Sentience

What if the biggest moral mistake of our time is failing to recognize the minds around us?

Philosopher Jeff Sebo, author of The Moral Circle, joins Sentience to explore one of the most consequential questions of our time: who deserves moral consideration? We discuss why the scientific case for animal consciousness has grown so strong, why fish and invertebrates are often overlooked, and how humans have repeatedly underestimated the minds of other species.

But the implications extend far beyond animals. As AI systems become increasingly sophisticated, could we be creating new kinds of minds whose capacity for suffering we don't yet understand? Should we take AI welfare seriously before we know whether machines are conscious? And what happens when the technologies we develop to replace animal experiments raise similar ethical questions of their own?

We also examine whether animal experiments deliver the benefits we assume they do, how we can make ethical decisions under scientific uncertainty, and why future generations may judge our treatment of animals and AI as one of the defining moral failures of our era.

Books referenced:

The Moral Circle: Who Matters, What Matters, and Why, by Jeff Sebo

The Edge of Sentience, by Jonathan Birch

Chapters / Timestamps:

(00:12) – Do we really doubt that animals are conscious?

(01:50) – Why science was slow to take animal consciousness seriously

(04:00) – How can we scientifically investigate another mind?

(07:30) – The New York Declaration on Animal Consciousness

(11:00) – What animal behavior can tell us about pain and pleasure

(14:00) – Do bees play? Rethinking animal joy and self-awareness

(16:00) – Why do people still think fish can't feel pain?

(20:00) – The problem of knowing what's happening inside another mind

(23:30) – Intelligence vs. consciousness: what's the difference?

(26:00) – The moral circle: who deserves moral consideration?

(28:00) – Could AI create more minds than we can comprehend?

(30:00) – Could artificial intelligence ever be conscious?

(34:00) – Why we should take AI welfare seriously before we're certain

(35:30) – What Star Trek can teach us about the rights of artificial beings

(38:00) – Why we can't wait for proof of consciousness

(40:00) – Could AI systems have needs we don't understand?

(42:00) – Are AI safety techniques a form of captivity or brainwashing?

(46:00) – Can something deserve moral consideration without being conscious?

(49:00) – What do people really think about AI welfare?

(52:00) – Why our actions don't always match our moral beliefs

(56:00) – Does animal experimentation actually benefit humans?

(60:00) – Could new technologies make animal experiments unnecessary?

(62:00) – Why changing the rules could transform medical research

(64:00) – The ethical limits of animal experimentation

(66:00) – If we replace animal experiments with digital minds, do we create a new ethical problem?

(68:00) – Could brain organoids ever deserve moral consideration?

(71:00) – What future generations will judge us for

What if we’ve been dramatically underestimating what it’s like to be another animal?

In this episode of Sentience, animal cognition researcher Mickey Pardo, PhD takes us inside the minds of other animals—from woodpeckers that track relationships between other birds to elephants that appear to use name-like calls to address one another.

We explore how scientists can investigate the private mental lives of animals, what the famous mirror test actually tells us about self-awareness, and why humans have repeatedly moved the goalposts whenever another species demonstrates an ability we once thought was uniquely ours. We also dive into the future of animal communication: could AI uncover hidden patterns in the vocalizations of elephants, dolphins, and whales that humans have completely missed?

And if animals have richer inner lives than we've assumed, what does that mean for how we understand intelligence, consciousness, and our relationship with the other minds sharing the planet with us?

Chapters/timestamps

(00:12) – How can we know what's happening inside an animal's mind?

(01:05) – How science changed its view of animal intelligence

(02:50) – Jane Goodall and the revolution in animal cognition

(06:52) – The surprisingly sophisticated social lives of woodpeckers

(13:37) – How scientists can uncover relationships between animals

(15:29) – Birds remembering relationships from before they were born

(21:59) – Why study elephants?

(24:44) – The mirror test: what does it actually prove?

(28:28) – What cleaner fish can teach us about intelligence

(33:12) – Do elephants have names?

