Quick takes

I'm continuing the work on my SPAR project, and I met with a hiring manager at an AI Safety org this week. Here's some interesting things I've learned: - They use a multiple of tools and processes  - a mixture of HR tools, AI and human review to score and review applications and work tests. They stated the process felt "disjointed". - Work tests are developed for multiple roles are broken down into 2-3 types of tests: - General - a foundational test that each applicant receives at this stage to map to cultural fit - Role specific - work problem (based upon junior or senior role type) that is reviewed by human reviewers.  - Work tests are developed by an internal team (not just the hiring manager) and they use archives of past (and high scoring responses) + team to create tests. Scoring (initial screening is done by AI with human review). - Their biggest frictions in the hiring process:  - Not being able to review in a timely manner completed work tests, and therefore their concern is they are losing high quality candidates to other orgs. - When the potential tidal increase of more candidates coming, due to expected IPO's, then their own  hiring strategy may be to only hire from referrals.  This conversation makes me realize that the hiring process is not only being hindered by processes (disjointed or non-existent) but also that most orgs (even with processes or tools) are not ready for the influx of people that could potentially look to AI Safety orgs as how they can contribute.  Therefore, my two may crux's that I need to explore more (with more discussions with AI Safety hiring managers) are the following: - Is the hiring process inefficient due to the process (or lack of it) and a Playbook for hiring managers would help? - How are work tests being written, how are the being scored and is the "reviewer" knowledgeable about the specific role that they can determine if the applicant has not only done this work in the past (and knows what they are talking a

With the large expected inflow of philanthropic capital, there has been a surge in interest in entrepreneurship among those who otherwise might not have pursued it. As we pursue promising new ideas, many ventures will fail. Some of these failures will be because the idea was not as good as it seemed, but many will fail because the entrepreneurs executed it poorly. I'm concerned we will abandon good ideas due to poor execution. Founders new and old should strive to make their successes and failures publicly visible (though these stories should be read critically given founder biases). More importantly, we should be skeptical of claims that an idea is bad based solely on the failure of one attempt, and we should be willing to pursue ideas more than once if we have a strong account for why the previous attempt(s) failed.

AI safety needs people everywhere but quickly stated, current talent bottlenecks to me look like: -- Founders -- Grantmakers -- (technical) Research leads  -- Policy entrepreneurs and implementors (which includes a lot of technical work) -- bets in international coordination and/or cooperation -- All manner of supporting talent -- program leads, ops proper, public outreach, content creators, comms   Most sought-after qualities for talent are: -- context, mission alignment, domain understanding, sophisticated views on AI strategy and threat modelling etc. -- "good judgement", "sound epistemics", "reasoning transparency" and other similar ideas/meta-skills from the EA/rationalist cannon -- a willingness to get shit done/bias for action (rather than be in learning mode, or people who need a lot of management and oversight, or folks with too many preferences/constraints) -- low ego, similar to above -- ambitious folks, since they would be really trying to be their own managers, take on bigger projects, grow themselves and their teams etc.    Finally, even having these, it's not enough to just claim to have these; job-seekers mainly trip up in being able to demonstrate and be legible about having them.

The "Desperate" AI Safety Talent Bottleneck is deeply misleading. Please.stop. I worked at Google. Google does not tell applicants that there is a talent bottleneck and they are desperately hiring. They say "we're cool, join us!"  I didn't get into Harvard.  Harvard does not tell applicants that they are desperately seeking students. I am not misled.  I've gotten rejected from countless AI Safety organizations. AI Safety tells applicants - we are in desperate need of talent (operators/generalists)! Please join us!  I've coached 70+ aspiring career pivoters on navigating the ecosystem. A common theme is the dejection & disappointment from all this rejection. Because of this false marketing, I am the one picking up the pieces to calibrate professionals on how difficult, competitive, and picky organizations are, how to build context, and how to endure the marathon, which is like any other job hunt.  Please.stop. Some alternatives: * We are an exciting, growing organization and you should apply for our roles! * Many people are excited to work for us - here are qualities of candidates we're especially excited about. * EA roles are competitive, and we would still love to see your application. Relevant posts: * https://forum.effectivealtruism.org/posts/B6d8Wzk4gNzHsXvdi/ai-safety-is-extremely-bottlenecked-on-grantmakers?commentId=n2Rd7RR4y9EnKP42F * https://forum.effectivealtruism.org/posts/b82SLXwEHRCs3TFJA/why-experienced-professionals-fail-to-land-high-impact-roles * https://forum.effectivealtruism.org/posts/jmbP9rwXncfa32seH/after-one-year-of-applying-for-ea-jobs-it-is-really-really 

