# Rethinking Technology Stack Selection with AI Coding Proficiency

arXiv: https://arxiv.org/abs/2509.11132 · HTML: https://mlsanity.com/paper/2509.11132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 11 of its 100 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 11 of 11)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [A Survey on Evaluating Large Language Models in Code Generation Tasks](https://mlsanity.com/paper/2408.16498/) (2024)
- [EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code](https://mlsanity.com/paper/2505.13004/) (2025)
- [A Study of LLMs' Preferences for Libraries and Programming Languages](https://mlsanity.com/paper/2503.17181/) (2025)
- [CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models](https://mlsanity.com/paper/2502.16614/) (2025)
- [BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?](https://mlsanity.com/paper/2503.15242/) (2025)
- [Does Few-Shot Learning Help LLM Performance in Code Synthesis?](https://mlsanity.com/paper/2412.02906/) (2024)

## Abstract (arXiv)

Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. However, existing technology selection methods mainly focus on the inherent attributes of technologies, overlooking whether the LLM can effectively leverage the chosen technology. Therefore, teams using LLM assistants risk choosing technologies that cannot be used effectively by LLMs, yielding high debugging effort and mounting technical debt. We foresee a practical question in the LLM era, is a technology ready for AI-assisted development? In this paper, we first propose the concept, AI coding proficiency, the degree to which LLMs can utilize a given technology to generate high-quality code snippets. We then conduct the first large-scale empirical study examining AI coding proficiency across 170 third-party libraries and six LLMs. Our findings reveal that libraries with similar functionalities can exhibit up to 84% differences in the quality score of LLM-generated code. These gaps can be translated into real engineering costs and steer developer choices toward a narrow set of technologies, threatening technological diversity in the ecosystem. We call on the community to integrate AI coding proficiency into technology selection frameworks and develop mitigation strategies, preserving competitive balance in AI-driven development.