It might not sound like much, but as a result of Borlaug’s research, wheat production in India and Pakistan almost doubled between 1965 and 1970, and formerly famine-stricken countries across the world were suddenly able to produce enough food for their entire populations. These developments have been credited with saving up to a billion people from famine,¹ and in 1970, Borlaug was awarded the Nobel Peace Prize.

Many of the highest-impact people in history, whether well-known or completely obscure, have been researchers.

Why are research skills valuable?

Not everyone can be a Norman Borlaug, and not every discovery gets adopted. Nevertheless, we think research can often be one of the most valuable skill sets to build — if you’re a good fit.

We’ll argue that:

• Research seems to have been extremely high-impact historically
• There are good theoretical reasons to think that research will be high-impact
• Research skills seem extremely useful to the problems we think are most pressing
• If you’re a good fit, you can have much more impact than the average
• And, depending on which subject you focus on, you may have good backup options.

Together, this suggests that research skills could be particularly useful for having an impact.

Later, we’ll look at:

• How to evaluate your fit for building research skills
• How to get started building research skills
• How you can use these skills to have an impact once you’ve started

Research seems to have been extremely high-impact historically

If we think about what has most improved the modern world, much can be traced back to research: advances in medicine such as the development of vaccines against infectious diseases, developments in physics and chemistry that led to steam power and the industrial revolution, and the invention of the modern computer, an idea which was first proposed by Alan Turing in his seminal 1936 paper On Computable Numbers.²

Many of these ideas were discovered by a relatively small number of researchers — but they changed all of society. This suggests that these researchers may have had particularly large individual impacts.

That said, research today is probably lower-impact than in the past. Research is much less neglected than it used to be: there are nearly 25 times as many researchers today as there were in 1930.³ It also turns out that more and more effort is required to discover new ideas, so each additional researcher probably has less impact than those that came before.⁴

However, even today, a relatively small fraction of people are engaged in research. As an approximation, only 0.1% of the population are academics,⁵ and only about 2.5% of GDP is spent on research and development. If a small number of people account for a large fraction of progress, then on average each person’s efforts are significant.

Moreover, we still think there’s a good case to be made for research being impactful on average today, which we cover in the next two sections.

There are good theoretical reasons to think that research will be high-impact

There’s little commercial incentive to focus on the most socially valuable research. And most researchers don’t get rich, even if their discoveries are extremely valuable. Alan Turing made no money from the discovery of the computer, and today it’s a multibillion-dollar industry. This is because the benefits of research often come a long time in the future and can’t usually be protected by patents. This means if you care more about social impact than profit, then it’s a good opportunity to have an edge.

Research is also a route to leverage. When new ideas are discovered, they can be spread incredibly cheaply, so it’s a way that a single person can change a field. And innovations are cumulative — once an idea has been discovered, it’s added to our stock of knowledge and, in the ideal case, becomes available to everyone. Even ideas that become outdated often speed up the important future discoveries that supersede it.

Research skills seem extremely useful to the problems we think are most pressing

When you look at our list of the world’s most pressing problems — like preventing future pandemics or reducing risks from AI systems — expert researchers seem like a key bottleneck.

For example, to reduce the risk posed by engineered pandemics, we need people who are talented at research to identify the biggest biosecurity risks and to develop better vaccines and treatments.

To ensure that developments in AI are implemented safely and for the benefit of humanity, we need technical experts thinking hard about how to design machine learning systems safely and policy researchers to think about how governments and other institutions should respond. (See this list of relevant research questions.)

And to decide which global priorities we should spend our limited resources on, we need economists, mathematicians, and philosophers to do global priorities research. For example, see the research agenda of the Global Priorities Institute at Oxford.

We’re not sure why so many of the most promising ways to make progress on the problems we think are most pressing involve research, but it may well be due to the reasons in the section above — research offers huge opportunities for leverage, so if you take a hits-based approach to finding the best solutions to social problems, it’ll often be most attractive.

In addition, our focus on neglected problems often means we focus on smaller and less developed areas, and it’s often unclear what the best solutions are in these areas. This means that research is required to figure this out.

For more examples, and to get a sense of what you might be able to work on in different fields, see this list of potentially high-impact research questions, organised by discipline.

If you’re a good fit, you can have much more impact than the average

The sections above give reasons why research can be expected to be impactful in general. But as we’ll show below, the productivity of individual researchers probably varies a great deal (and more than in most other careers). This means that if you have reason to think your degree of fit is better than average, your expected impact could be much higher than the average.

Depending on which subject you focus on, you may have good backup options

Pursuing research helps you develop deep expertise on a topic, problem-solving, and writing skills. These can be useful in many other career paths. For example:

• Many research areas can lead to opportunities in policymaking, since relevant technical expertise is valued in some of these positions. You might also have opportunities to advise policymakers and the public as an expert.
• The expertise and credibility you can develop by focusing on research (especially in academia) can put you in a good position to switch your focus to communicating important ideas, especially those related to your speciality, either to the general public, policymakers, or your students.
• If you specialise in an applied quantitative subject, it can open up certain high-paying jobs, such as quantitative trading or data science, which offer good opportunities for earning to give.

Some research areas will have much better backup options than others — lots of jobs value applied quantitative skills, so if your research is quantitative you may be able to transition into work in effective nonprofits or government. A history academic, by contrast, has many fewer clear backup options outside of academia.

What does building research skills typically involve?

By ‘research skills’ we broadly mean the ability to make progress solving difficult intellectual problems.

We find it especially useful to roughly divide research skills into three forms:

Academic research

Building academic research skills is the most predefined route. The focus is on answering relatively fundamental questions which are considered valuable by a specific academic discipline. This can be impactful either through generally advancing a field of research that’s valuable to society or finding opportunities to work on socially important questions within that field.

Turing was an academic. He didn’t just invent the computer — during World War II he developed code-breaking machines that allowed the Allies to be far more effective against Nazi U-boats. Some historians estimate this enabled D-Day to happen a year earlier than it would have otherwise.⁶ Since World War II resulted in 10 million deaths per year, Turing may have saved about 10 million lives.

We’re particularly excited about academic research in subfields of machine learning relevant to reducing risks from AI, subfields of biology relevant to preventing catastrophic pandemics, and economics — we discuss which fields you should enter below.

Academic careers are also excellent for developing credibility, leading to many of the backup options we looked at above, especially options in communicating important ideas or policymaking.

Academia is relatively unique in how flexibly you can use your time. This can be a big advantage — you really get time to think deeply and carefully about things — but can be a hindrance, depending on your work style.

See more about what academia involves in our career review on academia.

Practical but big picture research

Academia rewards a focus on questions that can be decisively answered with the methods of the field. However, the most important questions can rarely be answered rigorously — the best we can do is look at many weak forms of evidence and come to a reasonable overall judgement. which means while some of this research happens in academia, it can be hard to do that.

Instead, this kind of research is often done in nonprofit research institutes, e.g. the Centre for the Governance of AI or Our World in Data, or independently.

Your focus should be on answering the questions that seem most important (given your view of which global problems most matter) through whatever means are most effective.

Some examples of questions in this category that we’re especially interested in include:

• How likely is a pandemic worse than COVID-19 in the next 10 years?
• How difficult is the AI alignment problem going to be to solve?
• Which global problems are most pressing?
• Is the world getting better or worse over time?
• What can we learn from the history of philanthropy about which forms of philanthropy might be most effective?

You can see a longer list of ideas in this article.

Someone we know who’s had a big impact with research skills is Ajeya Cotra. Ajeya initially studied electrical engineering and computer science at UC Berkeley. In 2016, she joined Open Philanthropy as a grantmaker.⁷ Since then she’s worked on a framework for estimating when transformative AI might be developed, how worldview diversification could be applied to allocating philanthropic budgets, and how we might accidentally teach AI models to deceive us.

Applied research

Then there’s applied research. This is often done within companies or nonprofits, like think tanks (although again, there’s also plenty of applied research happening in academia). Here the focus is on solving a more immediate practical problem (and if pursued by a company, where it might be possible to make profit from the solution) — and there’s lots of overlap with engineering skills. For example:

• Developing new vaccines
• Creating new types of solar cells or nuclear reactors
• Developing meat substitutes

Neel was doing an undergraduate degree in maths when he decided that he wanted to work in AI safety. Our team was able to introduce Neel to researchers in the field and helped him secure internships in academic and industry research groups. Neel didn’t feel like he was a great fit for academia — he hates writing papers — so he applied to roles in commercial AI research labs. He’s now a research engineer at DeepMind. He works on mechanistic interpretability research which he thinks could be used in the future to help identify potentially dangerous AI systems before they can cause harm.

We also see “policy research” — which aims to develop better ideas for public policy — as a form of applied research.

Stages of progression through building and using research skills

These different forms of research blur into each other, and it’s often possible to switch between them during a career. In particular, it’s common to begin in academic research and then switch to more applied research later.

However, while the skill sets contain a common core, someone who can excel in intellectual academic research might not be well-suited to big picture practical or applied research.

The typical stages in an academic career involve the following steps:

1. Pick a field. This should be heavily based on personal fit (where you expect to be most successful and enjoy your work the most), though it’s also useful to think about which fields offer the best opportunities to help tackle the problems you think are most pressing, give you expertise that’s especially useful given these problems, and use that at least as a tie-breaker. (Read more about choosing a field.)
2. Earn a PhD.
3. Learn your craft and establish your career — find somewhere you can get great mentorship and publish a lot of impressive papers. This usually means finding a postdoc with a good group and then temporary academic positions.
4. Secure tenure.
5. Focus on the research you think is most socially valuable (or otherwise move your focus towards communicating ideas or policy).

Academia is usually seen as the most prestigious path…within academia. But non-academic positions can be just as impactful — and often more so since you can avoid some of the dysfunctions and distractions of academia, such as racing to get publications.

At any point after your PhD (and sometimes with only a master’s), it’s usually possible to switch to applied research in industry, policy, nonprofits, and so on, though typically you’ll still focus on getting mentorship and learning for at least a couple of years. And you may also need to take some steps to establish your career enough to turn your attention to topics that seem more impactful.

Note that from within academia, the incentives to continue with academia are strong, so people often continue longer than they should!

If you’re focused on practical big picture research, then there’s less of an established pathway, and a PhD isn’t required.

Besides academia, you could attempt to build these skills in any job that involves making difficult, messy intellectual judgement calls, such as investigative journalism, certain forms of consulting, buy-side research in finance, think tanks, or any form of forecasting.

Personal fit is perhaps more important for research than other skills

The most talented researchers seem to differ hugely in their impact compared to typical researchers across a wide variety of metrics and according to the opinions of other researchers.

For instance, when we surveyed biomedical researchers, they said that very good researchers were rare, and they’d be willing to turn down large amounts of money if they could get a good researcher for their lab.⁸ Professor John Todd, who works on medical genetics at Cambridge, told us:

The best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people[…] One good person can cover the ground of five, and I’m not exaggerating.

This makes sense if you think the distribution of research output is very wide — that the very best researchers have a much greater output than the average researcher.

