138 episodes

Unusually in-depth conversations about the world's most pressing problems and what you can do to solve them.

Subscribe by searching for '80,000 Hours' wherever you get podcasts.

Produced by Keiran Harris. Hosted by Rob Wiblin, Head of Research at 80,000 Hours.

80,000 Hours Podcast with Rob Wiblin The 80000 Hours team

    • Education
    • 4.8 • 184 Ratings

Unusually in-depth conversations about the world's most pressing problems and what you can do to solve them.

Subscribe by searching for '80,000 Hours' wherever you get podcasts.

Produced by Keiran Harris. Hosted by Rob Wiblin, Head of Research at 80,000 Hours.

    #111 – Mushtaq Khan on using institutional economics to predict effective government reforms

    #111 – Mushtaq Khan on using institutional economics to predict effective government reforms

    If you’re living in the Niger Delta in Nigeria, your best bet at a high-paying career is probably ‘artisanal refining’ — or, in plain language, stealing oil from pipelines.

    The resulting oil spills damage the environment and cause severe health problems, but the Nigerian government has continually failed in their attempts to stop this theft.

    They send in the army, and the army gets corrupted. They send in enforcement agencies, and the enforcement agencies get corrupted. What’s happening here?

    According to Mushtaq Khan, economics professor at SOAS University of London, this is a classic example of ‘networked corruption’. Everyone in the community is benefiting from the criminal enterprise — so much so that the locals would prefer civil war to following the law. It pays vastly better than other local jobs, hotels and restaurants have formed around it, and houses are even powered by the electricity generated from the oil.

    Links to learn more, summary and full transcript.

    In today's episode, Mushtaq elaborates on the models he uses to understand these problems and make predictions he can test in the real world.

    Some of the most important factors shaping the fate of nations are their structures of power: who is powerful, how they are organized, which interest groups can pull in favours with the government, and the constant push and pull between the country's rulers and its ruled. While traditional economic theory has relatively little to say about these topics, institutional economists like Mushtaq have a lot to say, and participate in lively debates about which of their competing ideas best explain the world around us.

    The issues at stake are nothing less than why some countries are rich and others are poor, why some countries are mostly law abiding while others are not, and why some government programmes improve public welfare while others just enrich the well connected.

    Mushtaq’s specialties are anti-corruption and industrial policy, where he believes mainstream theory and practice are largely misguided.

    Mushtaq's rule of thumb is that when the locals most concerned with a specific issue are invested in preserving a status quo they're participating in, they almost always win out.

    To actually reduce corruption, countries like his native Bangladesh have to follow the same gradual path the U.K. once did: find organizations that benefit from rule-abiding behaviour and are selfishly motivated to promote it, and help them police their peers.

    Trying to impose a new way of doing things from the top down wasn't how Europe modernised, and it won't work elsewhere either.

    In cases like oil theft in Nigeria, where no one wants to follow the rules, Mushtaq says corruption may be impossible to solve directly. Instead you have to play a long game, bringing in other employment opportunities, improving health services, and deploying alternative forms of energy — in the hope that one day this will give people a viable alternative to corruption.

    In this extensive interview Rob and Mushtaq cover this and much more, including:

    • How does one test theories like this?
    • Why are companies in some poor countries so much less productive than their peers in rich countries?
    • Have rich countries just legalized the corruption in their societies?
    • What are the big live debates in institutional economics?
    • Should poor countries protect their industries from foreign competition?
    • How can listeners use these theories to predict which policies will work in their own countries?

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 3 hr 20 min
    #110 – Holden Karnofsky on building aptitudes and kicking ass

    #110 – Holden Karnofsky on building aptitudes and kicking ass

    Holden Karnofsky helped create two of the most influential organisations in the effective philanthropy world. So when he outlines a different perspective on career advice than the one we present at 80,000 Hours — we take it seriously.

    Holden disagrees with us on a few specifics, but it's more than that: he prefers a different vibe when making career choices, especially early in one's career.

    Links to learn more, summary and full transcript.

    While he might ultimately recommend similar jobs to those we recommend at 80,000 Hours, the reasons are often different.

    At 80,000 Hours we often talk about ‘paths’ to working on what we currently think of as the most pressing problems in the world. That’s partially because people seem to prefer the most concrete advice possible.

    But Holden thinks a problem with that kind of advice is that it’s hard to take actions based on it if your job options don’t match well with your plan, and it’s hard to get a reliable signal about whether you're making the right choices.

