#176 – Nathan Labenz on the final push for AGI, understanding OpenAI’s leadership drama, and red-teaming frontier models

OpenAI says its mission is to build AGI — an AI system that is better than human beings at everything. Should the world trust them to do this safely?

That’s the central theme of today’s episode with Nathan Labenz — entrepreneur, AI scout, and host of The Cognitive Revolution podcast. Nathan saw the AI revolution coming years ago, and, astonished by the research he was seeing, set aside his role as CEO of Waymark and made it his full-time job to understand AI capabilities across every domain. He has been obsessively tracking the AI world since — including joining OpenAI’s “red team” that probed GPT-4 to find ways it could be abused, long before it was public.

Whether OpenAI was taking AI safety seriously enough became a topic of dinner table conversation around the world after the shocking firing and reinstatement of Sam Altman as CEO last month.

Nathan’s view: it’s complicated. Discussion of this topic has often been heated, polarising, and personal. But Nathan wants to avoid that and simply lay out, in a way that is impartial and fair to everyone involved, what OpenAI has done right and how it could do better in his view.

When he started on the GPT-4 red team, the model would do anything from diagnose a skin condition to plan a terrorist attack without the slightest reservation or objection. When later shown a “Safety” version of GPT-4 that was almost the same, he approached a member of OpenAI’s board to share his concerns and tell them they really needed to try out GPT-4 for themselves and form an opinion.

In today’s episode, we share this story as Nathan told it on his own show, The Cognitive Revolution, which he did in the hope that it would provide useful background to understanding the OpenAI board’s reservations about Sam Altman, which to this day have not been laid out in any detail.

But while he feared throughout 2022 that OpenAI and Sam Altman didn’t understand the power and risk of their own system, he has since been repeatedly impressed, and came to think of OpenAI as among the better companies that could hypothetically be working to build AGI.

Their efforts to make GPT-4 safe turned out to be much larger and more successful than Nathan was seeing. Sam Altman and other leaders at OpenAI seem to sincerely believe they’re playing with fire, and take the threat posed by their work very seriously. With the benefit of hindsight, Nathan suspects OpenAI’s decision to release GPT-4 when it did was for the best.

On top of that, OpenAI has been among the most sane and sophisticated voices advocating for AI regulations that would target just the most powerful AI systems — the type they themselves are building — and that could make a real difference. They’ve also invested major resources into new ‘Superalignment’ and ‘Preparedness’ teams, while avoiding using competition with China as an excuse for recklessness.

At the same time, it’s very hard to know whether it’s all enough. The challenge of making an AGI safe and beneficial may require much more than they hope or have bargained for. Given that, Nathan poses the question of whether it makes sense to try to build a fully general AGI that can outclass humans in every domain at the first opportunity. Maybe in the short term, we should focus on harvesting the enormous possible economic and humanitarian benefits of narrow applied AI models, and wait until we not only have a way to build AGI, but a good way to build AGI — an AGI that we’re confident we want, which we can prove will remain safe as its capabilities get ever greater.

By threatening to follow Sam Altman to Microsoft before his reinstatement as OpenAI CEO, OpenAI’s research team has proven they have enormous influence over the direction of the company. If they put their minds to it, they’re also better placed than maybe anyone in the world to assess if the company’s strategy is on the right track and serving the interests of humanity as a whole. Nathan concludes that this power and insight only adds to the enormous weight of responsibility already resting on their shoulders.

In today’s extensive conversation, Nathan and host Rob Wiblin discuss not only all of the above, but also:

  • Speculation about the OpenAI boardroom drama with Sam Altman, given Nathan’s interactions with the board when he raised concerns from his red teaming efforts.
  • Which AI applications we should be urgently rolling out, with less worry about safety.
  • Whether governance issues at OpenAI demonstrate AI research can only be slowed by governments.
  • Whether AI capabilities are advancing faster than safety efforts and controls.
  • The costs and benefits of releasing powerful models like GPT-4.
  • Nathan’s view on the game theory of AI arms races and China.
  • Whether it’s worth taking some risk with AI for huge potential upside.
  • The need for more “AI scouts” to understand and communicate AI progress.
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire and Dominic Armstrong
Transcriptions: Katy Moore

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#175 – Lucia Coulter on preventing lead poisoning for $1.66 per child

Lead is one of the most poisonous things going. A single sugar sachet of lead, spread over a park the size of an American football field, is enough to give a child that regularly plays there lead poisoning. For life they’ll be condemned to a ~3-point-lower IQ; a 50% higher risk of heart attacks; and elevated risk of kidney disease, anaemia, and ADHD, among other effects.

