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.

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