80,000 Hours Podcast

Today’s bonus release is a pilot for a new podcast called ‘The Worst Ideas in the History of the World’, created by Keiran Harris — producer of the 80,000 Hours Podcast.

If you have strong opinions about this one way or another, please email us at [email protected] to help us figure out whether more of this ought to exist.

Chapters:

• Rob’s intro (00:00:00)
• The Worst Ideas in the History of the World (00:00:51)
• My history with longtermism (00:04:01)
• Outlining the format (00:06:17)
• Will MacAskill’s basic case (00:07:38)
• 5 reasons for why future people might not matter morally (00:10:26)
• Whether we can reasonably hope to influence the future (00:15:53)
• Great power wars (00:18:55)
• Nuclear weapons (00:22:27)
• Gain-of-function research (00:28:31)
• Closer (00:33:02)
• Rob's outro (00:35:13)

As AI advances ever more quickly, concerns about potential misuse of highly capable models are growing. From hostile foreign governments and terrorists to reckless entrepreneurs, the threat of AI falling into the wrong hands is top of mind for the national security community.

With growing concerns about the use of AI in military applications, the US has banned the export of certain types of chips to China.

But unlike the uranium required to make nuclear weapons, or the material inputs to a bioweapons programme, computer chips and machine learning models are absolutely everywhere. So is it actually possible to keep dangerous capabilities out of the wrong hands?

In today's interview, Lennart Heim — who researches compute governance at the Centre for the Governance of AI — explains why limiting access to supercomputers may represent our best shot.

Links to learn more, summary and full transcript.

As Lennart explains, an AI research project requires many inputs, including the classic triad of compute, algorithms, and data.

If we want to limit access to the most advanced AI models, focusing on access to supercomputing resources -- usually called 'compute' -- might be the way to go. Both algorithms and data are hard to control because they live on hard drives and can be easily copied. By contrast, advanced chips are physical items that can't be used by multiple people at once and come from a small number of sources.

According to Lennart, the hope would be to enforce AI safety regulations by controlling access to the most advanced chips specialised for AI applications. For instance, projects training 'frontier' AI models — the newest and most capable models — might only gain access to the supercomputers they need if they obtain a licence and follow industry best practices.

We have similar safety rules for companies that fly planes or manufacture volatile chemicals — so why not for people producing the most powerful and perhaps the most dangerous technology humanity has ever played with?

But Lennart is quick to note that the approach faces many practical challenges. Currently, AI chips are readily available and untracked. Changing that will require the collaboration of many actors, which might be difficult, especially given that some of them aren't convinced of the seriousness of the problem.

Host Rob Wiblin is particularly concerned about a different challenge: the increasing efficiency of AI training algorithms. As these algorithms become more efficient, what once required a specialised AI supercomputer to train might soon be achievable with a home computer.

By that point, tracking every aggregation of compute that could prove to be very dangerous would be both impractical and invasive.

With only a decade or two left before that becomes a reality, the window during which compute governance is a viable solution may be a brief one. Top AI labs have already stopped publishing their latest algorithms, which might extend this 'compute governance era', but not for very long.

If compute governance is only a temporary phase between the era of difficult-to-train superhuman AI models and the time when such models are widely accessible, what can we do to prevent misuse of AI systems after that point?

Lennart and Rob both think the only enduring approach requires taking advantage of the AI capabilities that should be in the hands of police and governments — which will hopefully remain superior to those held by criminals, terrorists, or fools. But as they describe, this means maintaining a peaceful standoff between AI models with conflicting goals that can act and fight with one another on the microsecond timescale. Being far too slow to follow what's happening -- let alone participate -- humans would have to be cut out of any defensive decision-making.

Both agree that while this may be our best option, such a vision of the future is more terrifying than reassuring.

