The AI Fundamentalists

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!
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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.
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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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