The View from Below Deck

Most AI commentary comes from the bridge—executives announcing strategy, analysts tracking markets, consultants offering frameworks. This series comes from a different vantage point: the engine room.

The engine room is where you hear the machinery. Where the gap between what's promised and what's practical becomes tangible. It's not a better vantage point than the bridge—just a different one, with different things visible.

The engine room isn't a better vantage point than the bridge—just a different one, with different things visible.

I've spent my career in data engineering and ML research, watching waves of technology hype come and go. Some delivered. Some didn't. The patterns are recognizable if you've seen a few cycles.

Why This Series Now

There's a lot of excellent AI coverage available—from researchers explaining breakthroughs to executives sharing implementation stories. What I've found harder to find is the middle layer: practical explanations of how these systems work that connect to real decisions about data, governance, and interfaces.

That's the gap this series tries to fill. Not because other perspectives are wrong, but because this one might be useful to people navigating similar questions.

There's a lot of excellent AI coverage. What's harder to find is the middle layer: how these systems work, connected to real decisions about data and governance.

What You'll Find Here

The series covers three areas over thirteen articles:

AI Mechanisms (Articles 1-5): How attention, training, and context actually work. The goal is intuition, not exhaustive technical detail.

The Proprietary Data Paradox (Articles 6-9): Why data strategy is harder than it looks. Knowledge architecture, tacit expertise, interface design.

Forward-Looking Governance (Articles 10-13): Hallucination, effective prompting, what it all adds up to, and why AI readiness is a governance question.

A Note on Tone

I'll share what I've observed and what I think it means. I'll try to be clear about what's well-established versus what's my interpretation. Reasonable people will disagree with some of this—AI is a fast-moving field where even experts have honest disagreements.

My goal isn't to be the definitive voice on these topics. It's to offer a practitioner perspective that might help you form your own views.

The Takeaway

Understanding how something works changes the questions you ask. That's what I'm hoping this series provides—better questions, not final answers.

Worth Considering

What AI decision have you made in the last 90 days based on vendor claims you haven't verified? What would it take to pressure-test those assumptions?