Kevin Surace on The Future of Generative AI and QA Testing | Ep 270 | DevReady Podcast

In this episode of the DevReady Podcast, host Anthony Sapountzis is joined by Kevin Surace, CEO and CTO of Appvance.ai and one of the original pioneers of voice AI and virtual assistants. Kevin’s work dates back to the early days of AI driven speech interfaces, and his career spans innovations in semiconductors, aerospace, building materials, cybersecurity, and generative AI. Together, Anthony and Kevin unpack how generative AI is reshaping the software development lifecycle, especially enterprise QA testing, and why AI literacy has become a defining advantage for developers and teams.

Kevin begins by reflecting on his early role in building voice AI long before it became mainstream, and on how inventions can create unexpected ripple effects, including job displacement in customer support. He frames this not as a reason to slow innovation, but as a reminder that technology must be developed responsibly and used thoughtfully. Drawing on experience across semiconductors, aerospace, building materials, cybersecurity, and AI, Kevin positions curiosity and problem solving as the through line of his career. That mindset now drives AI-Driven Autonomous Software Testing Tools | Appvance ’s mission to automate end to end testing against business requirements, tackling one of the most expensive and disliked bottlenecks in modern software delivery.

A central theme of the conversation is the hidden scale and cost of enterprise QA. Kevin explains that most organisations test only a small fraction of real user flows, often around 10 percent, because thorough coverage is too slow and costly for human teams. The result is that customers regularly uncover bugs in common scenarios that were never validated across the many states of complex applications. Appvance’s AI script generation tackles this gap by producing thousands of meaningful tests in hours and identifying the vast majority of defects, which Kevin argues will soon make AI the dominant force in regression and end to end testing. They also discuss resistance inside organisations, where fear of change can lead to quiet sabotage of AI tools, echoing the historical backlash against automation.

From there, Anthony and Kevin broaden the lens to AI adoption across industries and business models. They note rising client scrutiny around pricing when AI is used, using the Deloitte Australia fake citation incident as a cautionary tale about choosing the wrong model and skipping basic human verification. Kevin stresses that AI value comes from pairing the right tool with expert oversight and points out that some models are far better than others at tasks like citation accuracy. He predicts that AI will keep pushing costs down towards near zero, making hourly labour based outsourcing models increasingly untenable, especially in QA and customer support. Appvance’s use of digital twins, instant simulation environments that generate scripts at machine speed before validating on real systems, is presented as a practical example of where autonomous testing is heading.

The conversation closes on a pragmatic and motivational note about skills, productivity, and the future of work. Kevin argues that AI is not replacing good developers so much as accelerating what they already do, like adapting open-source solutions, and that the real differentiator is how well you can direct AI with clear context and outcomes. He cites productivity gains of around 55 percent for developers who embrace these tools and warns that entry level roles are shrinking unless graduates are genuinely GenAI literate. Anthony agrees, highlighting the lag in education and the risk of training people on outdated workflows. Their shared message is simple: AI will not take your job, but someone who uses AI expertly will, and the best way forward is consistent, curious, hands-on adoption.

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