Sudhanshu Kasewa

Karma: 256

Sudhanshu Kasewa 20 Nov 2025 14:54 UTC
1 point
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on: How to network: lessons from 80,000 Hours’ advising team
Not to karma-farm, but added a few more thoughts here

Sudhanshu Kasewa 20 Nov 2025 14:53 UTC
3 points
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on: Sudhanshu Kasewa’s Quick takes
Following on from this post:
A few more things I often say that obliquely relate to networking:
1. Build in public and seek feedback. People like engaging with stuff online. Do stuff in public—writing, github projects, YouTube shorts, anything—and ask people for feedback. Repeatedly doing interesting things increases the quality of feedback and engagement in a positive cycle, and opens doors for deeper collaboration. Consider being pseudonymous if it is stressful to do it with own name.
2. Offer your services, volunteer, collaborate. Stolen from Laura’s piece on How to have an impact when the job market is not cooperating
3. Host events to attract the kind of people you want to engage with. IIUC this is how Mox SF started.
4. Consider doing things in-person. Lots of relationships are forged in post-event social settings.

Sudhanshu Kasewa 10 Nov 2025 15:35 UTC
4 points
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on: Humanizing Expected Value
Great post! Was just thinking about an intuition pump of my own re: EV earlier today, and it has a similar backdrop, of vaccine development. Also, you gave me a line with which to lead into it:
The work I do doesn’t end up helping other researchers get closer to coming up with a cure.
Oh but it could have helped! It probably does (but there are exceptions like if your work is heavily misguided to the degree that nobody would have worked on it, or is gated).
By doing the work and showing it doesn’t lead to a cure, you’re freeing someone else who would have done that work to do some other work instead. Assuming they would still be searching for a cure, you’ve increased the probability that the remaining researchers do in fact find a cure.
I encounter “in 99.9% of worlds, I end up making no progress” a lot in my work, and I offer in its place that it is important and valuable to chase down many different bets to their conclusions, that the vaccine is not developed by a single party alone in isolation from all the knowledge being generated around them, but through the collected efforts of thousands of failed attempts from as many groups. The victor can claim only the lion’s share of the credit, not all of it; every (plausible) failed attempted gets some part of the value generated from the endeavour as a whole, even ex post.

Sudhanshu Kasewa 8 Nov 2025 12:01 UTC
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in reply to: Benevolent_Rain’s comment on: Sudhanshu Kasewa’s Quick takes
“anyone” is a high bar! Maybe worth looking at what notable orgs might want to fund, as a way of spotting “useful safety work not covered by enough people”?
I notice you’re already thinking about this in some useful ways, nice. I’d love to see a clean picture of threat models overlaid with plans/orgs that aim to address them.
I think the field is changing too fast for any specific claim here to stay true in 6-12m.

Sudhanshu Kasewa 7 Nov 2025 14:36 UTC
3 points
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on: Sudhanshu Kasewa’s Quick takes
Signal boost: Check out the “Stars” and “Follows” on my github account for ideas of where to get stuck into AI safety.
A lot of people want to understand AI safety by playing around with code and closing some issues, but don’t know where to find such projects. So I’ve recently starting scanning github for AI safety relevant projects and repositories. I’ve starred some, and followed some orgs/coders there as well, to make it easy for you to find these and get involved.
Excited to get more suggestions too! Feel to comment here, or send them to me at sk@80000hours.org

Sudhanshu Kasewa 7 Nov 2025 14:13 UTC
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in reply to: Mjreard’s comment on: 12 Theses on EA
Thanks. I sort of don’t buy that that’s what the Mechanize piece says, and in any case “no matter what you do” sounds a bit fatalistic, similar to death. Sure, we all die, but does that really mean we shouldn’t try and live healthier for longer?
Not directly relating to your claim, but:
The Mechanize piece claims “Full automation is desirable”, which I don’t think I agree with both a priori and after reading their substantiation. It does not contend with the possibilities of catastrophic risks from fully automating, say, bioweapon research and development; it might be inevitable, but on desirability I think it’s clear that it’s only desirable once—at the bare minimum—substantial risks have been planned for and/or suitably mitigated. It’s totally reasonable to delay the inevitable!

