Artificial intelligence is rapidly changing how software is built, customized, and deployed. In this episode of All in on Data Pod, hosts Peter Memon and David Gleason explore the growing debate around the so-called “SaaS-pocalypse”—the theory that AI coding tools could replace software-as-a-service platforms.

The conversation breaks down the fundamentals of SaaS (software as a service), why it became the dominant software model, and what new AI development tools mean for the future of enterprise software. With generative AI accelerating coding productivity, some companies are experimenting with building their own internal tools instead of paying for SaaS subscriptions.

But does that mean the end of SaaS?

Peter and David explain why AI-generated software is powerful but still requires deep domain knowledge, security expertise, and operational infrastructure. They also discuss which types of SaaS tools may be most vulnerable to replacement, how AI is changing the economics of software development, and what SaaS companies must do to stay competitive in an AI-driven world.

If you're interested in AI software development, SaaS platforms, enterprise technology strategy, or the future of software engineering, this episode provides a thoughtful and balanced perspective.

Chapters:
00:00:00 Introduction and recent AI news
00:04:30 What “SaaS-pocalypse” actually means
00:09:15 What software-as-a-service is and why it became dominant
00:15:40 The pros and cons of SaaS platforms
00:21:30 How AI code generation is changing software development
00:30:20 Can companies replace SaaS with AI-built software?
00:39:10 The hidden costs of building your own applications
00:48:45 Which SaaS tools are most vulnerable to AI replacement
00:57:30 AI layoffs and the risk of losing institutional knowledge
01:05:30 The future of SaaS in an AI-driven world

About All In on Data:
We’re a full-service Data and AI consulting firm helping companies align strategy with scalable solutions. From roadmap to implementation, we’re your trusted partner for ethical, production-ready AI.

Follow Us for More Content:
David Gleason's LinkedIn: https://linkedin.com/in/david-r-gleason
Peter Memon's LinkedIn: https://linkedin.com/in/petermemon
AIoD LinkedIn: https://www.linkedin.com/company/all-in-on-data
Spotify: http://bit.ly/44BhxGs
Apple Podcast: http://bit.ly/3GHBe6i

Why does generative AI feel so threatening to workers?

In Episode 51 of All in on Data Pod, hosts Peter Memon and David Gleason break down a recent Harvard Business Review article titled Why Gen AI Feels So Threatening to Workers and explore the deeper psychological and organizational forces behind AI fear.

They examine the three core psychological needs affected by AI adoption — competence, autonomy, and relatedness — and explain why employees are feeling overwhelmed as AI transforms both white-collar and technical roles. But the problem isn’t just the technology.

This episode dives into AI leadership failures, poor communication, misaligned incentives, culture breakdowns, and the dangerous knowledge gap forming between AI-native junior employees and experienced domain experts. Peter and David argue that successful AI transformation requires education, transparency, business-led process redesign, and thoughtful incentive alignment — not just tool deployment.

They also discuss real-world AI failures, including AI agents deleting production code and inbox data, highlighting the risks of uncontrolled experimentation.

If you’re a business leader, technologist, or employee navigating AI-driven change, this episode offers practical insight into AI adoption strategy, workforce psychology, and organizational transformation.

In Episode 50 of All in on Data Pod, David Gleason and Peter Memon tackle one of the most overlooked challenges in artificial intelligence: how workers develop judgment in the AI era. As AI automates repetitive tasks, companies may unintentionally eliminate the very experiences that build critical thinking, leadership skills, and decision-making capability. The hosts explore the limitations of human-in-the-loop AI oversight, the long-term risks to talent pipelines, and why education must refocus on systems thinking, logic, history, science, and creativity. They also discuss quantum computing predictions and the growing threat of deepfakes eroding public trust. If you're interested in AI workforce strategy, AI governance, critical thinking skills, or the future of leadership in an AI-driven economy, this episode delivers deep insight.

Chapters:
00:00:00 Episode 50 Milestone & Reflection
00:05:06 Introducing Judgment in the AI Era
00:07:20 The AI Paradox: Automation vs Judgment
00:09:31 Human-in-the-Loop Failure Modes
00:12:41 Why Grunt Work Builds Real Expertise
00:21:26 The Leadership Pipeline Risk
00:23:52 Creation vs Evaluation: How Thinking Changes
00:35:35 What It Actually Means to “Think”
00:39:24 Designing a Curriculum for Critical Thinking
00:55:15 Lightning Round: Quantum Computing & Deepfakes

About All In on Data:
We’re a full-service Data and AI consulting firm helping companies align strategy with scalable solutions. From roadmap to implementation, we’re your trusted partner for ethical, production-ready AI.

Follow Us for More Content:
David Gleason's LinkedIn: https://linkedin.com/in/david-r-gleason
Peter Memon's LinkedIn: https://linkedin.com/in/petermemon
AIoD LinkedIn: https://www.linkedin.com/company/all-in-on-data
Spotify: http://bit.ly/44BhxGs
Apple Podcast: http://bit.ly/3GHBe6i