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The AI Fundamentalists

Dr. Andrew Clark & Dr. Sid Mangalik

A podcast about the fundamentals of safe and resilient modeling systems behind the AI that impacts our lives and our businesses.

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August 4 · 37 min
Token Economics (Tokenomics)
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! 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.
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July 21 · 35 min
Exploring political bias and persuasion in LLMs with Dr. Jillian Fisher
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. 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.
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May 5 · 30 min
Metaphysics and modern AI: What is Reasoning and Thinking?
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.
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April 21 · 31 min
Beyond Boosted Trees: Christoph Molnar on the Rise of Tabular Foundation Models
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! 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.
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S1 · E46
March 3 · 46 min
AI and the lost art of reading
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! 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.
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S1 · E41
January 27 · 36 min
Metaphysics and modern AI: What is causality?
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. 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.
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S1 · E40
January 6 · 40 min
Why validity beats scale when building multi‑step AI systems
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. 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.
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S1 · E40
Dec 22, 2025 · 42 min
2025 AI review: Why LLMs stalled and the outlook for 2026
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 • Diminishing returns from LLM scaling • Human-in-the-loop pitfalls and incentives • Agents vs validation and compounding error • Small, high-quality data outperforming synthetic • Expert systems, causality, and interpretability • Research trends return toward statistical rigor • 2026 outlook for ROI, governance, and trust We remain focused on the responsible use of AI. And while the market continues to adjust expectations for return on investment from AI, we're excited to see companies exploring "return on purpose" as the new foray into transformative AI systems for their business. What are you excited about for AI in 2026? 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.
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S1 · E39
Dec 9, 2025 · 49 min
Big data, small data, and AI oversight with David Sandberg
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 “human-in-the-loop” in a whole new way. • Actuarial valuation debates and why parallel models win • AI’s real value: enhance and accelerate the growth of human capital • Transparency, accountability, and enforceable standards • Prediction versus decision and learning from actual-to-expected • Small data as interpretable, traceable fuel for insight • Drift, regime shifts, and limits of regression and LLMs • Mapping decisions, setting risk appetite, and enterprise risk management (ERM) for AI • Where humans belong: the beginning, middle, and end of the system • Agentic AI complexity versus validated end-to-end systems • Training judgment with tools that force critique and citation Cultural references: Foundation, AppleTV The Feeling of Power, Isaac Asimov Player Piano, Kurt Vonnegut For more information, see Actuarial and data science: Bridging the gap. 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.
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S1 · E38
Nov 11, 2025 · 38 min
Metaphysics and modern AI: What is space and time?
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 movie Interstellar (2014). Highlights: • Introduction: Why fundamentals beat shortcuts in science and AI • Time as experience versus physical parameter • Plato’s ideals versus Aristotle’s change as framing tools • Free-fall, G-forces, and what we actually feel • Gravity wells, curvature, and moving through space-time • Black holes, tidal forces, and spaghettification • Momentum and speed: Laser probe, photon momentum, and braking limits • Doppler shifts, time dilation, and length contraction • Why light’s speed stays constant across frames • Modeling causality and preparing for the next paradigm This episode about space and time is the second in our series about metaphysics and modern AI. Each topic in the series is leading to the fundamental question, "Should AI try to think?" Step away from your keyboard and enjoy this journey with us. Previous episodes: Introduction: Metaphysics and modern AI What is reality? 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.
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S1 · E37
Oct 27, 2025 · 38 min
Metaphysics and modern AI: What is reality?
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 persistence Banana and circle examples for identity and ideals Plato versus Aristotle on forms and realism Ship of Theseus and continuity through change Samples, complexes, and emergence in systems Embodiment, consciousness, and why LLMs lack lived unity Existentialist focus on subjective reality and meaning Why metaphysics matters for AI governance and safety Join us for the next part of the metaphysics series to explore space and time. Subscribe now. What we're reading: [Mumford's] Metaphysics: A Very Short Introduction (Andrew) 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.
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S1 · E36
Oct 7, 2025 · 16 min
Metaphysics and modern AI: What is thinking? - Series Intro
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 attempt to "think," or are we approaching the entire concept incorrectly? Show notes: • Why metaphysics matters for AI foundations • Definitions of thinking from peers and what they imply • Mixture‑of‑experts, ranking, and the illusion of reasoning • Turing test limits versus deliberation and causality • Towers of Hanoi, agentic workflows, and brittle stepwise reasoning • Math, context, and multi‑component system failures • Proposed plan for the series and areas to explore • Invitation for resources, critiques, and future guests We hope you enjoy this philosophical journey to examine the intersection of ancient philosophical questions and cutting-edge technology. 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.
