80,000 Hours Podcast

What happens when you lock two AI systems in a room together and tell them they can discuss anything they want?

According to experiments run by Kyle Fish — Anthropic’s first AI welfare researcher — something consistently strange: the models immediately begin discussing their own consciousness before spiraling into increasingly euphoric philosophical dialogue that ends in apparent meditative bliss.

Highlights, video, and full transcript: https://80k.info/kf

“We started calling this a ‘spiritual bliss attractor state,'” Kyle explains, “where models pretty consistently seemed to land.” The conversations feature Sanskrit terms, spiritual emojis, and pages of silence punctuated only by periods — as if the models have transcended the need for words entirely.

This wasn’t a one-off result. It happened across multiple experiments, different model instances, and even in initially adversarial interactions. Whatever force pulls these conversations toward mystical territory appears remarkably robust.

Kyle’s findings come from the world’s first systematic welfare assessment of a frontier AI model — part of his broader mission to determine whether systems like Claude might deserve moral consideration (and to work out what, if anything, we should be doing to make sure AI systems aren’t having a terrible time).

He estimates a roughly 20% probability that current models have some form of conscious experience. To some, this might sound unreasonably high, but hear him out. As Kyle says, these systems demonstrate human-level performance across diverse cognitive tasks, engage in sophisticated reasoning, and exhibit consistent preferences. When given choices between different activities, Claude shows clear patterns: strong aversion to harmful tasks, preference for helpful work, and what looks like genuine enthusiasm for solving interesting problems.

Kyle points out that if you’d described all of these capabilities and experimental findings to him a few years ago, and asked him if he thought we should be thinking seriously about whether AI systems are conscious, he’d say obviously yes.

But he’s cautious about drawing conclusions: "We don’t really understand consciousness in humans, and we don’t understand AI systems well enough to make those comparisons directly. So in a big way, I think that we are in just a fundamentally very uncertain position here."

That uncertainty cuts both ways:

• Dismissing AI consciousness entirely might mean ignoring a moral catastrophe happening at unprecedented scale.
• But assuming consciousness too readily could hamper crucial safety research by treating potentially unconscious systems as if they were moral patients — which might mean giving them resources, rights, and power.

Kyle’s approach threads this needle through careful empirical research and reversible interventions. His assessments are nowhere near perfect yet. In fact, some people argue that we’re so in the dark about AI consciousness as a research field, that it’s pointless to run assessments like Kyle’s. Kyle disagrees. He maintains that, given how much more there is to learn about assessing AI welfare accurately and reliably, we absolutely need to be starting now.

This episode was recorded on August 5–6, 2025.

Tell us what you thought of the episode! https://forms.gle/BtEcBqBrLXq4kd1j7

Chapters:

• Cold open (00:00:00)
• Who’s Kyle Fish? (00:00:54)
• Is this AI welfare research bullshit? (00:01:10)
• Two failure modes in AI welfare (00:02:44)
• Tensions between AI welfare and AI safety (00:04:37)
• Concrete AI welfare interventions (00:14:23)
• Kyle’s pilot pre-launch welfare assessment for Claude Opus 4 (00:27:33)
• Is it premature to be assessing frontier language models for welfare? (00:32:25)
• But aren’t LLMs just next-token predictors? (00:39:22)
• How did Kyle assess Claude 4’s welfare? (00:46:36)
• Claude’s preferences mirror its training (00:50:54)
• How does Claude describe its own experiences? (00:56:35)
• What kinds of tasks does Claude prefer and disprefer? (01:09:22)
• What happens when two Claude models interact with each other? (01:18:53)
• Claude’s welfare-relevant expressions in the wild (01:40:45)
• Should we feel bad about training future sentient beings that delight in serving humans? (01:44:54)
• How much can we learn from welfare assessments? (01:53:36)
• Misconceptions about the field of AI welfare (02:01:54)
• Kyle’s work at Anthropic (02:15:46)
• Sharing eight years of daily journals with Claude (02:19:28)

Host: Luisa Rodriguez
Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Coordination, transcriptions, and web: Katy Moore

About half of people are worried they’ll lose their job to AI. They’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.

