Bohlin came up with one very simple invention: the modern seat belt.

Within a year, Volvo began equipping their cars with seat belts as standard, and — as a result of its importance to safety — opened up the patent so that other manufacturers could do the same. Volvo claims that Bohlin’s invention has saved over a million lives. That would make Bohlin one of the highest-impact people in history, alongside David Nalin, the inventor of oral rehydration therapy for diarrhoea.

We’d guess Bohlin’s impact wasn’t quite that large. For one thing, seat belts already existed: in 1951, a Y-shaped three-point seat belt was patented that avoided the risks of internal injuries from simple lap belts. Bohlin’s innovation was doing this with just one strap, making it simple and convenient to use. For another thing, it seems likely that someone else would have come up with Bohlin’s design eventually.

Nevertheless, a simple estimate suggests that Bohlin saved hundreds of lives at the very least² — incredible for such a simple piece of engineering.

Thanks to Jessica Wen and Sean Lawrence at High Impact Engineers for their help with this article. Much of the content is based on their website.

Why are engineering skills valuable?

Bohlin’s story shows that engineering — by which we mean all kinds of engineering other than software engineering, which we cover separately — can clearly be hugely valuable for the world. But we think it’s most valuable when:

• You can really speed up development. This might be because you’re working on something that’s relatively neglected by others, or because you’re working in an area where you have high personal fit, so you can make particularly helpful contributions (or, ideally, both).
• You’re producing something which will practically be used to help people. One reason Bohlin had such a large impact — and Griswold, the inventor of the Y-shaped three-point seat belt didn’t — is that Volvo opened up the patent for use by other manufacturers.
• You’re working on a particularly pressing problem. For example, vaccines for common and deadly diseases — like malaria — are much more useful for the world than vaccines for rare diseases.

Ultimately, many of the potential solutions to the top problems we recommend working on include developing and deploying technology — and this often requires engineers.

Below, we look more closely at how engineering could be used to solve some of the world’s most pressing problems.

Jobs in engineering are often highly paid and in-demand. So learning engineering skills can give you great back-up options, and — depending on the specific discipline — can be a decent choice for earning to give.

Good pay combined with intellectually rewarding work means that engineers often have high job satisfaction (although we’d expect job satisfaction to be lower in academia than in industry).

Finally, it’s worth noting that it’s possible to accidentally cause harm through engineering. While we’re generally hugely in favour of technological development, many of the risks we’re most concerned about arise directly from the development of future technologies. Many technologies are dual use and could have both positive and negative applications. So it’s worth thinking carefully about the work you’re doing and whether it could be used to cause harm. (For an example of how you might think about this, see this article on whether it’s good to work on advancing AI capabilities. This example primarily applies to software engineers, but could also apply more broadly — to computer hardware engineers for instance.)

What specific discipline of engineering is most valuable?

There are many different types of engineering. Typically, you’ll eventually specialise in one (often during an undergraduate degree).

There are ways of using any engineering discipline to have an impact.

That said, we’re most excited about:

• Biomedical engineering
• Chemical engineering
• Electrical and electronic engineering.

That’s because these areas are most relevant to some of our top problems, in particular preventing catastrophic pandemics and reducing the risk of an AI-related catastrophe.

Some engineering disciplines also pay much better than others. In particular, nuclear, aerospace, petroleum and computer hardware engineers are paid best (although we wouldn’t generally recommend becoming a petroleum engineer, as we’d worry it causes harm), while agricultural and civil engineers are paid least.

What does using an engineering skill set typically involve?

An engineering skill set usually involves developing technologies faster and deploying existing technologies in novel ways. (This is in contrast with research skills, which focus on finding answers to unanswered questions, although there’s a fair bit of overlap between the two.)

Engineers typically do one of the following:

• Work in academia
• Work in industry
• Work at small startups (or found them)

Work in academia

Work in academia tends to focus on more speculative, early-stage technology (e.g. using ultraviolet light to sterilise rooms). This work is much more similar to research, so if you’re interested we’d suggest looking at our articles on research skills and working in academia. This route almost always involves getting a PhD in a subfield of engineering.

Academic research can be difficult for many people. It often involves long deadlines, self-driven work, and very little structure. Beyond engineering, academic work is also likely to include grant applications, teaching courses, publishing papers, mentoring students, and other responsibilities.

(We’ll look more at what to consider when choosing to do a PhD below.)

Work in industry or startups

As the technology becomes more viable, businesses tend to get involved — either startups or large engineering firms, or both. There are also some nonprofits focused on high-impact technology.

When working on engineering in industry, you can choose to become a subject matter expert (more similar to research) or instead become a manager, increasing the scope of your responsibilities. Either way, you can try learning faster by getting temporary placements in other parts of a company, taking part in engineering competitions, or working towards professional registration (which can be a helpful credential for engineering careers).

Generally, the work you focus on will be dictated by the business needs of the company, and, compared to academia, you’re more likely to have a standard 9-5 workday (rather than more flexible hours). Deadlines are often much shorter than in academia.

If you choose to become a manager or work for a small startup, you’ll be using organisation-building skills alongside your engineering skill set.

How to evaluate your fit

How to predict your fit in advance

You’ll need a quantitative background, and ideally you’ll have studied (or plan to study) engineering or a highly related subject at undergraduate level.

If you’re considering doing an engineering degree (or otherwise moving your career into engineering), signs you’d be a great fit could include:

• You’re comfortable working on scientific subjects.
• You’re good at practical, hands-on work: in many areas of engineering, you’ll end up working with physical objects in a lab.
• You enjoy understanding how and why physical things work.
• You enjoy fixing or building things, for example, around your home.
• You are good at “systems thinking”: for example, you’d notice when people ask you similar questions multiple times and then think about how to prevent the issue from coming up again.
• You might also be good at learning quickly and have high attention to detail.

With academic engineering, you’ll need to be comfortable with the academic research environment and generally happy to be self-motivated while working on things with few clear deadlines. If you’re doing a degree, you could try doing some sort of academic research (like a summer research project) and think about how that goes. (Read more about evaluating your fit for research.)

If you want to become a manager or work for a startup, you’ll probably need more social skills (including things like clear communication and people management skills).

Assessing your fit for different disciplines of engineering

One way to start is to think about which of the natural sciences you most enjoy learning about. Some examples:

Another way to determine what kind of engineering you might be good at is to figure out where you lie on the spectrum from scientist to engineer. If you enjoy the more theoretical, abstract, or precise side of physics or mathematics, then something like materials science or electrical engineering could be a better fit. If you lean more towards optimisation, application of knowledge, or practicalities, then civil or chemical engineering might be more interesting. If you are somewhere in the middle, then mechanical engineering could be for you.

However, don’t place too much weight on these crude tests — all these areas involve design testing and innovation, as well as research and studying new phenomena.

Your discipline also may not matter that much when it comes to getting a job. For example, many larger companies will hire graduate engineers from a range of different disciplines for the same role, relying on on-the-job training for specialisation.

How to tell if you’re on track

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

Within academia, there’s generally very defined progression (e.g. completing a PhD, getting a postdoc, etc.).

In both cases, it’s worth trying to find some engineers whose work you respect, and who you trust to be honest with you, to give you feedback on how you’re getting along.

How to get started building engineering skills

Getting an engineering degree

The main way to get started is to do an undergraduate degree in engineering — although if you have a different quantitative degree, you may well be able to get an engineering job. (Read our advice on how to spend your time while at college.)

Engineering degrees are usually in a particular discipline of engineering. However, it can often be fairly easy to switch between engineering courses at university if you find that you’re not enjoying one kind of engineering.

Some universities may offer a ‘general first year’ for engineering in which you can take classes from different engineering disciplines to get a feel for what you enjoy.

Universities may have a range of student clubs or teams that work together to design, fabricate, test, and operate a complex vehicle or device in a national or worldwide competition with other universities. Examples include Formula SAE, the University Rover Challenge, UAS challenge, rocketry competitions (e.g. Australian Universities Rocket Competition), and human-powered vehicle challenges.

These sorts of competitions teach important skills that are invaluable in an engineering career — but they do typically require a large time commitment. Employers often view participation in these sorts of student teams very favourably, so it can give you a leg up in getting a job after graduating.

If you can, do internships in industry. Most large engineering companies run summer internships, and they are a good opportunity to see how industry works and gain some career capital. You could also do an engineering research project over the summer with a research group or join a startup. If all else fails, using the summer to create something also gives you valuable skills and experience — plus it lets you test out how much you like it.

Going into academia

If you want to do engineering in academia, you probably need to do a PhD.

Many people find PhDs very difficult. They can be isolating and frustrating, and take a very long time (4–6 years). What’s more, both your quality of life and the amount you’ll learn will depend on your supervisor — and it can be really difficult to figure out in advance whether you’re making a good choice.

So, if you’re considering doing a PhD, here are some things to consider:

• The topic of your research: It’s easy to let yourself be tied down to a PhD topic you’re not confident in. If the PhD you’re considering would let you work on something that seems relevant to a pressing problem you want to work on, it’s probably — all else equal — better for your career, and the research itself might have a positive impact as well.
• Mentorship: What are the supervisors or managers like at the opportunities open to you? You might be able to find engineering roles in industry where you could learn much more than you would in a PhD — or vice versa. When picking a supervisor, try reaching out to the current or former students of a prospective supervisor to ask them some frank questions. You can also use your final year undergraduate research project to evaluate your fit with a supervisor. (Also, see this article on how to choose a PhD supervisor.)
  Your fit for the work environment: Doing a PhD could mean working on your own with very little supervision or feedback for long periods of time. Some people thrive in these conditions! But some people really don’t and find PhDs extremely difficult.

PhD competitiveness varies by field. To get into any PhD, you’ll probably need high undergraduate grades and some research experience — including a reference from one or more professors. More competitive PhDs might require you to have published papers or extremely strong references. To get those, you might need to spend 1–3 years as a research assistant before applying for PhDs.

Entering industry

You can likely use an undergraduate degree to get an entry-level position in anything ranging from large engineering companies to startups.

In some countries (like the UK), large engineering companies offer graduate programs where you do rotations in different teams in the company. These allow you to build up lots of different skills and knowledge quickly (your ability to choose your rotation depends on the company, the department, and your manager).

Large companies are also likely to have a structured professional development scheme with training, assigned mentors, and regular check-ins to set you up for professional registration as an engineer.

Joining a startup generally means that you have a lot of responsibilities very quickly and less structure around you. This might mean more freedom with what you can do and lots of variety. You might learn a ton, but you won’t get much feedback or mentorship, and there will also be more stress and uncertainty.

Find jobs that use engineering

If you think you might be a good fit for this skill and you’re ready to start looking at job opportunities that are currently accepting applications, see our curated list of opportunities:

View all opportunities

Once you have an engineering skill set, how can you best apply it to have an impact?

Having a big impact as an engineer means finding a particularly pressing global issue and finding a way to use engineering to develop solutions.

Below is a list of pressing global problems and how engineers can help with each.

If you’re already an engineer, you can read through to see if any of these issues appeal to you — and then aim to speak to some people in each area about how your skills could be applied and what the current opportunities are.

You could also apply to speak to our team or get in touch with High Impact Engineers.

Preventing catastrophic pandemics

A future pandemic that is much worse than COVID-19 could pose a significant risk to society.

There’s a key role for bioengineers and chemical engineers to play in mitigating these risks, including:

• Developing vaccine platform technologies to help us rapidly produce new vaccines in response to novel threats
• Developing and implementing metagenomic sequencing to improve our ability to detect new pandemics

Other engineering disciplines are also needed. For example, engineers could:

• Help design better pathogen containment systems for labs and systems to reduce pathogen spread in buildings or vehicles. (There are roles here for materials, civil, industrial, aerospace, and HVAC engineers, among others.)
• Help improve stockpiling and management of PPE (personal protective equipment), such as gloves and masks. (There are possibly roles here for industrial engineers.)
• Help improve technologies for monitoring pathogens, like systems for sampling environments and processes for managing and examining samples. (There are roles here for industrial, mechanical, and automation engineers, among others.)

To learn more, take a look at Biosecurity needs engineers by Will Bradshaw and this overview of using engineering in biosecurity from High Impact Engineers.

AI alignment

We expect AI hardware to be a crucial component of the development of AI. Given the importance of positively shaping the development of AI, experts in AI hardware could be in a position to have a substantive positive impact.

Useful disciplines include:

• Electrical, electronic, and computer engineering (probably the most relevant discipline for AI hardware)
• Materials engineering with a focus on semiconductors
• Industrial engineering with a focus on the semiconductor supply chain

To learn more, read our full career review on becoming an expert in AI hardware.

If you have hardware expertise, you might also consider moving into AI policy. Read our career review of AI governance and coordination to learn more.

Improving civilisational resilience

One very neglected potential way to reduce existential threats is through generally increasing the resilience of our society to catastrophes.

All kinds of engineers can play a big role in this issue — for example by developing alternative foods, refuges, and knowledge stores that will be able to survive a near-apocalypse.

For instance, David Denkenberger is an engineer developing alternative foods that could be rapidly scaled up in the event of a global famine, perhaps caused by nuclear winter or a major volcanic eruption. We have two podcasts with him:

• Using paper mills and seaweed to feed everyone in a catastrophe
• We could feed all eight billion people through a nuclear winter

To learn more about refuges, see this review by Open Philanthropy. Or learn about how to increase the chance of recovery from a catastrophic event in two of our podcast episodes:

• Luisa Rodriguez on why global catastrophes seem unlikely to kill us all
• Paul Christiano on whether we should leave messages for future civilisations

Fight climate change

We think further developing and rolling out green energy is one of the best ways to tackle climate change, and engineers have a major role to play in this. This includes not just generating more green electricity, but also things like ensuring that there is enough electricity to meet seasonal changes in electricity demand and trying to find ways to make other forms of energy greener (like replacing fossil fuel use in blast furnaces or transportation).

You can further increase your impact by focusing on technology that’s either not widely known (e.g. hot rock geothermal) or unsexy (e.g. decarbonising cement rather than developing electric cars).

We have more notes on how to most effectively tackle climate change. We’d also recommend What can a technologist do about climate change? by Bret Victor.

Other problem areas that need engineers

In addition to the top problems mentioned above, there are many other pressing areas where engineers are needed. For example, you could:

• Create alternative proteins through chemical engineering to help reduce meat consumption and prevent the suffering of animals in factory farms.
• Work on tech and logistics for global health, like producing and rolling out vaccines for polio and malaria.
• Try to develop products for people living in poverty — see more about why that can be impactful in our interview with SendWave.

Options outside engineering that can use engineering aptitude

Engineers often have a systems mindset that can make them a particularly good fit for operations management or entrepreneurship. If that work interests you, it’s worth considering whether to spend some time building the skills you’d need to make the transition.

