The world of software development is rapidly changing, driven by "Shift Left" methodologies and the powerful influence of Artificial Intelligence. Are traditional software testers becoming obsolete? In this episode, we explore the provocative idea that the core job of a software tester is indeed on a path to extinction, as developers and analysts, empowered by AI, increasingly take on testing responsibilities.

Join us as we unpack:

• The "Shift Left" imperative: how moving testing earlier in the SDLC has reshaped team dynamics and why companies like Yahoo and Microsoft began moving away from traditional QA teams years ago.
• AI as the ultimate accelerant: how generative AI is revolutionizing test case generation, enabling self-healing tests, and driving predictive defect detection, leading to unprecedented efficiency.
• Real-world examples: discover how platforms like Functionize, SQAI Suite, and Momentic.ai are enabling organizations to operate with minimal or no dedicated QA teams, shifting quality ownership across the board.
• The evolution, not extinction, of the tester: learn about the rise of the Software Development Engineer in Test (SDET) and the new, highly technical profile of modern QA professionals.
• The indispensable human element: despite AI's advancements, we discuss its inherent limitations, particularly with subtle edge cases, creativity, and human judgment, underscoring why human expertise remains crucial.

Tune in to understand the profound transformation of the software quality landscape and what it means for the future of testing careers.

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In this episode, we examine how Meta's autonomous testing agent, Sapienz, is redefining software testing strategy and rendering the classic Testing Pyramid increasingly irrelevant. Once a foundational model in agile and DevOps environments, the pyramid prioritized a heavy reliance on unit tests with fewer integration and end-to-end tests. That hierarchy no longer reflects the capabilities or constraints of modern AI-powered testing.

We explore the academic rationale behind the original model and analyze how tools like Sapienz, SQAI Suite, Applitools, Mabl, Functionize, and WireMock are shifting testing priorities. These platforms use intelligent test generation, self-healing automation, visual regression analysis, and knowledge graphs to reshape the economics and logic of quality assurance.

We also discuss the trade-offs of moving away from unit test-centric strategies, and how organizations can build scalable, resilient, and AI-augmented test architectures that adapt to change and complexity.

This episode is for QA professionals, test architects, engineering leads, and anyone involved in modern software delivery who wants to understand the future of testing beyond the pyramid.

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In this episode, we dive deep into how cutting-edge AI techniques — Self-RAG (Self-Reflective Retrieval-Augmented Generation) and Agentic RAG — are fundamentally transforming the software testing landscape. From increasing test coverage by 40% at Goldman Sachs to enabling autonomous exploratory agents that simulate users in real-time, AI is no longer a futuristic idea in QA — it’s here and it's powerful.

Join us as we explore:

• What RAG, Self-RAG, and Agentic RAG really are
• How they reduce testing time, cost, and maintenance effort
• Real-world results from companies like Goldman Sachs, Swisscom, and Vodafone
• Where AI-driven QA is heading next
• Why your current testing strategy might already be obsolete

Whether you're a QA professional, test manager, or curious technologist, this episode delivers essential insights into the tools and strategies defining the future of quality engineering.

🎧 Tune in to find out if your testing strategy is ready for what’s next.

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In this episode, we explore how artificial intelligence is fundamentally reshaping the software testing landscape. We start with Shopify’s 2025 directive requiring teams to prove that AI cannot handle a task before hiring a new employee. We then examine how Siemens Digital Industries Software is embedding AI into its Tessent testing tools, using analytical, predictive, and generative AI to optimize chip and software testing.

Topics covered include the current pace of AI adoption in testing, practical examples of AI-powered quality engineering, and the skills QA professionals will need in the coming years. Whether you are a software engineer, QA lead, or tech executive, this episode offers actionable insights into the future of AI-augmented software testing.

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AI is changing everything, including the world of software testing. In this episode, we break down the real impact of AI on testing careers: Will it eliminate jobs or create better ones? We explore global trends, short- and long-term forecasts, emerging QA roles, essential new skills, and expert insights from WEF, Gartner, McKinsey, and more. Whether you're a manual tester, SDET, or team lead, this one's for you.

🔍 Learn how to future-proof your testing career and thrive in the age of AI.

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In this episode, we dive deep into one of the most provocative questions in the world of digital transformation: can AI-powered digital twins ever replicate the mind of a human software tester?

Digital twins have transformed industries like aerospace, manufacturing, and healthcare. Now, the software world is asking whether we can virtualize not only the systems under test but the testers themselves. But behind the hype lies a set of cognitive, ethical, and technical challenges that most conversations avoid.

From the promise of predictive QA environments to the failures of autonomous testing agents, this episode explores:

• What digital twins are and how they’re being applied in software testing
• Why replicating human intuition, doubt, and risk awareness is so difficult
• Lessons from other industries that have implemented digital twin technology
• The cognitive blind spots AI still can’t see
• Real-world data points, ethical dilemmas, and a grounded vision for the future

If you work in quality assurance, AI, software development, or digital strategy, this episode will challenge your assumptions and offer a fresh lens on where digital twins truly fit in—and where they do not.

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In this episode of Dean Does QA, I walk through the research and analysis behind my latest article on the dismantling of the SaaS model. From AI copilots to fully autonomous operators, I break down the three phases of this transformation, how traditional SaaS metrics are becoming misleading, and why the next generation of software may not look like software at all.

We explore:

• How AI agents are bypassing user interfaces
• Why workflows and expertise are being unbundled from software
• The implications for pricing, APIs, and product design
• Startup opportunities in vertical AI and agent orchestration
• How SaaS incumbents can pivot before the ground disappears beneath them

Whether you are a founder, product leader, investor, or tester, this is not a trend to watch. It is a paradigm to respond to.

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Are generative AI’s skeptics right to question its cost, energy use, and unclear business model? In this episode, we dig into the historical parallels between today’s AI challenges and the rocky beginnings of railroads, electricity, and the internet. We examine how previous breakthrough technologies also faced hype, bubbles, and early doubts before reshaping entire industries. If you are curious whether generative AI’s current setbacks are a sign of failure or a natural phase in a larger transformation, this discussion is for you.

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