The AI Fundamentalists

32:29

Revisiting AI Agents: What's Changed? What's the...

The AI Fundamentalists ·

2026/09/29

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...

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37:26

Token Economics (Tokenomics)

The AI Fundamentalists ·

2026/08/04

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...

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35:13

Exploring political bias and persuasion in LLMs with...

The AI Fundamentalists ·

2026/07/21

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...

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30:12

Metaphysics and modern AI: What is Reasoning and...

The AI Fundamentalists ·

2026/05/05

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 "...

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31:32

Beyond Boosted Trees: Christoph Molnar on the Rise of...

The AI Fundamentalists ·

2026/04/21

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 ...

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46:08

AI and the lost art of reading

The AI Fundamentalists ·

2026/03/03

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 attenti...

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36:29

Metaphysics and modern AI: What is causality?

The AI Fundamentalists ·

2026/01/27

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 structuresGestalt theory: Why correlation misleads and how pattern-seeking tricks usStatistical association vs causal...

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40:16

Why validity beats scale when building multi‑step AI...

The AI Fundamentalists ·

2026/01/06

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 hypeLimits of scale and the need for multi‑step controlTool use, compounding errors, and guardrailsSystems...

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42:06

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

The AI Fundamentalists ·

2025/12/22

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 •...

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49:48

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

The AI Fundamentalists ·

2025/12/09

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 ...

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38:04

Metaphysics and modern AI: What is space and time?

The AI Fundamentalists ·

2025/11/11

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 ...

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38:32

Metaphysics and modern AI: What is reality?

The AI Fundamentalists ·

2025/10/27

In the first episode of our series on metaphysics, Michael Herman joins us from Episode #14 on “What is consciousness?” to discuss reality. More specifically, the question of objects in reality. The team explores Plato’s forms, Aristotle’s realism, emergence, and embodiment to determine whether AI models can approximate from what humans uniquely experience. Defining objects via properties, perception, and persistenceBanana and circle examples for identity and idealsPlato versus Aristo...

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16:19

Metaphysics and modern AI: What is thinking? - Series...

The AI Fundamentalists ·

2025/10/07

This episode is the intro to a special project by The AI Fundamentalists’ hosts and friends. We hope you're ready for a metaphysics mini‑series to explore what thinking and reasoning really mean and how those definitions should shape AI research. Join us for thought-provoking discussions as we tackle basic questions: What is metaphysics and its relevance to AI? What constitutes reality? What defines thinking? How do we understand time? And perhaps most importantly, should AI systems att...

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35:11

AI in practice: Guardrails and security for LLMs

The AI Fundamentalists ·

2025/09/30

In this episode, we talk about practical guardrails for LLMs with data scientist Nicholas Brathwaite. We focus on how to stop PII leaks, retrieve data, and evaluate safety with real limits. We weigh managed solutions like AWS Bedrock against open-source approaches and discuss when to skip LLMs altogether. • Why guardrails matter for PII, secrets, and access control • Where to place controls across prompt, training, and output • Prompt injection, jailbreaks, and adversarial handling • RAG des...

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42:28

AI in practice: LLMs, psychology research, and mental...

The AI Fundamentalists ·

2025/09/04

We’re excited to have Adi Ganesan, a PhD researcher at Stony Brook University, Penn University, and Vanderbilt, on the show. We’ll talk about how large language models LLMs) are being tested and used in psychology, citing examples from mental health research. Fun fact: Adi was Sid's research partner during his Ph.D. program. Discussion highlights Language models struggle with certain aspects of therapy including being over-eager to solve problems rather than building understandingCurrent mode...

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22:42

LLM scaling: Is GPT-5 near the end of exponential...

The AI Fundamentalists ·

2025/08/19

The release of OpenAI GPT-5 marks a significant turning point in AI development, but maybe not the one most enthusiasts had envisioned. The latest version seems to reveal the natural ceiling of current language model capabilities with incremental rather than revolutionary improvements over GPT-4. Sid and Andrew call back to some of the model-building basics that have led to this point to give their assessment of the early days of the GPT-5 release. • AI's version of Moore's Law is slow...

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37:25

AI governance: Building smarter AI agents from the...

The AI Fundamentalists ·

2025/07/22

Sid Mangalik and Andrew Clark explore the unique governance challenges of agentic AI systems, highlighting the compounding error rates, security risks, and hidden costs that organizations must address when implementing multi-step AI processes. Show notes: • Agentic AI systems require governance at every step: perception, reasoning, action, and learning • Error rates compound dramatically in multi-step processes - a 90% accurate model per step becomes only 65% accurate over four steps •...

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29:46

Linear programming: Building smarter AI agents from...

The AI Fundamentalists ·

2025/07/08

We continue with our series about building agentic AI systems from the ground up and for desired accuracy. In this episode, we explore linear programming and optimization methods that enable reliable decision-making within constraints. Show notes: Linear programming allows us to solve problems with multiple constraints, like finding optimal flights that meet budget requirementsThe Lagrange multiplier method helps find optimal solutions within constraints by reformulating utility f...

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41:36

Utility functions: Building smarter AI agents from...

The AI Fundamentalists ·

2025/06/12

The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks. This episode is part 2 of our series about building smarter AI agents from the fundamentals. Listen to Part 1 about mechanism ...

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37:06

Mechanism design: Building smarter AI agents from the...

The AI Fundamentalists ·

2025/05/20

What if we've been approaching AI agents all wrong? While the tech world obsesses over larger language models (LLMs) and prompt engineering, there'a a foundational approach that could revolutionize how we build trustworthy AI systems: mechanism design. This episode kicks off an exciting series where we're building AI agents "the hard way"—using principles from game theory and microeconomics to create systems with predictable, governable behavior. Rather than hoping an LLM can magically handl...

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