The thing is, lots of jobs at charities just aren’t that impactful.

Some charities focus on programmes that don’t work, like Scared Straight, which actually caused kids to commit more crimes. Others focus on ways of helping that, while thoughtful and helpful, don’t have much leverage, like knitting individual sweaters for penguins affected by oil spills (this actually happened!) instead of funding large-scale ocean cleanup projects.

But there are also many organisations out there — both for-profit and nonprofit — focused on pressing problems, implementing effective and scalable solutions, run by great teams, and in need of people.

If you can build skills that are useful for helping an organisation like this, it could well be one of the highest-impact things you can do.

In particular, organisations often need generalists able to do the bread and butter of building an organisation — hiring people, management, administration, communications, running software systems, crafting strategy, fundraising, and so on.

We call these ‘organisation-building’ skills. They can be high impact because you can increase the scale and effectiveness of the organisation you’re working at, while also gaining skills that can be applied to a wide range of global problems in the future (and make you generally employable too).

Why are organisation-building skills valuable?

A well-run organisation can take tens, hundreds, or even thousands of people working on solving the world’s most pressing problems and help them work together far more effectively.

An employee with the right skills can often be a significant boost to an organisation, either by directly helping them deliver an impactful programme or by building the capacity of the organisation so that it can operate at a greater scale in the future. You could, for example, set up organisational infrastructure to enable the hiring of many more people in the future.

What’s more, organisation-building skills can be applied at most organisations, which means you’ll have opportunities to help tackle many different global problems in the future. You’ll also be flexibly able to work on many different solutions to any given problem if you find better solutions later in your career.

As an added bonus, the fact that pretty much all organisations need these skills means you’ll be employable if you decide to earn to give or step back from doing good all together. In fact, organisational management skills seem like some of the most useful and highest paid in the economy in general.

It can be even more valuable to help found a new organisation rather than build an existing one, though this is a particularly difficult step to take when you’re early in your career. (Read more on whether you should found an organisation early in your career.) See our profile on founding impactful organisations to learn more.

What does organisation-building typically involve?

A high-impact career using organisation-building skills typically involves these rough stages:

1. Building generally useful organisational skills, such as operations, people management, fundraising, administration, software systems, finance, etc.
2. Then applying those skills to help build (or found) high-impact organisations

The day-to-day of an organisation-building role is going to vary a lot depending on the job.

Here’s a possible description that could help build some intuition.

Picture yourself working from an office or, increasingly, from your own home. You’ll spend lots of time on your computer — you might be planning, organising tasks, updating project timelines, reworking a legal brief, or contracting out some marketing. You’ll likely spend some time communicating via email or chatting with colleagues. Your day will probably involve a lot of problem solving, making decisions to keep things going.

If you work for a small organisation, especially in the early stages, your “office” could be anywhere — a home office, a local coffee shop, or a shared workspace. If you manage people, you’ll conduct one-on-one meetings to provide feedback, set goals, and discuss personal development. In a project-oriented role, you might spend lots of time developing strategy, or analysing data to evaluate your impact.

What skills are needed to build organisations?

Organisation builders typically have skills in areas like:

• Operations management
• Project management (including setting objectives, metrics, etc.)
• People management and coaching (Some manager jobs require specialised skills, but some just require general management-associated skills like leadership, interpersonal communication, and conflict resolution.)
• Executive leadership (setting and achieving organisation-wide goals, making top-level decisions about budgeting, etc.)
• Entrepreneurship
• Recruiting
• Fundraising
• Marketing (which also benefits from communications skills)
• Communications and public relations (which also benefits from communications skills)
• Human resources
• Office management
• Events management
• Assistant and administrative work
• Finance and accounting
• Corporate and nonprofit law

Many organisations have a significant need for generalists who span several of these areas. If your aim is to take a leadership position, it’s useful to have a shallow knowledge of several.

You can also pick just one skill to specialise in — especially for areas like law and accounting that tend to be their own track.

Generally, larger organisations have a greater need for specialists, while those with under 50 employees hire more generalists.

Example people

How to evaluate your fit

How to predict your fit in advance

There’s no need to focus on the specific job or sector you work in now — it’s possible to enter organisation-building from a very wide variety of areas. We’ve even known academic philosophers who have transitioned to organisation-building!

Some common initial indicators of fit might include:

• You have an optimisation mindset. You frequently notice how things could be done more efficiently and have a strong internal drive to prevent avoidable errors and make things run more smoothly.
• You intuitively engage in systems thinking and enjoy going meta. This is a bit difficult to summarise, but involves things like: you’d notice when people ask you similar questions multiple times and then think about how to prevent the issue from coming up again. For example: “Can you give me access to this doc” turns into “What went wrong such that this person didn’t already have access to everything they need? How can we improve naming conventions or sharing conventions in the future?”
• You’re reliable, self-directed, able to manage your time well, and you can create efficient and productive plans and keep track of complex projects.
• You might also be good at learning quickly and have high attention to detail.

Of course, different types of organisation-building will require different skills. For example, being a COO or events manager requires greater social and system building skills, whereas working in finance requires fewer social skills, but does require basic quantitative skills and perhaps more conscientiousness and attention to detail.

If you’re really excited by a particular novel idea and have lots of energy and excitement for the idea, you might be a good fit for founding an organisation. (Read more about what it takes to successfully found a new organisation.)

You should try doing some cheap tests first — these might include talking to someone who works at the organisation you’re interested in helping to build, volunteering to do a short project, or doing an internship. Then you might commit to working there for 2–24 months (being prepared to switch to something else if you don’t think you’re on track).

How to tell if you’re on track

All of these — individually or together — seem like good signs of being on track to build really useful organisation-building skills:

• You get job offers (as a contractor or staff) 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.
• 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).
• You’re able to multiply a superior’s time by over 2–20X, depending on the role type.
• If you’re aiming to build a new organisation, write out some one-page summaries of ideas for new organisations you’d like to exist and get feedback from grantmakers and experts.
• If founding a new organisation, you get seed funding from a major grantmaker, like Open Philanthropy, Longview Philanthropy, EA Funds, or a private donor.

This said, if you don’t hit these milestones, you might still be a good fit for organisation-building — the issue might be that you’re at the wrong organisation or have the wrong boss.

How to get started building organisation-building skills

You can get started by finding any role that will let you start learning one of the skills listed above. Work in one specialisation will often give you exposure to the others, and it’s often possible to move between them.

If you can do this at a high-performing organisation that’s also having a big impact right away, that’s great. If you’re aware of any organisations like these, it’s worth applying just in case.

But, unfortunately, this is often not possible, especially if you’re fresh out of college, for a number of reasons:

• The organisations have limited mentorship capacity, so they most often hire people with a couple of years of experience rather than those fresh out of college (though there are exceptions) and often aren’t in a good position to help you become excellent at these skills.
• These organisations usually hire people who already have some expertise in the problem area they’re working on (e.g. AI safety, biosecurity), as these issues involve specialised knowledge.
• We chose our recommended problems in large part because they’re unusually neglected. But the fact that they’re neglected also means there aren’t many open positions or training programmes.

As a result, early in your career it can easily be worth pursuing roles at organisations that don’t have much impact in order to build your skills.

The way to do this is to work at any organisation that’s generally high-performing, especially if you can work under someone who’s a good manager and will mentor you — the best way to learn how to run an organisation is to learn from people who are already excellent at this skill.

Then, try to advance as quickly as you can within that organisation or move to higher-responsibility roles in other organisations after 1–3 years of high-performance.

It can also help if the organisation is small but rapidly growing, since that usually makes it much easier to get promoted — and if the organisation succeeds in a big way, that will give you a lot of options in the future.

In a small organisation you can also try out a wider range of roles, helping you figure out which aspects of organisation-building are the best fit for you and giving you the broad background that’s useful for leadership roles in the future. Moreover, many of the organisations we think are doing the best work on the most pressing problems are startups, so being used to this kind of environment can be an advantage.

One option within this category we especially recommend is to consider becoming an early employee at a tech startup.

If you pick well, working at a tech startup gives you many of the advantages of working at a small, growing, high-performing organisation mentioned above, while also offering high salaries and an introduction to the technology sector. (This is even better if you can find an organisation that will let you learn about artificial intelligence or synthetic biology.)

We’ve advised many people who have developed organisation-building skills in startups and then switched to nonprofit work (or earned to give), while having good backup options.

That said, smaller organisations have downsides such as being more likely to fail and less mentorship capacity. Many are also poorly run. So it’s important to pick carefully.

Another option to consider in this category is working at a leading AI lab, because they can often offer good training, look impressive on your CV, and let you learn about AI. That said, you’ll need to think carefully about whether your work could be accelerating the risks from AI as well.

One of the most common ways to build these skills is to work in large tech companies, consulting or professional services (or more indirectly, to train as a lawyer or in finance). These are most useful for learning how to apply these skills in very large corporate and government organisations, or to build a speciality like accounting. We think there are often more direct ways to do useful work on the problems we think are most pressing, but these prestigious corporate jobs can still be the best option for some.

However, it’s important to remember you can build organisation-building skills in any kind of organisation: from nonprofits to academic research institutes to government agencies to giant corporations. What most matters is that you’re working with people who have this skill, who are able to train you.

Should you found your own organisation early in your career?

For a few people, founding an organisation fairly early in your career could be a fantastic career step. Whether or not the organisation you start succeeds, along the way you could gain strong organisation-building (and other) skills and a lot of career capital.

We think you should be ambitious when deciding career steps, and it often makes sense to pursue high-upside options first when you’re doing some career exploration.

