The AI Fundamentalists

Hosted by Dr. Andrew Clark & Dr. Sid Mangalik

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Aug 2026

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About the show

A podcast about the fundamentals of safe and resilient modeling systems behind the AI that impacts our lives and our businesses.

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50 recent

August 4, 202637 min

Token Economics (Tokenomics)

Andrew and Sid break down the hidden costs of AI tokens, why current prices are artificially low, and whether AI tokens could become the next global commodity. As AI adoption surges and agentic workflows burn through compute, the underlying economics of large language models are reaching a critical inflection point. Join us to explore the rapidly shifting landscape of "tokenomics," the staggering hardware constraints behind the scenes, and what the true market clearing price for AI might actually look like. To help us unpack this, the hosts dive into the downstream effects of "token maxing," why true economic equilibrium in AI is far off, and how historical technological shifts like electricity can predict our AI future. Defining what a token actually is and how text is chunked and processed by specific models. The illusion of current token pricing and why heavy subsidization by tech giants obscures the true cost of production. Exploring the flawed "token maxing" trend and why organizations are improperly prioritizing raw AI usage over actual return on investment. The severe hardware constraints and geopolitical pressures, including skyrocketing GPU and RAM costs, that make running local infrastructure incredibly difficult. Analyzing the criteria for money to see if AI tokens can become a true currency, or if they are destined to act as a tradable commodity like oil. The "Jevons Paradox" of AI efficiency and why cheaper compute actually leads to massively increased, rather than decreased, usage. How the future of work will rely on "cyborging"—combining human talent with AI—to increase productivity, using the surprising resurgence of human travel agents as an example. This episode is full of economic insights and forward-looking predictions that are sure to change how you think about your next API bill. As we move into a new era of AI, it's the perfect time to explore the fundamentals of the next frontier! What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

July 21, 202635 min

Exploring political bias and persuasion in LLMs with Dr. Jillian Fisher

In this episode of The AI Fundamentalists, hosts Andrew and Sid are joined by AI alignment and safety researcher Dr. Jillian Fisher to unpack the complex realities of political bias in Large Language Models. Dr. Fisher explains that bias isn't just a byproduct of noisy training data; it is also embedded directly into the architectural choices of the models, such as relying on a "majority vote" mechanism to determine the right answer. The conversation explores why achieving true political neutrality in AI is widely considered impossible due to the inescapable human element involved in AI development. Instead, developers must rely on imperfect approximations of neutrality. Dr. Fisher breaks down approaches like "reasonable pluralism"—which attempts to present all reasonable sides of an argument—and flat-out refusal to answer, noting that both strategies come with distinct trade-offs for user agency and safety. Listeners will also discover fascinating insights into the psychology of AI persuasion. Dr. Fisher highlights research showing that unlike humans, who typically persuade through empathy and storytelling, AI is most convincing to users through "information packing". Delivering dense walls of facts, combined with natural conversational fluency, can trick our brains into viewing the model as an unquestionable authority. Finally, the group discusses the critical need for socio-technical AI literacy, exploring how teaching the public about AI's limitations and its reliance on flawed internet data could be the ultimate tool for inoculating users against sycophantic behaviors and unwanted persuasion. What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

May 5, 202630 min

Metaphysics and modern AI: What is Reasoning and Thinking?

In this episode we conclude our series about Metaphysics and modern AI, we explore the definitions of consciousness, reasoning, and thinking to understand if AI possesses these traits. From examining legal accountability and the concept of personhood to analyzing human cognitive frameworks, we map out the differences between actual contemplative problem-solving and probabilistic pattern recognition. The episode covers: Defining consciousness, reasoning, and what it means to be a "thinking thing" The Turing Test as a low bar and why natural language capabilities create the illusion of intelligence Accountability and agency: Why AI models like Claude are not legally recognized as persons Daniel Kahneman’s System 1 ( fast heuristics ) vs. System 2 ( contemplative reasoning ) thinking Why LLMs function primarily as System 1 pattern recognizers rather than true reasoners Complex systems, Descartes' dualism, and whether thinking is an emergent property requiring a physical body How chatbots use psychological mirroring, filler words, and pauses to trick human biases The dangers of anthropomorphizing AI driven by fear of change or financial incentives This is the final episode in our metaphysics and AI series. You can find the previous episodes here: Metaphysics and modern AI: What is causality? Metaphysics and modern AI: What is reality? Metaphysics and modern AI: What is thinking? - Series Intro What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

