We invest heavily in fellowships, but do we know exactly where people go and the impact the fellowships have? To begin answering this question I manually analyzed over 600 alumni profiles from 9 major late-stage fellowships (fellowships that I believe could lead directly into a job following). These profiles represent current participants and alumni from MATS, GovAI, ERA, Pivotal, Talos Network, Tarbell, Apart Labs, IAPS, and PIBBS.
Executive Summary
Iâve compiled a dataset of over 600 alumni profiles of 9 major âlate stageâ AI Safety and Governance Fellowships.
I found over 10% of fellows did another fellowship after their fellowship. This doesnât feel enormously efficient.
This is more directional than a conclusion, but according to preliminary results around 80% of alumni are still working in AI Safety.
Iâm actively looking for collaborators/âmentors to analyse counterfactual impact.
Key Insights from mini-project
Of the target fellowships I looked at, 21.5% (139) did at least one other fellowship alongside their target fellowship. 12.4% of fellows (80) had done a fellowship before the fellowship and 11.1% (72) did a fellowship after.
Since these fellowships are âlate-stageâ - none of them are designed to be much more senior than many of the othersâI think it is quite surprising that over 10% of alumni do another fellowship following the target fellowship.
I also think itâs quite surprising that only 12.4% of fellows had done an AI Safety fellowship beforeâonly slightly higher than those who did one after. This suggests that fellowships are most of the time taking people from outside of the âstandard fellowship streamâ.
Individual fellowships
Whilst most fellowships tended to stick around the average, here are some notable trends:
Firstly, 20.2% (17) of ERA fellows did a fellowship after ERA, whilst only 9.5% (8) had done a fellowship before. This suggests ERA is potentially, and somewhat surprisingly, an earlier stage fellowship than other fellowships, and more of a feeder fellowship. I expect this will be somewhat surprising to people, since ERA is as prestigious and competitive as most of the others.
Secondly, MATS was the other way round, with 15.1% (33) having done a fellowship before and only 6.9% (15) doing a fellowship after. This is unsurprising, as MATS is often seen as one of the most prestigious AI Safety Fellowships.
Thirdly, Talos Network had 32.3% overall doing another fellowship before or after Talos, much higher than the 21.5% average. This suggests Talos is more enmeshed in the fellowship ecosystem than other fellowships.
Fellowship
Alumni
Alumni who did another fellowship
Percentage who did another fellowship
Alumni who did a fellowship before
Percentage before
Alumni who did a fellowship after
Percentage after
Total
647
139
21.5%
80
12.4%
72
11.1%
MATS
218
45
20.6%
33
15.1%
15
6.9%
GovAI
118
24
20.3%
15
12.7%
12
10.2%
ERA
84
25
29.8%
8
9.5%
17
20.2%
Pivotal
67
17
25.4%
8
11.9%
10
14.9%
Talos
62
20
32.3%
11
17.7%
12
19.4%
Apart
52
11
21.2%
6
11.5%
9
17.3%
PIBBS
31
8
25.8%
5
16.1%
3
9.7%
Tarbell
21
1
4.8%
1
4.8%
0
0.0%
IAPS
12
4
33.3%
4
33.3%
0
0.0%
Links between fellowships
On the technical side, I found very strong links between MATS and SPAR, AI Safety Camp and ARENA (13, 9 and 7 fellows respectively had gone directly between one and the other), which is unsurprising.
Perhaps more surprisingly, on the governance side I found equally strong links between GovAI and ERA, IAPS and Talos, which also had 13, 9 and 7 links respectively. All of these fellowships are also half the size of MATS, which makes this especially surprising.
Strongest Bidirectional Links between Fellowships
Fellowships
Number of Links
MATS x SPAR
13
GovAI x ERA
13
MATS x AI Safety Camp
9
GovAI x IAPS
9
MATS x ARENA
7
GovAI x Talos
7
MATS x ERA
6
APART x SPAR
5
GovAI x Pivotal
4
MATS x Talos
4
For fun, I also put together a Sankey Visualisation of this. Itâs a little jankey but I think it gives a nice visual view of the network. View the Sankey Diagram Here.
Preliminary Directional Signals: IRG Data
As part of the IRG project I participated in this summer (during which I produced this database) I used this data to produce the following datapoints:
That 80% of fellowship alumni are now working in AI Safety. This put the average fellowship in line with MATS in terms of retention rate, which is very encouraging.
That the majority of those working in AI Safety are now working in the Non-Profit sector.
However, these results were produced very quickly. They used both AI tools to extract data and a manual, subjective judgement to decide whether someone worked in AI Safety or not. Whilst I expect they are in the right ballpark, view them as directional rather than conclusional.