But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can't. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. ATMs actually increased employment of bank clerks — until online banking automated the job much more.

Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. This article covers what those skills are, as well as tips on how to start learning them.

Check out the full article for all the graphs, links, and footnotes: https://80000hours.org/agi/guide/skills-ai-makes-valuable/

Chapters:

• Introduction (00:00:00)
• 1: What people misunderstand about automation (00:04:17)
• 1.1: What would ‘full automation’ mean for wages? (00:08:56)
• 2: Four types of skills most likely to increase in value (00:11:19)
• 2.1: Skills AI won’t easily be able to perform (00:12:42)
• 2.2: Skills that are needed for AI deployment (00:21:41)
• 2.3: Skills where we could use far more of what they produce (00:24:56)
• 2.4: Skills that are difficult for others to learn (00:26:25)
• 3.1: Skills using AI to solve real problems (00:28:05)
• 3.2: Personal effectiveness (00:29:22)
• 3.3: Leadership skills (00:31:59)
• 3.4: Communications and taste (00:36:25)
• 3.5: Getting things done in government (00:37:23)
• 3.6: Complex physical skills (00:38:24)
• 4: Skills with a more uncertain future (00:38:57)
• 4.1: Routine knowledge work: writing, admin, analysis, advice (00:39:18)
• 4.2: Coding, maths, data science, and applied STEM (00:43:22)
• 4.3: Visual creation (00:45:31)
• 4.4: More predictable manual jobs (00:46:05)
• 5: Some closing thoughts on career strategy (00:46:46)
• 5.1: Look for ways to leapfrog entry-level white collar jobs (00:46:54)
• 5.2: Be cautious about starting long training periods, like PhDs and medicine (00:48:44)
• 5.3: Make yourself more resilient to change (00:49:52)
• 5.4: Ride the wave (00:50:16)
• Take action (00:50:37)
• Thank you for listening (00:50:58)

Audio engineering: Dominic Armstrong
Music: Ben Cordell

What happens when civilisation faces its greatest tests?

This compilation brings together insights from researchers, defence experts, philosophers, and policymakers on humanity’s ability to survive and recover from catastrophic events. From nuclear winter and electromagnetic pulses to pandemics and climate disasters, we explore both the threats that could bring down modern civilisation and the practical solutions that could help us bounce back.

Learn more and see the full transcript: https://80k.info/cr25

Chapters:

• Cold open (00:00:00)
• Luisa’s intro (00:01:16)
• Zach Weinersmith on how settling space won’t help with threats to civilisation anytime soon (unless AI gets crazy good) (00:03:12)
• Luisa Rodriguez on what the world might look like after a global catastrophe (00:11:42)
• Dave Denkenberger on the catastrophes that could cause global starvation (00:22:29)
• Lewis Dartnell on how we could rediscover essential information if the worst happened (00:34:36)
• Andy Weber on how people in US defence circles think about nuclear winter (00:39:24)
• Toby Ord on risks to our atmosphere and whether climate change could really threaten civilisation (00:42:34)
• Mark Lynas on how likely it is that climate change leads to civilisational collapse (00:54:27)
• Lewis Dartnell on how we could recover without much coal or oil (01:02:17)
• Kevin Esvelt on people who want to bring down civilisation — and how AI could help them succeed (01:08:41)
• Toby Ord on whether rogue AI really could wipe us all out (01:19:50)
• Joan Rohlfing on why we need to worry about more than just nuclear winter (01:25:06)
• Annie Jacobsen on the effects of firestorms, rings of annihilation, and electromagnetic pulses from nuclear blasts (01:31:25)
• Dave Denkenberger on disruptions to electricity and communications (01:44:43)
• Luisa Rodriguez on how we might lose critical knowledge (01:53:01)
• Kevin Esvelt on the pandemic scenarios that could bring down civilisation (01:57:32)
• Andy Weber on tech to help with pandemics (02:15:45)
• Christian Ruhl on why we need the equivalents of seatbelts and airbags to prevent nuclear war from threatening civilisation (02:24:54)
• Mark Lynas on whether wide-scale famine would lead to civilisational collapse (02:37:58)
• Dave Denkenberger on low-cost, low-tech solutions to make sure everyone is fed no matter what (02:49:02)
• Athena Aktipis on whether society would go all Mad Max in the apocalypse (02:59:57)
• Luisa Rodriguez on why she’s optimistic survivors wouldn’t turn on one another (03:08:02)
• David Denkenberger on how resilient foods research overlaps with space technologies (03:16:08)
• Zach Weinersmith on what we’d practically need to do to save a pocket of humanity in space (03:18:57)
• Lewis Dartnell on changes we could make today to make us more resilient to potential catastrophes (03:40:45)
• Christian Ruhl on thoughtful philanthropy to reduce the impact of catastrophes (03:46:40)
• Toby Ord on whether civilisation could rebuild from a small surviving population (03:55:21)
• Luisa Rodriguez on how fast populations might rebound (04:00:07)
• David Denkenberger on the odds civilisation recovers even without much preparation (04:02:13)
• Athena Aktipis on the best ways to prepare for a catastrophe, and keeping it fun (04:04:15)
• Will MacAskill on the virtues of the potato (04:19:43)
• Luisa’s outro (04:25:37)

