#35 Jiayin Zhi: When AI Helps Thinking—and When It Replaces It
AI can speed up your work flow. But does it make you any smarter?
In this episode of the AITEC Philosophy Podcast, Roberto and Sam talk with Jiayin Zhi, a PhD student in computer science at the University of Chicago, about what large language models are doing to our thinking. Her research asks a question that is becoming harder to avoid: when we use AI to interpret, write, and reason, are we extending our minds—or outsourcing them?
The conversation begins with human-centered computing, the idea that technology should be designed around human needs, values, and real cognitive habits. From there, Jiayin shares findings from her research on AI-assisted close reading. One surprising result: AI-generated interpretations can improve the final written product, but too much AI can crowd out the pleasure of discovery and leave people feeling like there is little room for their own interpretation.
The episode then turns to critical thinking. Here, timing matters. Using AI from the start—especially under time pressure—can produce polished work without deep understanding. But using AI later, after doing independent thinking first, may help reduce myside bias by introducing counterarguments and alternative perspectives.
Along the way, we discuss poetry, interpretation, cognitive offloading, copy-and-paste learning, time pressure, myside bias, and the difference between using AI as a shortcut and using it as a genuine thinking partner.
This episode is for anyone who wants to use AI well without surrendering the struggle that makes learning real.
#34 John MacCormick: Can Machines Think Like Us?
When an AI tells a joke, writes an essay, or solves a complex programming problem, is it just performing a statistical magic trick—or is there something deeper happening under the hood?
On this episode of The AITEC Philosophy Podcast, Roberto and Sam sit down with computer scientist John McCormick, author of Thinking AI: How Artificial Intelligence Emulates Human Understanding (Princeton University Press).
For over 70 years, we've been asking Alan Turing's classic question: Can a machine think?. John reframes this debate by taking us past the polished, fluent surface of modern Large Language Models to look directly at the underlying code. We discuss whether AI merely mirrors human output or if its internal structures have begun to replicate pockets of human cognition. Despite the existential dread surrounding superintelligence, John shares a surprisingly optimistic perspective: even if we build entities that exceed human intellect, they will never replace the deeply rooted, biological fulfillment of our shared humanity.
Uncover the machinery behind the curtain and join the conversation at ethicscircle.org.
#33 Michael Gerlich: How AI is Stealing Your Ability to Think
Are we trading our critical thinking skills for the sake of digital convenience?
In this episode of The AITEC Philosophy Podcast, Roberto Carlos García sits down with Michael Gerlich. Michael is the Head of the Center for Strategic Corporate Foresight and Sustainability, the Head of Executive Education, and a Senior Faculty member at SBS Swiss Business School. Most recently, Michael summarized his research on the interaction between LLMs and humans in The Convenience Trap: What Happens When AI Becomes the Mind Behind Our Lives.
In this conversation, Michael shares his interdisciplinary research into how AI is "creeping" into nearly every aspect of our existence. We explore the dangerous phenomenon of "cognitive offloading"—the tendency to let algorithms make our choices, from the music we hear to the news we consume—and how this creates a "convenience trap" that narrows our perspective and weakens our mental "musculature". Michael argues that for AI to be a truly beneficial "sparring partner," we must do the hard work of thinking first before engaging with the machine.
Links:
Michael’s book
One of Michael’s articles on cognitive offloading
Michael’s article on societal bifurcation
#32 Yochai Ataria: Why Blade Runner is Secretly About Fake Realities
Have you ever suspected that the technology you use isn't just a tool, but an entirely fake reality replacing the natural world?
On this episode of The AITEC Philosophy Podcast, Sam Bennett sits down with Israeli philosopher Yochai Ataria to explore the brilliant philosophical undercurrents of Ridley Scott's 1982 classic, Blade Runner. Ataria reveals how the film functions as a profound Heideggerian critique of the modern technological age.
