Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New 80k problem profile - Climate change, published by Benjamin Hilton on May 18, 2022 on The Effective Altruism Forum.

This is a cross-post of 80,000 Hours' new problem profile on climate change. We're cross posting the first section, which is our research into the question of the extent to which climate change is a direct extinction risk. For more, including the indirect impacts of climate change, and career recommendations, see the full profile.

Could climate change lead to the end of civilisation?

Across the world, over half of young people believe that, as a result of climate change, humanity is doomed.And it’s common for media reports to suggest that climate change will plausibly destroy civilisation or even make Earth uninhabitable.

But this isn’t true.

The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report is, to our knowledge, the most authoritative and comprehensive source on climate change. The report is clear: climate change will be hugely destructive. We’ll see floods, famines, fires, and droughts — and the world’s poorest people will be affected the most.

But even when we try to account for unknown unknowns, nothing in the IPCC’s report suggests that civilisation will be destroyed.

Instead, the scientific consensus is that it’s pretty much impossible for climate change to directly cause the end of civilisation or the extinction of humanity. We’ll look at why this is in more detail, and why if your concern is the collapse of civilisation, there are bigger and even more neglected risks, such as engineered pandemics, risks from AI, or nuclear war.

This isn’t to say society shouldn’t do far more to tackle climate change. If you want to make climate change the focus of your career, we include some thoughts below on the most effective ways to help tackle it.

Climate change makes people feel angry, powerless, and — above all — afraid. If society doesn’t act more decisively, climate change will affect all our lives and gravely damage livelihoods around the world.

So it’s no wonder people are scared, and angry at our failure to do more.

But we’re not powerless.

And we’re far from doomed.

Summary

Climate change is going to significantly and negatively impact the world. Its impacts on the poorest people in our society and our planet’s biodiversity are cause for particular concern. Looking at the worst possible scenarios, while it looks extremely unlikely that climate change could directly make the Earth uninhabitable, it could be an important factor that increases existential threats from other sources, like great power conflicts, nuclear war, or pandemics. That said, we think most readers are likely to have a greater impact working directly on one of these other risks.

We think worrying about your personal carbon footprint is largely a distraction, and that some ways of making a difference on climate change are likely to be much more effective than others. In particular, you could use your career to help develop technology or advocate for policy that would reduce our current emissions, or research technology that could remove carbon from the atmosphere in the future.

Our overall view

Sometimes recommended

We’d love to see more people working on this issue, but — given our general worldview — all else equal we’d be even more excited for someone to work on one of our top priority problem areas.

Scale

We think work to materially reduce the probability of the worst outcomes of climate change would have a large positive impact. However, climate change seems hundreds of times less likely to directly cause human extinction than other risks we’re concerned about, like catastrophic pandemics. As a result, if climate change does have catastrophic and potentially long-lasting consequences for human civilisation, this will likely be through aggravating ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Many Faces of Effective Altruism, published by Devin Kalish on May 18, 2022 on The Effective Altruism Forum.

Author’s Note: This post based on a Discord rant

Epistemic Status: Very very unrigorous, just a distillation of some things I’ve been thinking about recently that some of my friends encouraged me to post somewhere

The Effective Altruism movement is, in my opinion, not a single subculture, but a coalition of several related ones that occupy a similar memespace, and often overlap. Recently I have been thinking about this undertheorized aspect of the movement, and wanted to share the beginnings of some distinctions that I see within the movement, and how these might be useful for understanding key things about it. Like all labeling schemes of this sort, this is also of limited value, will undoubtedly be controversial, especially when I get into my specific impressions of what fits where, and will probably be significantly shaped in various ways by my own personal sympathies. Nevertheless, and occasionally against my better judgement, I appreciate when other people make new labeling schemes like this, so I hope I can produce one that is valuable myself. My current thinking divides Effective Altruism into roughly six aesthetics/subcultures that seem meaningfully distinct:

Wholesome Effective Altruism: This is the more infographic/nerdy stuff, often with an interest in popular appeal. It is also more concerned with political causes outside of the common EA priorities, and adheres to EA on top of this basically because doing good is good. Lots of sort of adjacent, mildly sympathetic non-EA media leans this way, like “The Good Place” or Vlogbrothers or Kurzgesagt. Think Future Perfect or Our World in Data for more directly movement aligned media. Example figure: Ezra Klein