Notes on the Data
Proportion of Alumni: Of course, this does not cover every alumnus of each fellowshipâonly the ones that posted their involvement on LinkedIn. I estimate this population represents â - ½ of all alumni.
Choice of fellowships: The selection was somewhat arbitrary, focusing on âlate-stage fellowshipsâ where we expect graduates to land roles in AI Safety.
Seniority of Fellowships: Particularly for my link analysis, fellows are much less likely to post about less competitive and senior fellowships on their LinkedIn than later stage ones.
Fellowship Diversity: These programs vary significantly. ERA, Pivotal, MATS, GovAI, PIBBS, and IAPS are primarily research-focused, whereas Tarbell and Talos prioritize placements.
Experience Levels: Some fellowships (like PIBBS, targeting PhDs) aim for experienced researchers, while others welcome newcomers. This disparity suggests an interesting area for future research: analyzing the specific âselection tastesâ of different orgs.
Scale: Sizes vary drastically; MATS has over 200 alumni profiles, while IAPS has 11.
Open Questions: What can this dataset answer?
Beyond the basic flow of talent, this dataset is primed to answer deeper questions about the AIS ecosystem. Here are a few useful questions I believe the community could tackle directly with this data. For the first 4, the steps are quite straightforward and would make a good project. The last may require some thinking (and escapes me at the moment):
Retention Rates: What percentage of alumni are still working in AI Safety roles 1, 2, or 3 years post-fellowship?
The âFeeder Effectâ: Which fellowships serve as the strongest pipelines into specific top labs (e.g., Anthropic, DeepMind) versus independent research?
Background Correlation: How does a candidateâs academic background (e.g., CS vs. Policy degrees) correlate with their path through multiple fellowships?
Fellowship tastes: How do the specialism and experience of people different fellowships select differ?
The âGolden Eggâ: Counterfactual Impact.
What proportion of people would have entered AI Safety without doing a given fellowship?
What is the marginal value-add of a specific fellowship in a candidateâs trajectory? (Multiple fellowship leads have expressed a strong desire for this metric).
The Dataset Project
I wanted to release this dataset responsibly to the community, as I believe fellowship leads, employers, and grantmakers could gain valuable insights from it.
Request Access: If youâd like access to the raw dataset, please message me or fill in this form. Since the dataset contains personal information, I will be adding people on a person-by-person basis.
Note: If youâre not affiliated with a major AI Safety Organization, please provide a brief explanation of your intended use for this data.
Next Steps
Firstly, Iâd be very interested in working on one of these questions, particularly over the summer. If youâd be interested in collaborating with or mentoring me, have an extremely low bar for reaching out to me.
I would be especially excited to hear from people who have ideas for how to deal with the counterfactual impact question.
Secondly, if youâre an organisation and would like some kind of similar work done for your organisation or field, also have an extremely low bar for reaching out.
If you have access or funding for AI tools like clay.com, Iâd be especially interested.
Where do AI Safety Fellows go? Analyzing a dataset of 600+ alumni
We invest heavily in fellowships, but do we know exactly where people go and the impact the fellowships have? To begin answering this question I manually analyzed over 600 alumni profiles from 9 major late-stage fellowships (fellowships that I believe could lead directly into a job following). These profiles represent current participants and alumni from MATS, GovAI, ERA, Pivotal, Talos Network, Tarbell, Apart Labs, IAPS, and PIBBS.
Executive Summary
Iâve compiled a dataset of over 600 alumni profiles of 9 major âlate stageâ AI Safety and Governance Fellowships.
I found over 10% of fellows did another fellowship after their fellowship. This doesnât feel enormously efficient.
This is more directional than a conclusion, but according to preliminary results around 80% of alumni are still working in AI Safety.
Iâm actively looking for collaborators/âmentors to analyse counterfactual impact.
Key Insights from mini-project
Of the target fellowships I looked at, 21.5% (139) did at least one other fellowship alongside their target fellowship. 12.4% of fellows (80) had done a fellowship before the fellowship and 11.1% (72) did a fellowship after.
Since these fellowships are âlate-stageâ - none of them are designed to be much more senior than many of the othersâI think it is quite surprising that over 10% of alumni do another fellowship following the target fellowship.
I also think itâs quite surprising that only 12.4% of fellows had done an AI Safety fellowship beforeâonly slightly higher than those who did one after. This suggests that fellowships are most of the time taking people from outside of the âstandard fellowship streamâ.
Individual fellowships
Whilst most fellowships tended to stick around the average, here are some notable trends:
Firstly, 20.2% (17) of ERA fellows did a fellowship after ERA, whilst only 9.5% (8) had done a fellowship before. This suggests ERA is potentially, and somewhat surprisingly, an earlier stage fellowship than other fellowships, and more of a feeder fellowship. I expect this will be somewhat surprising to people, since ERA is as prestigious and competitive as most of the others.
