John Samuel

Business Unit Leader @ Seventh State

John Samuel is an experienced technologist and business founder of Seventh State technology company based out of the UK. His interest in computing started in the 80s and still maintains a child like interest in dismantling and putting together things whether that’s technical or business related.

Today technology is reshaping the world across many dimensions and he remains optimistic that this will be for the betterment of society.

When he’s not deep diving into the latest technologies, John enjoys soaking up culture and history on city breaks with his family.

Talk:
How Do You Eliminate a RabbitMQ Engineer? Addressing the Elephant in the Room…

It’s the question nobody in the room is saying out loud: if AI can answer RabbitMQ questions, what exactly are we all here for? As RabbitMQ specialists, we decided to find out.

Seventh State spent a decade keeping other people’s RabbitMQ systems alive. This year we set out to systematically capture, structure, and make callable everything we know. Not as a knowledge base. As infrastructure.

This talk is an honest account of what happened.

We’ll walk through what it actually takes to turn a decade of operational knowledge into something an AI system can reason with, the gap between “we know this” and “this is structured well enough to be useful,” the decisions about where human judgment stays essential, and what we found when we drew the boundary between what a machine can reliably do and what it cannot.

The short answer to the title question: you can’t. But the attempt changes what the engineer is for. And that turns out to be more interesting than the original question.

Key Takeaways:

  • Not reassurance, and not hype. An honest account of what one team found when they stopped avoiding the question and started answering it. You’ll leave with a practical understanding of what it actually takes to turn deep operational knowledge into something AI can reason with, how to identify and structure knowledge assets, where the human-AI boundary belongs in a production context, and what that process reveals about where technical expertise genuinely sits. The goal isn’t to convince you AI is fine. It’s to show that leaning into the disruption, rather than waiting for it to pass, is the more useful response, and what that looks like when you actually do the work.

Target Audience:

  • Engineers feeling the question personally. Team leads and hiring managers weighing up what AI means for headcount. Training managers thinking about where to invest in capability. Anyone with a say in how technical roles evolve, whether they’re sitting in the uncertainty themselves or making decisions that shape it for others. The existential question this talk addresses doesn’t respect job titles. It just lands differently depending on where you’re sitting.