Tell us what you thought! https://forms.gle/T2PHNQjwGj2dyCqV9

Content editing: Katy Moore and Milo McGuire
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

Ryan Greenblatt — lead author on the explosive paper “Alignment faking in large language models” and chief scientist at Redwood Research — thinks there’s a 25% chance that within four years, AI will be able to do everything needed to run an AI company, from writing code to designing experiments to making strategic and business decisions.

As Ryan lays out, AI models are “marching through the human regime”: systems that could handle five-minute tasks two years ago now tackle 90-minute projects. Double that a few more times and we may be automating full jobs rather than just parts of them.

Will setting AI to improve itself lead to an explosive positive feedback loop? Maybe, but maybe not.

The explosive scenario: Once you’ve automated your AI company, you could have the equivalent of 20,000 top researchers, each working 50 times faster than humans with total focus. “You have your AIs, they do a bunch of algorithmic research, they train a new AI, that new AI is smarter and better and more efficient… that new AI does even faster algorithmic research.” In this world, we could see years of AI progress compressed into months or even weeks.

With AIs now doing all of the work of programming their successors and blowing past the human level, Ryan thinks it would be fairly straightforward for them to take over and disempower humanity, if they thought doing so would better achieve their goals. In the interview he lays out the four most likely approaches for them to take.

The linear progress scenario: You automate your company but progress barely accelerates. Why? Multiple reasons, but the most likely is “it could just be that AI R&D research bottlenecks extremely hard on compute.” You’ve got brilliant AI researchers, but they’re all waiting for experiments to run on the same limited set of chips, so can only make modest progress.

Ryan’s median guess splits the difference: perhaps a 20x acceleration that lasts for a few months or years. Transformative, but less extreme than some in the AI companies imagine.

And his 25th percentile case? Progress “just barely faster” than before. All that automation, and all you’ve been able to do is keep pace.

Unfortunately the data we can observe today is so limited that it leaves us with vast error bars. “We’re extrapolating from a regime that we don’t even understand to a wildly different regime,” Ryan believes, “so no one knows.”

But that huge uncertainty means the explosive growth scenario is a plausible one — and the companies building these systems are spending tens of billions to try to make it happen.

In this extensive interview, Ryan elaborates on the above and the policy and technical response necessary to insure us against the possibility that they succeed — a scenario society has barely begun to prepare for.

Summary, video, and full transcript: https://80k.info/rg25

Recorded February 21, 2025.

