The pitch for AI in the workplace usually sounds like a trade: hand the task to a faster tool, and the work gets done. The part that gets skipped is who carries the result when the work goes wrong. Chris Coyier makes the point plainly. You cannot ask Claude to own the outcome. You need Jeremy.

That line separates two things companies routinely confuse. One is producing work. The other is being responsible for it. An AI assistant can do a large share of the first. Only a person can do the second.

Coyier's framing works because it names the actual problem. Claude stands in for every AI tool that generates output. Jeremy stands in for the human whose name is on the decision. A person owns the result of every contract that gets signed and every release that gets approved. That person absorbs the risk and makes the judgment calls. The same person answers when something fails. AI tools do not change that arrangement. They change how quickly work gets produced, not who is accountable for it.

The distinction matters more as AI tools get better at producing credible work. A draft that looks finished invites less scrutiny. A model that writes a confident proposal or a clean piece of code gives reviewers less reason to dig in. The danger is that responsibility becomes diffuse, spread across a tool and a team until no single person feels the weight of the result. Naming a Jeremy is the fix for that.

Why a model cannot own an outcome

Ownership means being the person who can explain and defend the work when it is questioned. A model produces a result and moves on. It has no reputation at stake and no grasp of the context beyond what it was given. The people affected by a bad call want the person who made that call, not a log of the model's output.

That becomes obvious in the moments that define critical work. A customer reports a failure and wants to know what happened. A regulator asks why a particular decision was made. A colleague needs a judgment call that depends on knowledge from outside the prompt and the history of the project. None of that lives in the model. All of it lives in Jeremy. A situation can require weighing consequences and accepting the weight of them. The model cannot do the accepting.

The counterpoint: capability and trust

The natural objection is that AI tools are already reliable, and human oversight is fallible too. Both points are true and beside the point. The argument concerns the structure of accountability, not error rates. Even a model with a flawless track record has no stake in the outcome. Jeremy has a stake in it. His reputation and his job depend on the result. The people who rely on him depend on his judgment. That is what makes his judgment different from a model's output.

There is also a question of what ownership does to the work itself. A person who knows they will answer for the outcome behaves differently. They ask harder questions and check assumptions. They push back when something looks off. None of that happens when the deliverable belongs to no one. Accountability is the mechanism that forces careful work. Remove it, and the careful work tends to leave with it.

A practical rule for teams

The practical rule is simple: assign a named person to every critical deliverable, even one produced with heavy AI assistance. The human does not have to write every word or check every line. The human has to review the result and accept it as their own. That acceptance is the moment responsibility transfers from a tool to a person.

This rule does not slow things down as much as it appears. Reviewing a draft is faster than producing it. The rule exists to keep the output inside a system that ends with a human signature. Companies that skip this step save a little time on the front end and spend it on the back end, when someone has to untangle a failure without a clear owner. The untangling is always slower and more expensive than the review would have been.

Coyier's phrasing also points at a useful habit. Say the name out loud. Jeremy deploys the thing, and Jeremy answers for it. Teams that talk about work in terms of tools keep responsibility vague. Teams that talk in terms of people keep the chain of accountability visible.

Keep the responsibility human

The conversation about AI in the workplace keeps circling the same question: what can this tool do? The more useful question is whether the work has a person who can be asked to explain and defend the outcome. For critical work, the only safe arrangement is one with a named human in charge. An AI assistant can produce the work. It cannot own it.

Coyier's point is a reminder that capability and accountability are separate. The companies that handle AI well keep the responsibility human. You need Jeremy, because Jeremy can be held to account. That is the whole difference.