The AI Fundamentalists

As multi-step agentic AI systems evolve, performance is increasingly driven by orchestration harnesses and stepwise outcome verification rather than raw model scale. While gated sub-agent architectures help prevent error cascades, the industry faces a sharp reckoning around vibe coding security vulnerabilities and unsustainable token costs. Paradoxically, generic AI travel planning tools are causing widespread itinerary homogenization, which in turn is driving up the market value and prestige...

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 actu...

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 neut...

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 "...

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 ...