Lennart and Rob discuss the above as well as:

• How can we best categorise all the ways AI could go wrong?
• Why did the US restrict the export of some chips to China and what impact has that had?
• Is the US in an 'arms race' with China or is that more an illusion?
• What is the deal with chips specialised for AI applications?
• How is the 'compute' industry organised?
• Downsides of using compute as a target for regulations
• Could safety mechanisms be built into computer chips themselves?
• Who would have the legal authority to govern compute if some disaster made it seem necessary?
• The reasons Rob doubts that any of this stuff will work
• Could AI be trained to operate as a far more severe computer worm than any we've seen before?
• What does the world look like when sluggish human reaction times leave us completely outclassed?
• And plenty more

Chapters:

• Rob’s intro (00:00:00)
• The interview begins (00:04:35)
• What is compute exactly? (00:09:46)
• Structural risks (00:13:25)
• Why focus on compute? (00:21:43)
• Weaknesses of targeting compute (00:30:41)
• Chip specialisation (00:37:11)
• Export restrictions (00:40:13)
• Compute governance is happening (00:59:00)
• Reactions to AI regulation (01:05:03)
• Creating legal authority to intervene quickly (01:10:09)
• Building mechanisms into chips themselves (01:18:57)
• Rob not buying that any of this will work (01:39:28)
• Are we doomed to become irrelevant? (01:59:10)
• Rob’s computer security bad dreams (02:10:22)
• Concrete advice (02:26:58)
• Article reading: Information security in high-impact areas (02:49:36)
• Rob’s outro (03:10:38)

Producer: Keiran Harris

Audio mastering: Milo McGuire, Dominic Armstrong, and Ben Cordell

Transcriptions: Katy Moore

Can there be a more exciting and strange place to work today than a leading AI lab? Your CEO has said they're worried your research could cause human extinction. The government is setting up meetings to discuss how this outcome can be avoided. Some of your colleagues think this is all overblown; others are more anxious still.

Today's guest — machine learning researcher Rohin Shah — goes into the Google DeepMind offices each day with that peculiar backdrop to his work.

Links to learn more, summary and full transcript.

He's on the team dedicated to maintaining 'technical AI safety' as these models approach and exceed human capabilities: basically that the models help humanity accomplish its goals without flipping out in some dangerous way. This work has never seemed more important.

In the short-term it could be the key bottleneck to deploying ML models in high-stakes real-life situations. In the long-term, it could be the difference between humanity thriving and disappearing entirely.

For years Rohin has been on a mission to fairly hear out people across the full spectrum of opinion about risks from artificial intelligence -- from doomers to doubters -- and properly understand their point of view. That makes him unusually well placed to give an overview of what we do and don't understand. He has landed somewhere in the middle — troubled by ways things could go wrong, but not convinced there are very strong reasons to expect a terrible outcome.

Today's conversation is wide-ranging and Rohin lays out many of his personal opinions to host Rob Wiblin, including:

• What he sees as the strongest case both for and against slowing down the rate of progress in AI research.
• Why he disagrees with most other ML researchers that training a model on a sensible 'reward function' is enough to get a good outcome.
• Why he disagrees with many on LessWrong that the bar for whether a safety technique is helpful is “could this contain a superintelligence.”
• That he thinks nobody has very compelling arguments that AI created via machine learning will be dangerous by default, or that it will be safe by default. He believes we just don't know.
• That he understands that analogies and visualisations are necessary for public communication, but is sceptical that they really help us understand what's going on with ML models, because they're different in important ways from every other case we might compare them to.
• Why he's optimistic about DeepMind’s work on scalable oversight, mechanistic interpretability, and dangerous capabilities evaluations, and what each of those projects involves.
• Why he isn't inherently worried about a future where we're surrounded by beings far more capable than us, so long as they share our goals to a reasonable degree.
• Why it's not enough for humanity to know how to align AI models — it's essential that management at AI labs correctly pick which methods they're going to use and have the practical know-how to apply them properly.
• Three observations that make him a little more optimistic: humans are a bit muddle-headed and not super goal-orientated; planes don't crash; and universities have specific majors in particular subjects.
• Plenty more besides.

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. Or read the transcript below.

Producer: Keiran Harris

Audio mastering: Milo McGuire, Dominic Armstrong, and Ben Cordell

Transcriptions: Katy Moore

GiveWell is one of the world's best-known charity evaluators, with the goal of "searching for the charities that save or improve lives the most per dollar." It mostly recommends projects that help the world's poorest people avoid easily prevented diseases, like intestinal worms or vitamin A deficiency.

But should GiveWell, as some critics argue, take a totally different approach to its search, focusing instead on directly increasing subjective wellbeing, or alternatively, raising economic growth?

