Brian Teller
Founder of Teller’s Tech, staff-level DevOps engineer, and podcast host
Greencastle, Pennsylvania, United States
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Brian Teller is the founder of Teller’s Tech and host of Ship It Weekly, a podcast for practitioners working in DevOps, SRE, platform engineering, and cloud. He brings real-world experience from staff-level infrastructure work across Terraform, Kubernetes, AWS, GCP, Kafka, CI/CD, incident response, and operational guardrails. Brian is also a DevOps Institute Ambassador and ITIL Ambassador, and much of his work centers on helping engineers cut through hype, think critically, and make better decisions in production environments.
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The AI-Generated Terraform Plan Looked Fine
AI is getting good enough to generate Terraform, Kubernetes manifests, CI/CD workflows, and cloud configuration that looks reasonable at first glance. That is exactly what makes it risky.
This lightning talk walks through a simple but dangerous pattern: an AI-assisted infrastructure change that appears clean in a pull request, passes a quick human skim, and still carries bad assumptions into production. The issue usually is not that the AI wrote obviously broken code. The issue is that it produced something plausible enough that the reviewer stopped asking the right questions.
In five minutes, I’ll cover the review habits I now treat as non-negotiable for AI-assisted infrastructure work: checking intent against the actual plan, looking for hidden blast radius, validating provider behavior, separating generated convenience from operational ownership, and making sure policy guardrails catch what humans miss.
This is not an anti-AI talk. It is a practical reminder that infrastructure code is not just text. It changes real systems, real permissions, real network paths, and real production behavior. AI can help you move faster, but the Terraform plan still needs an adult in the room.
Fast, Helpful, and Wrong: Critical Thinking for Shipping Software with AI
AI can write code, summarize incidents, generate Terraform, and sound incredibly confident while being completely wrong. For engineering teams, that creates a new kind of risk: the better the output sounds, the easier it is to stop thinking critically.
This talk is about what critical thinking looks like when AI becomes part of software delivery. I’ll break down where AI actually helps in platform, cloud, and DevOps work, where it tends to fail in dangerous ways, and how those failure modes show up in practice. That includes hallucinated infrastructure changes, shallow root cause analysis, bad assumptions hidden inside generated code, and overconfidence in summaries that skip key context.
I’ll also cover practical ways teams can use AI without outsourcing judgment: source verification, review loops, better prompting, sandboxing, policy guardrails, and clear ownership when AI is involved in production-facing work.
This is not a talk about whether AI is good or bad. It is a practitioner-focused look at how experienced engineers can use it productively without letting speed replace thinking. Attendees will leave with a framework they can use immediately to make AI-assisted engineering safer, sharper, and more useful.
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