AI Can Replicate. It Cannot Reciprocate.
I overheard a debate recently about whether AI-generated art counts as art. The image in question was striking. Technically impressive. The kind of thing that would stop you if you saw it in a gallery. But the people arguing about it were not talking about quality. They were talking about something they could not quite name.
I think I know what it is. And I think it matters for a lot more than art.
What We Are Actually Responding To
The usual argument goes like this: AI art is not real art because a human did not make it. But a human did prompt it, select it, refine it. So the counter-argument goes: a human was involved, just differently. And the debate spirals from there, into questions about originality, intent, and craft.
But I don’t think the objection is really about whether a human was present. A human can be present and still feel nothing. A painting made cynically, a song written to manipulate, a novel produced to fill a market gap. Humans made those. They can still feel empty.
What people are responding to, whether they name it or not, is the absence of relationship. Art at its best is not a product delivered from maker to consumer. It is a relational act. Someone struggled to make sense of their experience, shaped it into form, and offered it to someone else. The viewer or listener or reader receives it and is changed, not just informed but moved, challenged, seen. The meaning is not in the object. It is in the exchange. It lives between people.
AI breaks that exchange. Not because it is artificial, but because there is no relationship in it. Nothing was risked. Nobody was changed in the making. There is no reciprocity. You receive an image, but nobody offered it to you. That is what feels hollow.
Evaluation Is the Same
I have been an evaluation practitioner for a long time, and I have slowly come to believe that the same thing is true about evaluation.
We talk about evaluation as if the value is in the product: the report, the findings, the recommendations, the data visualizations. We measure the success of an evaluation by whether the report was delivered on time, whether the methodology was sound, whether the findings were actionable.
But the most valuable evaluations I have been part of were not the ones with the best reports. They were the ones where something happened between people. A program officer and a community organizer looking at the same evidence and realizing they understood the problem differently. A team sitting with findings that contradicted what they expected and being willing to say so out loud. A funder hearing directly from the people a program serves and having their assumptions rearranged.
The value was not in the data. It was in the relationship that made the data meaningful. It was in the willingness to be changed by what someone else showed you.
Nicole Bowman’s work in Indigenous evaluation names this more clearly than most Western evaluation theory does. In her framework, evaluation is not something done to communities or even for them. It is done with them, as relatives. Knowledge belongs to the community. The evaluator’s role is relational, not extractive. The purpose of inquiry is not just to know but to strengthen the bonds between people in the process of knowing.
That framing has changed how I think about all evaluation, not just Indigenous evaluation. If the value of evaluation is relational, then anything that weakens the relational core weakens the evaluation itself. It does not matter how rigorous the methods are. If nobody was in relationship during the process, the findings will sit on a shelf.
The Hollow Version
You do not need AI to produce the hollow version of evaluation. Organizations have been doing it for decades. Reports that nobody reads. Data dashboards that nobody discusses. Surveys collected from communities who never see the results. Findings delivered upward to funders while the people who shared their stories are left with nothing.
That is evaluation without relationship. The product exists, but the relational exchange never happened. Nobody was changed. Nobody was seen.
AI makes the hollow version faster. You can use AI to generate a survey, analyze the responses, summarize the findings, and produce a report without anyone sitting together to make sense of what it means. The output will be competent. It might even be accurate. But if the meaning was never made between people, you have not done evaluation. You have done data processing with a nice cover page.
The risk is not that AI will replace evaluators. The risk is that it will make it easier to skip the part that matters.
What Relationship Looks Like in Practice
The parts of evaluation that are relational are also the parts that produce the most change.
Sitting with a community partner and hearing something that surprises you. Presenting preliminary findings to a team and watching the room get quiet because everyone has to rethink something. Disagreeing about what the evidence means and staying in the conversation long enough to learn from the disagreement. Going back to the people who gave you their stories and asking whether your interpretation gets it right.
These moments require trust. They require presence. They require a willingness to be changed by the encounter. None of that can be automated. None of it should be.
Where AI Belongs
This is not an argument against AI in evaluation. It is an argument for knowing which parts are relational and protecting them.
AI is good at the parts that do not require relationship. Processing large datasets. Identifying patterns across documents. Drafting interview protocols. Cleaning and organizing qualitative data. Summarizing long reports. Translating materials. These tasks take time, and time is the scarcest resource in the social sector.
If AI handles the mechanical work, it can free people up for the relational work. More time in the room together. More time making sense of what the data means. More time going back to communities with findings and asking what they think.
But that trade only works if you actually use the time you save for relationship. If you use it to do more evaluations faster with fewer people in the room, you have just automated the hollow version.
How Anthralytic Approaches This
This is the principle behind how I build tools at Anthralytic. Technology as scaffolding for human connection, not a substitute for it. Every tool I design starts with a question: does this bring people closer to understanding each other, or does it let them skip the conversation?
People cannot do this work alone in front of their screens. Evaluation, like art, is relational. The meaning is made between people, in the exchange, in the reciprocity, in the willingness to be changed by what you hear. Take that out and you might still have data. But you do not have understanding.
And understanding, in the end, is the whole point.
Anthralytic is a strategy and evaluation studio that helps mission-driven teams clarify and amplify their impact. If you make decisions about resources in the social sector, whether you call yourself an evaluator or not, this newsletter is for you.