We’ve known lead is a health nightmare for at least 50 years, and that got lead out of car fuel everywhere. So is the situation under control? Not even close.

Around half the kids in poor and middle-income countries have blood lead levels above 5 micrograms per decilitre; the US declared a national emergency when just 5% of the children in Flint, Michigan exceeded that level. The collective damage this is doing to children’s intellectual potential, health, and life expectancy is vast — the health damage involved is around that caused by malaria, tuberculosis, and HIV combined.

This week’s guest, Lucia Coulter — cofounder of the incredibly successful Lead Exposure Elimination Project (LEEP) — speaks about how LEEP has been reducing childhood lead exposure in poor countries by getting bans on lead in paint enforced.

Various estimates suggest the work is absurdly cost effective. LEEP is in expectation preventing kids from getting lead poisoning for under $2 per child (explore the analysis here). Or, looking at it differently, LEEP is saving a year of healthy life for $14, and in the long run is increasing people’s lifetime income anywhere from $300–1,200 for each $1 it spends, by preventing intellectual stunting.

Which raises the question: why hasn’t this happened already? How is lead still in paint in most poor countries, even when that’s oftentimes already illegal? And how is LEEP able to get bans on leaded paint enforced in a country while spending barely tens of thousands of dollars? When leaded paint is gone, what should they target next?

With host Robert Wiblin, Lucia answers all those questions and more:

  • Why LEEP isn’t fully funded, and what it would do with extra money (you can donate here).
  • How bad lead poisoning is in rich countries.
  • Why lead is still in aeroplane fuel.
  • How lead got put straight in food in Bangladesh, and a handful of people got it removed.
  • Why the enormous damage done by lead mostly goes unnoticed.
  • The other major sources of lead exposure aside from paint.
  • Lucia’s story of founding a highly effective nonprofit, despite having no prior entrepreneurship experience, through Charity Entrepreneurship’s Incubation Program.
  • Why Lucia pledges 10% of her income to cost-effective charities.
  • Lucia’s take on why GiveWell didn’t support LEEP earlier on.
  • How the invention of cheap, accessible lead testing for blood and consumer products would be a game changer.
  • Generalisable lessons LEEP has learned from coordinating with governments in poor countries.
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire and Dominic Armstrong
Transcriptions: Katy Moore

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#174 – Nita Farahany on the neurotechnology already being used to convict criminals and manipulate workers

In today’s episode, host Luisa Rodriguez speaks to Nita Farahany — professor of law and philosophy at Duke Law School — about applications of cutting-edge neurotechnology.

They cover:

  • How close we are to actual mind reading.
  • How hacking neural interfaces could cure depression.
  • How companies might use neural data in the workplace — like tracking how productive you are, or using your emotional states against you in negotiations.
  • How close we are to being able to unlock our phones by singing a song in our heads.
  • How neurodata has been used for interrogations, and even criminal prosecutions.
  • The possibility of linking brains to the point where you could experience exactly the same thing as another person.
  • Military applications of this tech, including the possibility of one soldier controlling swarms of drones with their mind.
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#173 – Jeff Sebo on digital minds, and how to avoid sleepwalking into a major moral catastrophe

In today’s episode, host Luisa Rodriguez interviews Jeff Sebo — director of the Mind, Ethics, and Policy Program at NYU — about preparing for a world with digital minds.

They cover:

  • The non-negligible chance that AI systems will be sentient by 2030
  • What AI systems might want and need, and how that might affect our moral concepts
  • What happens when beings can copy themselves? Are they one person or multiple people? Does the original own the copy or does the copy have its own rights? Do copies get the right to vote?
  • What kind of legal and political status should AI systems have? Legal personhood? Political citizenship?
  • What happens when minds can be connected? If two minds are connected, and one does something illegal, is it possible to punish one but not the other?
  • The repugnant conclusion and the rebugnant conclusion
  • The experience of trying to build the field of AI welfare
  • What improv comedy can teach us about doing good in the world
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#172 – Bryan Caplan on why you should stop reading the news

Is following important political and international news a civic duty — or is it our civic duty to avoid it?