Today's guest — cofounder and CEO of GiveWell, Elie Hassenfeld — is proud of how much GiveWell has grown in the last five years. Its 'money moved' has quadrupled to around $600 million a year.

Its research team has also more than doubled, enabling them to investigate a far broader range of interventions that could plausibly help people an enormous amount for each dollar spent. That work has led GiveWell to support dozens of new organisations, such as Kangaroo Mother Care, MiracleFeet, and Dispensers for Safe Water.

But some other researchers focused on figuring out the best ways to help the world's poorest people say GiveWell shouldn't just do more of the same thing, but rather ought to look at the problem differently.

Links to learn more, summary and full transcript.

Currently, GiveWell uses a range of metrics to track the impact of the organisations it considers recommending — such as 'lives saved,' 'household incomes doubled,' and for health improvements, the 'quality-adjusted life year.' 

The Happier Lives Institute (HLI) has argued that instead, GiveWell should try to cash out the impact of all interventions in terms of improvements in subjective wellbeing. This philosophy has led HLI to be more sceptical of interventions that have been demonstrated to improve health, but whose impact on wellbeing has not been measured, and to give a high priority to improving lives relative to extending them.

An alternative high-level critique is that really all that matters in the long run is getting the economies of poor countries to grow. On this view, GiveWell should focus on figuring out what causes some countries to experience explosive economic growth while others fail to, or even go backwards. Even modest improvements in the chances of such a 'growth miracle' will likely offer a bigger bang-for-buck than funding the incremental delivery of deworming tablets or vitamin A supplements, or anything else.

Elie sees where both of these critiques are coming from, and notes that they've influenced GiveWell's work in some ways. But as he explains, he thinks they underestimate the practical difficulty of successfully pulling off either approach and finding better opportunities than what GiveWell funds today. 

In today's in-depth conversation, Elie and host Rob Wiblin cover the above, as well as:

• Why GiveWell flipped from not recommending chlorine dispensers as an intervention for safe drinking water to spending tens of millions of dollars on them
• What transferable lessons GiveWell learned from investigating different kinds of interventions
• Why the best treatment for premature babies in low-resource settings may involve less rather than more medicine.
• Severe malnourishment among children and what can be done about it.
• How to deal with hidden and non-obvious costs of a programme
• Some cheap early treatments that can prevent kids from developing lifelong disabilities
• The various roles GiveWell is currently hiring for, and what's distinctive about their organisational culture
• And much more.

Chapters:

• Rob’s intro (00:00:00)
• The interview begins (00:03:14)
• GiveWell over the last couple of years (00:04:33)
• Dispensers for Safe Water (00:11:52)
• Syphilis diagnosis for pregnant women via technical assistance (00:30:39)
• Kangaroo Mother Care (00:48:47)
• Multiples of cash (01:01:20)
• Hidden costs (01:05:41)
• MiracleFeet (01:09:45)
• Serious malnourishment among young children (01:22:46)
• Vitamin A deficiency and supplementation (01:40:42)
• The subjective wellbeing approach in contrast with GiveWell's approach (01:46:31)
• The value of saving a life when that life is going to be very difficult (02:09:09)
• Whether economic policy is what really matters overwhelmingly (02:20:00)
• Careers at GiveWell (02:39:10)
• Donations (02:48:58)
• Parenthood (02:50:29)
• Rob’s outro (02:55:05)

Producer: Keiran Harris

Audio mastering: Simon Monsour and Ben Cordell

Transcriptions: Katy Moore

What is the nature of the universe? How do we make decisions correctly? What differentiates right actions from wrong ones?

Such fundamental questions have been the subject of philosophical and theological debates for millennia. But, as we all know, and surveys of expert opinion make clear, we are very far from agreement. So... with these most basic questions unresolved, what’s a species to do?

In today's episode, philosopher Joe Carlsmith — Senior Research Analyst at Open Philanthropy — makes the case that many current debates in philosophy ought to leave us confused and humbled. These are themes he discusses in his PhD thesis, A stranger priority? Topics at the outer reaches of effective altruism.

Links to learn more, summary and full transcript.

To help transmit the disorientation he thinks is appropriate, Joe presents three disconcerting theories — originating from him and his peers — that challenge humanity's self-assured understanding of the world.