Some engineers may also excel at other options that require good quantitative abilities, such as:

• Working on AI safety technical research
• Software engineering
• Earning to give in a high-paying role, such as quantitative trading

Engineers may be able to easily develop skills in translating technically complex topics to less technical audiences, such as policymakers, which means you could also consider building a policy skill set. For example, TechCongress aims to get engineers, and other technologists, involved as technical advisors for policymakers.

Career paths we’ve reviewed that use engineering skills

• Expert in AI hardware
• Impactful climate solutions
• Biomedical research

Learn more about engineering

• High Impact Engineers
• Will Bradshaw on why biosecurity needs engineers and materials scientists
• A list of potential projects in biosecurity

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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In a nutshell:

You can start building software and tech skills by trying out learning to code, and then doing some programming projects before applying for jobs. You can apply (as well as continue to develop) your software and tech skills by specialising in a related area, such as technical AI safety research, software engineering, or information security. You can also earn to give, and this in-demand skill set has great backup options.

Why are software and tech skills valuable?

By “software and tech” skills we basically mean what your grandma would call “being good at computers.”

When investigating the world’s most pressing problems, we’ve found that in many cases there are software-related bottlenecks.

For example, machine learning (ML) engineering is a core skill needed to contribute to AI safety technical research. Experts in information security are crucial to reducing the risks of engineered pandemics, as well as other risks. And software engineers are often needed by nonprofits, whether they’re working on reducing poverty or mitigating the risks of climate change.

Also, having skills in this area means you’ll likely be highly paid, offering excellent options to earn to give.

Moreover, basic programming skills can be extremely useful whatever you end up doing. You’ll find ways to automate tasks or analyse data throughout your career.

What does a career using software and tech skills involve?

A career using these skills typically involves three steps:

1. Learn to code with a university course or self-study and then find positions where you can get great mentorship. (Read more about how to get started.)
2. Optionally, specialise in a particular area, for example, by building skills in machine learning or information security.
3. Apply your skills to helping solve a pressing global problem. (Read more about how to have an impact with software and tech.)

There’s no general answer about when to switch from a focus on learning to a focus on impact. Once you have some basic programming skills, you should look for positions that both further improve your skills and have an impact, and then decide based on which specific opportunities seem best at the time.

Software and tech skills can also be helpful in other, less directly-related career paths, like being an expert in AI hardware (for which you’ll also need a specialist knowledge skill set) or founding a tech startup (for which you’ll also need an organisation-building skill set). Being good with computers is also often part of the skills required for quantitative trading.

Programming also tends to come in handy in a wide variety of situations and jobs; there will be other great career paths that will use these skills that we haven’t written about.

How to evaluate your fit

How to predict your fit in advance

Some indications you’ll be a great fit include:

• The ability to break down problems into logical parts and generate and test hypotheses
• Willingness to try out many different solutions
• High attention to detail
• Broadly good quantitative skills

The best way to gauge your fit is just to try out programming.

It seems likely that some software engineers are significantly better than average — and we’d guess this is also true for other technical roles using software. In particular, these very best software engineers are often people who spend huge amounts of time practicing. This means that if you enjoy coding enough to want to do it both as a job and in your spare time, you are likely to be a good fit.

How to tell if you’re on track

If you’re at university or in a bootcamp, it’s especially easy to tell if you’re on track. Good signs are that you’re succeeding at your assigned projects or getting good marks. An especially good sign is that you’re progressing faster than many of your peers.

In general, a great indicator of your success is that the people you work with most closely are enthusiastic about you and your work, especially if those people are themselves impressive!

If you’re building these skills at an organisation, signs you’re on track might include:

• You get job offers at organisations you’d like to work for.
• You’re promoted within your first two years.
• You receive excellent performance reviews.
• You’re asked to take on progressively more responsibility over time.
• After some time, you’re becoming someone in your team who people look to solve their problems, and people want you to teach them how to do things.
• You’re building things that others are able to use successfully without your input.
• Your manager / colleagues suggest you might take on more senior roles in the future.
• You ask your superiors for their honest assessment of your fit and they are positive (e.g. they tell you you’re in the top 10% of people they can imagine doing your role).

How to get started building software and tech skills

Independently learning to code

As a complete beginner, you can write a Python program in less than 20 minutes that reminds you to take a break every two hours.

A great way to learn the very basics is by working through a free beginner course like Automate the Boring Stuff with Python by Al Seigart.

Once you know the fundamentals, you could try taking an intro to computer science or intro to programming course. If you’re not at university, there are plenty of courses online, such as:

• Udacity’s Intro to Theoretical Computer Science
• MIT’s Introduction to Computer Science and Programming
• Stanford’s Programming Methodology

Don’t be discouraged if your code doesn’t work the first time — that’s what normally happens when people code!

A great next step is to try out doing a project with other people. This lets you test out writing programs in a team and working with larger codebases. It’s easy to come up with programming projects to do with friends — you can see some examples here.

Once you have some more experience, contributing to open-source projects in particular lets you work with very large existing codebases.

Attending a coding bootcamp

We’ve advised many people who managed to get junior software engineer jobs in less than a year by going to a bootcamp.

Coding bootcamps are focused on taking people with little knowledge of programming to as highly paid a job as possible within a couple of months. This is a great entry route if you don’t already have much background, though some claim the long-term prospects are not as good as if you studied at university or in a particularly thorough way independently because you lack a deep understanding of computer science. Course Report is a great guide to choosing a bootcamp. Be careful to avoid low-quality bootcamps. To find out more, read our interview with an App Academy instructor.

Studying at university

Studying computer science at university (or another subject involving lots of programming) is a great option because it allows you to learn to code in an especially structured way and while the opportunity cost of your time is lower.

It will also give you a better theoretical understanding of computing than a bootcamp (which can be useful for getting the most highly-paid and intellectually interesting jobs), a good network, some prestige, and a better understanding of lower-level languages like C. Having a computer science degree also makes it easier to get a US work visa if you’re not from the US.

Doing internships

If you can find internships, ideally at the sorts of organisations you might want to work for to build your skills (like big tech companies or startups), you’ll gain practical experience and the key skills you wouldn’t otherwise pick up from academic degrees (e.g. using version control systems and powerful text editors). Take a look at our our list of companies with software and machine learning internships.

AI-assisted coding

As you’re getting started, it’s probably worth thinking about how developments in AI are going to affect programming in the future — and getting used to AI-assisted coding.

We’d recommend trying out using GitHub CoPilot, which writes code for you based on your comments. Cursor is a popular AI-assisted code editor based on VSCode.

You can also just ask AI chat assistants for help. ChatGPT is particularly helpful (although only if you use the paid version).

We think it’s reasonably likely that many software and tech jobs in the future will be heavily based on using tools like these.

Building a specialty

Depending on how you’re going to use software and tech skills, it may be useful to build up your skills in a particular area. Here’s how to get started in a few relevant areas:

Depending on how you’re aiming to have an impact with these skills (see the next section), you may also need to develop other skills. We’ve written about some other relevant skill sets:

• How to get started building research skills
• How to get started building specialist knowledge relevant to a pressing problem
• How to get started building skills for communicating ideas

For more, see our full list of impactful skills.

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

The problem you work on is probably the biggest driver of your impact. The first step is to make an initial assessment of which problems you think are most pressing (even if you change your mind over time, you’ll need to decide where to start working).

Once you’ve done that, the next step is to identify the highest-potential ways to use software and tech skills to help solve your top problems.

There are five broad categories here:

• Use software and tech skills in research. Lots of technical research relevant to the world’s most pressing problems makes heavy use of software and tech skills — most notably, AI safety technical research. To be successful, you might also need a research skill set, which we’ve written about separately. For some paths, you’ll also need specialist knowledge in an area related to a pressing problem — e.g. hardware for becoming an expert in AI hardware.
• ML engineering for AI safety research. Most AI safety researchers work closely with engineers (and in many organisations, no clear distinction is made). This is a particularly high-impact way of using software and tech skills because we think risks from AI is one of the world’s most pressing problems.
• Build software for organisations working on pressing problems. Most organisations working on everything from global health to reducing the risk of nuclear war need software engineers to manage computer systems, apps, and websites. The key feature that draws this work together is that you’ll be building a product for others to use. Read more about software engineering careers and organisation-building skills.
• Protect sensitive information. Some organisations need help protecting information that could be hugely dangerous if it was known more widely, such as harmful genetic sequences or powerful AI technology. Breaches in areas like these could have disastrous consequences — which makes information security a great option for people who want to have a high-impact career. Read more about information security.
• Earn to give. Most jobs that use software and tech skills, whether software engineering, information security, data science, or something else entirely, command high salaries (particularly in the US) — and so they offer a great option for earning to give. Skilled software engineers can earn $300,000 a year or more at big tech companies. Probably the highest-paying routes are trading in quantitative hedge funds or founding a tech startup.

While some of these options (like protecting dangerous information) will require building up some more specialised skills, being a great programmer will let you move around most of these categories relatively easily, and the earning to give options means you’ll always have a pretty good backup plan.

Find jobs that use software and tech skills

See our curated list of job opportunities for this path.

View all opportunities

Career paths we’ve reviewed that use these skills

• AI safety technical research
• Information security in high-impact areas
• Expert in AI hardware
• Forecasting and related research and implementation
• Data collection for AI alignment
• Software engineering
• Found a tech startup
• Earning to give in quantitative trading
• Machine learning PhDs
• Early-stage startup employee
• Working at a leading AI lab
• Computer science PhD
• Data science
• Product manager in tech
• Web designer

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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Many highly specific areas of knowledge seem applicable to solving the world’s most pressing problems, especially risks posed by biotechnology and artificial intelligence.

In particular we’d highlight:

• Subfields of biology relevant to pandemic prevention. Working on many of the possible technical solutions to reduce the risk of pandemics will require expertise in parts of biology. We’d particularly highlight synthetic biology, mathematical biology, virology, immunology, pharmacology, and vaccinology. This expertise can also be helpful for pursuing a biorisk-focused policy career. (Read more about careers to prevent catastrophic pandemics.)
• AI hardware. Specialised hardware is a crucial input to the development of frontier AI systems. As a result, we expect expertise in AI hardware to become increasingly important to the governance of AI systems. (Read more about becoming an expert in AI hardware).
• Economics. Understanding economics can be valuable in a huge range of impactful roles when combined with another skill set. For example, economics research is crucial for conducting global priorities research and improving decision making in large institutions. And a knowledge of economics can also support you in building policy and political skills, particularly for policy design and governance research.
• Other areas we sometimes recommend include history, knowledge of China, and law.

Of course, whatever skill set you focus on, you’ll likely need to build some specialist knowledge — for example, if you focus on policy and political skills, you’ll need to gain specialist knowledge in the area of policy you’re working in. Similarly, if you build software and tech skills, you could consider gaining specialist knowledge in machine learning or information security. The idea of the above list is just to highlight areas we think seem particularly valuable that you might not otherwise consider learning about.

How should you get started building specialist knowledge?

Each area is very different, so it’s hard to give any specific advice that applies to all of them.

Besides the articles on specific areas linked above, we’d suggest checking out:

• Whether to go to grad school
• How to find a career that fits you best
• How to be more successful in any job, especially the sections on learning how to learn and how to master a field and make creative contributions

All our career reviews relevant to building specialist knowledge

• Biorisk research, strategy, and policy
• Expert in AI hardware
• Investigate a potentially pressing but unexplored global issue
• Data collection for AI alignment
• Engineering
• Economics PhDs
• Law school in the US

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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The post Specialist knowledge relevant to a top problem appeared first on 80,000 Hours.

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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This logic doesn’t just apply to criminal justice. For almost any global issue you’re interested in, roles in powerful institutions like governments often offer unique and high-leverage ways to address some of the most pressing challenges of our time.

Why are policy and political skills valuable?

We’ll argue that:

• Governments (and other powerful institutions) have a huge impact in the world in general.
• Powerful institutions are often important for finding solutions to the problems we think are most pressing.
• You can create change if you’re able to build the skills that will let you do so.
• You’ll need to use your influence well and avoid causing harm.

Together, this suggests that building the skills needed to get things done in large institutions could give you a lot of opportunities to have an impact.

Later, we’ll look at:

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

Governments (and other powerful institutions) have a huge impact in the world

National governments are hugely powerful.

For a start, they command the spending of huge sums of money. The US government’s federal budget is approximately $6.4 trillion/year — that’s approximately the annual revenue of the world’s 14 largest companies by revenue (although only around $1.7 trillion/year is discretionary spending). Many other Western countries spend hundreds of billions of dollars a year.

And it’s not just money. Governments produce laws governing the actions of millions — or billions — and have unique tools at their disposal, including taxation and tax breaks, regulation, antitrust actions, and, ultimately, the use of force.

The US spends nearly a trillion dollars a year on its military (although this is an outlier — in other Western countries it’s more like tens of billions).

Why does this scale matter?

Well, we’ll argue that your chances of reaching a government role in which you can have a large influence are probably high enough that in expectation you can have a significant impact, given the huge scale of government action.

And it’s not just governments. Most of the advice in this article can be applied to any powerful institution, such as an international body or organisation like the United Nations. Much of what we say even applies to jobs at large corporations.

Governments and other major institutions play a major role in addressing the world’s most pressing problems

National governments and international bodies — in particular the US, UK, and EU — are already working on some of the problems we have identified as most pressing. For example:

• Biorisk: The UK government released the UK Biological Security Strategy aimed at preventing future pandemics in June 2023. The US Centers for Disease Control and Prevention (CDC) works on public health in the US and is also one of the most important organisations working on global disease control. The US defence and intelligence community also works in this area. For instance, the Department of Defense does a lot of work on infectious diseases and assists other countries’ efforts to prevent the proliferation of biological weapons.
• AI safety and public policy: In her annual State of the Union Address, the President of the European Commission told the European Parliament that the EU should be working to mitigate the risk of extinction from AI. The White House Office issued an executive order on AI, which — among other things — requires developers of the most powerful AI systems to develop safety standards and tests and share these results with the US government. The Defense Advanced Research Projects Agency (DARPA) has a program on explainable AI, which is a component of AI safety research. The UK government has set up the AI Safety Institute. And as AI becomes more important, governments will likely become more involved.
• Nuclear security: The US has the world’s most powerful military and the second biggest stockpile of nuclear weapons. Federal agencies such as the Department of Defense, the Department of Energy, and the State Department are important for preventing nuclear catastrophe.

Governments also play a major role in pretty much every other global issue you can think of (including basically every issue we have have profiles on, such as global health, climate change, and factory farming).

Throughout this article, we focus on the US because we think it has particular influence in areas related to the problems we think are most pressing, and because it’s where we have the most readers. However, we think these skills are also valuable to build if you’re based in many other countries (and we also have advice specifically about the UK).

Beyond governments, there are also international organisations and large companies that are important for solving certain problems. For example, the Biological Weapons Convention plays a unique role in preventing biological catastrophes, while leading AI labs and large tech companies have a crucial influence over the development of AI.

To see lists of particularly relevant institutions for various problems, see our problem profiles and job board.

