TestGuild Automation Podcast

Everyone is talking about AI replacing testers, writing tests, and transforming software quality. But what if we're asking the wrong question?
In this solo episode, Joe Colantonio shares a growing concern he's seen while traveling across the country for TestGuild IRL events: a decline in testing fundamentals at the exact moment AI hype is reaching a fever pitch.
Drawing insights from Carissa Véliz's book Prophecy: Prediction, Power, and the Fight for the Future, Wayne Roseberry's work on AI and meaning, and Tariq King's concept of Human Experience Testing, Joe explores why AI systems may be far less intelligent than many believe, and why human testers remain more important than ever.
You'll discover:
✅ Why large language models generate plausible answers without understanding truth
✅ The difference between prediction, correlation, and genuine understanding
✅ Why AI can test software but cannot experience software
✅ What "Everything is tested, but nothing is experienced" really means
✅ How AI hype may be distracting teams from critical testing fundamentals
✅ Why empathy, context, and human judgment are becoming competitive advantages for testers
Whether you're excited about AI, skeptical of it, or somewhere in between, this episode will challenge you to think more deeply about the future of testing and your role in it.
Resources Mentioned 📖 Prophecy: Prediction, Power, and the Fight for the Future by Carissa Véliz 📖 Work and presentations by Wayne Roseberry 🎓 Free course: thebullshitmachines.com 🎤 Learn more about TestGuild IRL events: TestGuild.com/irl
If you enjoy this episode, be sure to subscribe, leave a review, and share it with a fellow tester who's trying to navigate the AI era without losing sight of the fundamentals.
#SoftwareTesting #AI #QualityEngineering #TestAutomation #SoftwareQuality #HumanExperienceTesting #ArtificialIntelligence #TestGuild #QA #TechPodcast

AI coding tools promised to make development faster — and they delivered. But here's the problem nobody talks about enough: when you speed up coding, you don't eliminate the bottleneck in the SDLC. You just move it. And for most teams, it lands squarely in QA.
In this episode, Joe sits down with Vilhelm von Ehrenheim, Co-founder and Chief AI Officer of QA.tech, to dig into how agentic AI is reshaping software testing from the ground up. Vilhelm brings serious ML credibility, he helped build Motherbrain, one of the earliest production LLM systems in venture capital, and he's now applying that experience to one of the hardest problems in software delivery: testing at AI development velocity.
You'll learn how QA.tech's behavioral knowledge graph gives AI agents the context they need to actually understand your application, why validating user intent beats checking element identifiers every time, how autonomous agents can review PRs, reproduce bugs from Slack messages, and generate targeted tests without a single line of test code ,and what the tester's role actually looks like when agents do the heavy lifting.
If you're wondering whether your QA practice can survive the pace of AI-driven development, this one's required listening.
🔗 Book a demo now: https://testgld.link/qatechdemo

What happens when AI agents can not only write mobile app code, but also validate their own work automatically?
In this episode, I sit down with Maestro Co-founder and CEO Leland Takamine to explore one of the biggest shifts happening in software testing right now: agentic mobile testing.
Leland shares how his team went from solving mobile performance testing challenges to building one of the fastest-growing mobile automation frameworks used by companies like Microsoft, Meta, Amazon, and DoorDash.
We dive deep into:
How AI coding agents are changing mobile testing workflows
What "closing the agentic feedback loop" actually means
Why deterministic testing still matters in the age of AI
How Maestro MCP lets AI agents validate mobile app changes automatically
Why mobile test maintenance may finally become manageable
The future role of testers as AI-generated code explodes
Leland also gives a live demo showing an AI agent building, validating, debugging, and generating a reusable mobile test completely autonomously.
If you care about AI testing, mobile automation, MCP servers, or the future of QA engineering, this episode will likely change how you think about testing workflows over the next few years.
Try it out now for yourself: 
Maestro Studio: https://testgld.link/mstudio
Maestro MCP docs: https://testgld.link/maestromcp

AI-powered testing tools are exploding across software engineering teams… but so are the hidden costs.
In this episode, Joe sits down with Arthur Hicken to unpack the growing problem of runaway AI token usage, unexpected LLM billing, and the operational risks of deploying AI agents into testing and DevOps pipelines. Inspired by Arthur's article on the emerging "Token Tax," this conversation explores why many teams are underestimating the true cost of AI automation.
You'll learn:
Why AI-generated testing can create unexpected scaling costs
How runaway AI agents and infinite loops happen
Real-world examples of massive AI billing surprises
Why deterministic problems shouldn't always use LLMs
The hidden risks of "vibe testing" and autonomous AI remediation
How QA teams can monitor, test, and control token usage
Why performance testing and service virtualization matter more than ever in AI systems
Practical strategies to avoid expensive AI deployment mistakes
Whether you're a software tester, automation engineer, QA leader, or DevOps practitioner, this episode will help you think more strategically about AI testing before costs spiral out of control.

AI-powered testing tools promise faster automation and less maintenance, but most require teams to abandon their existing frameworks.
In this episode, we explore Alumnium, an opensource AI-native end-to-end testing solution created by Alex Rodionov, an engineer at Airbnb and a tech lead on the Selenium project.
Instead of replacing tools like Playwright or Selenium, Alumnium adds an AI layer on top, helping teams:
Reduce test maintenance by removing brittle locators
Build more resilient, self-healing tests
Write less code while increasing coverage
Run tests across web and mobile using intent-based steps
We also go beyond the hype and break down what actually matters for real teams:
Why AI-driven tests can still become flaky
The performance and cost tradeoffs of LLM-based execution
What "context rot" is—and how it impacts long test runs
How to safely introduce AI into existing test suites without rewriting everything
Check it out now: https://testguild.me/alumAI