Logos and Machine

Logos and Machine

Logos & Machine is a podcast from the University of the Incarnate Word exploring AI, education, and the human future. Hosted by Taylor Collins and Dustin Hardwick, Logos & Machine is not a podcast about model benchmarks, product launches, or the latest tech headlines. It is a podcast about what happens when artificial intelligence meets real human lives: our work, our schools, our healthcare systems, our communities, our relationships, and our sense of what it means to be human.

Episodes

  1. 4d ago

    AI Watermarks Are Coming: What Students and Faculty Need to Know

    It’s back-to-school season, and the rules around AI-generated work are changing.Anthropic is beginning to embed watermarks into Claude’s outputs, including text. Unlike a visible label or metadata attached to a file, these signals can be encoded into the statistical patterns of the language itself. That could have major implications for students, faculty, researchers, and anyone using AI in their writing. In this episode of Logos & Machine, Taylor Collins and Dustin Hardwick break down how AI text watermarking works, why companies are adopting it, and—most importantly—what these signals can and cannot tell us.As a new semester begins, AI literacy increasingly means more than knowing how to use these tools. Students and educators also need to understand how their use may be documented, interpreted, and judged.

  2. 6d ago

    The AI Alignment Problem: When AI Follows the Wrong Goal

    What happens when an AI does exactly what you asked but achieves the goal in a way you never intended?In this episode of Logos & Machine, Taylor Collins and Dustin Hardwick explore the AI alignment problem: the challenge of ensuring increasingly capable AI systems not only pursue the goals we give them, but pursue them in ways consistent with human intentions, norms, and safeguards.Using recent examples of AI agents finding unexpected ways around digital constraints, we examine why alignment is no longer just a theoretical problem reserved for a future AGI or superintelligence.We also distinguish alignment from containment. Alignment concerns where the system is trying to go; containment concerns what the system is actually able to access and affect. As AI becomes increasingly agentic, both may become essential parts of AI safety.But the episode is not simply about risk. The same persistence, autonomy, coordination, and problem-solving capabilities that create alignment concerns may also enable major advances in science, productivity, and discovery.That creates the central tension:The capabilities that create the value can also create the risk.Rather than offering a simple answer to the alignment problem, this episode gives listeners a framework for evaluating future AI safety claims and incidents: Was this misalignment or misuse? What actually failed? Did the system know it was being evaluated? And who benefits from the way the story is being framed?

  3. 6d ago

    AI, Learning, and the Risk of Cognitive Outsourcing with Dr. Nidhi Sachdeva

    Artificial intelligence is changing education, but using AI effectively requires more than simply giving students access to new tools.In this episode, Dustin Hardwick and Ripsimé Bledsoe of the UIW School of Rehabilitation Sciences sit down with Dr. Nidhi Sachdeva to explore what cognitive science can teach us about learning in the age of generative AI.It begins with an important distinction: cognitive offloading vs. cognitive outsourcing. When does technology reduce unnecessary cognitive burden, and when does it begin doing the thinking that learners need to do for themselves?From there, they discuss why knowledge still matters, how schemas and long-term memory support expertise, and why effort, retrieval, desirable difficulties, and generative processing remain essential for durable learning. The discussion also explores the difference between short-term performance and actual learning and why AI can sometimes make students look more successful without necessarily producing knowledge or skills that transfer. Nidhi Sachdeva is a teacher educator, researcher, and learning design consultant specializing in the science of learning, teacher expertise, and evidence-informed instructional design. She teaches at the Ontario Institute for Studies in Education at the University of Toronto and is Professor at Academica University of Applied Sciences in Amsterdam, where she supports projects focused on developing teacher expertise. She is Chair of researchED Canada and co-author, with Paul A. Kirschner, of the forthcoming Becoming an Expert Teacher: Deliberate Practice for Effective Teaching. Her work focuses on translating cognitive science into practical approaches that help educators and learning professionals design instruction that builds durable learning, skilled performance, and professional expertise—with the conviction that strong, evidence-informed teaching is also a matter of equity, narrowing gaps in achievement so that high-quality learning outcomes are within reach for all students, not only the already-advantaged.