You can create change

You might think that, even if you work at an important institution, you won’t have much impact because you won’t really be able to affect anything. You’ll have to carry out the will of elected officials, who are bound to the electorate, institutional constraints, and special interests. And while this is definitely true in many cases, we do think there are opportunities to have at least a small effect on the actions of these large and powerful institutions.

Frances Kelsey was an academic and a pharmacologist. But, in 1960, she took a major career step when she was hired by the FDA. Just one month into her new career in government, she was given her first assignment to review a drug: thalidomide. Despite considerable pressure from the drug’s manufacturer, Kelsey insisted that it be tested more rigorously.

And so, while more than 10,000 children across the world were born with birth defects as a result of thalidomide — living with life-long deformed limbs and defective organs — only 17 such children were born in the US. Kelsey was hailed by the American public as a hero and was awarded the President’s Award for Distinguished Federal Civilian Service in 1962.¹

But why was a mid-level official — only one month into her new job — able to have such an impact?

First, there’s just a huge amount to do, and senior officials don’t have that much time.

For example, in the US, there are 535 members of Congress and around 4,000 presidential appointees in the executive branch. That might sound like a lot, but think about it this way: each of these people, on average, has oversight over about 0.02% of the US federal budget — over $1 billion. It would be literally impossible to micromanage that amount of activity.

This is only a very rough heuristic, but by dividing the $1.7 trillion discretionary federal budget by the number of people at different levels of seniority, we can estimate the average budget that different subsets of people in the government oversee.²

Note that this method is just an estimate of the average and there are some reasons to think it’s probably too high.³

Nevertheless, these figures are so high that if you can help those budgets be used just a little more efficiently, it could be worth millions of dollars of additional spending in the area of focus.

And, in other ways, this is an underestimate of the responsibility of each individual because much of what the government does is not best thought of as setting budgets — rather it comes from regulation, foreign policy, changing social norms and so on. Budgets here are just being used as a proxy for one form of impact.

Second, the views and opinions of others in government aren’t completely fixed. Otherwise — whether you think it’s protected free speech or a distortion of democracy — it’s hard to explain why private companies spend around $4 billion a year on federal lobbying. For every dollar spent by a profit-oriented company on lobbying, it’s probably getting more than a dollar back on average by affecting government policy. This suggests that people interested in social change can have an impact, especially if they’re focused on global issues with little other lobbying, or they can find neglected ways to affect policy.

And so it’s not surprising that when we’ve spoken to people working in and around governments, we’ve found that — as in the case of Frances Kelsey — people have actually had the opportunity to influence things even in junior roles (if they had the skills).

In the US, we spoke to a number of mid-level and senior federal employees, and most were able to give us an example of how they had a large positive impact through their role. Some of their examples involved starting new impactful programs worth $10s of millions, saving Americans $100s of millions, or moving billions to something potentially more impactful. We haven’t vetted these stories, but at the very least they persuaded us that mid-level and senior federal employees feel as though they can sometimes have a large positive influence on the government.

In the UK, one junior civil servant we spoke to determined how £250 million was spent in her policy area through careful discussions with senior civil servants, while ministers were only scrutinising larger chunks of money.

And it’s not just in the executive. For example, in the US Congress, huge amounts of work are done by congressional staffers. “Ninety-five percent of the nitty-gritty work of drafting bills and negotiating their final form is now done by staff,” according to former Senator Ted Kennedy.⁴

Often this work is done by very junior people. One junior staff member in a Congressional office told us that more senior individuals (like Chiefs of Staff) are often tasked with substantial managerial responsibilities that crowd out their ability to focus on nitty-gritty policy research. Because of this, they have to defer to more junior staff (such as legislative assistants) who have the capacity and time to dig into a specific policy area and make concrete proposals.

This all suggests that you can effect change in large institutions (even when you’re just getting started), and in particular:

• On issues where people care enough for changes to be made, but not enough to micromanage the changes
• Where powerful figures like elected officials have vague goals, but no specific idea of what they want
• When details have a large impact, e.g. the details of one piece of legislation can affect many other laws

All other things being equal, the more senior you are, the more influence you’ll have.

If you’re a motivated graduate from a top university, over the course of your career, the chance of reaching high levels in the government is significant.

Approximately 1 in 30 federal employees in DC are in the senior executive service. What’s more, we found that students with a strong academic background and great social skills (and an interest in politics) in the UK could have an around 1 in 3 chance of becoming an MP. Meanwhile, if you became a Congressional staffer in the US, you’d have something like a 1 in 40 chance of being elected to Congress.

Other factors will also affect your ability to create change, such as how politicised your area is (the more political, the more your moves will be countered by others).

All that said, many people we speak to in the civil service don’t feel that they have a lot of influence. That’s because many roles don’t have opportunities for a lot of impact. (We’ll discuss finding ones that do later, and it can be hard to see your impact even in those that do.)

But the potential for change is there. You can think of decision making in large institutions as a negotiation between different groups with power. Most of the time you won’t tip the balance, but occasionally you might be able to — and it could have a large impact.

But you’ll need to use your influence responsibly

Having influence is a double-edged sword.

If you use your position poorly, then you might make things worse than they would have been otherwise. This is especially easy in policy, because it’s hard to know what truly makes things better, and policy can have unintended consequences. This is especially disturbing if you end up working on critical problems, such as preventing pandemics or nuclear crises.

This doesn’t mean you should avoid these positions altogether. For a start, someone has to take these positions, and it’ll probably be better for the world if more altruistic people enter them. Hopefully, if you’re reading this article, you’re more likely than average to be one of these people.

However, it does mean that if you succeed in advancing you have a huge responsibility to use the position well — and the higher you advance, the more responsibility you have.

This means trying to do the best job you can to help the institution do more good for society, and being especially careful to avoid actions that could cause significant harm.

Unfortunately, the more you advance, the easier it is to lose touch with people who will give you frank feedback, and the more temptations you’ll face to do unethical or dishonest actions in order to preserve your influence or “for the greater good” — i.e. to get corrupted.

This means we’d especially encourage people considering this path to focus on building good character and making sure they have friends around them who can keep them honest at the early stages, so these are in place in case they gain a lot of influence.

It’s also important to make sure you have a clear ‘edge’ that will allow you to do more good than a typical employee. For instance, you might be able to give ministers more evidence-based advice, contribute specialist knowledge, or pay more attention to the effect of policies on the long-term future than typical.

That said, even talented and very well-meaning people can fail to do good in government and even do harm, so it is worth learning constantly and thinking carefully and critically about what will actually help. Read more advice on avoiding harm.

What does using a policy and political skill set involve?

Any career path that ends up in an influential institutional position could be a way of using these skills, though some options are more likely to be relevant to the problems we think are most pressing.

This typically involves the following steps:

1. Identify some institutions that could play an important role tackling some of the problems you think are most pressing. See an introduction to comparing global problems in terms of impact and lists of institutions that are important to each area in our problem profiles and job board.

2. Learn to make useful contributions to an institution (or group of institutions) by gaining experience, credibility, seniority, and authority.

3. Often, it involves developing a speciality that’s especially relevant to the problems you want to focus on. For instance, if you want to work on tackling engineered pandemics, you might specialise in counter-terrorism, technology policy, or biomedical policy. This is both to help you advance into more relevant roles, but also to improve your understanding of which policies are actually helpful. That said, many policy makers remain generalists. In that case, you need to make sure you find trusted expert advisors to help you understand which policy changes would be most helpful.

4. Move into roles that put you in a better position to help tackle these problems. Focusing on pandemics again, you might aim to work at the Center for Disease Control and Prevention and then advance to more senior positions.

5. Have an impact by using your position and expertise to improve policies and practices relevant to pressing global problems or bringing attention to neglected but important priorities.

Within this skill set, it’s possible to focus more on policy research or policy implementation. The first is about developing ideas for new policies, and involves an element of applied research skills, while the second is a bit more like an organisation building skill set and has an impact via making an important institution more efficient.

There’s also a spectrum of roles from roles that are more like being a technical specialist to those — like roles in political parties or running for elected office — that are more political and closer to engagement with the general public and current affairs.

In addition to roles actually within the relevant institutions, there are also “influencer” roles which aim to shape these institutions from the outside.

This includes jobs in think tanks, advocacy non-profits, journalism, academia, and even corporations, rather than within government.

The skills needed for influencer roles are similar to those needed for policy and political roles in many ways, but they also overlap a lot with skills in research and communicating ideas. These roles can be a better fit for someone who wants to work in a smaller organisation, is less comfortable with political culture, or wants to focus more on ideas rather than application.

In practice, people often move between influencer and government positions across their careers.

Some people think that to work in policy you have to be brilliant at networking.

That’s not quite true — as we’ve seen, depending on your role, you might focus more on understanding and researching policies, communicating ideas to a specific audience, or just really understanding your particular institution very well.

But it’s nevertheless true that networking skills are more important in building a policy and political skill set than, for example, if you wanted to work in a purely research — and you can learn more about how to network in our article on how to be successful in any job. In particular, multiple people — both in the US and in the UK — have told us that it’s important to be friendly and nice to others.

Finally, we’d like to emphasise the potential value of doing policy-style work in industry, especially if you’re interested in AI policy. While government policy is likely to play a key role in coordinating various actors interested in reducing the risks from advanced AI, internal policy, compliance work, lobbying, and corporate governance within the largest AI labs are also powerful tools. Collaboration between labs and government also requires work that may use similar skills, like stakeholder management, policy design, and trust-building.

Example people

How to evaluate your fit

This skill set is fairly broad, which also means it can potentially be a good fit for a wide variety of people. Don’t rule it out based on a hazy sense that government work isn’t for you!

For example, entering policy through building specific expertise can be a good fit for people interested in research careers but who would like to do something more practical. Many roles are totally unlike the stereotype of a politician endlessly shaking hands or what ‘government bureaucrat’ brings to mind.

How to predict your fit in advance

Here are some traits that seem likely to point towards being a great fit:

• You have the potential to succeed at relationship-building and fitting in. In many of these roles, you need to be able to develop good relationships with a wide range of people in a short amount of time, come across as competent and warm in your interactions, genuinely want to add value and help others achieve their goals, consistently follow up and stay in touch with people, and build a reputation and be remembered.

  It helps to have empathy and social intelligence so that you can model other people’s viewpoints and needs accurately. It also helps if you can remember small details about people! You don’t necessarily need all these skills when you start out, but you should be interested in improving them.

  These skills are most important in more public-facing party-political positions and are also needed to work in large institutions. However, there are also roles focused more on applying technical expertise to policy, which don’t require these skills as much (though they’re still probably more important than in e.g. academia).

• You can think of a relevant institution at which you can imagine yourself being relatively happy, productive, and motivated for a long time — while playing by the institution’s rules. Try speaking with later-career people at the institution to get as detailed a sense as possible of how long it will take to reach the kind of position you’re hoping for, what your day-to-day life will be like in the meantime, and what you will need to do to succeed.

• Having the right citizenship. There are lots of influential and important policy roles in every country, so you should consider them wherever you live. But some roles in the US seem especially impactful — as do certain roles at large institutions like the EU. In particular, any of the roles within the US most relevant to the problems we think are most pressing — particularly in the executive branch and Congress — are only open to, or at least will heavily favour, American citizens. All key national security roles that might be especially important will be restricted to those with US citizenship, which is required to obtain a security clearance.

  If you’re excited about US policy in particular and are curious about immigration pathways and types of policy work available to non-citizens, see this blog post. Consider also participating in the annual diversity visa lottery if you’re from an eligible country, as this is low-effort and allows you to win a US green card if you’re lucky (getting a green card is the only way to become a citizen).

• Being comfortable with political culture. The culture in politics, especially US federal politics, can be difficult to navigate. Some people we know have entered promising policy positions, but later felt like the culture was a terrible fit for them. Experts we’ve spoken to say that, in Washington, DC, there’s a big cultural focus on networking and internal bureaucratic politics to navigate. We’ve also been told that while merit matters to a degree in US government work, it is not the primary determinant of who is most successful. We’d expect this to be similar in other countries. People who think they wouldn’t feel able or comfortable to be in this kind of environment for the long term should consider whether other skills or institutions would be a better fit.

  That said, this does vary substantially by area and by role. Some roles, like working in a parliament or somewhere like the White House, are much more exposed to politics than others. Also, if you work on a hot button, highly partisan issue, you’re much more likely to be exposed to intense political dynamics than if you work on more niche, technocratic, or cross-party issues.

It’s useful if you can find ways to do cheap tests first, like speaking to someone in the area (which could take a couple of hours), or doing an internship (which could take a couple of months). But often, you’ll need to take a job in the area to tell whether this is a good fit for you — and be willing to switch after a year or more if it’s not. For more, read our article on finding a job that fits you.

How to tell if you’re on track

First, ask yourself “How quickly and impressively is my career advancing, by the standards of the institution I’m currently focused on?” People with more experience (and advancement) at the institution will often be able to help you get a clear idea of how this is going. (It’s also just generally important to have good enough relationships with some experienced people to get honest input from them — this is an additional indicator of whether you’re “on track” in most situations.)

One caveat to this is that the rate of advancement could really vary depending on the exact role you have in that institution. For example, in Congress, speed of promotion often has to do less with your abilities and more with timing and the turnover of the office. As a result, the better the office, the fewer people leave and the slower the pace of promotion; the opposite is often true for bad offices. So you need to make sure you’re judging yourself by relevant standards — again, people with more experience at the institution should be able to help here.

Another relevant question to ask is “How sustainable does this feel?” This question is relevant for all skills, but especially here — for government and policy roles, one of the main things that affects how well you advance is simply how long you can stick with it and how consistently you meet the institution’s explicit and implicit expectations. So, if you find you can enjoy government and political work, that’s a big sign you’re on track. Just being able to thrive in government work can be an extremely valuable comparative advantage.

One other way to advance your career in government, especially as it relates to a specific area of policy, is what some call “getting visibility” — that is, using your position to learn about the landscape and connect with the actors and institutions that affect the policy area you care about. You’ll want to be invited to meetings with other officials and agencies, be asked for input on decisions, and engage socially with others who work in the policy area. If you can establish yourself as a well-regarded expert on an important but neglected aspect of the issue, you’ll have a better shot at being included in key discussions and events.

How to get started building policy and political skills

There are two main ways you might get started:

1. Institution-first. You’d start your career by trying to find a set of institutions that are a good fit for you and that seems at least relevant to the problems you think are most pressing (e.g. the executive branch of the US government or tech companies). You’d then try to move up the ranks of those institutions.
2. Expertise-first. In this route, you initially focus on building a relevant speciality or area of expertise (e.g. in academia or think tanks) and then use that to switch into institutional positions later. In addition, people with impressive credentials and accomplishments outside of government (e.g. in business, consulting, or law) can sometimes enter important departments and agencies at particularly senior and influential levels.

If you take the institution-first approach, you can try for essentially any job at this institution and focus on performing well by the institution’s standards. All else being equal, it’d be better to work on jobs relevant to a pressing problem, but just trying to advance should probably be your main goal early in your career.

