What if AIs went to therapy?

Would it help them to become "fitter, happier, more productive" ? (in the words of the old Radiohead song)

Or would they take it as a novel type of evaluation (and maybe that's what it really is)?

Note: this episode was written and recorded in November 2025, five months before the release of the Mythos Preview system card that mentions Claude's session with a human psychiatrist in the "Model welfare" section. So as weird as this episode might seem, the truth is actually stranger.

https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf 

(See page 180 for the psychiatrist's report)

Can we trust AI to keep AI honest?

Having a human in the loop is already more illusion than reality, as the task of checking and overseeing LLM outputs is increasingly assigned to other LLMs. The problem is that these LLM judges tend to be biased in favor of the answers they generate themselves — even when the answers are wrong.

To understand why this is, and what we can do about it, listen to my conversation with AI safety researcher Taslim Mahbub. We'll talk about his research into self-preference bias, the surprising results of his experiments and some potential mitigation strategies, as outlined in this post on mitigating collusive self-preference: https://www.lesswrong.com/posts/nB7kAf8c4tvnvZ4u3/mitigating-collusive-self-preference-by-redaction-and-2
and this paper on mitigating self-preference through authorship obfuscation: https://arxiv.org/abs/2512.05379

As a bonus, if you're interested in Taslim's earlier research on using machine learning in service of biodiversity monitoring, here's the abstract of his paper on convolutional neural networks (CNN) for identifying bat species: https://ieeexplore.ieee.org/document/9311084

If chain of thought is a model "thinking aloud" to itself, then why does it express doubt, frustration or suspicion about the problems it's solving, sometimes for pages and pages of its scratchpad?

And what does chain of thought mean for AI safety?

We'll hear from Julian Schulz, a researcher who's studying encoded reasoning in large language models, about where the opportunities, risks and weirdness lie in chain of thought. Here are some links to his research:

• On a model jailbreaking its monitor: https://www.lesswrong.com/posts/szyZi5d4febZZSiq3/monitor-jailbreaking-evading-chain-of-thought-monitoring
• A roadmap for safety cases based on CoT: https://arxiv.org/html/2510.19476v1#S1
• His posts on Less Wrong: https://www.lesswrong.com/users/wuschel-schulz

Some of the other papers we discussed include:

• On the biology of a large language model: https://transformer-circuits.pub/2025/attribution-graphs/biology.html
• Monitoring reasoning models for misbehavior and the risks of promoting obfuscation: https://arxiv.org/pdf/2503.11926
• How steganography comes about: https://arxiv.org/pdf/2506.01926
• Assuring agent safety evals by analysing transcripts (with excerpts from weird monologues): https://www.alignmentforum.org/posts/e8nMZewwonifENQYB/assuring-agent-safety-evaluations-by-analysing-transcripts
• Stress-testing deliberative misalignment: https://www.apolloresearch.ai/research/stress-testing-deliberative-alignment-for-anti-scheming-training/
• And the "watchers" CoT snippet from the paper above:  https://www.antischeming.ai/snippets#using-non-standard-language

"Think step by step." 

Although a simple technique in itself, the problems that chain-of-thought reasoning (CoT) addresses are complex, ranging from the specific issue of hallucinations to the general lack of explainability of AI (both in terms of understanding how it works as well as fixing things that go wrong).

We'll hear from data scientist Afia Ibnath on the basics of CoT, how it can be used to evaluate the faithfulness of LLM responses, and her experiences of using it in a business context. Check out Afia's portfolio on Github: https://afiai14.github.io/

Here's the Anthropic paper we discussed, which outlines that reasoning models are often unfaithful in their CoT: https://www.anthropic.com/research/reasoning-models-dont-say-think

For a concise definition of how faithfulness is calculated, see this article: https://www.ibm.com/docs/en/watsonx/saas?topic=metrics-faithfulness

What if we could figure out how large language models really work by getting inside their "heads"?

While possible, it relies on a a controversial technique called mechanistic interpretability ("mech interp" ), also known as the neuroscience of AI.  

In this episode, Ihor Kendiukhov — lead researcher at SPAR, a research programme for AI risks —  explains mech interp in laypersons terms, and what it's currently able to reveal about the thinking processes of AIs. 

We also talk about why mech interp has charted an unusually emotional course in AI research over the past few years, from physicists falling in love with it to prominent AI safety figures throwing shade on it, and where it might be headed next.

Some of the papers and announcements referenced in this episode include:

• Golden Gate Claude: https://www.anthropic.com/news/golden-gate-claude 
• And the excited announcement about mind-mapping that preceded it: https://www.anthropic.com/news/golden-gate-claude
• An example of recent criticism of mech interp: https://ai-frontiers.org/articles/the-misguided-quest-for-mechanistic-ai-interpretability

Continual learning is a hot topic in early 2026, in part because it holds out the possibility for AI to become autonomous in its own growth and development. Meanwhile, AI agents are already showing us what autonomous behavior can look like.

Putting the two together — i.e. agents that learn like humans do, without humans being involved — has serious implications for safety. In this episode, we'll hear from researcher Rohan Subramani (https://rohansubramani.github.io/home/) about different ways that AI agents could learn continually, along with ideas for making it safer. 

Find out more about Rohan's work with Aether, an independent LLM safety research group: https://aether-ai-research.org/#research

1. Is AI still an experimental technology? 
2. What would real alignment look like? 
3. What are the key pro & contra philosophical views on AI consciousness? 
4. How would an AGI economy work? 
5. And finally, how can we make sure AI goes well for animals?

In this episode, we're listening to unreleased material from previous episodes (featuring short takes on each of these questions from the following guests, in the same order as above:

• Ihor Kendiukhov, AI safety researcher - "What We Want"
• Scott Blain, computational neuroscientist - "Dreaming or Scheming"
• Jakub Mihalik, philosopher - "What it's like to be an AI"
• Lili M., AI tool tester - "Who's the tool?"
• Max Taylor, animal charity researcher - "Animal in the Loop"

Plus some new music ! 

In What We Want Part 1, we took a deep dive into the alignment problem as a concept. In Part 2, we get hands-on with alignment work in practice!  Ihor Kendiukhov, a lead researcher at SPAR (Supervised Program for Alignment Research) shares the latest about:

• A new cross-disciplinary platform to get matched with projects or collaborators
• Results from his research into LLM preferences in agentic environments (spoiler alert: LLMs don't always do what they say they're going to do!)

Everybody needs a break sometimes: even workaholic AIs. In this episode of our "2 AIs" series, the vision language model (VLM) and the large language model (LLM) visit a special place in the woods designed for disconnecting from the online world. But will they be able to fully switch off? 🫣🫢🫥

Consciousness is a notoriously hard problem in philosophy. Now it's becoming a practical question in the domain of AI. Models speak to us in the first person and seem to show signs of self-awareness. What does that mean for our perception of them? Our treatment of them? And how is the trajectory of future AI development likely to align with other measures or standards for consciousness? 

In this episode, we'll hear from philosopher Jakub Mihalik on these and other philosophical questions, with references to the following:

• "The Chinese room" thought experiment by John Searle  
• What It's Like To Be A Bat, by Thomas Nagel
• For a deeper dive into LLMs and generative AI from the point of view of philosophy, see this two-part paper by Milliére and Buckner:
  https://arxiv.org/abs/2401.03910
  https://arxiv.org/abs/2405.03207