Episode 9: From Cult to Code: Tracing the History of the Aura in Art
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Walter Benjamin, AI, & the Aura of Art: In this episode of The Jewish futurism Lab, host Mike Wirth unpacks Walter Benjamin’s “aura” of art and asks what presence means when every image can be copied, remixed, and generated on demand. Moving from Byzantine icons and ritual objects to photography, social media, NFTs, and AI image models trained on his own work, Mike maps six value eras of art, from cult value and exhibition value to digital manipulation, circulation, synthetic scarcity, and generative value.
Along the way, he explores why a family Hanukkiah, a live performance, or a handmade painting still feel different from a viral post or a blockchain-certified NFT, and how Jewish ritual and textual tradition offer a counter-story to purely market-driven ideas of originality and authenticity.
The episode lands on a haunting, guiding question for our AI age: when you stand in front of an image, an object, or an artwork today, was anyone present when this was made?
Walter Benjamin is not a household name. But he should be.
In 1936, this German philosopher and cultural critic wrote an essay that predicted, with startling precision, almost everything that has happened to art since. He described what it would feel like when images became infinitely copyable. He anticipated the strange hollowness of standing in front of a famous painting you have already seen a thousand times on a screen. He named the feeling you get in front of a great original that no photograph ever quite captures. And he called it, simply, the aura.
“Even the most perfect reproduction of a work of art is lacking in one element: its presence in time and space, its unique existence at the place where it happens to be.” — Walter Benjamin, Illuminations (214)
You already know what aura is. You have felt it. It is the difference between seeing a photo of the Grand Canyon and standing at its edge. It is why people still cry in front of paintings in museums. It is why a vinyl record from a musician you love feels different from the same album on a streaming service. It is why your grandmother’s ring means something her ring’s photograph does not. Benjamin just gave it a name and asked what happens to it when technology makes everything reproducible.
I have been asking that same question for most of my adult life. The answer, it turns out, is complicated. And beautiful. And a little devastating.
What Benjamin Actually Said
Benjamin’s core argument is simple enough to fit on a Post-it note: every original artwork has a presence tied to the specific place and moment it occupies in history. He called this its “here and now” (Benjamin 214). A painting carries the weight of every hand that ever touched it, every room it ever hung in, every century it survived. That accumulated presence is its aura. And the moment you photograph it, print it, digitize it, or copy it in any way, something essential leaks out. The copy is everywhere. The original is still only here.
I remember reading this in college and thinking it sounded romantic, maybe even a little precious. It took years of making things, and years of watching how people relate to things I made, before I understood he was not being romantic at all. He was being precise.
He was writing at a moment when photography and film were brand new cultural forces, and he watched them doing something no previous technology had managed: not just reproducing art, but changing what people expected from it. The museum poster, the art history textbook, the film still. Suddenly the image of the artwork was more familiar than the artwork itself.
“That which withers in the age of mechanical reproduction is the aura of the work of art.” — Walter Benjamin, Illuminations (221)
What makes Benjamin so prescient is that he was not simply mourning this loss. He saw something potentially liberating in it too. If art was no longer locked inside churches and palaces and the reverence of the elite, maybe it could become something more democratic. Maybe it could be politically alive in ways sacred objects never were. I find that tension in his thinking genuinely useful. He does not give you a clean answer because there is not one. He holds the grief and the possibility at the same time, which is, I think, the only honest way to engage with what technology does to culture.
The Six Lives of Aura
What Benjamin could not have predicted was how many more transformations were coming. Aura did not simply wane and disappear. It kept reinventing itself, finding new containers, mutating into new forms of value with each new technology. When I map these transformations out, what strikes me most is not how much has changed but how consistent the underlying human longing remains. Every era destroys one version of presence and immediately starts trying to rebuild it.
“The desire for authenticity, for the unrepeatable, for the original: this is what drives the market’s endless attempts to reconstruct aura under new conditions.” — Jos de Mul, Cyberspace Odyssey
Here is how that history maps across six eras.
Before the camera, art had cult value. It existed in one place, for one community, embedded in ritual (Benjamin 217). You had to travel to it. The gap between you and the object was not an obstacle. It was the point. Think of a Byzantine icon, a cathedral fresco, a Torah scroll passed down through generations. These things were not primarily decorative. They were alive with the weight of where they had been and who had held them.
I think about this constantly in my work with Jewish material culture. A Hanukkiah that has been in a family for four generations is not the same object as an identical one bought last year. It carries a history in its scratches and its dents and its smell. That is cult value. And I want to be clear about something that often gets lost in discussions of Benjamin: cult value has not disappeared from contemporary practice. Studio artists working in slow, material-intensive disciplines, oil painting, ceramics, hand-pulled printmaking, still generate genuine aura through the ritual of making. The visible trace of time, the irreproducible encounter with an original surface: these conditions still produce something real. I have stood in front of works that stopped me cold in ways I could not explain, and I believe that experience is not nostalgia. It is recognition.
Photography gave us exhibition value. Art could now travel to you, flattened and portable (Benjamin 225). More people than ever could access it, which was genuinely democratic and genuinely good. But the form of that access had changed fundamentally. The Mona Lisa on a postcard belongs to no place and no moment. It has been liberated from its context and, in that liberation, hollowed out a little. I do not say this with contempt for the postcard. I own plenty of them. I say it because the hollowing is real, and pretending otherwise does not serve anyone.
“The technique of reproduction detaches the reproduced object from the domain of tradition.” — Walter Benjamin, Illuminations (221)
The market fought back almost immediately: signed editions, numbered prints, certificates of authenticity. I find this reflex fascinating and a little poignant. The demand for aura did not disappear when the technology changed. It went underground and started looking for new containers. That pattern repeats in every era that follows, and once you see it you cannot unsee it.
The digital age brought manipulation value. Theorist Lev Manovich argued in 1998 that the database had replaced narrative as the dominant logic of new media culture (Manovich, “Database”). In a database, nothing has a fixed place or hierarchy. Everything is a node, waiting to be queried, remixed, and recombined. Art became raw material. Its worth shifted from what it was to how generative it could be.