This is particularly true if you:

• Have an idea that you’ve seriously thought about, stress tested, and got positive feedback on from relevant experts
• Have real energy and excitement for your idea (not for the idea of being an entrepreneur)
• Understand that you’re likely to fail, and have good backup plans in place for that

It can be hard to figure out if your idea is any good, or if you’ll be any good at this, in advance. One rule of thumb is that if, after six months to a year of work, you can be accepted to a top incubator (like Y Combinator), you’re probably on track. But if you can’t get into a top incubator, you should consider trying to build organisation-building skills in a different way (or try building a completely different skill set).

There are many downsides of working on your own projects. In particular, you’ll get less direct feedback and mentorship, and your efforts will be spread thinly across many different types of tasks and skills, making it harder to develop specialist expertise.
To learn more, see our article on founding new projects tackling top problems.

Find jobs that use organisation-building skills

See our curated list of job opportunities for this path, which you can filter by ‘management’ and ‘operations’ to find opportunities in this category (though there will also be jobs outside those filters where you can apply organisation-building skills).

View all opportunities

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, so the first step is to decide which problems you think are most pressing.

Once you’ve done that, the next step is to identify the highest-potential organisations working on your top problems.

In particular, look for organisations that:

1. Implement an effective solution, or one that has a good chance of having a big impact (even if it might not work)
2. Have the potential to grow
3. Are run by a great team
4. Are in need of your skills

These organisations will most often be nonprofits, but they could also be research institutes, political organisations, or for-profit companies with a social mission.¹

For specific ideas, see our list of recommended organisations. You can also find longer lists of suggestions within each of our problem profiles.

Finally, see if you can get a job at one of these organisations that effectively uses your specific skills. If you can’t, that’s also fine — you can apply your skills elsewhere, for example through earning to give, and be ready to switch into working for a high-impact organisation in the future.

Career paths we’ve reviewed that use organisation-building skills

These are some reviews of career paths we’ve written that use ‘organisation-building’ skills:

• Operations management
• Helping build the effective altruism community
• Non-technical roles in leading AI labs
• Earning to give
• Founding new projects tackling top problems
• Become an executive assistant for someone doing especially high-impact work
• Working at effective nonprofits
• Research management

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 Organisation-building appeared first on 80,000 Hours.

On December 31, 2020, the four most valuable companies were: Apple, Microsoft, Amazon, and Google.

On December 31, 2019, the four most valuable companies were: Apple, Microsoft, Google, and Amazon.

And on December 31, 2018, the four most valuable companies were: Microsoft, Apple, Amazon, and Google.

If you’re anything like me, you’re starting to spot a pattern here.

Revenue in software has grown from $400 billion in 2016 to $500 billion in 2021, and is projected to reach $800 billion by 2026.

Software has an increasing and overwhelming importance in our economy — and everything else in our society. High demand and low supply makes software engineering well-paid, and often enjoyable.

But we also think that, if you’re trying to make the world a better place, software engineering could be a particularly good way to help.

This review owes a lot to helpful discussions with (and comments from) Andy Jones, Ozzie Gooen, Jeff Kaufman, Sasha Cooper, Ben Kuhn, Nova DasSarma, Kamal Ndousse, Ethan Alley, Ben West, Ben Mann, Tom Conerly, Zac Hatfield-Dodds, and George McGowan. Special thanks go to Roman Duda for our previous review of software engineering, on which this was based.

Why might software engineering be high impact?

Software engineers are in a position to meaningfully contribute directly to solving a wide variety of the world’s most pressing problems.

In particular, there is a shortage of software engineers at the cutting edge of research into AI safety.

We’ve also found that software engineers can contribute greatly to work aiming at preventing pandemics and other global catastrophic biological risks.

Aside from direct work on these crucial problems, while working for startups or larger tech companies you can gain excellent career capital (especially technical skills), and, if you choose, earn and donate substantial amounts to the world’s best charities.

How to do good as a software engineer

Even for skilled engineers who could command high salaries, we think that working directly on a problem will probably be more impactful than earning to give.

Some examples of projects where software engineering is central to their impactful work:

• Ought is an AI research lab attempting to build systems that apply machine learning to the task of helping people think.
• The Secure DNA Project is attempting to build a secure global screening system for DNA sequences that could be used to engineer a global pandemic.
• Momentum is a startup building donation pages that encourage recurring donations to the world’s most effective charities.
• Lightcone Infrastructure builds software-based infrastructure for longtermist projects.
• Telis Bioscience is a startup attempting to radically accelerate drug development to mitigate risks from future pandemics.
• Anthropic is a research company working to build reliable, interpretable, and steerable AI systems.
• Redwood Research conducts applied research into the challenge of aligning artificial intelligence.
• Wave is a startup building a way for people in developing countries to access financial services.

Most organisations, even ones that don’t focus on developing large software products, need software engineers to manage computer systems, apps, and websites. For example:

• Effective Altruism Funds provides expert-managed funds to facilitate donors maximising the impact of their donations. They use software engineers to design and maintain their online platform.
• Our World in Data, founded by Max Roser, collects and presents data on many of the world’s most pressing problems. They use software engineers to maintain their website, analyse data, and develop their open-source data visualisation tool.
• Here at 80,000 Hours, our team includes two software engineers working on our website and other technology.
• The Centre for Effective Altruism‘s tech team supports a variety of projects across the effective altruism community, such as the Effective Altruism Forum.

Many people we’ve spoken to at these and other organisations have said that they have real difficulty hiring extremely talented software engineers. Many nonprofits want to hire people who believe in their missions (just as they do with operations staff), which indicates that talented, altruistic-minded software engineers are sorely needed and could do huge amounts of good.

Smaller organisations that don’t focus on engineering often only have one or two software engineers. And because things at small organisations can change rapidly, they need unusually adaptable and flexible people who are able to maintain software with very little help from the wider team.¹

It seems likely that, as the community of people working on helping future generations grows, there will be more opportunities for practical software development efforts to help. This means that even if you don’t currently have any experience with programming, it could be valuable to begin developing expertise in software engineering now.

Software engineers can help with AI safety

We’ve argued before that artificial intelligence could have a deeply transformative impact on our society. There are huge opportunities associated with this ongoing transformation, but also extreme risks — potentially even threatening humanity’s survival.

With the rise of machine learning, and the huge success of deep learning models like GPT-3, many experts now think it’s reasonably likely that our current machine learning methods could be used to create transformative artificial intelligence.

This has led to an explosion in empirical AI safety research, where teams work directly with deep neural networks to identify risks and develop frameworks for mitigating them. Examples of organisations working in empirical AI safety research include Redwood Research, DeepMind, OpenAI, and Anthropic.

These organisations are doing research directly with extremely large neural networks, which means each experiment can cost millions of dollars to run. This means that even small improvements to the efficiency of each experiment can be hugely beneficial.

There’s also often overlap between experimental results that will help further AI safety and results that could accelerate the development of unsafe AI, so it’s also important that the results of these experiments are kept secure.

As a result, it’s likely to remain incredibly valuable to have talented engineers working on ensuring that these experiments are as efficient and safe as possible. Experts we spoke to expect this to remain a key bottleneck in AI safety research for many years.

However, there is a serious risk associated with this route: it seems possible for engineers to accidentally increase risks from AI by generally accelerating the technical development of the field. We’re not sure of the more precise contours of this risk (e.g. exactly what kinds of projects you should avoid), but think it’s important to watch out for. That said, there are many more junior non-safety roles out there than roles focused specifically on safety, and experts we’ve spoken to expect that most non-safety projects aren’t likely to be causing harm. If you’re uncertain about taking a job for this reason, our team may be able to help you decide.

Software engineer salaries mean you can earn to give

In general, if you can find a job you can do well, you’ll have a bigger impact working on a problem directly than you would by earning money and donating. However, earning to give can still be a high-impact option, especially if you focus on donating to the most effective projects that could use the extra funds.

If you’re skilled enough to work at top companies, software engineering is a well-paid career. In the US, entry-level software engineer salaries start at around $110,000. Engineers at Microsoft start at $150,000, and engineers at Google start at around $180,000 (including stock and bonuses). If you’re successful, after a few years on the job you could be earning over $500,000 a year.

Pay is generally much lower in other countries. Median salaries in Australia are around 20% lower than salaries in the US (approximately US$80,000), and around 40% lower in the UK, Germany, Canada, and Japan (approximately US$60,000). While much of your earnings as a software engineer come from bonuses and equity, rather than just your salary, these are also lower outside the US.

If you do want to make a positive difference through donating part of your income as a software engineer, you may be able to increase your impact by using donation-matching programmes, which are common at large tech companies (although these are often capped at around US$10,000 per year).

You can read more about salaries at large tech companies below.

It’s important to note that many nonprofit organisations, including those focusing on AI safety, will offer salaries and benefits that compete with those at for-profit firms.

If you work at or found a startup, your earnings will be highly variable. However, the expected value of your earnings — especially as a cofounder — could be extremely high. For this reason, if you’re a particularly good fit, founding a tech startup and donating your earnings could be hugely impactful, as you could earn and donate extraordinary amounts.

What does a software engineering career involve?

Ultimately, the best ways to have an impact with software engineering are probably things like working at an AI lab or a particularly effective nonprofit.

To get there, there are two broad paths that you could follow to build software engineering skills (and, given the high salaries in software engineering, you can earn to give along the way):

1. Working for a large, stable company (e.g. Microsoft, Google, Amazon)
2. Working for a small, fast-growing startup

In general, you will gain broadly transferable skills through either of these options. To gain experience as quickly and effectively as possible, look for roles that offer good management and mentorship opportunities. You should also make sure you gain a really deep understanding of the basics of software development.