How much do researchers differ in productivity?

It’s hard to know exactly how spread out the distribution is, but there are several strands of evidence that suggest the variability is very high.

Firstly, most academic papers get very few citations, while a few get hundreds or even thousands. An analysis of citation counts in science journals found that ~47% of papers had never been cited, more than 80% had been cited 10 times or less, but the top 0.1% had been cited more than 1,000 times. A similar pattern seems to hold across individual researchers, meaning that only a few dominate — at least in terms of the recognition their papers receive.

Citation count is a highly imperfect measure of research quality, so these figures shouldn’t be taken at face-value. For instance, which papers get cited the most may depend at least partly on random factors, academic fashions, and “winner takes all” effects — papers that get noticed early end up being cited by everyone to back up a certain claim, even if they don’t actually represent the research that most advanced the field.

However, there are other reasons to think the distribution of output is highly skewed.

William Shockley, who won the Nobel Prize for the invention of the transistor, gathered statistics on all the research employees in national labs, university departments, and other research units, and found that productivity (as measured by total number of publications, rate of publication, and number of patents) was highly skewed, following a log-normal distribution.

Shockley suggests that researcher output is the product of several (normally distributed) random variables — such as the ability to think of a good question to ask, figure out how to tackle the question, recognize when a worthwhile result has been found, write adequately, respond well to feedback, and so on. This would explain the skewed distribution: if research output depends on eight different factors and their contribution is multiplicative, then a person who is 50% above average in each of the eight areas will in expectation be 26 times more productive than average.⁹

When we looked at up-to-date data on how productivity differs across many different areas, we found very similar results. The bottom line is that research seems to perhaps be the area where we have the best evidence for output being heavy-tailed.

Interestingly, while there’s a huge spread in productivity, the most productive academic researchers are rarely paid 10 times more than the median, since they’re on fixed university pay-scales. This means that the most productive researchers yield a large “excess” value to their field. For instance, if a productive researcher adds 10 times more value to the field than average, but is paid the same as average, they will be producing at least nine times as much net benefit to society. This suggests that top researchers are underpaid relative to their contribution, discouraging them from pursuing research and making research skills undersupplied compared to what would be ideal.

Can you predict these differences in advance?

Practically, the important question isn’t how big the spread is, but whether you could — early on in your career — identify whether or not you’ll be among the very best researchers.

There’s good news here! At least in scientific research, these differences also seem to be at least somewhat predictable ahead of time, which means the people entering research with the best fit could have many times more expected impact.

In a study, two IMF economists looked at maths professors’ scores in the International Mathematical Olympiad — a prestigious maths competition for high school students. They concluded that each additional point scored on the International Mathematics Olympiad “is associated with a 2.6 percent increase in mathematics publications and a 4.5 percent increase in mathematics citations.”

We looked at a range of data on how predictable productivity differences are in various areas and found that they’re much more predictable in research.

What does this mean for building research skills?

The large spread in productivity makes building strong research skills a lot more promising if you’re a better fit than average. And if you’re a great fit, research can easily become your best option.

And while these differences in output are not fully predictable at the start of a career, the spread is so large that it’s likely still possible to predict differences in productivity with some reliability.

This also means you should mainly be evaluating your long-term expected impact in terms of your chances of having a really big success.

That said, don’t rule yourself out too early. Firstly, many people systematically underestimate their skills. (Though others overestimate them!) Also, the impact of research can be so large that it’s often worth trying it out, even if you don’t expect you’ll succeed. This is especially true because the early steps of a research career often give you good career capital for many other paths.

How to evaluate your fit

How to predict your fit in advance

It’s hard to predict success in advance, so we encourage an empirical approach: see if you can try it out and look at your track record.

You probably have some track record in research: many of our readers have some experience in academia from doing a degree, whether or not they intended to go into academic research. Standard academic success can also point towards being a good fit (though is nowhere near sufficient!):

• Did you get top grades at undergraduate level (a 1st in the UK or a GPA over 3.5 in the US)?
• If you do a graduate degree, what’s your class rank (if you can find that out)? If you do a PhD, did you manage to author an article in a top journal (although note that this is easier in some disciplines than others)?

Ultimately, though, your academic track record isn’t going to tell you anywhere near as much as actually trying out research. So it’s worth looking for ways to cheaply try out research (which can be easy if you’re at college). For example, try doing a summer research project and see how it goes.

Some of the key traits that suggest you might be a good fit for a research skills seem to be:

• Intelligence (Read more about whether intelligence is important for research.)
• The potential to become obsessed with a topic (Becoming an expert in anything can take decades of focused practice, so you need to be able to stick with it.)
• Relatedly, high levels of grit, self-motivation, and — especially for independent big picture research, but also for research in academia — the ability to learn and work productively without a traditional manager or many externally imposed deadlines
• Openness to new ideas and intellectual curiosity
• Good research taste, i.e. noticing when a research question matters a lot for solving a pressing problem

There are a number of other cheap ways you might try to test your fit.

Something you can do at any stage is practice research and research-based writing. One way to get started is to try learning by writing.

You could also try:

• Finding out what the prerequisites/normal backgrounds of people who go into a research area are to compare your skills and experience to them
• Reading key research in your area, trying to contribute to discussions with other researchers (e.g. via a blog or twitter), and getting feedback on your ideas
• Talking to successful researchers in a field and asking what they look for in new researchers

How to tell if you’re on track

Here are some broad milestones you could aim for while becoming a researcher:

• You’re successfully devoting time to building your research skills and communicating your findings to others. (This can often be the hardest milestone to hit for many — it can be hard to simply sustain motivation and productivity given how self-directed research often needs to be.)
• In your own judgement, you feel you have made and explained multiple novel, valid, nontrivially important (though not necessarily earth-shattering) points about important topics in your area.
• You’ve had enough feedback (comments, formal reviews, personal communication) to feel that at least several other people (whose judgement you respect and who have put serious time into thinking about your area) agree, and (as a result) feel they’ve learned something from your work. For example, lots of this feedback could come from an academic supervisor. Make sure you’re asking people in a way that gives them affordance to say you’re not doing well.
• You’re making meaningful connections with others interested in your area — connections that seem likely to lead to further funding and/or job opportunities. This could be from the organisations most devoted to your topics of interest; but, there could also be a “dissident” dynamic in which these organisations seem uninterested and/or defensive, but others are noticing this and offering help.

If you’re finding it hard to make progress in a research environment, it’s very possible that this is the result of that particular environment, rather than the research itself. So it can be worth testing out multiple different research jobs before deciding this skill set isn’t for you.

Within academic research

Academia has clearly defined stages, so you can see how you’re performing at each of these.

Very roughly, you can try asking “How quickly and impressively is my career advancing, by the standards of my institution and field?” (Be careful to consider the field as a whole, rather than just your immediate peers, who might be very different from average.) Academics with more experience than you may be able to help give you a clear idea of how things are going.

We go through this in detail in our review of academic research careers.

Within independent research

As a very rough guideline, people who are an excellent fit for independent research can often reach the broad milestones above with a year of full-time effort purely focusing on building a research skill set, or 2–3 years of 20%-time independent effort (i.e. one day per week).

Within research in industry or policy

The stages here can look more like an organisation-building career, and you can also assess your fit by looking at your rate of progression through the organisation.

How to get started building research skills

As we mentioned above, if you’ve done an undergraduate degree, one obvious pathway into research is to go to graduate school (read our advice on choosing a graduate programme) and then attempt to enter academia before deciding whether to continue or pursue positions outside of academia later in your career.

If you take the academic path, then the next steps are relatively clear. You’ll want to try to get excellent grades in undergraduate and in your master’s, ideally gain some kind of research experience in your summers, and then enter the best PhD programme you can. From there, focus on learning your craft by working under the best researcher you can find as a mentor and working in a top hub for your field. Try to publish as many papers as possible since that’s required to land an academic position.

It’s also not necessary to go to graduate school to become a great researcher (though this depends a lot on the field), especially if you’re very talented.
For instance, we interviewed Chris Olah, who is working on AI research without even an undergraduate degree.

You can enter many non-academic research jobs without a background in academia. So one starting point for building up research skills would be getting a job at an organisation specifically focused on the type of question you’re interested in. For examples, take a look at our list of recommended organisations, many of which conduct non-academic research in areas relevant to pressing problems.

More generally, you can learn research skills in any job that heavily features making difficult intellectual judgement calls and bets, preferably on topics that are related to the questions you’re interested in researching. These might include jobs in finance, political analysis, or even nonprofits.

Another common route — depending on your field — is to develop software and tech skills and then apply them at research organisations. For instance, here’s a guide to how to transition from software engineering into AI safety research.

If you’re interested in doing practical big-picture research (especially outside academia), it’s also possible to establish your career through self-study and independent work — during your free time or on scholarships designed for this (such as EA Long-Term Future Fund grants and Open Philanthropy support for individuals working on relevant topics).

Some example approaches you might take to self-study:

• Closely and critically review some pieces of writing and argumentation on relevant topics. Explain the parts you agree with as clearly as you can and/or explain one or more of your key disagreements.
• Pick a relevant question and write up your current view and reasoning on it. Alternatively, write up your current view and reasoning on some sub-question that comes up as you’re thinking about it.
• Then get feedback, ideally from professional researchers or those who use similar kinds of research in their jobs.

It could also be beneficial to start with some easier versions of this sort of exercise, such as:

• Explaining or critiquing interesting arguments made on any topic you find motivating to write about
• Writing fact posts
• Reviewing the academic literature on any topic of interest and trying to reach and explain a bottom-line conclusion

In general, it’s not necessary to obsess over being “original” or having some new insight at the beginning. You can learn a lot just by trying to write up your current understanding.

Choosing a research field

When you’re getting started building research skills, there are three factors to consider in choosing a field:

1. Personal fit — what are your chances of being a top researcher in the area? Even if you work on an important question, you won’t make much difference if you’re not particularly good at it or motivated to work on the problem.
2. Impact — how likely is it that research in your field will contribute to solving pressing problems?
3. Back-up options — how will the skills you build open up other options if you decide to change fields (or leave research altogether)?

One way to go about making a decision is to roughly narrow down fields by relevance and back-up options and then pick among your shortlist based on personal fit.

We’ve found that, especially when they’re getting started building research skills, people sometimes think too narrowly about what they can be good at and enjoy. Instead, they end up pigeonholing themselves in a specific area (for example being restricted by the field of their undergraduate degree). This can be harmful because it means people who could contribute to highly important research don’t even consider it. This increases the importance of writing a broad list of possible areas to research.