“The database represents the world as a list of items, and it refuses to order this list.” — Lev Manovich, “Database as a Symbolic Form”
Hip-hop producers understood this intuitively before any theorist named it. Joseph Schloss establishes in Making Beats that producers sample not because it is convenient but because it is aesthetically beautiful, governed by a strict ethics of creativity and reverence for the source (Schloss 60–61). I find this argument genuinely moving. A Madlib record is built from hundreds of samples, each one carrying the aura of its source: a 1972 soul session, a Brazilian jazz recording, a forgotten film score, all folded into something new. The manipulation is also an act of love. He knew what he was taking. He was accountable to it.
“Sampling itself is an embodiment of this active process of engaging with history.” — Jeff Chang, Can’t Stop Won’t Stop (qtd. in DiCola and McLeod 74)
That accountability is everything. It is what separates sampling from mere recombination, and it becomes the critical distinction when we get to AI.
Social media created circulation value. In the age of Instagram, TikTok, and viral sharing, what an artwork is worth is inseparable from how far and fast it moves (Eryani). I remember when this shift started to feel real to me, not as a theoretical idea but as something I was actually living. Works I made that circulated widely took on a kind of social weight I had not anticipated. Works I made that did not circulate felt invisible regardless of how much they meant to me. That asymmetry disturbed me. It still does.
“In the digital age, the aura of an artwork is no longer tied to its physical uniqueness but to its cultural resonance and the collective experience it generates.” — Rulla Eryani, “Aura Reimagined”
A work now risks losing significance not by being too widely reproduced but by not being reproduced widely enough. Obscurity, not ubiquity, is the threat. Benjamin would have found this deeply strange. I find it both funny and genuinely disorienting.
NFTs tried to engineer scarcity value. When digital technology made reproduction totally free and infinite, the market did not accept the loss of aura gracefully. It built a financial instrument to simulate it. A blockchain certificate acted as a surrogate original, a unique claim of ownership over an infinitely copyable file (Jin). I watched this happen in real time and felt something like recognition mixed with exhaustion. Of course the market did this. It always does.
“NFTs don’t reinvent the aura — they show us what it always was: a structure of power, hierarchy, and exclusivity dressed in spiritual language.” — Laurie Rojas, Caesura Magazine
What NFTs revealed, more nakedly than anything in recent art history, is that the desire for aura was never purely spiritual. It was always also about property, exclusivity, and the economics of being the one person who owns the real thing. The container was synthetic. The longing was genuine. I think that distinction matters enormously.
AI generation has brought us to generative value. This is the strangest and most unsettled territory of all, and I say that as someone who is inside it. AI does not reproduce existing works. It generates entirely new ones, trained on millions of images, producing outputs that look like art, circulate like art, and affect people the way art does, but which were made by no one in particular, in no specific moment, with no hand, no resistance, no decision under pressure.
“AI systems trained on cultural databases continue the database logic of new media, generating new narratives and images from accumulated cultural archives.” — Lev Manovich and Emanuele Arielli, Artificial Aesthetics
I have fine-tuned my own image models on my own work. I fed them my visual language, my aesthetic history, my accumulated decisions as an artist, and watched them generate images that look, in some meaningful way, like me. I want to be honest about how strange that experience is. The outputs are genuinely useful. I use them for ideation, for unlocking directions I might not have found otherwise, for seeing my own sensibility reflected back at unexpected angles. But I have never used AI output in a final work. Something stops me every time. I have spent a lot of time trying to name what that something is, and I think Benjamin finally gives me the language: the generated image carries the shape of my aura but not its weight. The model learned from objects I made in specific moments. It was not there when I made them.
This is also where collage becomes a useful and genuinely complicated contrast. Hannah Höch, Kurt Schwitters, Romare Bearden built entire practices on the deliberate rupture of aura in source materials. And yet their works carry unmistakable aura of their own. The cut is a decision. The placement is a decision. The tension between fragments is authored, lived, physically enacted in a specific moment by a specific person. AI image generation looks like collage from the outside but the difference is exactly what Benjamin would have identified: there is no hand, no moment, no resistance.
“A work of art produced by a human hand communicates something of the artist’s presence, their struggle with materials, their decision-making under pressure — none of which a machine can replicate.” — Eva Cetinic and James She, Leonardo (Cetinic)
A collage artist ruptures aura intentionally and then reconstructs something from the rupture. An AI model has no relationship to rupture because it was never present to the wholeness of what it borrowed from. Collage and hip-hop sampling both taught me that context can be destroyed and meaning can still be made. AI is asking me whether that is still true when the displacement is total and no one was accountable to the source. I genuinely do not know the answer yet.
Why This Matters Now
Here is the thing about Benjamin’s argument that keeps bringing me back to it after all these years: the desire for aura never disappears. Every technological shift triggers an almost immediate cultural attempt to reconstruct what was just lost. Signed prints, authentication certificates, blockchain tokens, the slow craft revival, the vinyl resurgence, the return to film photography among young artists. These are not nostalgic accidents. They are symptoms of a persistent human need for the irreplaceable encounter, for the object or experience that cannot be anywhere else because it is only here.
I see this in my students. I see it in collectors. I see it in myself every time I walk into a room with an object that stops me. The need is real. What changes is only the form it takes and how honestly we reckon with whether the form is delivering what we actually hunger for.
“The authenticity of a thing is the essence of all that is transmissible from its beginning, ranging from its substantive duration to its testimony to the history which it has experienced.” — Walter Benjamin, Illuminations (221)
The question Benjamin leaves us with, and the one I find most urgent right now, is not whether aura survives. It clearly does, in some form, in every era. The question is what conditions make genuine aura possible and what conditions produce only its simulation. The handmade object, the live performance, the face-to-face encounter: these still generate something real. The blockchain certificate, the AI output, the viral image: these generate something that rhymes with aura but plays by different rules. Knowing the difference, and caring about the difference, might be the most important thing an artist, a designer, or a thoughtful consumer of culture can do right now.
Walter Benjamin died in 1940, at the Spanish border, fleeing the Nazis, carrying a manuscript no one has ever found. He did not live to see television, the internet, the smartphone, or the AI image generator. But he understood the essential dynamic that drives all of them: every new technology promises to bring art closer to everyone, and every new technology changes what art is in the process of doing so. The question he asked in 1936 is the same one I keep asking.
Was anyone present when this was made?
This essay is part of an ongoing exploration of Jewish Futurism, design thinking, and the cultural stakes of emerging technology.