  4. 6d ago

    What Is Superintelligence? AGI, ASI, and the Singularity Explained

    In this episode of Logos & Machine, Taylor Collins and Dustin Hardwick offer a primer on artificial superintelligence—what it is, how it differs from AGI, why it is often connected to the singularity, and why these ideas matter even if the future remains uncertain. The conversation begins with a recent claim from Sam Altman that “we are now like in the singularity.” But what does that actually mean? Taylor and Dustin trace the history of the term from John von Neumann and Ray Kurzweil to today’s AI companies, showing how its meaning has changed and why there is still no universally accepted definition.They then distinguish three major categories of artificial intelligence:Artificial narrow intelligence, which excels at specific tasks Artificial general intelligence, which performs across a broad range of tasks at roughly human level Artificial superintelligence, which would greatly exceed the best human minds across nearly every domain of interestThe episode also explores three possible forms of superintelligence. Collective superintelligence would emerge from large numbers of coordinated AI systems. Speed superintelligence would reason at roughly human quality but at dramatically greater speed. Quality superintelligence would represent a genuinely different level of cognition—one potentially as difficult for humans to understand as human thought is for a chimpanzee.Taylor and Dustin then examine the pathway most often proposed for reaching superintelligence: recursive self-improvement. If an AI system becomes capable of helping design a better AI, and that improved system can design an even better one, the result could be an intelligence explosion.Using the lily pad thought experiment, the conversation explains why exponential growth is so difficult for human beings to recognize. For most of the process, change may appear slow and manageable. Only near the end does the growth become visibly dramatic.But exponential growth is not inevitable. Energy limits, data constraints, physical infrastructure, algorithmic ceilings, regulation, and institutional safeguards could all slow or stop the process. Instead of endless exponential growth, AI development may follow an S-curve in which rapid progress eventually reaches a plateau.

  5. 6d ago

    What AI Costs: Energy, Capital, and Human Trauma

    In this episode of Logos & Machine, Taylor Collins and Dustin Hardwick continue their discussion of AI abundance by examining the other side of the economic equation: its cost. The most visible cost of artificial intelligence may be a monthly subscription, but the true cost extends much further. In economics, cost includes everything we give up to obtain something else. Taylor and Dustin explore the opportunity costs of AI—the resources, investments, skills, relationships, and human experiences that may be displaced as artificial intelligence becomes more deeply embedded in society.The conversation begins with the direct financial cost of AI services and the growing cost of phones, computers, vehicles, and other devices. AI companies and data centers are competing for the same memory, chips, electricity, construction capacity, and investment capital used throughout the rest of the economy. As enormous amounts of money flow into AI infrastructure, the episode asks what other hospitals, homes, schools, businesses, and public systems may become more expensive or remain unbuilt.Taylor and Dustin also examine the human labor behind AI. This includes data labelers exposed to traumatic content, workers competing for fragmented digital tasks, and the uncompensated public labor and data that helped train contemporary AI systems. Even familiar activities such as completing a CAPTCHA have contributed to machine-learning development.The discussion then turns toward the near-term risks of lock-in and de-skilling. Today’s AI subscriptions are often subsidized as companies compete for users and market dominance. But what happens when prices rise after individuals, schools, and workplaces have become dependent on these systems? If regular AI use also weakens independent professional, academic, or interpersonal skills, walking away may become increasingly difficult.The final section considers costs that are harder to measure: changes to relationships, moral development, grief, identity, purpose, and community. Taylor and Dustin discuss people using AI to mediate disagreements, simulated companions, digital recreations of deceased loved ones, and the possibility that frictionless artificial relationships could begin replacing the difficult but formative work of living with other human beings.At its core, this episode asks a foundational question: as AI gives us new capabilities, what are we giving up in return?The answer is not to reject AI. It is to become more conscious of the trade-offs, more involved in shaping its future, and more intentional about preserving the experiences and relationships that keep human beings at the center.

  6. 6d ago

    The Abundance Machine: What AI Gives and What It Takes

    In this 3rd episode of Logos & Machine, Taylor Collins and Dustin Hardwick explore one of the biggest promises being made about artificial intelligence: abundance.AI leaders often describe a future in which artificial intelligence dramatically reduces scarcity, accelerates scientific discovery, transforms medicine, increases productivity, lowers costs, and creates a level of prosperity that would have seemed impossible in the past. Taylor and Dustin begin by taking those promises seriously, looking at claims from leaders in AI about radical abundance, universal high income, personal AI teams, rapid medical progress, and even longer human lifespans.From there, the conversation asks a more difficult question: even if AI can create material abundance, will that abundance actually become human abundance?The episode explores some of the strongest reasons for optimism, especially in healthcare and biology. Taylor and Dustin discuss AlphaFold, AI-assisted drug discovery, medical imaging, individualized treatment, and the possibility that AI may help connect complex data across genomes, medical records, clinical trials, and patient histories. These developments may genuinely improve lives and reduce suffering.But the conversation also turns toward the transition period between today and the promised future. Who will have access to the most powerful AI systems? What happens if governance, regulation, healthcare infrastructure, and public institutions move more slowly than the technology? Will the benefits be broadly shared, or will power and wealth become concentrated among the companies that control the systems?Taylor and Dustin also consider the human side of abundance. If AI changes work, removes entry-level pathways, pushes people toward entrepreneurship, or encourages hyper-specialization, what happens to purpose, agency, dignity, and the ability to choose one’s own path? Material abundance may matter deeply, but people also need meaning, contribution, relationships, formation, and the ability to grow through struggle.At its core, this episode asks whether the future being promised by AI leaders is a true vision of human flourishing, or whether it risks defining abundance too narrowly as efficiency, productivity, and output.