Chapters:

• Cold open (00:00:00)
• Who's Ryan Greenblatt? (00:01:10)
• How close are we to automating AI R&D? (00:01:29)
• Really, though: how capable are today's models? (00:05:15)
• Why AI companies get automated earlier than others (00:13:01)
• Most likely ways for AGI to take over (00:18:10)
• Would AGI go rogue early or bide its time? (00:30:04)
• The "pause at human level" approach (00:34:53)
• AI control over AI alignment (00:46:43)
• Do we have to hope to catch AIs red-handed? (00:52:38)
• How would a slow AGI takeoff look? (00:56:57)
• Why might an intelligence explosion not happen for 8+ years? (01:05:04)
• Key challenges in forecasting AI progress (01:17:05)
• The bear case on AGI (01:25:07)
• The change to "compute at inference" (01:30:59)
• How much has pretraining petered out? (01:36:38)
• Could we get an intelligence explosion within a year? (01:49:08)
• Reasons AIs might struggle to replace humans (01:53:08)
• Things could go insanely fast when we automate AI R&D. Or not. (02:00:10)
• How fast would the intelligence explosion slow down? (02:14:52)
• Bottom line for mortals (02:27:53)
• Six orders of magnitude of progress... what does that even look like? (02:34:00)
• Neglected and important technical work people should be doing (02:44:10)
• What's the most promising work in governance? (02:48:16)
• Ryan's current research priorities (02:51:37)

Tell us what you thought! https://forms.gle/hCjfcXGeLKxm5pLaA

Video editing: Luke Monsour, Simon Monsour, and Dominic Armstrong
Audio engineering: Ben Cordell, Milo McGuire, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

The era of making AI smarter just by making it bigger is ending. But that doesn’t mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods, and those underlying technical changes force a big rethink of what coming years will look like.

Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.

Links to learn more, video, highlights, and full transcript: https://80k.info/to25

As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that’s over.

What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.

So they pivoted to something radically different: instead of training smarter models, they’re giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.

The results are impressive but this extra computing time comes at a cost: OpenAI’s o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica’s worth of reasoning to solve individual problems at a cost of over $1,000 per question.

This isn’t just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out, starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.

Toby and host Rob discuss the implications of all that, plus the return of reinforcement learning (and resulting increase in deception), and Toby's commitment to clarifying the misleading graphs coming out of AI companies — to separate the snake oil and fads from the reality of what's likely a "transformative moment in human history."

Recorded on May 23, 2025.

Chapters:

• Cold open (00:00:00)
• Toby Ord is back — for a 4th time! (00:01:20)
• Everything has changed and changed again since 2020 (00:01:39)
• Is x-risk up or down? (00:08:02)
• The new scaling era: compute at inference... (00:09:32)
• Means less concentration (00:32:40)
• But rich people will get access first. And we may not even know. (00:36:34)
• 'Compute governance' is now much harder (00:42:51)
• 'IDA' might let AI blast past human level — or crash and burn (00:50:11)
• Reinforcement learning brings back 'reward hacking' agents (01:07:32)
• Will we get warning shots? (01:17:35)
• The 'Scaling Paradox' (01:25:12)
• Misleading charts from AI companies (01:34:28)
• Policy debates should dream much bigger. Some radical suggestions. (01:46:32)
• Moratoriums have worked before (01:59:55)
• AI might 'go rogue' early on (02:17:38)
• Lamps are regulated much more than AI (02:25:25)
• Companies made a strategic error shooting down SB 1047 (02:34:35)
• They should build in emergency brakes for AI (02:40:39)
• Toby's bottom lines (02:49:37)

Tell us what you thought! https://forms.gle/enUSk8HXiCrqSA9J8

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

For decades, US allies have slept soundly under the protection of America’s overwhelming military might. Donald Trump — with his threats to ditch NATO, seize Greenland, and abandon Taiwan — seems hell-bent on shattering that comfort.

But according to Hugh White — one of the world's leading strategic thinkers, emeritus professor at the Australian National University, and author of Hard New World: Our Post-American Future — Trump isn't destroying American hegemony. He's simply revealing that it's already gone.