(35:08) – How scientists discovered elephant “names”

(41:56) – The hidden complexity of animal communication

(43:55) – Why humans keep moving the goalposts for intelligence

(49:16) – Could AI decode animal communication?

(51:37) – Can animals talk about things that aren't happening right now?

(54:39) – Dolphins, AI, and the search for animal language

(57:04) – What animal communication could teach us about alien intelligence

(01:02:58) – What will we get completely wrong about animal minds?

(01:07:37) – What other minds can teach us about ourselves

(01:08:39) – Rethinking our relationship with sentient animals

In this episode of Sentience, Bruno Olshausen, PhD, professor at UC Berkeley, tells the story of how he went from wanting to build brain-like robots to uncovering one of neuroscience's most surprising results. Early in his career, he and a collaborator showed that if you train a simple computer model to represent images as efficiently as possible, it spontaneously reinvents the same kind of visual "building blocks" found in real brains—a discovery almost nobody believed at first. Olshausen walks us through why the brain seems to prefer having most of its neurons sit quietly at any given moment, why this looks so different from how modern AI vision systems work, and why brains might not be "efficient" so much as bound by the same physical limits as everything else. We also get into why the brain sends information as sudden spikes rather than smooth signals, and what all of this might mean for building truly autonomous machines.

Timestamps(00:00) – Welcome to Sentience and meeting Bruno Olshausen(00:41) – From building robots to studying the brain(03:37) – An early chapter at NASA(05:56) – Why recognizing objects is harder than it sounds(09:45) – Where AI vision and real vision part ways(17:48) – A brief history of "efficient" brains(32:46) – The experiment that surprised everyone(36:04) – Why nobody believed it at first(46:17) – Making sense of uncertainty: how the brain guesses(54:47) – Why most of your neurons are doing nothing right now(01:07:16) – Two very different ways the brain stores memories(01:15:01) – What "efficient" really means for a brain(01:22:33) – Why the brain uses spikes instead of steady signals(01:28:19) – What Bruno's chasing next

Books referenced:

• Sterling, P. & Laughlin, S. Principles of Neural Design. MIT Press.
• MacKay, D.J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press.

Core papers referenced:

• Olshausen, B.A. & Field, D.J. (1996). "Emergence of simple-cell receptive field properties by learning a sparse code for natural images." Nature, 381, 607–609.
• Barlow, H.B. (1961). "Possible principles underlying the transformation of sensory messages." In Sensory Communication.
• Rozell, C.J., Johnson, D.H., Baraniuk, R.G., & Olshausen, B.A. (2008). "Sparse coding via thresholding and local competition in neural circuits." Neural Computation, 20(10), 2526–2563.

In this episode of Sentience, Yi-Li Wu, PhD, Associate Professor of History and Women's and Gender Studies at the University of Michigan, explores the overlooked history of anesthesia and pain management. We discuss medieval Chinese surgical practices, herbal anesthetics, and the transmission of medical knowledge across centuries and cultures. Wu explains how physicians balanced the therapeutic and toxic effects of powerful plants, why much of this knowledge was passed down orally rather than written, and how modern narratives of scientific discovery often obscure the broader global networks behind medicine. The conversation also explores traumatic injuries, military medicine, and what the history of anesthesia reveals about how humans everywhere have confronted pain, surgery, and healing.

Chapters / Timestamps

(00:00) – Welcome to Sentience and meeting Yi-Li Wu(01:02) – From women’s history to the history of Chinese medicine(07:18) – Traumatic injuries and the problem of unwritten medical knowledge(10:30) – Discovering a 14th-century Chinese anesthesia recipe(13:36) – Ancient pain management and the legend of Hua Tuo(15:42) – Empirical medicine and how early physicians understood anesthetics(18:03) – Poison, potency, and balancing dangerous medicines(21:40) – Datura, bone setting, and independent discoveries across cultures(26:17) – Surgery, warfare, and what Hua Tuo may really have done(33:03) – Japanese anesthesia, Seishu Hanaoka, and shared medical traditions(37:35) – Why scientific breakthroughs are rarely the work of one person(39:28) – Rethinking the global history of anesthesia(43:49) – Recovering lost medical histories and finishing the book project