Secondly, MATS was the other way round, with 15.1% (33) having done a fellowship before and only 6.9% (15) doing a fellowship after. This is unsurprising, as MATS is often seen as one of the most prestigious AI Safety Fellowships.
Thirdly, Talos Network had 32.3% overall doing another fellowship before or after Talos, much higher than the 21.5% average. This suggests Talos is more enmeshed in the fellowship ecosystem than other fellowships.
Links between fellowships
On the technical side, I found very strong links between MATS and SPAR, AI Safety Camp and ARENA (13, 9 and 7 fellows respectively had gone directly between one and the other), which is unsurprising.
Perhaps more surprisingly, on the governance side I found equally strong links between GovAI and ERA, IAPS and Talos, which also had 13, 9 and 7 links respectively. All of these fellowships are also half the size of MATS, which makes this especially surprising.
For fun, I also put together a Sankey Visualisation of this. Itâs a little jankey but I think it gives a nice visual view of the network. View the Sankey Diagram Here.
Preliminary Directional Signals: IRG Data
As part of the IRG project I participated in this summer (during which I produced this database) I used this data to produce the following datapoints:
That 80% of fellowship alumni are now working in AI Safety. This put the average fellowship in line with MATS in terms of retention rate, which is very encouraging.
That the majority of those working in AI Safety are now working in the Non-Profit sector.
However, these results were produced very quickly. They used both AI tools to extract data and a manual, subjective judgement to decide whether someone worked in AI Safety or not. Whilst I expect they are in the right ballpark, view them as directional rather than conclusional.
Notes on the Data
Proportion of Alumni: Of course, this does not cover every alumnus of each fellowshipâonly the ones that posted their involvement on LinkedIn. I estimate this population represents â - ½ of all alumni.
Choice of fellowships: The selection was somewhat arbitrary, focusing on âlate-stage fellowshipsâ where we expect graduates to land roles in AI Safety.
Seniority of Fellowships: Particularly for my link analysis, fellows are much less likely to post about less competitive and senior fellowships on their LinkedIn than later stage ones.
Fellowship Diversity: These programs vary significantly. ERA, Pivotal, MATS, GovAI, PIBBS, and IAPS are primarily research-focused, whereas Tarbell and Talos prioritize placements.
Experience Levels: Some fellowships (like PIBBS, targeting PhDs) aim for experienced researchers, while others welcome newcomers. This disparity suggests an interesting area for future research: analyzing the specific âselection tastesâ of different orgs.
Scale: Sizes vary drastically; MATS has over 200 alumni profiles, while IAPS has 11.
Open Questions: What can this dataset answer?
Beyond the basic flow of talent, this dataset is primed to answer deeper questions about the AIS ecosystem. Here are a few useful questions I believe the community could tackle directly with this data. For the first 4, the steps are quite straightforward and would make a good project. The last may require some thinking (and escapes me at the moment):
Retention Rates: What percentage of alumni are still working in AI Safety roles 1, 2, or 3 years post-fellowship?
The âFeeder Effectâ: Which fellowships serve as the strongest pipelines into specific top labs (e.g., Anthropic, DeepMind) versus independent research?
Background Correlation: How does a candidateâs academic background (e.g., CS vs. Policy degrees) correlate with their path through multiple fellowships?
Fellowship tastes: How do the specialism and experience of people different fellowships select differ?
The âGolden Eggâ: Counterfactual Impact.
What proportion of people would have entered AI Safety without doing a given fellowship?
What is the marginal value-add of a specific fellowship in a candidateâs trajectory? (Multiple fellowship leads have expressed a strong desire for this metric).
The Dataset Project
I wanted to release this dataset responsibly to the community, as I believe fellowship leads, employers, and grantmakers could gain valuable insights from it.
Request Access: If youâd like access to the raw dataset, please message me or fill in this form. Since the dataset contains personal information, I will be adding people on a person-by-person basis.
Note: If youâre not affiliated with a major AI Safety Organization, please provide a brief explanation of your intended use for this data.
Next Steps
Firstly, Iâd be very interested in working on one of these questions, particularly over the summer. If youâd be interested in collaborating with or mentoring me, have an extremely low bar for reaching out to me.
I would be especially excited to hear from people who have ideas for how to deal with the counterfactual impact question.
Secondly, if youâre an organisation and would like some kind of similar work done for your organisation or field, also have an extremely low bar for reaching out.
If you have access or funding for AI tools like clay.com, Iâd be especially interested.