The best way to learn how to perform and advance is to speak to people a couple of steps ahead of you in the path. Also look at cases of people who advanced unusually quickly and try to unpack what they did.

Sometimes the best way to advance will involve going somewhere other than the institution itself temporarily. For instance, going to law school, public policy school, or working at think tanks can give you credentials and connections that open up positions in government later.

If you’re focused on developing expertise in a particular area of policy, then it’s common to go to graduate school in a subject relevant to that area (e.g. economics, machine learning, biology).

As always, whether these paths are a good way of building your skills depends on the specific job or programme and people you’ll be working with:

• Will you get good mentorship?
• What’s their reputation in the field?
• Do they have good character?
• Does their policy agenda seem positive?
• Will the culture be a good fit for you?

With all that in mind, here are a few next steps that are especially good for building these skills:

Fellowships and leadership schemes

Fellowships can be an effective way to gain experience inside government or think tanks and can help you advance quickly into more senior government positions.

Some fellowships are aimed at people who already have some professional experience outside of policy but want to pivot into government roles, while others are aimed at recent graduates.

In the US, consider the Presidential Management Fellows for recent graduates of advanced degrees, the Horizon Fellowship, the AAAS fellowship for people with science PhDs or engineering master’s, or the TechCongress fellowship for mid-career tech professionals. If you have completed a STEM graduate degree, also consider the Mirzayan Science and Technology Policy Graduate Fellowship Program.

In the UK, try the Civil Service Fast Stream. And if you’re interested in EU AI policy, you can apply for the EU Tech Policy Fellowship. We also curate a list of UK / EU policy master’s options through our job board.

Graduate school

In general, we’d most recommend grad school for economics or machine learning. (Read more about why these are the best subjects to study at grad school.)

Some other useful subjects to highlight, given our list of pressing problems, include:

• Other applied quantitative subjects, like computer science, physics, and statistics
• Security studies, international relations, public policy, or law school, particularly for entering government and policy careers
• Subfields of biology relevant to pandemic prevention (like synthetic biology, mathematical biology, virology, immunology, pharmacology, or vaccinology)

Many master’s programmes offer specific coursework on public policy, science and society, security studies, international relations, and other topics. Having a graduate degree or law degree will give you a leg up for many positions.

In the US, a policy master’s, a law degree, or a PhD is particularly useful if you want to climb the federal bureaucracy. Choosing a graduate school near or close to DC is often a good idea, especially if you’re hoping to work part- or even full-time in public policy alongside graduate school.

While you’re studying (either at grad school or as an undergraduate), internships — for example in DC — are a promising route to evaluate your fit for policy work and to establish early career capital. Many academic institutions in the US offer a “Semester in DC” programme, which can let you explore placements of choice in Congress, federal agencies, or think tanks. The Virtual Student Federal Service (VSFS) also offers part-time, remote government internships.

Just bear in mind that graduate schools present the risk that you could spend a long time there without learning much about the actual career you’re pursuing itself or the problem you want to work on. It may sometimes make sense to try out a junior role or internship, see how it feels, and make sure you’re expecting a graduate degree to be worth it before going for it.

Read more about going to grad school.

Working for a politician or on a political campaign

Working for a politician as a researcher or staffer (e.g. as a parliamentary researcher in the UK, legislative staff for a Member of Congress, or as campaign staff for an electoral candidate) can be one useful step into political and policy positions. It’s also demanding, prestigious (especially in the US, less so in the UK), and gives you lots of connections. From this step, it’s also common to move into the executive branch or to later run for office. Read more in our career review on becoming a congressional staffer.

You don’t strictly need a master’s or other advanced degree to work in the US Congress. But many staffers still eventually pursue a graduate degree, in part because federal agencies and think tanks commonly care more about formal credentials, and many congressional staffers at some point switch to these institutions.

You can also work for a politician on a particular campaign — some of the top people who work on winning campaigns eventually get high-impact positions in the federal government. This is a high-risk strategy: it often only pays off if your candidate wins, and even then, not everybody on the campaign staff will get influential jobs or jobs in the areas they care about, especially if you’re a junior campaign staffer. (Running for office yourself involves a similar high-risk, high-reward dynamic.)

Roles in the executive branch

Look for entry-level roles in your national government, again focusing on positions at the executive-branch equivalent or those most relevant to policy-making.

In the US, you could take an entry-level role as a federal employee, ideally working on something relevant to a problem you want to help solve or will give you the flexibility to potentially work on multiple pressing problems. The most influential positions are usually in the executive branch.

That said, most people have told us that, in the US, it’s even better to get a graduate degree first because it will allow you to reach higher levels of career advancement and seniority more quickly. A graduate degree could also qualify you for fellowships.

In the UK, see our profile on civil service careers.

Think tank roles

Think tanks are organisations that aren’t part of government but still focus on informing and ultimately influencing policymaking.

Research roles at policy think tanks involve conducting in-depth research on specific policy areas and formulating relevant recommendations. These researchers also often collaborate with experts, host events, engage with policymakers, and liaise with the media to influence and inform public policy discourse. This often involves fundraising, grant writing, and staying updated on political trends — and it can teach you many of the skills that are useful in government.

These roles are relatively competitive and you may have your reputation tied to particular institutions you work for — which can have upsides and downsides.

Think tanks also employ non-research staff in communications, HR, finance, and other areas; these roles are less likely to meaningfully impact policy outcomes, though they could still be a reasonable way to build policy career capital.

Also, think tank staff are often fairly cleanly split between entry-level employees and senior employees with advanced degrees (often PhDs), with relatively few mid-level roles. For this reason, it’s fairly uncommon for people to stay and rise through the ranks at a think tank without leaving for graduate school or another role.

These roles let you learn about important policy issues and can open up many options in policy. One option is to continue working in think tanks or other influencer positions, perhaps specialising in an area of policy. Otherwise, it’s common to switch from think tanks to the executive branch, a campaign, or other policy positions.

(Read more in our career review on working in think tanks.)

Other options

It’s also common to enter policy and government jobs from consulting and law, as well as other professional services, public relations, and business in general.

More broadly, having organisation-building skills (e.g. public relations, organisational communications, finance, and accounting knowledge) or research skills can help you find policy and political roles.

Find jobs that use policy and political skills

If you think you might be a good fit for this skill set and you’re ready to start looking at job opportunities that are currently accepting applications, see our curated list of opportunities.

View all opportunities

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

Let’s suppose you now have a position with some ability to get things done in an important institution, and, from building expertise or an advisory network in particular pressing problems, you also have some ideas about the most important things you’d like to see happen. Then what should you do?

Depending on the issue and your position, you might then seek to have an impact via:

1. Improving the implementation of policy relevant to a pressing problem. For example, you could work at an agency regulating synthetic biology.

2. Gathering support for policy ideas. For example, you could highlight the top areas of consensus in the field about promising ways the government could reduce global poverty to a politician you work for.

3. Coming up with ideas for new policies. For example, you might craft new proposals for implementing compute governance policies.

Improving the implementation of policies

When people think about political careers, they usually think of people in suits having long debates about what to do.

But fundamentally, a policy is only an idea. For an idea to have an impact, someone actually has to carry it out.

The difference between the same policy carried out badly vs. competently can be enormous. For instance, during COVID-19, some governments reacted much faster than others, saving the lives of thousands of citizens.

What’s more, many policies are by necessity, only defined vaguely. For instance, a set of drug safety standards might need to show there is “reasonable evidence” a drug is safe, but — as shown by Frances Kelsey — how that is interpreted is left up to the relevant agency and may even change over time.

Many details are often left undecided when the policy is created, and again, these get filled out by government employees.

This option especially requires skills like people and project management, planning, coordination in and out of government, communication, resource allocation, training, and more.

So, if you can become great at one or more of these things (and really know your way around the institution you work in), it’s worth trying to identify large projects that might help solve the problems you think are most pressing — and then helping them run better.

These roles are most commonly found in the executive branch such as the Defense Department, the State Department, intelligence agencies, or the White House. (See also our profile on the UK civil service.)

Bringing ideas for new policies to the attention of important decision makers

One way to have an impact is to help get issues “on the agenda” by getting the attention and buy-in of important people.

For example, when politicians take office, they often enter on a platform of promises made to their constituents and their supporters about which policy agendas they want to pursue. They can be, to varying degrees, problem-specific — for example, having a broad remit of “improving health care.” Or, it could be more solution-specific — for example, aiming to create a single-payer health system or remove red tape facing critical industries. These agendas are formed through public discussion, media narratives, internal party politics, deliberative debate, interest group advocacy, and other forms of input. Using any of these ways to get something on the agenda is a great way to help make sure it happens.

You can contribute to this process in political advisory positions (e.g. being a staffer for a congressperson) or through influencer positions, such as think tanks.

As a rule of thumb, if you’re working within an institution (such as a large corporation or a government department), you want to be as senior as possible while still being responsible for a specific set of issues. In such a position, you’ll be in contact with all the key stakeholders, from the most senior people to those more on your level.

But it’s important to remember that, for many important issues, policymakers or officials at various levels of government can also prioritise solving certain problems or enacting specific proposals that aren’t the subject of national debate. In fact, sometimes making issues too salient, framing them in divisive ways, or allowing partisanship and political polarisation to shape the discussion, can make it harder to successfully get things done.

Coming up with ideas for new policies

In many areas relevant to particularly pressing problems, there’s a lack of concrete policies that are ready to implement.

Policy creation is a long process, often starting from broad intellectual ideas, which are iteratively developed into more practical proposals by think tanks, civil servants, political parties, advocates, and others, and then adjusted in response to their reception by peers, the media and the electorate, as well as political reality at the time.

Once concrete policy options are on the table, they must be put through the relevant decision-making process and negotiations. In countries with strong judicial review like the US, special attention often has to be paid to make sure laws and regulations will hold up under the scrutiny of the courts.

All this means there are many ways to contribute to policy creation in roles ranging from academia to government employees.

Many policy details are only hashed out at the later stages by civil servants and political advisors. This also means there isn’t a bright line between policy creation and policy implementation — more a spectrum that blurs from one into the other.

In the corporate context, internal policy creation can serve similar functions. Though they may be less enforceable unless backed up with contracts, the norms policies create can shape behaviour considerably.

While policy research is the bread and butter of think tank work, many staffers in Congress, agencies, and the White House also develop policy ideas or translate existing ideas into concrete policy proposals. For many areas of technical policy, especially AI policy, some of the best policy research is being done at industry labs, like OpenAI and DeepMind. (Read more about whether you should take a job at a top AI lab.)

For more details on the complex work of policy creation, we recommend Thomas Kalil’s article Policy Entrepreneurship in the White House: Getting Things Done in Large Organisations.

Career paths we’ve reviewed that use these skills

• AI governance and coordination
• Biorisk research, strategy, and policy
• Policy careers focused on other pressing global issues
• Non-technical roles in leading AI labs
• Research and advocacy promoting impactful climate solutions
• Congressional staffer
• Economics PhDs
• Law school in the US
• Working at a leading AI lab
• Party politics in the UK
• UK civil service jobs
• US policy master’s degrees

Learn more about government and policy

• The entire emerging tech policy careers site
• Working in US AI policy
• Podcast: Ezra Klein on existential risk from AI and what DC could do about it
• Podcast: Tom Kalil on how to have a big impact in government & huge organisations, based on 16 years’ experience in the White House
• Podcast: Tantum Collins on what he’s learned as an AI policy insider
• Collection of work on ‘Should you should focus on the EU if you’re interested in AI governance for longtermist/x-risk reasons?’ on the Effective Altruism Forum

See all our materials on policy and political 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 Policy and political skills appeared first on 80,000 Hours.

• Read the new and updated career guide here, by our founder Benjamin Todd and the 80,000 Hours team.

Our 2023 career guide isn’t just a fancy new design — here’s a rundown of how the content has been updated:

1. Career capital: get good at something useful

In our previous career guide, we argued that your primary focus should be on building very broadly applicable skills, credentials, and connections — what we called transferable career capital.

We also highlighted jobs like consulting as a way to get this.

However, since launching the 2017 version of the career guide, we came to think a focus on transferable career capital might lead you to neglect experience that can be very useful to enter the most impactful jobs — for example, experience working in an AI lab or studying synthetic biology.

OK, so how should you figure out the best career capital option for you?

Our new advice: get good at something useful.

In more depth — choose some valuable skills to learn, and that are a good fit for you, and then find opportunities that let you practise those skills. And then have concrete back-up plans and plan Bs in mind, rather than relying on general ‘transferability.’

This focus on skills is important because you’re much more likely to have an impact if you’re good at what you do — and research suggests it can take years of experience to reach your peak abilities. It also becomes much easier to build up other components of career capital — like gaining credentials or making connections — once you have something useful to offer.

We’ve supplemented this with an updated list of impactful role types to aim at long-term and common types of next steps that help to learn skills useful for those.

These steps still often involve learning skills that can be applied to many different global problems or sectors (since all else equal, more transferability is better), but we don’t emphasise transferability as much. We’re also less keen on consulting as a route into working on the most pressing problems (though it’s still best for some).

More:

• The chapter of our new guide on career capital
• Why you should consider other things before going into consulting

2. How to plan your career

We have greatly expanded our content on how to plan your career.

Our chapter on career planning leads you through planning for both a longer-term vision and immediate next steps:

• Your longer-term vision is useful for helping shape your plans, although it shouldn’t be more than a vague idea about where you’d like to end up (read more).
• You can then work backwards from that vision to help come up with next steps — but you should also work forward from your current situation, looking at any opportunities immediately in front of you (read more).

And to help you develop your career plan, we also have a new career planning template, designed to be used alongside our career guide.

More:

• The chapter of our new guide on career planning

3. Other changes and improvements

• New types of impactful careers. We added sections on why government and policy and organisation-building careers could have a high impact.
• A new chapter on which global problems are most pressing. The previous version of the printed book (although not the website) didn’t contain anything about which problems we think are most pressing and why. The new chapter tells the story of how our views have evolved, and why we focus on reducing existential risks today.
• Avoiding doing harm with your career. In the past years, we’ve become more concerned about the risk of people potentially causing harm with their careers, despite attempts to do good. Our new career guide more carefully and explicitly warns against this, and provides advice on how to avoid causing harm. Relatedly, we suggest considering your character as part of your career capital, and so considering how any job you take will shape and form your virtues.
• We greatly expanded the chapter on assessing personal fit and exploring your options.
• We’ve fully updated the more empirical sections of the guide using more up-to-date papers and data.

By working together, in our lifetimes, we can prevent the next pandemic and mitigate the risks of AI, we can end extreme global poverty and factory farming — and we can do this while having interesting, fulfilling lives too.

Our hope is that this new guide will help you do exactly that.

Learn more:

• Read the entire new career guide.
• Get the guide as a printed book (you can buy it on Amazon or email book.giveaway@80000hours.org from an address subscribed to our newsletter, and we’ll send you a free copy), on Audible, or for free as a podcast series and ebook (available as a .pdf or .epub).

