The AI Fundamentalists

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 
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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!
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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!

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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.

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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.

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Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:
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Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.