Emo Effective Altruism: Emphasizes feeling the appropriate weight from things like suffering and death, lots of the art associated with EA comes from this corner in some way. Made up of people who are real into stuff like “Fable of the Dragon Tyrant” and “500 Million, but not a Single One More” and “On Caring”. I also tend to group the more “emotionally intelligent” side of EA in here, though that’s a debatable connection. Example figure: Eliezer Yudkowsky

IDW Effective Altruism: This is the more centrist or right-leaning, anti-woke side of EA. Often embraces some version of Bryan Caplan’s “EA is what SJ ought to be” idea. Sort of the right’s answer to Wholesome EA, sharing a “doing good is good” type motivation, and also featuring figures who are sympathetic but not that centrally involved, like Steven Pinker and Coleman Hughes. Example figure: Sam Harris

A-aesthetic Effective Altruism: Don’t want to put out much of a vibe at all, and prefer purely unbiased, rigorous analysis. The source of many-a-long, nuanced document Effective Altruists are keen to cite (but perhaps uneager to read), though there is more accessible work that also fits this tone in my opinion. Relatively few people purely fit into this category, though many combine other categories with it, or occasionally wear the a-aesthetic hat. Example Figure: Hilary Greaves

Contrarian Effective Altruism: This is not necessarily about liking unpopular ideas, but more about being super into weird ideas because they seem interesting and cool. Usually I picture the type of person who is into the simulation hypothesis and grabby aliens but doesn’t seem that worried about them viscerally. Not many major figures in the movement seem to fit that well into this category, but I’ve met lots of people on the ground within EA clubs who fit here more solidly. Example figure: Robin Hanson

Cheery utilitarian Effective Altruism: People in this category basically see some underlying tenants of utilitarianism as uncomplicatedly true and good without much o...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What's up with the recent monkeypox cases?, published by johnswentworth on May 18, 2022 on LessWrong.

Is it a thing I should be allocating attention to?

Wikipedia tells me that human-to-human transmission of monkeypox is pretty rare/difficult. There has been some community spread recently at least in the UK, but that's been speculated to have been from sex. On the other hand, there's been a bunch of cases reported in Portugal and Spain recently as well. Is that just normal background rate being given more attention than usual by the news cycle? Or is this actually an unusually high number of cases? Most importantly, is the number of cases significant evidence of increased human-to-human transmission?

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We have achieved Noob Gains in AI, published by Aniruddha Nrusimha on May 18, 2022 on The AI Alignment Forum.

TL;DR I explain why I think AI research has been slowing down, not speeding up, in the past few years.

How have your expectations for the future of AI research changed in the past three years? Based on recent posts in this forum, it seems that results in text generation, protein folding, image synthesis, and other fields have accomplished feats beyond what was thought possible. From a bird's eye view, it seems as though the breakneck pace of AI research is already accelerating exponentially, which would make the safe bet on AI timelines quite short.

This way of thinking misses the reality on the front lines of AI research. Innovation is stalling beyond just throwing more computation at the problem, and the forces that made scaling computation cheaper or more effective are slowing. The past three years of AI results have been dominated by wealthy companies throwing very large models at novel problems. While this expands the economic impact of AI, it does not accelerate AI development.

To figure out whether AI development is actually accelerating, we need to answer a few key questions:

What has changed in AI in the past three years?

Why has it changed, and what factors have allowed that change?

How have those underlying factors changed in the past three years?

By answering these fundamental questions, we can get a better understanding of how we should expect AI research to develop over the near future. And maybe along the way, you'll learn something about lifting weights too. We shall see.

What has changed in AI research in the past three years?

Gigantic models have achieved spectacular results on a large variety of tasks.

How large is the variety of tasks? In terms of domain area, quite varied. Advances have been made in major hard science problems like protein synthesis, imaginative tasks like creating images from descriptions, and playing complex games like Starcraft.

How large is the variety of models used? While each model features many domain specific model components and training components, the core of each of these models is a giant transformer trained with a variant of gradient descent, usually ADAM.

How large are these models? That depends. DALLE2 and AlphaFold are O(10GB), AlphaStar is O(1GB), and the current state of the art few shot NLP models (Chinchilla) are O(100GB).

