DevSecOps Engineer, Author, and Mentor

Artificial intelligence has made old questions feel newly urgent.

We can now collect, store, analyze, imitate, synthesize, and reproduce human expression at a scale that would have been difficult to imagine only a few years ago. A photograph can become image data. A voice can become a sample. A body of writing can become a statistical pattern. A lifetime of work can become material for a system that learns, imitates, and responds.

The legal questions matter. Who owns the data? Who may train on it? What does a license permit? What do terms of service allow? Where does fair use begin and end?

Yet this series is concerned with a deeper set of questions.

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

What does genuine consent look like when information can be copied, retained, recombined, and transformed indefinitely?

Does public availability imply permission?

Does usefulness create entitlement?

Can a system learn from a person's work without beginning to appropriate the person behind it?

What happens when AI moves from learning from someone to speaking as someone?

These essays explore consent as more than a legal mechanism. Consent is one of the ways we recognize another person's dignity, agency, and right to participate in decisions that affect them. When consent is ignored, bypassed, or reduced to a technical defense, something human begins to disappear.

The question is not merely whether AI can do these things.

It can.

The question is whether our ability to do something relieves us of the responsibility to ask whether we should.

Posts in this Series

1. Consent in the Age of AI (Introduction)

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In my news feed, I recently saw a post from TechCrunch that talked about "tokenmaxxing" and how the use of LLM token usage as a metric for developer productivity wasn't having the desired effect. The article pointed out how various metrics over the years have been varying degrees of ineffective when it came to quantifying how productive developers were being. Other metrics included the number of lines of code (LoC) added to a project, the number of commits pushed to a repository, code acceptance rates, and many more.

The goal with these metrics is to find some way that the value of a developer can be reduced to a number that can be easily compared to other numbers. These numbers can be added to a spreadsheet, tracked over time, turned into graphs, and presented as objective signals of progress.

That approach is understandable, but it's also incomplete.

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I was in a conversation about AI and Leadership recently and a number of themes were common while some were less so.

We were discussing someone who was trying to position themselves as a thought leader by regularly posting articles that showed all of the hallmarks of having been written by AI.

Using AI is fine. I have no problem with people who use AI. That's not my problem. My problem is when someone posts, publishes, disseminates, etc. a piece of content and calls it their own when they did not, in fact, craft it. My problem is when the responsibility for human judgment is abdicated in preference for expedience and speed. My problem is when fear and a desire for comfort edges out creativity and risk.

In my mind, these are not the traits of a leader.

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