# Artificial Intelligence Index Report 2024

arXiv: https://arxiv.org/abs/2405.19522 · HTML: https://mlsanity.com/paper/2405.19522/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2024.

## Built on by (most cited first, 24 of 24)

- [RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts](https://mlsanity.com/paper/2411.15114/) (2024)
- [When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation](https://mlsanity.com/paper/2602.16763/) (2026)
- [SLIP: Securing LLMs IP Using Weights Decomposition](https://mlsanity.com/paper/2407.10886/) (2024)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [Casper: Prompt Sanitization for Protecting User Privacy in Web-Based Large Language Models](https://mlsanity.com/paper/2408.07004/) (2024)
- [aiXamine: Simplified LLM Safety and Security](https://mlsanity.com/paper/2504.14985/) (2025)
- [Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations](https://mlsanity.com/paper/2511.05613/) (2025)
- [Small is Sufficient: Reducing the World AI Energy Consumption Through Model Selection](https://mlsanity.com/paper/2510.01889/) (2025)
- [Impact of ML Optimization Tactics on Greener Pre-Trained ML Models](https://mlsanity.com/paper/2409.12878/) (2024)
- [From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning](https://mlsanity.com/paper/2602.23729/) (2026)
- [Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption](https://mlsanity.com/paper/2604.00186/) (2026)
- [The ATOM Report: Measuring the Open Language Model Ecosystem](https://mlsanity.com/paper/2604.07190/) (2026)
- [Bias in Text Embedding Models](https://mlsanity.com/paper/2406.12138/) (2024)
- [Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia](https://mlsanity.com/paper/2504.00652/) (2025)
- [Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms](https://mlsanity.com/paper/2509.00167/) (2025)
- [Universal computation is intrinsic to language model decoding](https://mlsanity.com/paper/2601.08061/) (2026)
- [AI Research is not Magic, it has to be Reproducible and Responsible: Challenges in the AI field from the Perspective of its PhD Students](https://mlsanity.com/paper/2408.06847/) (2024)
- [An Efficient Plugin Method for Metric Optimization of Black-Box Models](https://mlsanity.com/paper/2503.02119/) (2025)
- [Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects](https://mlsanity.com/paper/2506.02214/) (2025)
- [Uncovering Scaling Laws for Large Language Models via Inverse Problems](https://mlsanity.com/paper/2509.07909/) (2025)
- [The Future of AI is Many, Not One](https://mlsanity.com/paper/2603.29075/) (2026)
- [Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion](https://mlsanity.com/paper/2607.14371/) (2026)
- [aiXamine: Unified Black-Box Evaluation of Cross-Dimensional Trade-offs in LLM Safety, Security, and Privacy](https://mlsanity.com/paper/2608.20554/) (2026)
- [The AI Evaluation Ecosystem](https://mlsanity.com/paper/2610.09296/) (2026)

## Abstract (arXiv)

The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year's edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.