Given our list of the world’s most pressing problems, we think some of the most promising fields to do research within are as follows:

• Fields relevant to artificial intelligence, especially machine learning, but also computer science more broadly. This is mainly to work on AI safety directly, though there are also many opportunities to apply machine learning to other problems (as well as many back-up options).
• Biology, particularly synthetic biology, virology, public health, and epidemiology. This is mainly for biosecurity.
• Economics. This is for global priorities research, development economics, or policy research relevant to any cause area, especially global catastrophic risks.
• Engineering — read about developing and using engineering skills to have an impact.
• International relations/political science, including security studies and public policy — these enable you to do research into policy approaches to mitigating catastrophic risks and are also a good route into careers in government and policy more broadly.
• Mathematics, including applied maths or statistics (or even physics). This may be a good choice if you’re very uncertain, as it teaches you skills that can be applied to a whole range of different problems — and lets you move into most of the other fields we list. It’s relatively easy to move from a mathematical PhD into machine learning, economics, biology, or political science, and there are opportunities to apply quantitative methods to a wide range of other fields. They also offer good back-up options outside of research.
• There are many important topics in philosophy and history, but these fields are unusually hard to advance within, and don’t have as good back-up options. (We do know lots of people with philosophy PhDs who have gone on to do other great, non-philosophy work!)

However, many different kinds of research skills can play a role in tackling pressing global problems.

Choosing a sub-field can sometimes be almost as important as choosing a field. For example, in some sciences the particular lab you join will determine your research agenda — and this can shape your entire career.

And as we’ve covered, personal fit is especially important in research. This can mean it’s easily worth going into a field that seems less relevant on average if you are an excellent fit. (This is due both to the value of the research you might produce and the excellent career capital that comes from becoming top of an academic field.)

For instance, while we most often recommend the fields above, we’d be excited to see some of our readers go into history, psychology, neuroscience, and a whole number of other fields. And if you have a different view of global priorities from us, there might be many other highly relevant fields.

Once you have these skills, how can you best apply them to have an impact?

Richard Hamming used to annoy his colleagues by asking them “What’s the most important question in your field?”, and then after they’d explained, following up with “And why aren’t you working on it?”

You don’t always need to work on the very most important question in your field, but Hamming has a point. Researchers often drift into a narrow speciality and can get detached from the questions that really matter.

Now let’s suppose you’ve chosen a field, learned your craft, and are established enough that you have some freedom about where to focus. Which research questions should you focus on?

Which research topics are the highest-impact?

Charles Darwin travelled the oceans to carefully document different species of birds on a small collection of islands — documentation which later became fuel for the theory of evolution. This illustrates how hard it is to predict which research will be most impactful.

What’s more, we can’t know what we’re going to discover until we’ve discovered it, so research has an inherent degree of unpredictability. There’s certainly an argument for curiosity-driven research without a clear agenda.

That said, we think it’s also possible to increase your chances of working on something relevant, and the best approach is to try to find topics that both personally motivate you and seem more likely than average to matter. Here are some approaches to doing that.

Using the problem framework

One approach is to ask yourself which global problems you think are most pressing, and then try to identify research questions that are:

• Important to making progress on those problems (i.e. if this question were answered, it would lead to more progress on these problems)
• Neglected by other researchers (e.g. because they’re at the intersection of two fields, unpopular for bad reasons, or new)
• Tractable (i.e. you can see a path to making progress)

The best research questions will score at least moderately well on all parts of this framework. Building a perpetual motion machine is extremely important — if we could do it, then we’d solve our energy problems — but we have good reason to think it’s impossible, so it’s not worth working on. Similarly, a problem can be important but already have the attention of many extremely talented researchers, meaning your extra efforts won’t go very far.

Finding these questions, however, is difficult. Often, the only way to identify a particularly promising research question is to be an expert in that field! That’s because (when researchers are doing their jobs), they will be taking the most obvious opportunities already.

However, the incentives within research rarely perfectly line up with the questions that most matter (especially if you have unusual values, like more concern for future generations or animals). This means that some questions often get unfairly neglected. If you’re someone who does care a lot about positive impact and have some slack, you can have a greater-than-average impact by looking for them.

Below are some more ways of finding those questions (which you can use in addition to directly applying the framework above).

Rules of thumb for finding unfairly neglected questions

• There’s little money in answering the question. This can be because the problem mostly affects poorer people, people who are in the future, or non-humans, or because it involves public goods. This means there’s little incentive for businesses to do research on this question.
• The political incentives to answer the question are missing. This can happen when the problem hurts poorer or otherwise marginalised people, people who tend not to organise politically, people in countries outside the one where the research is most likely to get done, people who are in the future, or non-humans. This means there’s no incentive for governments or other public actors to research this question.
• It’s new, doesn’t already have an established discipline, or is at the intersection of two disciplines. The first researchers in an area tend to take any low hanging fruit, and it gets harder and harder from there to make big discoveries. For example, the rate of progress within machine learning is far higher than the rate of progress within theoretical physics. At the same time, the structure of academia means most researchers stay stuck within the field they start in, and it can be hard to get funding to branch out into other areas. This means that new fields or questions at the intersection of two disciplines often get unfairly neglected and therefore provide opportunities for outsized impact.
• There is some aspect of human irrationality that means people don’t correctly prioritise the issue. For instance, some issues are easy to visualise, which makes them more motivating to work on. People are scope blind which means they’re likely to neglect the issues with the very biggest scale. They’re also bad at reasoning about issues with low probability, which can make them either over-invest or under-invest in them.
• Working on the question is low status. In academia, research that’s intellectually interesting and fits the research standards of the discipline are high status. Also, mathematical and theoretical work tends to be seen as higher status (and therefore helps to progress your career). But these don’t correlate that well with the social value of the question.
• You’re bringing new skills or a new perspective to an established area. Progress often comes in science from bringing the techniques and insights of one field into another. For instance, Kahneman started a revolution in economics by applying findings from psychology. Cross-over is an obvious approach but is rarely used because researchers tend to be immersed in their own particular subject.

If you think you’ve found a research question that’s short on talent, it’s worth checking whether the question is answerable. People might be avoiding the question because it’s just extremely difficult to find an answer. Or perhaps progress isn’t possible at all. Ask yourself, “If there were progress on this question, how would we know?”

Finally, as we’ve discussed, personal fit is particularly important in research. So position yourself to work on questions where you maximise your chances of producing top work.

Find jobs that use a research skills

If you have these skills already or are developing it and you’re ready to start looking at job opportunities that are currently accepting applications, see our curated list of opportunities for this skill set:

View all opportunities

Career paths we’ve reviewed that use these skills

• AI safety technical research and engineering
• AI governance and coordination
• Biorisk research
• China-related AI safety and governance paths
• Grantmaker focused on pressing world problems
• Research into global priorities
• Forecasting and related research and implementation
• Historian of large societal trends, inflection points, progress or collapse
• Expert in AI hardware

• Specialist in emerging global powers

• Investigate a potentially pressing but unexplored global issue
• Research management
• Think tank research
• Academic research
• Research and advocacy promoting impactful climate solutions
• Improving China-Western coordination on global catastrophic risks
• Engineering
• Economics PhDs
• Machine learning PhDs
• Biomedical research
• Computer science PhDs
• Data science
• Philosophy academia

Learn more about research

• High Impact Science by Carl Shulman
• How to succeed as an early-stage researcher: the “lean startup” approach
• Podcast: Luisa and Robert Long on how to make independent research more fun
• A list of potentially high-impact research questions, organised by discipline

See all our articles and podcasts on research careers.

Read next:  Explore other useful skills

Want to learn more about the most useful skills for solving global problems, according to our research? See our list.

Continue →

The post Research skills appeared first on 80,000 Hours.

This article is our full report into reducing global catastrophic biological risks. For a shorter introduction, see our problem profile on preventing catastrophic pandemics.

What is our analysis based on?

I, Gregory Lewis, wrote this profile. I work at the Future of Humanity Institute on GCBRs. It owes a lot to helpful discussions with (and comments from) Christopher Bakerlee, Haydn Belfield, Elizabeth Cameron, Gigi Gronvall, David Manheim, Thomas McCarthy, Michael McClaren, Brenton Mayer, Michael Montague, Cassidy Nelson, Carl Shulman, Andrew Snyder-Beattie, Bridget Williams, Jaime Yassif, and Claire Zabel. Their kind help does not imply they agree with everything I write. All mistakes remain my own.

This profile is in three parts. First, I explain what GCBRs are and why they could be a major global priority. Second, I offer my impressions (such as they are) on the broad contours of the risk landscape, and how these risks are best addressed. Third, I gesture towards the best places to direct one’s career to reduce this danger.

Motivation

What are global catastrophic biological risks?

Global catastrophic risks (GCRs) are roughly defined as risks that threaten great worldwide damage to human welfare, and place the long-term trajectory of humankind in jeopardy.¹ Existential risks are the most extreme members of this class. Global catastrophic biological risks (GCBRs) are a catch-all for any such risk that is broadly biological in nature (e.g. a major pandemic).

I write from a broadly longtermist perspective: roughly, that there is profound moral importance in how humanity’s future goes, and so trying to make this future go better is a key objective in our decision-making (I particularly recommend Joseph Carlsmith’s talk).² When applying this perspective to biological risks, the issue of whether a given event threatens the long-term trajectory of humankind becomes key. This question is much harder to adjudicate than whether a given event threatens severe worldwide damage to human welfare. My guesswork is the ‘threshold’ for when a biological event starts to threaten human civilisation is high: a rough indicator is a death toll of 10% of the human population, at the upper limit of all disasters ever observed in human history.

As such, I believe some biological catastrophes, even those which are both severe and global in scope, would not be GCBRs. One example is antimicrobial resistance (AMR): AMR causes great human suffering worldwide, threatens to become an even bigger problem, and yet I do not believe it is a plausible GCBR. An attempt to model the worst case scenario of AMR suggests it would kill 100 million people over 35 years, and reduce global GDP by 2%-3.5%.³ Although disastrous for human wellbeing worldwide, I do not believe this could threaten humanity’s future – if nothing else, most of humanity’s past occurred during the ‘pre-antibiotic age’, to which worst-case scenario AMR threatens a return.

To be clear, a pandemic that killed less than 10% of the human population could easily still be among the worst events in our species’ history. For example, the ongoing COVID-19 pandemic is already a humanitarian crisis and threatens to get much worse, though it is very unlikely to threaten extinction according to this threshold. It is well worth investing great resources to mitigate such disasters and prevent more from arising.

The reason to focus here on events that kill a larger fraction of the population is firstly, that they are not so unlikely, secondly, that the damage they could do would be vastly greater still — and potentially even more long-lasting.

These impressions have pervasive influence on judging the importance of GCBRs in general, and choosing what to prioritise in particular. They are also highly controversial: One may believe that the ‘threshold’ for when an event poses a credible threat to human civilisation is even higher than I suggest (and the risk of any biological event reaching this threshold is very remote). Alternatively, one may believe that this threshold should be set much lower (or at least set with different indicators) so a wider or different set of risks should be the subject of longtermist concern.⁴ On all of this, more later.

The plausibility of GCBRs

The case that biological global catastrophic risks are a credible and urgent threat to humankind arises from a few different sources of evidence. All are equivocal.