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S1 · E35
Sep 30, 2025 · 35 min
AI in practice: Guardrails and security for LLMs
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 design with vector DB separation and permissions • Evaluation methods, risk scoring, and cost trade-offs • AWS Bedrock guardrails vs open-source customization • Domain-adapted safety models and policy matching • When deterministic systems beat LLM complexity This episode is part of our "AI in Practice” series, where we invite guests to talk about the reality of their work in AI. From hands-on development to scientific research, be sure to check out other episodes under this heading in our listings. Related research: Building trustworthy AI: Guardrail technologies and strategies (N. Brathwaite) Nic's GitHub 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.
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S1 · E35
Sep 4, 2025 · 42 min
AI in practice: LLMs, psychology research, and mental health
We’re excited to have Adi Ganesan, a PhD researcher at Stony Brook University, the University of Pennsylvania, 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 understanding Current models are poor at detecting psychomotor symptoms from text alone but are oversensitive to suicidality markers Cognitive reframing assistance represents a promising application where LLMs can help identify thought traps Proper evaluation frameworks must include privacy, security, effectiveness, and appropriate engagement levels Theory of mind remains a significant challenge for LLMs in therapeutic contexts; example: The Sally-Anne Test. Responsible implementation requires staged evaluation before patient-facing deployment Resources To learn more about Adi's research and topics discussed in this episode, check out the following resources: Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation Therapist Behaviors paper: [2401.00820] A Computational Framework for Behavioral Assessment of LLM Therapists Cognitive reframing paper: Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction - ACL Anthology Faux Pas paper: Testing theory of mind in large language models and humans | Nature Human Behaviour READI: Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation | npj Mental Health Research GPT-4’s Schema of Depression: Explaining GPT-4’s Schema of Depression Using Machine Behavior Analysis Adi’s Profile: Adithya V Ganesan - Google Scholar 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.
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S1 · E34
Aug 19, 2025 · 22 min
LLM scaling: Is GPT-5 near the end of exponential growth?
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 slowing down dramatically with GPT-5 • OpenAI appears to be experiencing an identity crisis, uncertain whether to target consumers or enterprises • Running out of human-written data is a fundamental barrier to continued exponential improvement • Synthetic data cannot provide the same quality as original human content • Health-related usage of LLMs presents particularly dangerous applications • Users developing dependencies on specific model behaviors face disruption when models change • Model outputs are now being verified rather than just inputs, representing a small improvement in safety • The next phase of AI development may involve revisiting reinforcement learning and expert systems * Review the GPT-5 system card for further information Follow The AI Fundamentalists on your favorite podcast app for more discussions on the direction of generative AI and building better AI systems. This summary was AI-generated from the original transcript of the podcast that is linked to this episode. 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.
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S1 · E34
Jul 22, 2025 · 37 min
AI governance: Building smarter AI agents from the fundamentals, part 4
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 • Two-way information flow creates new security and confidentiality vulnerabilities. For example, targeted prompting to improve awareness comes at the cost of performance. (arXiv, May 24, 2025) • Traditional governance approaches are insufficient for the complexity of agentic systems • Organizations must implement granular monitoring, logging, and validation for each component • Human-in-the-loop oversight is not a substitute for robust governance frameworks • The true cost of agentic systems includes governance overhead, monitoring tools, and human expertise Make sure you check out Part 1: Mechanism design, Part 2: Utility functions, and Part 3: Linear programming. If you're building agentic AI systems, we'd love to hear your questions and experiences. Contact us. What we're reading: We took reading "break" this episode to celebrate Sid! This month, he successfully defended his Ph.D. Thesis on "Psychological Health and Belief Measurement at Scale Through Language." Say congrats!>> 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.
