Without commenting too much on a specific org (anonymity commitments, sorry!), I think we’re in agreement here and that the information you provided doesn’t conflict with the findings of the report (although, since your comment is focused on a single org in a way that the report is simply not licensed to be, your comment is somewhat higher resolution).

One manager creates bandwidth for 5-10 additional Iterator hires, so the two just aren’t weighted the same wrt something like ‘how many of each should we have in a MATS cohort?’ In a sense, a manger is responsible for ~half the output of their team, or is “worth” 2.5-5 employees (if, counterfactually, you wouldn’t have been able to hire those folks at all). This is, of course, conditional on being able to get those employees once you hire the manager. Many orgs also hire managers from within, especially if they have a large number of folks in associate positions who’ve been with the org > 1 year and have the requisite soft skills to manage effectively.

If you told me “We need x new safety teams from scratch at an existing org”, probably I would want to produce (1-2)x Amplifiers (to be managers), and (5-10)x Iterators. Keeping in mind the above note about internal hires, this pushes the need (in terms of ‘absolute number of heads that can do the role’) for Amplifiers relative to Iterators down somewhat.

Fwiw, I think that research engineer is a pretty Iterator-specced role, although with different technical requirements from, i.e. “Member of Technical Staff” and “Research Scientist”, and that pursuing an experimental agenda that requires building a lot of your own tools (with an existing software development background) is probably great prep for that position. My guess is that MATS scholars focused on evals, demos, scalable oversight, or control could make strong research engineers down the line, and that things like CodeSignal tests would help catch strong Research Engineers in the wild.

...we’re looking for someone with experience in a research or engineering environment, who is excited about and experienced with people and project management, and who is enthusiastic about our research agenda and mission.

I’d also predict that, if management becomes a massive bottleneck to Anthropic scaling, they would restructure somewhat to make the prerequisites for these roles a little less demanding (as has DeepMind, with their People Managers, as opposed to Research Leads, and as have several growing technical orgs, as mentioned in the post).

This is a good point, and something that I definitely had in mind when putting this post together. There are a few thoughts, though, that would temper my phrasing of a similar claim: 

Many interviewees said things like "I want 50 more iterators, 10 amplifiers to manage them, and 1-2 connectors." Interviewees were also working on diverse research agendas, meaning that each of these agendas could probably absorb >100 iterators if not for managerial bottlenecks and, to a lesser extent, funding constraints. This is even more true if those iterators have sufficient research taste (experience) to design their own followup experiments.

This points toward abundant low hanging fruit and a massive experimental backlog field-wide. For this reason and others, I'd probably bump up the 100 number in your hypothetical by a few oom which, given the (fast in an absolute sense but, relative to our actual needs/funds) slow growth of the field, probably means the need for iterators holds even in long timelines, particularly if read as "for at least a few months, please prioritize making more iterators and amplifiers" and not "for all time, no more connectors are needed."

If we just keep tasting the soup, and figuring out what it needs as we go, we'll get better results than if any one-time appraisal or cultural mood becomes dogma.

There's a line I hear paraphrased a lot by the ex-physicists around here, from Paul Dirac, about physics  in the immediate wake of relativity: it was a time when "second-rate physicists could do first-rate work." The AI safety situation seems similar: the rate of growth, the large number of folks who've made meaningful contributions, the immaturity of the paradigm, the proliferation of divergent conceptual models, all point to a landscape in which a lot of dry scientific churning needs doing.

I definitely agree that marginal 'more-of-the-same' talent has diminishing returns. But I also think diverse teams have a multiplicative effect, and my intention in the post is to advocate for a diversified talent portfolio (as in the numbered takeaways section, which is in some sense a list of priorities, but in another sense a list of considerations that I would personally refuse to trade off against if I were the Dictator of AI Safety Field-building). That is, you get more from 5 iterators, one amplifier, and one connector working together on mech interp, than you do from 30 iterators doing the same. But I wasn't thinking about building the mech interp talent pool from scratch in a frictionless vacuum; I was looking at the current mech interp talent pool and trying to see how far it is, right now, from its ideal composition, then fill those gaps (where job openings, especially at small safety orgs, and preferences of grant makers, are a decent proxy for the gaps).

Sorry to go so hard in this response! I've just been living inside thinking about this for 4-5 months, and a lot of this type of background was cut from the initial post for concision and legibility (neither of which are particularly native to me). I'd hoped the comment section might be a good place for me to provide more context and tempering, so thanks so much for engaging!