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?

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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)

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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.

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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

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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 

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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.

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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!>>

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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

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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.

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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!

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