April 21, 202631 min

Beyond Boosted Trees: Christoph Molnar on the Rise of Tabular Foundation Models

As the AI landscape evolves, the methods we use to process structured data are undergoing a silent revolution. Join us to explore how Tabular Foundation Models (TFMs) are challenging the decade-long reign of tree-based algorithms, why the traditional "train and predict" workflow is being replaced by "in-context learning," and what this shift means for the future of resilient modeling. To help us, Christoph Molnar , renowned expert in machine learning interpretability and author of the Mindful Modeler newsletter, joins us to share his perspective on the emergence of tabular transformers, the surprising power of synthetic data, and how to maintain model safety in a world without parameter updates. The decline of the "fit and predict" paradigm in tabular data Transformer architectures vs. traditional models like XGBoost and LightGBM In-context learning: Predicting without traditional training steps The role of Structural Causal Models (SCMs) in generating training data Why models trained on "math and probability" succeed on real-world datasets Hardware accessibility and running foundation models on local MacBooks Integrating SHAP values and conformal prediction for model interpretability The future of the data science workflow: One tool among many or a total shift? This episode is full of technical insights and forward-looking predictions that are sure to change how you approach your next dataset. As we move into a new era of AI, it’s the perfect time to explore the fundamentals of the next frontier! What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

March 3, 2026Episode 4646 min

AI and the lost art of reading

As information sources have become abundant and attention spans have shortened in the age of AI, we take on the lost art of reading. Join us to explore why reading rates are falling, how that shift affects judgment and opportunity, and how interdisciplinary books help us see patterns across history, economics, and technology. To help us, Alisa Rusanoff , CEO of Eltech AI , joins us to share her perspective on reading, debate volume versus depth, and offer practical ways to reclaim attention and read with intention. Evidence on declining reading rates among adults, teens and children Noise versus signal in the attention economy Mental models and interdisciplinary synthesis for better decisions AI’s limits and why human integration still matters Cycles in debt, trade, demography, and geopolitics Fiction as a cultural sensor for lived experience Wealth gaps, polarization and the need for critical thinking Practical habits to train feeds and protect reading time Challenge to read, reflect, and apply insights For people worried if they are reading enough: Reading just 1 book a year puts you in the top 60% of readers Read 4 books a year to be in the top 50% of readers Read 10 books a year to be in the top 20% of readers For those looking to be in the top 5% of readers, expect to read at least 50 books This episode is full of research and fun connections that are sure to make you think positively about your commitment to reading. At the time of this episode, it's not too late to join the top 20% in 2026! What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

January 27, 2026Episode 4136 min

Metaphysics and modern AI: What is causality?

In this episode of our series about Metaphysics and modern AI, we break causality down to first principles and explain how to tell factual mechanisms from convincing correlations. From gold-standard Randomized Control Trials (RCT) to natural experiments and counterfactuals, we map the tools that build trustworthy models and safer AI. Defining causes, effects, and common causal structures Gestalt theory: Why correlation misleads and how pattern-seeking tricks us Statistical association vs causal explanation RCTs and why randomization matters Natural experiments as ethical, scalable alternatives Judea Pearl’s do-calculus, counterfactuals, and first-principles models Limits of causality, sample size, and inference Building resilient AI with causal grounding and governance This is the fourth episode in our metaphysics series. Each topic in the series is leading to the fundamental question, "Should AI try to think?" Check out previous episodes: Series Intro What is reality? What is space and time? If conversations like this sharpen your curiosity and help you think more clearly about complex systems, then step away from your keyboard and enjoy this journey with us. What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

