# 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
-----------------

*   I’ve compiled a dataset of over **600** alumni profiles of **9** major 'late stage' AI Safety and Governance Fellowships.
*   I found over **10%** of fellows did another fellowship after their fellowship. This doesn’t feel enormously efficient.
*   **Almost ⅓** of ERA and Talos Network fellows (**29.8%** and **32.3%** respectively) did another fellowship before or after, much higher than the average of **21.5%**.
*   **ERA** particularly seemed to be a ‘feeder’ fellowship for other fellowships. Only **9.5%** of ERA fellows had done a fellowship before ERA, but **20.2%** did another fellowship following, almost double the **11.1%** average.
*   **GovAI Fellowship** had strong direct links with other governance fellowships - i.e. many people went directly to or from **other governance fellowships** to GovAI. There were **13**, **9** and **7** direct links between **GovAI** and **ERA**, **IAPS** and **Talos Network** respectively.
*   This is more **directional** than a conclusion, but according to preliminary results **around 80%** of alumni are still working in AI Safety.
*   I'm **actively looking for collaborators/mentors** 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 ‘late-stage’ - none of them are designed to be much more senior than many of the others - I think it is quite surprising that over 10% of alumni do another fellowship following the target fellowship.  
  
I also think it’s quite surprising that only **12.4%** of fellows had done an AI Safety fellowship before - only slightly higher than those who did one after. This suggests that fellowships are most of the time taking people from outside of the ‘standard fellowship stream’.

### 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. It’s a little jankey but I think it gives a nice visual view of the network. [View the Sankey Diagram Here](https://christopherlus.github.io/).

![](https://39669.cdn.cke-cs.com/cgyAlfpLFBBiEjoXacnz/images/2b7561ddeb3f276d41c8d3c13ab4af2598390a27f9f5573c.png)

Preliminary Directional Signals: IRG Data
-----------------------------------------

As part of the [IRG project](https://docs.google.com/document/d/1SIQmvQbZb1u1YzMX9mnq3gORH9T_TMsJB0USjthQcYQ/edit?usp=sharing) I participated in this summer (during which I produced this database) I used this data to produce the following datapoints:

1.  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.
2.  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.**

![](https://res.cloudinary.com/cea/image/upload/f_auto,q_auto/v1/mirroredImages/0b5ebbe2fe6d2e5236ebc5ff8d872f8e8528a9489c6013eb4c38f62467ac776d/y4ftmbfnk51jcypzl91l)

![](https://res.cloudinary.com/cea/image/upload/f_auto,q_auto/v1/mirroredImages/40903bec5efba690eb88bac9d20dc9a23f02af057185dfce4456d9b52affc10e/jvgegd704wdqudhutpqi)

Notes on the Data
-----------------

*   **Proportion of Alumni:** Of course, this does not cover every alumnus of each fellowship - only the ones that posted their involvement on LinkedIn. I estimate this population represents **⅓ \- ½** of all alumni.
*   **Choice of fellowships**: The selection was somewhat arbitrary, focusing on 'late-stage fellowships' 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 "selection tastes" 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):

1.  **Retention Rates:** What percentage of alumni are still working in AI Safety roles 1, 2, or 3 years post-fellowship?
2.  **The "Feeder Effect":** Which fellowships serve as the strongest pipelines into specific top labs (e.g., Anthropic, DeepMind) versus independent research?
3.  **Background Correlation:** How does a candidate’s academic background (e.g., CS vs. Policy degrees) correlate with their path through multiple fellowships?
4.  **Fellowship tastes:** How do the specialism and experience of people different fellowships select differ?
5.  **The "Golden Egg": 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 candidate's 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 you'd like access to the raw dataset, please message me or fill in [this form](https://forms.gle/EmU8FfutBQhZwBY3A). Since the dataset contains personal information, I will be adding people on a person-by-person basis.

*Note: If you're not affiliated with a major AI Safety Organization, please provide a brief explanation of your intended use for this data.*

Next Steps
----------

Firstly, I’d be very interested in working on one of these questions, **particularly over the summer**. If you’d 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 you’re 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](http://clay.com/), I’d be especially interested.