Got sent a set of questions from ARBOx to handle async; thought I'd post my answers publicly:

• Can you explain more about mundane utility? How do you find these opportunities?
  • Lots of projects need people and help! E.g. Can you contribute to EleutherAI, or close issues in Neuronpedia? Some more ideas:
    • Contribute to the projects within SK’s github follows and stars
    • Make some contributions within Big list of lists of AI safety project ideas 2025
  • Reach out to projects that you think are doing cool work and ask if you can help!
  • BlueDot ideas for SWEs
    • I'm an experienced software engineer. How can I contribute to AI safety? 
    • The software engineer’s guide to making your first AI safety contribution in <1 week 
  • From a non-coding perspective, you could e.g. 
    • Facilitate BlueDot courses
    • Give people feedback on their research proposals, drafts, etc.
    • Be accountability partners
    • Offer to talk to people and share what you know with those who know less than you
  • Check out these pieces from my colleagues:
    • How to have an impact when the job market is not cooperating by Laura G Salmeron
    • Your Goal Isn’t Really to Get a Job by Matt Beard
• What is your theory of change?
  • As an 80k advisor, my ToC is “Try and help someone to do something more impactful than if they had not spoken to me.”
    • Mainly, this is helping get people more familiar with/excited about/doing things related to AI safety. It’s also about helping them with resources and sometimes warm introductions to people who can help them even more.
• Are there any particular pipelines / recommended programs for control research?
  • Just the things you probably already know about – MATS, Astra are likely your best bets, but look through these papers to see if there are any low hanging fruit as future work
• What are the most neglected areas of work in the AIS space? 
  • Hard question, with many opinions! I’m particularly concerned that “making illegible problems legible” is neglected. See Wei Dai’s writing about this
    • Legible vs. Illegible AI Safety Problems 
    • Problems I've Tried to Legibilize
  • More groundedly, I’m concerned we’re not doing enough work on Gradual Disempowerment and more broadly questions of {how to have a flourishing future/what is a flourishing future} even if we avoid catastrophic risks
  • In general, AI safety work needs to contend with a collection of subproblems. See davidad’s opinion – A list of core AI safety problems 
    • There are many other such opinions, and it’s good to scan through them to work out how they’re all connected, so that you can see the forest for the trees; and also to work out which problems you’re drawn to/compelled by, and seek out what’s neglected within those 🙂
• Some questions about ops-roles: 
  • What metrics should I use to evaluate my performance in ops/fieldbuilding roles? I find ops to be really scattered and messy, and so it's hard to point to consistent metrics. 
    • Hard to talk about this in concrete terms, because ops is so varied; every task can have its own set of metrics. Instead, think through this strategically:
      • Be clear on the theory(ies) of change, and your roles/activities/tasks in it(them). Once you can articulate those things, the metrics worth measuring become a lot clearer
      • Sometimes we’re not tracking impact because impact evaluation is notoriously difficult. Look for proxies. Red-team them with people you admire
    • Fieldbuilding metrics can be easier to generate, but I don’t claim to be an expert here – ask folks at BlueDot, or the fellowships for better input.
      • How many people completed the readings?
      • How many people did I get to sign up for the bluedot course?
        • How many of those finished the bluedot course?
      • How many people did I get into an Apart Hackathon?
        • Did any of my people win?
      • And so on…
  • Likewise, I have a hard time discerning what “ops” really means. What are the best tangible “ops” skills I should go out of my way to skill up on if I want to work in the field building/programmes space? Are there “hard” ops skills I should become really good at (like, familiarity with certain software programmes, etc)
    • Ops is usually a “get stuff done” bucket of work. Yes, it can help to have functional experience in an ops domain like “Finance” or “IT/office tech infra/website” (and especially “Legal”), but a LOT of ops can be learned on the job/on your own; AI safety is stacked full of folks who didn’t let “I don’t know anything about ops” stop them from figuring it out and getting it done
• Under what circumstances should a “technical person” consider switching their career to fieldbuilding? 
  • First things first: 
    • Fieldbuilding is not a consolation prize. Do fieldbuilding if you’re really passionate about helping AI go well, and fieldbuilding is your comparative advantage.
  • And doubling down on that:
    • It really really really helps if fieldbuilders are very competent. A fieldbuilder who doesn’t know their shit about AI risk and AI safety can propagate bad ideas among the people they’re inducting into the field.
      • This can have incredibly high costs
        • Pollutes the commons
        • Wastes time downstream where all this would need to be corrected
        • Bounces people who might be able to quickly get up to speed, because their initial contact with these fieldbuilders is of poor quality, poor argumentation, poor epistemics
    • Conversely a great fieldbuilder is one who knows how to tend their flock, what they need to prosper and grow to become competent at thinking about AI safety properly, and being able to do AI safety things
• How would you recommend going about doing independent project work for upskilling in-place of doing something like SPAR or MATS?
  • Why not both? In general, I want people to ask themselves this question when making decisions. You can do a lot more than you give yourself credit for.
  • At the current margins SPAR, MATS etc. are probably better than independent work
    • Some of these fellowships have pretty high signal to employers (based on evidence that has been generated over time)
    • There is a lot that these fellowships offer that are sometimes hard to get without them
      • Research support, mentorship, community engagement, well-scoped projects with deliverables and accountability
        • Also softer things like physical space , some money
  • But if you’re great at doing stuff independently, go for it! Neel Nanda didn’t need a fellowship.
    • A key idea is to keep your eye on the ball – be productive!
      • The point is generate outputs
        • That make you learn
        • That show that you have learned
        • That are related to AI safety
        • That get feedback
          • That show that you update based on (relevant/good/high-quality) feedback

lfg!

Not to karma-farm, but added a few more thoughts here

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.

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.

"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.

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

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!

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.

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!

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!