Blog post by Matt Beard · Last updated September 10th, 2025 · First published June 4th, 2025
Sometimes, our advising team speaks to people who have enthusiasm for technical AI safety and a related skill set but need concrete ideas for how to enter the field. This list was developed in consultation with our advisors to find the resources they commonly share, including articles, courses, organisations, and fellowships.
While we recommend applying to speak to an advisor for 1-1 tailored guidance, this page gives a practical, non-comprehensive snapshot of how you might move from ‘interested in technical AI safety’ to ‘starting to work on technical AI safety.’
Podcast by Robert Wiblin · Published September 15th, 2025
At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”
This means creating as many opportunities as possible for surprisingly good things to happen:
Write publicly.
Reach out to researchers whose work you admire.
Say yes to unusual projects that seem a little scary.
Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.
His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.
Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”
His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.
In this extended conversation, Neel discusses all that and some other hot takes from his four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)
This episode was recorded on July 21.
Video editing: Simon Monsour and Luke Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Camera operator: Jeremy Chevillotte Coordination, transcriptions, and web: Katy Moore
Given that we’re not currently fundraising, we initially considered not publishing an external review this year so that we could focus on other priorities. However, since we think these reviews might be informative for our audience, the EA/AIS community, and other stakeholders, we decided to publish a minimal version instead — we’ll stick to backward-looking updates rather than opinionated reflections or future plans, and largely draw on resources we’d already produced for other purposes.
The focus on brevity also means it’s more positively framed than it otherwise would be — both because challenges are harder to write about clearly, and because the programme updates are primarily focused on developments from this year (which have been going relatively well so far).
High-level org vision
80,000 Hours provides research and support to help talented people move into careers that tackle the world’s most pressing problems. We’re currently focusing our proactive effort on helping people work on safely navigating the transition to a world with powerful AGI, since we think this is the most pressing problem. We are broadly trying to:
Be a great source of information to bring people up to speed on how and why to use their careers to make AGI go well
Build automated systems for getting people into the roles that help make AGI go well
Podcast by Robert Wiblin · Published September 8th, 2025
We don’t know how AIs think or why they do what they do. Or at least, we don’t know much. That fact is only becoming more troubling as AIs grow more capable and appear on track to wield enormous cultural influence, directly advise on major government decisions, and even operate military equipment autonomously. We simply can’t tell what models, if any, should be trusted with such authority.
Neel Nanda of Google DeepMind is one of the founding figures of the field of machine learning trying to fix this situation — mechanistic interpretability (or “mech interp”). The project has generated enormous hype, exploding from a handful of researchers five years ago to hundreds today — all working to make sense of the jumble of tens of thousands of numbers that frontier AIs use to process information and decide what to say or do.
Neel now has a warning for us: the most ambitious vision of mech interp he once dreamed of is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are simply too great to get us there in time, before competitive pressures push us to deploy human-level or superhuman AIs. Indeed, Neel argues no one approach will guarantee alignment, and our only choice is the “Swiss cheese” model of accident protection, layering multiple safeguards on top of one another.
But while mech interp won’t be a silver bullet for AI safety, it has nevertheless had some major successes and will be one of the best tools in our arsenal.
For instance: by inspecting the neural activations in the middle of an AI’s thoughts, we can pick up many of the concepts the model is thinking about — from the Golden Gate Bridge, to refusing to answer a question, to the option of deceiving the user. While we can’t know all the thoughts a model is having all the time, picking up 90% of the concepts it is using 90% of the time should help us muddle through — so long as mech interp is paired with other techniques to fill in the gaps.
In today’s episode, Neel takes us on a tour of everything you’ll want to know about this race to understand what AIs are really thinking. He and host Rob Wiblin cover:
The best tools we’ve come up with so far, and where mech interp has failed
Why the best techniques have to be fast and cheap
The fundamental reasons we can’t reliably know what AIs are thinking, despite having perfect access to their internals
What we can and can’t learn by reading models’ ‘chains of thought’
Whether models will be able to trick us when they realise they’re being tested
The best protections to add on top of mech interp
Why he thinks the hottest technique in the field (SAEs) are overrated
His new research philosophy
How to break into mech interp and get a job — including applying to be a MATS scholar with Neel as your mentor (applications close September 12!)
This episode was recorded on July 17 and 21, 2025.