One of the most consistent findings of the past decade of AI research is that larger models trained with more data get better results, especially transformers. If all of these models are built on top of the same underlying architecture, why is there so much variation in size?

Think of training models like lifting weights. What limits your ability to lift heavy weights?

Data availability: (Nutrition) If you don't eat enough food, you'll never gain muscle! Data is the food that makes models learn, and the more "muscle" you want the more "food" you need. When looking for text on the internet, it is easy to get terabytes of data to train a model. This is harder for other tasks

Cost (exhaustion): No matter how rich your corporation is, training a model is expensive. Each polished model you see comes after a lot of experimentation and trials, which uses a lot of computational resources. AI labs are notorious cost sinks. The talent they acquire is expensive, and in addition to their salaries the talent demands access to top of the line computational resources.

Training methodology (What exercises you do). NLP models only require to train one big transformer. More complex models like DALLE-2 and AlphaFold have many subcomponents optimized for their use cases. Training an NLP model is like deadlifting a loaded barbell and training AlphaFold is like lifting a box filled with stuff: at equiva...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Maxent and Abstractions: Current Best Arguments, published by johnswentworth on May 18, 2022 on The AI Alignment Forum.

This post is not-very-distilled and doesn’t contain much background; it’s intended for people who already have the context of at least these four posts. I’m putting it up mainly as a reference for people who might want to work directly on the math of natural abstractions, and as a technical reference post.

There’s various hints that, in most real-world cases, the distribution of low-level state given high-level natural abstractions should take the form of a maximum entropy distribution, in which:

The “features” are sums over local terms, and

The high-level variables are (isomorphic to) the Lagrange multipliers

More formally: we have a low-level causal model (aka Bayes net) P[XL]=∏iP[XLi|XLpa(i)]. Given the high-level variables XH, the distribution of low-level variable values should look like

P[XL|XH]=1ZP[XL]eλT(XH)∑ifi(XLi,XLpa(i))

. i.e. the maximum-entropy distribution subject to constraints of the form E[∑ifi(XLi,XLpa(i))|XH]=μ(XH). (Note: λ, fi, and μ are all vector-valued.)

This is the sort of form we see in statistical mechanics. It’s also the form which the generalized Koopman-Pitman-Darmois (gKPD) theorem seems to hint at.

I don’t yet have a fully-satisfying general argument that this is the main form which abstractions should take, but I have two partial arguments. This post will go over both of them.

Maxent Telephone Argument

Quick recap of the Telephone Theorem: information about some variable X passes through a nested sequence of Markov blankets M1,M2,.. Information about X can only be lost as it propagates. In the limit, all information is either perfectly conserved or completely lost. Mathematically, in the limit P[X|Mn]=P[X|Fn(Mn)] for some F such that Fn(Mn)=Fn+1(Mn+1) with probability approaching 1 as n→∞; F is the perfectly-conserved-in-the-limit information carrier.

In this setup, we can also argue that the limiting distribution limn→∞P[X|Mn] should have a maxent form. (Note: this is a hand-wavy argument, not a proper proof.)

Think about how the distribution (x↦P[X=x|Mn]) transforms as we increment n by 1. We have

P[X|Mn+1]=∑MnP[X|Mn]P[Mn|Mn+1]

First key property of this transformation: it’s a convex combination for each Mn+1 value, i.e. it’s mixing. Mixing, in general, cannot decrease the entropy of a distribution, only increase it or leave it the same. So, the entropy of P[X|Mn] will not decrease with n.

When will the entropy stay the same? Well, our transformation may perfectly conserve some quantities. Since the transformation is linear, those quantities should have the form ∑Xf(X)P[X|Mn] for some f, i.e. they’re expected values. They’re conserved when E[f(X)|Mn]=E[f(X)|Mn+1] with probability 1.

Intuitively, we’d expect the entropy of everything except the conserved quantities to strictly increase. So, we’d expect the distribution P[X|Mn] to approach maximum entropy subject to constraints of the form E[f(X)|Mn]=μ(Mn), where E[f(X)|Mn]=E[f(X)|Mn+1] with probability 1 (at least in the limit of large n). Thus, we have the maxent form

P[X|Mn]=1ZP[X]eλT(Mn)f(X)

(Note on the P[X] in there: I’m actually maximizing relative entropy, relative to the prior on X, which is almost always what one should actually do when maximizing entropy. That results in a P[X] term. We should find that E[lnP[X]|Mn] is a conserved quantity anyway, so it shouldn’t actually matter whether we include the P[X] multiplier or not; we’ll get the same answer either way.)