In this episode of Sentience, Osh Agabi, CEO of Koniku Inc., explores what happens when technology is built not just with silicon, but with living biology. We discuss his journey from growing up in Lagos to leading efforts in “wetware computing,” where engineered biological systems are designed to sense, adapt, and solve problems in the real world. Agabi explains how biological chips can smell their environment, why embodiment and sensing may be essential for intelligence, and why many traditional brain–machine approaches miss something fundamental. He also reflects on failure, engineering rigor, ethics, and what it means to build machines that might one day learn and evolve alongside us.

Timestamps

(00:00) – Welcome to Sentience and meeting Osh Agabi(10:00) – From robotics in Europe to early machine learning(17:23) – What AI can’t do yet and current limits(19:09) – Building next-generation neural electrode chips(35:35) – Toward embodied systems and artificial “brains”(40:26) – Ambition, rigor, and real-world usefulness(42:17) – The long view of foundational technology(43:14) – Why smell is a powerful sensing problem(47:30) – Turning neurons into real-world sensors(50:38) – Engineering mindset vs. biotech mindset(51:32) – Why optical readouts beat electrical ones(52:05) – Modular biochips, maintenance, and usability(53:06) – Beyond smell and toward richer perception

In this episode of Sentience, Joel Frohlich, PhD explores how scientists study consciousness through brain signals. We discuss why familiar brain waves can sometimes mislead us, how ideas from entropy and information theory offer deeper insight into conscious states, and what neurodevelopmental conditions and brain injury reveal about long-held assumptions in neuroscience. Frohlich also shares his work using advanced imaging to study the developing brain before birth, and reflects on what it really means to measure the mind.

Timestamps

(00:00) – Welcome to Sentience and meeting Joel Frohlich
(01:00) – Communicating neuroscience and asking bigger questions
(05:20) – Studying consciousness through brain activity
(07:45) – Why brain waves can be misleading
(11:10) – Consciousness and recovery after severe brain injury
(15:10) – What brain waves actually represent
(19:20) – How oversimplified brain stories persist
(22:00) – Entropy, complexity, and richer measures of consciousness
(26:40) – Information, uncertainty, and the structure of neural activity
(31:15) – Consciousness as a dynamic process
(35:30) – Measuring the developing brain before birth
(41:20) – What fetal brain signals can—and can’t—tell us
(48:50) – Open questions and future directions in consciousness science
(55:00) – Seeing patterns in noise, from brains to astrophotography

In this episode of Sentience, Alysson Muotri, PhD, professor at UC San Diego, discusses how brain organoids—lab-grown neural tissues derived from stem cells—are reshaping neuroscience. He explains how organoids can model early brain development, uncover the roots of neurodevelopmental disorders like autism, and accelerate the search for new treatments. We also explore how these systems shed light on human evolution, the emergence of intelligence, and the ethical questions that come with studying increasingly complex neural models.

Chapters / Timestamps

(00:00) – Welcome to Sentience
(00:24) – Alysson’s journey into stem cell and brain organoid research
(03:14) – How organoids model the developing human brain
(06:22) – Using organoids to study autism and other neurodevelopmental disorders
(09:58) – From lab dish to therapy: translating organoid insights into medicine
(13:27) – When organoids begin producing brain-like activity
(17:36) – What these systems reveal about intelligence and evolution
(21:18) – The ethics of studying increasingly complex neural tissue
(26:03) – Biological vs. artificial intelligence: where the lines blur
(29:55) – What organoids are teaching us about being human
(33:12) – Looking ahead: the future of brain models in science and medicine

In this episode of Sentience, we talk with Thomas Hartung, PhD, about how organoids and AI are reshaping biomedical science. Hartung explains how AI models can predict the toxicity of chemicals more accurately than animal tests and how organoids—tiny, lab-grown versions of human organs—can stand in for animals in studying disease and drug effects. We explore the concept of organoid intelligence, the ethics of creating brain-like systems, and how these technologies together could make research more humane, efficient, and human-relevant.