1. Experts express alarm about biological risks in general, and some weak evidence of expert concern about GCBRs in particular. (Yet other experts are sceptical.)
2. Historical evidence of ‘near-GCBR’ events, suggesting a ‘proof of principle’ there could be risks of something even worse. (Yet none have approached extinction-level nor had discernable long-run negative impacts on global civilisation that approached GCBR levels.)
3. Worrying features of advancing biotechnology.
4. Numerical estimates and extrapolation. (Yet the extrapolation is extremely uncertain and indictable.)

Expert opinion

Various expert communities have highlighted the danger of very-large scale biological catastrophe, and have assessed that existing means of preventing and mitigating this danger are inadequate.⁵

Yet, as above, not all large scale events would constitute a GC(B)R. The balance of expert opinion on the likelihood of these sorts of events is hard to assess, although my impression is that there is substantial scepticism.⁶ The only example of expert elicitation addressed to this I am aware of is a 2008 global catastrophic risks survey, which offers these median estimates of a given event occurring before 2100:

Table 1: Selected risk estimates from 2008 survey

This data should be weighed lightly. As Millett and Snyder-Beattie (2017) note:

The disadvantage is that the estimates were likely highly subjective and unreliable, especially as the survey did not account for response bias, and the respondents were not calibrated beforehand.

The raw data also shows considerable variation in estimates,⁷ although imprecision in risk estimates is generally a cause for greater concern.

‘Near-GCBR’ events in the historical record

‘Naturally arising’ biological extinction events seem unlikely given the rarity of ‘pathogen driven’ extinction events in natural history, and the 200,000 year lifespan of anatomically modern humans. The historical record also rules against a very high risk of ‘naturally arising’ GCBRs (on which more later). Nonetheless history has four events that somewhat resemble a global biological catastrophe, and so act as a partial ‘proof of principle’ for the danger:⁸

1. Plague of Justinian (541-542 CE): Thought to have arisen in Asia before spreading into the Byzantine Empire around the Mediterranean. The initial outbreak is thought to have killed ~6 million (~3% of world population),⁹ and contributed to reversing the territorial gains of the Byzantine empire around the Mediterranean rim as well as (possibly) the success of its opponent in the subsequent Arab-Byzantine wars.
2. The Black Death (1335-1355 CE): Estimated to have killed 20-75 million people (~10% of world population), and believed to have had profound impacts on the subsequent course of European history.
3. The Columbian Exchange (1500-1600 CE): A succession of pandemics (likely including smallpox and paratyphoid) brought by the European colonists devastated Native American populations: it is thought to contribute in large part to the ~80% depopulation of native populations in Mexico over the 16th century, and other groups in the Americas are suggested to have suffered even starker depopulation – up to 98% proportional mortality.¹⁰
4. The 1918 Influenza Pandemic (1918 CE): A pandemic which ranged almost wholly across the globe, and killed 50-100 million people (2.5% – 5% of world population) – probably more than either World War.

COVID-19, which the World Health organization declared a global pandemic on March 11th 2020, has already caused grave harm to humankind, and regrettably is likely to cause much more. Fortunately, it seems unlikely to cause as much harm as the historical cases noted here.

All of the impacts of the cases above are deeply uncertain, as:

• Vital statistics range from at best very patchy (1918) to absent. Historical populations (let alone their mortality rate, and let alone mortality attributable to a given outbreak) are very imprecisely estimated.
• Proxy indicators (e.g. historical accounts, archaeology) have very poor resolution, leaving a lot to educated guesswork and extrapolation (e.g. “The evidence suggests, in European city X, ~Y% of the population died due to the plague – how should one adjust this to the population of Asia?”)
• Attribution of historical consequences of an outbreak are highly contestable: other coincident events can offer competing (or overdetermining) explanations.

Although these factors add ‘simple’ uncertainty, I would guess academic incentives and selection effects introduce a bias to over-estimates for historical cases. For this reason I’ve used Muelhauser’s estimates for ‘death tolls’ (generally much more conservative than typical estimates, such as ’75-200 million died in the black death’), and reiterate the possible historical consequences are ‘credible’ rather than confidently asserted.

For example, it’s not clear the plague of Justinian should be on the list at all. Mordechai et al. (2019) survey the circumstantial archeological data around the time of the Justinian Plague, and find little evidence of a discontinuity over this period suggestive of a major disaster: papyri and inscriptions suggest stable rates of administrative activity, and pollen measures suggest stable land-use (they also offer reasonable alternative explanations for measures which did show a sharp decline – new laws declined during the ‘plague period’, but this could be explained by government efforts at legal consolidation having coincidentally finished beforehand).

Even if one takes the supposed impacts of each at face value, each has features that may disqualify it as a ‘true’ global catastrophe. The first three, although afflicting a large part of humanity, left another large part unscathed (the Eurasian and American populations were effectively separated). 1918 Flu had a very high total death toll and global reach, but not the highest proportional mortality, and relatively limited historical impact. The Columbian Exchange, although having high proportional mortality and crippling impact on the affected civilisations, had comparatively little effect on global population owing to the smaller population in the Americas and the concurrent population growth of the immigrant European population.

Yet even though these historical cases were not ‘true’ GCBRs, they were perhaps near-GCBRs. They suggest that certain features of a global catastrophe (e.g. civilisational collapse, high proportional mortality) can be driven by biological events. And the current COVID-19 outbreak illustrates the potential for diseases to spread rapidly across the world today, despite efforts to control it. There seems to be no law of nature that prevents a future scenario more extreme than these, or that combines the worst characteristics of those noted above (even if such an event is unlikely to naturally arise).

Whether the risk of ‘natural’ biological catastrophes is increasing or decreasing is unclear

The cases above are ‘naturally occuring’ pandemic diseases, and most of them afflicted much less technically advanced civilisations in the past. Whether subsequent technological progress has increased or decreased this danger is unclear.

Good data is hard to find: Burden of endemic infectious disease is on a downward trend, but this gives little reassurance for changes in the far right tail of pandemic outbreaks. One (modelling) datapoint comes from an AIR worldwide study to estimate the impact if the 1918 influenza outbreak happened today. They suggest that although the absolute numbers of deaths would be similar (tens of millions), the proportional mortality of the global population would be much lower, due to a 90% reduction in case fatality risk.

From first principles, considerations point in both directions. On the side of natural GCBR risk getting lower:

• A healthier (and more widely geographically spread) population.
• Better hygiene and sanitation.
• The potential for effective vaccination and therapeutics.
• Understanding of the mechanisms of disease transmission and pathogenesis.

On the other hand:

• Trade and air travel allow much faster and wider transmission.¹¹ For example, air travel seems to have played a large role in the spread of COVID-19.
• Climate change may (among other effects) increase the likelihood of new emerging zoonotic diseases.
• Greater human population density.
• Much larger domestic animal reservoirs.

There are many other relevant considerations. On balance, my (highly uncertain) view is that the danger of natural GCBRs has declined.

Artificial GCBRs are very dangerous, and increasingly likely

‘Artificial’ GCBRs are a category of increasing concern, owed to advancing biotechnological capacity alongside the increasing risk of its misuse.¹² The current landscape (and plausible forecasts of its future development) have concerning features which, together, make the accidental or deliberate misuse of biotechnology a credible global catastrophic risk.

Replaying the worst outbreaks in history

Polio, the 1918 pandemic influenza strain, and most recently horsepox (a close relative of smallpox) have all been synthesised ‘from scratch’. The genetic sequence of all of these disease-causing organisms (and others besides) are publicly available, and the progress and democratisation of biotechnology may make the capacity to perform similar work more accessible to the reckless or malicious.¹³ Biotechnology therefore poses the risk of rapidly (and repeatedly) recreating the pathogens which led to the worst biological catastrophes observed in history.

Engineered pathogens could be even more dangerous

Beyond repetition, biotechnology allows the possibility of engineering pathogens more dangerous than those that have occurred in natural history. Evolution is infamously myopic, and its optimisation target is reproductive fitness, rather than maximal damage to another species (cf. optimal virulence). Nature may not prove a peerless bioterrorist; dangers that emerge by evolutionary accident could be surpassed by deliberate design.

Hints of this can be seen in the scientific literature. The gain-of-function influenza experiments, suggested that artificial selection could lead to pathogens with properties that enhance their danger.¹⁴ There have also been instances of animal analogues of potential pandemic pathogens being genetically modified to reduce existing vaccine efficacy.

These cases used techniques well behind the current cutting edge of biotechnology, and were produced somewhat ‘by accident’ by scientists without malicious intent. The potential for bad actors to intentionally produce new or modified pathogens using modern biotechnology is harrowing.

Ranging further, and reaching higher, than natural history

Natural history constrains how life can evolve. One consequence is the breadth of observed biology is a tiny fraction of the space of possible biology.¹⁵ Bioengineering may begin to explore this broader space.

One example is enzymes: proteins that catalyse biological reactions. The repertoire of biochemical reactions catalysed by natural enzymes is relatively narrow, and few are optimised for very high performance, due to limited selection pressure or ‘short-sighted evolution’.¹⁶ Enzyme engineering is a relatively new field, yet it has already produced enzymes that catalyse novel reactions (1, 2, 3), and modifications of existing enzymes with improved catalytic performance and thermostability (1, 2).

Similar stories can be told for other aspects of biology, and together they suggest the potential for biological capabilities unprecedented in natural history. It would be optimistic to presume that in this space of large and poorly illuminated ‘unknown unknowns’ there will only be familiar dangers.

Numerical estimates

Millett and Snyder-Beattie (2017) offer a number of different models to approximate the chance of a biological extinction risk:

Table 2: Estimates of biological extinction risk ¹⁷

These rough approximations may underestimate by virtue of the conservative assumptions in the models, that the three scenarios do not mutually exhaust the risk landscape, and that the extrapolation from historical data is not adjusted for trends that (I think, in aggregate) increase the risk. That said, the principal source of uncertainty is the extremely large leap of extrapolation: power-law assumptions guarantee a heavy right tail, yet in this range other factors may drive a different distribution (either in terms of type or scale parameter). The models are (roughly) transcribed into a guesstimate here.¹⁸

GCBRs may be both neglected and tractable

Even if GCBRs are a ‘big problem’, this does not entail more people should work on it. Some big problems are hard to make better, often because they are already being addressed by many others, or that there are no good available interventions.¹⁹

This doesn’t seem to apply to GCBRs. There are good reasons to predict this is a problem that will continue to be neglected; surveying the area provides suggestive evidence of under-supply and misallocation; and examples of apparently tractable shortcomings are readily found.

A prior of low expectations

Human cognition, sculpted by the demands of the ancestral environment, may fit poorly with modern challenges. Yudkowsky surveys heuristics and biases that tend to mislead our faculties: GC(B)Rs, with their unpredictability, rarity, and high consequence, appear to be a treacherous topic for our minds to navigate.

Decisions made by larger groups can sometimes mitigate these individual faults. But the wider social and political environment presents its own challenges. There can be value divergence: a state may regard its destruction and outright human extinction as similarly bad, even if they starkly differ from the point of view of the universe. Misaligned incentives can foster very short time horizons, parochial concern, and policy driven by which constituents can shout the loudest instead of who is the most deserving.²⁰ Concern for GCBRs – driven in large part by cosmopolitan interest in the global population, concern for the long-run future, and where most of its beneficiaries are yet to exist – has obvious barriers to overcome.