The post What you should know about our updated career guide appeared first on 80,000 Hours.

Today, we’re re-launching that guide. Among many other changes, in the new version:

• We’ve substantially changed our recommendations on career capital.¹
• We have significantly improved and extended our article on career planning.
• We improved our advice on personal fit and career exploration.
• We added sections on why community-building, government and policy, and organisation-building careers could be high impact.
• We focus more on avoiding harm (in line with our updates following the collapse of FTX), and explicitly discuss Sam Bankman-Fried when talking about earning to give.
• We are more upfront about 80,000 Hours’ focus on existential risk in particular (while also discussing a wide variety of cause areas, including global health, animal welfare, existential risk and meta-causes).²
• We’ve updated the more empirical sections of the guide using more up-to-date papers and data.

You can read the guide here or start with a 2-minute summary.

It’s also available as a printed book (you can get a free copy by signing up for our newsletter or buy it on Amazon), audiobook, podcast series or ebook (available as a .pdf or .epub).

We’d appreciate you sharing the new guide with a friend! You can send them a free copy using this link. Many of the people who’ve found our advice most useful in the past have found us via a friend, so we think the time you take to share it could be really worthwhile.

What’s in the guide?

The career guide aims to cover the most important basic concepts in career planning. (If instead you’d like to see something more in-depth, see our advanced series and podcast.)

The first article is about what to look for in a fulfilling job:

• Part 1: What makes for a dream job?

The next five are about which options are most impactful for the world:

• Part 2: Can one person make a difference?
• Part 3: How to have a real positive impact in any job
• Part 4: How to choose which problems to focus on
• Part 5: What are the world’s biggest and most urgent problems?
• Part 6: What types of jobs help the most?

The next four cover how to find the best option for you and invest in your skills:

• Part 7: Which jobs put you in a better position?
• Part 8: How to find the right career for you
• Part 9: How to be more successful in any job
• Part 10: How to write a career plan

The last two cover how to take action and launch your dream career:

• Part 11: How to get a job
• Part 12: How community can help

Why did we make this change?

In 2019, we deprioritised 80,000 Hours’ career guide in favour of our key ideas series.

Our key ideas series had a more serious tone and was more focused on impact. It represented our best and most up-to-date advice. We expected that this switch would reduce engagement time on our site, but that the key ideas series would better appeal to people more likely to change their careers to do good.

However, the drop in engagement time which we could attribute to this change was larger than we’d expected. In addition, data from our user survey suggested that people who changed their careers were more, not less, likely to have found and used the older, more informal career guide (which we kept up on our site).

As a result, we decided to bring the advice in our career guide in line with our latest views, while attempting to retain its structure, tone, and engagingness.

We’re retaining the content in our key ideas series: it’s been re-released as our advanced series.

Has it been successful so far?

Yes!

We’ve had positive feedback on the quality of the content in the guide, and we’ve also seen many more people reading this guide than our key ideas series. Since soft launching the guide in May, we’ve seen about a 30% increase in total weekly engagement time on our site.

How can you help?

Please take a look at the guide and, if possible, share it with a friend! You can send them a free copy using this link.

You can also give us some feedback on the guide using this form.

Here are the links to the guide again:

• Online version
• Printed book (buy it or sign up for our newsletter)
• Ebook (.pdf or .epub)
• Audiobook
• Podcast series

Thank you so much!

The post Announcing the new 80,000 Hours career guide appeared first on 80,000 Hours.

In a nutshell: Working at a leading AI lab is an important career option to consider, but the impact of any given role is complex to assess. It comes with great potential for career growth, and many roles could be (or lead to) highly impactful ways of reducing the chances of an AI-related catastrophe — one of the world’s most pressing problems. However, there’s a risk of doing substantial harm in some cases. There are also roles you should probably avoid.

Why might it be high-impact to work for a leading AI lab?

We think AI is likely to have transformative effects over the coming decades. We also think that reducing the chances of an AI-related catastrophe is one of the world’s most pressing problems.

So it’s natural to wonder — if you’re thinking about your career — whether it would be worth working in the labs that are doing the most to build, and shape, these future AI systems.

Working at a top AI lab, like Google DeepMind, OpenAI, or Anthropic, might be an excellent way to build career capital to work on reducing AI risk in the future. Their work is extremely relevant to solving this problem, which suggests you’ll likely gain directly useful skills, connections, and credentials (more on this later).

In fact, we suggest working at AI labs in many of our career reviews; it can be a great step in technical AI safety and AI governance and coordination careers. We’ve also looked at working in AI labs in our career reviews on information security, software engineering, data collection for AI alignment, and non-technical roles in AI labs.

What’s more, the importance of these organisations to the development of AI suggests that they could be huge forces for either good or bad (more below). If the former, they might be high-impact places to work. And if the latter, there’s still a chance that by working in a leading lab you may be able to reduce the risks.

All that said, we think it’s crucial to take an enormous amount of care before working at an organisation that might be a huge force for harm. Overall, it’s complicated to assess whether it’s good to work at a leading AI lab — and it’ll vary from person to person, and role to role. But we think this is an important option to consider for many people who want to use their careers to reduce the chances of an existential catastrophe (or other harmful outcomes) resulting from the development of AI.

What relevant considerations are there?

Labs could be a huge force for good — or harm

We think that a leading — but careful — AI project could be a huge force for good, and crucial to preventing an AI-related catastrophe. Such a project could, for example:

• Engage in defensive deployment: using early, safe, but powerful AI systems to make the situation safer — for example, by using AI systems to contribute to AI safety research, produce evidence and demonstrations of risks, contribute to information security, and help with monitoring the risks. (Note that whether this will be possible is debated.)
• Perform valuable empirical research into making sure that AI systems are safe, using state-of-the-art systems (possibly ones far more advanced than would be available outside a leading AI project)
• Put huge effort into designing tests for danger, and credibly warning others if it does see designs of danger in its own systems
• Set examples for other projects on governance, security, and adherence to standards.
• Coordinate effectively with other AI companies and projects — for example, by sharing important safety findings and techniques, and possibly, if needed acquiring other projects or otherwise gaining visibility, influence, and control with which to prevent the deployment of any dangerous systems
• Credibly and effectively lobby the government for helpful measures to reduce the risk

(Read more about what AI companies can do today to reduce risks).¹

But a leading and uncareful — or just unlucky — AI project could be a huge danger to the world. It could, for example, generate hype and acceleration (which we’d guess is harmful), make it more likely (through hype, open-sourcing or other actions) that incautious players enter the field, normalise disregard for governance, standards and security, and ultimately it could even produce the very systems that cause a catastrophe.

So, in order to successfully be a force for good, a leading AI lab would need to balance continuing their development of powerful AI (and possibly even retaining a leadership position), whilst also appropriately prioritising doing things that reduce the risk overall.

This tightrope seems difficult to walk, with constant tradeoffs to make between success and caution. And it seems hard to assess from the outside which labs are doing this well. The top labs — as of 2023, OpenAI, Google DeepMind, and Anthropic — seem reasonably inclined towards safety, and it’s plausible that any or all of these could be successfully walking the tightrope, but we’re not really sure.

We don’t feel confident enough to give concrete recommendations on which of these labs people should or should not work for. We can only really recommend that you put work into forming your own views about whether a company is a force for good. But the fact that labs could be such a huge force for good is part of why we think it’s likely there are many roles at leading AI labs that are among the world’s most impactful positions.

It’s often excellent career capital

Top AI labs are high-performing, rapidly growing organisations. In general, one of the best ways to gain career capital is to go and work with any high-performing team — you can just learn a huge amount about getting stuff done. They also have excellent reputations more widely (AI is one of the world’s most sought-after fields right now, and the top labs are top for a reason). So you get the credential of saying you’ve worked in a leading lab, and you’ll also gain lots of dynamic, impressive connections. So even if we didn’t think the development of AI was a particularly pressing problem, they’d already seem good for career capital.

But you will also learn a huge amount about and make connections within AI in particular, and, in some roles, gain technical skills which could be much harder to learn elsewhere.

We think that, if you’re early in your career, this is probably the biggest effect of working for a leading AI lab, and the career capital is (generally) a more important consideration than the direct impact of the work. You’re probably not going to be having much impact at all, whether for good or for bad, when you’re just getting started.

However, your character is also shaped and built by the jobs you take, and matters a lot for your long-run impact, so is one of the components of career capital. Some experts we’ve spoken to warn against working at leading AI labs because you should always assume that you are psychologically affected by the environment you work in. That is, there’s a risk you change your mind without ever encountering an argument that you’d currently endorse (for example, you could end up thinking that it’s much less important to ensure that AI systems are safe, purely because that’s the view of people around you). Our impression is that leading labs are increasingly concerned about the risks, which makes this consideration less important — but we still think it should be taken into account in any decision you make. There are ways of mitigating this risk, which we’ll discuss later.

Of course, it’s important to compare working at an AI lab with other ways you might gain career capital. For example, to get into technical AI safety research, you may want to go do a PhD instead. Generally, the best option for career capital will depend on a number of factors, including the path you’re aiming for longer term and your personal fit for the options in front of you.

You might advance AI capabilities, which could be (really) harmful

We’d guess that, all else equal, we’d prefer that progress on AI capabilities was slower.

This is because it seems plausible that we could develop transformative AI fairly soon (potentially in the next few decades). This suggests that we could also build potentially dangerous AI systems fairly soon — and the sooner this occurs the less time society has to successfully mitigate the risks. As a broad rule of thumb, less time to mitigate risks seems likely to mean that the risks are higher overall.

But that’s not necessarily the case. There are reasons to think that advancing at least some kinds of AI capabilities could be beneficial. Here are a few:

• This distinction between ‘capabilities’ research and ‘safety’ research is extremely fuzzy, and we have a somewhat poor track record of predicting which areas of research will be beneficial for safety work in the future. This suggests that work that advances some (and perhaps many) kinds of capabilities faster may be useful for reducing risks.
• Moving faster could reduce the risk that AI projects that are less cautious than the existing ones can enter the field.
• Lots of work that makes models more useful — and so could be classified as capabilities (for example, work to align existing large language models) — probably does so without increasing the risk of danger . This kind of work might allow us to use these models to reduce the risk overall, for example, through the kinds of defensive deployment discussed earlier.
• It’s possible that the later we develop transformative AI, the faster (and therefore more dangerously) everything will play out, because other currently-constraining factors (like the amount of compute available in the world) could continue to grow independently of technical progress. Slowing down advances now could increase the rate of development in the future, when we’re much closer to being able to build transformative AI systems. This would give the world less time to conduct safety research with models that are very similar to ones we should be concerned about but which aren’t themselves dangerous. (When this is caused by a growth in the amount of compute, it’s often referred to as a hardware overhang.)

Overall, we think not all capabilities research is made equal — and that many roles advancing AI capabilities (especially more junior ones) will not be harmful, and could be beneficial. That said, our best guess is that the broad rule of thumb that there will be less time to mitigate the risks is more important than these other considerations — and as a result, broadly advancing AI capabilities should be regarded overall as probably harmful.

This raises an important question. In our article on whether it’s ever OK to take a harmful job to do more good, we ask whether it might be morally impermissible to do a job that causes serious harm, even if you think it’s a good idea on net.

It’s really unclear to us how jobs that advance AI capabilities fall into the framework proposed in that article.

This is made even more complicated by our view that a leading AI project could be crucial to preventing an AI-related catastrophe — and failing to prevent a catastrophe seems, in many value systems, similarly bad to causing one.

Ultimately, answering the question of moral permissibility is going to depend on ethical considerations about which we’re just hugely uncertain. Our guess is that it’s good for us to sometimes recommend that people work in roles that could harmfully advance AI capabilities — but we could easily change our minds on this.

For another article, we asked the 22 people we thought would be most informed about working in roles that advance AI capabilities — and who we knew had a range of views — to write a summary of their takes on the question: if you want to help prevent an AI-related catastrophe, should you be open to roles that also advance AI capabilities, or steer clear of them? There’s a range of views among the 11 responses we received, which we’ve published here.

You may be able to help labs reduce risks

As far as we can tell, there are many roles at leading AI labs where the primary effects of the roles could be to reduce risks.

Most obviously, these include research and engineering roles focused on AI safety. Labs also often don’t have enough staff in relevant teams to develop and implement good internal policies (like on evaluating and red-teaming their models and wider activity), or to figure out what they should be lobbying governments for (we’d guess that many of the top labs would lobby for things that reduce existential risks). We’re also particularly excited about people working in information security at labs to reduce risks of theft and misuse.

Beyond the direct impact of your role, you may be able to help guide internal culture in a more risk-sensitive direction. You probably won’t be able to influence many specific decisions, unless you’re very senior (or have the potential to become very senior), but if you’re a good employee you can just generally become part of the ‘conscience’ of an organisation. Just like anyone working at a powerful institution, you can also — if you see something really harmful occurring — consider organising internal complaints, whistleblowing, or even resigning. Finally, you could help foster good, cooperative working relationships with other labs as well as the public.

To do this well, you’d need the sorts of social skills that let you climb the organisational ladder and bring people round to your point of view. We’d also guess that you should spend almost all of your work time focused on doing your job well; criticism is usually far more powerful coming from a high performer.

There’s a risk that doing this badly could accidentally cause harm, for example, by making people think that arguments for caution are unconvincing.

How can you mitigate the downsides of this option?

There are a few things you can do to mitigate the downsides of taking a role in a leading AI lab:

• Don’t work in certain positions unless you feel awesome about the lab being a force for good. This includes some technical work, like work that improves the efficiency of training very large models, whether via architectural improvements, optimiser improvements, improved reduced-precision training, or improved hardware. We’d also guess that roles in marketing, commercialisation, and fundraising tend to contribute to hype and acceleration, and so are somewhat likely to be harmful.
• Think carefully, and take action if you need to. Take the time to think carefully about the work you’re doing, and how it’ll be disclosed outside the lab. For example, will publishing your research lead to harmful hype and acceleration? Who should have access to any models that you build? Be an employee who pays attention to the actions of the company you’re working for, and speaks up when you’re unhappy or uncomfortable.
• Consult others. Don’t be a unilateralist. It’s worth discussing any role in advance with others. We can give you 1-1 advice, for free. If you know anyone working in the area who’s concerned about the risks, discuss your options with them. You may be able to meet people through our community, and our advisors can also help you make connections with people who can give you more nuanced and personalised advice.
• Continue to engage with the broader safety community. To reduce the chance that your opinions or values will drift just because of the people you’re socialising with, try to find a way to spend time with people who more closely share your values. For example, if you’re a researcher or engineer, you may be able to spend some of your working time with a safety-focused research group.
• Be ready to switch. Avoid being in a financial or psychological situation where it’s just going to be really hard for you to switch jobs into something more exclusively focused on doing good. Instead, constantly ask yourself whether you’d be able to make that switch, and whether you’re making decisions that could make it harder to do so in the future.