    How can you know you’ve chosen the right cause? How can you know the job you’re aiming for will be helpful to that cause? And what if you can’t get a job in this area at all?

    Holden prefers to focus on ‘aptitudes’ that you can build in all sorts of different roles and cause areas, which can later be applied more directly.

    Even if the current role doesn’t work out, or your career goes in wacky directions you’d never anticipated (like so many successful careers do), or you change your whole worldview — you’ll still have access to this aptitude.

    So instead of trying to become a project manager at an effective altruism organisation, maybe you should just become great at project management. Instead of trying to become a researcher at a top AI lab, maybe you should just become great at digesting hard problems.

    Who knows where these skills will end up being useful down the road?

    Holden doesn’t think you should spend much time worrying about whether you’re having an impact in the first few years of your career — instead you should just focus on learning to kick ass at something, knowing that most of your impact is going to come decades into your career.

    He thinks as long as you’ve gotten good at something, there will usually be a lot of ways that you can contribute to solving the biggest problems.

    But Holden’s most important point, perhaps, is this: Be very careful about following career advice at all.

    He points out that a career is such a personal thing that it’s very easy for the advice-giver to be oblivious to important factors having to do with your personality and unique situation.

    He thinks it’s pretty hard for anyone to really have justified empirical beliefs about career choice, and that you should be very hesitant to make a radically different decision than you would have otherwise based on what some person (or website!) tells you to do.

    Instead, he hopes conversations like these serve as a way of prompting discussion and raising points that you can apply your own personal judgment to.

    That's why in the end he thinks people should look at their career decisions through his aptitude lens, the '80,000 Hours lens', and ideally several other frameworks as well. Because any one perspective risks missing something important.

    Holden and Rob also cover:

    • Ways to be helpful to longtermism outside of careers
    • Why finding a new cause area might be overrated
    • Historical events that deserve more attention
    • And much more

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 2 hr 46 min
    #109 – Holden Karnofsky on the most important century

    #109 – Holden Karnofsky on the most important century

    Will the future of humanity be wild, or boring? It's natural to think that if we're trying to be sober and measured, and predict what will really happen rather than spin an exciting story, it's more likely than not to be sort of... dull.

    But there's also good reason to think that that is simply impossible. The idea that there's a boring future that's internally coherent is an illusion that comes from not inspecting those scenarios too closely.

    At least that is what Holden Karnofsky — founder of charity evaluator GiveWell and foundation Open Philanthropy — argues in his new article series titled 'The Most Important Century'. He hopes to lay out part of the worldview that's driving the strategy and grantmaking of Open Philanthropy's longtermist team, and encourage more people to join his efforts to positively shape humanity's future.

    Links to learn more, summary and full transcript.

    The bind is this. For the first 99% of human history the global economy (initially mostly food production) grew very slowly: under 0.1% a year. But since the industrial revolution around 1800, growth has exploded to over 2% a year.

    To us in 2020 that sounds perfectly sensible and the natural order of things. But Holden points out that in fact it's not only unprecedented, it also can't continue for long.

    The power of compounding increases means that to sustain 2% growth for just 10,000 years, 5% as long as humanity has already existed, would require us to turn every individual atom in the galaxy into an economy as large as the Earth's today. Not super likely.

    So what are the options? First, maybe growth will slow and then stop. In that case we today live in the single miniscule slice in the history of life during which the world rapidly changed due to constant technological advances, before intelligent civilization permanently stagnated or even collapsed. What a wild time to be alive!

    Alternatively, maybe growth will continue for thousands of years. In that case we are at the very beginning of what would necessarily have to become a stable galaxy-spanning civilization, harnessing the energy of entire stars among other feats of engineering. We would then stand among the first tiny sliver of all the quadrillions of intelligent beings who ever exist. What a wild time to be alive!

    Isn't there another option where the future feels less remarkable and our current moment not so special?

    While the full version of the argument above has a number of caveats, the short answer is 'not really'. We might be in a computer simulation and our galactic potential all an illusion, though that's hardly any less weird. And maybe the most exciting events won't happen for generations yet. But on a cosmic scale we'd still be living around the universe's most remarkable time.

    Holden himself was very reluctant to buy into the idea that today’s civilization is in a strange and privileged position, but has ultimately concluded "all possible views about humanity's future are wild".