It’s common to think that ‘staying informed’ and checking the headlines every day is just what responsible adults do.

But in today’s episode, host Rob Wiblin is joined by economist Bryan Caplan to discuss the book Stop Reading the News: A Manifesto for a Happier, Calmer and Wiser Life — which argues that reading the news both makes us miserable and distorts our understanding of the world. Far from informing us and enabling us to improve the world, consuming the news distracts us, confuses us, and leaves us feeling powerless.

In the first half of the episode, Bryan and Rob discuss various alleged problems with the news, including:

  • That it overwhelmingly provides us with information we can’t usefully act on.
  • That it’s very non-representative in what it covers, in particular favouring the negative over the positive and the new over the significant.
  • That it obscures the big picture, falling into the trap of thinking ‘something important happens every day.’
  • That it’s highly addictive, for many people chewing up 10% or more of their waking hours.
  • That regularly checking the news leaves us in a state of constant distraction and less able to engage in deep thought.
  • And plenty more.

Bryan and Rob conclude that if you want to understand the world, you’re better off blocking news websites and spending your time on Wikipedia, Our World in Data, or reading a textbook. And if you want to generate political change, stop reading about problems you already know exist and instead write your political representative a physical letter — or better yet, go meet them in person.

In the second half of the episode, Bryan and Rob cover:

  • Why Bryan is pretty sceptical that AI is going to lead to extreme, rapid changes, or that there’s a meaningful chance of it going terribly.
  • Bryan’s case that rational irrationality on the part of voters leads to many very harmful policy decisions.
  • How to allocate resources in space.
  • Bryan’s experience homeschooling his kids.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: Katy Moore

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#171 – Alison Young on how top labs have jeopardised public health with repeated biosafety failures

In today’s episode, host Luisa Rodriguez interviews award-winning investigative journalist Alison Young on the surprising frequency of lab leaks and what needs to be done to prevent them in the future.

They cover:

  • The most egregious biosafety mistakes made by the CDC, and how Alison uncovered them through her investigative reporting
  • The Dugway life science test facility case, where live anthrax was accidentally sent to labs across the US and several other countries over a period of many years
  • The time the Soviets had a major anthrax leak, and then hid it for over a decade
  • The 1977 influenza pandemic caused by vaccine trial gone wrong in China
  • The last death from smallpox, caused not by the virus spreading in the wild, but by a lab leak in the UK
  • Ways we could get more reliable oversight and accountability for these labs
  • And the investigative work Alison’s most proud of

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#170 – Santosh Harish on how air pollution is responsible for ~12% of global deaths — and how to get that number down

In today’s episode, host Rob Wiblin interviews Santosh Harish — leader of Open Philanthropy’s grantmaking in South Asian air quality — about the scale of the harm caused by air pollution.

They cover:

  • How bad air pollution is for our health and life expectancy
  • The different kinds of harm that particulate pollution causes
  • The strength of the evidence that it damages our brain function and reduces our productivity
  • Whether it was a mistake to switch our attention to climate change and away from air pollution
  • Whether most listeners to this show should have an air purifier running in their house right now
  • Where air pollution in India is worst and why, and whether it’s going up or down
  • Where most air pollution comes from
  • The policy blunders that led to many sources of air pollution in India being effectively unregulated
  • Why indoor air pollution packs an enormous punch
  • The politics of air pollution in India
  • How India ended up spending a lot of money on outdoor air purifiers
  • The challenges faced by foreign philanthropists in India
  • Why Santosh has made the grants he has so far
  • And plenty more

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: Katy Moore

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#169 – Paul Niehaus on whether cash transfers cause economic growth, and keeping theft to acceptable levels

In today’s episode, host Luisa Rodriguez interviews Paul Niehaus — cofounder of GiveDirectly — on the case for giving unconditional cash to the world’s poorest households.

