AI is changing how software is built. More can be generated, changed and shipped faster than before. So where does that leave testing and quality engineering, and what do we bring with us as the way we build software continues to change?
In this episode, Chris, Russell and Tara are joined by Richard Hinton to explore what the AI era might mean for people working in testing and quality. Rather than trying to protect the role we have today, the conversation looks at how our skills can evolve alongside the technology and where a testing mindset becomes particularly valuable when the pace of change increases.
There’s plenty that is new, from non-deterministic systems and AI-generated code to agents reviewing the work of other agents. There’s also plenty that feels remarkably familiar. We still need to understand risk. We still need useful feedback. We still need to challenge assumptions, understand what changed, ask whether everyone actually understood the same thing, and make decisions about when something is good enough.
The conversation explores:
- Why speed and scale make critical thinking and fast feedback increasingly important.
- What happens when generating software becomes easier than understanding everything we have generated.
- The temptation to outsource our thinking alongside the more mundane parts of our work.
- Why small changes, experimentation and good engineering practices still matter when AI is doing some of the building.
- Accountability when humans and AI are increasingly involved in the same decisions and outputs.
- Bias, hallucinations and what happens when AI is trained on imperfect human behaviours, practices and data.
- Keeping our own skills sharp enough to question, direct and understand the tools we’re using.
- How testing and quality roles might evolve towards risk, systems thinking and helping teams make sense of increasing amounts of information.
- The importance of connecting intent with outcomes, rather than assuming that faster creation automatically gives us better software.
There’s no call here to stand on the sidelines waiting to say “we told you so”. AI gives us new capabilities, and we are going to experiment, get things wrong, learn and adapt. Testing has plenty to contribute to that process.
Along the way, we also encounter AI reviewing AI, spaghetti monster systems, sunk costs, 99 bugs on the wall, overly agreeable LLMs and the possible need for critical-thinking rehab centres.
The tools are changing. How we apply what we know will need to change with them.
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