How to predict your fit in advance

In general, we think you’ll be a better fit for working at an AI lab if you have an excellent understanding of risks from AI. If the positive impact of your role comes from being able to persuade others to make better decisions, you’ll also need very good social skills. You’ll probably have a better time if you’re pragmatic and comfortable with making decisions that can, at times, be difficult, time-pressured, and morally ambiguous.

While a career in a leading AI lab can be rewarding and high impact for some, it’s not suitable for everyone. People who should probably not work at an AI lab include:

• People who can’t follow tight security practices: AI labs often deal with sensitive information that needs to be handled responsibly.
• People who aren’t able to keep their options open — that is, they aren’t (for a number of possible reasons) financially or psychologically prepared to leave if it starts to seem like the right idea. (In general, whatever your career path, we think it’s worth trying to build at least 6-12 months of financial runway.)
• People who are more sensitive than average to incentives and social pressure: you’re just more likely to do things you wouldn’t currently endorse.

More specifically than that, predicting your fit will depend on the exact career path you’re following, and for that you can check out our other related career reviews.

How to enter

Some labs have internships (e.g. at Google DeepMind) or residency programmes (e.g. at OpenAI) — but the path to entering a leading AI lab can depend substantially on the specific role you’re interested in. So we’d suggest you look at our other career reviews for more detail, as well as plenty of practical advice.

Recommended organisations

We’re really not sure. It seems like OpenAI, Google DeepMind, and Anthropic are currently taking existential risk more seriously than other labs. Some people we spoke to have strong opinions about which of these is best, but they disagree with each other substantially.

Big tech companies like Apple, Microsoft, Meta, Amazon, and NVIDIA — which have the resources to potentially become rising stars in AI — are also worth considering, as there’s a need for more people in these companies who care about AI safety and ethics. Relatedly, plenty of startups can be good places to gain career capital, especially if they’re not advancing dangerous capabilities. However, the absence of teams focused on existential safety means that we’d guess these are worse choices for most of our readers.

Find a job in this path

If you think you might be a good fit for this path and you’re ready to start looking at job opportunities that are currently accepting applications, see our list of opportunities for this path:

View all opportunities

Learn more about working at AI labs

Learn more about making career decisions where there’s a risk of harm:

• Anonymous advice about advancing AI capabilities
• Is it ever OK to take a harmful job in order to do more good?
• Ways people trying to do good accidentally make things worse, and how to avoid them

Relevant career reviews (for more specific and practical advice):

• AI safety technical research
• AI governance and coordination
• Information security
• Software engineering
• Data collection for AI alignment
• Non-technical roles in leading AI labs

Read next:  Learn about other high-impact careers

Want to consider more paths? See our list of the highest-impact career paths according to our research.

Continue →

The post Should you work at a leading AI lab? appeared first on 80,000 Hours.

But these risks can be tackled.

With further progress in AI safety, we have an opportunity to develop AI for good: systems that are safe, ethical, and beneficial for everyone.

This article explains how you can help.

Thanks to Adam Gleave, Jacob Hilton and Rohin Shah for reviewing this article. And thanks to Charlie Rogers-Smith for his help, and his article on the topic — How to pursue a career in technical AI alignment.

Why AI safety technical research is high impact

As we’ve argued, in the next few decades, we might see the development of hugely powerful machine learning systems with the potential to transform society. This transformation could bring huge benefits — but only if we avoid the risks.

We think that the worst-case risks from AI systems arise in large part because AI systems could be misaligned — that is, they will aim to do things that we don’t want them to do. In particular, we think they could be misaligned in such a way that they develop (and execute) plans that pose risks to humanity’s ability to influence the world, even when we don’t want that influence to be lost.

We think this means that these future systems pose an existential threat to civilisation.

Even if we find a way to avoid this power-seeking behaviour, there are still substantial risks — such as misuse by governments or other actors — which could be existential threats in themselves.

There are many ways in which we could go about reducing the risks that these systems might pose. But one of the most promising may be researching technical solutions that prevent unwanted behaviour — including misaligned behaviour — from AI systems. (Finding a technical way to prevent misalignment in particular is known as the alignment problem.)

In the past few years, we’ve seen more organisations start to take these risks more seriously. Many of the leading industry labs developing AI — including Google DeepMind and OpenAI — have teams dedicated to finding these solutions, alongside academic research groups including at MIT, Oxford, Cambridge, Carnegie Mellon University, and UC Berkeley.

That said, the field is still very new. We think there are only around 300 people working on technical approaches to reducing existential risks from AI systems,¹ which makes this a highly neglected field.

Finding technical ways to reduce this risk could be quite challenging. Any practically helpful solution must retain the usefulness of the systems (remaining economically competitive with less safe systems), and continue to work as systems improve over time (that is, it needs to be ‘scalable’). As we argued in our problem profile, it seems like it might be difficult to find viable solutions, particularly for modern ML (machine learning) systems.

(If you don’t know anything about ML, we’ve written a very very short introduction to ML, and we’ll go into more detail on how to learn about ML later in this article. Alternatively, if you do have ML experience, talk to our team — they can give you personalised career advice, make introductions to others working on these issues, and possibly even help you find jobs or funding opportunities.)

Although it seems hard, there are lots of avenues for more research — and the field really is very young, so there are new promising research directions cropping up all the time. So we think it’s moderately tractable, though we’re highly uncertain.

In fact, we’re uncertain about all of this and have written extensively about reasons we might be wrong about AI risk.

But, overall, we think that — if it’s a good fit for you — going into AI safety technical research may just be the highest-impact thing you can do with your career.

What does this path involve?

AI safety technical research generally involves working as a scientist or engineer at major AI labs, in academia, or in independent nonprofits.

These roles can be very hard to get. You’ll likely need to build up career capital before you end up in a high-impact role (more on this later, in the section on how to enter). That said, you may not need to spend a long time building this career capital — we’ve seen exceptionally talented people move into AI safety from other quantitative fields, sometimes in less than a year.

Most AI safety technical research falls on a spectrum between empirical research (experimenting with current systems as a way of learning more about what will work), and theoretical research (conceptual and mathematical research looking at ways of ensuring that future AI systems are safe).

No matter where on this spectrum you end up working, your career path might look a bit different depending on whether you want to aim at becoming a research lead — proposing projects, managing a team and setting direction — or a contributor — focusing on carrying out the research.

Finally, there are two slightly different roles you might aim for:

• In academia, research is often led by professors — the key distinguishing feature of being a professor is that you’ll also teach classes and mentor grad students (and you’ll definitely need a PhD).
• Many (but not all) contributor roles in empirical research are also engineers, often software engineers. Here, we’re focusing on software roles that directly contribute to AI safety research (and which often require some ML background) — we’ve written about software engineering more generally in a separate career review.

We think that research lead roles are probably higher-impact in general. But overall, the impact you could have in any of these roles is likely primarily determined by your personal fit for the role — see the section on how to predict your fit in advance.

Next, we’ll take a look at what working in each path might involve. Later, we’ll go into how you might enter each path.

What does work in the empirical AI safety path involve?

Empirical AI safety tends to involve teams working directly with ML models to identify any risks and develop ways in which they might be mitigated.

That means the work is focused on current ML techniques and techniques that might be applied in the very near future.

Practically, working on empirical AI safety involves lots of programming and ML engineering. You might, for example, come up with ways you could test the safety of existing systems, and then carry out these empirical tests.

You can find roles in empirical AI safety in industry and academia, as well as some in AI safety-focused nonprofits.

Particularly in academia, lots of relevant work isn’t explicitly labelled as being focused on existential risk — but it can still be highly valuable. For example, work in interpretability, adversarial examples, diagnostics and backdoor learning, among other areas, could be highly relevant to reducing the chance of an AI-related catastrophe.

We’re also excited by experimental work to develop safety standards that AI companies might adhere to in the future — for example, the work being carried out by METR.

To learn more about the sorts of research taking place at labs focused on empirical AI safety, take a look at:

• OpenAI’s approach to alignment research
• Anthropic’s views on AI safety
• Redwood Research’s recent research highlights
• Publications from Google DeepMind’s safety team

While programming is central to all empirical work, generally, research lead roles will be less focused on programming; instead, they need stronger research taste and theoretical understanding. In comparison, research contributors need to be very good at programming and software engineering.

What does work in the theoretical AI safety path involve?

Theoretical AI safety is much more heavily conceptual and mathematical. Often it involves careful reasoning about the hypothetical behaviour of future systems.

Generally, the aim is to come up with properties that it would be useful for safe ML algorithms to have. Once you have some useful properties, you can try to develop algorithms with these properties (bearing in mind that to be practically useful these algorithms will have to end up being adopted by industry). Alternatively, you could develop ways of checking whether systems have these properties. These checks could, for example, help hold future AI products to high safety standards.

Many people working in theoretical AI safety will spend much of their time proving theorems or developing new mathematical frameworks. More conceptual approaches also exist, although they still tend to make heavy use of formal frameworks.

Some examples of research in theoretical AI safety include:

• Risks from learned optimisation in advanced machine learning systems by Hubinger et al.
• Eliciting latent knowledge by Christiano, Cotra and Xu.
• Formalizing the presumption of independence by Christiano, Neyman, and Xu
• Discovering agents by Kenton et al.
• Active reward learning from multiple teachers by Barnett et al.

There are generally fewer roles available in theoretical AI safety work, especially as research contributors. Theoretical research contributor roles exist at nonprofits (primarily the Alignment Research Center), as well as at some labs (for example, Anthropic’s work on conditioning predictive models and the Causal Incentives Working Group at Google DeepMind). Most contributor roles in theoretical AI safety probably exist in academia (for example, PhD students in teams working on projects relevant to theoretical AI safety).

Some exciting approaches to AI safety

There are lots of technical approaches to AI safety currently being pursued. Here are just a few of them:

• Scalably learning from human feedback. Examples include iterated amplification, AI safety via debate, building AI assistants that are uncertain about our goals and learn them by interacting with us, and other ways to get AI systems trained with stochastic gradient descent to report truthfully what they know.
• Threat modelling. An example of this work would be demonstrating the possibility of (allowing us to study) dangerous capabilities, like deceptive or manipulative AI systems. You can read an overview in a recent Google DeepMind paper. This work splits into work that evaluates whether a model has dangerous capabilities (like the work of METR in evaluating GPT-4), and work that evaluates whether a model would cause harm in practice (like Anthropic’s research into the behaviour of large language models and this paper on goal misgeneralisation).
• Interpretability research. This work involves studying why AI systems do what they do and trying to put it into human-understandable terms. For example, this paper examined how AlphaZero learns chess, and this paper looked into finding latent knowledge in language models without supervision. This category also includes mechanistic interpretability — for example, Zoom In: An Introduction to Circuits by Olah et al.). For more, see this survey paper, as well as Hubinger’s a transparency and interpretability tech tree, and Nanda’s A Longlist of Theories of Impact for Interpretability for overviews of of how interpretability research could reduce existential risk from AI.
• Other anti-misuse research to reduce the risks of catastrophe caused by misuse of systems. (We’ve written more on this in our problem profile on AI risk). For example, this work includes training AIs so they’re hard to use for dangerous purposes. (Note there’s lots of overlap with the other work on this list).
• Research to increase the robustness of neural networks. This work involves ensuring that the sorts of behaviour neural networks display when exposed to one set of inputs continues when exposed to inputs they haven’t previously been exposed to, in order to prevent AI systems changing to unsafe behaviour. See section 2 of Unsolved Problems in AI safety for more.
• Work to build cooperative AI. Find ways to ensure that even if individual AI systems seem safe, they don’t produce bad outcomes through interacting with other sociotechnical systems. For more, see Open Problems in Cooperative AI by Dafoe et al. or the Cooperative AI Foundation. This seems particularly relevant for the reduction of ‘s-risks.’
• More generally, there are some unified safety plans. For more, see Hubinger’s 11 possible proposals for building safe advanced AI, or Karnofsky’s How might we align transformative AI if it’s developed very soon.²

It’s worth noting that there are many approaches to AI safety, and people in the field strongly disagree on what will or won’t work.

This means that, once you’re working in the field, it can be worth being charitable and careful not to assume that others’ work is unhelpful just because it seemed so on a quick skim. You should probably be uncertain about your own research agenda as well.

What’s more, as we mentioned earlier, lots of relevant work across all these areas isn’t explicitly labelled ‘safety.’

So it’s important to think carefully about how or whether any particular research helps reduce the risks that AI systems might pose.

What are the downsides of this career path?

AI safety technical research is not the only way to make progress on reducing the risks that future AI systems might pose. Also, there are many other pressing problems in the world that aren’t the possibility of an AI-related catastrophe, and lots of careers that can help with them. If you’d be a better fit working on something else, you should probably do that.

Beyond personal fit, there are a few other downsides to the career path:

• It can be very competitive to enter (although once you’re in, the jobs are well paid, and there are lots of backup options).
• You need quantitative skills — and probably programming skills.
• The work is geographically concentrated in just a few places (mainly the California Bay Area and London, but there are also opportunities in places with top universities such as Oxford, New York, Pittsburgh, and Boston). That said, remote work is increasingly possible at many research labs.
• It might not be very tractable to find good technical ways of reducing the risk. Although assessments of its difficulty vary, and while making progress is almost certainly possible, it may be quite hard to do so. This reduces the impact that you could have working in the field. That said, if you start out in technical work you might be able to transition to governance work, since that often benefits from technical training and experience with the industry, which most people do not have.)
• Relatedly, there’s lots of disagreement in the field about what could work; you’ll probably be able to find at least some people who think what you’re working on is useless, whatever you end up doing.
• Most importantly, there’s some risk of doing harm. While gaining career capital, and while working on the research itself, you’ll have to make difficult decisions and judgement calls about whether you’re working on something beneficial (see our anonymous advice about working in roles that advance AI capabilities). There’s huge disagreement on which technical approaches to AI safety might work — and sometimes this disagreement takes the form of thinking that a strategy will actively increase existential risks from AI.

Finally, we’ve written more about the best arguments against AI being pressing in our problem profile on preventing an AI-related catastrophe. If those are right, maybe you could have more impact working on a different issue.

How much do AI safety technical researchers earn?

Many technical researchers work at companies or small startups that pay wages competitive with the Bay Area and Silicon Valley tech industry, and even smaller organisations and nonprofits will pay competitive wages to attract top talent. The median compensation for a software engineer in the San Francisco Bay area was $222,000 per year in 2020.³ (Read more about software engineering salaries).

This $222,000 median may be an underestimate, as AI roles, especially in top AI labs that are rapidly scaling up their work in AI, often pay better than other tech jobs, and the same applies to safety researchers — even those in nonprofits.

However, academia has lower salaries than industry in general, and we’d guess that AI safety research roles in academia pay less than commercial labs and nonprofits.

Examples of people pursuing this path

How to predict your fit in advance

You’ll generally need a quantitative background (although not necessarily a background in computer science or machine learning) to enter this career path.