They unpack how the film's protagonist, Rick Deckard, serves as a direct stand-in for René Descartes, undergoing a radical crisis of certainty and identity. The conversation also delves into ancient Greek frameworks, exploring how the replicant Roy Batty mirrors the Oedipus myth and how the rare flashes of lightning in a polluted sky tie back to Zeus. Ultimately, this episode asks whether the representations we rely on—from futuristic photo-analyzers to modern social media algorithms—are actually elaborate lies designed to disconnect us from reality.
#31: Jacob Browning: Unmasking the Fake Minds of Large Language Models
Have you ever wondered if AI models actually understand the words they generate, or if they are just really good at faking it?
On this episode of The AITEC Podcast, Roberto García and Sam Bennett are joined by philosopher Jacob Browning (Baruch College, CUNY) to unpack his article, Intentionality All-Stars Redux: Do language models know what they are talking about?
Using a clever baseball diamond metaphor and drawing on the philosophy of Immanuel Kant, Jacob explains why Large Language Models lack the "intentionality" required for genuine comprehension. We cover:
First Base (Formal Competence): Why LLMs struggle with basic logic and negation, revealing the absence of an underlying logical engine.
Second Base (Rationality): Why true understanding requires purposive behavior, and how LLMs hilariously fail at "intuitive physics" (like trying to inflate a couch to get it onto a roof).
Shortstop (Objectivity and World Models): Why genuine understanding requires grasping an objective, mind-independent world that determines whether sentences are true or false. This position explores how LLMs lack a coherent "world model," causing them to fail at tasks that require intuitive physics and planning for counterfactual situations (like predicting where a billiard ball will go or playing simple video games).
Third Base (The Unified Self): Why making a claim requires a persistent self that takes responsibility for its beliefs—something a next-token predictor simply cannot do.
Whether you're exploring the intersection of AI, technology, and ethics, or just trying to figure out if your chatbot actually knows what it's saying, this conversation will give you the philosophical toolkit to see through the illusion.
#30 Andrea Pinotti: Beyond the Frame—Virtual Reality, Narcissus, and the Desire to Enter the Image
Philosopher Andrea Pinotti joins us to discuss At the Threshold of the Image: From Narcissus to Virtual Reality. What begins as a conversation about image theory quickly becomes a sweeping exploration of immersion, identity, and the strange pull of simulated worlds.
Why do we long to enter the image? What do we gain—and lose—when the frame disappears? Pinotti guides us from Paleolithic caves to VR headsets, through myths of Narcissus and Pygmalion, to Black Mirror’s digital afterlives.
Along the way, we consider how virtual environments blur fiction and reality, evoke religious promises, and reshape what it means to be human.
If you've ever wondered why virtual reality feels so real—or so dangerous—this episode is for you.
#29 Justin Tiehen: Why AI Can't Make a Promise—The Hidden Limits of Large Language Models
Have you ever felt like ChatGPT genuinely understands you? What if the reality is that it doesn't even have the foundational capacity to "speak" to you at all?
On this episode of The AITEC Podcast, Roberto Carlos García and Sam Bennett sit down with philosopher Justin Tiehen (University of Puget Sound) to unpack his fascinating new paper, LLM's Lack a Theory of Mind and So Can't Perform Speech Acts--A Causal Argument.
Justin takes us on a deep dive into the philosophy of mind to explain why current Large Language Models, despite their impressive output, are essentially just faking it. We explore why next-token predictors are completely missing the causal architecture required to have a "Theory of Mind," and why, without that, they are fundamentally incapable of making assertions, giving orders, or performing true speech acts.
Key Takeaways from this Episode:
The Ladder of Causation: Why AI is stuck observing statistical correlations and cannot grasp true causal interventions or counterfactuals (drawing on Judea Pearl’s work).
The Speech Act Problem: Why performing a true "speech act" requires the deliberate intention to influence another person's mind.
Cheating the Benchmarks: How LLMs "cheat" on psychological exams like the Sally-Anne false-belief test simply by memorizing statistical patterns in text.
The Threat of AI Blackmail: What it would actually look like if an AI possessed a Theory of Mind and strategically tried to manipulate human behavior to achieve its goals.
Whether you are deeply invested in the philosophy of language or just trying to figure out how much you should trust your favorite AI assistant, this conversation will completely reframe how you view generative AI.