Working at a top-tier tech company also holds comparable prestige to working in finance or consulting, and gives you the opportunity to make connections with wealthy and influential people, many of whom are impact-minded and interested in doing good.

You’ll need different skills, and work at different jobs, depending on whether you want to be a front-end, back-end (including machine learning), or full-stack developer.

Working for a large software company

The best way to develop software skills is to practise writing code and building software through years of experience. Direct one-on-one mentorship is extremely valuable when developing skills, and this is often provided through software engineering jobs at large tech companies.

Top firms (e.g. Microsoft, Google, Amazon) are particularly good at providing training to develop particular skill sets, such as management and information security. After talking with people who have experience in training at both tech giants and elsewhere, we think that this internal training is likely the best way to develop knowledge in software engineering (other than on-the-job practice), and will be better than training provided outside of these big tech companies.

However, it’s important to ensure that your role provides you with a variety of experiences: five years of software development experience is not the same as having the same year of experience five times over.

For example, it can be harder to gain full-stack or transferable front-end development experience at a large company. Many large mature products have a large front-end team making many small tweaks and analysing their performance in experiments. This provides good training in experiment design and analysis, but often isn’t very transferable to the sorts of front-end work you’d do at smaller companies or nonprofits, where you’ll often be working in a much smaller team with a focus on developing the experience as a whole rather than running experiments on small changes.

It generally takes around two years for new starters at big tech companies to have the experience they need to independently work on software, and another two years to reach a position where they are able to give advice and support to others in the company and manage projects.

Key career stages at large tech companies

First you’ll need some basic experience. You can get this from a relevant degree; working on a job at a smaller, less prestigious company; or from a bootcamp (see how to enter below for more).

New graduates, and other people with a couple of years of relevant experience, will start out as junior engineers. As a junior engineer, you’d complete small, clearly specified tasks and gain a preliminary understanding of the software development lifecycle. You’ll generally be given lots of guidance and support from more experienced engineers. You usually stay in this role for around three years, gradually expanding your scope. In the US, you’d be paid an entry-level compensation of $100,000 to $200,000 (as of early 2022).

Once you’ve successfully demonstrated that you can work on projects without needing much support, you’ll be given more responsibility. For a couple of years, you’ll work on more complex projects (often in one or two languages in which you’ve specialised), and with less support from others.

After five to eight years², you’ll generally progress to a senior engineer position. As a senior engineer, you write complex applications and have a deep understanding of the entire software lifecycle. You may lead small teams or projects, and you’ll be expected to provide mentorship and guidance to junior engineers. You can stay in this role for much of your career, though it becomes harder to compete with younger talent as you get older. Compensation in 2022 at this level is around $300,000 to $400,000 in the US.

At this point you may have the skills to leave and become a technical founder or CTO of a startup. This is a highly variable option (since most startups fail), but could be one of the highest expected value ways to earn to give given a chance of wild success.

Progressing past senior engineers, you’re typically responsible for defining as well as doing your job. You may go into management positions, or could become a staff engineer. Staff engineers, while still building software, also set technical direction, provide mentorship, input an engineering perspective to organisational decisions, and do exploratory work. At this level, at top firms in the US, you can earn upwards of $500,000 and sometimes more than $1,000,000 a year.

Software engineering is unusual in that you can have a senior position without having to do management, and many see this as a unique benefit of the career. (To learn more about post-senior roles, we recommend The Staff Engineer’s Path by Tanya Reilly and the StaffEng website.)

Working for a startup as a software engineer

Working for a startup can give you a much broader range of experience, including problem-solving, project management, and other ‘soft’ skills — because unlike in large companies, there is no one else at the organisation to do these things for you. You can gain a strong understanding of the entire development process as well as general software engineering principles.

Startups often have a culture that encourages creative thinking and resourcefulness. This can be particularly good experience for working in small software-focused nonprofits later in your career.

However, the experience of working in small organisations varies wildly. You’ll be less likely to have many very senior experienced engineers around to give you the feedback you need to improve. At very small startups, the technical cofounder may be the only experienced engineer, and they are unlikely to provide the level of mentorship provided at big tech companies (in part because there’s so much else they will need to be doing). That said, we’ve spoken to some people who have had great mentorship at small startups.

You also gain responsibility much faster at a fast-growing startup, as there is a desperate need for employees to take on new projects and gain the skills required. This can make startups a very fertile learning ground, if you can teach yourself what you need to know.

Pay at startups is very variable, as you will likely be paid (in large part) in equity, and so your earnings will be heavily tied to the success of the organisation. However, the expected value of your earnings may be comparable to, and in some cases higher than, earnings at large companies.

Many startups exit by selling to large tech companies. If this happens, you may end up working for a large company anyway.

Take a look at our list of places to find startup roles.

Moving to a direct impact software engineering role

Working in AI safety

If you are looking to work in an engineering role in an AI safety or other research organisation, you will probably want to focus on back-end software development (although there are also front-end roles, particularly those focusing on gathering data from humans on which models can be trained and tested). There are recurring opportunities for software engineers with a range of technical skills (to see examples, take a look at our job board).

If you have the opportunity to choose areas in which you could gain expertise, the experienced engineers we spoke to suggested focusing on:

• Distributed systems
• Numerical systems
• Security

In general, it helps to have expertise in any specific, hard-to-find skill sets.

This work uses a range of programming languages, including Python, Rust, C++ and JavaScript. Functional languages such as Haskell are also common.

We’ve previously written about how to move into a machine learning career for AI safety. We now think it is easier than we previously thought to move into an AI-safety-related software engineering role without explicit machine learning experience.

The Effective Altruism Long-Term Future Fund and the Survival and Flourishing Fund may provide funding for promising individuals to learn skills relevant to helping future generations, including new technologies such as machine learning. If you already have software engineering experience, but would benefit from explicit machine learning or AI safety experience, this could be a good option for you.

If you think you could, with a few weeks’ work, write a new feature or fix a bug in a major machine learning library, then you could probably apply directly for engineering roles at top AI safety labs (such as Redwood Research, DeepMind, OpenAI, and Anthropic), without needing to spend more time building experience in software engineering. These top labs offer pay that is comparable to pay at large tech firms. (Read more about whether you should take a job at a top AI lab.)

If you are considering joining an AI safety lab in the near future, our team may be able to help.

Working on reducing global catastrophic biological risks

Reducing global catastrophic biological risks — for example, research into screening for novel pathogens to prevent future pandemics — is likely to be one of the most important ways to help solve the world’s most pressing problems.

Through organisations like Telis Bioscience and SecureDNA (and other projects that might be founded in the future), there are significant opportunities for software engineers to contribute to reducing these risks.

Anyone with a good understanding of how to build software can be useful in these small organisations, even if they don’t have much experience. However, if you want to work in this space, you’ll need to be comfortable getting your hands dirty and doing whatever needs to be done, even when the work isn’t the most intellectually challenging. For this reason, it could be particularly useful to have experience working in a software-based startup.

Much of the work in biosecurity is related to handling and processing large amounts of data, so knowledge of how to work with distributed systems is in demand. Expertise in adjacent fields such as data science could also be helpful.

There is also a big focus on security, particularly at organisations like SecureDNA.

Most code in biosecurity is written in Python.

If you’re interested in working on biosecurity and pandemic preparedness as a software engineer, you can find open positions on our job board.

Other important direct work

Nonprofit organisations and altruistic-minded startups often have very few team members. And no matter what an organisation does, they almost always have some need for engineers (for example, 80,000 Hours is not a software organisation, but we employ two developers). So if you find an organisation you think is doing something really useful, working as a software engineer for them might be an excellent way to support that work.

Engineering for a small organisation likely means doing work across the development process, since there are few other engineers.

Often these organisations are focused on front-end development, with jobs ranging from application development and web development to data science and project management roles. There are often also opportunities for full-stack developers with a broad range of experience.

Founding an organisation yourself is more challenging, but can be even more impactful. And if you’ve worked in a small organisation or a startup before, you might have the broad skills and entrepreneurialism that’s required to succeed. See our profile on founding new high-impact projects for more.

Reasons not to go into software engineering

We think that most people with good general intelligence will be able to do well at software engineering. And because it’s very easy to test out (see the section on how to predict your fit in advance), you’ll be able to tell early on whether you’re likely to be a good fit.

However, there are lots of other paths that seem like particularly promising ways to help solve the world’s most pressing problems, and it’s worth looking into them. If you find programming difficult, or unenjoyable, your personal fit for other career paths may be higher. And even if you enjoy it and you’re good at it, we think that will be true for lots of people, so that’s not a good reason to think you won’t be even better at something else!

As a result, it’s important to test your fit for a variety of options. Try taking a look at our other career reviews to find out more.

How much do software engineers earn?

It’s difficult to make claims about software engineer earnings in general.

For a start, almost all of the official (especially government) data on this is on salaries rather than total compensation. By the time you’re a senior engineer, less than half of what you earn will be from your salary — the rest will be from bonuses, stock, and other benefits.

Most government data also reports median salaries, but as we saw when looking at progression in big tech firms, very senior software engineers can earn seven-figure compensations. So we should expect the distribution of total compensation to be positively skewed, or possibly even bimodal.

As a result, you should think of the figures below as representing salaries for early- to mid- career software developers.

Even given all these caveats, the figures we present here are instructive for understanding the relative salary levels (e.g. between locations), even if the absolute values given aren’t perfect.

