TL;DR: Training data taken without consent, labor displacement, environmental cost and power concentrated in a few companies are all legitimate reasons people give for refusing AI. Refusing the technology entirely doesn’t follow from any of them, and the refusal usually has no effect on the underlying problems.
I take the ethical objections to AI seriously, and some of them are fully justified on the creators’ behalf. Writers had their work scraped without anyone asking them, and nobody should wave that away. What I can’t get behind is the idea that one professional refusing the tools fixes any of it. Refusal feels like a moral act. In practice it mostly removes the person with the conscience from the conversation that decides how people use this technology.
The ethical concerns, taken seriously
I want to engage with the ethical doubter position on its strongest version. The strong version goes like this.
AI companies trained their models on text and images scraped without permission from creators who never agreed to the use. The benefits flow to a few large companies. Costs flow to the workers whose labor the companies took and to the workers whose jobs the resulting tools will affect. Environmental cost of training runs is real and concentrated in regions that didn’t choose to host it.
The economic surplus from the technology will compound to a few firms in a few cities. The workers employers are asking to use the tools are the same workers whose displacement the tools are designed to enable.
That argument is coherent and has serious people behind it. I don’t think it’s correct in its strongest form, but the doubter who holds it is engaging with real problems. Where the position goes wrong is the conclusion it draws about what an individual professional should do with that diagnosis.
I don’t think it’s correct in its strongest form, but I think the doubter who holds it’s engaging with real problems instead of imagining them.Share on X
What does individual AI refusal accomplish?
An individual professional refusing to use AI changes the trajectory of the technology approximately zero. The training has happened.
Models exist. Companies that built them have capitalization beyond what individual customer decisions can affect. The labor displacement concerns work at policy and labor-organizing levels. The environmental concerns are addressable through energy policy and data center regulation. Your refusal is a personal stance, which has its own value, but it’s not a lever on the underlying problems.
The closest analogy is quitting social media because of what it does to democracy and mental health.
Those concerns are legitimate, and quitting has real personal benefits for many people. It doesn’t change the platforms, the algorithms, the spread of misinformation or the effects on civic life. Individual refusal is a coherent personal practice. Treating it as a strategy for changing the technology buys moral satisfaction and leaves the problem exactly where it was.
Is AI training data an ethical problem?
The training data complaint is the most specific and legally fluid of the ethical concerns.
Writers had their work scraped without anyone asking them, and nobody should wave that away.
Courts in multiple jurisdictions are working out whether training on copyrighted material without licensing constitutes infringement, and the answers will shape how companies build the next generation of models. The question is open. Individual professionals can have legitimate positions on it. The professional uncomfortable using current models because of how companies trained them is taking a defensible stance. The defensibility doesn’t require pretending the question is settled in either direction.
The complication is that the professional who refuses on training data grounds usually doesn’t refuse other technologies built on similarly contested foundations. Most software stacks include open source components used in ways the original authors didn’t anticipate.
Most professional research depends on aggregating sources in ways the sources didn’t consent to. The internet itself runs on protocols whose creators imagined a different use than what emerged.
I lose patience with the loudest refusers over that inconsistency.
Somebody who types objections on a laptop full of open-source code, looks things up with search engines that indexed everybody’s work without asking, and posts it all to a platform that sells their attention owes readers an explanation of why AI is the line. Maybe there’s a good one. I’d like to hear it said out loud.
Selectively applying the training-data objection to AI but not to the rest of the professional stack is a position that needs to be argued explicitly instead of treated as obvious. A piece on the actual quality problem with AI takes the related concern about output quality seriously without resorting to the categorical-refusal frame.
Will AI displace writing jobs?
The labor concern is the most emotionally weighty and the most variable across professions.
Some workers will lose ground to AI tools. Others will become considerably more productive and capture the gains. Many will see their work change in ways that are neither pure loss nor pure gain. The distribution of those outcomes is uneven, hard to predict, and a real subject of policy debate.
The position that an individual professional should refuse AI because of labor concerns runs into the same problem as the training data version. The professional’s refusal doesn’t preserve the jobs that AI affects. Development continues regardless. The workers most affected are usually not the worker doing the refusing. The refusal is symbolic at best and self-defeating at worst, because the refusing professional sometimes ends up displaced by professionals who did engage with the technology.
That last outcome is the one I’d warn anyone about. The professional who refuses on principle gets no credit for the principle when the work dries up. Clients don’t ask why you’re slower; they hire someone who isn’t. A more useful position is to engage with the technology, develop real working judgment about it, and use that judgment to advocate within your industry and in policy conversations for how the technology should be governed. A piece on AI and the realistic labor picture covers the working version of this engagement.
What is the environmental cost of AI?
The environmental cost of AI training and inference is real and worth tracking. The numbers are also smaller than the rhetoric sometimes suggests, and they’re improving rapidly as hardware efficiency increases and energy mixes shift. Individual professional decisions about whether to use AI don’t meaningfully affect the environmental footprint, because the major commercial deployments of the technology and data center energy policy determine that footprint, and neither of those is downstream of individual customer choices.
The professional who’s concerned about the environmental cost of AI has better levers than personal abstention: supporting policies that require data centers to use clean energy, choosing AI vendors whose disclosure on energy use is transparent, advocating for reporting standards on AI carbon footprints. Those are the lever points where individual professional voice can matter, and the lever points are different from “don’t use the tools.” Individual refusal is a personal practice. The energy mix question is a policy and procurement question.
A worried professional who spends one afternoon pressing a vendor for its energy disclosures does more good than one who spends a year feeling virtuous about abstaining. Only one of those puts pressure on somebody who can change the numbers.
My take
Personal refusal changes nothing about training data, jobs or energy use. Development goes on without you, and the professional who refused gets no credit when the work dries up. Clients don’t ask why you’re slower. They hire someone who isn’t. If the ethics bother you, engage and push where it counts. One afternoon pressing a vendor for its energy disclosures does more than a year of feeling virtuous.
The more useful position for a thoughtful doubter
The thoughtful doubter has another option besides refusal, and the option is more demanding instead of less.
The option is to engage with the technology, develop working judgment about it, and use that judgment to push the industry toward better practice. Professionals who understand AI well enough to evaluate it are the people whose voices matter when companies decide how to deploy it, when policymakers write rules, and when norms get set in industries. The doubter who refuses has no voice in those conversations because they have no working knowledge.
The doubter who engages and disagrees has a voice because they have credibility.
That position is harder than refusal, because it means using a technology you have reservations about.
It’s also the more honest one, because the technology exists, companies will keep developing it, and whatever norms come out of the conversations professionals are having now will govern it. Walking away from those conversations hands the decisions to exactly the companies the doubter distrusts, and I suspect those companies are glad to see the critics leave. If your ethics are serious, stay in the room and make them uncomfortable.
Refusal leaves the ethics unsolved and you less capable. My book The Birth of the Augmented Human is about the path where AI amplifies thinking instead of replacing it.
Frequently Asked Questions
The book on this: The Death of Thinking is 314 pages on what outsourcing judgment costs. That’s the question a thoughtful refusal is really trying to answer.