January 6, 2026Episode 4040 min

Why validity beats scale when building multi‑step AI systems

In this episode, Dr. Sebastian (Seb) Benthall joins us to discuss research from his and Andrew's paper entitled “ Validity Is What You Need ” for agentic AI that actually works in the real world. Our discussion connects systems engineering, mechanism design, and requirements to multi‑step AI that creates enterprise impact to achieve measurable outcomes. Defining agentic AI beyond LLM hype Limits of scale and the need for multi‑step control Tool use, compounding errors, and guardrails Systems engineering patterns for AI reliability Principal–agent framing for governance Mechanism design for multi‑stakeholder alignment Requirements engineering as the crux of validity Hybrid stacks: LLM interface, deterministic solvers Regression testing through model swaps and drift Moving from universal copilots to fit‑for‑purpose agents You can also catch more of Seb's research on our podcast. Tune in to Contextual integrity and differential privacy: Theory versus application . What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

December 22, 2025Episode 4042 min

2025 AI review: Why LLMs stalled and the outlook for 2026

Here it is! We review the year where scaling large AI models hit its ceiling, Google reclaimed momentum with efficient vertical integration, and the market shifted from hype to viability. Join us as we talk about why human-in-the-loop is failing, why generative AI agents validating other agents compounds errors, and how small expert data quietly beat the big models. • Google’s resurgence with Gemini 3.0 and TPU-driven efficiency • Monetization pressures and ads in co-pilot assistants • Diminishing returns from LLM scaling • Human-in-the-loop pitfalls and incentives • Agents vs validation and compounding error • Small, high-quality data outperforming synthetic • Expert systems, causality, and interpretability • Research trends return toward statistical rigor • 2026 outlook for ROI, governance, and trust We remain focused on the responsible use of AI. And while the market continues to adjust expectations for return on investment from AI, we're excited to see companies exploring "return on purpose" as the new foray into transformative AI systems for their business. What are you excited about for AI in 2026? What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

December 9, 2025Episode 3949 min

Big data, small data, and AI oversight with David Sandberg

In this episode, we look at the actuarial principles that make models safer: parallel modeling, small data with provenance, and real-time human supervision. To help us, long-time insurtech and startup advisor David Sandberg, FSA, MAAA, CERA, joins us to share more about his actuarial expertise in data management and AI. We also challenge the hype around AI by reframing it as a prediction machine and putting human judgment at the beginning, middle, and end. By the end, you might think about “human-in-the-loop” in a whole new way. • Actuarial valuation debates and why parallel models win • AI’s real value: enhance and accelerate the growth of human capital • Transparency, accountability, and enforceable standards • Prediction versus decision and learning from actual-to-expected • Small data as interpretable, traceable fuel for insight • Drift, regime shifts, and limits of regression and LLMs • Mapping decisions, setting risk appetite, and enterprise risk management (ERM) for AI • Where humans belong: the beginning, middle, and end of the system • Agentic AI complexity versus validated end-to-end systems • Training judgment with tools that force critique and citation Cultural references: Foundation , AppleTV The Feeling of Power , Isaac Asimov Player Piano , Kurt Vonnegut For more information, see Actuarial and data science: Bridging the gap . What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

November 11, 2025Episode 3838 min

Metaphysics and modern AI: What is space and time?

We explore how space and time form a single fabric, testing our daily beliefs through questions about free-fall, black holes, speed, and momentum to reveal what models get right and where they break. To help us, we’re excited to have our friend David Theriault, a science and sci-fi afficionado; and our resident astrophysicist, Rachel Losacco , to talk about practical exploration in space and time. They'll even unpack a few concerns they have about how space and time were depicted in the movie Interstellar (2014) . Highlights: • Introduction: Why fundamentals beat shortcuts in science and AI • Time as experience versus physical parameter • Plato’s ideals versus Aristotle’s change as framing tools • Free-fall, G-forces, and what we actually feel • Gravity wells, curvature, and moving through space-time • Black holes, tidal forces, and spaghettification • Momentum and speed: Laser probe, photon momentum, and braking limits • Doppler shifts, time dilation, and length contraction • Why light’s speed stays constant across frames • Modeling causality and preparing for the next paradigm This episode about space and time is the second in our series about metaphysics and modern AI. Each topic in the series is leading to the fundamental question, "Should AI try to think?" Step away from your keyboard and enjoy this journey with us. Previous episodes: Introduction: Metaphysics and modern AI What is reality? What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.