Sudhanshu Kasewa 6 Nov 2025 15:10 UTC
5 points
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on: 12 Theses on EA
Thanks Matt. Good read.
A stronger technological determinism tempers this optimism by saying that the kinds of minds you get will be whichever are easiest to build or maintain, and that those quite-specific minds will dominate no matter what you do.
Is there a thing you would point to that substantiates or richly argues for this claim? It seems non-obvious to me.

Sudhanshu Kasewa 6 Nov 2025 13:53 UTC
17 points
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on: Sudhanshu Kasewa’s Quick takes
I try to maintain this public doc of AI safety cheap tests and resources, although it’s due a deep overhaul.
Suggestions and feedback welcome!

Sudhanshu Kasewa 6 Nov 2025 13:51 UTC
43 points
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on: Sudhanshu Kasewa’s Quick takes
Scrappy note on the AI safety landscape. Very incomplete, but probably a good way to get oriented to (a) some of the orgs in the space, and (b) how the space is carved up more generally.
(A) Technical
(i) A lot of the safety work happens in the scaling-based AGI companies (OpenAI, GDM, Anthropic, and possibly Meta, xAI, Mistral, and some Chinese players). Some of it is directly useful, some of it is indirectly useful (e.g. negative results, datasets, open-source models, position pieces etc.), and some is not useful and/or a distraction. It’s worth developing good assessment mechanisms/instincts about these.
(ii) A lot of safety work happens in collaboration with the AGI companies, but by individuals/organisations with some amount of independence and/or different incentives. Some examples: METR, Redwood, UK AISI, Epoch, Apollo. It’s worth understanding what they’re doing with AGI cos and what their theories of change are.
(iii) Orgs that don’t seem to work directly with AGI cos but are deeply technically engaging with frontier models and their relationship to catastrophic risk: places like Palisade, FAR AI, CAIS. These orgs maintain even more independence, and are able to do/say things which maybe the previous tier might not be able to. A recent cool thing was CAIS finding that models don’t do well on remote work tasks—only 2.5% of tasks—in contrast to OpenAI’s findings in GDPval suggests models have an almost 50% win-rate against industry professionals on a suite of “economically valuable, real-world tasks” tasks.
(iv) Orgs that are pursuing other* technical AI safety bets, different from the AGI cos: FAR AI, ARC, Timaeus, Simplex AI, AE Studio, LawZero, many independents, some academics at e.g. CHAI/Berkeley, MIT, Stanford, MILA, Vector Institute, Oxford, Cambridge, UCL and elsewhere. It’s worth understanding why they want to make these bets, including whether it’s their comparative advantage, an alignment with their incentives/grants, or whether they’re seeing things that others haven’t been able to see yet. (*Some of the above might be pursuing similar bets to AGI cos but with fewer resources or with increased independence etc.)
(v) Orgs pursuing non-software technical bets: e.g. FlexHEG, TamperSec
(B) Non-technical or less technical, but still aimed (or could be aimed) at directly** working the problem
(i) Orgs that do more policy-focussed/outreach/advocacy/other-non-technical things: e.g. MIRI, CAIS, RAND, CivAI, FLI, Safe AI forum, SaferAI, EU AI office, CLTR, GovAI, LawAI, CSET, CSER
(ii) AGI cos policy and governance teams, e.g. the RSP teams, the government engagement teams, and maybe even some influence and interaction with product teams and legal departments.
** “directly” here means something like “make a strong case to delay the development of AGI giving us more time to technically solve the problem”, a first-order effect, rather than something like “fund someone who can make a case to delay...”, which is a higher order effect
(C) Field-building/Talent development/Physical infrastructure
(i) Direct talent development: Constellation, Kairos, BlueDot, ARENA, MATS, LASR, Apart Research, Tarbell, etc. These orgs aim to increase the number of people going into above categories or speed them up. They don’t usually (aim to) work directly on the problem, but sometimes incidentally do (e.g. via high quality outputs from MATS). There can be a multiplier effect for working in such orgs.
(ii) Infra: Constellation, FAR AI, Mox, LISA
(iii) Incubators: e.g. Seldon Labs, Constellation, Catalyze, EF, Fifty-Fifty
(D) Moving money
(i) Non-profit/philanthropic donors: e.g. OpenPhil, SFF, EA Funds, LongView, Schmidt Futures
(ii) VCs: e.g. Halcyon, Fifty-Fifty
For added coverage,
(E) Others
(i) Multipolar scenarios: CLR, ACS Prague, FOCAL (CMU), CAIF
(ii) Digital consciousness type-things: CLR, Eleos, NYU Center for Mind, Ethics, and Policy
(iii) Post-AGI futures: Forethought, MIT FutureTech
(F) For-profits trying to translate AI safety work into some kind of business model to validate research and possibly be well situated should more regulation mandate evals, audit, certifications etc.: e.g. Goodfire, Lakera, GraySwan, possibly dozens more startups + big professional services firms would be itching to get in on this when the regulations happen.
It is very worth investigating whether to work on any of these: The field is wide open and there are many approaches to pursue. “Defence in depth” (1, 2, 3) implies that there is work to be done across a lot of different attack surfaces, and so it’s maybe not so central to identify a singular best thing to work on; it’s enough to find something that has a plausible theory of change, that seems to be neglected and/or is patching some hole in a huge array of defences—we need lots of people/orgs/resources to help with finding and patching the countless holes!

