The AI Fundamentalists

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!

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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.

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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.

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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? 

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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.

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