An artist's take on AI in math
Short summary
Mitchell Zemil, a freelance animator and adjunct professor, writes in response to 3blue1brown's AI-in-math discussion, arguing that artists and mathematicians both value the human labor, intentionality, and personal "DNA" behind their work. He contends that generative AI turns making into tastemaking, and that mathematics is more than an answer-producing machine.
- Art is valued for the artist's labor, skill development, and intentional process, so generative AI's ability to produce polished output quickly threatens that value and raises ethical issues around training data.
- Past technologies like cameras and digital tools democratized art without erasing the human practice behind it, but generative AI is different because it shifts the work toward selecting from outputs rather than making them.
- Mathematics is often treated as a purely objective pursuit that machines could finish off, but Zemil argues people still value living mathematicians who explain, interpret, and push the frontier, much as audiences still value dancers at the ballet.
Full summary
Mitchell Zemil, an animator and adjunct professor who describes himself as an outsider to mathematics, wrote this essay as a guest reaction on 3Blue1Brown's Substack about AI's rapid spread into creative and mathematical work. He uses "AI" in the narrow, current sense: large language models and GPT-style systems. His core argument is that the art world is structured in ways that make it instinctively hostile to generative AI. Artists are celebrated for labor, years of cultivated skill, and a traceable "DNA" of influences, and a tool that can produce polished work from a prompt cuts against all of that. He points to real economic pressure, namely artists' work being devalued and careers becoming precarious, along with the unresolved ethics of training data built partly from human artists' output, plus environmental and infrastructure costs. He also admits his gut says "no" to the idea of AI supplanting creative workers, though he struggles to say exactly why.
Zemil places this reaction in a longer history. Technology has repeatedly widened who gets to make and own art, and each shift, from cameras to digital painting software, unsettled the field before the field adapted. Photography did not kill painting; it pushed painters toward styles a camera could not reproduce. His distinction is that generative AI may be a different kind of tool. For the optimist it is just another step toward faster output by smaller teams. For the skeptic it turns artmaking into tastemaking: request, select, tweak the prompt, repeat. The value of art, in this view, lies in the visible relationship between an artist's instinct, intent, and hand, which an output-selection process does not replicate.
In the final section he turns to mathematics, which he sees as valued for objective truth rather than personal expression. That framing invites the tempting idea that a machine answering any math problem would be pure gain. He argues this view is cynical and partial, since math is more than a pipeline of answers, and he notes that a few parties with large financial stakes in AI adoption are shaping the debate. The open question he leaves hanging is whose autonomy and whose interests get expressed as these tools spread.