Video editing: Simon Monsour, Luke Monsour, Dominic Armstrong, and Milo McGuire Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Camera operator: Jeremy Chevillotte Coordination, transcriptions, and web: Katy Moore
The interview in a nutshell
Neel Nanda, who runs the mechanistic interpretability team at Google DeepMind, has shifted from hoping mech interp would fully reverse-engineer AI models to seeing it as one useful tool among many for AI safety:
Auditing hidden goals: Sam Marks at Anthropic ran competitions where teams had to find secret objectives in models — teams with mech interp’s most popular technique (sparse autoencoders) won
Detecting harmful prompts: Probes achieved 99.9% accuracy identifying harmful requests even when jailbreaks bypassed normal refusals
2. Simple techniques often outperform complex ones
Recent experiments have revealed the surprising effectiveness of basic approaches:
Probes beat fancy techniques:
In Neel’s team’s experiments, linear probes (simple correlations) detected harmful prompts better than sophisticated methods
They work by immediately noticing the model is using a ‘direction’ correlated with a concept like “this prompt is harmful”
They’re cheap enough to run on every inference in production (unlike LLM monitors which might cost 100x)
Chain of thought reveals what AIs are thinking:
Self-preservation was just confusion: When Palisade Research showed models resisting shutdown to complete math problems, Sen Rajamanoharan discovered by reading chain of thought that models thought “if I get shut down, I can’t finish these 10 problems” — not self-preservation for its own sake
Simple prompt changes eliminated the behaviour: Telling models “another AI will finish the task” or “letting yourself be shut down is more important” reduced shutdown resistance to zero
Even “blackmail” wasn’t real scheming: Models recommended self-preservation when told they were advising other AIs, showing they were roleplaying rather than genuinely trying to survive themselves
But chain of thought has limitations:
Models can give “unfaithful” reasoning and may not even know why they said what they said — e.g. models answering “yes” to both “Is magnesium’s atomic number bigger than aluminium’s?” and the reverse
OpenAI’s o3 already uses abbreviated shorthand like “poly not work, try different” — which could obscure thoughts from human overseers in the future
Chain of thought could break if we switch to ‘continuous chain of thought’ that doesn’t require them to output reasoning text
Fun demos: Golden Gate Claude, which couldn’t stop talking about the Golden Gate Bridge
Where SAEs were disappointing:
Finding known concepts: When looking for harmfulness, simple probes outperform SAEs
Feature absorption problems: SAEs create nonsensical concepts like “starts with E but isn’t the word elephant” to maximise sparsity — an issue that can be solved with effort
Neel advocates for the following research philosophy:
Start simple: Reading chain of thought solved the self-preservation mystery — no fancy tools needed
Beware false confidence: The Rome paper on editing facts (making models think the Eiffel Tower is in Rome) actually just added louder signals rather than truly editing knowledge
Expect to be wrong: Neel’s own “Toy model of universality” paper needed two followups to correct errors — “I think I believe the third paper, but I’m not entirely sure”
Why mech interp is probably too popular relative to other alignment research:
“An enormous nerd snipe” — the romance of understanding alien minds attracts researchers
Lower compute requirements for getting started than most ML research
The field still needs people because:
AI safety overall is massively underinvested in relative to its importance
Some people are much better suited to mech interp than other research projects
Practical career advice:
Don’t read 20 papers before starting — mech interp is learned by doing
Start with tiny two-week projects; abandoning them is fine if you’re learning
The MATS Program takes people from zero to conference papers in a few months — and Neel is currently accepting applications for his next cohort (apply by September 12)
Math Olympiad skills aren’t required — just linear algebra basics and good intuition
Blog post by Bella Forristal · Published September 8th, 2025
So, we finally gave in to peer pressure — 80,000 Hours is trying out Substack as a new way to publish our content. If you like reading things on Substack (or want to try it out), subscribe to our new publication!
For readers unfamiliar with Substack: it’s an online blogging platform that has risen steeply in popularity in recent years, and has become a home to some of the best longform written content about AI and its risks.
So, over the coming weeks, we’ll be cross-posting some of our favourite (and best-reviewed) pieces to our new Substack.
This is an experiment, and we might publish more depending on how much interest we get — so let us know what you’d like to see, by sending us an email (or tell us not to bother with Substack!).
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.
“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.
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Coordination, transcriptions, and web: Katy Moore
Help make spectacular videos that reach a huge audience.
80,000 Hours provides free research and support to help people find careers tackling the world’s most pressing problems.
We want a great video programme to be a huge part of 80,000 Hours’ communication about why and how our audience can help society safely navigate a transition to a world with transformative AI.
The video programme has created a new YouTube Channel — AI in Context. Its first video, We’re Not Ready For Superintelligence, which is about the AI 2027 scenario, was released in July 2025 and has already been viewed over three million times. The channel has over 100,000 subscribers.
To support our new video programme’s growth, we are on the lookout for excellent editors, scriptwriters, videographers, and producers to work on a contracting basis to make great videos. We want these videos to start changing and informing the conversation about transformative AI and its risks.
Why?
In 2025 and beyond, 80,000 Hours is planning to focus especially on helping explain why and how our audience can help society safely navigate a transition to a world with transformative AI. Right now, not nearly enough people are talking about these ideas and their implications.