More data is available at Levels.fyi, which collects data from people self-reporting their total compensation, and also has data on the distribution of what people earn, rather than just averages.

Software engineering salaries in the US

Here are the median US salaries for software developers, from the US Bureau of Labor Statistics:

Median US salaries for software engineers in 2020 (excluding bonuses)³

Here are the median salaries at different levels of progression, both in the US as a whole and in Mountain View and Palo Alto (i.e. Silicon Valley).⁴ In general, salaries rise quite rapidly in the early stages of the career, but then level off and grow by only a few percent per year after around a decade. However, this is probably offset by increases in other forms of compensation.

Median US salaries for software engineers in 2020 at different levels of progression

For figures on total compensation, especially at top companies, we can again look at Levels.fyi. These figures are far higher. Entry-level compensation is around $150,000, rising to $300,000 to $400,000 for senior engineers, and above $500,000 for late-career engineers. The top compensation levels reported are over $1,000,000.

Salaries also vary by location within the US; they are generally significantly higher in California (although web developers are best paid in Seattle).

Mean salary by US region in 2020⁵

These data are supported by Levels.fyi data on various locations in the US (e.g. Atlanta, New York City, Seattle, and the Bay Area).

Notably, the differences between locations in salaries at the 90th percentile is much higher than the differences in median salaries.

Compensation by US region in 2020⁶

It’s worth noting, however, that the cost of living in Silicon Valley is higher than in other parts of the US (Silicon Valley’s cost of living is 1.5 times the US national average⁷), reducing disposable income. (In general, data on average cost of living is particularly representative of the costs you’d expect to pay if you have a family or want to own a house.)

If you want to estimate your own disposable income given different scenarios, you can try these tools:

• Estimate your post-tax income
• Find out the cost of living in different cities
• Calculate your donatable income

Software engineering pay in other countries

Software engineers are paid significantly less outside the US. The UK Office for National Statistics found that the mean salary for “programmers and software development professionals” in 2020 was £46,000 (US$59,000 in 2020).⁸ Even when looking at full compensation, we see similar trends across the world.

Software engineer compensation outside the US⁶

The only countries with earnings as high as the US are Israel and Switzerland, and no countries have earnings as high as Seattle or the San Francisco Bay Area. The cost of living in major cities in Israel and Switzerland is around 20% higher than in Silicon Valley.⁹

Compensation across the world is often higher if you work from a major city.

Software engineer compensation in major cities outside the US⁶

It can be difficult to get a visa to work in the US. For example, US immigration law mandates that a maximum of 65,000 H-1B visas (one of the most common types for software engineers) are issued a year. Also, because of the cost of flying you out for an interview, there will often be a higher bar for international applicants passing phone interviews.

There are some things that can make it easier to get a visa:

• Having a degree in computer science or other field related to your job
• Applying to companies with enough capital and flexibility to bear the time and financial costs of the visa process
• Having a specific unusual skill set that may be hard to find in the US

Take a look at this blog to find out more.

Despite all of this, remote work in software development is becoming far more common. There’s a growing trend for a few companies to hire globally for remote roles, and pay US-market compensation. If you manage to get one of those roles, you can earn a lot from anywhere.

Software engineering job outlook

The future demand for software engineers is promising. The US Bureau of Labor Statistics projects 22% growth in US employment of software engineers from 2020–30, which is much higher than the growth rate for all occupations (8%). The main reason given for this growth is a large projected increase in the demand for software for mobile technology, the healthcare industry, and computer security.

The number of web development jobs is projected to grow by 13% from 2020–2030. The main reasons for this are the expected growth of e-commerce and an increase in mobile devices that access the web.

What does this mean for future salaries? Strong growth in demand provides the potential for salary growth, but it also depends on how easily the supply of engineers can keep up with demand.

Software engineering job satisfaction

The same high demand for software engineers that leads to high pay also leads to high bargaining power. As a result, job satisfaction among software engineers is high.

Many software engineers we have spoken to say the work is engaging, often citing the puzzles and problems involved with programming, and being able to enter a state of flow (which is one of the biggest predictors of job satisfaction). On the other hand, working with large existing codebases and fixing bugs are often less pleasant. Read our five interviews with software engineers for more details.

Work-life balance in software engineering is generally better than in jobs with higher or comparable pay. According to one survey, software engineers work 8.6 hours per day (though hours are likely to be longer in higher-paid roles and at startups).

Tech companies are progressive, often having flexible hours, convenient perks, remote working, and a results-driven culture. The best companies are widely regarded as among the best places to work in the world.

Examples of people pursuing this path

How to predict your fit in advance

The best way to gauge your fit is to try it out. You don’t need a computer science degree to do this. We recommend that you:

1. Try out writing 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. Once you know the fundamentals, try taking an intro to computer science and programming class, or work through free resources. If you’re in college, you could try taking CS 101 (or an equivalent course outside the US).
2. Do 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. Contributing to open-source projects in particular lets you work with very large existing codebases.
3. Take an internship or do a coding bootcamp.

It seems likely that a few software engineers could be significantly better than average. These very best software engineers are often people who spend huge amounts of time practising. 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 enter this field

While a degree in computer science or a quantitative subject is often helpful, many entry-level jobs don’t require one, meaning that software engineering is open to people with backgrounds in humanities and social sciences.

To enter, you need some basic programming skills and to be able to demonstrate a strong interest in software engineering. We’ve seen many people with humanities and social science degrees get junior software engineer jobs with high salaries, just through learning on their own or through coding bootcamps.

Learning to program

Basic computer programming skills can be extremely useful whatever you end up doing. You’ll find ways to automate tasks or analyse data throughout your career. This means that spending a little time learning to code is a very robustly useful option.

• Learning on your own. There 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. Don’t be discouraged if your code doesn’t work the first time — that’s what normally happens when people code!
• Attending a coding bootcamp. We’ve advised many people who managed to get junior software engineer jobs in less than a year through 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 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 computer science at university (or another subject involving lots of programming). 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. It will also give you a better theoretical understanding of computing than a bootcamp will (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 CS 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 your target employers (whether big tech companies or nonprofits), you’ll gain practical experience and the key skills you otherwise wouldn’t pick up from academic degrees (e.g. using version control systems and powerful text editors). Take a look at our list of software engineering (and machine learning) internships at top companies.

Getting your first job in software engineering

Larger companies will broadly advertise entry-level roles. For smaller companies, you may have to reach out directly and through your network. You can find startup positions on job boards such as AngelList, and many top venture capital firms have job boards for their portfolio companies.

Large software firms can have long and in-depth interview processes. You will be asked about general software knowledge, and later rounds of interviews are likely to give you problems around coding and algorithms, during which you will be asked to collaborate with the interviewer to solve the problem.

It’s worth practising software engineering interview questions in advance; often this means apply for companies you are less likely to want to work at first, and use these applications to get used to the process. This can be a stressful process (in part because you might face some early rejections, in part because it’s tricky to navigate applying if you don’t really want the job that much), so it’s important to take care of your mental health throughout the process.

It will also probably help to study the most popular interview guide, Cracking the Coding Interview. You can also practise by doing TopCoder problems.

We think that this guide to getting a software engineering job is particularly helpful. There are six rough steps:

1. Send a company your resume. Make it as specific as possible to the job you’re applying for, and proofread it carefully. If you can get a referral from a friend, that will significantly increase your chances of success.
2. Speak to a recruiter. Read up about the company in advance, and make sure you have questions. Be nice — it’s going to help if the recruiter is on your side.
3. Have a technical phone interview. You’ll solve some problems together. Make sure you ask questions to clarify the problem, and strategise about the best possible approach before you start writing code. Finish by checking for bugs and make sure you’re handling errors correctly. When you’re done, ask the interviewer some questions!
4. Have a three- to six-hour on-site interview. It’s key to talk out loud as you work through a problem. And again, ask your interviewer some questions about them and the company.
5. Get an offer from the recruiter. You should make sure they think you are seriously considering the company or you may not get an offer. If you don’t get an offer, ask for feedback (though it’s not always possible for companies to give detailed feedback). If you need more time to think (or to apply elsewhere), tell them in advance, and they may choose to wait to give you details when you’re more ready to go through with an offer.
6. Accept the offer!

Want one-on-one advice on pursuing this path?

If you think software engineering might be a great option for you, but you need help deciding or thinking about what to do next, our team might be able to help.

We can help you compare options, make connections, and possibly even help you find jobs or funding opportunities.

APPLY TO SPEAK WITH OUR TEAM

Learn more

Top recommendations

• The 80,000 Hours Podcast on Artificial Intelligence (a collection of 10 key AI episodes from our podcast)
• Our review of careers in information security
• Our review of being an early employee at a startup

Further recommendations

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 for jobs, see our curated list of opportunities:

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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.

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A forthcoming paper, by economists at UPenn and Yale,¹ reports a survey of the economic literature on these spillover benefits for the 11 highest-earning professions.

There’s very little literature, so all these estimates are very, very uncertain, and should be not be taken literally. But it’s interesting reading – it represents a survey of what economists think they know about this topic, and it’s surprisingly little.

Here are the bottom lines – see more detail on the estimates below. (Note that we already discussed an older version of this paper, but the estimates have been updated since then.)

We calculated mean income for 2005 in an earlier article. We increased income by 30% to account for nominal wage growth since then.

The paper uses the expressions spillover and ‘externality’. An ‘externality’ is a technical term for a ‘cost or benefit that affects a party who did not choose to incur that cost or benefit.’ The authors of the paper call it an ‘externality’ when someone who buys a service does (or does not) benefit after taking account of the cost of purchasing it. This is a nonstandard usage, but fine for our purpose of assessing the overall social value of different professions.