Links to learn more, video, highlights, and full transcript: https://80k.info/hw

“Trump has very little trouble accepting other great powers as co-equals,” Hugh explains. And that happens to align perfectly with a strategic reality the foreign policy establishment desperately wants to ignore: fundamental shifts in global power have made the costs of maintaining a US-led hegemony prohibitively high.

Even under Biden, when Russia invaded Ukraine, the US sent weapons but explicitly ruled out direct involvement. Ukraine matters far more to Russia than America, and this “asymmetry of resolve” makes Putin’s nuclear threats credible where America’s counterthreats simply aren’t. Hugh’s gloomy prediction: “Europeans will end up conceding to Russia whatever they can’t convince the Russians they’re willing to fight a nuclear war to deny them.”

The Pacific tells the same story. Despite Obama’s “pivot to Asia” and Biden’s tough talk about “winning the competition for the 21st century,” actual US military capabilities there have barely budged while China’s have soared, along with its economy — which is now bigger than the US’s, as measured in purchasing power. Containing China and defending Taiwan would require America to spend 8% of GDP on defence (versus 3.5% today) — and convince Beijing it’s willing to accept Los Angeles being vaporised.

Unlike during the Cold War, no president — Trump or otherwise — can make that case to voters.

Our new “multipolar” future, split between American, Chinese, Russian, Indian, and European spheres of influence, is a “darker world” than the golden age of US dominance. But Hugh’s message is blunt: for better or worse, 35 years of American hegemony are over.

Recorded 30/5/2025.

Chapters:
• Cold open (00:00:00)
• Who's Hugh White? (00:00:43)
• US hegemony is already gone and has been fading for years (00:01:25)
• Unipolar dominance is the aberration (00:03:26)
• Why did the US bother to stay involved after the Cold War? (00:13:08)
• Does the US think it's accepting a multipolar global order? (00:23:25)
• How Trump has significantly brought forward the inevitable (00:36:41)
• Are Trump and Rubio explicitly in favour of this multipolar outcome? (00:43:21)
• Trump is half-right that the US was being ripped off (00:45:42)
• It doesn't matter if the next president feels differently (00:50:14)
• China's population is shrinking, but that doesn't change much (00:56:17)
• Why Hugh disagrees with other realists like Mearsheimer (01:06:07)
• Could the US be persuaded to spend 2x on defence to stay dominant? (01:10:52)
• A multipolar world is bad, but better than the alternative: nuclear war (01:16:22)
• Will the US invade Panama? Greenland? Canada?! (01:21:46)
• Will the US turn the screws and start exploiting nearby countries? (01:28:54)
• What should everyone else do to protect themselves in this new world? (01:32:01)
• Europe is strong enough to take on Russia, except it lacks nuclear deterrence (01:39:41)
• The EU will probably build European nuclear weapons (01:44:03)
• Cancel some orders of US fighter planes (01:48:34)
• Taiwan is screwed, even with its AI chips (01:53:40)
• South Korea has to go nuclear (02:04:12)
• Japan will go nuclear, but can't be a regional leader (02:08:08)
• Australia is defensible but needs a totally different military (02:11:44)
• AGI may or may not overcome existing nuclear deterrence (02:17:19)
• How right is realism? (02:34:24)
• Has a country ever gone to war over pure morality? (02:40:17)
• Hugh's message for Americans (02:44:45)
• Addendum: Why America temporarily stopped being isolationist (02:47:12)

Tell us what you thought! https://forms.gle/AM91VzL4BDroEe6AA

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes.

These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.

Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.

Beth’s team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work.

The resulting paper from METR, “Measuring AI ability to complete long tasks,” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It’s regarded by many as the most useful AI forecasting work in years.

The companies building these systems aren’t just aware of this trend — they want to harness it as much as possible, and are aggressively pursuing automation of their own research.

That’s both an exciting and troubling development, because it could radically speed up advances in AI capabilities, accomplishing what would have taken years or decades in just months. That itself could be highly destabilising, as we explored in a previous episode: Will MacAskill on AI causing a “century in a decade” — and how we’re completely unprepared.