They cover:

  • The empirical evidence on whether giving cash directly can drive meaningful economic growth
  • How the impacts of GiveDirectly compare to USAID employment programmes
  • GiveDirectly vs GiveWell’s top-recommended charities
  • How long-term guaranteed income affects people’s risk-taking and investments
  • Whether recipients prefer getting lump sums or monthly instalments
  • How GiveDirectly tackles cases of fraud and theft
  • The case for universal basic income, and GiveDirectly’s UBI studies in Kenya, Malawi, and Liberia
  • The political viability of UBI
  • Plenty more

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#168 – Ian Morris on whether deep history says we’re heading for an intelligence explosion

In today’s episode, host Rob Wiblin speaks with repeat guest Ian Morris about what big-picture history says about the likely impact of machine intelligence.

They cover:

  • Some crazy anomalies in the historical record of civilisational progress
  • Whether we should think about today’s technology from an evolutionary perspective
  • Whether war will make a resurgence
  • Why we can’t end up living like The Jetsons
  • Whether stagnation or cyclical futures are realistic
  • What it means that over the very long term the rate of economic growth has increased
  • Whether violence between humans and powerful AI systems is likely
  • The most likely reasons for Rob and Ian to be really wrong about all of this
  • How professional historians react to this sort of talk
  • The future of Ian’s work
  • Plenty more

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire
Transcriptions: Katy Moore

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#167 – Seren Kell on the research gaps holding back alternative proteins from mass adoption

In today’s episode, host Luisa Rodriguez interviews Seren Kell — Senior Science and Technology Manager at the Good Food Institute Europe — about making alternative proteins as tasty, cheap, and convenient as traditional meat, dairy, and egg products.

They cover:

  • The basic case for alternative proteins, and why they’re so hard to make
  • Why fermentation is a surprisingly promising technology for creating delicious alternative proteins
  • The main scientific challenges that need to be solved to make fermentation even more useful
  • The progress that’s been made on the cultivated meat front, and what it will take to make cultivated meat affordable
  • How GFI Europe is helping with some of these challenges
  • How people can use their careers to contribute to replacing factory farming with alternative proteins
  • The best part of Seren’s job
  • Plenty more

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong and Milo McGuire
Additional content editing: Luisa Rodriguez and Katy Moore
Transcriptions: Katy Moore

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#166 – Tantum Collins on what he’s learned as an AI policy insider at the White House, DeepMind and elsewhere

In today’s episode, host Rob Wiblin gets the rare chance to interview someone with insider AI policy experience at the White House and DeepMind who’s willing to speak openly — Tantum Collins.

They cover:

  • How AI could strengthen government capacity, and how that’s a double-edged sword
  • How new technologies force us to confront tradeoffs in political philosophy that we were previously able to pretend weren’t there
  • To what extent policymakers take different threats from AI seriously
  • Whether the US and China are in an AI arms race or not
  • Whether it’s OK to transform the world without much of the world agreeing to it
  • The tyranny of small differences in AI policy
  • Disagreements between different schools of thought in AI policy, and proposals that could unite them
  • How the US AI Bill of Rights could be improved
  • Whether AI will transform the labour market, and whether it will become a partisan political issue
  • The tensions between the cultures of San Francisco and DC, and how to bridge the divide between them
  • What listeners might be able to do to help with this whole mess
  • Panpsychism
  • Plenty more

Producer and editor: Keiran Harris
Audio engineering lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: Katy Moore

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#165 – Anders Sandberg on war in space, whether civilisations age, and the best things possible in our universe

In today’s episode, host Rob Wiblin speaks with repeat guest and audience favourite Anders Sandberg about the most impressive things that could be achieved in our universe given the laws of physics.

They cover:

  • The epic new book Anders is working on, and whether he’ll ever finish it
  • Whether there’s a best possible world or we can just keep improving forever
  • What wars might look like if the galaxy is mostly settled
  • The impediments to AI or humans making it to other stars
  • How the universe will end a million trillion years in the future
  • Whether it’s useful to wonder about whether we’re living in a simulation
  • The grabby aliens theory
  • Whether civilizations get more likely to fail the older they get
  • The best way to generate energy that could ever exist
  • Black hole bombs
  • Whether superintelligence is necessary to get a lot of value
  • The likelihood that life from elsewhere has already visited Earth
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: Katy Moore

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#164 – Kevin Esvelt on cults that want to kill everyone, stealth vs wildfire pandemics, and how he felt inventing gene drives

In today’s episode, host Luisa Rodriguez interviews Kevin Esvelt — a biologist at the MIT Media Lab and the inventor of CRISPR-based gene drive — about the threat posed by engineered bioweapons.