There are two main approaches you can take to predict your fit, and it’s helpful to do both:

• Try it out: try out the first few steps in the section below on learning the basics. If you haven’t yet, try learning some python, as well as taking courses in linear algebra, calculus, and probability. And if you’ve done that, try learning a bit about deep learning and AI safety. Finally, the best way to try this out for many people would be to actually get a job as a (non-safety) ML engineer (see more in the section on how to enter).
• Talk to people about whether it would be a good fit for you: If you want to become a technical researcher, our team probably wants to talk to you. We can give you 1-1 advice, for free. If you know anyone working in the area (or something similar), discuss this career path with them and ask for their honest opinion. You may be able to meet people through our community. Our advisors can also help make connections.

It can take some time to build expertise, and enjoyment can follow expertise — so be prepared to take some time to learn and practice before you decide to switch to something else entirely.

If you’re not sure what roles you might aim for longer term, here are a few rough ways you could make a guess about what to aim for, and whether you might be a good fit for various roles on this path:

• Testing your fit as an empirical research contributor: In a blog post about hiring for safety researchers, the Google DeepMind team said “as a rough test for the Research Engineer role, if you can reproduce a typical ML paper in a few hundred hours and your interests align with ours, we’re probably interested in interviewing you.”
  • Looking specifically at software engineering, one hiring manager at Anthropic said that if you could, with a few weeks’ work, write a complex new feature or fix a very serious bug in a major ML library, they’d want to interview you straight away. (Read more.)
• Testing your fit for theoretical research: If you could have got into a top 10 maths or theoretical computer science PhD programme if you’d optimised your undergrad to do so, that’s a decent indication of your fit (and many researchers in fact have these PhDs). The Alignment Research Center (one of the few organisations that hires for theoretical research contributors, as of 2023) said that they were open to hiring people without any research background. They gave four tests of fit: creativity (e.g. you may have ideas for solving open problems in the field, like Eliciting Latent Knowledge); experience designing algorithms, proving theorems, or formalising concepts; broad knowledge of maths and computer science; and having thought a lot about the AI alignment problem in particular.
• Testing your fit as a research lead (or for a PhD): The vast majority of research leads have a PhD. Also, many (but definitely not all) AI safety technical research roles will require a PhD — and if they don’t, having a PhD (or being the sort of person that could get one) would definitely help show that you’re a good fit for the work. To get into a top 20 machine learning PhD programme, you’d probably need to publish something like a first author workshop paper, as well as a third author conference paper at a major ML conference (like NeurIPS or ICML). (Read more about whether you should do a PhD).

Read our article on personal fit to learn more about how to assess your fit for the career paths you want to pursue.

How to enter

You might be able to apply for roles right away — especially if you meet, or are near meeting, the tests we just looked at — but it also might take you some time, possibly several years, to skill up first.

So, in this section, we’ll give you a guide to entering technical AI safety research. We’ll go through four key questions:

1. How to learn the basics
2. Whether you should do a PhD
3. How to get a job in empirical research
4. How to get a job in theoretical research

Hopefully, by the end of the section, you’ll have everything you need to get going.

Learning the basics

To get anywhere in the world of AI safety technical research, you’ll likely need a background knowledge of coding, maths, and deep learning.

You might also want to practice enough to become a decent ML engineer (although this is generally more useful for empirical research), and learn a bit about safety techniques in particular (although this is generally more useful for empirical research leads and theoretical researchers).

We’ll go through each of these in turn.

Learning to program

You’ll probably want to learn to code in python, because it’s the most widely used language in ML engineering.

The first step is probably just trying it out. As a complete beginner, you can write a Python program in less than 20 minutes that reminds you to take a break every two hours. Don’t be discouraged if your code doesn’t work the first time — that’s what normally happens when people code!

Once you’ve done that, you have a few options:

• Teach yourself to program. Try working through a free beginner course like Automate the boring stuff with Python by Al Seigart. There also are many great introductory computer science and programming courses online, including: Udacity’s Intro to Computer Science, MIT’s Introduction to Computer Science and Programming, and Stanford’s Programming Methodology. Then, try finding something you want to build, and building it — or getting involved in an open-source project. For interview practice, try leetcode or TopCoder, or the exercises in Cracking the Coding Interview by Gayle McDowell.
• Take a college course. If you’re in university, this is a great option because it allows you to learn programming while the opportunity cost of your time is lower. You can even consider majoring in computer science (or another subject involving lots of programming).
• Learn on the job. If you can find internships, you’ll gain practical experience and skills you otherwise wouldn’t pick up from academic degrees.
• Go to a bootcamp. Coding bootcamps are focused on taking people with little knowledge of programming to as highly paid a job as possible within a couple of months — though some claim the long-term prospects are not as good because you lack a deep understanding of computer science. Course Report is a great guide to choosing a bootcamp. Be careful to avoid low-quality bootcamps. You can also find online bootcamps — for people completely new to programming — focused on ML, like Udemy’s Python for Data Science and Machine Learning Bootcamp.

You can read more about learning to program — and how to get your first job in software engineering (if that’s the route you want to take) — in our career review on software engineering.

Learning the maths

The maths of deep learning relies heavily on calculus and linear algebra, and statistics can be useful too — although generally learning the maths is much less important than programming and basic, practical ML.

We’d generally recommend studying a quantitative degree (like maths, computer science or engineering), most of which will cover all three areas pretty well.

If you want to actually get good at maths, you have to be solving problems. So, generally, the most useful thing that textbooks and online courses provide isn’t their explanations — it’s a set of exercises to try to solve, in order, with some help if you get stuck.

If you want to self-study (especially if you don’t have a quantitative degree) here are some possible resources:

• Calculus: 3blue1brown’s video series on calculus could be a good place to start. You may also be able to follow recorded university courses: MIT’s single variable calculus (which requires only high school algebra and trigonometry) followed by MIT’s course in vector and multivariable calculus.
• Linear algebra: Again, we’d suggest 3blue1brown’s video series on linear algebra as a place to start. In his post about technical alignment careers, Rogers-Smith recommends Linear Algebra Done Right by Sheldon Axler. Finally, if you prefer lectures, try MIT’s undergraduate course in linear algebra (although note that this course assumes knowledge of multivariate calculus).
• Probability: Take a look at MIT’s undergraduate course in probability and random variables.

You might be able to find resources that cover all these areas, like Imperial College’s Mathematics for Machine Learning.

Learning basic machine learning

You’ll likely need to have a decent understanding of how AI systems are currently being developed. This will involve learning about machine learning and neural networks, before diving into any specific subfields of deep learning.

Again, there’s the option of covering this at university. If you’re currently at college, it’s worth checking if you can take an ML course even if you’re not majoring in computer science.

There’s one important caveat here: you’ll learn a huge amount on the job, and the amount you’ll need to know in advance for any role or course will vary hugely! Not even top academics know everything about their fields. It’s worth trying to find out how much you’ll need to know for the role you want to do before you invest hundreds of hours into learning about ML.

With that caveat in mind, here are some suggestions of places you might start if you want to self-study the basics:

• 3blue1brown’s series on neural networks is a really great place to start for beginners.
• When I was learning, I used Neural Networks and Deep Learning — it’s an online textbook, good if you’re familiar with the maths, with some helpful exercises as well.
• Online intro courses like fast.ai (focused on practical applications), Full Stack Deep Learning, and the various courses at deeplearning.ai.
• For more detail, see university courses like MIT’s *Introduction to Machine Learning, NYU’s Deep Learning for even more detail. We’d also recommend Google DeepMind’s lecture series.

PyTorch is a very common package used for implementing neural networks, and probably worth learning! When I was first learning about ML, my first neural network was a 3-layer convolutional neural network with L2 regularisation classifying characters from the MNIST database. This is a pretty common first challenge, and a good way to learn PyTorch.

Learning about AI safety

If you’re going to work as an AI safety researcher, it usually helps to know about AI safety.

This isn’t always true — some engineering roles won’t require much knowledge of AI safety. But even then, knowing the basics will probably help land you a position, and can also help with things like making difficult judgement calls and avoiding doing harm. And if you want to be able to identify and do useful work, you’ll need to learn about the field eventually.

Because the field is still so new, there probably aren’t (yet) university courses you can take. So you’ll need to do some self-study. Here are some places you might start:

• Section 3 of our problem profile about preventing an AI-related catastrophe provides an introduction to the problems that AI safety attempts to solve (with a particular focus on alignment).
• Rob Miles’ YouTube channel is full of popular and well-explained introductory videos that don’t need much background knowledge of ML.
• AXRP – the AI X-risk Research Podcast — is full of in-depth (and enjoyable) conversations with researchers about their research.
• The courses from AGI Safety Fundamentals, in particular the AI Alignment Course, possibly followed by Alignment 201, which provide an introduction to research on the alignment problem.
• Intro to ML Safety, a course from the Center for AI Safety focuses on withstanding hazards (“robustness”), identifying hazards (“monitoring”), and reducing systemic hazards (“systemic safety”), as well as alignment.

For more suggestions — especially when it comes to reading about the nature of the risks we might face from AI systems — take a look at the top resources to learn more from our problem profile.

Should you do a PhD?

Some technical research roles will require a PhD — but many won’t, and PhDs aren’t the best option for everyone.

The main benefit of doing a PhD is probably practising setting and carrying out your own research agenda. As a result, getting a PhD is practically the default if you want to be a research lead.

That said, you can also become a research lead without a PhD — in particular, by transitioning from a role as a research contributor. At some large labs, the boundary between being a contributor and a lead is increasingly blurry.

Many people find PhDs very difficult. They can be isolating and frustrating, and take a very long time (4–6 years). What’s more, both your quality of life and the amount you’ll learn will depend on your supervisor — and it can be really difficult to figure out in advance whether you’re making a good choice.

So, if you’re considering doing a PhD, here are some things to consider:

• Your long-term vision: If you’re aiming to be a research lead, that suggests you might want to do a PhD — the vast majority of research leads have PhDs. If you mainly want to be a contributor (e.g. an ML or software engineer), that suggests you might not. If you’re unsure, you should try doing something to test your fit for each, like trying a project or internship. You might try a pre-doctoral research assistant role — if the research you do is relevant to your future career, these can be good career capital, whether or not you do a PhD.
• The topic of your research: It’s easy to let yourself become tied down to a PhD topic you’re not confident in. If the PhD you’re considering would let you work on something that seems useful for AI safety, it’s probably — all else equal — better for your career, and the research itself might have a positive impact as well.
• Mentorship: What are the supervisors or managers like at the opportunities open to you? You might be able to find ML engineering or research roles in industry where you could learn much more than you would in a PhD — or vice versa. When picking a supervisor, try reaching out to the current or former students of a prospective supervisor and asking them some frank questions. (Also, see this article on how to choose a PhD supervisor.)
• Your fit for the work environment: Doing a PhD means working on your own with very little supervision or feedback for long periods of time. Some people thrive in these conditions! But some really don’t and find PhDs extremely difficult.

Read more in our more detailed (but less up-to-date) review of machine learning PhDs.

It’s worth remembering that most jobs don’t need a PhD. And for some jobs, especially empirical research contributor roles, even if a PhD would be helpful, there are often better ways of getting the career capital you’d need (for example, working as a software or ML engineer). We’ve interviewed two ML engineers who have had hugely successful careers without doing a PhD.

Whether you should do a PhD doesn’t depend (much) on timelines

We think it’s plausible that we will develop AI that could be hugely transformative for society by the end of the 2030s.

All else equal, that possibility could argue for trying to have an impact right away, rather than spending five (or more) years doing a PhD.

Ultimately, though, how well you, in particular, are suited to a particular PhD is probably a much more important factor than when AI will be developed.

That is to say, we think the increase in impact caused by choosing a path that’s a good fit for you is probably larger than any decrease in impact caused by delaying your work. This is in part because the spread in impact caused by the specific roles available to you, as well as your personal fit for them, is usually very large. Some roles (especially research lead roles) will just require having a PhD, and others (especially more engineering-heavy roles) won’t — and people’s fit for these paths varies quite a bit.

We’re also highly uncertain about estimates about when we might develop transformative AI. This uncertainty reduces the expected cost of any delay.

Most importantly, we think PhDs shouldn’t be thought of as a pure delay to your impact. You can do useful work in a PhD, and generally, the first couple of years in any career path will involve a lot of learning the basics and getting up to speed. So if you have a good mentor, work environment, and choice of topic, your PhD work could be as good as, or possibly better than, the work you’d do if you went to work elsewhere early in your career. And if you suddenly receive evidence that we have less time than you thought, it’s relatively easy to drop out.

There are lots of other considerations here — for a rough overview, and some discussion, see this post by 80,000 Hours advisor Alex Lawsen, as well as the comments.

Overall, we’d suggest that instead of worrying about a delay to your impact, think instead about which longer-term path you want to pursue, and how the specific opportunities in front of you will get you there.

How to get into a PhD

ML PhDs can be very competitive. To get in, you’ll probably need a few publications (as we said above, something like a first author workshop paper, as well as a third author conference paper at a major ML conference (like NeurIPS or ICML), and references, probably from ML academics. (Although publications also look good whatever path you end up going down!)

To end up at that stage, you’ll need a fair bit of luck, and you’ll also need to find ways to get some research experience.

One option is to do a master’s degree in ML, although make sure it’s a research masters — most ML master’s degrees primarily focus on preparation for industry.

Even better, try getting an internship in an ML research group. Opportunities include RISS at Carnegie Mellon University, UROP at Imperial College London, the Aalto Science Institute international summer research programme, the Data Science Summer Institute, the Toyota Technological Institute intern programme and MILA. You can also try doing an internship specifically in AI safety, for example at CHAI. However, there are sometimes disadvantages to doing internships specifically in AI safety directly — in general, it may be harder to publish and mentorship might be more limited.

Another way of getting research experience is by asking whether you can work with researchers. If you’re already at a top university, it can be easiest to reach out to people working at the university you’re studying at.

PhD students or post-docs can be more responsive than professors, but eventually, you’ll want a few professors you’ve worked with to provide references, so you’ll need to get in touch. Professors tend to get lots of cold emails, so try to get their attention! You can try:

• Getting an introduction, for example from a professor who’s taught you
• Mentioning things you’ve done (your grades, relevant courses you’ve taken, your GitHub, any ML research papers you’ve attempted to replicate as practice)
• Reading some of their papers and the main papers in the field, and mention them in the email
• Applying for funding that’s available to students who want to work in AI safety, and letting people know you’ve got funding to work with them

Ideally, you’ll find someone who supervises you well and has time to work with you (that doesn’t necessarily mean the most famous professor — although it helps a lot if they’re regularly publishing at top conferences). That way, they’ll get to know you, you can impress them, and they’ll provide an amazing reference when you apply for PhDs.

It’s very possible that, to get the publications and references you’ll need to get into a PhD, you’ll need to spend a year or two working as a research assistant, although these positions can also be quite competitive.