#28 Mathilda Marie Mulert: Sex Robots, Simulation, and the Question of Moral Harm
In this episode of the AITEC Podcast, we’re joined by philosopher Mathilda Marie Mulert, a doctoral researcher at the Oxford Internet Institute, to explore one of the most difficult questions in contemporary tech ethics: when, if ever, is it morally permissible to simulate sexual violence?
Drawing on her recent work on simulation ethics, Mulert examines video games, virtual environments, sex robots, and consensual role-play to challenge the assumption that “it’s just pretend.” We discuss the Gamers’ Dilemma, the limits of consent, and why moral context—not just content—matters when evaluating simulated wrongdoing.
This conversation is philosophical, careful, and candid. Listener discretion is advised.
Links:
Mathilda’s Oxford Internet Institute Webpage
Mathilda’s recent article
#27 Matheus Ferreira de Barros: Technology, Spheres, and the Human Being
In this episode of the AITEC podcast, Sam Bennett and Roberto Carlos speak with Matheus Ferreira de Barros, a philosopher of technology at PUC-Rio and the Federal University of Rio de Janeiro, about the work of Peter Sloterdijk. Ferreira de Barros introduces Sloterdijk’s philosophy of technology, focusing on the idea that human beings and technology co-evolve and that technology plays a constitutive role in human life rather than merely serving as an external tool.
The conversation explores Sloterdijk’s Spheres project, including his account of insulation, distance from nature, and the creation of protective interiors that stabilize human existence at biological, psychological, and symbolic levels. The discussion also examines the loss of large-scale meaning structures in modernity, the role of religion and culture as technologies of existential security, and how contemporary technologies, including AI, may both disrupt and reshape the spheres through which human life becomes livable.
#26 Iwan Williams: Do Language Models Have Intentions?
In this episode of the AITEC podcast, Sam Bennett speaks with philosopher of mind and AI researcher Iwan Williams about his paper “Intention-like representations in language models?” Williams is a postdoctoral researcher at the University of Copenhagen and received his PhD from Monash University.
The conversation explores whether large language models exhibit internal representations that resemble intentions, as distinct from beliefs or credences. Focusing on features such as directive function, planning, and commitment, Williams evaluates several empirical case studies and explains why current models may appear intention-like in some respects while falling short in others. The discussion also considers why intentions matter for communication, safety, and our broader understanding of artificial intelligence.
#25 Pilar López-Cantero: The Ethics of Breakup Chatbots
What if your ex never really left—because you trained a chatbot to be them? In this episode of the AITEC Podcast, we’re joined by philosopher Pilar López-Cantero to explore her provocative article, The Ethics of Breakup Chatbots. From the haunting potential of AI relationships to the dangers of narrative stagnation, we dive into what it means to love, let go, and maybe linger too long—with a machine. Are these bots helping us heal, or are they shaping a lonelier, more controllable kind of intimacy?
#24 Kevin Crowston and Francesco Bolici: The Death of Expertise?
In this episode of the show, we sit down with Kevin Crowston and Francesco Bolici—two leading scholars of information science and organizational behavior—to explore the hidden risks of generative AI in the workplace and the classroom.
Their recent paper on deskilling and upskilling with AI serves as the foundation for a conversation that ranges from ChatGPT in programming to the future of education. The key concern? AI systems may offer short-term productivity boosts—but they quietly erode the very skills people need to think, solve problems, and make decisions when things go wrong.
We unpack:
The tension between efficiency and learning: how AI tools give us answers but rob us of “learning by doing”
Why novice users might look as good as experts—but only because AI is flattening the skill curve
The “leveling effect” vs. the “multiplier effect”: when AI empowers novices vs. when it amplifies expert performance
What happens to organizations—and societies—when no one remembers how to do things manually
How educators can respond: should we stop students from using AI? Or teach them how to use it without becoming dependent?
From sales to software engineering, and from university classrooms to global labor markets, this episode explores how generative AI reshapes human learning, power, and value—and what we must do now to avoid a future of mass deskilling.