A great video programme could change this. Time spent on the internet is increasingly spent watching video, and for many people in our target audience,
In a recent study by Anthropic, frontier AI models faced a choice: fail at a task, or succeed by taking a harmful action like blackmail. And they consistently chose harm over failure.
We’ve just published a new article, on the risks from power-seeking AI systems, which explains the significance of unsettling results like these.
Our 2022 piece on preventing an AI-related catastrophe also explored this idea, but a lot has changed since then.
So, we’ve drawn together the latest evidence to get a clearer picture of the risks — and what you can do to help.
We’ve been worried that advanced AI systems could disempower humanity since 2016, when it was purely a theoretical possibility.
Unfortunately, we’re now seeing real AI systems show early warning signs of power-seeking behaviour — and deception, which could make this behaviour hard to detect and prevent in the future. In our new article, we discuss recent evidence that AI systems may:
Career review by Benjamin Todd · Last updated August 11th, 2025 · First published November 2021
In 2010, a group of founders with experience in business, practical medicine, and biotechnology launched a new project: Moderna, Inc.
After witnessing recent groundbreaking research into RNA, they realised there was an opportunity to use this technology to rapidly create new vaccines for a wide range of diseases. But few existing companies were focused on that application.
They decided to found a company. And 10 years later, they were perfectly situated to develop a highly effective vaccine against COVID-19 — in a matter of weeks. This vaccine played a huge role in curbing the pandemic and has likely saved millions of lives.
This illustrates that if you can find an important gap in a pressing problem area and found an organisation that fills this gap, that can be one of the highest-impact things you can do — especially if that organisation can persist and keep growing without you.
Why might founding a new project be high impact?
If you can find an important gap in what’s needed to tackle a pressing problem, and create an organisation to fill that gap, that’s a highly promising route to having a huge impact.
But here are some more reasons it seems like an especially attractive path to us, provided you have a compelling idea and the right personal fit — which we cover in the next section.
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have \~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations team oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
The role
As our IT Security, Data Privacy, and Systems Lead, you would:
Evaluate and implement security controls
Research and make recommendations on security tools (endpoint protection, email security, etc.)
Lead the rollout of chosen solutions across our distributed team
Balance security needs with operational efficiency
Initially, you’ll make recommendations to leadership, but as you grow in the role,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The role
This role joins Niel and Jess in the Office of the CEO, working closely with them to keep 80,000 Hours running smoothly and focusing on its highest priorities.
Your responsibilities will likely include:
Managing Niel’s calendar, inbox, and daily planning
Supporting with meeting preparation and follow-up
Taking on a variety of ad hoc tasks for Niel. Some recent examples include:
Researching metrics for a speech
Recommending how to integrate Claude and Asana
Booking a restaurant for a meeting
Creating a record of Niel’s hiring decisions
Owning the logistics for recurring projects that the Office of the CEO is responsible for, such as:
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The role
This role would be great for building career capital in operations, especially if you could one day see yourself in a more senior operations role (e.g. specialising in a particular area, taking on management, or eventually being a Head of Operations or COO).
We plan to hire people at both the associate and specialist levels during this round. The associate role is a more junior position, and we expect to match candidates to the appropriate level as part of the application process so you don’t need to decide which one to apply for. To give an idea of how the roles might differ:
Associates are more likely to focus on owning and implementing our processes, identifying improvements and optimisations, and will take on more complex projects over time.
Specialists are more likely to manage larger areas of responsibility,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations team oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
The role
This role would be great for building career capital in operations, especially if you could one day see yourself in a more senior operations role (e.g. specialising in a particular area, taking on management, or eventually being a Head of Operations or COO).
We plan to hire people at both the associate and specialist levels during this round. The associate role is a more junior position,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations team oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
The role
This role would be great for building career capital in operations, especially if you could one day see yourself in a more senior operations role (e.g. specialising in a particular area, taking on management, or eventually being a Head of Operations or COO).
We plan to hire people at both the associate and specialist levels during this round. The associate role is a more junior position,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations team oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
The role
This role would be great for building career capital in operations, especially if you could one day see yourself in a more senior operations role (e.g. specialising in a particular area, taking on management, or eventually being a Head of Operations or COO).
We plan to hire people at both the associate and specialist levels during this round. The associate role is a more junior position,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations function oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
The role
This role would be great for building career capital in operations, by helping us design and run high-quality events that strengthen our team, culture, and connections in the AI safety space. We’re looking for an Events Associate/Specialist who can take ownership of the day-to-day logistics and execution of our events.
We plan to hire people at both the associate and specialist levels during this round. The associate role is a more junior position,
80,000 Hours’ goal is to get talented people working on the world’s most pressing problems. After more than 10 years of research into dozens of problem areas, we’re putting most of our focus on helping people work on positively shaping the trajectory of AI, because we think it presents the most serious and urgent challenge that the world is facing right now.
