Recorded: April 14, 2026

Most people using AI today are still users.

They open ChatGPT, call an API, and get an answer.

And honestly… it works.

But that’s not the same as building with AI.

In this episode of Domesticating AI, we break down the difference between AI users and AI practitioners—and why that shift matters if you want reliable systems.

We’re joined by Alexandra “Lexi” Pasi, PhD, CEO of Lucidity Sciences, to talk about what it actually means to own the system around AI:

why calling an API is still user behavior
what changes when you build the harness
how agent systems actually fail (loops, cost, drift)
why switching models isn’t a reliability strategy
how to add layers—constraints, validation, and control flow
why engineering discipline matters more with AI, not less

If you’ve built your first AI agent, workflow, or coding loop—this is the “now what?” episode.

Alexandra Pasi is the CEO of Lucidity Sciences, where she works at the intersection of mathematics, machine learning, and real-world system design.

She holds a PhD in Mathematics from Baylor University and specializes in building analytical and algorithmic systems that bring structure to complex, uncertain environments.

🔗 LinkedIn: https://www.linkedin.com/in/alexandrapasi/
🔗 Lucidity Sciences: https://luciditysciences.com

Google TurboQuant (LLM compression research)
https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/
Anthropic Claude Mythos Preview (security-focused model)
https://red.anthropic.com/2026/mythos-preview/
Project Glasswing (Anthropic security initiative)
https://www.anthropic.com/glasswing
Karpathy Autoresearch (self-improving training loop)
https://github.com/karpathy/autoresearch
Kitaru (durable agent execution framework)
https://github.com/zenml-io/kitaru

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Keep your AI on a leash.

🧾 Episode Summary👤 Guest: Alexandra “Lexi” Pasi, PhD🔗 Topics & Links Mentioned🔔 Follow & Support