# Trends in Frontier AI Model Count: A Forecast to 2028

arXiv: https://arxiv.org/abs/2504.16138 · HTML: https://mlsanity.com/paper/2504.16138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [Training Compute Thresholds: Features and Functions in AI Regulation](https://mlsanity.com/paper/2405.10799/) (2024)
- [On Regulating Downstream AI Developers](https://mlsanity.com/paper/2503.11922/) (2025)

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

- [RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models](https://mlsanity.com/paper/2604.03291/) (2026)

## Abstract (arXiv)

Governments are starting to impose requirements on AI models based on how much compute was used to train them. For example, the EU AI Act imposes requirements on providers of general-purpose AI with systemic risk, which includes systems trained using greater than 1025 floating point operations (FLOP). In the United States' AI Diffusion Framework, a training compute threshold of 1026 FLOP is used to identify "controlled models" which face a number of requirements. We explore how many models such training compute thresholds will capture over time. We estimate that by the end of 2028, there will be between 103-306 foundation models exceeding the 1025 FLOP threshold put forward in the EU AI Act (90% CI), and 45-148 models exceeding the 1026 FLOP threshold that defines controlled models in the AI Diffusion Framework (90% CI). We also find that the number of models exceeding these absolute compute thresholds each year will increase superlinearly -- that is, each successive year will see more new models captured within the threshold than the year before. Thresholds that are defined with respect to the largest training run to date (for example, such that all models within one order of magnitude of the largest training run to date are captured by the threshold) see a more stable trend, with a median forecast of 14-16 models being captured by this definition annually from 2025-2028.