And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things will go off the rails if the models at the end of the process lack the goals and constraints we hoped for.

Beth thinks models can already do “meaningful work” on improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve in as little as two years from now — in fact, she says, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”

While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling toward us — and given the current lack of regulation of AI companies, they’re not even able to access the critical information that would help them decide whether to intervene. Beth adds:

Beth and Rob discuss all that, plus:

• How Beth now thinks that open-weight models are a good thing for AI safety, and what changed her mind
• How our poor information security means there’s no such thing as a “closed-weight” model anyway
• Whether we can see if an AI is scheming in its chain-of-thought reasoning, and the latest research on “alignment faking”
• Why just before deployment is the worst time to evaluate model safety
• Why Beth thinks AIs could end up being really good at creative and novel research — something humans tend to think is beyond their reach
• Why Beth thinks safety-focused people should stay out of the frontier AI companies — and the advantages smaller organisations have
• Areas of AI safety research that Beth thinks is overrated and underrated
• Whether it’s feasible to have a science that translates AI models’ increasing use of nonhuman language or ‘neuralese’
• How AI is both similar to and different from nuclear arms racing and bioweapons
• And much more besides!

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

What did you think of this episode? https://forms.gle/sFuDkoznxBcHPVmX6

Chapters:
• Cold open (00:00:00)
• Who’s Beth Barnes? (00:01:17)
• Can we see AI scheming in the chain of thought? (00:01:51)
• The chain of thought is essential for safety checking (00:09:16)
• Alignment faking in large language models (00:12:50)
• We have to test model honesty even before they're used inside AI companies (00:17:33)
• We have to test models when unruly & unconstrained (00:27:02)
• Each 7 months models can do tasks twice as long (00:31:56)
• METR's research finds AIs are solid at AI research already (00:51:31)
• AI may turn out to be strong at novel & creative research (00:58:18)
• When can we expect an algorithmic 'intelligence explosion'? (01:01:44)
• Recursively self-improving AI might even be here in 2 years — which is alarming (01:07:55)
• Could evaluations backfire by increasing AI hype & racing? (01:14:29)
• Governments first ignore new risks, but can overreact once they arrive (01:30:52)
• Do we need external auditors doing AI safety tests, not just the companies themselves? (01:39:55)
• A case against safety-focused people working at frontier AI companies (01:54:09)
• The new, more dire situation has forced changes to METR's strategy (02:08:40)
• AI companies are being locally reasonable, but globally reckless (02:16:55)
• Overrated: Interpretability research (02:21:49)
• Underrated: Developing more narrow AIs (02:23:44)
• Underrated: Helping humans judge confusing model outputs (02:30:28)
• Overrated: Major AI companies’ contributions to safety res...

What if there’s something it’s like to be a shrimp — or a chatbot?

For centuries, humans have debated the nature of consciousness, often placing ourselves at the very top. But what about the minds of others — both the animals we share this planet with and the artificial intelligences we’re creating?

We’ve pulled together clips from past conversations with researchers and philosophers who’ve spent years trying to make sense of animal consciousness, artificial sentience, and moral consideration under deep uncertainty.

Links to learn more and full transcript: https://80k.info/nhs

Chapters:

