Many of my recent posts have been about ethics and Generative Artificial Intelligence, particularly where Large Language Models (LLMs) are concerned. They're about retaining humanity and ethics and personhood in a world that decreasingly values people, particularly when it comes to profit margins and returns on investment.

It might appear that I'm against AI. I am not. I am for respect and dignity and humanity. This post, therefore, will appear to be different from my latest writings.

This post talks about an experiment working with an AI to develop a well-engineered tool that is reliable, maintainable, understandable, and correct. This is about writing something that's boring. Beyond the Principle of Least Surprise, my goal was to make this feel quiet and stable and honest.

This is not a demonstration of programming wizardry, excellence in Bash development, or fanciness. I'm not here to show any of that. I have a passion for Bash and script development because shell scripts are often seen as a list of commands to be executed in order of appearance. Bash is not associated with serious development. In fact, when I've taught classes on Bash development to senior, experienced engineers, the most common reaction is that they had no idea Bash could do the things I was demonstrating.

So, this is about my journey in building a tool that I needed using a language that I enjoy with the principles I use to shape my professional practices.

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This is a three-part series of posts discusses an experiment I undertook. I wanted to try a purely AI-built tool that adhered to strong engineering standards and a documentation-first approach to development.

Posts in this Series

1. Bootstrap, an engineering exercise (part 1)
2. Bootstrap, an engineering exercise (intro)

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In the first two parts of this series, I explored a question that began in an unexpected place. What initially appeared to be a discussion about artificial intelligence gradually became a discussion about consent. What began as concerns about security, data stewardship, and information governance eventually led to something deeper: the realization that many debates about AI are not really about technology at all. They are about people. More specifically, they are about the obligations that arise when one person benefits from the labor, creativity, likeness, voice, experiences, or identity of another. Once that connection becomes visible, the conversation changes. We are no longer talking merely about data, artifacts, or outputs. We are talking about the people whose lives made those things possible.

Along the way, I found myself returning to a simple but increasingly important observation. Artificial intelligence has a remarkable ability to separate artifacts from the human beings connected to them. A photograph becomes image data, a voice recording becomes a sample, a body of writing becomes a statistical pattern, and a lifetime of experience becomes a dataset. None of these descriptions are technically incorrect. However, as the artifact becomes easier to analyze, store, reproduce, and transform, the person behind it becomes easier to overlook. The central question of consent emerges precisely at that point:

When does our desire to benefit from another person begin to eclipse our obligation to respect them?

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