Ryan Kidd

Give me feedback! :)



  • Ph.D. in Physics from the University of Queensland (2017-2022)
  • Group organizer at Effective Altruism UQ (2018-2021)

Wiki Contributions


Ryan Kidd472

I am a Manifund Regrantor. In addition to general grantmaking, I have requests for proposals in the following areas:

You might be interested in this breakdown of gender differences in the research interests of the 719 applicants to the MATS Summer 2024 and Winter 2024-25 Programs who shared their gender. The plot shows the difference between the percentage of male applicants who indicated interest in specific research directions from the percentage of female applicants who indicated interest in the same.

The most male-dominated research interest is mech interp, possibly due to the high male representation in software engineering (~80%), physics (~80%), and mathematics (~60%). The most female-dominated research interest is AI governance, possibly due to the high female representation in the humanities (~60%). Interestingly, cooperative AI was a female-dominated research interest, which seems to match the result from your survey where female respondents were less in favor of "controlling" AIs relative to men and more in favor of "coexistence" with AIs.

I interpret your comment as assuming that new researchers with good ideas produce more impact on their own than in teams working towards a shared goal; this seems false to me. I think that independent research is usually a bad bet in general and that most new AI safety researchers should be working on relatively few impactful research directions, most of which are best pursued within a team due to the nature of the research (though some investment in other directions seems good for the portfolio).

I've addressed this a bit in thread, but here are some more thoughts:

  • New AI safety researchers seem to face mundane barriers to reducing AI catastrophic risk, including funding, infrastructure, and general executive function.
  • MATS alumni are generally doing great stuff (~46% currently work in AI safety/control, ~1.4% work on AI capabilities), but we can do even better.
  • Like any other nascent scientific/engineering discipline, AI safety will produce more impactful research with scale, albeit with some diminishing returns on impact eventually (I think we are far from the inflection point, however).
  • MATS alumni, as a large swathe of the most talented new AI safety researchers in my (possibly biased) opinion, should ideally not experience mundane barriers to reducing AI catastrophic risk.
  • Independent research seems worse than team-based research for most research that aims to reduce AI catastrophic risk:
    • "Pair-programming", builder-breaker, rubber-ducking, etc. are valuable parts of the research process and are benefited by working in a team.
    • Funding insecurity and grantwriting responsibilities are larger for independent researchers and obstruct research.
    • Orgs with larger teams and discretionary funding can take on interns to help scale projects and provide mentorship.
    • Good prosaic AI safety research largely looks more like large teams doing engineering and less like lone geniuses doing maths. Obviously, some lone genius researchers (especially on mathsy non-prosaic agendas) seem great for the portfolio too, but these people seem hard to find/train anyways (so there is often more alpha in the former by my lights). Also, I have doubts that the optimal mechanism to incentivize "lone genius research" is via small independent grants instead of large bounties and academic nerdsniping.
  • Therefore, more infrastructure and funding for MATS alumni, who are generally value-aligned and competent, is good for reducing AI catastrophic risk in expectation.

Also note that historically many individuals entering AI safety seem to have been pursuing the "Connector" path, when most jobs now (and probably in the future) are "Iterator"-shaped, and larger AI safety projects are also principally bottlenecked by "Amplifiers". The historical focus on recruiting and training Connectors to the detriment of Iterators and Amplifiers has likely contributed to this relative talent shortage. A caveat: Connectors are also critical for founding new research agendas and organizations, though many self-styled Connectors would likely substantially benefit as founders by improving some Amplifier-shaped soft skills, including leadership, collaboration, networking, and fundraising.

In theory, sure! I know @yanni kyriacos recently assessed the need for an ANZ AI safety hub, but I think he concluded there wasn't enough of a need yet?

@Elizabeth, Mesa nails it above. I would also add that I am conceptualizing impactful AI safety research as the product of multiple reagents, including talent, ideas, infrastructure, and funding. In my bullet point, I was pointing to an abundance of talent and ideas relative to infrastructure and funding. I'm still mostly working on talent development at MATS, but I'm also helping with infrastructure and funding (e.g., founding LISA, advising Catalyze Impact, regranting via Manifund) and I want to do much more for these limiting reagents.

I would amend it to say "sometimes struggles to find meaningful employment despite having the requisite talent to further impactful research directions (which I believe are plentiful)"

Why does the AI safety community need help founding projects?

  1. AI safety should scale
    1. Labs need external auditors for the AI control plan to work
    2. We should pursue many research bets in case superalignment/control fails
    3. Talent leaves MATS/ARENA and sometimes struggles to find meaningful work for mundane reasons, not for lack of talent or ideas
    4. Some emerging research agendas don’t have a home
    5. There are diminishing returns at scale for current AI safety teams; sometimes founding new projects is better than joining an existing team
    6. Scaling lab alignment teams are bottlenecked by management capacity, so their talent cut-off is above the level required to do “useful AIS work”
  2. Research organizations (inc. nonprofits) are often more effective than independent researchers
    1. Block funding model” is more efficient, as researchers can spend more time researching, rather than seeking grants, managing, or other traditional PI duties that can be outsourced
    2. Open source/collective projects often need a central rallying point (e.g., EleutherAI, dev interp at Timaeus, selection theorems and cyborgism agendas seem too delocalized, etc.)
  3. There is (imminently) a market for for-profit AI safety companies and value-aligned people should capture this free energy or let worse alternatives flourish
    1. If labs or API users are made legally liable for their products, they will seek out external red-teaming/auditing consultants to prove they “made a reasonable attempt” to mitigate harms
    2. If government regulations require labs to seek external auditing, there will be a market for many types of companies
    3. “Ethical AI” companies might seek out interpretability or bias/fairness consultants
  4. New AI safety organizations struggle to get funding and co-founders despite having good ideas
    1. AIS researchers are usually not experienced entrepeneurs (e.g., don’t know how to write grant proposals for EA funders, pitch decks for VCs, manage/hire new team members, etc.)
    2. There are not many competent start-up founders in the EA/AIS community and when they join, they don’t know what is most impactful to help
    3. Creating a centralized resource for entrepeneurial education/consulting and co-founder pairing would solve these problems

AI that obeys the intention of a human user can be asked to help build unsafe AGI, such as by serving as a coding assistant.

I think a better example of your point is "Corrigible AI can be used by a dictator to enforce their rule."

Yep, it was pointed out to me by @LauraVaughan (and I agree) that e.g. working for RAND or a similar government think tank is another high-impact career pathway in the "Nationalized AGI" future.

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