Works Cited
Benjamin, Walter. “The Work of Art in the Age of Mechanical Reproduction.” Illuminations: Essays and Reflections, edited by Hannah Arendt, translated by Harry Zohn, Schocken Books, 1969, pp. 214–240.
Cetinic, Eva, and James She. “The ‘Aura’ of Artworks in the Era of Artificial Intelligence.” Leonardo, vol. 58, no. 4, MIT Press, 2025, pp. 352–360.
Chang, Jeff. Can’t Stop Won’t Stop: A History of the Hip-Hop Generation. St. Martin’s Press, 2005.
de Mul, Jos. Cyberspace Odyssey: Towards a Virtual Ontology and Anthropology. Cambridge Scholars Publishing, 2010.
DiCola, Peter, and Kembrew McLeod. Creative License: The Law and Culture of Digital Sampling. Duke University Press, 2011.
Episode 8: Speed Kills : Why Every futurist must confront the past
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In this episode, we confront the rise and collapse of Italian Futurism, the avant garde movement that worshiped speed, technology, youth, and rupture at any cost. What began as radical artistic rebellion under Filippo Tommaso Marinetti quickly blurred into nationalism and ultimately aligned itself with fascism.
So what went wrong?
We examine how aesthetic obsession with acceleration became political extremism, and how the rhetoric of disruption can mask ethical blind spots. For today’s futurists working in AI, design, and innovation, the lesson is clear: progress without moral grounding is dangerous. If you build the future, you are responsible for its consequences.
In this episode, I look at how AI is impacting Jewish artistry itself: from how I and other Jewish artists research, sketch, and prototype with AI-generated imagery, to how algorithms are beginning to influence our visual language, ritual design, and the stories our communities tell about themselves. I raise concrete questions about authorship, ownership, and credit when AI systems remix Jewish symbols and styles at scale, and I ask what happens to kavannah, memory, and responsibility when part of the “hand” in Jewish art is computational. Throughout, I frame AI as both a powerful tool for midrashic reinterpretation and speculative Jewish futures, and a disruptive force that can flatten nuance, decontextualize heritage, or sideline human makers if we do not respond with clear ethical commitments.
Let’s not wrestle with this golem alone. Check out this episode.
What does it mean to build a Jewish future through scissors, glue, and pixels? In this episode, I sit down with collage artist Alex Woz, who I met at the Jerusalem Biennale. We talk about the graphic design industry, swap stories about our favorite Jewish artists, and get honest about why we make what we make.
Alex grew up in an antisemitic city and turned that experience into an artistic mission. We explore the weird parallels between cutting and pasting found images and prompting AI, what makes art original, and how we’re both in conversation with Jewish creative lineage from Moritz Daniel Oppenheim to today.
This conversation goes deep on legacy: What are we leaving behind for our descendants? What does Jewish creativity look like when it refuses to disappear? And why is Alex a practitioner of Jewish futurism, even if he works with analog and digital hand tools instead of code ?
Episode 1: Welcome to The Jewish futurism Lab: Torah, Tech, Tomorrow
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In this first episode, I’m introducing The Jewish futurism Lab and what this podcast is here to build: a space where Torah learning, creative practice, and emerging technology meet. I’ll share a quick bit about who I am, what Jewish futurism is, and why I’m drawn to Jewish futurism, then lay out what you can expect in future episodes, essays, and projects connected to my work at mikewirthart.com. We’ll start with the foundation, what Jewish futurism is, why it matters right now, and how we can imagine bold, ethical Jewish futures without losing our roots.
This article is a teacher’s (me) journey out of the AI shadows and into classroom transformation. This article is a companion to a recorded lecture I gave on how I use AI in the classroom. I recommend watching the video in addition to reading this post, as it offers a deeper dive and helps contextualize the experiments and perspectives summarized here.
AI Isn’t a Hammer, It’s a Screwdriver
A teacher’s journey out of the AI shadows and into classroom transformation. This article is a companion to a recorded lecture I gave on how I use AI in the classroom. I recommend watching the video in addition to reading this post, as it offers a deeper dive and helps contextualize the experiments and perspectives summarized here.
We’ve successfully scared the hell out of ourselves about AI. That’s the truth. Despite the helpful Wall-E’s and Rosie the Robots, the likes of HAL 9000 locking astronauts out in space to the death machines of The Terminator, the cultural imagination has been fed a steady diet of dystopian dread. And now, with the hype and hysteria churned out by the media and social media, we’ve triggered a collective fight, flight, or freeze response. So it’s no surprise that when AI entered the classroom, a lot of educators felt like they were witnessing the start of an apocalypse, like all of us were each our own John Connors’ watching the dreaded Skynet come online for the first time.
But I’m here to tell you that’s not what’s happening. At least not in my classroom.
In fact, this post is about how I crawled out of the AI shadows and learned to see it not as a threat but as a tool. Not a hammer, but a screwdriver. Not something that does my job for me, but something that helps me do my job better. Especially the parts that grind me down or eat away at my time.
If you’re skeptical, hesitant, angry, or just plain confused about what AI is doing to education, pull up a chair. I’ve been there. But I’ve also experimented, adjusted, and seen the light and the darkness. I cannot dispel all of the implications of AI use, but I want to share what I’ve learned so you don’t have to build the spaceship from scratch.
We Owe It to Our Students to Model Bravery
Students are already using AI. They’re exploring it in secret, often at night, often with shame. They’re wondering if they’re doing something wrong. And if we meet them with fear, avoidance, or silence, we’re sending the message that they’re on their own. In a 2023 talk at ASU+GSV, Ethan Mollick noted that nearly all of his students had already used ChatGPT, often without disclosure. He emphasized that faculty need to assume AI is already in the room and should focus on teaching students how to use it wisely, ethically, and with reflection. That means our job isn’t to police usage—it’s to guide it.
I don’t want my students wandering through this new terrain without a map. So I model what I want them to do: ask questions, explore ethically, think critically, and most of all—reflect. I also model the discipline of not using AI output as a final product, but only as inspiration. If I use AI to brainstorm or generate language, I always make sure to rewrite it into something that reflects my own thinking and voice. That’s how we teach students to be creators, not copy machines. Map out where you have been and where you are going in your journey.
