Your ethics are your ethics.

The line hit me hard. I had been describing how I use AI in my work, the actual mechanics of it, what I hand off and what I keep, where the tool helps and where it gets in the way. And this was the response. It was ambiguous, foreboding, confusing.

Why is my use of AI being framed as an ethical position in the first place? Not a methodological choice, not a workflow decision, not a question of craft. An ethical one. With the implication, sitting just under the surface, that a verdict is pending and the wrong answer, whatever that is, has consequences.

Thus, I felt compelled to write. Because I think this quote explains why fields are stalling out while the world accelerates away from them.

When confronted by AI, many see the road diverging into two paths. Ban it or delegate to it.

Ban it because the technology is unreliable, or threatens the craft, or produces work that isn’t really yours. And underneath those reasons, often, an inherent resistance to change. That isn’t the way we did it in my day. We must preserve what made the field worth practicing in the first place.

Delegate to it. Hit the “easy button.” Paste in the prompt and accept the output. This is now called cognitive offloading, when the thinking gets outsourced.

For many, the choice really is binary, and the AI conversation aligns with these two paths, as if those were the only directions available.

My own field faced this challenge when ChatGPT arrived in late 2022.

Three years later, most institutions still do not have a coherent position. Some ban, some allow, some leave it to individual instructors, some mandate disclosure without defining what disclosure means. But students kept using the tool throughout, because the tool kept getting better and the work kept being assignable. What academia did not generally do was figure out what good practice looks like.

But there is a third option: AI as a collaborator.

You may ask, “What does this even mean? How do you collaborate with a tool?”

The answer is amazingly simple. Just like any craftsman, you experiment, you learn, you refine, you improve, until one day the tool is a seamless part of your workflow.

This option rarely appears in most conversations about AI because it is harder and more nebulous than the first two. It is not a position you can declare. It is a practice, and practices are harder to talk about than postures.

But it works, just look at software development.

Coders were among the first to feel real pressure from AI, and they were among the first to figure out how to work with it. The model that has emerged is orchestration. The developer no longer types every line, but they also don’t look for an easy button. They specify what needs to be done, delegate tasks to the AI, monitor what is built, review the results, and move on to the next piece.

The skill is not just coding in the old sense. It is knowing what to ask for, knowing what good looks like when it arrives, and knowing when to throw something out and try again. The developer’s judgment is doing more work than ever, and they do more with less typing.

This is craftsmanship. It is what a working path forward looks like when a field starts to figure out how to collaborate with the tool.

This is also how I work, and how I teach.

AI is part of nearly everything I do. The model is a thinking partner, not a ghostwriter. It pushes back, it gets things wrong, it surfaces angles I had not considered, and our back-and-forth is where the work actually happens. What I publish is mine. But it results through collaboration.

This is what I teach students. We don’t ban the tool and pretend it isn’t there. We also don’t hand the assignment over and accept whatever comes back. Instead, they use this tool the way a professional in their field is expected to use it now. The pedagogy I have arrived at is straightforward: work with AI, critically evaluate what it produces, and form your own insight. The skills that matter are the ones that remain when the tool changes. And it will.

I see journalism following the same binary paths. Wired published a near-blanket ban⊕Wired’s policy bars AI-generated or AI-edited text and AI-generated images, with narrow carve-outs for headline suggestions and idea brainstorming. How WIRED Will Use Generative AI Tools, by Gideon Lichfield. on AI-generated text in stories, with carve-outs for things like headline brainstorming and story idea generation.

At the other end, outlets with thinner resources have leaned in hard, publishing AI-generated articles with minimal review (what is derogatorily called “AI slop”). The worst offenders have handed the ban-it crowd exactly the evidence they sought.

In between them sits the disclosure regime. Outlets and institutions mandate that AI use be declared, but no one has defined what exactly “AI was used” actually means. Used to research? To draft? To check a fact? The vagueness becomes a loophole. People can skip disclosures or shade them, and feel fine doing so, because they are ambiguous.

Which brings us back to the starting line: Your ethics are your ethics.

When scared about job security, people look for moats. A defensible position that protects the work, the field, the profession. If they can’t find one, fear will often drive them to invent one.

I see the ethics framing as one such invention. The argument runs: AI use is an ethical question, my answer is to limit it, and if everyone agreed with my answer the field would be protected. Ban AI in journalism and journalism stays journalism. Ban it in the classroom and the degree still means something. We have a moat.

But that moat only works if everyone honors it. And the moment you invoke individual ethics, you open the door to them making different decisions. Some will tell a white lie on disclosures. Some institutions will carve out exceptions. Many will skip disclosure entirely because no one has settled on what disclosure means. The imaginary moat dissolves the moment anyone steps across it, and people are stepping across it constantly.

What is left is the field, undefended, with practitioners who refused to engage with the tool now competing against practitioners who did.

This is the pattern I keep seeing. Professions whose job is to interpret the world for everyone else flinching at the moment they should be leaning in. Treating the most important tool of their working lives as an ethical hazard rather than a skill to develop. Building moats out of moral language and watching the moats dissolve.

Your ethics are your ethics. Fine, I get it. But the corollary is your field is your field. And the question is not whether you approve, but whether you cling to the past or lead the way forward.

This article was developed with AI assistance for research, outlining, drafting, and editing. All ideas, experiences, and perspectives are my own.