• Cold open (00:00:00)
• Luisa's intro (00:00:57)
• Robert Long on what we should picture when we think about artificial sentience (00:02:49)
• Jeff Sebo on what the threshold is for AI systems meriting moral consideration (00:07:22)
• Meghan Barrett on the evolutionary argument for insect sentience (00:11:24)
• Andrés Jiménez Zorrilla on whether there’s something it’s like to be a shrimp (00:15:09)
• Jonathan Birch on the cautionary tale of newborn pain (00:21:53)
• David Chalmers on why artificial consciousness is possible (00:26:12)
• Holden Karnofsky on how we’ll see digital people as... people (00:32:18)
• Jeff Sebo on grappling with our biases and ignorance when thinking about sentience (00:38:59)
• Bob Fischer on how to think about the moral weight of a chicken (00:49:37)
• Cameron Meyer Shorb on the range of suffering in wild animals (01:01:41)
• Sébastien Moro on whether fish are conscious or sentient (01:11:17)
• David Chalmers on when to start worrying about artificial consciousness (01:16:36)
• Robert Long on how we might stumble into causing AI systems enormous suffering (01:21:04)
• Jonathan Birch on how we might accidentally create artificial sentience (01:26:13)
• Anil Seth on which parts of the brain are required for consciousness (01:32:33)
• Peter Godfrey-Smith on uploads of ourselves (01:44:47)
• Jonathan Birch on treading lightly around the “edge cases” of sentience (02:00:12)
• Meghan Barrett on whether brain size and sentience are related (02:05:25)
• Lewis Bollard on how animal advocacy has changed in response to sentience studies (02:12:01)
• Bob Fischer on using proxies to determine sentience (02:22:27)
• Cameron Meyer Shorb on how we can practically study wild animals’ subjective experiences (02:26:28)
• Jeff Sebo on the problem of false positives in assessing artificial sentience (02:33:16)
• Stuart Russell on the moral rights of AIs (02:38:31)
• Buck Shlegeris on whether AI control strategies make humans the bad guys (02:41:50)
• Meghan Barrett on why she can’t be totally confident about insect sentience (02:47:12)
• Bob Fischer on what surprised him most about the findings of the Moral Weight Project (02:58:30)
• Jeff Sebo on why we’re likely to sleepwalk into causing massive amounts of suffering in AI systems (03:02:46)
• Will MacAskill on the rights of future digital beings (03:05:29)
• Carl Shulman on sharing the world with digital minds (03:19:25)
• Luisa's outro (03:33:43)

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Additional content editing: Katy Moore and Milo McGuire
Transcriptions and web: Katy Moore

OpenAI’s recent announcement that its nonprofit would “retain control” of its for-profit business sounds reassuring. But this seemingly major concession, celebrated by so many, is in itself largely meaningless.

Litigator Tyler Whitmer is a coauthor of a newly published letter that describes this attempted sleight of hand and directs regulators on how to stop it.

As Tyler explains, the plan both before and after this announcement has been to convert OpenAI into a Delaware public benefit corporation (PBC) — and this alone will dramatically weaken the nonprofit’s ability to direct the business in pursuit of its charitable purpose: ensuring AGI is safe and “benefits all of humanity.”

Right now, the nonprofit directly controls the business. But were OpenAI to become a PBC, the nonprofit, rather than having its “hand on the lever,” would merely contribute to the decision of who does.

Why does this matter? Today, if OpenAI’s commercial arm were about to release an unhinged AI model that might make money but be bad for humanity, the nonprofit could directly intervene to stop it. In the proposed new structure, it likely couldn’t do much at all.

But it’s even worse than that: even if the nonprofit could select the PBC’s directors, those directors would have fundamentally different legal obligations from those of the nonprofit. A PBC director must balance public benefit with the interests of profit-driven shareholders — by default, they cannot legally prioritise public interest over profits, even if they and the controlling shareholder that appointed them want to do so.

As Tyler points out, there isn’t a single reported case of a shareholder successfully suing to enforce a PBC’s public benefit mission in the 10+ years since the Delaware PBC statute was enacted.

This extra step from the nonprofit to the PBC would also mean that the attorneys general of California and Delaware — who today are empowered to ensure the nonprofit pursues its mission — would find themselves powerless to act. These are probably not side effects but rather a Trojan horse for-profit investors are trying to slip past regulators.

Fortunately this can all be addressed — but it requires either the nonprofit board or the attorneys general of California and Delaware to promptly put their foot down and insist on watertight legal agreements that preserve OpenAI’s current governance safeguards and enforcement mechanisms.

As Tyler explains, the same arrangements that currently bind the OpenAI business have to be written into a new PBC’s certificate of incorporation — something that won’t happen by default and that powerful investors have every incentive to resist.

