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

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.