Beyond the Prompt: Training OSS Masters When AI Takes over the Basics

“In vain do we build the city if we do not first build the man.”
Edwin Markham

AI will surely run the OSS cities of the future.

AI may even build the OSS cities of the future.

What can we do now to build the OSS masters of the future?

.

We’re already witnessing the dawn of an AI-infused era in OSS. From fault-fix to network designs to code generation to infrastructure provisioning, AI tools increasingly handle the repetitive, predictable and procedural elements of OSS design.

This speed and accessibility of AI are undeniably powerful and alluring. However, if AI is doing the volume of activities, it will cut “apprenticeship opportunities” – limiting learning opportunities for developing the next generation of OSS Masters.

However, AI also risks creating a false sense of completeness and confidence. Relying solely on AI creates a shallow layer of human understanding and an inability to think critically or identify gaps in what the algorithms are telling us.

The more we rely on AI, the more we must train for exceptions and exception-handling.

This is where human expertise is irreplaceable – handling what the AI wasn’t trained on, resolving emergent behaviours and leading in situations where ambiguity prevails.

NONA as a Blueprint: Designing Human-Centric Learning Models

The Network Operations Ninja Academy (NONA) offers a model for human development in AI-enhanced OSS landscapes. It’s not just about technical certification – it’s about cultivating layered mastery across systems thinking, handling “black swan” situations under the stresses of outage situations, unlocking operational insight and strategic reasoning where no precedent exists.

NONA frames learning as a deliberate, purpose-driven journey. It emphasises situational awareness, architectural intuition and the ability to operate across OSS toolset and network / domain diversity. It empowers learners to connect the dots that have never been connected before, not just execute rote instructions.

Without a purpose-driven learning model, skill atrophy is inevitable. If teams only engage with AI as a tool for output, they lose the deeper expertise of OSS Masters today (Masters who are needed to guide, challenge and refine what the AI produces).

.

The Atrophy Risk: How AI Can Undermine Expertise

AI, for all its capabilities, is bounded by its training data. It cannot envision architectures that haven’t been seen before. The most significant breakthroughs – adaptive orchestration strategies, new visualisation paradigms, or black-swan fault mitigation – are born from human intuition, not interpolation. They emerge when individuals connect cross-domain insights, reframe problems and take risks that AI cannot model.

When AI handles the “easy” parts of OSS, the harder parts don’t necessarily get easier – they just get deferred or handed off to today’s Masters. What if there are no Masters tomorrow? As automation takes over foundational tasks, people can forget how those systems really work. The result? Engineers who can deploy rapidly but struggle to explain, adapt, or troubleshoot under pressure.

This creeping erosion of expertise often goes unnoticed until something breaks – or until a truly novel / black-swan situations emerges. We don’t want there to ever be a gap between what teams understand and their ability to decipher the new challenges that arise. The video in the tweet below provides a humourous example:

https://x.com/javilopen/status/1920909544198201552

Naturally, we don’t want tomorrow’s OSS people to be wasting their time on the repetitive, known events  – the noise. A new model of learning is required. It requires deeper skill cultivation and training how to make the right decisions in the unknown, using models like NONA.

Real innovation happens beyond the training data. And that’s where we must focus our energy: building OSS masters who think, question and create at (or beyond?) the edges.

Edwin Markham was right. The foundation of any great system is the spirit and understanding of the person who shaped it. If we want our OSS “cities” to endure, we must start by building the people who have the knowledge to guide them.