The externalities per dollar were calculated from table 3 in the paper. This gives the externality share as a fraction of income. We divided this by the total income share for the profession to get the externalities per dollar of income. We then multiplied this by the mean income. The working is in the table below.

For research, the authors made three estimates of the externality share, and for management they made two (as explained in the quoted section of the two). We took the averages of these estimates, which is why the values we report are different from those that appear in table 3 in the paper.

How did the economists estimate the size of the externalities?

Here is more explanation of how the externality estimates were made in the paper:

To calculate the marginal social output from each profession, we draw on the literatures that estimate economy-wide externalities from various professions. Although we have done our best to faithfully represent the current literature, we emphasize that these estimates are highly uncertain extrapolations from heterogeneous and not easily comparable studies primarily aimed at different estimands than those we draw from them.

Our prior is that Coasian bargaining should eliminate externalities, so when these literatures do not offer a clear finding, we set the aggregate externality to 0. In the cases in which these literatures offer conflicting results, we adopt one value as a baseline and use an alternate value for sensitivity analysis. (Note: In the tables above, we just took the average.)

We ultimately care about marginal externalities rather than the average over the whole profession, but the authors didn’t try to estimate the difference between the two. One industry that might be good on average, but harmful if it becomes larger is finance.

Here is more info on the estimates for specific jobs (the papers used are listed at the end):

Arts Although some evidence, and a number of good theoretical arguments, suggest the arts generate some positive externalities, we are unable to find a plausible basis for estimating the magnitude of these externalities, and consequently assume 0 to be conservative.

Engineering The only study we found of externalities from engineering is a cross-country ordinary least-squares regression by Murphy et al. (1991). They investigate the impact of the allocation of talent on GDP growth rates rather than on GDP levels. To be conservative and fit within our static framework, we interpret these impacts as one-time effects on the level of output rather than impacts on growth rates. We multiply their estimate of the GDP impact of an increase in the fraction of students studying engineering by the number of students studying engineering according to the OECD to obtain an externality of 0.6% of total income.

Finance French (2008) estimates the cost of resources expended to “beat the market” by subtracting passive management fees from active management fees. Bai et al. (Forthcoming) show the informativeness of stock and bond prices (measured in their ability to predict earnings) has stayed constant since 1960, despite a vast growth of the finance profession documented by Philippon (2010). We therefore interpret the entirety of French (2008)’s estimates, which amount to 1.5% of total income in 2005, as negative externalities from finance.

Law Murphy et al. (1991) estimate externalities from law in the same manner they calculate externalities from engineering, and we apply the same methodology to yield a −0.2% externality as a percent of total income. Kaplow and Shavell (1992) present several models of why the provision of legal advice may exceed the social optimum.

Management Two strands in the literature offer competing views on the externalities of management. According to the first strand (Bertrand and Mullainathan, 2001; Malmendier and Tate, 2009), chief executive officer (CEO) compensation shifts resources from shareholders to managers in ways that do not actually reflect the CEO’s marginal product. Piketty et al. (2014) argue that 60% of the CEO earnings elasticity with respect to taxes represents this rent-seeking behavior, implying the negative externalities from management are 8.1% of total income. The other half of the literature argues market forces can explain CEO compensation (Gabaix and Landier, 2008) and suggests that therefore externalities are 0. Most managers in our sample work at lower levels of firms where the problems of measuring marginal product highlighted by the critics of CEO compensation are less likely to apply, so we take the figure of 0 as our baseline and use the −8.1% figure in sensitivity analysis.

Medicine We could find no literature estimating the externalities of (non-research) medicine and so set the externality to 0 to be conservative.

Operations This profession is comprised of consultants and IT professionals. Bloom et al. (2013) conducted a field experiment to determine the causal impact of management consulting on profits. They interpreted their results as consistent with the view that consultants earn approximately their marginal product, and thus we assume no externality for consulting.

Real Estate We could find no literature estimating the externalities of brokers, property managers, and appraisers and so set the externality to 0 to be conservative.

Research Our baseline estimate for the externalities from research comes from the value of medical research, measured in terms of people’s willingness to pay for the additional longevity this research makes possible. Murphy and Topel (2006) estimate the annual gains of medical research equaled 25.72% of GDP from 1980-2000. Traditional GDP accounting does not capture this externality, in contrast to our model, so we divide it by GDP augmented with this externality to obtain .2572 1+.2572 = 20.5%. Although this externality may be the largest externality from academia and science, this estimate is still conservative in assuming no gains accrue from other research fields. An alternative measure of research externalities comes from the literature that calculates the social returns to R&D. Jones and Williams (1998) suggest the socially optimal amount of R&D activity is four times the observed amount, which we loosely translate into a three times externality or 5.6% of GDP. A narrower benchmark for this externality focuses only on the externalities of universities to profits made by geographically proximate firms as studied in Jaffe (1989). We use his estimates to calculate a much smaller 3.0% externality, which we use as a lower-bound estimate in our sensitivity analysis.

Sales Although an extensive theoretical literature argues the welfare effects of advertising can be positive or negative (Bagwell, 2007), we are not aware of any work attempting a comprehensive estimate of externalities, and therefore, as with medicine, we use an externality of 0.

Teaching We calculate the social product of teaching as the impact of an additional year of schooling on aggregate earnings of all workers in the economy. The spillover from teaching is then this social product less the annual earnings of all teachers. As our estimate of the effect of a year of schooling on earnings, we use a 10.3% gain, which equals the midpoint of the numbers collected in Card (1999)’s review. Because teachers earn 3.4% of economy income, we use a spillover from teaching of 6.9% of economy income. We also compute the aggregate effect of teaching on earnings using Chetty et al. (2014)’s measure of teacher quality and its long-run impact on eventual student earnings. We use the ratio of total teacher pay to its standard deviation in our data multiplied by the social product Chetty et al. (2014) estimate for a standard deviation in teacher quality to obtain an aggregate effect equal to 10.2% of economy income. This figure leads to a spillover of 6.8% of economy income. Given the similarity between the two spillover estimates and the fact that the estimate based on returns to schooling is more easily interpretable in the aggregate, we use the Card (1999) number as our estimate.

How many extra externalities will result if you take a job in the industry?

The estimates given are supposed to apply to additional ‘marginal workers considering joining an industry. However, if you join an industry, the additional externalities will probably be less.

First, diminishing returns have been ignored, and, in established industries like these, diminishing returns will probably mean that additional workers will have less impact – positive or negative – than the average.

Second, by taking a job in the industry, you won’t lead to a whole extra person’s salary worth of income earned in the industry. We could model you taking the job as an increase in the labour supply for that industry by one. This will slightly decrease wages in the industry, and won’t lead to a whole extra worker employed (the amounts will depend on the supply and demand elasticities in the industry). This means the income earned by the workers in the industry will increase by less than a whole salary. If the externality-income ratio stays the same, then by working in the industry, you change externalities by less than what’s in the table. (Though note the externality-income ratio could easily change – it seems more natural to tie it to the revenue of the industry than worker income.)

On the other hand, if your salary would be higher due to unusually good personal fit, then you’d need to increase the estimates.

To do a full analysis, there are other adjustments we’d want to make as well (see our upcoming article on replaceability).

What conclusions can we draw from these estimates? Donations are more important

The main point that strikes me is that, with the exception of research, all the estimates are fairly small relative to salary. And, that’s even if we ignore the points in the section right above.

This suggests that at least for “normal jobs”, your donations to charity are a more significant component of your social impact.

For instance, if we suppose making a random American $1 wealthier is worth one “unit” of impact, then I think donations to GiveDirectly are at least 20 times better, mainly because the recipients are about 100 times poorer than the average American. And I think Against Malaria Foundation (AMF) is about 4 times more effective than GiveDirectly, so it creates 80 units of social value per dollar of donations.

This means that, if someone earning $100,000 per year donates 10% of their income to AMF, they create $0.8m of social value per year, which is about as large as the largest externality estimates in the table.

Moreover, I think there are charities that are 10 times more effective than AMF, and if you earn $100,000, it would be possible to give 30% rather than 10%. Putting the two effects together, it’s plausible your donations could be another 30 times more valuable.

I also think the externality estimates are more likely too large than too small. My prior is that the externalities are close to zero, and the authors seem to have updated too aggressively based on speculative calculations. Moreover, I don’t think “dollars of wealth created” is a very good proxy for humanity having a good long-run future, which is the main thing I care about. I think it’s more important to focus on issues around technology and existential risk, and most jobs don’t have much impact on that.

This would mean that how much and where you donate is normally far more important than the “direct” impact of your job, which is an argument for earning to give. The exception would be if you work at a very high-impact organisation, such as AMF, you’re a good fit with research, you work in government or politics, or take other unusually important positions. Advocacy could also be a path that’s competitive with earning to give in terms of impact. There could also be exceptions if you take an unusually harmful career.

That aside, it seems true that most people could have more impact by earning to give. But, that doesn’t mean most members of our community should earn to give, because they might be able to take many of the even better jobs just listed.

What about the estimates for specific jobs? Unfortunately, we can’t draw many conclusions about these. The estimates are based on rough calculations in just one or two papers. I’d prefer to see a more thorough process where (i) we start with a prior estimate near zero, (ii) we consider theoretical arguments as well as empirical estimates and (iii) we update away from the prior depending on how robust the evidence is.

If this were done, my guess is that the ordering would be similar, though art and medicine would also have positive externalities, marketing might be a small negative, and the size of the externalities would generally be smaller (perhaps with the exception of research).

