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?

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:

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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.

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.