Shortcomings of This Argument

Obviously it’s a bit handwavy. Other than that, the main issue is that the Telephone Theorem doesn’t really leverage the spatial distribution of information; information only propagates along a single dimension. As a result, there’s not really a way to talk about th...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Fertility and Infertility: less related than you might think, published by isabel on May 17, 2022 on The Effective Altruism Forum.

Thanks to the Forethought Foundation for providing research support during the creation of this post.

People are bad at naming things

When people ask what I’ve been working on and I say that I’m looking into declining global fertility rates, they sometimes think that I’m talking about rising rates of infertility. Which makes sense as an interpretation – fertility is a very overloaded term!

But the way that I (and most economists and biologists) usually use the term fertility is to discuss the rate at which children are actually born, rather than their potential for being born. Infertility refers to the inability of a heterosexual couple “to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse". So if there were widespread infertility in some group, this group would have low fertility rates, but low fertility rates are not necessarily indicative of widespread infertility.

Note that fertility in this sense is usually talked about on a population level, and there are different measures that try to capture different relevant features of it. Two popular measures of it are the total fertility rate and completed cohort fertility. See my discussion of those in my earlier piece here .The term that gets closest to the opposite of infertility is fecundity, the “physiological potential to bear children” or “the biologic capacity to reproduce”.

Once I clarify that I’m working on studying the decline in the rate at which people are having children, I get the follow up question: but is some of the decline in global fertility rates due to a decline in fecundity and an increase in infertility?

TL;DR: Decreasing fecundity?

This is a tricky question to answer, because there isn’t one marker that can be used to evaluate fecundity, and so we have limited information on whether physical ability to conceive and bear children has changed over time.

I won’t address all possible measures of reproductive health here, and will instead focus on sperm, because when the question comes up, people are often referring to the results of this paper: Temporal trends in sperm count: a systematic review and meta-regression analysis (Levine et al, 2017) and the subsequent book by one of the authors on the paper, “Count Down: How Our Modern World Is Threatening Sperm Counts, Altering Male and Female Reproductive Development, and Imperiling the Future of the Human Race”. The paper does not draw a connection between falling sperm counts and global fertility decline, but the book does, claiming that "the dramatic decline in sperm counts is an important factor in the fertility slump that’s being seen in many parts of the world".

My conclusion from reading the paper, skimming the book and looking through the related literature, is that:

there is evidence of a decline in global sperm counts, but the data is less clear cut than is often implied

Levine et al. meta-analysis finds decline, but has issues I will discuss

the evidence for sperm count and quality declines is strongest among sperm donors and among those seeking fertility treatments

there are very limited studies comparing different points in time that don't fall into one of those two groups, but the ones that I've been able to find all find no decline

this potential decline in sperm count is unlikely to have been a substantial driver of the global fertility decline

the Levine et al. paper finds a decline in sperm counts specifically among Western men, but the countries from which the Western sample was drawn already experienced most of their fertility decline by the start of the sample, at a time when, according to the paper, sperm counts were high

evidence that age-adjusted rates of infertilit...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How to get into AI safety research, published by Stuart Armstrong on May 18, 2022 on The AI Alignment Forum.

Recently, I had a conversation with someone from a math background, asking how they could get into AI safety research. Based on my own path from mathematics to AI alignment, I recommended the following sources. It may prove useful to others contemplating a similar change in career:

Superintelligence by Nick Bostrom. It condenses all the main arguments for the power and the risk of AI, and gives a framework in which to think of the challenges and possibilities.

Sutton and Barto's Book: Reinforcement Learning: An Introduction. This gives the very basics of what ML researchers actually do all day, and is important for understanding more advanced concepts. It gives (most of) the vocabulary to understand what ML and AI papers are talking about.

Gödel without too many tears. This is how I managed to really grok logic and the completeness/incompleteness theorems. Important for understanding many of MIRI's and LessWrong's approaches to AI and decision theory.

Safely Interruptible agents. It feels bad to recommend one of my own papers, but I think this is an excellent example of bouncing between ML concepts and alignment concepts to make some traditional systems interruptible (so that we can shut them down without them resisting the shutdown).