That’s what it means to teach AI literacy. It’s not about having all the answers. It’s about being brave enough to stay in the conversation. I was also wandering aimlessly with AI—unsure how to use it, uncertain about what was ethical—until I took this course from Wharton on Leveraging ChatGPT for Teaching. That course changed my mindset, my emotional state, and my entire classroom practice. It gave me a framework for using AI ethically, strategically, and with care for student development. If you’re looking for a place to start, that’s a great one.
AI Isn’t a Hammer. It’s a Screwdriver.
Here’s a metaphor I use a lot: AI is not a hammer. It’s a screwdriver.
Too many people try to use AI for the wrong task. They ask it to be a mindreader or a miracle worker. When it fails, they say it’s dumb. But that’s like trying to hammer in a screw and then blaming the hammer.
When you learn what AI actually does well, like pattern recognition, remixing ideas, filtering, and translating formats, you start to use AI for its actual strengths. As Bender et al. (2021) explain in their paper On the Dangers of Stochastic Parrots, large language models are fundamentally pattern-matching systems. They can generate fluent, creative-sounding language, but they do not possess understanding, emotional awareness, or genuine creativity. They remix what already exists. That is why we must use these tools to support our thinking, not replace it. It becomes a tool in your toolkit. Not a black box. Not a crutch. A screwdriver.
I don’t want AI to do my art and writing so I can do dishes. I want AI to do my dishes so I can do art and writing. As Joanna Maciejewska put it: “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” It won’t do your dishes. But it might give you time back so you can do something that matters more.
How I Actually Use AI in Class With Students
I teach graphic design, motion, UX, and interactive design. AI is already a mainstay in each of these disciplines—from tools that enhance layout and animation to systems that evaluate accessibility and automate UX testing. But even though AI had become part of the professional design landscape, I was still skeptical. I wasn’t sure how to bring it into my classroom in a meaningful way. So I started small.
Using AI for minor efficiencies—generating rubrics, reformatting documents, cleaning up language—felt good. It felt safe. And it gave me just enough momentum to try it on bigger, more impactful tasks. What made the difference was a mindset shift. I stopped seeing myself as a single musician trying to play every part of a complex score and started seeing myself as the conductor of the orchestra. I didn’t need to play every part, I just needed to know how the parts worked together. That gave me the confidence to use AI—and to teach with it.
Here’s how I integrate AI into our learning:
Students design chatbots that simulate clients, so they can roleplay conversations. I used to pretend to be clients and interact with students through Canvas discussion boards. Now I can read their chat logs and have conversations with them about their questions and intentions.
In Motion Graphics, students use “vibe coding”—a form of sketching in code with the help of GPT to simulate motion, like moons orbiting planets.
In Interactive Design, they use Copilot** to debug code** in HTML, CSS, and JavaScript.
They learn to generate placeholder images for mockups, not final artwork.
We create custom Copilot agents, like “RUX”—a UX-focused bot trained to give scaffolded feedback based on accessibility standards.
I’m not handing them shortcuts. I’m handing them power tools and asking them to build something that’s still theirs.
The Creative Process Needs Scaffolding—AI Can Help
I believe in the creative process. I’ve studied models like the Double Diamond and the 4C Model. I’ve seen how students get stuck during the early stages, especially when self-doubt creeps in.
That’s where AI shines.
AI helps my students generate more ideas in the divergent phase. This echoes research by Mollick and Terwiesch (2024) showing that structured AI prompting increases idea variance and originality during the creative process. It helps them compare, sort, and edit during the convergent phase. And when I ask them to submit their chat logs as part of their final deliverable, I can see their thinking. It’s like watching a time-lapse of the creative process.
We’re not assessing just artifacts anymore. We’re assessing growth. And that includes how students use AI as part of their process. I make it clear that AI-generated outputs are not to be submitted as final work. Instead, we treat those outputs as inspiration or scaffolding—starting points that must be reshaped, edited, or reimagined by the human at the center of the learning. That’s a critical behavior we need to model as teachers. If we want students to be creative thinkers, not copy-paste artists, then we have to show them how that transformation happens.
Accessibility and AI Should Be Friends
I also use AI to make my course materials more accessible. I format assignments to follow TILT and UDL principles. For example, I asked GPT to act as a TILT and UDL expert and reformat a complex assignment brief. It returned a clean layout with clear learning objectives, task instructions, and evaluation criteria. I pasted this directly into a Canvas Page to ensure full screen reader compatibility and ease of access.
For rubrics, I asked GPT to generate a Canvas rubric using a CSV file template. I specified category names, point scales, and descriptors, and GPT returned a rubric that I could tweak and upload into Canvas. No more building from scratch in the Canvas UI.
To generate quizzes, I use OCR with my phone’s Notes app to scan printed textbook pages. I paste that text into GPT and ask it to write multiple-choice questions with answer keys. GPT can even generate QTI files, which I import directly into Canvas. This process saves me hours of manual quiz-writing and makes use of printed texts that don’t have digital versions.
AI helps me build ramps, not walls.
Faculty are also legally required to build those ramps. Under the Rehabilitation Act and the Americans with Disabilities Act (ADA), specifically Section 504, course content in learning management systems like Canvas must meet accessibility standards. But let’s be honest—retrofitting dozens or even hundreds of old documents, PDFs, and slide decks into fully accessible formats is a monumental task. It often gets pushed to the bottom of the to-do list, which leaves institutions vulnerable to non-compliance. Check out the WCAG standards for more details.
AI can help. It can reformat documents for screen reader compatibility, generate alt text, simplify layout structure, and audit for contrast and clarity. And it can do it in a fraction of the time it would take any one of us. By using AI thoughtfully here, we not only make our content better, we also help our institutions become more equitable and compliant faster.
When I use local LLMs to analyze student writing using tools like LM Studio, I keep student data safe, FERPA compliant, and private. This aligns with concerns raised by Liang et al. (2023) about how commercial LLMs may compromise the privacy of non-native English speakers and their content. It is ethical. It is efficient. And it respects the trust students place in me.
Let Students Build Their Own Tools
One of the best things I’ve done is empower students to create their own AI agents.
Yes, students can train their own Copilot bots. And when they do, they stop seeing AI as some alien threat. They start seeing it as a co-creator. A partner. A lab assistant. ChatGPT has a feature called Custom GPTs, which allows similar personalization, but it’s locked behind a paywall. That creates real inequity for students who can’t afford a subscription. Copilot, on the other hand, is free to students and provides the necessary capabilities to build custom agents or chatbots. Here’s a guide to get started building your own agents with Copilot.