They cover:

  • Why it makes sense to focus on deliberately released pandemics
  • Case studies of people who actually wanted to kill billions of humans
  • How many people have the technical ability to produce dangerous viruses
  • The different threats of stealth and wildfire pandemics that could crash civilisation
  • The potential for AI models to increase access to dangerous pathogens
  • Why scientists try to identify new pandemic-capable pathogens, and the case against that research
  • Technological solutions, including UV lights and advanced PPE
  • Using CRISPR-based gene drive to fight diseases and reduce animal suffering
  • And plenty more.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#163 – Toby Ord on the perils of maximising the good that you do

Effective altruism is associated with the slogan “do the most good.” On one level, this has to be unobjectionable: What could be bad about helping people more and more?

But in today’s interview, Toby Ord — moral philosopher at the University of Oxford and one of the founding figures of effective altruism — lays out three reasons to be cautious about the idea of maximising the good that you do. He suggests that rather than “doing the most good that we can,” perhaps we should be happy with a more modest and manageable goal: “doing most of the good that we can.”

Toby was inspired to revisit these ideas by the possibility that Sam Bankman-Fried, who stands accused of committing severe fraud as CEO of the cryptocurrency exchange FTX, was motivated to break the law by a desire to give away as much money as possible to worthy causes.

Toby’s top reason not to fully maximise is the following: if the goal you’re aiming at is subtly wrong or incomplete, then going all the way towards maximising it will usually cause you to start doing some very harmful things.

This result can be shown mathematically, but can also be made intuitive, and may explain why we feel instinctively wary of going “all-in” on any idea, or goal, or way of living — even something as benign as helping other people as much as possible.

Toby gives the example of someone pursuing a career as a professional swimmer. Initially, as our swimmer takes their training and performance more seriously, they adjust their diet, hire a better trainer, and pay more attention to their technique. While swimming is the main focus of their life, they feel fit and healthy and also enjoy other aspects of their life as well — family, friends, and personal projects.

But if they decide to increase their commitment further and really go all-in on their swimming career, holding back nothing back, then this picture can radically change. Their effort was already substantial, so how can they shave those final few seconds off their racing time? The only remaining options are those which were so costly they were loath to consider them before.

To eke out those final gains — and go from 80% effort to 100% — our swimmer must sacrifice other hobbies, deprioritise their relationships, neglect their career, ignore food preferences, accept a higher risk of injury, and maybe even consider using steroids.

Now, if maximising one’s speed at swimming really were the only goal they ought to be pursuing, there’d be no problem with this. But if it’s the wrong goal, or only one of many things they should be aiming for, then the outcome is disastrous. In going from 80% to 100% effort, their swimming speed was only increased by a tiny amount, while everything else they were accomplishing dropped off a cliff.

The bottom line is simple: a dash of moderation makes you much more robust to uncertainty and error.

As Toby notes, this is similar to the observation that a sufficiently capable superintelligent AI, given any one goal, would ruin the world if it maximised it to the exclusion of everything else. And it follows a similar pattern to performance falling off a cliff when a statistical model is ‘overfit’ to its data.

cliff

In the full interview, Toby also explains the “moral trade” argument against pursuing narrow goals at the expense of everything else, and how consequentialism changes if you judge not just outcomes or acts, but everything according to its impacts on the world.

Toby and Rob also discuss:

  • The rise and fall of FTX and some of its impacts
  • What Toby hoped effective altruism would and wouldn’t become when he helped to get it off the ground
  • What utilitarianism has going for it, and what’s wrong with it in Toby’s view
  • How to mathematically model the importance of personal integrity
  • Which AI labs Toby thinks have been acting more responsibly than others
  • How having a young child affects Toby’s feelings about AI risk
  • Whether infinities present a fundamental problem for any theory of ethics that aspire to be fully impartial
  • How Toby ended up being the source of the highest quality images of the Earth from space

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.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour
Transcriptions: Katy Moore

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#162 – Mustafa Suleyman on getting Washington and Silicon Valley to tame AI

Mustafa Suleyman was part of the trio that founded DeepMind, and his new AI project is building one of the world’s largest supercomputers to train a large language model on 10–100x the compute used to train ChatGPT.