Sudhanshu Kasewa 15 Oct 2025 11:19 UTC
23 points
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on: Sudhanshu Kasewa’s Quick takes
PSA: If you’re doing evals things, every now and then you should look back at OpenPhil’s page on capabilities evals to check against their desiderata and questions in sections 2.1-2.2, 3.1-3.4, 4.1-4.3 as a way to critically appraise the work you’re doing.

Sudhanshu Kasewa 29 Sep 2025 16:33 UTC
2 points
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on: Diversify Your Moral Risk Portfolio
I was reminded of this post (Purchase Fuzzies and Utilons Separately), and it’s something I do myself: work in some speculative EV-maximising space, but donate to “definitely doing good” things.

Sudhanshu Kasewa 27 Aug 2025 14:51 UTC
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on: Technical AI Safety research taxonomy attempt (2025)
Thanks for doing this, Ben!
Readers: Here’s a spreadsheet with the above Taxonomy, and some columns which I’m hoping we can collectively populate with some useful pointers for each topic:
1. Does [academic] work in this topic help with reducing GCRs/X-risks from AI?
2. What’s the theory of change[1] for this topic?
3. What skills does this build, that are useful for AI existential safety?
4. What are some Foundational Papers in this topic?
5. What are some Survey Papers in this topic?
6. Which academic labs are doing meaningful work on this topic?
7. What are the best academic venues/workshops/conferences/journals for this topic?
8. What other projects are working on this topic?
9. Any guidance on how to get involved, who to speak with etc. about this topic?
For security reasons, I have not made it ‘editable’, but please comment on the sheet and I’ll come by in a few days and update the cells.
[1] softly categorised as Plausible, Hope, Grand Hope

Sudhanshu Kasewa 27 Jul 2025 22:08 UTC
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in reply to: Sven Spehr’s comment on: AMA: Ask Career Advisors Anything
Hi Sven: Unfortunately this is a bit outside my wheelhouse, but you might want to reach out to the folks behind Integral—I bet Milan would have great ideas about what skills to build in order to work on this cause.

Sudhanshu Kasewa 24 Jul 2025 13:27 UTC
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in reply to: The_Lord's_Servant_280’s comment on: AMA: Ask Career Advisors Anything
Hi! Thanks for sharing your story. Some quick thoughts:
1. Could you quit your part-time job and instead use that time better? Could you take on a different part-time position that lets you build useful skills and networks?
2. I think it’s probably okay to finish your masters (since you’re already halfway through it); if you can find a job that’s going to get you hands-on experience on, say, LLMs, and/or build skills that could be robustly useful in a variety of roles, it might tip the scales in favour of dropping out (but by then you’ll be even closer to finishing, so maybe it makes sense to just complete it).
3. With or without your masters, with or without your job, I think it’s useful to get situationally aware about what’s happening in AI, and get comfortable (proficient, even) with using AI tools to enhance your own productivity and growth.
4. Benjamin Todd wrote about how not to lose your job to AI. I might not fully endorse this piece, but I think it’s directionally correct and has some good ideas to think about.
All the best!