As a way to model this behavior for students, I created a CoPilot Agent myself called RUX, short for “Rex UX”, honoring Rex, our beloved university mascot. I built it using Microsoft’s Copilot Studio, which lets you define an agent’s knowledge base, tone, and purpose. For RUX, I gave it specific documentation to pull from, including core sources like WCAG, UDL, and UX heuristics, and trained it to act as a guide and feedback coach for my UX students. It doesn’t give away answers. It asks questions, gives feedback, and helps students reflect.
Setting up an agent starts with defining your intent. I decided I wanted RUX to act like a mentor who knew the standards for accessibility and good UX practices, but also had the patience and tone of a coach. I uploaded key resources as reference material, wrote prompt examples, and added instructions to prevent the agent from simply giving away answers. This ensures students use it to reflect and improve rather than shortcut their learning.
The great part is that it took me about 30 minutes. And now my students use it to get feedback in between critiques, to check their work against accessibility standards, and to build their confidence.
And the students slowly start to ask better questions.
Final Thoughts: Be the Conductor, Not the Consumer
I tell my students this all the time: don’t just be a user. Be the conductor. That’s the heart of this whole article. I started this journey skeptical and unsure about how to use AI in my teaching, but I kept experimenting. And the more I leaned in, the more I realized I could use these tools to orchestrate the learning experience. I didn’t need to master every note, just guide the ensemble. Once I felt that shift, I was able to build my own practice and share it with students in ways that felt grounded and empowering.
Here are two simple but powerful GPT exercises that are from the UPenn AI in the Classroom course that I recommend for you to get started:
1. Role Playing (Assigning the AI a Persona)
This method helps shape AI responses by giving it a clear role.
Steps:
Tell the AI, “You are an expert in [topic].”
Provide a specific task, like “explain X to a 19-year-old art student” or “give feedback on a beginner-level UX portfolio.”
Refine the prompt with context about the student’s needs or your learning objectives.
Outcome: The AI behaves like a thoughtful tutor instead of a know-it-all. Students can use it as a low-stakes, judgment-free practice partner.
2. Chain of Thought Prompting
This is useful for step-by-step thinking and collaborative problem solving.
Steps:
Ask the AI to help you develop a lesson plan, solve a design challenge, or draft a workflow.
Break the task into steps: “What’s the first thing I should consider?” Then “What comes next?”
Let the AI ask you questions in return. Keep the conversation going.
Outcome: You model metacognition, and students learn how to refine ideas through iterative feedback. It supports both ideation and strategic planning.
Try these as warm-ups, homework tools, or reflection exercises. They’re simple, ethical, and illuminating ways to integrate AI in any classroom.
That’s what I want for my colleagues, too. You don’t have to know everything about AI. You just have to be curious. You have to be willing to ask: “What can this help me or my students do better?”
So here’s your first experiment:
Have students brainstorm ideas for a project.
Have them ask GPT the same question.
Compare the lists.
Reflect. (What worked? What didn’t? How will you approach brainstorming next time?, Repeat)
Then decide what to keep, what to toss, and what to remix. Just like we always have. Let’s stop building walls. Let’s start building labs. And let’s do it together.
Yes, there’s a problem, but It’s not just about AI.
I’ve heard passionate arguments against AI usage from fellow artists. I’ve also read in detail about the lawsuits filed by creators against companies who used their work without permission. I agree that this is wrong and that it has hurt the true validity of the tools. These concerns are real, and they’ve shaped how I approach the technology.
A central question remains: if we could make the source imagery for AI training completely copyright-free and ethical, would that actually end the argument over the use of these tools in art making? Or is the real issue an underlying belief in purity in the creative process? As a graphic designer, I know that purity in creation was disrupted long before AI ever existed.
From Generative Art to Generative AI
I was exhilarated the first time I started using AI in my art. I’ve been working as a generative artist since the late 90s, so I’ve seen a lot of shifts in how tech intersects with creativity. Back in the early 2000s, I was building generative art installations that used text, images, and sound. I was dreaming in code, digital sensors, and databases as these were the core elements to create art-making robots. I was even invited to exhibit my projects in the US and international media arts biennales in Split, Croatia and Wroclaw, Poland. But this new wave of AI tools? It felt like a leap. A serious one.
When the most recent wave of AI tools burst onto the scene in 2022, I found myself re-reading Walter Benjamin’s essay The Work of Art in the Age of Mechanical Reproduction and picking up Lev Manovich’s article Who is an Author in the Age of AI? for the first time. They both helped give me clarity on the debate developing. The first was written over a century ago, and the second in the present moment, but both challenged me to think beyond the surface of the debate.
That said, I didn’t jump in without questions. I was skeptical about where the image data was coming from. Most of the image models were trained on LAION400M, a huge dataset scraped from the internet. It was meant for research, not commercial use. As an artist, I care deeply about copyright and creative ownership. That part bothered me.
But the power of the tool was undeniable. AI helps me iterate quickly. It pushes my image-making forward and challenges me to try things I wouldn’t have done on my own. New poses, wild color combinations, unusual compositions. Sometimes, I don’t even recognize what I’m capable of until I see what AI reflects back at me.
Through my experiments and explorations, it became very true that it’s not about replacing my work at all. It’s about expanding it.
AI as a Catalyst and Sharpener for Creativity
One of the things I love most about AI is how it helps me start. Sometimes, it’s like having an oracle. I might not know exactly where a project is headed, but I can toss in a few ideas and see what comes back. That response helps me clarify what I want. Or what I don’t want. And that’s part of the creative process, too. The ability to think divergently and then convergently is the true poetry of the creative process and having a “helper” in that process allows greater tracking and reflection on the process.
I use Stable Diffusion most often because it’s open-source and highly controllable. I like that I can run it locally on my machine without paying for credits or cloud storage. Not having a paywall gives me the freedom to really dig deep. I can generate a hundred versions of an idea, explore unexpected paths, and move fast without overthinking costs.