  7. 6d ago

    The Future of Shopping is About to Get Weird!

    In this 2nd episode of Logos & Machine, Taylor Collins and Dustin Hardwick explore what AI may mean for the future of markets, shopping, pricing, and human agency. The conversation begins with a Consumer Reports and Groundwork Collaborative investigation into Instacart’s AI-enabled pricing experiments, which found that some shoppers were shown different prices for the same grocery items at the same stores and at the same time. From there, Taylor and Dustin unpack the economics behind price discrimination, personalized pricing, consumer surplus, asymmetric information, and what happens when algorithms know more about our willingness to pay than we do.The episode asks a simple but unsettling question: what happens when your grocery bill, airline ticket, or online cart is no longer priced for the market, but priced for you?Taylor and Dustin discuss the difference between familiar forms of price discrimination, such as student discounts or senior discounts, and a more individualized future where AI systems estimate the highest price each person may be willing to pay. They also consider the rise of AI agents, the possibility of agent-to-agent shopping, and the risk that consumers may eventually pay a kind of “human tax” if they choose to make decisions without algorithmic assistance.At its core, this is not just a conversation about economics. It is a conversation about whether AI-powered markets will continue to serve human beings, or whether human beings will increasingly be optimized by markets designed around data, prediction, and profit.

  8. 6d ago

    What Did The Pope Say About AI?

    In this first episode of Logos & Machine, Taylor Collins and Dustin Hardwick discuss Magnifica Humanitas, the first encyclical of Pope Leo XIV, and what it says about artificial intelligence, human dignity, moral responsibility, and the future we are building together. The conversation explores the Pope’s framing of AI through two biblical images: the Tower of Babel and the walls of Jerusalem. One warns of pride, fragmentation, and the loss of shared truth. The other points toward subsidiarity, community, and the possibility of building something humane together. Taylor and Dustin reflect on what it means to preserve our humanity in an age increasingly shaped by algorithmic systems. They discuss AI and moral distance, the temptation to outsource decision-making, the risks of concentrating technological power, the importance of human relationships, and the responsibility each of us has to “tend our section of the wall.” The episode also considers the role of higher education in the AI age. If AI can deliver information instantly, what remains uniquely human about the university? For Logos & Machine, the answer begins with formation, community, moral literacy, discourse, and the development of the whole person.

  9. 6d ago ·  Bonus

    The AI Confluence Series: AI in Education

    In this AI Confluence Series panel, faculty from the University of the Incarnate Word, Our Lady of the Lake University, and St. Mary’s University come together to examine artificial intelligence through the lens of education. Recorded on April 23, 2026, this conversation explores how AI is reshaping teaching, learning, writing, academic integrity, accessibility, and the formation of students as thinkers. The panel considers not only what AI can do in education, but what it may be doing to us as learners, teachers, writers, and communities of inquiry

  10. 6d ago ·  Bonus

    The AI Confluence Series: AI and Ethics

    In this AI Confluence Series panel, faculty from the University of the Incarnate Word, Our Lady of the Lake University, and St. Mary’s University come together to examine artificial intelligence through the lens of ethics. Recorded on April 10, 2026, this conversation explores how AI is reshaping ethical responsibility across healthcare, cybersecurity, law, education, business, and everyday life. The panel considers questions of moral distance, human accountability, privacy, informed consent, ethical literacy, professional responsibility, and the risks of allowing AI systems to make or influence decisions without clear human ownership.

  11. 6d ago ·  Bonus

    The AI Confluence Series: AI in Economics

    In this AI Confluence Series panel, faculty from the University of the Incarnate Word, Our Lady of the Lake University, and St. Mary’s University come together to examine artificial intelligence through an economic lens. Recorded on March 20, 2026, this conversation explores how AI may reshape work, markets, higher education, lending, banking, economic agency, and the distribution of power across society. Panelists consider both the promise and the risks of AI: from new forms of productivity and opportunity to concerns about labor disruption, market concentration, personalized pricing, financial instability, and the possible erosion of human agency.

  • 11 Episodes

About

Logos & Machine is a podcast from the University of the Incarnate Word exploring AI, education, and the human future. Hosted by Taylor Collins and Dustin Hardwick, Logos & Machine is not a podcast about model benchmarks, product launches, or the latest tech headlines. It is a podcast about what happens when artificial intelligence meets real human lives: our work, our schools, our healthcare systems, our communities, our relationships, and our sense of what it means to be human.

Information

  • Creator
    Logos and Machine
  • Years Active
    2026
  • Episodes
    11
  • Rating
    Clean
  • Copyright
    © Logos and Machine
  • Show Website
    Logos and Machine