As AI tools race into every corner of software development, a simple question keeps coming back to me. Will AI replace human testers, or will it force us to rethink what great testing looks like in the first place. In today’s conversation, I talk with Santiago Komadina Geffroy, a Software Engineer at Jalasoft and an educator with Jala University, about what changes, what stays, and what teams should do next.
Santiago shares how his day job and teaching intersect. He points to a gap he sees often. Engineers are experimenting with large language models without fully understanding how they work, which leads to overconfidence and avoidable rework. He argues for clearer interaction patterns between tools and people. Think less about magic prompts and more about protocols, context sharing, and agent to agent collaboration. That shift frees testers to do the thinking work that AI still struggles with, from exploratory testing and usability judgment to spotting the weird edge cases that only show up when real humans use real products.
We also get into bias and ethics. AI is only as fair as the data it learns from, and that matters in healthcare, finance, and hiring where a mistake can carry life changing consequences. Santiago calls for stronger education around data quality, authorship, privacy, and environmental impact, not as a side note but as part of how engineers are trained. He believes governance helps teams move faster with fewer regrets when they take AI into production.
Security sits in the mix too. Many AI tools need deep system access. If compromised, they can distort results or leak sensitive information. Santiago is candid about the limits of any single safeguard. He recommends a culture of shared responsibility where engineers understand when to call in security specialists and how to design workflows that keep humans in the loop for consequential decisions.
We close with what Jalasoft has learned from building with AI inside a nearshore model in South America. More thinking time. Smaller, controllable scopes. Clear lines between routine automation and human judgment. The headline is simple. AI will change testing. Human testers will remain at the heart of quality.