I Crashed an MBA Class to Talk AI. Here's What They Needed to Hear.

Dec 2

Elijah recaps a talk he gave to University of Utah MBA students on how AI is reshaping work and why most professionals are still underusing it. He walks through the evolution from rule-based systems to today’s generative models, clarifies the difference between narrow AI and AGI, and explains singularity-style futures without pretending anyone knows the timeline. He contrasts media skepticism and bubble talk with hard data, like autonomous vehicle safety and Amazon’s AI powered recommendations. Then he gets practical, sharing a pyramid for adoption, the “clerks, colleagues, coaches” model of AI at work, and a roadmap for governance, safe experimentation, and turning AI from time saver into revenue driver.

AI at Work — I Crashed an MBA Class to Talk AI. Here's What They Needed to Hear.

AI at Work Podcast — I Crashed an MBA Class to Talk AI. Here's What They Needed to Hear. with Elijah Szasz

Takeaways

Most professionals, even in MBA programs, are barely using AI, which creates a huge edge for anyone who builds literacy now.
Modern AI is narrow but rapidly broadening, and while AGI timelines are uncertain, capability curves are clearly exponential, not linear.
Skepticism about bubbles and hallucinations is valid, yet many real world systems, like autonomous driving and fraud detection, are already outperforming humans.
The fastest wins come from automating tasks you dislike, then moving up the pyramid to work AI simply cannot do well and finally to revenue generating personalization.
Sustainable adoption requires an AI manifesto, governance, role specific training, clear ownership, sandboxes for experimentation, and a focus on AI as clerk, colleague, and coach rather than human replacement.

Chapters

00:00 Introduction to AI in Education

02:51 The Evolution of AI: From Rule-Based to Generative

06:05 Understanding Narrow AI vs. AGI

08:41 The Turing Test and Its Implications

11:33 The Singularity: Predictions and Possibilities

14:27 Current Applications of AI Across Industries

17:45 Skepticism and Challenges in AI Adoption

20:39 The Future of AI: Opportunities and Limitations

28:51 The Negativity Bias in AI Innovation

33:36 The AI Supercycle: A New Era of Technology

35:55 Generative AI: Capabilities and Limitations