Alignment for Advanced Machine Learning Systems. Helps give an overall perspective on different alignment methods, and some understanding of MIRI's view on the subject (for a deeper understanding, I recommend diving into MIRI's or Eliezer's publications/writings).

You mileage may vary, but these are the sources that I would recommend. And I encourage you to post any sources you'd recommend, in the comments.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Choosing causes re Flynn for Oregon, published by Zach Stein-Perlman on May 18, 2022 on The Effective Altruism Forum.

Andrea Salinas just beat EA candidate Carrick Flynn in the Democratic primary for Oregon's 6th congressional district. She got about 38% and he got about 19% of the vote. This outcome was (very) disappointing but expected.

Individual EAs donated some $900K (my estimate) to Carrick's campaign, presumably mostly because they thought it was highly effective in expectation. Several prominent EAs publicly donated the maximum amount, and the general feeling on this forum (and elsewhere, like twitter) was "I . . . recommend this highly to people looking for impactful individual donations" (source). In retrospect, this investment didn't pay off. But independent of this particular election, what can we learn about prioritizing donations?

I wouldn't want everyone to spend forever researching donations of a few thousand dollars. But when the community is donating on the scale of $100K or more, particularly in novel ways, and particularly when many donors are driven by prominent EAs' enthusiasm, we should really have reason to believe that the intervention is effective. Before donating and encourage others to donate, we should share reason to believe that the intervention has a reasonable probability of success, rather than just the promise of great outcomes if it does succeed. I almost think it's irresponsible for community leaders to say things like "I donated $5800 and recommend this highly to people looking for impactful individual donations" without (1) pointing to at least a back-of-the-envelope reason to believe that marginal donations have a reasonable probability of improving the outcome and (2) checking to make sure at least a couple people with relevant subject knowledge agree.

Whether donating is effective depends not just on whether electing Carrick would be good, but also on how effective donations are, and I saw little discussion in the community of expected value of marginal dollars. I would guess that relatively few EAs donating to Carrick's campaign had either a good understanding of elections that told them that donating was highly effective or meta-level justification such as the opinion of an independent person with relevant knowledge. Again setting aside this specific campaign, I think we should beware of bandwagoning before perceiving much reason to believe that an intervention is likely to succeed.

Also, I think we should be particularly cautious around causes that feel tribal-y. I noticed that a couple objectively bad pro-Carrick election takes got highly upvoted in the endorsement comments, while some objectively good Carrick-skeptical election takes and some objectively reasonable meta-level comments got downvoted. We should be good enough at scout mindset to welcome informed criticism without seeing it as an attack on our values; I'm not saying that went wrong here, but the forum votes suggest it's possible. In such situations, we should be particularly encouraging of counterevidence (see Habryka's comment). It should be easy to disagree with the community without it feeling hard, much less feeling like you might risk social status. Regardless of the actual consequences of expressing dissent, a reasonable commenter thought it was "obvious" that dissent would entail "severe" consequences, so others likely had similar fears.

I happen to think that donating to Carrick's campaign was totally reasonable, as an effective intervention, but I fear that the community's decision-making process wasn't very grounded in donations' probability of tipping the election, so I'm concerned that future causes might get support despite being predictably ineffective. Anyway, I'm excited to see Carrick's future research and policy work.

Edit: this post is ge...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Try to sell me on [ EA idea ] if I'm [ person with a viewpoint ], published by Matt Goodman on May 17, 2022 on The Effective Altruism Forum.

I recently came across this post on the r/neoliberal subreddit:

Try to sell me on immigration(meaning give me a pro immigration pitch) if I'm a blue collar working class guy who's afraid of someone taking my job for lower pay or my wages going down

I like the format of this request and I’d like to suggest some EA ones in the same format:

Try to sell me on working with large food companies to improve animal welfare, if I’m a vegan abolitionist.

Try to sell me on donating millions of dollars made from crypto trading to the most effective causes, if I’m an environmentalist concerned with the CO2 generated by bitcoin mining.

Try to sell me on donating to the global poor if I live in the developed world and have a very strong sense of responsibility to my family and local community.

Try to sell me on the dangers of AGI if I’m interested in evidence-based ways to reduce global poverty, and think EA should be pumping all its money into bednets and cash transfers.