Sudhanshu Kasewa 24 Jul 2025 12:23 UTC
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in reply to: MarcKrüger’s comment on: AMA: Ask Career Advisors Anything
Hi Marc, thanks for the question.
Lots has been said about the value of PhDs:
1. Lewis Hammond gives advice here about doing PhDs
2. Rohin Shah talks a little about PhDs here in relation to AI safety work
3. Adam Gleave makes a positive case here for AI safety
[Caveat: having dropped out of a PhD myself, I might be biased against doing one.] I think our piece on doing PhDs mostly holds up, but I’d make a few updates away from doing one:
1. AI things might happen soon, and in many worlds it would be better to do good soon rather than build a lot of skill first. (This obviously doesn’t apply if you are able to do good via your PhD as well.)
2. For many of us, we’re likely to really succeed at a PhD only if we are obsessed about it: an An advisee once said to me “Only do a PhD, if it’s something that you would do for free, in your free time, after work, on nights and weekends.”
  1. A softer version of this: I think many of us are not calibrated about what attitudes and behaviours help to do a PhD—I certainly wasn’t! Before committing, try to come to grips with what you’re signing up for.
3. Finally, I subscribe to a PhD not being an end-in-itself, and instead a way to get some role/job/opportunity, which otherwise might be very unlikely without it. In impact-focussed spaces, I don’t think there are that many opportunities gate-kept by a PhD credential: academia/professorship, the “research scientist” title (but not necessarily the work), and maybe some policy positions. Orgs and managers who care about impact, care more about “can do you the work?” rather than “do you have the credential?”; could you get the legible skills to “do the work” in a paid job with better hours in fewer years, or do you have to do a PhD instead?
Hope this helps! All the best.

Sudhanshu Kasewa 24 Jul 2025 11:55 UTC
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in reply to: Adam-the-apteryx’s comment on: AMA: Ask Career Advisors Anything
Hi Adam, so exciting that you want to use your skills for doing good. I’d go even further and say that “doing good” is its own goal to shoot for, and I want more folks thinking about “What are the best opportunities to do the most good?” first, and only then filtering by some subset of their relevant skills that might make them a good fit.
This is described more in Part 4 our career guide, where we outline a framework—Scale, Tractability, Neglectedness—to identify global issues with some of the highest opportunities for positive impact. We’ve applied such an analysis in our work for many years, and have in-depth articles on what we believe to be some of the world’s most pressing problems.
We also host a job board to aggregate opportunities aimed at helping with these. If you’re looking for more “goal-directed” next steps, perhaps scanning through those jobs can give you a sense of what the world needs right now, and how you can help. All the best!

Sudhanshu Kasewa 24 Jul 2025 11:27 UTC
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in reply to: alphaplus’s comment on: AMA: Ask Career Advisors Anything
Hi alphaplus, thank you for the questions. I’m glad to hear your health is improving.
I want to start by saying: your (written) English seems fine! Even if you’re concerned about your speaking skills, you can always lean in to your written ability to connect, exchange ideas, and grow.
Without knowing more, it’s hard to give very tailored advice, so here are some messages I think more folks should take seriously:
1. AI could be a big deal, soon. It could create huge dangers.
  1. In light of this, lots of stuff needs doing, e.g. technical research, governance, cybersecurity, international cooperation.
    even if the most egregious risks of AI don’t materialise soon (or at all), I’ll claim (albeit without justification) that having an understanding of how these technologies are transforming the world puts one is a good place to help out in many future scenarios.
As a result, I advocate more people develop ‘situational awareness’, and make their plans keeping (the possibility of) rapid AI progress in mind.
To your main question of “What would you do if you were in my position?”, there are several ways to progress. One procedure is articulated here:
1. Make some best guesses (hypotheses) about which options seem best.
2. Identify your key uncertainties about those hypotheses.
3. Go and investigate those uncertainties.
The key point is to try things, get feedback, and update your beliefs, and try again. Once you have more clarity, you’ll be able to aim for and commit to specific paths.
Finally, there are no real barriers to entry to engaging with Effective Altruism! If you think you’ll find value in connecting with folks in the community, you absolutely should. In addition to this Forum, there are plenty of other spaces, e.g. EAG(x) events you can attend, or slack channels you can join.

