The Beyond Capture Podcast

Do We Really Trust AI?

Umony Season 1 Episode 7

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0:00 | 49:40

In this wide-ranging conversation, Dean Elwood, CEO of Umony, sits down with Alan Charbonneau, CTO of Umony, to explore one of the most pressing questions in today’s AI landscape: what does it really mean to trust intelligent systems?

From hallucinations and explainability to hybrid lexicons, human-in-the-loop workflows, and the limits of agentic systems, Dean and Alan break down where AI delivers, where it fails, and why progress may be shifting from “jobs” to “tasks.”

They discuss the plateau of model quality, the risks of synthetic data polluting the internet, the economics of failed AI initiatives, and whether we’re chasing AGI for innovation or for the trophy. Along the way, they examine how UX, curation, and “AI seasoning” may hold the key to making AI actually useful, safe, and trustworthy.

It’s a conversation about technology, but also about ethics, governance, and what remains fundamentally human as automation scales.
 

Chapters:

00:00 Intro

01:11 Trusting AI: Where Do We Begin?

03:35 Explainability, Citations & Transparency

06:44 Regulators, Risk & 100% Data Coverage

08:13 Beyond Red Flags: Business Insights & Green Flags

11:14 The Limitations of Lexicons & Fuzzy Models

13:44 The Future Without Lexicons

15:22 Human in the Loop: Why It’s Not Going Anywhere

20:09 AGI: A Goal or a Distraction?

23:39 Big Tech, Valuations & the Trophy Problem

25:59 Apple, Trust & Risk

30:36 The AI Hype Cycle & ROI Reality

33:05 Radiologists, Tasks & Human Judgment

34:57 Chat Interfaces vs Better UX

40:31 When AI Gets Things Wrong 

44:58 The Future of Dashboards

47:29 AI in Small Doses