Dynamic Complement in Generative AI Systems](https://mlsanity.com/paper/2407.14333/) (2024)
- [Algorithmic Monocultures in Hiring](https://mlsanity.com/paper/2605.27371/) (2026)
- [An Exploratory Study on LLM-Generated Code and Comments in Code Repositories](https://mlsanity.com/paper/2607.01867/) (2026)
- [Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code](https://mlsanity.com/paper/2605.13280/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have been widely reported to homogenize human expression and thought. However, I argue that convergence in language need not imply convergence in ideas, and existing evidence rarely distinguishes between the two. I demonstrate this through a study of software development, where AI assistants have diffused fastest and where syntax can be readily separated from semantics (conceptual approach or intent). Using Kaggle contest submissions from 2019 to mid-2026, I first document convergence toward the random seed value 42, consistent with LLMs reinforcing a longstanding programming-culture convention associated with Douglas Adams' comedy novel The Hitchhiker's Guide to the Galaxy. I then measure homogenization in code syntax and approach more generally, quantifying within-contest code submission similarity using term-frequency-inverse-document-frequency (TF-IDF) n-gram representations, which capture surface syntax, and Voyage code-3 retrieval embeddings, which capture intent and approach. Submissions have become more alike in literal syntax, but I find no evidence of homogenization in approach or intent. I conclude that shared AI tools are homogenizing how Kaggle contestants express their solutions, not the problem-solving strategies they employ.