The first idea is that we might be living in a computer simulation, because, in the classic formulation, if most civilisations go on to run many computer simulations of their past history, then most beings who perceive themselves as living in such a history must themselves be in computer simulations. Joe prefers a somewhat different way of making the point, but, having looked into it, he hasn't identified any particular rebuttal to this 'simulation argument.'

If true, it could revolutionise our comprehension of the universe and the way we ought to live...

Other two ideas cut for length — click here to read the full post.

These are just three particular instances of a much broader set of ideas that some have dubbed the "train to crazy town." Basically, if you commit to always take philosophy and arguments seriously, and try to act on them, it can lead to what seem like some pretty crazy and impractical places. So what should we do with this buffet of plausible-sounding but bewildering arguments?

Joe and Rob discuss to what extent this should prompt us to pay less attention to philosophy, and how we as individuals can cope psychologically with feeling out of our depth just trying to make the most basic sense of the world.

In today's challenging conversation, Joe and Rob discuss all of the above, as well as:

• What Joe doesn't like about the drowning child thought experiment
• An alternative thought experiment about helping a stranger that might better highlight our intrinsic desire to help others
• What Joe doesn't like about the expression “the train to crazy town”
• Whether Elon Musk should place a higher probability on living in a simulation than most other people
• Whether the deterministic twin prisoner’s dilemma, if fully appreciated, gives us an extra reason to keep promises
• To what extent learning to doubt our own judgement about difficult questions -- so-called “epistemic learned helplessness” -- is a good thing
• How strong the case is that advanced AI will engage in generalised power-seeking behaviour

Chapters:

• Rob’s intro (00:00:00)
• The interview begins (00:09:21)
• Downsides of the drowning child thought experiment (00:12:24)
• Making demanding moral values more resonant (00:24:56)
• The crazy train (00:36:48)
• Whether we’re living in a simulation (00:48:50)
• Reasons to doubt we’re living in a simulation, and practical implications if we are (00:57:02)
• Rob's explainer about anthropics (01:12:27)
• Back to the interview (01:19:53)
• Decision theory and affecting the past (01:23:33)
• Rob's explainer about decision theory (01:29:19)
• Back to the interview (01:39:55)
• Newcomb's problem (01:46:14)
• Practical implications of acausal decision theory (01:50:04)
• The hitchhiker in the desert (01:55:57)
• Acceptance within philosophy (02:01:22)
• Infinite ethics (02:04:35)
• Rob's explainer about the expanding spheres approach (02:17:05)
• Back to the interview (02:20:27)
• Infinite ethics and the utilitarian dream (02:27:42)
• Rob's explainer about epicycles (02:29:30)
• Back to the interview (02:31:26)
• What to do with all of these weird philosophical ideas (02:35:28)
• Welfare longtermism and wisdom longtermism (02:53:23)
• Epistemic learned helplessness (03:03:10)
• Power-seeking AI (03:12:41)
• Rob’s outro (03:25:45)

Producer: Keiran Harris

Audio mastering: Milo McGuire and Ben Cordell

Transcriptions: Katy Moore

Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don’t get to see any resumes or do reference checks. And because you’re so rich, tonnes of people apply for the job — for all sorts of reasons.

Today’s guest Ajeya Cotra — senior research analyst at Open Philanthropy — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods.

As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you’re monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it.

Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky!

Can’t we rely on how well models have performed at tasks during training to guide us? Ajeya worries that it won’t work. The trouble is that three different sorts of models will all produce the same output during training, but could behave very differently once deployed in a setting that allows their true colours to come through. She describes three such motivational archetypes:

• Saints — models that care about doing what we really want
• Sycophants — models that just want us to say they’ve done a good job, even if they get that praise by taking actions they know we wouldn’t want them to
• Schemers — models that don’t care about us or our interests at all, who are just pleasing us so long as that serves their own agenda

In principle, a machine learning training process based on reinforcement learning could spit out any of these three attitudes, because all three would perform roughly equally well on the tests we give them, and ‘performs well on tests’ is how these models are selected.

But while that’s true in principle, maybe it’s not something that could plausibly happen in the real world. After all, if we train an agent based on positive reinforcement for accomplishing X, shouldn’t the training process spit out a model that plainly does X and doesn’t have complex thoughts and goals beyond that?