The upshot is GC(B)Rs lie within the shadows cast by defects in our individual reasoning, and their reduction to a global and intergenerational public good standard theory suggests markets and political systems will under-provide.

The imperfectly allocated portfolio

Very large efforts are made on mitigating biological risks in the general sense. The US government alone planned to spend around $3 billion on biosecurity in 2019.²¹ Even if only a small fraction of this is ‘GCBR-relevant’ (see later), it looks much larger than (say) $10s of millions yearly spending on AI safety, another 80,000 Hours priority area.

Most things are relatively less neglected than AI safety, yet they can still be neglected in absolute terms. A clue for this being the case in biological risk generally is evidence of high marginal cost-effectiveness. One example is pandemic preparedness. The World Bank suggests an investment of 1.9B to 3.4B in ‘One Health‘ initiatives would reduce the likelihood of pandemic outbreaks by 20%. At this level, the economic rate of return is a (highly favourable) 14-49%. Although I think this ‘bottom line’ is optimistic,²² it is probably not so optimistic for its apparent outperformance to be wholly overestimation.

There is a story to be told of insufficient allocation towards GCBRs in particular, as well as biosecurity in general. Millett and Snyder-Beattie (2017) offer a ‘black-box’ approach (e.g. “X billion dollars would reduce biological existential risk by Y% in expectation”) to mitigating extremely high consequence biological disasters. ‘Pricing in’ the fact extinction not only kills everyone currently alive, but also entails the loss of all people who could live in the future, they report ‘cost per QALY’ of a 250 billion dollar programme that reduces biological extinction risks by 1% from their previous estimates (i.e an absolute risk reduction of 0.02 to ~2/million over a century) to be between $0.13 to $1600,²³ superior to marginal health spending in rich countries. Contrast the billion dollar efforts to develop and stockpile anthrax vaccines.

Illustrative examples

I suggest the following two examples are tractable shortcomings in the area of GCBR reduction (even if they are not necessarily the best opportunities), and so suggest opportunities to make a difference are reasonably common.

State actors and the Biological Weapons Convention

Biological weapons have some attractive features for state actors to include in their portfolio of violence: they provide a novel means of attack, are challenging to attribute, and may provide a strategic deterrent more accessible than (albeit inferior to) nuclear weapons.²⁴ The trend of biotechnological progress may add to or enhance these attractive features, and thus deliberate misuse by a state actor developing and deploying a biological weapon is a plausible GCBR (alongside other risks which may not be ‘globally catastrophic’ as defined before, but are nonetheless extremely bad).

The principal defence against proliferation of biological weapons among states is the Biological Weapons Convention. Of 197 state parties eligible to ratify the BWC, 183 have done so. Yet some states which have signed or ratified the BWC have covertly pursued biological weapons programmes. The leading example was the Biopreparat programme of the USSR,²⁵ which at its height spent billions and employed tens of thousands of people across a network of secret facilities, and conducted after the USSR signed onto the BWC:²⁶ their activities are alleged to have included industrial-scale production of weaponised agents like plague, smallpox and anthrax, alongside successes in engineering pathogens for increased lethality, multi-resistance to therapeutics, evasion of laboratory detection, vaccine escape, and novel mechanisms of disease not observed in nature.²⁷ Other past and ongoing violations in a number of countries are widely suspected.²⁸

The BWC faces ongoing difficulties. One is verification: the Convention lacks verification mechanisms for countries to demonstrate their compliance,²⁹ and the technical and political feasibility of such verification is fraught – similarly, it lacks an enforcement mechanism. Another is states may use disarmament treaties (BWC) included as leverage for other political ends: decisions must be made by unanimity, and thus the 8th review conference in 2017 ended without agreement due to the intransigence of one state.³⁰ Finally (and perhaps most tractably) is that the BWC struggles for resources; it has around 3 full-time staff, a budget less than the typical McDonalds, and many states do not fulfil their financial obligations: the 2017 meeting of states parties was only possible thanks to overpayment by some states, and the 2018 meeting had to be cut short by a day due to insufficient funds.³¹

Dual-use research of concern

The gain-of-function influenza experiments is an example of dual-use research of concern (DURC): research whose results have the potential for misuse. De novo horsepox synthesis is a more recent case. Good governance of DURC remains more aspiration than actuality.

A lot of decision making about whether to conduct a risky experiment falls on an individual investigator, and typical scientific norms around free inquiry and challenging consensus may be a poor fit for circumstances where the downside risks ramify far beyond the practitioners themselves. Even in the best case, where the scientific community is solely composed of those who only perform work which they sincerely believe is on balance good for the world, this independence of decision making gives rise to a unilateralist curse: the decision on ‘should this be done’ defaults to the most optimistic outlier, as only one needs to mistakenly believe it should be done for it to be done, even if it should not.

In reality, scientists are subject to other incentives besides the public good (e.g. publications, patents). This drives the scientific community to make all accessible discoveries as quickly as possible, even if the sequence of discoveries that results is not the best from the perspective of the public good: it may be better that safety-enhancing discoveries occur before (easier to make) dangerous discoveries (cf. differential technological development).

Individually, some scientists may be irresponsible or reckless. Ron Fouchier, when first presenting his work on gain of function avian influenza, did not describe it in terms emblematic of responsible caution: saying that he first “mutated the hell out of the H5N1 virus” to try and make it achieve mammalian transmission. Although it successfully attached to mammalian cells (“which seemed to be very bad news”) it could not transmit from mammal to mammal. Then “someone finally convinced [Fouchier] to do something really, really stupid” – using serial passage in ferrets of this mutated virus, which did successfully produce an H5N1 strain that could transmit from mammal-to-mammal (“this is very bad news indeed”).³²

Governance and oversight can mitigate risks posed by individual foibles or mistakes. The track record of these identifying concerns in advance is imperfect. The gain of function influenza work was initially funded by the NIH (the same body which would subsequently declare a moratorium on gain of function experiments), and passed institutional checks and oversight – concerns only began after the results of the work became known. When reporting de novo horsepox synthesis to the WHO advisory committee on Variola virus research, the scientists noted:

Professor Evans’ laboratory brought this activity to the attention of appropriate regulatory authorities, soliciting their approval to initiate and undertake the synthesis. It was the view of the researchers that these authorities, however, may not have fully appreciated the significance of, or potential need for, regulation or approval of any steps or services involved in the use of commercial companies performing commercial DNA synthesis, laboratory facilities, and the federal mail service to synthesise and replicate a virulent horse pathogen.

One underlying challenge is there is no bright line one can draw around all concerning research. ‘List based’ approaches, such as select agent lists or the seven experiments of concern are increasingly inapposite to current and emerging practice (for example, neither of these would ‘flag’ horsepox synthesis, as horsepox is not a select agent, and de novo synthesis, in itself, is not one of the experiments of concern). Extending the lists after new cases are demonstrated does not seem to be a winning strategy, yet the alternative to lists is not clear: the consequences of scientific discovery are not always straightforward to forecast.

Even if a more reliable governance ‘safety net’ could be constructed, there would remain challenges in geographic scope. Practitioners inclined (for whatever reason) towards more concerning work can migrate to where the governance is less stringent; even if one journal declines to publish on public safety grounds, one can resubmit to another who might.³³

Yet these challenges are not insurmountable: research governance can adapt to modern challenges; greater awareness of (and caution around) biosecurity issues can be inculcated into the scientific community; one can attempt to construct better means of risk assessment than blacklists (cf. Lewis et al. (2019)); broader intra- and inter-national cooperation can mitigate some of the dangers of the unilateralist’s curse. There is ongoing work in all of these areas. All could be augmented.

Impressions on the problem area

Even if the above persuades that GCBRs should be an important part of the ‘longtermist portfolio’,³⁴ it does not answer either how to prioritise this problem area relative to other parts of the ‘far future portfolio’ (e.g. AI safety, Nuclear security), nor which areas under the broad heading of ‘GCBRs’ are the best to work on. I survey some of the most important questions, and (where I have them) offer my impressions as a rough guide.

Key uncertainties

What is the threshold for an event to threaten global catastrophe?

Biological events vary greatly in their scale. At either extreme, there is wide agreement of whether to ‘rule out’ or ‘rule in’ an event as a credible GCBR: a food poisoning outbreak is not a GCBR; an extinction event is. Disagreement is widespread between these limits. I offered before a rough indicator of ‘10% of the population’, which suggests a threshold for concern for GCBRs at the upper limit of events observed in human history.

As this plays a large role in driving my guesses over risk share, a lower threshold would tend to push risk ‘back’ from the directions I indicate (and generally towards ‘conventional’ or ‘commonsense’ prioritisation), and vice-versa.

How likely is humanity to get back on track after global catastrophe?

I have also presumed that a biological event which causes human civilisation to collapse (or ‘derail’) threatens great harm to humanity’s future, and thus such risks would have profound importance to longtermists alongside those of outright human extinction. This is commonsensical, but not inarguable.

Much depends on how likely humanity is to recover from extremely large disasters which nonetheless are not extinction events. An event which kills 99% of humankind would leave a population of around 78 million, still much higher than estimates of prehistoric total human populations (which survived the 200,000 year duration of prehistory, suggesting reasonable resilience to subsequent extinction). Unlike prehistory, the survivors of a ‘99%’ catastrophe likely have much greater knowledge and access to technology than earlier times, better situating them for a speedy recovery, at least relative to the hundreds of millions of years remaining of the earth’s habitable period.³⁵ The likelihood of repeated disasters ‘resetting’ human development again and again through this interval looks slim.

If so, a humanity whose past has been scarred by such a vast disaster nonetheless still has good prospects to enjoy a flourishing future. If this is true, from a longtermist perspective, more effort should be spent upon disasters which would not offer reasonable prospect of recovery, of which risks of outright extinction are the leading (but may not be the only) candidate.³⁶

Yet it may not be so:

• History may be contingent and fragile, and the history we have observed can at best give limited reassurance that recovery would likely occur if we “blast (or ‘bio’) ourselves back to the stone age”.
• We may also worry about interaction terms between catastrophic risks: perhaps one global catastrophe is likely to precipitate others which ‘add up’ to an existential risk.
• We may take the trajectory of our current civilisation to be unusually propitious out of those which were possible (consider reasonably-nearby possible worlds with totalitarian state hyper-power, or roiling great power warfare). Even if a GCBR ‘only’ causes a civilisational collapse which is quickly recovered from, it may still substantially increase risk indirectly if the successor civilisations tend to be worse at navigating subsequent existential risks well.³⁷
• Risk factors may be shared between GCBRs and other global catastrophes (e.g. further proliferation of weapons of mass destruction do not augur well for humanity navigating other challenges of emerging technology). Thus the risk of large biological disasters may be a proxy indicator for these important risk factors.³⁸

The more one is persuaded by a ‘recovery is robust and reliable’ view, the more one should focus effort on existential rather than globally catastrophic dangers (and vice versa). Such a view would influence not only how to allocate efforts ‘within’ the GCBR problem area, but also in allocation between problem areas. The aggregate risk of GCBRs appears to be mainly composed of non-existential dangers, and so this problem area would be relatively disfavoured compared to those, all else equal, where the danger is principally one of existential risk (AI perhaps chief amongst these).