Read next: Which jobs are highest-impact?

Which are the 10 most harmful jobs?

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Wave is one of the most high potential social impact for-profit startups we’re aware of, and it was co-founded by someone in our effective altruism community – Lincoln Quirk. Wave allows immigrants to send money from North America to relatives in Kenya, Uganda, Tanzania and Ethiopia with much lower fees than if they used Western Union or MoneyGram. (Though Wave existing is nothing to do with 80,000 Hours, someone we recently coached chose to work for Wave and help them expand into the UK.)

Why is Wave such an important company? Previously, if immigrants wanted to send remittances, they had to use Western Union or MoneyGram. Both the sender and receiver would have to go to a physical outlet to make the transfer, and worst of all, the sender would have to pay 10% in transfer costs! Lincoln Quirk and his cofounder Drew Durbin have built software that allows instant transfers from a mobile phone in the US or Canada to a mobile phone in Eastern Africa or Ethiopia – and they only charge 3%, a saving of 7%.

For each dollar of revenue that they make, they are saving $2.33 for someone in the world’s poorest countries. Assuming a 20% profit margin, the figure is $12 in savings for each $1 of profit.

The potential positive impact of this idea is huge. Annual global remittances are $450 billion, several times the total global foreign aid budget. The potential impact Wave could have, by lowering the cost of sending remittances by 70 percent, amounts to billions of dollars in extra financial flow from rich countries to poorer countries every year.

This business has became viable where it previously hadn’t been because of the rapid growth of M-PESA and other mobile phone based payments systems in Eastern Africa. These services are better than what’s available in the US or UK today.

Lincoln was a coder and spent 1.5 years working in the video game industry. He preferred the idea of starting companies, but wasn’t convinced he was good enough at it, and lacked good enough ideas. However when he met his co-founder Drew, he was sufficiently motivated by the product ideas they came up with together to try.

People in the Centre for Applied Rationality and the associated community encouraged him to pursue entrepreneurship to have a larger impact. Though he thinks he would have done so anyway he found their advice helpful in other ways:

“CFAR material was highly impactful in making me far better at startups than I otherwise would have been: For choosing what to work on, noticing rationalization and aversions; for being a good communicator, asking for examples and listening to what people actually say instead of what you want them to say; for individual productivity, trading off time and money for attention, and installing conscientiousness systems.”

Early on he applied and got into Y Combinator. Their first attempted product was a dating app. This is a classic sexy Silicon Valley idea, that, while useful, is unlikely to change the world in a really big way because a huge number of such apps are already being funded by venture capitalists. Fortunately, their dating app didn’t work out, and neither did texting, location sharing or video chat. Lincoln’s explanation was that “although we created great products and were effective at distribution, we weren’t working on something people really wanted.”

They considered a bunch of other options, but decided to pursue Wave because of the larger potential to help people, and the fact that the Kenyans they spoke to were desperate to use the product even before it existed. In Lincoln’s words:

“The most sticky moment was the first week of researching the idea when we talked to some Kenyans and described our idea, and they were very excited and said “this will grow like wildfire” and “when can I use it?” That was by far the most excited anyone had been about one of our startup ideas, and it was multiple people. We looked at each other and said “we have to make this work”.”

Lincoln is glad he stopped trying to do good indirectly by just trying to become successful first:

“…during our prior startups we had previously been in the mindset of “make something successful first, in order to generate experience and get the resources to do something truly impactful”.

We realized that was a bad idea – we called it the “deferred life plan”. If you want a thing in the world, it’s much better to directly work on it even if your leverage seems tiny; we were rationalizing not working on it because the other path was more comfortable. In retrospect, the deferred life plan was one of the worst lifestyle choices I had made.”

Wave has the wind behind its back in part thanks to the many benefits of being a for-profit (rather than government program or nonprofit):

• You can fund your growth through your own revenue;
• You can raise capital more easily because you have the promise of future revenue;
• Because you have access to money, you can hire talented staff who are motivated by a higher salary;
• Thanks to your growth, you can reach a huge number of users and do a lot of good, even if you benefit each one less because you have to charge.

If Lincoln had chosen to become a software engineer, he likely would have made between $150,000 and $300,000 a year, half of which he might have given away to people in poverty in Kenya through GiveDirectly. As it is, his company only needs to move $8 million in remittances to have a similar level of impact.¹ While the company is tight-lipped about its exact size, it’s likely well above that. The biggest argument against this approach relative to earning to give is that a similar would have happened soon anyway, as competition between remittance companies has increased a great deal in recent years. Nevertheless, because the scale of the upside is so large, even just accelerating the process a little does a lot of good.

Lincoln thinks Wave can also do good by increasing the growth of electronic currencies across Africa. Lincoln commented:

“A huge aspect of our potential impact is that we’re trying to create our own mobile money. Mobile money systems have transformed Kenya – Kenya is rapidly moving to a cashless economy (40% of its GDP already goes through M-Pesa) – reducing transaction costs, theft risk, and providing a platform on which lots of incredible products are built, including Wave, M-Kopa Solar and GiveDirectly. If we can create the M-Pesa of Africa we could move the needle on the rate of Africa’s development as a whole.”

If you’d like to learn more, you can follow Wave on Facebook, or check out this interview with Lincoln on a Kenyan TV show. Lincoln also has a personal website.

For more on how to do good through startups, join our newsletter and we’ll send you the rest of our series when it’s finished. Or check out our interview with a startup founder who’s pledged to give all his income above minimum wage to charity.

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I recently interviewed Ben West (second to left), the founder of Health eFilings. After reading 80,000 Hours’ website, Ben entered tech entrepreneurship – from software engineering – in order to ‘earn to give’. Amazingly, Ben pledged to donate any money he made above the minimum wage. His company helps American physicians file paperwork with the US government, and collect ‘performance based pay’, much more easily. Several other 80,000 Hours alumni have ended up working in his company. You can read a summary of the key points from the interview below.

Summary of the interview

• Ben West was influenced by Peter Singer’s work when he was young to start donating his income. Four years ago he was a software engineer donating to New Harvest, a meat substitute organisation.
• He spent almost a decade at a large healthcare IT company, which helped to prepare him for what he’s doing now. He doesn’t think he could have successfully started this company without having experience in the health IT sector first.
• He learned about 80,000 Hours through a link on the blog Overcoming Bias. Reading our work on entrepreneurship made him willing to consider starting his own business despite the fact that he’s risk averse by nature. He then spoke with some other well-informed people, including Carl Shulman (who volunteered for 80,000 Hours in the early days), who gave him more information about what the path involved.
• Ben was turned down by Y Combinator but soon afterwards teamed up with a serial entrepreneur, which combined with initial growth, meant they were able to raise about $1m of venture capital funding. If you believe the current valuation of the company, last year he earned 20 times what he would have in his previous software engineering role, and expects to donate $10-$20m. Though, he points out there’s still at least a 50% chance the company fails and he has nothing to give!
• Before making the switch, he took some time off from work to research startups. The main concern was the risk involved, and coming to believe on a gut level that this path was for him. He was highly uncertain what ‘reference class’ to use to predict his likely earnings. He decided to experiment for a year and test if he could get any customers. They got their first customer within the first three months.
• He initially worked in a co-working space to meet other people attempting tech entrepreneurship, and would recommend doing something similar if you’re considering making the switch. It makes it seem much more possible.
• In his first direct sales push he sought people within the effective altruism community to help out, and found 50 people who were interested. He’s also had a lot of mentoring from another 80,000 Hours plan changer, Alex, which helped him get early traction.
• The company saves doctors a lot of time filing information on patient outcomes with the government and helps them avoid errors.
• Ben agrees about the importance of concrete exceptional achievements in opening doors. Being able to mention impressive sounding achievements in one sentence helped him get started with the company.
• Ben isn’t convinced that starting a tech company is a great way to build up career capital. For people who try once and fail, they’re not any more likely to succeed the second time around, which suggests they don’t learn much. If your company doesn’t take off you probably also won’t walk away with a great network, because that comes when your business gets traction. This suggests that luck may be a huge factor in this industry.

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Tell us about your background

I did my undergraduate work at UCLA in Political Science and Public Policy and then continued with postgraduate study at the University of Cambridge focusing on International Relations. My plan was to get a PhD and then stay in academics and shape International Security policy. However a year in, I realised that the practical impact of my work would be marginal at best, so I finished with a Master’s degree and began to look for opportunities that would actually have a discernible effect on the world. I got involved with Founders Forum For Good — the precursor to what I do now with the Founders Pledge — where I focused on helping social entrepreneurs build and scale businesses. Before all that, I spent a couple years in finance in the US, started and sold a business in Europe, and ran a chain of Segway dealerships in California. All in, a pretty bizarre career trajectory if I’m honest, which was largely the result of having no clear idea of what I wanted to do with my life until recently.

What is the Founders Pledge?

Through the Founders Pledge, entrepreneurs make a commitment to donate at least 2% of their personal proceeds to charity when they sell their business. Keeping this simple idea in mind, I’ve been able to provide tech founders and investors with a tax efficient way to do good in the world without taking time and effort away from the projects and businesses they’re working on. We’re a small team now and will be expanding soon. More on that later.

What led you to set up the Founders Pledge?

I had been working with social entrepreneurs for a while through Founders Forum For Good, giving away grants to help this community start and scale socially positive businesses. Unfortunately, this didn’t produce the level of impact I was hoping for, so I had to shift gears. There is a lot of nuance to starting a business, and having enough funds right at the beginning isn’t the only hurdle — it certainly wasn’t for the community we were supporting. So rather than continuing down this path, I flipped the model around and focused on a sector that makes some of the best businesses: technology. The idea is simple — make it exceedingly easy for tech start-ups/founders to do good in the world with their success.