But far from the stereotype of the incorrigibly optimistic tech founder, Mustafa is deeply worried about the future, for reasons he lays out in his new book The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma (coauthored with Michael Bhaskar). The future could be really good, but only if we grab the bull by the horns and solve the new problems technology is throwing at us.

On Mustafa’s telling, AI and biotechnology will soon be a huge aid to criminals and terrorists, empowering small groups to cause harm on previously unimaginable scales. Democratic countries have learned to walk a ‘narrow path’ between chaos on the one hand and authoritarianism on the other, avoiding the downsides that come from both extreme openness and extreme closure. AI could easily destabilise that present equilibrium, throwing us off dangerously in either direction. And ultimately, within our lifetimes humans may not need to work to live any more — or indeed, even have the option to do so.

And those are just three of the challenges confronting us. In Mustafa’s view, ‘misaligned’ AI that goes rogue and pursues its own agenda won’t be an issue for the next few years, and it isn’t a problem for the current style of large language models. But he thinks that at some point — in eight, ten, or twelve years — it will become an entirely legitimate concern, and says that we need to be planning ahead.

In The Coming Wave, Mustafa lays out a 10-part agenda for ‘containment’ — that is to say, for limiting the negative and unforeseen consequences of emerging technologies:

  1. Developing an Apollo programme for technical AI safety
  2. Instituting capability audits for AI models
  3. Buying time by exploiting hardware choke points
  4. Getting critics involved in directly engineering AI models
  5. Getting AI labs to be guided by motives other than profit
  6. Radically increasing governments’ understanding of AI and their capabilities to sensibly regulate it
  7. Creating international treaties to prevent proliferation of the most dangerous AI capabilities
  8. Building a self-critical culture in AI labs of openly accepting when the status quo isn’t working
  9. Creating a mass public movement that understands AI and can demand the necessary controls
  10. Not relying too much on delay, but instead seeking to move into a new somewhat-stable equilibria

As Mustafa put it, “AI is a technology with almost every use case imaginable” and that will demand that, in time, we rethink everything.

Rob and Mustafa discuss the above, as well as:

  • Whether we should be open sourcing AI models
  • Whether Mustafa’s policy views are consistent with his timelines for transformative AI
  • How people with very different views on these issues get along at AI labs
  • The failed efforts (so far) to get a wider range of people involved in these decisions
  • Whether it’s dangerous for Mustafa’s new company to be training far larger models than GPT-4
  • Whether we’ll be blown away by AI progress over the next year
  • What mandatory regulations government should be imposing on AI labs right now
  • Appropriate priorities for the UK’s upcoming AI safety summit

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.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire
Transcriptions: Katy Moore

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#161 – Michael Webb on whether AI will soon cause job loss, lower incomes, and higher inequality — or the opposite

In today’s episode, host Luisa Rodriguez interviews economist Michael Webb of DeepMind, the British Government, and Stanford about how AI progress is going to affect people’s jobs and the labour market.

They cover:

  • The jobs most and least exposed to AI
  • Whether we’ll we see mass unemployment in the short term
  • How long it took other technologies like electricity and computers to have economy-wide effects
  • Whether AI will increase or decrease inequality
  • Whether AI will lead to explosive economic growth
  • What we can we learn from history, and reasons to think this time is different
  • Career advice for a world of LLMs
  • Why Michael is starting a new org to relieve talent bottlenecks through accelerated learning, and how you can get involved
  • Michael’s take as a musician on AI-generated music
  • And plenty more

If you’d like to work with Michael on his new org to radically accelerate how quickly people acquire expertise in critical cause areas, he’s now hiring! Check out Quantum Leap’s website.

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.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#160 – Hannah Ritchie on why it makes sense to be optimistic about the environment

In today’s episode, host Luisa Rodriguez interviews the head of research at Our World in Data — Hannah Ritchie — on the case for environmental optimism.

They cover:

  • Why agricultural productivity in sub-Saharan Africa could be so important, and how much better things could get
  • Her new book about how we could be the first generation to build a sustainable planet
  • Whether climate change is the most worrying environmental issue
  • How we reduced outdoor air pollution
  • Why Hannah is worried about the state of biodiversity
  • Solutions that address multiple environmental issues at once
  • How the world coordinated to address the hole in the ozone layer
  • Surprises from Our World in Data’s research
  • Psychological challenges that come up in Hannah’s work
  • 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.