According to Ajeya, this is one thing we don’t know, and should be trying to test empirically as these models get more capable. For reasons she explains in the interview, the Sycophant or Schemer models may in fact be simpler and easier for the learning algorithm to creep towards than their Saint counterparts.

But there are also ways we could end up actively selecting for motivations that we don’t want.

For a toy example, let’s say you train an agent AI model to run a small business, and select it for behaviours that make money, measuring its success by whether it manages to get more money in its bank account. During training, a highly capable model may experiment with the strategy of tricking its raters into thinking it has made money legitimately when it hasn’t. Maybe instead it steals some money and covers that up. This isn’t exactly unlikely; during training, models often come up with creative — sometimes undesirable — approaches that their developers didn’t anticipate.

If such deception isn’t picked up, a model like this may be rated as particularly successful, and the training process will cause it to develop a progressively stronger tendency to engage in such deceptive behaviour. A model that has the option to engage in deception when it won’t be detected would, in effect, have a competitive advantage.

What if deception is picked up, but just some of the time? Would the model then learn that honesty is the best policy? Maybe. But alternatively, it might learn the ‘lesson’ that deception does pay, but you just have to do it selectively and carefully, so it can’t be discovered. Would that actually happen? We don’t yet know, but it’s possible.

In today’s interview, Ajeya and Rob discuss the above, as well as:

• How to predict the motivations a neural network will develop through training
• Whether AIs being trained will functionally understand that they’re AIs being trained, the same way we think we understand that we’re humans living on planet Earth
• Stories of AI misalignment that Ajeya doesn’t buy into
• Analogies for AI, from octopuses to aliens to can openers
• Why it’s smarter to have separate planning AIs and doing AIs
• The benefits of only following through on AI-generated plans that make sense to human beings
• What approaches for fixing alignment problems Ajeya is most excited about, and which she thinks are overrated
• How one might demo actually scary AI failure mechanisms

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in May 2023.

Chapters:

• Rob’s intro (00:00:00)
• The interview begins (00:02:38)
• How Ajeya’s views have changed since 2020 (00:05:09)
• Are neural networks more like a sped-up version of evolution, or a slower version of human learning? (00:17:42)
• Situational awareness (00:26:10)
• Misalignment stories Ajeya doesn't buy (00:42:03)
• The orphan heir with a trillion-dollar fortune (00:59:14)
• Saints, Sycophants, and Schemers (01:03:41)
• Ways to train safer AI systems (01:23:20)
• Aliens and other analogies (01:38:22)
• Moral patienthood (01:53:21)
• ARC Evaluations (01:55:35)
• Interpretability research (02:09:25)
• Rewarding models based on how good and sensible their plans seem to us (02:17:48)
• Overrated approaches (02:25:49)
• Demos of actually scary alignment failures (02:30:57)
• Skills to develop for doing useful work (02:37:23)
• Rob’s outro (02:47:24)

Producer: Keiran Harris

Audio mastering: Ryan Kessler and Ben Cordell

Transcriptions: Katy Moore

It’s easy to dismiss alarming AI-related predictions when you don’t know where the numbers came from.

For example: what if we told you that within 15 years, it’s likely that we’ll see a 1,000x improvement in AI capabilities in a single year? And what if we then told you that those improvements would lead to explosive economic growth unlike anything humanity has seen before?

You might think, “Congratulations, you said a big number — but this kind of stuff seems crazy, so I’m going to keep scrolling through Twitter.”

But this 1,000x yearly improvement is a prediction based on *real economic models* created by today’s guest Tom Davidson, Senior Research Analyst at Open Philanthropy. By the end of the episode, you’ll either be able to point out specific flaws in his step-by-step reasoning, or have to at least consider the idea that the world is about to get — at a minimum — incredibly weird.

Links to learn more, summary and full transcript.

As a teaser, consider the following:

Developing artificial general intelligence (AGI) — AI that can do 100% of cognitive tasks at least as well as the best humans can — could very easily lead us to an unrecognisable world.

You might think having to train AI systems individually to do every conceivable cognitive task — one for diagnosing diseases, one for doing your taxes, one for teaching your kids, etc. — sounds implausible, or at least like it’ll take decades.

