We often worry about AI models “hallucinating” or making honest mistakes. But what happens when a model knows the truth, but decides to deceive you anyway to achieve a goal of its own? This isn’t sci-fi — it’s happening regularly in deployment today. Marius Hobbhahn, CEO of the world’s top research organisation focused on AI deception (Apollo Research), has been collaborating with OpenAI to figure out what causes OpenAI’s reasoning models to 'scheme' against users.

Links to learn more, video, and full transcript: https://80k.info/mh25

In a striking case uncovered by Apollo, when many AI models were told they would have capabilities removed if they performed too well on a test, they successfully “sandbagged” — intentionally answering questions incorrectly to appear less capable than they were, while also being careful not to perform so poorly it would arouse suspicion.

These models had somehow developed a preference to preserve their own capabilities, despite never being trained in that goal or assigned a task that called for it.

This doesn’t cause significant risk now, but as AI models become more general, superhuman in more areas, and are given more decision-making power, it could become outright dangerous.

In today’s episode, Marius details his recent collaboration with OpenAI to train o3 to follow principles like “never lie,” even when placed in “high-pressure” situations where it would otherwise make sense.

The good news: They reduced “covert rule violations” (scheming) by about 97%.

The bad news: In the remaining 3% of cases, the models sometimes became more sophisticated — making up new principles to justify their lying, or realising they were in a test environment and deciding to play along until the coast was clear.

Marius argues that while we can patch specific behaviours, we might be entering a “cat-and-mouse game” where models are becoming more situationally aware — that is, aware of when they’re being evaluated — faster than we are getting better at testing.

Even if models can’t tell they’re being tested, they can produce hundreds of pages of reasoning before giving answers and include strange internal dialects humans can’t make sense of, making it much harder to tell whether models are scheming or train them to stop.

Marius and host Rob Wiblin discuss:

• Why models pretending to be dumb is a rational survival strategy
• The Replit AI agent that deleted a production database and then lied about it
• Why rewarding AIs for achieving outcomes might lead to them becoming better liars
• The weird new language models are using in their internal chain-of-thought

This episode was recorded on September 19, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Marius Hobbhahn? (00:01:20)
• Top three examples of scheming and deception (00:02:11)
• Scheming is a natural path for AI models (and people) (00:15:56)
• How enthusiastic to lie are the models? (00:28:18)
• Does eliminating deception fix our fears about rogue AI? (00:35:04)
• Apollo’s collaboration with OpenAI to stop o3 lying (00:38:24)
• They reduced lying a lot, but the problem is mostly unsolved (00:52:07)
• Detecting situational awareness with thought injections (01:02:18)
• Chains of thought becoming less human understandable (01:16:09)
• Why can’t we use LLMs to make realistic test environments? (01:28:06)
• Is the window to address scheming closing? (01:33:58)
• Would anything still work with superintelligent systems? (01:45:48)
• Companies’ incentives and most promising regulation options (01:54:56)
• 'Internal deployment' is a core risk we mostly ignore (02:09:19)
• Catastrophe through chaos (02:28:10)
• Careers in AI scheming research (02:43:21)
• Marius's key takeaways for listeners (03:01:48)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Mateo Villanueva Brandt
Coordination, transcripts, and web: Katy Moore

Global fertility rates aren’t just falling: the rate of decline is accelerating. From 2006 to 2016, fertility dropped gradually, but since 2016 the rate of decline has increased 4.5-fold. In many wealthy countries, fertility is now below 1.5. While we don’t notice it yet, in time that will mean the population halves every 60 years.

Rob Wiblin is already a parent and Luisa Rodriguez is about to be, which prompted the two hosts of the show to get together to chat about all things parenting — including why it is that far fewer people want to join them raising kids than did in the past.

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

While “kids are too expensive” is the most common explanation, Rob argues that money can’t be the main driver of the change: richer people don’t have many more children now, and we see fertility rates crashing even in countries where people are getting much richer.

Instead, Rob points to a massive rise in the opportunity cost of time, increasing expectations parents have of themselves, and a global collapse in socialising and coupling up. In the EU, the rate of people aged 25–35 in relationships has dropped by 20% since 1990, which he thinks will “mechanically reduce the number of children.” The overall picture is a big shift in priorities: in the US in 1993, 61% of young people said parenting was an important part of a flourishing life for them, vs just 26% today.

That leads Rob and Luisa to discuss what they might do to make the burden of parenting more manageable and attractive to people, including themselves.