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire and Dominic Armstrong
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#159 – Jan Leike on OpenAI’s massive push to make superintelligence safe in 4 years or less

In July, OpenAI announced a new team and project: Superalignment. The goal is to figure out how to make superintelligent AI systems aligned and safe to use within four years, and the lab is putting a massive 20% of its computational resources behind the effort.

Today’s guest, Jan Leike, is Head of Alignment at OpenAI and will be co-leading the project. As OpenAI puts it, “…the vast power of superintelligence could be very dangerous, and lead to the disempowerment of humanity or even human extinction. … Currently, we don’t have a solution for steering or controlling a potentially superintelligent AI, and preventing it from going rogue.”

Given that OpenAI is in the business of developing superintelligent AI, it sees that as a scary problem that urgently has to be fixed. So it’s not just throwing compute at the problem — it’s also hiring dozens of scientists and engineers to build out the Superalignment team.

Plenty of people are pessimistic that this can be done at all, let alone in four years. But Jan is guardedly optimistic. As he explains:

Honestly, it really feels like we have a real angle of attack on the problem that we can actually iterate on… and I think it’s pretty likely going to work, actually. And that’s really, really wild, and it’s really exciting. It’s like we have this hard problem that we’ve been talking about for years and years and years, and now we have a real shot at actually solving it. And that’d be so good if we did.

Jan thinks that this work is actually the most scientifically interesting part of machine learning. Rather than just throwing more chips and more data at a training run, this work requires actually understanding how these models work and how they think. The answers are likely to be breakthroughs on the level of solving the mysteries of the human brain.

The plan, in a nutshell, is to get AI to help us solve alignment. That might sound a bit crazy — as one person described it, “like using one fire to put out another fire.”

But Jan’s thinking is this: the core problem is that AI capabilities will keep getting better and the challenge of monitoring cutting-edge models will keep getting harder, while human intelligence stays more or less the same. To have any hope of ensuring safety, we need our ability to monitor, understand, and design ML models to advance at the same pace as the complexity of the models themselves.

And there’s an obvious way to do that: get AI to do most of the work, such that the sophistication of the AIs that need aligning, and the sophistication of the AIs doing the aligning, advance in lockstep.

Jan doesn’t want to produce machine learning models capable of doing ML research. But such models are coming, whether we like it or not. And at that point Jan wants to make sure we turn them towards useful alignment and safety work, as much or more than we use them to advance AI capabilities.

Jan thinks it’s so crazy it just might work. But some critics think it’s simply crazy. They ask a wide range of difficult questions, including:

  • If you don’t know how to solve alignment, how can you tell that your alignment assistant AIs are actually acting in your interest rather than working against you? Especially as they could just be pretending to care about what you care about.
  • How do you know that these technical problems can be solved at all, even in principle?
  • At the point that models are able to help with alignment, won’t they also be so good at improving capabilities that we’re in the middle of an explosion in what AI can do?

In today’s interview host Rob Wiblin puts these doubts to Jan to hear how he responds to each, and they also cover:

  • OpenAI’s current plans to achieve ‘superalignment’ and the reasoning behind them
  • Why alignment work is the most fundamental and scientifically interesting research in ML
  • The kinds of people he’s excited to hire to join his team and maybe save the world
  • What most readers misunderstood about the OpenAI announcement
  • The three ways Jan expects AI to help solve alignment: mechanistic interpretability, generalization, and scalable oversight
  • What the standard should be for confirming whether Jan’s team has succeeded
  • Whether OpenAI should (or will) commit to stop training more powerful general models if they don’t think the alignment problem has been solved
  • Whether Jan thinks OpenAI has deployed models too quickly or too slowly
  • The many other actors who also have to do their jobs really well if we’re going to have a good AI future
  • 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 and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore

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#158 – Holden Karnofsky on how AIs might take over even if they’re no smarter than humans, and his 4-part playbook for AI risk

Back in 2007, Holden Karnofsky cofounded GiveWell, where he sought out the charities that most cost-effectively helped save lives. He then cofounded Open Philanthropy, where he oversaw a team making billions of dollars’ worth of grants across a range of areas: pandemic control, criminal justice reform, farmed animal welfare, and making AI safe, among others. This year, having learned about AI for years and observed recent events, he’s narrowing his focus once again, this time on making the transition to advanced AI go well.