    In the conversation Holden and Rob cover each part of the 'Most Important Century' series, including:

    • The case that we live in an incredibly important time
    • How achievable-seeming technology - in particular, mind uploading - could lead to unprecedented productivity, control of the environment, and more
    • How economic growth is faster than it can be for all that much longer
    • Forecasting transformative AI
    • And the implications of living in the most important century

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 2 hr 19 min
    #108 – Chris Olah on working at top AI labs without an undergrad degree

    #108 – Chris Olah on working at top AI labs without an undergrad degree

    Chris Olah has had a fascinating and unconventional career path.

    Most people who want to pursue a research career feel they need a degree to get taken seriously. But Chris not only doesn't have a PhD, but doesn’t even have an undergraduate degree. After dropping out of university to help defend an acquaintance who was facing bogus criminal charges, Chris started independently working on machine learning research, and eventually got an internship at Google Brain, a leading AI research group.

    In this interview — a follow-up to our episode on his technical work — we discuss what, if anything, can be learned from his unusual career path. Should more people pass on university and just throw themselves at solving a problem they care about? Or would it be foolhardy for others to try to copy a unique case like Chris’?

    Links to learn more, summary and full transcript.

    We also cover some of Chris' personal passions over the years, including his attempts to reduce what he calls 'research debt' by starting a new academic journal called Distill, focused just on explaining existing results unusually clearly.

    As Chris explains, as fields develop they accumulate huge bodies of knowledge that researchers are meant to be familiar with before they start contributing themselves. But the weight of that existing knowledge — and the need to keep up with what everyone else is doing — can become crushing. It can take someone until their 30s or later to earn their stripes, and sometimes a field will split in two just to make it possible for anyone to stay on top of it.

    If that were unavoidable it would be one thing, but Chris thinks we're nowhere near communicating existing knowledge as well as we could. Incrementally improving an explanation of a technical idea might take a single author weeks to do, but could go on to save a day for thousands, tens of thousands, or hundreds of thousands of students, if it becomes the best option available.

    Despite that, academics have little incentive to produce outstanding explanations of complex ideas that can speed up the education of everyone coming up in their field. And some even see the process of deciphering bad explanations as a desirable right of passage all should pass through, just as they did.

    So Chris tried his hand at chipping away at this problem — but concluded the nature of the problem wasn't quite what he originally thought. In this conversation we talk about that, as well as:

    • Why highly thoughtful cold emails can be surprisingly effective, but average cold emails do little
    • Strategies for growing as a researcher
    • Thinking about research as a market
    • How Chris thinks about writing outstanding explanations
    • The concept of 'micromarriages' and ‘microbestfriendships’
    • And much more.

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 1 hr 33 min
    #107 – Chris Olah on what the hell is going on inside neural networks

    #107 – Chris Olah on what the hell is going on inside neural networks

    Big machine learning models can identify plant species better than any human, write passable essays, beat you at a game of Starcraft 2, figure out how a photo of Tobey Maguire and the word 'spider' are related, solve the 60-year-old 'protein folding problem', diagnose some diseases, play romantic matchmaker, write solid computer code, and offer questionable legal advice.

    Humanity made these amazing and ever-improving tools. So how do our creations work? In short: we don't know.

    Today's guest, Chris Olah, finds this both absurd and unacceptable. Over the last ten years he has been a leader in the effort to unravel what's really going on inside these black boxes. As part of that effort he helped create the famous DeepDream visualisations at Google Brain, reverse engineered the CLIP image classifier at OpenAI, and is now continuing his work at Anthropic, a new $100 million research company that tries to "co-develop the latest safety techniques alongside scaling of large ML models".

    Links to learn more, summary and full transcript.

    Despite having a huge fan base thanks to his explanations of ML and tweets, today's episode is the first long interview Chris has ever given. It features his personal take on what we've learned so far about what ML algorithms are doing, and what's next for this research agenda at Anthropic.

    His decade of work has borne substantial fruit, producing an approach for looking inside the mess of connections in a neural network and back out what functional role each piece is serving. Among other things, Chris and team found that every visual classifier seems to converge on a number of simple common elements in their early layers — elements so fundamental they may exist in our own visual cortex in some form.

    They also found networks developing 'multimodal neurons' that would trigger in response to the presence of high-level concepts like 'romance', across both images and text, mimicking the famous 'Halle Berry neuron' from human neuroscience.

    While reverse engineering how a mind works would make any top-ten list of the most valuable knowledge to pursue for its own sake, Chris's work is also of urgent practical importance. Machine learning models are already being deployed in medicine, business, the military, and the justice system, in ever more powerful roles. The competitive pressure to put them into action as soon as they can turn a profit is great, and only getting greater.

