When AI models hallucinate confidently while burning through your token budget, you need more than better prompts you need better processes.

In this episode, Kevin and Eli explore the reality of working with AI that lies while you pay, the evolution from saved prompts to voice-triggered workflows, and why validation loops matter more than model updates. From enterprise governance requirements to tech stack consolidation, this conversation cuts through the hype to reveal what actually works when deploying AI in real business contexts.

Key topics covered:

✅ Why model reliability requires systematic validation, not perfect AI

✅ The shift from skills to snippets and voice-triggered workflows

✅ Enterprise governance requirements when litigation risk is involved

✅ Tech stack consolidation as AI tools mature beyond experimentation

✅ Cost economics driving procurement strategy changes

✅ Building quality control loops that catch hallucinations before implementation

TIMESTAMPS:

00:00 — Intro and Sora commercial viability discussion

05:30 — Video generation landscape: Veo, SeeDance, Higgs Field

15:00 — Model quality regression and hallucination experiences

25:00 — Skills vs snippets: workflow evolution deep dive