Try to sell me on the need for strong international governing bodies to reduce existential risk if I’m a libertarian skeptical of large government.

I like the format of these questions (ok they're not strictly questions, but they have an asker and answerer format) for a few reasons:

They encourage the answerer to imagine a variety of viewpoints they don’t hold, which may change their views on certain issues.

They encourage the answerer to consider how EA may appear to those outside the movement.

They encourage the answerer to consider how people in EA or the causes EA is interested in may have very different opinions to one another. This is probably good for fostering positive disagreement within EA.

They allow the asker to pose the question without seeming as if they hold the viewpoint described. The asker may have heard the concern raised by people with the viewpoints described, but don’t want to seem as if they hold that viewpoint themselves.

They allow the answerer to address criticism of EA from a very safe, low pressure environment. The hypothetical nature of the questions makes them feel less like severe criticism. This is good practice, for when you do come into a debate with people who hold these views. The fact that you’ve considered the question beforehand, imagined some answers and actively extended your imagination to consider how the person your debating may think, should make the debate more constructive.

I think these kinds of questions could be run on the forum each month. Each month, the top voted question gets a cash prize, and each month, the top voted answer(s) get a cash prize. To think of this another way, we could ask the question "which ideas should we pitch to which kinds of people... and what are the best ways of doing that?"

If you like this idea, feel free to comment on this post with "Try to sell me... if I'm" pitches, or write answers someone else's, or the ones I asked in this post.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Try working on something random with someone cool, published by eca on May 18, 2022 on The Effective Altruism Forum.

I occasionally get referred people. Those messages look something like this.

"From: [Person I respect and trust]

Hey, have you met [Someone]? linkedin.com/someone

They're interested in [some biosecurity thing] and have [some relevant qualifications]. They seem really promising, and I thought they might be a good fit for [something I'm doing].

Should I intro you?"

If I'm feeling particularly underwater and/or antisocial, I have a followup like this.

"Wow cool background. Have you worked with [Someone] on anything? Or do you know anyone who has?"

The answer is almost always no.

And I think this is bad.

Why?

Matching people to high-impact work is really hard.

Hiring by work trust network is the only cheat code I know.

While working together on a very real project very extensively is most informative, working on a random thing is still probably informative.

Working with people on some random thing is relatively easy.

Time spent on the counterfactual-more casual introduction-y conversations- is comparatively less valuable.

If you buy this, the prescription is simple. Find the coolest EAs you know, find some random thing to work on, and work on it together.

Appendix 1. Community builders, consider spending more time working on projects with promising people.

I imagine there are lots of good reasons I haven't thought about explaining the prioritization of volume v.s. depth in the community building funnel, and I don't know what I'm talking about in general. But in my individual experience, I'd trade at least 5 high-quality introductions like the one above for a single intro from the same distribution, but where the [Person I respect or trust] has direct work experience with whoever they are introing. Say, experience on a project of similar scope to a final project in an intro CS class at a good university.

Appendix 2. Some quick justification, I guess

Matching people to high-impact work is really hard.

Something needs to explain 'talent bottleneck in EA orgs' but also 'loads of talented people seeking direct work'. Lots of posts relevant to this, e.g. here:

This is typically called "vetting" from what I know. I think its more like matching, in the sense that vetting for one team and role should be very different from another.

Hiring by work trust network is the only cheat code I know.

Earlier this year I hired a lot of people in a period of several weeks. And I think they're pretty amazing folks. The only way this could work was by asking people I was working with, or had worked with in the past, to tell me who they had worked with who was a great fit for our needs. Thats what I've been calling work trust networks (almost certainly stolen from someone else, sorry)- a sequence of direct work experiences.

This is a general competence filter. But more importantly, if you trust the person giving you their reference to be totally honest, it gives you an oracle to query about the future of a potential candidate at your company. Work experience doesn't just tell you "good/ not good", it tells you how someone likes using slack, whether they tend to procrastinate on longform writing tasks, and how patient they are with a disorganized onboarding process. This information can not only allow you to evaluate whether your team is a match, but also find match(es) between the person and your most pressing needs as an organization.

Notably, when I've tried to hire through trust networks, e.g. allowing for connections by reputation or friendship instead of just work experience, it hasn't worked as well (purely anecdotal, but also makes sense from first principles IMO)

While working together on a very real project very extensively is most informative, working on a random thing ...