There was one recent project that brought this all into focus. A client in Houston wanted a mural with about 25 different visual elements. Honestly, it was overwhelming. I started by asking AI to look for patterns in the list text, riffing with the chatbot to explore visual ways to combine the many items into one space. The ideas that chat suggested, but it did mention the word surrealism that made me think of Salvador Dali’s haunting landscapes. That was it! A landscape like Dali’s is a place where all the elements could exist together logically. A dreamscape, so to speak. That unlocked the whole thing. Without AI, I might’ve taken much longer to get there.
Most AI images aren’t good. I’d say maybe 10 percent are worth a second look. I know I’ve hit something useful when it meets my “GE” standard: Good Enough to move forward. I’m looking for strong composition and clear visual hierarchy. Everything else can be worked on later. But if the image holds space in a striking way, I’ll keep going.
AI for Process, Not Just Product: An Ethical Approach
People often think of AI as a tool for generating a final image. That’s not how I use it. For me, AI is most powerful when it supports the process. It helps me evaluate, brainstorm, and reflect. Sometimes, I upload a rough idea or a brain dump and use a chatbot to ask questions or poke holes in it. That outside perspective—fast, responsive, and nonjudgmental—is gold.
If I’m stuck, I might ask the AI to generate some moodboards or rough compositions. I’m not expecting polished work. I’m looking for sparks. A direction to follow or a problem to solve. It’s the same way I’d sketch a dozen thumbnails on paper.
And no, I don’t fall in love with the first cool thing AI spits out. That novelty wore off fast. I’ve trained myself to be curatorial. Most results don’t hit the mark. But knowing I can always make more helps me stay loose. I push ideas until they’re solid.
One piece that stands out was an illustration I made for a Torah portion about Pharaoh’s dream. I had drawn a grim-looking cow skull and was thinking of placing it in a field of wheat. Then, the AI surprised me. It created a cow skull made out of wheat. That twist was mysterious and totally unexpected—perfect for the surreal nature of a dream. I never would have gone there on my own. But once I saw it, I knew exactly where to take it.
AI Won’t Replace Me. It Will Refine Me.
As an educator, I’ve brought AI into the classroom not just as a tool but as a way to help students understand how creativity works. I show them how to use it to ideate, test ideas, and refine their thinking. We do in-class exercises where students generate images or prompts and then share their chatlogs with me. It gives me a real window into how they’re thinking—and how they’re growing.
Some students are skeptical at first. Others dive in headfirst and sometimes know more than I do about certain tools. The ones who get it quickly start using AI not to shortcut the work but to deepen it. They realize that it’s not about letting AI do the thinking. It’s about using AI to push your thinking further.
I’ve even used AI as part of a critique. One time, I had students feed their near-final projects into ChatGPT and ask for feedback. With the right prompts, the feedback was surprisingly thoughtful. Not perfect. But useful. It opened a door for them to reflect and iterate in ways they hadn’t before and to be critical of comments that didn’t in fact, help improve their work.
What AI reveals, I think, is that creativity isn’t just about generating something new. It’s about discovering connections, asking better questions, and recognizing what’s missing. AI isn’t great at originality on its own, but it’s fantastic at remixing and showing what’s possible. It’s like a mirror that reflects potential back at you. I’ve worked closely with a few creative development systems like the Double Diamond and SCAMPER and I can say with confidence: AI can support both divergent and convergent thinking, especially when used intentionally.
Originality: The Collage Conversation
This comes up a lot: “Isn’t AI just stealing?” And my response usually starts with this: what about collage?
We’ve accepted collage as a legitimate art form for over a century. Artists like Hannah Höch,Romare Bearden, and Robert Rauschenberg all used found images, many of them copyrighted. They cut, glued, layered, and remixed to create something new. If we call that art, why are we drawing the line at AI?
To me, AI-generated images are collage-like. The human prompts them with intention. The AI recombines things based on patterns it has learned. The process is digital, but the creative act is still there. Cutting and pasting by hand doesn’t make something inherently more authentic. It’s the idea behind the work that matters.
Now, I don’t ignore the legal and ethical side of this. Most major AI image models are trained on datasets built from scraped web images, and that’s a problem. I’ve been exploring more ethically sourced options. For example, Adobe’s Firefly and Shutterstock’s model are trained on licensed stock images. Even better, I recently started working with a model called PixelDust. It’s a rebuild of Stable Diffusion, but trained only on public domain and Creative Commons Zero (CC0) images—think Wikipedia, museum archives, and open repositories. While it’s the closest public domain model out there, it still is not 100% certain it is copyright-free.
I fine-tuned that model using 380 of my own original works. That means when I prompt it now, it generates images in my style using my visual language. It’s still collaborative, but it feels more personal. And the results have seriously improved my ideation speed and image quality.
There’s a difference between copying and remixing. Collage artists have done it forever. Musicians sample. Writers quote. AI might be new, but it fits within a long tradition of borrowing, blending, and transforming. What complicates the conversation is “style”. People think style is protected by copyright, but it’s not. Only specific works are. So, while artists may be known for their style, that alone doesn’t make it off-limits.
Yes, people have questioned the validity of my AI-assisted work. When that happens, I explain. I describe how I use AI for ideation, how I fine-tune models on my own work, and how that affects the output. Once people understand that I’m building on my own images and ideas, they usually come around.
Co-Creating with the Machine: How AI Refines My Process
Over time, I’ve started experimenting with creating a kind of AI version of myself. Not in a sci-fi clone kind of way, but as a tool trained to think and see more like me. I fine-tuned a model using 360 of my own artworks, each paired with carefully written prompts. That way, when I generate new images, they come out in my visual language, not someone else’s.
I also use a tool called ControlNet. It lets me upload sketches or basic compositions, and then the AI fills in the style and detail. This setup allows me to keep control over layout and flow while still tapping into the speed and surprise of the AI. It doesn’t always work the first time, and it can be a long back-and-forth, but the results are worth it.
Eventually, I’d love to have a copyright-safe, fully custom model that supports my entire process. The goal isn’t automation for the sake of ease. I want to hand off the repetitive, procedural stuff so I can stay focused on creativity, strategy, and ideas.
And no, I don’t want my AI self to be autonomous. That would defeat the point. I’m the creative leader here. The AI is my partner. It helps me explore, test, and refine, but I make the final call.
I’ve also made peace with the idea of my style being encoded. I’ve been an illustrator long enough to know that you don’t really “own” a style. My style is a blend of influences I’ve absorbed over the years, and it’s always evolving. As a professional, I’ve had to learn multiple styles just to stay competitive. So, no, I don’t see style as sacred. It’s the ideas and the content that matter most to me.