Some guesses on risk

How do GCBRs compare to AI risk?

A relatively common view within effective altruism is that biological risks and AI risks comprise the two most important topics to work on from a longtermist perspective.³⁹ AI likely poses a greater overall risk, yet GCBRs may have good opportunities for people interested in increasing their impact given the very large pre-existing portfolio, more ‘shovel-ready’ interventions, and very few people working in the relevant fields who have this as their highest priority (see later).

My impression is that GCBRs should be a more junior member of an ideal ‘far future portfolio’ compared to AI.⁴⁰ But not massively more junior: some features of GCBRs look worrying, and many others remain unclear. When considered alongside relatively greater neglect (at least among those principally concerned with the longterm future), whatever gap lies between GCBRs and AI is unlikely so large as to swamp considerations around comparative advantage. I recommend those with knowledge, skills or attributes particularly well-suited to working on GCBRs explore this area first before contemplating changing direction into AI. I also suggest those for whom personal fit does not provide a decisive consideration consider this area as a reasonable candidate alongside AI.

Probably anthropogenic > natural GCBRs.

In sketch, the case for thinking anthropogenic risks are greater than natural ones is this:

Our observational data, such as it is, argues for a low rate of natural GCBRs:⁴¹

• Pathogen-driven extinction events appear to be relatively rare.
• As Dr Toby Ord argues in the section on natural risks in his book ‘The Precipice’, the fact that humans have survived for 200,000 years is evidence against there being a high baseline extinction risk from any cause (biology included), and so a low probability of occuring in (say) 100 years.⁴²
• A similar story applies to GCBRs, given we’ve (arguably) not observed a ‘true’ GCBR, and only a few (or none) near-GCBRs.

One should update this baseline risk by all the novel changes in recent history (e.g. antibiotics, air travel, public health, climate change, animal agriculture – see above). Estimates of the aggregate impact of these changes are highly non-resilient even with respect to sign (I think it is risk-reducing, but reasonable people disagree). Yet it seems reasonable given this uncertainty that one should probably not be adjusting the central risk estimate upwards by the orders of magnitude necessary to make natural GCBR at 1% or greater this century.⁴³

I think anthropogenic GCBR is around the 1% mark or greater this century, motivated partly by the troubling developments in biotechnology noted above, and partly by the absence of reassuring evidence of a long track record of safety from this type of risk. Thus this looks more dangerous.

Perhaps deliberate over accidental misuse

Within anthropogenic risks one can (imperfectly) subdivide them into deliberate versus accidental misuse (compare bioterrorism to a ‘science experiment gone wrong’ scenario).⁴⁴

Which is more worrying is hard to say – little data to go on, and considerations in both directions. For deliberate misuse, the idea is that scenarios of vast disaster are (thankfully) rare among the space of ‘bad biological events’ (however cashed out), and so are more likely to be found by deliberate search rather than chance; for accidents, the idea is that (thankfully) most actors are well intentioned, and so there will be a much higher rate of good actors making mistakes than bad actors doing things on purpose. I favour the former consideration more,⁴⁵ and so lean towards deliberate misuse scenarios being more dangerous than accidental ones.

Which bad actors pose the greatest risk?

Various actors may be inclined to deliberate misuse: from states, to terrorist groups, to individual misanthropes (and others besides). One key feature is what we might call actor sophistication (itself a rough summary of their available resources, understanding, and so on). There are fewer actors at a higher level of sophistication, but the danger arising from each is higher: there are many more possible individual misusers than possible state misusers, but a given state programme tends to be much more dangerous than a given individual scheme (and states themselves could vary widely in their sophistication).

My impression is that one should expect the aggregate risk to initially arise principally from highly sophisticated bad actors, this balance shifts over time towards less sophisticated ones. My reasoning, in sketch, is this:

For a given danger of misuse, the barrier to entry for an actor to utilise this starts very high, but inexorably falls (top left panel, below). Roughly, the risk window is opened by a vanguard risk where some bad actor can access it, and saturates when the danger has proliferated to the point where (virtually) any bad actor can access it.⁴⁶

Suppose the risk window for every danger ultimately closes, and there is some finite population of such dangers distributed over time (top right).⁴⁷ Roughly, this suggests cumulative danger first rises, and then declines (cf., also). This is similar to the danger posed by a maximally sophisticated bad actor over time, with lower sophistication corresponding to both a reduced magnitude of danger, and a skew towards later in time (bottom left – ‘sophisticated’ and ‘unsophisticated’ are illustrations; there aren’t two neat classes, but rather a spectrum). With actors becoming more numerous with less sophistication, this suggests the risk share of total danger shifts from the latter to the former over time (bottom right – again making an arbitrary cut in the hypothesised ‘sophistication spectrum).

Crowdedness, convergence, and the current portfolio

If we can distinguish GCBRs from ‘biological risks’ in general, ‘longtermist’ views would recommend greater emphasis be placed on reducing GCBRs in particular. Nonetheless, three factors align current approaches in biosecurity to GCBR mitigation efforts:⁴⁸

1. Current views imply some longtermist interest: Even though ‘conventional’ views would not place such a heavy weight on protecting the long-term future, they would tend not to wholly discount it. Insofar as they don’t, they value work that reduces these risks.
2. GCBRs threaten near-term interests too: Events that threaten to derail civilisation also threaten vast amounts of death and misery. Even if one discounts the former, the latter remains a powerful motivation.⁴⁹
3. Interventions tend to be ‘dual purpose’ between ‘GCBRs’ and ‘non-GCBRs’: disease surveillance can detect both large and small outbreaks, counter-proliferation efforts can stop both higher and lower consequence acts of deliberate use, and so on.

This broad convergence, although welcome, is not complete. The current portfolio of work (set mainly by the lights of ‘conventional’ views) would not be expected to be perfectly allocated by the lights of a longtermist view (see above). One could imagine these efforts as a collection of vectors, their length corresponding to the investment they currently receive, and of varying alignment with the cause of GCBR mitigation, the envelope of these having a major axis positively (but not perfectly) correlated with the ‘GCBR mitigation’ axis:

Approaches to intervene given this pre-existing portfolio can be split into three broad buckets. The first bucket simply channels energy into this portfolio without targeting – ‘buying the index’ of conventional biosecurity, thus generating more beneficial spillover into GCBR mitigation. The second bucket aims to complement the portfolio of biosecurity effort to better target GCBRs: assisting work particularly important to GCBRs, adapting existing efforts to have greater ‘GCBR relevance’, and perhaps advocacy within the biosecurity community to place greater emphasis on GCBRs when making decisions of allocation. The third bucket is pursuing GCBR-reducing work which is fairly independent of (and has little overlap with) efforts of the existing biosecurity community.

I would prioritise the latter two buckets over the first: I think it is possible to identify which areas are most important to GCBRs and so for directed effort to ‘beat the index’.⁵⁰ My impression is the second bucket should be prioritised over the third: although GCBRs have some knotty macro-strategic questions to disentangle,⁵¹ the area is less pre-paradigmatic than AI risk, and augmenting and leveraging existing effort likely harbours a lot of value (as well as a wide field where ‘GCBR-focused’ and ‘conventional’ efforts can mutually benefit).⁵² Of course, much depends on how easy interventions in the buckets are. If it is much lower cost to buy the biosecurity index because of very large interest in conventional biosecurity subfields, the approach may become competitive.

There is substantial overlap between ‘bio’ and other problem areas, such as global health (e.g. the Global Health Security Agenda), factory farming (e.g. ‘One Health‘ initiatives), or AI (e.g. due to analogous governance challenges between both). Although suggesting useful prospects for collaboration, I would hesitate to recommend ‘bio’ as a good ‘hedge’ for uncertainty between problem areas. The more indirect path to impact makes bio unlikely to be the best option by the lights of other problem areas (e.g. although I hope bio can provide some service to AI governance, one would be surprised if work on bio made greater contributions to AI governance than working on AI governance itself – cf.), and so further deliberation over one’s uncertainty (and then committing to one’s leading option) will tend to have a greater impact than a mixed strategy.⁵³

How to help

General remarks

What is the comparative advantage of Effective Altruists in this area?

The main advantage of Effective Altruists entering this area appears to be value-alignment – in other words, appreciating the great importance of the long-run future, and being able to prioritise by these lights. This pushes towards people seeking roles where they influence prioritisation and the strategic direction of relevant communities, rather than doing particular object level work: an ‘EA vaccinologist’ (for example) is reasonably replaceable by another competent vaccinologist; an ‘EA deciding science budget allocation’ much less so.⁵⁴

Desirable personal characteristics

There are some characteristics which make one particularly well suited to work on GCBRs.

1. Discretion: Biosecurity in general (and GCBRs in particular) are a delicate area – one where mistakes are easy to make yet hard to rectify. The ideal norm is basically the opposite of ‘move fast and break things’, and caution and discretion are essential. To illustrate:
   • First, GCBRs are an area of substantial information hazard, as a substantial fraction of risk arises from scenarios of deliberate misuse. As such certain information ‘getting into the wrong hands’ could prove dangerous. This not only includes particular ‘dangerous recipes’, but also general heuristics or principles which could be used by a bad actor to improve their efforts: the historical trend of surprising incompetence from those attempting to use disease as a weapon is one I am eager to see continue.⁵⁵ It is important to recognise when information could be hazardous; to judge impartially the risks and benefits of wider disclosure (notwithstanding personal interest in e.g. ‘publishing interesting papers’ or ‘being known to have cool ideas’); and to practice caution in decision-making (and not take these decisions unilaterally).⁵⁶
   • Second, the area tends to be politically sensitive, given it intersects many areas of well-established interests (e.g. state diplomacy, regulation of science and industry, security policy). One typically needs to build coalitions of support among these, as one can seldom ‘implement yourself’, and mature conflicts can leave pitfalls that are hard to spot without a lot of tacit knowledge.
   • Third, this also applies to EA’s entry into biosecurity itself. History suggests integration between a pre-existing expert community and an ‘outsider’ group with a new interest does not always go well. This has gone much better in the case of biorisk so far, but hard won progress can be much more easily reversed by inapt words or ill-chosen deeds.
2. Focus: If one believes the existing ‘humanity-wide portfolio’ of effort underweights GCBRs, it follows much of the work that best mitigates GCBRs may be somewhat different to typical work in biosafety, public health, and other related areas. A key ability is to be able to prioritise by this criterion, and not get side-tracked into laudable work which is less important.