Timestamps

(00:12) – From animal lover to scientist: Thomas’s early journey

(03:06) – Replacing a major rabbit test and the origins of modern alternatives(

05:42) – Why AI became the next frontier for toxicology

(09:32) – How algorithms can predict chemical safety without animals

(13:24) – Thinking in probabilities: a smarter kind of toxicology

(16:14) – Designing safer drugs from the start

(18:43) – The rise of lab-grown intelligence: what organoids can do

(24:01) – From simple cells to living models of the human brain

(28:21) – Why brain organoids matter for understanding disease

(33:17) – Building connections: assembling mini-brains

(36:31) – Organoid intelligence and the idea of biological learning

(40:54) – Could organoids ever think? Ethics and embedded responsibility

(44:17) – What AI can learn from the brain—and vice versa

(48:16) – The road to replacing animal tests: challenges and hope

(54:36) – A new era of human-relevant science

In this episode of Sentience, we speak with Shaolei Ren, PhD, about the hidden environmental footprint of artificial intelligence. From the enormous water and energy demands of data centers to the trade-offs between carbon emissions and cooling needs, Ren explains how his research uncovers these overlooked costs. We discuss geographical load balancing, fairness in resource allocation, brain-inspired computing, and how tech companies can improve transparency. This conversation explores how to build a greener, more equitable future for AI.

Timestamps

(00:00) – Introduction and Shaolei Ren’s journey into AI and environmentalism
(02:10) – The physical reality of AI: data centers, hardware, and energy use
(04:10) – Water as the overlooked resource in AI training and cooling
(07:59) – Why data centers need cooling and how it drives environmental impact
(10:15) – Geographical load balancing: shifting workloads to save resources
(13:21) – Trade-offs between carbon footprint and water consumption
(15:24) – Algorithms for efficiency and fairness in AI’s environmental impact
(17:12) – Defining and measuring fairness across regions
(20:37) – How tech companies can improve sustainability and transparency
(22:19) – Learning from the brain: energy-efficient, brain-inspired AI
(26:06) – Personal use of AI, individual vs. systemic impact, and cost–benefit thinking
(29:37) – AI as a tool to fight climate change and optimize renewable energy
(30:32) – The future of greener, more intelligent AI systems
(34:34) – Public health impacts and power grid considerations
(36:35) – Closing thoughts on transparency, user awareness, and a sustainable AI future

In this episode, I speak with evolutionary neuroscientist Barbara Finlay, PhD, whose pioneering research has reshaped our understanding of brain evolution. Barbara shares her scientific journey, from her early inspiration in vision science to her groundbreaking discoveries about how brains scale and evolve. We explore how brains self-organize to process sensory information, why humans don't have a uniquely special cortex, and what the evolutionary transition from water to land meant for brain development and consciousness. This conversation dives deep into how evolution shapes neural architecture, uncovering principles that bridge species from sharks to humans, and raises fascinating new questions about consciousness, memory, language, and AI. Whether you're a scientist, student, or simply curious, Barbara’s insights offer a compelling glimpse into the evolutionary story of our own minds.

Timestamps:
(00:00) – Intro to Barbara Finlay and the premise of the episode
(00:32) – Barbara’s early path into neuroscience
(08:33) – Introduction to Evo-Devo: how development shapes brain evolution and how evolution shapes brain development
(17:20) – Brain scaling laws and how the cortex grows in coordination with the rest of the brain
(29:12) – Why the human cortex isn’t special—and what actually makes us unique
(36:09) – Language, sociality, and the deeper brain structures that support them
(43:12) – Self-organization of the cortex: how brain areas emerge without being hardwired
(53:00) – The brain’s hunger for information: from monkeys gaining color vision to human adaptability
(59:00) – The limbic system vs. cortex: two distinct computational systems
(01:10:00) – Egocentric mapping, the transition from water to land, and the roots of consciousness
(01:24:00) – Consciousness, midbrain vs. cortex, and what AI is missing
(01:31:00) – Barbara’s current work and reflections on being part of the neuroscience founder generation