241 - Opportunities with AI (in 2026) with MZ Naser

About this episode

Is it too late to start with the AI in 2026? It wen't so far, does it still make sense to get interested in this technology?

Absolutely. Today we sit down with MZ Naser of Clemson University to map a clear, useful path for engineers who want results without the hype. We start with the basics - clean data, the right algorithm, and a realistic mindset - and climb toward explainability, causality, and even philosophy to show where AI informs decisions and where it can quietly mislead.

We dig into the limits of our experiments: when tests are expensive, we control only a few variables and then celebrate when explainable AI “finds” the same drivers. That’s not discovery; that’s confirmation. MZ explains how broader sampling, anomaly detection, and careful clustering can reveal patterns we miss, while acknowledging that physics is fixed but our datasets are narrow. We also talk scale: a model that predicts whole-building fire behavior from scratch is a fantasy without impossible data. The practical play is combining reasoning, physics, and simulation to guide where AI adds value - sometimes leading to a simpler equation that replaces the model altogether.

Then we get tactical. What is agentic AI, and how can it save engineers real time? Think delegated workflows: data gathering, parametric setup, code lookups, Excel design sheets, quality checks, and concise summaries. Train agents with explicit steps and tight guardrails, keep them away from money and safety-critical controls, and make human review mandatory. We also confront traceability and model retirement - why freezing working versions, documenting assumptions, and cross-verifying with independent methods matter for audits years down the line.

Throughout, we balance open local models versus cloud LLMs, the trade-offs between control and convenience, and the hard truth that black boxes don’t absolve us of understanding. The big takeaway: AI is a lever, not a miracle. Use it to widen your view, automate routine work, and challenge your priors - while keeping physics, data quality, and professional judgment at the center.

If this conversation helps you think clearer about where AI fits in your workflow, follow the show, share it with a colleague, and leave a quick review so more engineers can find it.

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