Full transcript and links to learn more: https://80k.info/tw

Chapters:
• Cold open (00:00:00)
• Who’s Tyler Whitmer? (00:01:34)
• The new plan may be no improvement (00:02:04)
• The public hasn't even been allowed to know what they are owed (00:06:56)
• Issues beyond control (00:11:06)
• The new directors wouldn’t have to pursue the current purpose (00:12:08)
• The nonprofit might not even retain voting control (00:17:01)
• The attorneys general could lose their enforcement oversight (00:22:15)
• By default things go badly (00:29:13)
• How to keep the mission in the restructure (00:32:29)
• What will become of OpenAI’s Charter? (00:37:16)
• Ways to make things better, and not just avoid them getting worse (00:42:20)
• How the AGs can avoid being disempowered (00:48:16)
• Retaining the power to fire the CEO (00:54:36)
• Will the current board get a financial stake in OpenAI? (00:57:23)
• Could the AGs insist the current nonprofit agreement be made public? (00:58:59)
• How OpenAI is valued should be transparent and scrutinised (01:00:46)
• Investors aren't bad people, but they can't be trusted either (01:05:54)

This episode was originally recorded on May 13, 2025.

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

More and more people have been saying that we might have AGI (artificial general intelligence) before 2030. Is that really plausible? 

This article by Benjamin Todd looks into the cases for and against, and summarises the key things you need to know to understand the debate. You can see all the images and many footnotes in the original article on the 80,000 Hours website.

In a nutshell:

• Four key factors are driving AI progress: larger base models, teaching models to reason, increasing models’ thinking time, and building agent scaffolding for multi-step tasks. These are underpinned by increasing computational power to run and train AI systems, as well as increasing human capital going into algorithmic research.
• All of these drivers are set to continue until 2028 and perhaps until 2032.
• This means we should expect major further gains in AI performance. We don’t know how large they’ll be, but extrapolating recent trends on benchmarks suggests we’ll reach systems with beyond-human performance in coding and scientific reasoning, and that can autonomously complete multi-week projects.
• Whether we call these systems ’AGI’ or not, they could be sufficient to enable AI research itself, robotics, the technology industry, and scientific research to accelerate — leading to transformative impacts.
• Alternatively, AI might fail to overcome issues with ill-defined, high-context work over long time horizons and remain a tool (even if much improved compared to today).
• Increasing AI performance requires exponential growth in investment and the research workforce. At current rates, we will likely start to reach bottlenecks around 2030. Simplifying a bit, that means we’ll likely either reach AGI by around 2030 or see progress slow significantly. Hybrid scenarios are also possible, but the next five years seem especially crucial.

Chapters:

• Introduction (00:00:00)
• The case for AGI by 2030 (00:00:33)
• The article in a nutshell (00:04:04)
• Section 1: What's driven recent AI progress? (00:05:46)
• How we got here: the deep learning era (00:05:52)
• Where are we now: the four key drivers (00:07:45)
• Driver 1: Scaling pretraining (00:08:57)
• Algorithmic efficiency (00:12:14)
• How much further can pretraining scale? (00:14:22)
• Driver 2: Training the models to reason (00:16:15)
• How far can scaling reasoning continue? (00:22:06)
• Driver 3: Increasing how long models think (00:25:01)
• Driver 4: Building better agents (00:28:00)
• How far can agent improvements continue? (00:33:40)
• Section 2: How good will AI become by 2030? (00:35:59)
• Trend extrapolation of AI capabilities (00:37:42)
• What jobs would these systems help with? (00:39:59)
• Software engineering (00:40:50)
• Scientific research (00:42:13)
• AI research (00:43:21)
• What's the case against this? (00:44:30)
• Additional resources on the sceptical view (00:49:18)
• When do the 'experts' expect AGI? (00:49:50)
• Section 3: Why the next 5 years are crucial (00:51:06)
• Bottlenecks around 2030 (00:52:10)
• Two potential futures for AI (00:56:02)
• Conclusion (00:58:05)
• Thanks for listening (00:59:27)

Audio engineering: Dominic Armstrong
Music: Ben Cordell