# Increased Compute Efficiency and the Diffusion of AI Capabilities

arXiv: https://arxiv.org/abs/2311.15377 · HTML: https://mlsanity.com/paper/2311.15377/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2023.

## Built on by (most cited first, 8 of 8)

- [Training Compute Thresholds: Features and Functions in AI Regulation](https://mlsanity.com/paper/2405.10799/) (2024)
- [A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search](https://mlsanity.com/paper/2507.00004/) (2025)
- [Position: Ensuring mutual privacy is necessary for effective external evaluation of proprietary AI systems](https://mlsanity.com/paper/2503.01470/) (2025)
- [Practical Principles for AI Cost and Compute Accounting](https://mlsanity.com/paper/2502.15873/) (2025)
- [Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape](https://mlsanity.com/paper/2507.07765/) (2025)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery](https://mlsanity.com/paper/2602.20684/) (2026)
- [MANTA -- Model Adapter Native generations that's Affordable](https://mlsanity.com/paper/2409.14363/) (2024)

## Abstract (arXiv)

Training advanced AI models requires large investments in computational resources, or compute. Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time - a concept we describe as increasing compute efficiency. We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to each actor. This potentially enables large compute investors to pioneer new capabilities, maintaining a performance advantage even as capabilities diffuse. Since large compute investors tend to develop new capabilities first, it will be particularly important that they share information about their AI models, evaluate them for emerging risks, and, more generally, make responsible development and release decisions. Further, as compute efficiency increases, governments will need to prepare for a world where dangerous AI capabilities are widely available - for instance, by developing defenses against harmful AI models or by actively intervening in the diffusion of particularly dangerous capabilities.