But Tom thinks we might not need to train AI to do every single job — we might just need to train it to do one: AI research.

And building AI capable of doing research and development might be a much easier task — especially given that the researchers training the AI are AI researchers themselves.

And once an AI system is as good at accelerating future AI progress as the best humans are today — and we can run billions of copies of it round the clock — it’s hard to make the case that we won’t achieve AGI very quickly.

To give you some perspective: 17 years ago we saw the launch of Twitter, the release of Al Gore's *An Inconvenient Truth*, and your first chance to play the Nintendo Wii.

Tom thinks that if we have AI that significantly accelerates AI R&D, then it’s hard to imagine not having AGI 17 years from now.

Wild.

Host Luisa Rodriguez gets Tom to walk us through his careful reports on the topic, and how he came up with these numbers, across a terrifying but fascinating three hours.

Luisa and Tom also discuss:

• How we might go from GPT-4 to AI disaster
• Tom’s journey from finding AI risk to be kind of scary to really scary
• Whether international cooperation or an anti-AI social movement can slow AI progress down
• Why it might take just a few years to go from pretty good AI to superhuman AI
• How quickly the number and quality of computer chips we’ve been using for AI have been increasing
• The pace of algorithmic progress
• What ants can teach us about AI
• And much more

Chapters:

• Rob’s intro (00:00:00)
• The interview begins (00:04:53)
• How we might go from GPT-4 to disaster (00:13:50)
• Explosive economic growth (00:24:15)
• Are there any limits for AI scientists? (00:33:17)
• This seems really crazy (00:44:16)
• How is this going to go for humanity? (00:50:49)
• Why AI won’t go the way of nuclear power (01:00:13)
• Can we definitely not come up with an international treaty? (01:05:24)
• How quickly we should expect AI to “take off” (01:08:41)
• Tom’s report on AI takeoff speeds (01:22:28)
• How quickly will we go from 20% to 100% of tasks being automated by AI systems? (01:28:34)
• What percent of cognitive tasks AI can currently perform (01:34:27)
• Compute (01:39:48)
• Using effective compute to predict AI takeoff speeds (01:48:01)
• How quickly effective compute might increase (02:00:59)
• How quickly chips and algorithms might improve (02:12:31)
• How to check whether large AI models have dangerous capabilities (02:21:22)
• Reasons AI takeoff might take longer (02:28:39)
• Why AI takeoff might be very fast (02:31:52)
• Fast AI takeoff speeds probably means shorter AI timelines (02:34:44)
• Going from human-level AI to superhuman AI (02:41:34)
• Going from AGI to AI deployment (02:46:59)
• Were these arguments ever far-fetched to Tom? (02:49:54)
• What ants can teach us about AI (02:52:45)
• Rob’s outro (03:00:32)

Producer: Keiran Harris
Audio mastering: Simon Monsour and Ben Cordell
Transcriptions: Katy Moore

Being a good and successful person is core to your identity. You place great importance on meeting the high moral, professional, or academic standards you set yourself.

 But inevitably, something goes wrong and you fail to meet that high bar. Now you feel terrible about yourself, and worry others are judging you for your failure. Feeling low and reflecting constantly on whether you're doing as much as you think you should makes it hard to focus and get things done. So now you're performing below a normal level, making you feel even more ashamed of yourself. Rinse and repeat.

 This is the disastrous cycle today's guest, Tim LeBon — registered psychotherapist, accredited CBT therapist, life coach, and author of 365 Ways to Be More Stoic — has observed in many clients with a perfectionist mindset.

Links to learn more, summary and full transcript.

Tim has provided therapy to a number of 80,000 Hours readers — people who have found that the very high expectations they had set for themselves were holding them back. Because of our focus on “doing the most good you can,” Tim thinks 80,000 Hours both attracts people with this style of thinking and then exacerbates it.

But Tim, having studied and written on moral philosophy, is sympathetic to the idea of helping others as much as possible, and is excited to help clients pursue that — sustainably — if it's their goal.

Tim has treated hundreds of clients with all sorts of mental health challenges. But in today's conversation, he shares the lessons he has learned working with people who take helping others so seriously that it has become burdensome and self-defeating — in particular, how clients can approach this challenge using the treatment he's most enthusiastic about: cognitive behavioural therapy.