As someone who hires people (I am hiring right now, see bottom of the post), I often get emails asking whether someone should apply for a role I've listed. If you're considering contacting a hiring manager to ask if you're a good fit for the role, you should just apply. Why? * Job descriptions are wish lists. Everyone wants someone who checks all the boxes. If the dream candidate applies, you'll probably get rejected. In reality, everyone has weaknesses, and an 80% match candidate is highly competitive. * Self-skepticism correlates with candidate quality. Applicant pools are filled with people who can't do the job but don't understand their abilities well enough to realize it. Conversely, many highly competent professionals self-select out. If you are humble and self-reflective enough to consider your chances honestly, you are probably more competitive than you think. * You probably won't get a response. Hiring is always a busy time, and hiring managers don't have time to review every email. Often, you'll receive nothing, leaving you with wasted effort. I try to reply as much as I can, but there's only so much that can be done. * Any response you get is biased toward yes. If an org has a good filtering process, the biggest determinant in hire quality is whether good candidates apply. More "bad fit" candidates pose little strain, while one more "great fit" candidate could double the success of the hiring round. The incentive is always to get more people to apply. It's unclear whether a seeming good fit is a great fit who is overly worried about their weaknesses or a bad fit exaggerating their strengths, but it doesn't matter. Hiring managers are better off if both apply so they encourage them to do so. As a result, you spend some time writing an email, the hiring manager spends some time reviewing your email, they take time to send a response, and you receive the answer that you probably should apply, so all that effort led to nothing. Skipping t

Applications to SPAR Fall 2026 are closing tomorrow Aug 18 EOD Anywhere on Earth. SPAR is the ecosystem's biggest AI safety research program, and it's part-time remote. We still have many strong projects across AI safety, AI policy, and biosecurity with very few applications[1], so please consider applying! This round, we also have 18 non-research/generalist projects that people can apply to, and we have much more mentee capacity than previous rounds; we expect to accept around 500 people into the program. If you have friends who have thought about going into AI safety, spread the word! 1. ^ To be specific, as of 2:25 PM PT, we had around 67 projects with fewer than 20 applications total!

Our board now has more roles than before (1600+), and a public Airtable version that you can use to set up custom views and automations (including with Slack). A quick guide for using the new Airtable: 1. Open the public Airtable and click "Use this data". Make sure "Create a synced table" is selected. Choose which Airtable base you'd like the jobs data to live in. This creates a read-only synced table with our published roles. 2. Create a filtered view in the new table (e.g., filter by cause area, location, or role type). 3. From here, you can set up Slack notifications: 1. Trigger: "When record enters view", selecting the filtered view you created in step 2.  2. Action: "Send a Slack message" (via Airtable’s built-in Slack integration) 3. Compose your message using field tokens to pull in live data from each role, e.g. New role: {Job Title} at {Org Name} | {Job URL}. Use markdown for basic formatting like bold or italics.  If you use our job board, here’s a few ways you can help us to help you: * Test out the new Airtable and let us know if there are any issues or if you do anything cool with it. * If you land a role that you found on the job board, please get in touch! Even a short message about how our services helped you makes a huge difference to our ability to continue providing these services. * If you know of any orgs you think we should monitor for the board (including ones you work for), please share them! * If you work at an org that's listed on the board, note that links to your roles from our job board automatically include utm_source=probablygood_board so if you track referral sources, you'll be able to see applications that came via us. If you have a question on your application forms regarding where candidates heard about the role, please also consider adding "Probably Good" as an option.  * If you’re a hiring manager/recruiter who ends up hiring a candidate who found your role through our job board, please let us know! Othe

We recently published an interview with Matthew Coleman - another entry in our Career Journeys series. Matthew is the Executive Director of Giving Multiplier, a platform that encourages donations to highly effective charities through donation matching. Before this, he completed a PhD in psychology, researching the psychology of altruism. The interview covers quite a lot of ground, but a few of the things we talked about include: * The gap between what a career looks like from the outside and what it's actually like day-to-day. * Advice for people wanting to make an impact through psychology. * The tension between keeping your options open and committing to a path. Here’s one of our favorite extracts from the full interview: On engaging with the (often mundane) realities of academic research: I learned a lot. By the time I started my lab manager role, I was fairly confident I wanted to do a PhD. But my research lab in undergrad, which I loved, was a very small lab where I was working closely with the faculty advisor, and I wanted to try out a larger lab studying different topics to explore a bit more. As the lab manager of an unusually large lab, I got a bird’s-eye view of a lot of the research projects going on and understood what the day-to-day looked like, whether that was grant applications, hiring and onboarding, or actually conducting research myself alongside my colleagues. I found the experience amazing and fascinating and really intellectually stimulating, which confirmed that I wanted to go the PhD route, so I followed through on my original plan from undergrad. […] I was certainly very fortunate to have gotten a lot of hands-on experience in research as an undergraduate, so I think I had a better sense of the day-to-day than many people do. But I do think it’s a very important point, and some related advice I like to give is: when you wake up on a random Tuesday in February, do you actually want to do the things that you have to do? Not just do y