3. Domain knowledge and relevant credentials: Prior advanced understanding and/or formal qualifications (e.g. a PhD in a bioscience field) are a considerable asset, for three reasons. First, for many relevant careers to GCBRs, advanced credentials (e.g. PhD, MD, JD) are essential or highly desirable. Second, close understanding of relevant fields seems important for doing good work: much of the work in GCBRs will be concrete, and even more abstract approaches will likely rely more on close understanding rather than flashes of ‘pure’ conceptual insight. Third, (non-pure) time discounting favours those who already have relevant knowledge and credentials, rather than those several years away from getting them.

4. US Citizenship: The great bulk of biosecurity activity is focused in the United States. For serious involvement with some major stakeholders (e.g. the US biodefense community), US citizenship is effectively pre-requisite. For many others, it remains a considerable advantage.

Work directly now, or build capital for later?

There are two broad families of approach to work on GCBRs, which we might call explicit versus implicit. Explicit approaches involve working on GCBRs expressly. Implicit (indirect) approaches involve pursuing a more ‘conventional’ career path in an area relevant to GCBRs, both to do work that has GCBR-relevance (even if that is not the primary objective of the work) and also aim to translate this influence and career capital to bear on the problem later.⁵⁷ Which option is more attractive is sensitive to the difficult topics discussed above (and other things besides).

Reasonable people differ on the ideal balance of individuals pursuing explicit versus implicit approaches (compare above on degree of convergence between GCBR mitigation and conventional biosecurity work): perhaps the strongest argument for the former is that such work can better uncover considerations that inform subsequent effort (e.g. if ‘technical fixes’ to GCBRs look unattractive compared to ‘political fixes’, this changes which areas the community should focus on); perhaps the strongest argument for the latter is that there are very large amounts of financial and human capital distributed nearby to GCBRs, so efforts to better target this portfolio are very highly leveraged.

That said, this discussion is somewhat overdetermined at present by two considerations: the first is both appear currently undersupplied with human capital, regardless of one’s view of what the ideal balance between them should be. The second is that there are few immediate opportunities for people to work directly on GCBRs (although that will hopefully change in a few years, see below), and so even for those inclined towards direct work, indirect approaches may be the best ‘holding pattern’ at present.

Tentative career advice

This section offers some tentative suggestions of what to do to contribute to this problem. It is hard to overstate how uncertain and non-resilient these recommendations are: I recommend folks considering a career in this area to heavily supplement this with their own research (and talking to others) before making big decisions.

1. What to study at university

There’s a variety of GCBR-relevant subjects that can be studied, and the backgrounds of people working in this space are highly diverse.⁵⁸ One challenge is GCBRs interface fuzzily with a number of areas, many of which themselves are interdisciplinary.

It can be roughly divided into two broad categories of technical and policy fields. Most careers require some knowledge of both. Of the two, it is probably better to ‘pick up’ technical knowledge first: this seems generally harder to pick up later-career than most non-technical subjects, and career trajectories of those with a technical background moving towards policy are much more common than the opposite.

Technical fields

Synthetic biology (roughly stipulated as ‘bioengineering that works’) is a key engine of biological capabilities, and so also one that drives risks and opportunities relevant to GCBRs. Synthetic biology is broad and nebulous, but it can be approached from the more mechanistic (e.g. molecular biology), computational (e.g. bioinformatics), or integrative (e.g. systems biology) aspects of biological science.

To ‘become a synthetic biologist’ a typical route is a biological sciences or chemistry undergrad, with an emphasis on one or more of these sub-fields alongside laboratory research experience, followed by graduate training in a relevant lab. iGEM is another valuable opportunity if one’s university participates. Other subfields in biology also provide background and experience relevant commensurate to their proximity to synthetic biology (e.g. biophysics is generally better than histology).

Another approach, particularly relevant for more macrostrategy-type research, are areas of biology with a more abstract bent (e.g. mathematical and theoretical biology, evolutionary biology, ecology). They tend to be populated by a mix of mathematically-inclined biologists and biologically inclined mathematicians (/computer scientists and other ‘math-heavy’ areas).

A further possibility is scientific training in an area whose subject-matter will likely be relevant to specific plausible GCBRs (examples might be virology or microbiology for certain infectious agents, immunology, pharmacology and vaccinology for countermeasures). Although a broad portfolio of individuals with domain-specific scientific expertise would be highly desirable in a mature GCBR ecosystem, its current small size disfavours lots of subspecialisation, especially with the possibility that our understanding of the risk landscape (and thus which specialties are most relevant) may change.

If there are clear new technologies that we will need to develop to mitigate GCBRs, it’s possible that you could also have a significant impact as a generalist engineer or tech entrepreneur. This could mean that general training in quantitative subjects, in particular engineering, would be helpful.

Policy fields

Unlike technical fields, policy fields are accessible to those with backgrounds in the humanities or social sciences as well as ‘harder’ science subjects.⁵⁹ They therefore tend to be better approaches for those who have already completed an undergraduate degree in the humanities or social sciences looking to move into this area than technical fields (but people with technical backgrounds are often highly desired for government jobs).

The most relevant policy subjects go under the labels of ‘health security’, ‘biosecurity’, or ‘biodefense’.⁶⁰ The principal emphasis of these areas is the protection of people and the environment from biological threats, so giving it the greatest ‘GCBR relevance’. Focused programmes exist, such as George Mason University’s Biodefense programmes (MS, PhD). Academic centres in this area, even if they do not teach, may take research interns or PhD students in related disciplines (e.g. Johns Hopkins Centre for Health Security, Georgetown Centre for Global Science and Security). The ELBI fellowship and SynbioLEAP are also good opportunities (albeit generally for mid or later-career people) to get further involved in this arena.

This area is often approached in the context of other (inter-)disciplines. Security studies/IR ‘covers’ aspects of biodefense, with (chemical and) biological weapon non-proliferation the centre of their overlap. Science and technology studies (STS), with its interest in socially responsible science governance, has some common ground with biosecurity (dual use research of concern is perhaps the centre of this shared territory). Public health is arguably a superset of health security (sometimes called health protection), and epidemiology a closely related practice. Public policy is another relevant superset, although fairly far-removed in virtue of its generality.

1. ‘Explicit’ work on GCBRs

There are only a few centres which work on GCBRs explicitly. To my knowledge, these are the main ones:

• The Centre for Health Security (CHS)
• The Nuclear Threat Initiative (NTI)
• The Future of Humanity Institute (FHI)
• Centre for the Study of Existential Risk (CSER)

As above, this does not mean these are the only places which contribute to reducing GCBRs: a lot of efforts by other stakeholders, even if not labelled as (or primarily directed towards) GCBR reduction nonetheless do so.

It is both hoped and expected that the field of ‘explicit’ GCBR-reduction work will grow dramatically over the next few years, and at maturity be capable of absorbing dozens of suitable people. At the present time, however, prospects for direct work are somewhat limited: forthcoming positions are likely to be rare and highly competitive – the same applies to internships and similar ‘limited term’ roles. More contingent roles (e.g. contracting work, self-contained projects) may be possible at some of these, but this work has features many will find unattractive (e.g. uncertainty, remote work, no clear career progression, little career capital).

2. Implicit approaches

Implicit approaches involve working at a major stakeholder in a nearby space, in the hopes of enhancing their efforts towards GCBR reduction and cultivating relevant career capital. I sketch these below, in highly approximate order of importance:

United States Government

The US government represents one of the largest accessible stakeholders in fields proximate to GCBRs. Positions are competitive, and many roles in a government career are unlikely to be solely focused on matters directly relevant to GCBRs. Individuals taking this path should not consider themselves siloed to a particular agency: career capital is transferable between agencies (and experience in multiple agencies is often desirable). Work with certain government contractors is one route to a position at some of these government agencies.

Relevant agencies include:

• Department of Defence (DoD)
  • Defence Advanced Research Projects Agency (DARPA)
  • Defence Threat Reduction Agency (DTRA)
  • Office of the Secretary of Defence
  • Offices that focus on oversight and implementation of the Cooperative Threat Reduction Program (Counter WMD Policy & Nuclear, Chem Bio Defense)
  • Office of Net Assessment, (including Health Affairs)
• State Department
  • Bureau of International Security and Nonproliferation
  • Biosecurity Engagement Program (BEP)
• Department of Health and Human Services (HHS)
  • Centers for Disease Control and Prevention (CDC)
  • Office of the Assistant Secretary for Preparedness and Response (ASPR)
  • Biomedical Advanced Project and Development Agency (BARDA)
  • Office of Global Affairs
• Department of Homeland Security
• Federal Bureau of Investigation, Weapons of Mass Destruction Directorate
• U.S. Agency for International Development, Bureau of Global Health, Global Health Security and Development Unit
• The US intelligence community (broadly)
  • Intelligence Advanced Research Projects Agency (IARPA)

Post-graduate qualifications (PhD, MD, MA, or engineering degrees) are often required. Good steps to move into these careers are the Presidential Management Fellowship, the AAAS Science and Technology Policy Fellowship, Mirzayan Fellowship, and the Epidemic Intelligence Service fellowship (for Public Health/Epidemiology).

Scientific community (esp. synthetic biology community)

It would be desirable for those with positions of prominence in academic scientific research or leading biotech start-ups, particularly in synthetic biology, to take the risk of GCBRs seriously. Professional experience in this area also lends legitimacy when interacting with these communities. SynbioLEAP is one useful programme to be aware of. A further dividend is this area is a natural fit for those wanting to work directly on technical contributions and counter-measures.⁶¹

International organisations

The three leading candidates are the UN Office for Disarmament Affairs (UNODA), the World Health Organisation (WHO), and the World Organization for Animal Health (OIE).

WHO positions tend to be entered into by those who spent their career at another relevant organisation. For more junior roles across the UN system, there is a programme for early-career professionals called The Junior Professional Officers (JPO) programme. Both sorts of positions are extraordinarily competitive. A further challenge is the limited number of positions orientated towards GCBRs specifically: as mentioned before, the biological weapons implementation and support unit (ISU) comprises three people.

Academia/Civil society

There are a relatively small number of academic centres working on related areas, as well as a similarly small diaspora of academics working independently on similar topics (some listed above). Additional work in these areas would be desirable.⁶²

Relevant civil society groups are thin on the ground, but Chatham House (International Security Department), and the National Academies of Sciences, Engineering, and Medicine (NASEM) are two examples.

(See also this guide on careers in think tanks).

Other nation states

Parallel roles to those mentioned for the United States in security, intelligence, and science governance in other nation states likely have high value (albeit probably less so than either the US or international organisations). My understanding of these is limited (some pointers on China are provided here).

Public Health/Medicine

Public health and medicine are natural avenues from the perspective of disease control and prevention, as well as the treatment of infectious disease, and medical and public health backgrounds are common in senior decision makers in relevant fields. That said, training in these areas is time-inefficient, and seniority in these fields may not be the most valuable career capital to have compared to those above (cf. medical careers).