    But if we don't know what these machines are doing, we can't be confident they'll continue to work the way we want as circumstances change. Before we hand an algorithm the proverbial nuclear codes, we should demand more assurance than "well, it's always worked fine so far".

    But by peering inside neural networks and figuring out how to 'read their minds' we can potentially foresee future failures and prevent them before they happen. Artificial neural networks may even be a better way to study how our own minds work, given that, unlike a human brain, we can see everything that's happening inside them — and having been posed similar challenges, there's every reason to think evolution and 'gradient descent' often converge on similar solutions.

    Among other things, Rob and Chris cover:

    • Why Chris thinks it's necessary to work with the largest models
    • What fundamental lessons we've learned about how neural networks (and perhaps humans) think
    • How interpretability research might help make AI safer to deploy, and Chris’ response to skeptics
    • Why there's such a fuss about 'scaling laws' and what they say about future AI progress

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 3 hr 9 min
    #106 – Cal Newport on an industrial revolution for office work

    #106 – Cal Newport on an industrial revolution for office work

    If you wanted to start a university department from scratch, and attract as many superstar researchers as possible, what’s the most attractive perk you could offer?

    How about just not needing an email address.

    According to today's guest, Cal Newport — computer science professor and best-selling author of A World Without Email — it should seem obscene and absurd for a world-renowned vaccine researcher with decades of experience to spend a third of their time fielding requests from HR, building management, finance, and so on. Yet with offices organised the way they are today, nothing could be more natural.

    Links to learn more, summary and full transcript.

    But this isn’t just a problem at the elite level — this affects almost all of us. A typical U.S. office worker checks their email 80 times a day, once every six minutes on average. Data analysis by RescueTime found that a third of users checked email or Slack every three minutes or more, averaged over a full work day.

    Each time that happens our focus is broken, killing our momentum on the knowledge work we're supposedly paid to do.

    When we lament how much email and chat have reduced our focus and filled our days with anxiety and frenetic activity, we most naturally blame 'weakness of will'. If only we had the discipline to check Slack and email once a day, all would be well — or so the story goes.

    Cal believes that line of thinking fundamentally misunderstands how we got to a place where knowledge workers can rarely find more than five consecutive minutes to spend doing just one thing.

    Since the Industrial Revolution, a combination of technology and better organization have allowed the manufacturing industry to produce a hundred-fold as much with the same number of people.

    Cal says that by comparison, it's not clear that specialised knowledge workers like scientists, authors, or senior managers are *any* more productive than they were 50 years ago. If the knowledge sector could achieve even a tiny fraction of what manufacturing has, and find a way to coordinate its work that raised productivity by just 1%, that would generate on the order of $100 billion globally each year.

    Since the 1990s, when everyone got an email address and most lost their assistants, that lack of direction has led to what Cal calls the 'hyperactive hive mind': everyone sends emails and chats to everyone else, all through the day, whenever they need something.

    Cal points out that this is so normal we don't even think of it as a way of organising work, but it is: it's what happens when management does nothing to enable teams to decide on a better way of organising themselves.

    A few industries have made progress taming the 'hyperactive hive mind'. But on Cal's telling, this barely scratches the surface of the improvements that are possible within knowledge work. And reigning in the hyperactive hive mind won't just help people do higher quality work, it will free them from the 24/7 anxiety that there's someone somewhere they haven't gotten back to.

    In this interview Cal and Rob also cover:

    • Is this really one of the world's most pressing problems?
    • The historical origins of the 'hyperactive hive mind'
    • The harm caused by attention switching
    • Who's working to solve the problem and how
    • Cal's top productivity advice for high school students, university students, and early career workers
    • And much more

    Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.


    Producer: Keiran Harris
    Audio mastering: Ben Cordell
    Transcriptions: Sofia Davis-Fogel

    • 1 hr 53 min

Customer Reviews

4.8 out of 5
184 Ratings

184 Ratings

Senessence ,

80,000 Stars

The key points, links and transcript on the website are great.

Gusbehid ,

Amazing

This show does a tour de force through the world’s most pressing problems. Every episode is packed with super interesting and useful information on issues related to nuclear catastrophe, artificial intelligence, climate change, animal welfare, global poverty, and more. Absolutely phenomenal interviewing!

Arlie K ,

Actionable + Inspiring

Rob is truly making the world a better place, one episode at a time. The deeply insightful conversations he's facilitating here are truly incredible - I learn *so* much from tuning in! Highly recommend giving it a listen.

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