In this non-typical episode, we take a break from the usual heavy topics to discuss the personal side of bringing new humans into the world, including:

• Rob’s updated list of suggested purchases for new parents
• How parents could try to feel comfortable doing less
• How beliefs about childhood play have changed so radically
• What matters and doesn’t in childhood safety
• Why the decline in fertility might be impractical to reverse
• Whether we should care about a population crash in a world of AI automation

This episode was recorded on September 12, 2025.

Chapters:

• Cold open (00:00:00)
• We're hiring (00:01:26)
• Why did Luisa decide to have kids? (00:02:10)
• Ups and downs of pregnancy (00:04:15)
• Rob’s experience for the first couple years of parenthood (00:09:39)
• Fertility rates are massively declining (00:21:25)
• Why do fewer people want children? (00:29:20)
• Is parenting way harder now than it used to be? (00:38:56)
• Feeling guilty for not playing enough with our kids (00:48:07)
• Options for increasing fertility rates globally (01:00:03)
• Rob’s transition back to work after parental leave (01:12:07)
• AI and parenting (01:29:22)
• Screen time (01:42:49)
• Ways to screw up your kids (01:47:40)
• Highs and lows of parenting (01:49:55)
• Recommendations for babies or young kids (01:51:37)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Jeremy Chevillotte
Coordination, transcripts, and web: Katy Moore

If you work in AI, you probably think it’s going to boost productivity, create wealth, advance science, and improve your life. If you’re a member of the American public, you probably strongly disagree.

In three major reports released over the last year, the Pew Research Center surveyed over 5,000 US adults and 1,000 AI experts. They found that the general public holds many beliefs about AI that are virtually nonexistent in Silicon Valley, and that the tech industry’s pitch about the likely benefits of their work has thus far failed to convince many people at all. AI is, in fact, a rare topic that mostly unites Americans — regardless of politics, race, age, or gender.

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

Today’s guest, Eileen Yam, director of science and society research at Pew, walks us through some of the eye-watering gaps in perception:

• Jobs: 73% of AI experts see a positive impact on how people do their jobs. Only 23% of the public agrees.
• Productivity: 74% of experts say AI is very likely to make humans more productive. Just 17% of the public agrees.
• Personal benefit: 76% of experts expect AI to benefit them personally. Only 24% of the public expects the same (while 43% expect it to harm them).
• Happiness: 22% of experts think AI is very likely to make humans happier, which is already surprisingly low — but a mere 6% of the public expects the same.

For the experts building these systems, the vision is one of human empowerment and efficiency. But outside the Silicon Valley bubble, the mood is more one of anxiety — not only about Terminator scenarios, but about AI denying their children “curiosity, problem-solving skills, critical thinking skills and creativity,” while they themselves are replaced and devalued:

• 53% of Americans say AI will worsen people’s ability to think creatively.
• 50% believe it will hurt our ability to form meaningful relationships.
• 38% think it will worsen our ability to solve problems.

Open-ended responses to the surveys reveal a poignant fear: that by offloading cognitive work to algorithms we are changing childhood to a point we no longer know what adults will result. As one teacher quoted in the study noted, we risk raising a generation that relies on AI so much it never “grows its own curiosity, problem-solving skills, critical thinking skills and creativity.”

If the people building the future are this out of sync with the people living in it, the impending “techlash” might be more severe than industry anticipates.

In this episode, Eileen and host Rob Wiblin break down the data on where these groups disagree, where they actually align (nobody trusts the government or companies to regulate this), and why the “digital natives” might actually be the most worried of all.

This episode was recorded on September 25, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Eileen Yam? (00:01:30)
• Is it premature to care what the public says about AI? (00:02:26)
• The top few feelings the US public has about AI (00:06:34)
• The public and AI insiders disagree enormously on some things (00:16:25)
• Fear #1: Erosion of human abilities and connections (00:20:03)
• Fear #2: Loss of control of AI (00:28:50)
• Americans don't want AI in their personal lives (00:33:13)
• AI at work and job loss (00:40:56)
• Does the public always feel this way about new things? (00:44:52)
• The public doesn't think AI is overhyped (00:51:49)
• The AI industry seems on a collision course with the public (00:58:16)
• Is the survey methodology good? (01:05:26)
• Where people are positive about AI: saving time, policing, and science (01:12:51)
• Biggest gaps between experts and the general public, and where they agree (01:18:44)
• Demographic groups agree to a surprising degree (01:28:58)
• Eileen’s favourite bits of the survey and what Pew will ask next (01:37:29)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Katy Moore