3. Speculative possibilities

Some further (even) more speculative routes to impact are worth noting:

Grant-making

The great bulk of grants to reduce GCBRs (explicitly) are made by Open Philanthropy (under the heading of ‘Biosecurity and Pandemic Preparedness‘).⁶³ Compared to other potential funders loosely in the ‘EA community’ interested in GCBRs, Open Phil has two considerable advantages: i) a much larger pool of available funding; ii) staff dedicated to finding opportunities in this area. Working at Open Phil, even if one’s work would not relate to GCBRs, may still be a strong candidate from a solely GCBR perspective (i.e. ignoring benefits to other causes).⁶⁴

Despite these strengths, Open Phil may not be able to fill all available niches of an ideal funding ecosystem, as Carl Shulman notes in his essay on donor lotteries.⁶⁵ Yet even if other funding sources emerge which can fill these niches, ‘GCBR grantmaking capacity’ remains in short supply. People skilled at this could have a considerable impact (either at Open Phil or elsewhere), but I do not know how such skill can be recognised or developed. See 80,000 Hours’ career review on foundation grantmaking for a general overview.

Operations and management roles

One constraint on expanding the number of people working directly on GCBRs is limited by operations and management capacity. Common to the wider EA community, people with these skills remain in short supply (although this seems to be improving).⁶⁶

A related area of need is roughly termed ‘research management’, comprising an overlap between management and operations talent and in depth knowledge of a particular problem area. These types of roles will be increasingly important as the area grows.

Public facing advocacy

It is possible public advocacy may be a helpful lever to push on to mitigate GCBRs, although also possible doing so is counter-productive. In the former case, backgrounds in politics, advocacy (perhaps analogous to nuclear disarmament campaigns), or journalism may prove valuable.⁶⁷ Per the previous discussion about information continence, broad coordination with other EAs working on existential risk topics is critical.

Engineering and Entrepreneurship

Many ways of reducing biorisk will involve the development of new technologies. This means there may also be opportunities to work as an engineer or tech entrepreneur. Following one of these paths could mean building expertise through working as an engineer at a “hard-tech” company or working at a startup, rather than building academic biological expertise at university. Though if you do take this route, make sure the project you’re helping isn’t advancing biotech capabilities that could make the problem worse.

Other things

Relevant knowledge

GCBRs are likely a cross-disciplinary problem, and although it is futile to attempt to become an ‘expert at everything’, basic knowledge of relevant fields outside one’s area of expertise is key. In practical terms, this recommends those approaching GCBRs from a policy angle acquaint themselves with the relevant basic science (particularly molecular and cell biology), and those with a technical background the policy and governance background.

Exercise care with original research

Although reading around the subject is worthwhile, independent original research on GCBRs should be done with care. The GCBR risk landscape has a high prevalence of potentially hazardous information, and in some cases the best approach will be prophylaxis: to avoid certain research directions which are likely to uncover these hazards. Some areas look more robustly positive, typically due to their defense-bias: better attribution techniques, technical and policy work to accelerate countermeasure development and deployment, and more effective biosurveillance would be examples of this. In contrast, ‘Red teaming’ or exploring what are the most dangerous genetic modifications that could be made to a given pathogen are two leading examples of research plausibly better not done at all, and certainly better not done publicly.

Decisions here are complicated, and likely to be better made by the consensus in the GCBR community, rather than amateurs working outside of it. Unleashing lots of brainpower on poorly-directed exploration of the risk landscape may do more harm than good. A list of self-contained projects and research topics suitable for ‘external’ researchers is in development: those interested are encouraged to get in touch.

Find opportunities on our job board

Our job board features opportunities in biosecurity and pandemic preparedness:

View all opportunities

Learn more

• All 80,000 Hours’ content on reducing risks of catastrophic pandemics
• Greg’s ultra-rough reading list.
• Chris Bakerlee’s reading list.
• Good online sources to keep abreast of developments in the broad field (from a primarily US perspective) are the GMU Pandora report and the CHS Health Security Headlines mailing list.
• The Precipice: Existential Risk and the Future of Humanity by Toby Ord — see Chapters 3 and 5
• Discussion of how GCBRs should be defined from the Centre for Health Security
• You can find a discussion of GCBRs in chapters 14 and 20 of Global Catastrophic Risks edited by Bostrom and Cirkovic (2007)
• Think about applying to work with one of the experts on this list.

Podcast interviews about GCBRs

• 

  Dr Pardis Sabeti on the Sentinel system for detecting and stopping pandemics

  Read more
• 

  Andy Weber on rendering bioweapons obsolete and ending the new nuclear arms race

  Read more
• 

  Dr Greg Lewis on COVID-19 and reducing global catastrophic biological risks

  Read more
• 

  Dr Cassidy Nelson on the twelve best ways to stop the next pandemic (and limit COVID-19)

  Read more
• 

  The careers and policies that can prevent global catastrophic biological risks, according to world-leading health security expert Dr Tom Inglesby

  Read more
• 

  Howie Lempel on why we aren’t worried enough about the next pandemic — and specifically what we can do to stop it.

  Read more
• 

  Beth Cameron fought Ebola for the White House. Now she works to stop something even worse.

  Read more

Other relevant 80,000 Hours articles

• Why despite global progress, humanity is probably facing its most dangerous time ever
• Introducing the long-term value thesis

The post Reducing global catastrophic biological risks appeared first on 80,000 Hours.

Later in her career, Jessica served as Deputy to the Undersecretary of State for Global Affairs, wrote a weekly column for the Washington Post, and most recently served as President of the Carnegie Endowment for International Peace, an influential Washington-based foreign policy think tank.

What launched such an successful career? In our conversation with Jessica, she argued it was the AAAS Science & Technology (S&T) Policy Fellowship. Jessica was selected as one of their inaugural fellows in 1973.

In this article² we argue that for eligible people interested in our top recommended problem areas and S&T policy careers the AAAS S&T Policy Fellowship is a valuable springboard that could rapidly advance your career as it did for Jessica.

The opportunity

At 80,000 Hours, we think the AAAS Policy Fellowship is one of the best routes into the US Government for people with a STEM or social science PhD, or an engineering masters and three years of industry experience.

Policy fellows work within the US Government for one year in policy-related roles relevant to science and technology. Nearly 300 fellows are accepted each year, and almost all of them take assignments within the executive branch, for example in the Departments of Defense and State or within the intelligence community. A small handful of fellows work in congress or within the judiciary.³

AAAS policy fellows have a good reputation in government. The fellowship has been running for over 40 years, and attracts high-caliber scientists and engineers interested in policy. Applicants come from a range of backgrounds, including early- to late-career researchers, but also people who have left research to work in industry or the not-for-profit sector. Government offices are often keen to hire AAAS Policy Fellows because of the expertise they bring to government. Jessica Tuchman Mathews described how in her day it was “a wonderful calling card”, a sentiment that was echoed by others we spoke with.

This makes the Fellowship among the best routes we’ve found into US Government S&T policy careers.

We think that these careers offer a substantial opportunity to contribute to policy-making on a range of issues from artificial intelligence (AI) to biosecurity, and from ending factory farming to ending extreme poverty.

We are particularly excited about people with advanced degrees in subjects relevant to machine learning entering government. The way AI is handled by governments is likely to shape this technology’s development, which in turn could impact humanity’s long-term trajectory. We outline our views on why we think AI public policy careers are particularly impactful in this article.

Fellows receive a stipend of $80,000 to $105,000 as well as other support including health insurance, a travel stipend and a year-long professional development programme.

For more information about the fellowship, read AAAS’s FAQ.

In the following sections, we’ll go into more detail about the fellowship’s eligibility requirements and its pros and cons.

Eligibility

To be eligible for the AAAS policy fellowship, you must be a US citizen with a PhD in a STEM or social or behavioural science subject. Engineers with an engineering masters degree and a minimum of three years of industry experience are also eligible.

We asked AAAS whether people with CS masters would be eligible. A spokesperson for AAAS said people with at least three years of industry experience and a computer science masters from an engineering school with may be eligible, and encouraged them to apply. They also explained that all final eligibility determinations are up to the reviewers.

If you have several years experience in the technology sector, then you should also consider the TechCongress Fellowship.

Potential benefits

The Fellowship seems to offer a significant boost in career capital in just one year, especially for those who want to focus on issues relevant to global catastrophic risks. This section outlines some of the reasons why.

During your AAAS S&T Policy Fellowship, it is usually possible to serve in more senior roles in government than you might have otherwise been offered. This is in large part because of the fellowship’s prestige within government, which is probably helped by its competitive application process and the positive reputation of past fellows.

The AAAS S&T Policy Fellowship boasts a 3000-person alumni network. Some of the most impressive mid-career policymakers we know at 80,000 Hours were once AAAS S&T Policy Fellows. Having a network within government that spans departments is also valuable for helping get things done.

The fellowship has an impressive placement record. The fellowship FAQ gives some statistics:

“In the year immediately following their fellowship, approximately 40-50% of fellows continue working in the policy realm (not necessarily in federal government); 20-25% return to the sector in which they worked previously; and 20-25% use the experience as a stepping stone to a new opportunity.”

Compared with most other US Government fellowships, the AAAS S&T Policy Fellowship is more relevant to those attempting to improve the long-term future because of its focus on science and technology policy.

Potential downsides

If there is a reasonably large chance that you will want to return to academia after the policy fellowship, then spending a year in government may make that harder. In general, once you leave academia, it can be difficult to return.

A similar argument may apply if you are thinking of returning to non-academic research after the fellowship. In this case it will probably be better to do research-related work instead of this fellowship.

After the year of the fellowship, you do not have a job in government by default. You will need to use the network that you develop over the course of the year to find a position in your second year. On occasion it is possible, however, to extend the fellowship to a second year at the mutual agreement of the host office, AAAS, and yourself.

You do not have complete control over which part of government you end up working in. Placements are allocated by agreement between AAAS, the host departments/agencies, and applicants. You do not know which office you will be placed with until the end of the process.

The application process for the fellowship is somewhat involved. In addition to the usual references and online application, you will be required to write a policy memo and present it in a video interview. Near the end of the process there are also in-person interviews in DC with potential placement offices.

The application process is also relatively competitive, though AAAS do not release information on the fraction of applicants that are accepted.

Conclusion

If you’re a US citizen with a science PhD or an engineering masters⁴, we think certain areas of S&T policy are among your highest impact career paths. The AAAS Fellowship is one of the best ways to launch a career in US S&T policy and we strongly encourage you to consider applying. The annual deadline is November 1st.

We are particularly interested to meet with potential applicants with PhDs or CS masters who are interested in working in one of our priority paths such as AI policy. If you fit this description, please get in touch below and we may be able to make introductions, help advise on how to apply, and provide career guidance.

Further Reading

• ‘40 years of impact’, a video by AAAS interviewing fellows from previous decades.
• ‘AAAS S&T Policy Fellowship FAQ’, which answers many common questions about the fellowship
• ‘Effective altruism in government’, a talk by Jason Matheny on government careers aiming at social impact
• 80,000 Hours’ AI policy careers guide describes how to move into AI public policy careers

The post American with a science PhD? Get a fast-track into AI and STEM policy by applying for the acclaimed AAAS Science & Technology Fellowship by Nov 1. appeared first on 80,000 Hours.