We’ve had over 10 million readers on our website, have ~600,000 subscribers to our newsletter, and have given one-on-one advice to over 6,000 people. We’ve also been one of the largest drivers of growth in the effective altruism community.
The operations team oversees 80,000 Hours’ HR, recruiting, finances, governance operations, org-wide metrics, and office management, as well as much of our fundraising, tech systems, and team coordination.
Currently, the operations team has ten full-time staff and some part-time staff. We’re planning to significantly grow the size of our operations team this year to stay on track with our ambitious goals and support a growing team.
To learn more about the other teams hiring during this round (video team, office of the CEO), see the individual job descriptions.
The role
This role would be great for building career capital in operations, especially if you could one day see yourself in a more senior operations role (e.g. specialising in a particular area, taking on management, or eventually being a Head of Operations or COO).
We plan to hire people at both the associate and specialist levels during this round.
We expect there will be substantial progress in AI in the coming years, potentially even to the point where machines come to outperform humans in many, if not all, tasks. This could have enormous benefits, helping to solve currently intractable global problems, but could also pose severe risks. These risks could arise accidentally (for example, if we don’t find technical solutions to concerns about the safety of AI systems), or deliberately (for example, if AI systems worsen geopolitical conflict). We think more work needs to be done to reduce these risks.
Some of these risks from advanced AI could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity.1 There have not yet been any satisfying answers to concerns — discussed below — about how this rapidly approaching, transformative technology can be safely developed and integrated into our society. Finding answers to these concerns is neglected and may well be tractable. We estimated that there were around 400 people worldwide working directly on this in 2022, though we believe that number has grown.2 As a result, the possibility of AI-related catastrophe may be the world’s most pressing problem — and the best thing to work on for those who are well-placed to contribute.
Promising options for working on this problem include technical research on how to create safe AI systems, strategy research into the particular risks AI might pose, and policy research into ways in which companies and governments could mitigate these risks. As policy approaches continue to be developed and refined, we need people to put them in place and implement them. There are also many opportunities to have a big impact in a variety of complementary roles, such as operations management, journalism, earning to give, and more — some of which we list below.
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.
You’ll hear from:
Zach Weinersmith on how settling space won’t help with threats to civilisation anytime soon (unless AI gets crazy good) (from episode #187)
Luisa Rodriguez on what the world might look like after a global catastrophe, how we might lose critical knowledge, and how fast populations might rebound (#116)
David Denkenberger on disruptions to electricity and communications we should expect in a catastrophe, and his work researching low-cost, low-tech solutions to make sure everyone is fed no matter what (#50 and #117)
Lewis Dartnell on how we could recover without much coal or oil, and changes we could make today to make us more resilient to potential catastrophes (#131)
Andy Weber on how people in US defence circles think about nuclear winter, and the tech that could prevent catastrophic pandemics (#93)
Toby Ord on the many risks to our atmosphere, whether climate change and rogue AI could really threaten civilisation, and whether we could rebuild from a small surviving population (#72 and #219)
Mark Lynas on how likely it is that widespread famine from climate change leads to civilisational collapse (#85)
Kevin Esvelt on the human-caused pandemic scenarios that could bring down civilisation — and how AI could help bad actors succeed (#164)
Joan Rohlfing on why we need to worry about more than just nuclear winter (#125)
Annie Jacobsen on the rings of annihilation and electromagnetic pulses from nuclear blasts (#192)
Christian Ruhl on thoughtful philanthropy that funds “right of boom” interventions to prevent nuclear war from threatening civilisation (80k After Hours)
Athena Aktipis on whether society would go all Mad Max in the apocalypse, and the best ways to prepare for a catastrophe (#144)
Will MacAskill on why potatoes are so cool (#130 and #136)
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
AI 2027, a research-based scenario and report from the AI Futures Project, combines forecasting and storytelling to explore a possible future where AI radically transforms the world by 2027.
The report goes through the creation of AI agents, job loss, the role of AIs improving other AIs (R&D acceleration loops), security crackdowns, misalignment — and then a choice: slow down or race ahead.
Kokotajlo’s predictions from 2021 (pre-ChatGPT) in What 2026 looks like have proved prescient, and co-author Eli Lifland is among the world’s top forecasters. So even if you don’t end up buying all its claims, the report’s grounding in serious forecaster views, research, and dozens of wargames makes it worth taking seriously.
Why watch our AI 2027 video
Containing expert interviews, our analysis, and discussion of what a sane world would be doing, we think the video will be an enjoyable and informative watch whether you’re familiar with the report or not.
The video:
Takes you through one of the most detailed and influential AI forecast to date and brings you into the story
Explains the key upshots — how AI progress could accelerate dramatically via powerful feedback loops,