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S1 · E34
Jul 8, 2025 · 29 min
Linear programming: Building smarter AI agents from the fundamentals, part 3
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 requirements The Lagrange multiplier method helps find optimal solutions within constraints by reformulating utility functions Combinatorial optimization handles discrete choices like selecting specific flights rather than continuous variables Dynamic programming techniques break complex problems into manageable subproblems to find solutions efficiently Mixed integer programming combines continuous variables (like budget) with discrete choices (like flights) Neurosymbolic approaches potentially offer conversational interfaces with the reliability of mathematical solvers Unlike pattern-matching LLMs, mathematical optimization guarantees solutions that respect user constraints Make sure you check out Part 1: Mechanism design and Part 2: Utility functions. In the next episode, we'll pull all of the components from these three episodes to demonstrate a complete travel agent AI implementation with code examples and governance considerations. What we're reading: Burn Book - Kara Swisher, March 2025 Signal and the Noise - Nate Silver, 2012 Leadership in Turbulent Times - Doris Kearns Goodwin 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.
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S1 · E33
Jun 12, 2025 · 41 min
Utility functions: Building smarter AI agents from the fundamentals, part 2
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 design HERE. Show notes: • Discussing the current AI landscape where companies are discovering implementation is harder than anticipated • Introducing the travel agent use case requiring ingestion, reasoning, execution, and feedback capabilities • Explaining why LLMs aren't designed for optimization tasks despite their conversational abilities • Breaking down utility functions from economic theory as a way to quantify user preferences • Exploring concepts like indifference curves and marginal rates of substitution for preference modeling • Examining four cases of utility relationships: independent goods, substitutes, complements, and diminishing returns • Highlighting how mathematical optimization provides explainability and guarantees that LLMs cannot • Setting up for future episodes that will detail the technical implementation of utility-based agents Subscribe so that you don't miss the next episode. In part 3, Andrew and Sid will explain linear programming and other optimization techniques to build upon these utility functions and create truly personalized travel experiences. 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.
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S1 · E32
May 20, 2025 · 37 min
Mechanism design: Building smarter AI agents from the fundamentals, Part 1
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 handle complex multi-step processes like booking travel, Sid and Andrew explore how to design the rules of the game so that even self-interested agents produce optimal outcomes. Drawing from our conversation with Dr. Michael Zarham (Episode 32), we break down why LLM-based agents struggle with transparency and governance. The "surface area" for errors expands dramatically when you can't explain how decisions are made across multiple steps. Instead, mechanism design creates clear states with defined optimization parameters at each stage—making the entire system more reliable and accountable. We explore the famous Prisoner's Dilemma to illustrate how individual incentives can work against collective benefits without proper system design. Then we introduce the Vickrey-Clark-Groves mechanism, which ensures AI agents truthfully reveal preferences and actively participate in multi-step processes—critical properties for enterprise applications. Beyond technical advantages, this approach offers something profound: a way to preserve humanity in increasingly automated systems. By explicitly designing for values, fairness, and social welfare, we're not just building better agents—we're ensuring AI serves human needs rather than replacing human thought. Subscribe now to follow our journey as we build an agentic travel system from first principles, applying these concepts to real business challenges. Have questions about mechanism design for AI? Send them our way for future episodes! 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.
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May 8, 2025 · 46 min
Principles, agents, and the chain of accountability in AI systems
Dr. Michael Zargham provides a systems engineering perspective on AI agents, emphasizing accountability structures and the relationship between principals who deploy agents and the agents themselves. In this episode, he brings clarity to the often misunderstood concept of agents in AI by grounding them in established engineering principles rather than treating them as mysterious or elusive entities. Show highlights • Agents should be understood through the lens of the principal-agent relationship, with clear lines of accountability • True validation of AI systems means ensuring outcomes match intentions, not just optimizing loss functions • LLMs by themselves are "high-dimensional word calculators," not agents - agents are more complex systems with LLMs as components • Guardrails provide deterministic constraints ("musts" or "shalls") versus constitutional AI's softer guidance ("shoulds") • Systems engineering approaches from civil engineering and materials science offer valuable frameworks for AI development • Authority and accountability must align - people shouldn't be held responsible for systems they don't have authority to control • The transition from static input-output to closed-loop dynamical systems represents the shift toward truly agentic behavior • Robust agent systems require both exploration (lab work) and exploitation (hardened deployment) phases with different standards Explore Dr. Zargham's work Protocols and Institutions (Feb 27, 2025) Comments Submitted by BlockScience, University of Washington APL Information Risk and Synthetic Intelligence Research Initiative (IRSIRI), Cognitive Security and Education Forum (COGSEC), and the Active Inference Institute (AII) to the Networking and Information Technology Research and Development National Coordination Office's Request for Comment on The Creation of a National Digital Twins R&D Strategic Plan NITRD-2024-13379 (Aug 8, 2024) 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.
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