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

Our planet is home to 10 quintillion insects, 3.5 trillion fish, 428 billion birds and 130 billion mammals other than humans. Soon AI will be as much a factor in their lives as it is in ours.

In this episode, we'll hear from Max Taylor, an expert on animals and AI, about the different ways this is already happening, such as:

• AI trainers for dogs and cats
• Smart watch-style wearables for cows and pigs
• Wild animal surveillance 
• Black-box insect farming

Follow Max's newsletter to stay up to date on all things animal and AI, and to find out when his book will be published!

 

If you're interested in both animal welfare and digital minds, you may want to check out the work of Sentient Futures.

Large language models are trained to respond to our preferences. It sounds logical enough in theory, but it turns out to spiral in strange and unexpected directions in practice, from AI-induced psychosis in humans to manipulation and power-seeking on the part of the AIs.

In this episode, hear from Ihor Kendiukhov from SPAR (Supervised Program for Alignment Research) about why he changed his career to work on AI safety, and some of the current approaches in understanding what it is that LLMs might want themselves.