This guide by Adam Gleave also goes into more detail on how to get a PhD, including where to apply and tips on the application process itself. We discuss ML PhDs in more detail in our career review on ML PhDs (though it’s outdated compared to this career review).

Getting a job in empirical AI safety research

Ultimately, the best way of learning to do empirical research — especially in contributor and engineering-focused roles — is to work somewhere that does both high-quality engineering and cutting-edge research.

The top three labs are probably Google DeepMind (who offer internships to students), OpenAI (who have a 6-month residency programme) and Anthropic. (Working at a leading AI lab carries with it some risk of doing harm, so it’s important to think carefully about your options. We’ve written a separate article going through the major relevant considerations.)

To end up working in an empirical research role, you’ll probably need to build some career capital.

Whether you want to be a research lead or a contributor, it’s going to help to become a really good software engineer. The best ways of doing this usually involve getting a job as a software engineer at a big tech company or at a promising startup. (We’ve written an entire article about becoming a software engineer.)

Many roles will require you to be a good ML engineer, which means going further than just the basics we looked at above. The best way to become a good ML engineer is to get a job doing ML engineering — and the best places for that are probably leading AI labs.

For roles as a research lead, you’ll need relatively more research experience. You’ll either want to become a research contributor first, or enter through academia (for example by doing a PhD).

All that said, it’s important to remember that you don’t need to know everything to start applying, as you’ll inevitably learn loads on the job — so do try to find out what you’ll need to learn to land the specific roles you’re considering.

How much experience do you need to get a job? It’s worth reiterating the tests we looked at above for contributor roles:

• In a blog post about hiring for safety researchers, the DeepMind team said “as a rough test for the Research Engineer role, if you can reproduce a typical ML paper in a few hundred hours and your interests align with ours, we’re probably interested in interviewing you.”
• Looking specifically at software engineering, one hiring manager at Anthropic said that if you could, with a few weeks’ work, write a new feature or fix a serious bug in a major ML library, they’d want to interview you straight away. (Read more.)

In the process of getting this experience, you might end up working in roles that advance AI capabilities. There are a variety of views on whether this might be harmful — so we’d suggest reading our article about working at leading AI labs and our article containing anonymous advice from experts about working in roles that advance capabilities. It’s also worth talking to our team about any specific opportunities you have.

If you’re doing another job, or a degree, or think you need to learn some more before trying to change careers, there are a few good ways of getting more experience doing ML engineering that go beyond the basics we’ve already covered:

• Getting some experience in software / ML engineering. For example, if you’re doing a degree, you might try an internship as a software engineer during the summer. DeepMind offer internships for students with at least two years of study in a technical subject,
• Replicating papers. One great way of getting experience doing ML engineering, is to replicate some papers in whatever sub-field you might want to work in. Richard Ngo, an AI governance researcher at OpenAI, has written some advice on replicating papers. But bear in mind that replicating papers can be quite hard — take a look at Amid Fish’s blog on what he learned replicating a deep RL paper. Finally, Rogers-Smith has some suggestions on papers to replicate. If you do spend some time replicating papers, remember that when you get to applying for roles, it will be really useful to be able to prove you’ve done the work. So try uploading your work to GitHub, or writing a blog on your progress. And if you’re thinking about spending a long time on this (say, over 100 hours), try to get some feedback on the papers you might replicate before you start — you could even reach out to a lab you want to work for.
• Taking or following a more in-depth course in empirical AI safety research. Redwood Research ran the MLAB bootcamp, and you can apply for access to their curriculum here. You could also take a look at this Deep Learning Curriculum by Jacob Hilton, a researcher at the Alignment Research Center — although it’s probably very challenging without mentorship.⁴ The Alignment Research Engineer Accelerator is a program that uses this curriculum. Some mentors on the SERI ML Alignment Theory Scholars Program focus on empirical research.
• Learning about a sub-field of deep learning. In particular, we’d suggest natural language processing (in particular transformers — see this lecture as a starting point) and reinforcement learning (take a look at Pong from Pixels by Andrej Karpathy, and OpenAI’s Spinning up in Deep RL). Try to get to the point where you know about the most important recent advances.

Finally, Athena is an AI alignment mentorship program for women with a technical background looking to get jobs in the alignment field

Getting a job in theoretical AI safety research

There are fewer jobs available in theoretical AI safety research, so it’s harder to give concrete advice. Having a maths or theoretical computer science PhD isn’t always necessary, but is fairly common among researchers in industry, and is pretty much required to be an academic.

If you do a PhD, ideally it’d be in an area at least somewhat related to theoretical AI safety research. For example, it could be in probability theory as applied to AI, or in theoretical CS (look for researchers who publish in COLT or FOCS).

Alternatively, one path is to become an empirical research lead before moving into theoretical research.

Compared to empirical research, you’ll need to know relatively less about engineering, and relatively more about AI safety as a field.

Once you’ve done the basics, one possible next step you could try is reading papers from a particular researcher, or on a particular topic, and summarising what you’ve found.

You could also try spending some time (maybe 10–100 hours) reading about a topic and then some more time (maybe another 10–100 hours) trying to come up with some new ideas on that topic. For example, you could try coming up with proposals to solve the problem of eliciting latent knowledge. Alternatively, if you wanted to focus on the more mathematical side, you could try having a go at the assignment at the end of this lecture by Michael Cohen, a grad student at the University of Oxford.

If you want to enter academia, reading a ton of papers seems particularly important. Maybe try writing a survey paper on a certain topic in your spare time. It’s a great way to master a topic, spark new ideas, spot gaps, and come up with research ideas. When applying to grad school or jobs, your paper is a fantastic way to show you love research so much you do it for fun.

There are some research programmes aimed at people new to the field, such as the SERI ML Alignment Theory Scholars Program, to which you could apply.

Other ways to get more concrete experience include doing research internships, working as a research assistant, or doing a PhD, all of which we’ve written about above, in the section on whether and how you can get into a PhD programme.

One note is that a lot of people we talk to try to learn independently. This can be a great idea for some people, but is fairly tough for many, because there’s substantially less structure and mentorship.

Recommended organisations

AI labs in industry that have empirical technical safety teams, or are focused entirely on safety:

• Anthropic is an AI safety company working on building interpretable and safe AI systems. They focus on empirical AI safety research. Anthropic cofounders Daniela and Dario Amodei gave an interview about the lab on the Future of Life Institute podcast. On our podcast, we spoke to Chris Olah, who leads Anthropic’s research into interpretability, and Nova DasSarma, who works on systems infrastructure at Anthropic.
• METR works on assessing whether cutting-edge AI systems could pose catastrophic risks to civilization, including early-stage, experimental work to develop techniques, and evaluating systems produced by Anthropic and OpenAI.
• The Center for AI Safety is a nonprofit that does technical research and promotion of safety in the wider machine learning community.
• FAR AI is a research nonprofit that incubates and accelerates research agendas that are too resource-intensive for academia but not yet ready for commercialisation by industry, including research in adversarial robustness, interpretability and preference learning.
• Google DeepMind is probably the largest and most well-known research group developing general artificial machine intelligence, and is famous for its work creating AlphaGo, AlphaZero, and AlphaFold. It is not principally focused on safety, but has two teams focused on AI safety, with the Scalable Alignment Team focusing on aligning existing state-of-the-art systems, and the Alignment Team focused on research bets for aligning future systems.
• OpenAI, founded in 2015, is a lab that is trying to build artificial general intelligence that is safe and benefits all of humanity. OpenAI is well known for its language models like GPT-4. Like DeepMind, it is not principally focused on safety, but has a safety team and a governance team. Jan Leike (co-lead of the superalignment team) has some blog posts on how he thinks about AI alignment, and has spoken on our podcast about the sorts of people he’d like to hire for his team.
• Ought is a machine learning lab building Elicit, an AI research assistant. Their aim is to align open-ended reasoning by learning human reasoning steps, and to direct AI progress towards helping with evaluating evidence and arguments.
• Redwood Research is an AI safety research organisation, whose first big project attempted to make sure language models (like GPT-3) produce output following certain rules with very high probability, in order to address failure modes too rare to show up in standard training.

Theoretical / conceptual AI safety labs:

• The Alignment Research Center (ARC) is attempting to produce alignment strategies that could be adopted in industry today while also being able to scale to future systems. They focus on conceptual work, developing strategies that could work for alignment and which may be promising directions for empirical work, rather than doing empirical AI work themselves. Their first project was releasing a report on Eliciting Latent Knowledge, the problem of getting advanced AI systems to honestly tell you what they believe (or ‘believe’) about the world. On our podcast, we interviewed ARC founder Paul Christiano about his research (before he founded ARC).
• The Center on Long-Term Risk works to address worst-case risks from advanced AI. They focus on conflict between AI systems.
• The Machine Intelligence Research Institute was one of the first groups to become concerned about the risks from machine intelligence in the early 2000s, and its team has published a number of papers on safety issues and how to resolve them.
• Some teams in commercial labs also do some more theoretical and conceptual work on alignment, such as Anthropic’s work on conditioning predictive models and the Causal Incentives Working Group at Google DeepMind.

AI safety in academia (a very non-comprehensive list; while the number of academics explicitly and publicly focused on AI safety is small, it’s possible to do relevant work at a much wider set of places):

• The Algorithmic Alignment Group in the Computer Science and Artificial Intelligence Laboratory at MIT, led by Dylan Hadfield-Menell
• The Center for Human-Compatible AI at UC Berkeley, led by Stuart Russell, focuses on academic research to ensure AI is safe and beneficial to humans. (Our podcast with Stuart Russell examines his approach to provably beneficial AI.)
• Jacob Steinhardt’s research group in the Department of Statistics at UC Berkeley
• The NYU Alignment research Group led by Sam Bowman
• David Krueger’s research group at the Computational and Biological Learning Laboratory at the University of Cambridge
• The Foundations of Cooperative AI Lab at Carnegie Mellon University
• The Future of Humanity Institute at the University of Oxford has an AI safety research group
• The Alignment of Complex Systems research group at Charles University, Prague

Find a job in this path

If you think you might be a good fit for this path and you’re ready to start looking at job opportunities that are currently accepting applications, see our curated list of opportunities for this path:

View all opportunities

Learn more about AI safety technical research

Top recommendations

• The AGI safety fundamentals technical alignment curriculum
• The 80,000 Hours Podcast on Artificial Intelligence (a collection of 10 key AI episodes from our podcast)
• Charlie Rogers-Smith’s step-by-step guide to AI safety careers (which this article is in large part based on) provides some helpful concrete advice, including ways you might get some funding to help you move into an AI safety technical research career.

Further recommendations

Read next:  Learn about other high-impact careers

Want to consider more paths? See our list of the highest-impact career paths according to our research.

Continue →

The post AI safety technical research appeared first on 80,000 Hours.

Last Tuesday’s statement on AI risk has hit headlines across the world. Hundreds of leading AI scientists and other prominent figures — including the CEOs of OpenAI, Anthropic and Google DeepMind — signed the one-sentence statement:

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.

This mainstreaming of concerns about the risk of extinction from AI represents a substantial shift to the strategic landscape — and should, as a result, have implications on how best to reduce the risk.

• Read more about extinction risks from AI.

How has the landscape shifted?

Pictures from the White House Press Briefing. Meme from @kristjanmoore. The relevant video is here.

So far, I think the most significant effect of the changes in the way these risks are viewed can be seen in changes in political activity.

World leaders — including Joe Biden, Rishi Sunak, Emmanuel Macron — have all met leaders in AI in the last few months. AI regulation was a key topic of discussion at the G7. And now it’s been announced that Biden and Sunak will discuss extinction risks from AI as part of talks in DC next week.

At the moment, it’s extremely unclear where this discussion will go. While I (tentatively) think that there are actions that governments could be taking, it’s possible that governments will act in a way that could increase the risk overall.

But it does seem like some kind of government action is now likely to take place, in the near future.

Looking a bit further forward, the other substantial change to the strategic landscape is that, probably, more people are going to end up working on AI risk sooner than I would previously have predicted.

What does this mean for our actions?

Our framework suggests thinking about the effects of all this news on the scale, neglectedness and solvability of risks from AI.

• Scale: It’s unclear whether the risk from AI has gone up or down in recent months. All else equal, I’d guess that more attention on the issue will be beneficial — but I’m very uncertain.
• Neglectedness: It seems likely that more people will be working on AI risk sooner. This reduces the neglectedness of the risk, making it harder for any one individual to have an impact.
• Solvability: For the moment, at least, it appears that it’s going to be easier to convince people to take action to reduce the risk.

Overall, I’d guess that the increase in solvability is, for now, more substantial than any decrease in neglectedness, making risks from AI overall slightly more pressing than they have been in the past. (It’s very plausible that this could change within the next few years.)

Our recommendations for action

There are still far too few people working on AI risk — and now might be one of the highest-impact times to get involved. We think more people should consider using their careers to reduce risks from AI, and not everyone needs a technical or quantitative background to do so.

(People often think that we think everyone should work on AI, but this isn’t the case. The impact you have over your career depends not just on how pressing the problems you focus on are, but also the effectiveness of the particular roles you might do, and your personal fit for working in them. I’d guess that less than half of our readers should work on reducing risks from AI — there are other problems that are similarly important. But I’d also guess that many of our readers underestimate their ability to contribute.)

There are real ways you could work to reduce this risk:

• Consider working on technical roles in AI safety. If you might be able to open up new research directions, that seems incredibly high impact. But there are lots of other ways to help. You don’t need to be an expert in ML — it can be really useful just to be a great software engineer.
• It’s vital that information on how to produce and run potentially dangerous AI models is kept safe. This means roles in information security could be highly impactful.
• Other jobs at AI companies — many of which don’t require technical skills — could be good to take, if you’re careful to avoid the risks.
• Jobs in government and policy could, if you’re well informed, position you to provide cautious and helpful advice as AI systems become more dangerous and powerful.

We’ll be updating our career reviews of technical and governance roles soon as part of our push to keep our advice in line with recent developments.

Getting to the point where you could secure any of these roles — and doing good with them — means becoming more informed about the issues, and then building career capital to put yourself in a better position to have an impact. Our rule of thumb would be “get good at something helpful.”

• You could pursue relevant graduate study like an ML PhD — or try doing some self-study.
• Work in entry-level software engineering roles, in big tech or at startups — or maybe even get a junior position at a top AI company.
• Take an entry-level route into policy careers.
• Or just do anything where you might really excel.

Take a look at our (new!) article on building career capital for more.

Finally, if you’re serious about working on reducing risks from AI, consider talking to our team.

I don’t think human extinction is likely. In fact, in my opinion, humanity is almost certainly going to survive this century. But the risk is real, and substantial, making this probably the most pressing problem currently facing the world.

And right now could be the best time to start helping. Should you?

Learn more:

• Article: What could an AI-caused existential catastrophe actually look like?
• Podcast: Catherine Olsson and Daniel Ziegler on fast paths into aligning AI as a machine learning engineer

The post Practical steps to take now that AI risk is mainstream appeared first on 80,000 Hours.