Rethinking and Remixing Creativity with AI
My relationship with AI has changed a lot since I started. At first, I believed the hype. I thought it would be a job killer that could replace me. But as I worked with it more, I realized its limitations. It isn’t a one-click creative solution. It’s a tool that depends on my input, my ideas, and my vision. It helps me move faster and reflect more deeply, but it doesn’t do the thinking or the feeling for me.
I’ve come to believe that AI isn’t replacing creativity. It’s revealing it. It shows us how we think, where we hesitate, and what we ignore. It challenges the old myths that artists work in isolation, drawing purely from inspiration or talent. That myth never held true for working designers and educators like me. And it definitely doesn’t reflect how creativity works in the real world.
Still, I respect the artists who are hesitant or resistant. I’ve listened to powerful critiques and concerns. The lawsuits over unauthorized dataset usage raise important ethical and legal questions. And they should. If we can’t build these tools on ethically sourced, copyright-free content, then we have no foundation to build from. But if we can create models trained on ethically gathered images, then we should be having different conversations. One would be about practice. Another about process. We’d also be talking about expanding what it means to be creative. Instead, we’re stuck in echo chamber-like debates with half-truths and misunderstandings.
AI is not a threat to purity in art because that purity never really existed. From collage to sampling to appropriation, art has always thrived on remix. This is what Benjamin meant when he spoke of the “aura” of artworks over a hundred years ago. Reproduction changes the way we relate to art, but it doesn’t remove its meaning. It shifts the space where meaning happens.
So I use AI not because it replaces me but because it helps me be more of who I already am. A generative artist. A question asker. A teacher. A remix thinker. A designer trained in collaboration, systems, and complexity. AI is now a part of that system. And I welcome it, carefully and critically, into my process.
The tech will keep evolving. But the core of creativity, being curiosity, play, rigor, surprise, and reflection, has not changed. AI just gives us more ways to explore it.
I had the pleasure of contributing both an interview and original artwork to the cover and interior of the AI Issue of AJS Perspectives, published by the Association for Jewish Studies. The issue explores how artificial intelligence is beginning to reshape Jewish scholarship, pedagogy, and creative practice, and it was meaningful to participate in that conversation from both a visual and conceptual standpoint.
Cover the AI Issue Summer 24′
I especially enjoyed working again with Doug Rosenberg, whose editorial vision I deeply admire and with whom I have collaborated in the past. Doug thoughtfully framed the issue by placing two distinct but complementary approaches into dialogue. He focused on Julie Wietz’s use of the Golem as a performative and robotic avatar alongside my own work around Sar Torah, a model of generative knowledge that treats Torah as a living, evolving system rather than a static archive.
Julie and I have also worked together previously, and seeing our practices paired in this context was especially rewarding. Her embodied, mythic approach and my systems-based, generative approach ask similar questions from different angles: how Jewish imagination, ethics, and inherited narratives shape our relationship to emerging technologies.
Feature spread by Doug Rosenberg- AJS Perspectives Journal Summer 24′
I also greatly enjoyed working with the editorial team to develop artwork that could serve as a cohesive visual theme for the issue. That collaboration gave me the opportunity to show my Jewish futurism work in action, not as speculation, but as a visual language actively engaging with contemporary Jewish scholarship. It felt meaningful to bring this work into conversation with this part of the Jewish academic world, where ideas, tradition, and future-facing inquiry meet.
Overall, the experience reaffirmed for me that discussions about AI within Jewish Studies are ultimately about people, values, and responsibility. They ask how we carry tradition forward, how knowledge is generated and shared, and how creativity remains a sacred act even as our tools continue to evolve.
Exporting cultural richness online through the worlds of Torah and NFTs
Originally Published by Challah Magazine.com (2022)
By
Mike Wirth
By now you’ve heard quite a bit about NFTs (non-fungible tokens) and may have jumped into their world yourself. NFTs are a creative financial technology phenomena that arose from the creation of platforms for digital creators and the like to list and value their artwork. The NFT marketplace has grown to a global multibillion-dollar cultural hub in only a few short years. I want to focus on how Jews and Jewish creators are making a niche for themselves in this volatile yet meteorically growing marketplace, and why the future of Jewish NFTs is still something that is shapeable by all of us.
Meditation on Aleph 2022 Digital Print 40″x54″
What is an NFT?
Firstly, NFTs are a part of a larger digital marketplace called cryptocurrency and follow the global digital ledger of transactions called the Blockchain. A lot of new vocabulary, but not overly complicated once you grasp a few simple concepts. I’ll share the way I explained it to my Bubbe.
Like what if I said that an NFT is like a unique stock certificate being issued by a newly public company to public investors. The price is set based on a formula that considers a company’s net worth and its speculated future potential earnings, which becomes the stock’s initial public offering, IPO price. Crypto is the capital that this new marketplace runs on and the Blockchain is a decentralized securely-encrypted version of the daily stock market trading ledger.
With me so far?
Then I explained that we speculate how that company stock is valued based on how “well” traders, investors, and we think it will do. If the company releases an innovative product then its stock will likely go up. Could the same not be applied to artists, and especially Jewish artists? Just like public companies who trade stock, we build brands, produce unique products/services, and contribute to the global economy. NFTs, Crypto, and the Blockchain allow us to participate in a similar financial system that is peer-to-peer-based rather than operated and mediated by private brokerage and or national entities.
Lastly and, in my opinion, the most amazing aspect of NFTs is the utility or the perks attached to the purchase of an NFT. Besides the glory and crypto value of the NFT, utility provides tangible value to the intangible digital media asset. NFT artists may attach real-world artwork, merchandise, or special access to an event or content. Intangibly, the benefits include status in key social circles, connections with other like-minded communities, and the simple joy of the investment in a community or individual.
“But, aren’t we just day-trading jpegs, Mike?”.
Shel Rosh 2021 Digital Print 24″x36″
What Can Be an NFT?
In short, anything that can be represented in digital form can be an NFT. The vast majority of NFTs now are jpeg images, but are also videos, audio recordings, writings, 3D models, interactive experiences in VR, video games, or computer code. Basically, any form of contemporary digital media that’s out there.
