Summary

The goal of this post is to analyze the growth of the technical and non-technical AI safety fields in terms of the number of organizations and number of FTEs working in these fields.

In 2022, I estimated that there were about 300 FTEs (full-time equivalents) working in the field of technical AI safety research and 100 on non-technical AI safety work (400 in total).

Based on updated data and estimates from 2025, I estimate that there are now approximately 600 FTEs working on technical AI safety and 500 FTEs working on non-technical AI safety (1100 in total).

Note that this post is an updated version of my old 2022 post Estimating the Current and Future Number of AI Safety Researchers.

Technical AI safety field growth analysis

The first step for analyzing the growth of the technical AI safety field is to create a spreadsheet listing the names of known technical AI safety organizations, when they were founded, and an estimated number of FTEs for each organization. The technical AI safety dataset contains 70 organizations working on technical AI safety and a total of 645 FTEs working at them (68 active organizations and 620 active FTEs in 2025).

Then I created two scatter plots showing the number of technical AI safety research organizations and FTEs working at them respectively. On each graph, the x-axis is the years from 2010 to 2025 and the y-axis is the number of active organizations or estimated number of total FTEs working at those organizations. I also created models to fit the scatter plots. For the technical AI safety organizations and FTE graphs, I found that an exponential model fit the data best.

The two graphs show relatively slow growth from 2010 to 2020 and then the number of technical AI safety organizations and FTEs starts to rapidly increase around 2020 and continues rapidly growing until today (2025).

The exponential models describe a 24% annual growth rate in the number of technical AI safety organizations and 21% growth rate in the number of technical AI safety FTEs.

I also created graphs showing the number of technical AI safety organizations and FTEs by category. The top three categories by number of organizations and FTEs are Misc technical AI safety research, LLM safety, and interpretability.

Misc technical AI safety research is a broad category that mostly consists of empirical AI safety research that is not purely focused on LLM safety research such as scalable oversight, adversarial robustness, jailbreaks, and otherwise research that covers a variety of different areas and is difficult to put into a single category.

Non-technical AI safety field growth analysis

I also applied the same analysis to a dataset of non-technical AI safety organizations. The non-technical AI safety landscape, which includes fields like AI policy, governance, and advocacy, has also expanded significantly. The non-technical AI safety dataset contains 45 organizations working on non-technical AI safety and a total of 489 FTEs working at them.

The graphs plotting the growth of the non-technical AI safety field show an acceleration in the rate of growth around 2023 though a linear model fits the data well from the years 2010 - 2025.

In the previous post from 2022, I counted 45 researchers on Google Scholar with the AI governance tag. There are now over 300 researchers with the AI governance tag, evidence that the field has grown.

I also created graphs showing the number of non-technical AI safety organizations and FTEs by category.

Acknowledgements

Thanks to Ryan Kidd from SERI MATS for sharing data on AI safety organizations which was useful for writing this post.

Appendix

• A Colab notebook for reproducing the graphs in this post can be found here.
• Technical AI safety organizations spreadsheet in Google Sheets.
• Non-Technical AI safety organizations spreadsheet in Google Sheets.

Old and new dataset and model comparison

The following graph shows the difference between the old dataset and model from the Estimating the Current and Future Number of AI Safety Researchers (2022) post compared with the updated dataset and model.

The old model is the blue line and the new model is the orange line.

The old model predicts a value of 484 active technical FTEs in 2025 and the true value is 620. The percentage error between the predicted and true value is 22%.

Technical AI safety organizations table

Non-technical AI safety organizations table