Sudhanshu Kasewa 24 Jul 2025 10:42 UTC
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in reply to: Stephanie123’s comment on: AMA: Ask Career Advisors Anything
Hi Stephanie,
Thanks for writing in. It’s great that you’re thinking of using your career to help AI go well, and have been building skills and applying for roles to that end.
I’m sorry to hear you’ve been struggling with landing a role. Here are some ideas:
1. Are you getting enough feedback on your application materials? It’s good to solicit input from trusted sources, mentors, others on similar journeys as you, so that you’re sure that you’re not missing some key pieces in your applications.
2. Are you talking/connecting to people in roles/orgs you’re interested in? As we get more experience, it’s more likely that we’ll find roles through our network and collaborators; so it’s useful to invest in those relationships, not just by being a jobseeker, but by aiming to help out and add value wherever you can (maybe by giving feedback/critiques, exchanging your expertise for something you want to learn, or volunteering in some way).
3. Are you continuing to make your skills and experience legible? As my colleague @Matt Beard puts it “you should obsessively improve at an in-demand skill set in a legible way”. Those skills could be within writing, speaking, research, analysis, code, hardware, interpersonal collaboration, project management, organisation building, strategic thinking, and so on. The idea here is analogous to “build it and they will come”, and countless folks have translated their visible expertise into high-impact roles. Check out our skills pages for more details.
  1. A related thing is to use such public productivity and output as a way to increase your feedback surface area, to pick up on where you can grow. Aim to post your work in places where folks are happy to engage in good faith and offer constructive input. The EA Forum and LessWrong are great places for this!
4. Are you maybe the right person to start a new org working on something because nobody else is doing it, and it’s incredibly important? I often say in advising calls “You can just do things”, because it’s true and sometimes we forget that. Yes, it can be daunting, and it’s worth considering your own personal circumstances, but all things considered, I want more people to be willing to take those kinds of risks.
  1. Similarly, you might even want to implement someone else’s idea, or replicate or improve on an existing project—there are plenty of excellent ideas out there that need more people executing them.
5. Some more resources:
  1. Advice for mid-career folks looking for high-impact roles
  2. What to do when the job market isn’t cooperating
  3. A (maybe slightly stale) list of social impact job boards
Hope this helps! All the best.

Sudhanshu Kasewa 23 Jul 2025 8:26 UTC
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in reply to: Tristan Trim’s comment on: AMA: Ask Career Advisors Anything
Quick thoughts:
1. Great that you have work like the arxiv paper! You could even explicitly ask for feedback on that work
2. Make it easy for people to understand your work: Try and answer questions like “Why did I do this? What did I learn and/or what update did I make? What is my theory of change?”, and so on...
3. Make it easy for people to engage with your work: Display it prominently, tweet about it, write a blogpost on lesswrong about it. Polish and publish the code base (see an example here), and so on...
4. Everyone has their own style of building relationships. I think a powerful way to do so is to try and add value to others: can you summarise/discuss their work in public, or give them feedback, or extend it in an interesting way? Are there volunteer or part-time opportunities that you can help out with? Can you identify issues in their codebases and improve them?

Sudhanshu Kasewa 22 Jul 2025 15:26 UTC
3 points
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in reply to: justsaying’s comment on: AMA: Ask Career Advisors Anything
Hi, thanks for the question!
I’m not sure anyone is confident what “optimal policy” is! Waiting to develop your own views on optimal policy can take some time; in the meanwhile you could check out what some researchers and think tanks have to say, e.g.:
Some longer reads:
1. International AI safety report
2. MIRI’s AI governance research agenda
3. OpenPhil’s AI Governance RFP might have some pointers to some reasonable ideas
Finally, for U.S. citizens, Emerging Tech Policy Careers is great! Check out their AI policy resources.
In general, applying is a great way to get feedback and get calibrated on what you bring to table and what you need to work on, as mentioned elsewhere in these comments, so, yes, I think, you should be biased towards applying to things. There are some nuances to this, including being aware of when a rejection is likely to result in a ‘cooling off period’ where you may not be able to reapply for 6-12 months.