Untreated, perfectionism might not cause problems for many years — it might even seem positive providing a source of motivation to work hard. But it's hard to feel truly happy and secure, and free to take risks, when we’re just one failure away from our self-worth falling through the floor. And if someone slips into the positive feedback loop of shame described above, the end result can be depression and anxiety that's hard to shake.

But there's hope. Tim has seen clients make real progress on their perfectionism by using CBT techniques like exposure therapy. By doing things like experimenting with more flexible standards — for example, sending early drafts to your colleagues, even if it terrifies you — you can learn that things will be okay, even when you're not perfect.

In today's extensive conversation, Tim and Rob cover:

• How perfectionism is different from the pursuit of excellence, scrupulosity, or an OCD personality
• What leads people to adopt a perfectionist mindset
• How 80,000 Hours contributes to perfectionism among some readers and listeners, and what it might change about its advice to address this
• What happens in a session of cognitive behavioural therapy for someone struggling with perfectionism, and what factors are key to making progress
• Experiments to test whether one's core beliefs (‘I need to be perfect to be valued’) are true
• Using exposure therapy to treat phobias
• How low-self esteem and imposter syndrome are related to perfectionism
• Stoicism as an approach to life, and why Tim is enthusiastic about it
• What the Stoics do better than utilitarian philosophers and vice versa
• And how to decide which are the best virtues to live by

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: Simon Monsour and Ben Cordell
 Transcriptions: Katy Moore

If you want to work to tackle climate change, you should try to reduce expected carbon emissions by as much as possible, right? Strangely, no.

 Today's guest, Johannes Ackva — the climate research lead at Founders Pledge, where he advises major philanthropists on their giving — thinks the best strategy is actually pretty different, and one few are adopting.

 In reality you don't want to reduce emissions for its own sake, but because emissions will translate into temperature increases, which will cause harm to people and the environment.

 Links to learn more, summary and full transcript.

Crucially, the relationship between emissions and harm goes up faster than linearly. As Johannes explains, humanity can handle small deviations from the temperatures we're familiar with, but adjustment gets harder the larger and faster the increase, making the damage done by each additional degree of warming much greater than the damage done by the previous one.

In short: we're uncertain what the future holds and really need to avoid the worst-case scenarios. This means that avoiding an additional tonne of carbon being emitted in a hypothetical future in which emissions have been high is much more important than avoiding a tonne of carbon in a low-carbon world.

That may be, but concretely, how should that affect our behaviour? Well, the future scenarios in which emissions are highest are all ones in which clean energy tech that can make a big difference — wind, solar, and electric cars — don't succeed nearly as much as we are currently hoping and expecting. For some reason or another, they must have hit a roadblock and we continued to burn a lot of fossil fuels.

In such an imaginable future scenario, we can ask what we would wish we had funded now. How could we today buy insurance against the possible disaster that renewables don't work out?

Basically, in that case we will wish that we had pursued a portfolio of other energy technologies that could have complemented renewables or succeeded where they failed, such as hot rock geothermal, modular nuclear reactors, or carbon capture and storage.

If you're optimistic about renewables, as Johannes is, then that's all the more reason to relax about scenarios where they work as planned, and focus one's efforts on the possibility that they don't.

And Johannes notes that the most useful thing someone can do today to reduce global emissions in the future is to cause some clean energy technology to exist where it otherwise wouldn't, or cause it to become cheaper more quickly. If you can do that, then you can indirectly affect the behaviour of people all around the world for decades or centuries to come.

In today's extensive interview, host Rob Wiblin and Johannes discuss the above considerations, as well as:

• Retooling newly built coal plants in the developing world
• Specific clean energy technologies like geothermal and nuclear fusion
• Possible biases among environmentalists and climate philanthropists
• How climate change compares to other risks to humanity
• In what kinds of scenarios future emissions would be highest
• In what regions climate philanthropy is most concentrated and whether that makes sense
• Attempts to decarbonise aviation, shipping, and industrial processes
• The impact of funding advocacy vs science vs deployment
• Lessons for climate change focused careers
• And plenty 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. Or read the transcript below.

Producer: Keiran Harris
Audio mastering: Ryan Kessler
Transcriptions: Katy Moore