How did you find out about effective altruism, and how does effective altruism affect your plans?

My first exposure to the ideas of effective altruism was Peter Singer’s TED talk on the subject. His ideas struck a chord with me, but I didn’t dive into EA substantively until 2015 as the Founders Pledge was taking shape. At that time, I was organising an event for tech founders in the UK (Founders Forum) and the EA stuff, and more specifically 80,000 Hours, seemed like a great fit for one of the sessions. I invited Will to participate and we ended up discussing it well into the night while everyone else went off to the after-parties. This discussion, and my involvement with EA since, has been crucial and has helped shape how the Founders Pledge deploys its donations worldwide.

The good thing about the tech sector is that, as the name implies, many people within it are both technical and prefer to do things that maximise their return on investment. One of our selling points with the pledge is that our members have complete control over their respective donations. In some cases, they have a specific organisation they would like to give to. Most often, however, there is a cause area they “like” or they just haven’t thought about it at all and merely want to “do some good at some point.” Starting to sound like a good fit for effective altruism, no? After a founder makes their pledge, they can use our resources at their leisure and either decide where they’d like their money to go, or let us handle it entirely. We want people to donate, and we want to educate them on how to deploy their money most effectively (and do the most amount of good).

Only 5 of our members had heard about effective altruism (or the associated charities) before getting involved with the Founders Pledge. As of today, nearly all of our members have been introduced to effective altruism. This is happened through face-to-face interactions at our dinners and events, reading Doing Good Better (which we give away to many of our new members, and aim to give to all of our new members), or post-exit as we begin to explore high-impact giving opportunities. Of our 5 members who have realised exits to date, 2 are now planning to give to effective altruism charities. Of the $64mm raised to date, I would expect at least 33% to go to EA-aligned charities. Absent their involvement with Founders Pledge, I expect a very small percentage (less than 10%) would have gone to these charities. Beyond that, a number of other HNW and UHNW supporters of ours are now exploring x-risk as a viable cause area to support.

Overall, 80,000 Hours has been instrumental. It will increase my lifetime impact many times over.

What have you achieved so far?

Since March (2015), we’ve received 230 pledges from founders and investors worth $64 million as of today. A few of our members have actually met success already and we’re in the process of donating roughly $1.3 million to charitable causes globally.

How much are you looking to raise in donations, and by when?

Billions. Given how well received the Founders Pledge has been in the tech community, this isn’t actually that far in the future, either. Our current growth shows us hitting our first billion in pledged donations by mid 2017. Naturally, the full extent of a business’ success and when it will happen are hard to nail down, so we primarily focus on supporting our founders along the way. We want them to do good with their money effectively, but we also want that giving to help their business succeed, right from the start.

Who are you looking to hire, and when? What skillsets are you looking for?

We have a couple major hires coming up in early 2016. Official postings aren’t quite ready, but here’s the general idea:

First, our Director of Deployment position is an exciting opportunity to put many aspects of effective altruism to use. Overall, this means that you’ll be in charge of giving away the money we raise. On the front end, you’ll advise our “undecided” founders and develop online resources to help educate them on how to do the most good with their money. On the back end, you’ll be finding, vetting, and interacting with the charities to which the donations will ultimately go. You should be aligned with the EA research methodologies and how to present them to others well. The job will require a nuanced understanding of international tax regulations with regards to charitable giving and non-profit status.

Second, we’re looking for a Community Coordinator. You’ll be in charge of making sure that we are best supporting our community of pledgers. This is very open ended, but will include running our hosted events and being the members’ point of contact with the Founders Pledge. You’ll have freedom to design programmes around the needs and wants of our members and must be able to recognise when they are effective and when they are failing. We’re here to support our community and their projects, not just give away money. You should have experience planning, booking, and executing events from a dozen people all the way up to a couple hundred. Strong organisational skills are required and you should have interest in and familiarity with modern collaboration tools, resources, and philosophy.

Each position needs an inventive doer. We don’t like the phrase “self-motivated” as much as “selfless-motivated.” Knowing that you’re helping other people keeps you going and you get antsy when you lose track of making an impact with your life.

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In summary, I’ll argue:

1. There’s two broad approaches to assessing projects – the marginal cost-effectiveness approach and the growth approach.
2. The community today often wrongly applies the marginal approach to fast growing startups.
3. This means we’re supporting the wrong projects and not investing enough in growth.

At the end I’ll give some guidelines on how to use the growth approach to evaluate nonprofits.

Two approaches to assessing projects

Suppose you want to identify the startup that will generate the most (time-discounted) profit. The social impact equivalent is to find the project that will generate the largest long-run social impact per dollar invested. What rules of thumb should you use to identify these projects?

Here’s two broad approaches (with the social impact equivalent in brackets):

1. The marginal approach: Which project generated the largest profit (social impact minus costs) per dollar invested over the last year? Can we expect these levels of return to hold up at the margin? If so, invest.

2. The growth approach: Does the project have a high growth rate, large total market (address an important problem), good product (have an effective solution to this problem) and great team?

The growth approach is how Y Combinator evaluates startups, and this seems clearly the right approach in their case. If you’d only invest in startups that had been profitable over the previous year, you would have missed almost all of the biggest winners e.g. Google didn’t make any revenue at all for the first two years; Amazon still isn’t profitable. And this makes sense – if you have the opportunity to be 100x larger, then you should invest all your spare money in growth rather than run a profit. All the value comes from the scenario where you get really big and that’s all you should focus on.

The marginal approach can be applied to companies that are already big and relatively stable, like Coca Cola or a utility company. But it doesn’t make sense to apply to a startup.

Here’s the problem. People in the effective altruism community often evaluate startup nonprofits using the marginal approach rather than the growth approach.

I know I was making this mistake several years ago. What attracted me to working at 80,000 Hours was the high probability of persuading a couple of extra people into high impact careers, and achieving a high short-run leverage ratio. I was vaguely aware the project had high long-term upside – perhaps 10% of all college grads could be users – but didn’t explicitly think much about the possibility of getting really big in ten years time. Over time, however, I realised we should be focusing on the scenario where we get really big. Going through Y Combinator made me more acutely aware of the differences.

I think many donors in the community also mostly think in terms of the marginal approach. Responding to demand, this is why many startups in the community put some kind of short-run leverage ratio at the centre of their fundraising: e.g. Giving What We Can reports pledge donations per dollar, and GiveWell reports money moved against costs. Many discussions about which organisation to donate to focus on (i) the robustness of these multipliers (ii) whether similar levels of return can be projected into the future of not. This was notable during recent discussion about whether to donate to Giving What We Can (GWWC), and I’ve noticed it in many other discussions about where to donate. I’ve seen much less little attention given to which projects have the best growth prospects.

I guess the community has a bias towards the marginal approach because in the past it has put a lot of focus on supporting evidence-backed global health interventions. These interventions have the character that $1 donated leads to some quantified effects, such as malaria nets being given out and QALYs saved. In this case, it makes sense to apply the marginal approach to evaluating the project. The mistake is to then try to apply the same approach to a tiny organisation that’s growing fast. (And to be clear, I’m not saying everyone is making this mistake – there’s plenty who aren’t, including GiveWell themselves – GiveWell broadly applies the marginal approach to their top recommended charities, but wouldn’t evaluate a startup nonprofit using the same criteria, and also originally recommended GiveDirectly partly due to growth potential).

These backward looking ratios are not the best guide to future potential. For instance, if you’d looked at GiveWell’s ratios in its first couple of years, then costs exceeded money moved, especially if you included the opportunity cost of staff time. GiveWell wouldn’t have been “cost-effective” by this measure, and so you wouldn’t have donated, which would have been a mistake. A few years later in 2012, GiveWell had about a 1:10 leverage ratio, so you would have thought it was good, though not exceptional. But again that would have been a mistake: GiveWell was about to partner with Good Ventures, a foundation with thousands of times more money than what GiveWell had moved to that point.

What problems might this be causing?

1) Radically underinvesting in growth. GWWC claims $8 of donations made per $1 of costs, and over $50 of value of future donations. If GWWC is under pressure not to let this ratio drop over the next year, then the maximum GWWC can spend next year is one eighth of the total amount of money GWWC expects to influence in that year. It seems likely that the optimal amount to spend is much higher than that if you want to have the greatest possible long-run impact. In particular, GWWC has little incentive to make investments that will pay off in more than a year, because this only lowers their short-term ratio and could cause their donors to abandon them. But there probably are good opportunities that will pay off in longer than a year.

All of this means we might be investing far less in growing small organisations than we should. (This is particularly true if you also incorrectly assume diminishing returns, which comes naturally with the marginal approach. See this post on why small organisations may have increasing marginal returns). We may also be incorrectly prioritising among organisations with similar ratios, but where one has much more growth potential than another (e.g. some in the community donate to organisations that they believe to have strongly diminishing returns to scale, which the growth approach wouldn’t advise).

2) Starting an overly narrow range of projects. Several of the organisations that have been founded recently seem to be motivated in large part by the possibility of obtaining a provably high cost-effectiveness ratio in the short-term, or providing some immediate value to the community. Charity Science and Raising for Effective Giving are notable examples. These are great projects, of course, and they clearly win over earning to give for the people involved. My worry is that there are whole areas of projects we haven’t even considered that could lead to even higher long-run impact.