In today’s conversation, Holden returns to the show to share his overall understanding of the promise and the risks posed by machine intelligence, and what to do about it. That understanding has accumulated over around 14 years, during which he went from being sceptical that AI was important or risky, to making AI risks the focus of his work.

(As Holden reminds us, his wife is also the president of one of the world’s top AI labs, Anthropic, giving him both conflicts of interest and a front-row seat to recent events. For our part, Open Philanthropy is 80,000 Hours’ largest financial supporter.)

One point he makes is that people are too narrowly focused on AI becoming ‘superintelligent.’ While that could happen and would be important, it’s not necessary for AI to be transformative or perilous. Rather, machines with human levels of intelligence could end up being enormously influential simply if the amount of computer hardware globally were able to operate tens or hundreds of billions of them, in a sense making machine intelligences a majority of the global population, or at least a majority of global thought.

As Holden explains, he sees four key parts to the playbook humanity should use to guide the transition to very advanced AI in a positive direction: alignment research, standards and monitoring, creating a successful and careful AI lab, and finally, information security.

In today’s episode, host Rob Wiblin interviews return guest Holden Karnofsky about that playbook, as well as:

  • Why we can’t rely on just gradually solving those problems as they come up, the way we usually do with new technologies.
  • What multiple different groups can do to improve our chances of a good outcome — including listeners to this show, governments, computer security experts, and journalists.
  • Holden’s case against ‘hardcore utilitarianism’ and what actually motivates him to work hard for a better world.
  • What the ML and AI safety communities get wrong in Holden’s view.
  • Ways we might succeed with AI just by dumb luck.
  • The value of laying out imaginable success stories.
  • Why information security is so important and underrated.
  • Whether it’s good to work at an AI lab that you think is particularly careful.
  • The track record of futurists’ predictions.
  • 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. Or read the transcript below.

Producer: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: Katy Moore

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#157 – Ezra Klein on existential risk from AI and what DC could do about it

In Oppenheimer, scientists detonate a nuclear weapon despite thinking there’s some ‘near zero’ chance it would ignite the atmosphere, putting an end to life on Earth. Today, scientists working on AI think the chance their work puts an end to humanity is vastly higher than that.

In response, some have suggested we launch a Manhattan Project to make AI safe via enormous investment in relevant R&D. Others have suggested that we need international organisations modelled on those that slowed the proliferation of nuclear weapons. Others still seek a research slowdown by labs while an auditing and licencing scheme is created.

Today’s guest — journalist Ezra Klein of The New York Times — has watched policy discussions and legislative battles play out in DC for 20 years. Like many people he has also taken a big interest in AI this year, writing articles such as “This changes everything.” In his first interview on the show in 2021, he flagged AI as one topic that DC would regret not having paid more attention to.

So we invited him on to get his take on which regulatory proposals have promise, and which seem either unhelpful or politically unviable.

Out of the ideas on the table right now, Ezra favours a focus on direct government funding — both for AI safety research and to develop AI models designed to solve problems other than making money for their operators. He is sympathetic to legislation that would require AI models to be legible in a way that none currently are — and embraces the fact that that will slow down the release of models while businesses figure out how their products actually work.

By contrast, he’s pessimistic that it’s possible to coordinate countries around the world to agree to prevent or delay the deployment of dangerous AI models — at least not unless there’s some spectacular AI-related disaster to create such a consensus. And he fears attempts to require licences to train the most powerful ML models will struggle unless they can find a way to exclude and thereby appease people working on relatively safe consumer technologies rather than cutting-edge research.

From observing how DC works, Ezra expects that even a small community of experts in AI governance can have a large influence on how the the US government responds to AI advances. But in Ezra’s view, that requires those experts to move to DC and spend years building relationships with people in government, rather than clustering elsewhere in academia and AI labs.

In today’s brisk conversation, Ezra and host Rob Wiblin cover the above as well as:

  • Whether it’s desirable to slow down AI research
  • The value of engaging with current policy debates even if they don’t seem directly important
  • Which AI business models seem more or less dangerous
  • Tensions between people focused on existing vs emergent risks from AI
  • Two major challenges of being a new parent

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 Engineering Lead: Ben Cordell
Technical editing: Milo McGuire
Transcriptions: Katy Moore

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