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.

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