3) Not optimising organisations for long-run growth, e.g. by investing in impact evaluation too early. If you’re thinking with the marginal approach, then you’ll want to do a rigorous impact evaluation as soon as possible to prove that you’re (short-run) cost-effective. And many effective altruist organisations do exactly that, sometimes taking evaluation and transparency efforts to extreme levels. Thinking in terms of the growth approach, however, it’s not so obvious this is the right move. Impact evaluation takes up a significant fraction of executive time. When you consider that nonprofit executives also have to spend a lot of time fundraising (unlike for-profit counterparts), a focus on early impact evaluation means executives are left with little time to make sure the programs are actually good and you have a good team. Under the growth approach, it may make sense to delay in-depth impact evaluation until you have more resources and capacity.

Another example is hiring too early. In the growth approach, you spend the first couple of years exploring different products, with the aim of hitting one that “takes off” achieving exponential growth. New hires however, unless exceptionally able and up to speed, aren’t very good at working out what product to make, so slow you down in this stage. Later on, once you achieve ‘product-market fit’, you want to hire as fast as you can. If you’re thinking with the marginal approach, however, then you should hire whenever it won’t decrease your short-run leverage ratio, which normally means hiring a steady stream of a small number of people. In the very early stage of CEA, I think we hired too many people, especially interns, because we were thinking with the marginal approach. There’s other examples too – for instance if your aim is to make an organisation that’s huge in ten years time, then having a good organisational culture is extremely important, which is another reason to hire more slowly early on than you would with the marginal approach.

The combined result of these three mistakes is that we’ll end up with lots of small organisations, but no big wins. And because the most of the impact comes from the big wins (because the distribution of outcomes has a log-normal or power law tail), the community will end up having far less impact than we could have.

It’s not obvious the community is making these mistakes, but the comparative lack of focus on the growth approach makes me concerned we are.

Why favour the marginal approach?

The benefit supporting projects that look good according marginal approach is that you can be confident you’re at least having some impact, whereas it’s harder to make accurate assessments with the growth approach (e.g. it’s easier to be duped into supporting something with zero impact on the promise of long-term benefits). If you’re relatively skeptical, you could argue this is the best we can do.

Taken to the extreme, this view can’t be right as it would preclude properly testing new projects. For example, if you had followed this line of argument, you would have never invested in early GiveWell, and that would have been a mistake.

A more defensible view is that donors with more limited time should focus on the marginal approach (or just give to GiveWell recommended charities), whereas growth donations to startups should be left to “angel” donors, who have the substantial time and expertise required to make good assessments.

So, I don’t think everyone in the community should move to using the growth approach, but I think the balance should move in that direction.

How to evaluate projects with the growth approach?

Here’s some questions to ask if you’d like to evaluate social impact projects using the growth approach¹ (in brackets I’ve written the for-profit equivalent):

1. Does the project have a replicable approach to solving an important problem? (Is the product good?) E.g. with GWWC the question is ‘do they have a repeatable approach for encouraging more people to take the pledge’?

2. Do they have a method to do this at a much larger scale, such that if done, the value created would be much larger than costs? (Do they have a distribution strategy, and if done at scale, would the cost of customer acquisition be lower than the value of a customer?) E.g. can GWWC acquire many more members at less than the value of a pledge? Note that in the nonprofit sector this can also include options like having your intervention taken over by the government, or implemented by other organisations.

3. If the project solved the problem it’s addressing, how good would it be? (market size and growth rate). E.g. if GWWC’s entire addressable market took the pledge, how much impact would that have?

4. Is the cause the project is addressing neglected and tractable? (competitive advantage and timing) E.g. For GWWC, are there lots of other people trying to promote effective giving relative to the size of the problem?

5. Is the project’s impact growing quickly, and is it plausibly exponential? (growth rate) E.g. For GWWC, what’s the growth rate of people taking the pledge? (Bear in mind assessing the growth rate isn’t appropriate at the earliest stages on the project, before you have product-market fit).

6. Is the team altruistic, smart and determined? Do they have a track record? Do they have the relevant skills and experience?

If you want to learn more about the growth approach, check out How to Start a Startup by Y Combinator or the Startup Playbook.

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I’ve had a lot of questions about YC over the last couple of days. Here’s answers to the most common questions, plus an update on our progress since we were admitted.

FAQ

What do you get as a nonprofit in YC, and why did you join?

Instead of $120,000 of investment, you get a $100,000 donation. Otherwise, you’re treated almost exactly the same as a for-profit. This means:

• There are two partners who look after you. You meet them once every 1-2 weeks. We’re looked after by Dalton Caldwell and Paul Bucheit, the founder of Gmail.
• Kate Courteau, head of the nonprofit program also looks out for us.
• You can also request office hours with any of the other 20 or so partners. We’ve had great office hours with Kevin Hale, the founder of Wufoo, on web design, and Sam Altman, the President of YC, on strategy. We’ve also had some very useful legal help from YC’s lawyers.
• Paul Graham is retired, but you get to meet him once. Unfortunately, we didn’t yet.
• There’s a dinner each Tuesday where they bring in a tech leader to talk off-the-record, and you get to chat to your batchmates and the partners.
• There’s also group sessions and workshops.
• At the end, you do a 2:30min minute pitch at Demo Day to the biggest investors in the Valley. There’s a lot of training on how to pitch in the lead up, and YC introduces you to leads. We’ll be asking for donations rather than investment.

Why is YC letting in nonprofits?

I think it’s because they want to make the world a better place, and they spot an opportunity to build a new nonprofit ecosystem that’s much more like the startup world, a vision we share.

YC has also been admitting many more ‘for good’ for-profit startups e.g. working on biotech, clean energy and education.

What have you got out of the program so far?

1. The atmosphere. Being around so many people who want to grow billion dollar businesses as fast as possible is pretty exciting. Whenever you say “we want to grow 2x,” there’s always someone to ask “how about 10x?” We’re working hard.
2. Internalising the YC approach to startups. We think YC are the world’s experts in how to run a startup, and there’s a clear YC philosophy. We’d learned about this philosophy before, but going through the program lets you really internalise it. It has felt like gaining a whole set of new tools for getting stuff done in the world. And I think it’s especially mind-blowing if you have a nonprofit background.
3. The network. Being in YC gives you credibility and membership of a community of over 1,000 of the most proactive, positive and productive people I’ve ever met.
4. Specific advice. We expected to get more specific advice than we have, but we’ve received a lot of great tips on how to improve the product, as well as interesting big picture feedback. Overall, I think the advice for for-profits is better (as you’d expect), but YC can still help you with many key issues such as product design, pitching, hiring, thinking of a big vision, and marketing.

What are you trying to grow / how are you scaling?

We’re focusing on our online career guide, so we’re scaling in the same way many tech companies do.

Ultimately, we’re aiming to grow the rate of significant plan changes per month. Unfortunately, this metric is very lagging, so week-to-week we’re focusing on growing active user reading time 10%+ per week.

What do they look for in a nonprofits, and why did they let you in?

YC is still figuring out the details of their nonprofit program, but basically what they look for is similar to for-profits:

• A productive, gritty, passionate team.
• An important problem
• Evidence you can solve this problem: traction and growth

For nonprofits, they also look for a sustainable funding model.

And they’re mainly looking for startups that use technology, and having a technical co-founder is a significant bonus (which we had), though they’re letting in more and more non-tech companies.

The selection rate is similar to for-profits at about 1%. There’s no fixed quota of companies, rather they aim to let in anyone who meets their criteria.

We think they let us in because they agree that career advice is bad, no-one else is working on the problem, and we could show that former users were having way more impact due to us. We also showed we could scale by partially automating our coaching, then hiring more coaches (or perhaps faster via online content). And we could show that we’d raised $200,000 of donations from former users, and that users were paying for coaching, so our funding model is scalable.

What other nonprofits are there?

We’re the fourth batch of nonprofits, and there’s been about 20 nonprofits in total. Some of the other cool nonprofits in previous batches include Watsi, DemocracyOS and Bayes Impact. See some more examples.

What progress have we made since the start of June?

• We moved to Mountain View in California, which has been great (we’ll be going back to Oxford at the end of Sept).
• We decided to focus on online content rather than coaching. The online content has changed plans in the past, can be scaled without hiring, improves our knowledge and is in-demand by our users, so makes sense to focus on first. We can always do more coaching later, and will likely do a round Sept-Oct. Based on this decision, we sorted out our metrics and targets.
• We’ve taken the career guide through several iterations, including adding a career quiz (something many at YC suggested).
• Rob Wiblin joined the team as a researcher and co-founder – he’s been working closely with us for three years and was the perfect fit for this role. Before this role, he was Executive Director of our umbrella organisation, the Centre for Effective Altruism, and Research Director at Giving What We Can.
• We’ve released 3-4 pieces of new content per week, driving traffic and getting a lot of positive feedback from users.
• We’ve met lots of awesome people, and gave eight talks at Effective Altruism Global.
• Will’s book was released and has had some great media.

Total reading time on the site was 170h per day in the last week of July, up from 50h per day at the start of YC. That’s over 14% weekly growth.

We had 300 newsletter sign ups in the last week of July, up from 50 per week in May.

What’s the plan for the rest of the program?

In the lead up to Demo Day and immediately after, we’ll be focused on fundraising. If you’d like to donate to us, please drop me an email.

Otherwise, we’ll keep improving the career guide and releasing new content with the aim of getting at least another 2x growth in our leading metrics by the end of the year.

We also plan to do outreach and coaching at the start of the academic year with our student groups in top universities.

Update April 2017: here’s a guide to how to apply, written by our co-founder, Will MacAskill

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