But looking at the media side of NFTs is only half the story. Coupled with unique utility, the media representation really serves as a certificate for perks in real life. For example, an artist could sell an NFT of their latest painting and then offer a common utility like a print of the work. Or they could offer something uncommon like a dozen MasterClass painting lessons with the artist. The difference in these kinds of utility perks would greatly influence the value of the NFT in my example. So, if we couple amazing media art with unique utility, then boom – we have a solid NFT to bring to market. This is where great creative questions come into play to decide what is valuable and worth putting on the market.
How Are NFTs Jewish?
Since NFTs are globally-based and community-focused, they mirror global creative financial trends. Simultaneously, there is a current Renaissance-like explosion of both implicit and explicit Jewish creativity and cultural expression which has similar trends globally. By implicit and explicit, I’m referring to the 20th to 21st-century shift in defining what Jewish art is. But more so than ever, we are seeing artwork made by Jewish-identifying artists and the content, aesthetic style, or form is also Jewish. We are at a point where we are rapidly learning about the great intersections of the Jewish story around the world and that we actually share a common future. Creative explorations of the bespoke and sublime of Jewish life have exponential cultural and spiritual implications.
There are a few major ways that Jews are affecting charity and culture in the NFT space by combining acts of Tzedakah with Hiddur Mitzvot to offer unique utility perks to supporters with uniquely-beautified digital objects.
Firstly, by using the real-world tiered fund drive features in their utility offerings with their NFTs to fundraise for their own brick-and-mortar organizations and beneficiaries, the NFTorah project by TechTribe minted a series of 18 (chai) curated Torah portions into NFTs to raise funds to support Torah-studying communities in need. They cite that the “Torah is the oldest unbroken blockchain” and that the utility of the NFTs is tzedakah going to further the completion of a newly-scribed Torah scroll to be donated to a community in need.
Cosmic Key 2021 Digital Print 24″x36″
While this project doesn’t put emphasis on the digital media asset side of the NFT, the 1-to-1 Torah parsha-to-NFT fundraising model is a strong case for why an NFT utility could be a real mitzvah in Tzedakah. Plus, it’s pretty cool to imagine a studious scribe painstakingly handmaking each Hebrew letter moments after receiving your contribution and the attached scripture.
No Weapon Formed Against Me Shall Prosper 2021 Digital Print 24″x36″
Secondly, the visually-dominated platforms of social media and NFT marketplaces have ignited a surge in Judaica and Jewish-themed creative objects. It’s fair to say that this era of Jewish creatives is intentionally making Hiddur Mitzvot quite prolific and are not only pushing the aesthetic boundaries of beautification of our cultural and spiritual objects, but joyfully celebrating the strata of Jewish identities in the world in new and unorthodox spaces. We now see Jewish themes emerging in global pop-cultural arenas of music, art, and fashion. Many contemporary Jewish creatives mine Jewish texts, history, and politics to produce world-class traditional Judaica, fine art, street art, commercial art spaces, and cutting-edge digital experiences.
I observe all of this creative activity as a sublime visual-Midrashic-like expression of the contemporary Jewish experience in action. NFTs provide a greater platform for cataloging this evolving Jewish art and Judaica on the blockchain that has the potential to make a real-world impact on the artist and their communities.
Explicit Cultural Expression
Is Jewish art defined by the Jewish content and themes featured in the work, or is it because it was created by a Jewish artist?
Jewish art was famously hard to define in the 19th and 20th centuries because many Jewish artists expressed themselves implicitly and in encrypted ways, but were very much Jewish people and had Jewish identities. Perhaps the most appropriate of Jewish expressions for the modern and postmodern art eras.
The 21st century has been a unique time for Jewish culture worldwide. Some would say that we’ve rebuilt a digital silk road and have entered an era past postmodernism to what theorists call metamodernism. For the first time in centuries, we can access an incredible amount of our thought-to-be-lost texts and cultural artifacts, a continuously unfolding archeological history, and we can connect and collaborate with other Jewish communities living outside of our own in a global Jewish culture jam.
The simple googling of “Jewish art” will send you down a rabbit hole of wonderful world-class artistry both contemporary and historic. This makes me feel a little less alone in the Diaspora knowing that elsewhere and in Israel there are strong communities of Jews that are actively exporting cultural richness online and in real life. This set of global circumstances has spurred a rise in the amount of explicitly Jewish creativity worldwide which has cascaded into the NFT space. Meaning the art features Jewish content, Jewish cultural experience, and/or is made by a Jewish artist.
The light body dance 2022 Digital Print 24″x36″
Jewish NFT projects include The Kiddush Club NFT Mensch collection, a JaDa organization NFT event at Miami art week 2021, to independent Jewish artists like MosheArt’s hamsa art becoming NFTs or myself in minting my Jewish Futurism artwork and digital experiences into NFTs. We take existing artwork and add that work as NFTs to our current output channels. Independent artists offer unique and interesting utility options, such as prints of the NFT art, access to exclusive content, or even providing the actual rights to the NFT artwork. These different perks would greatly impact the value of the NFT offered. As digital technology and utility offerings evolve into new spaces and screens, we’ll see this grow and evolve in value and utility.
You better believe how thrilled I am that I get to directly engage my audience with the Jewish art that I am making as original work, prints, merch, and now NFTs.
Where Is It All Going?
In the end, we’ve seen examples that demonstrate the promising qualities of NFTs that appeal to creatives, fundraising communities, and fin-tech communities. The examples I shared and the growing number of Jewish creatives, organizations, and institutions adding their NFT projects to the marketplace daily indicate that working with NFTs does actually extend the representation and creative utility of the Jewish experience into emerging global markets and spaces.
That sounds like a fantastic opportunity for high-tech Hiddur Mitzvot and Tzedaka that puts Jewish culture into the midst of new and innovative spaces and conversations on our own terms.
Mike Wirth is a visual artist, digital experience designer, and muralist, best known for his thoughtful murals, public art installations, and client-driven commercial design work that focus on major social justice issues and his identity as a Southern, Jewish-American.
Over the past 20 years, Wirth’s murals, published design projects, and digital museum exhibits have appeared in New York, Miami, Austin, Charlotte, NC, and internationally in Croatia, Poland, and Germany.
Currently, Wirth is a scholar at the Stan Greenspon Center for Holocaust and Social Justice Education and Professor of Art and Design at Queens University of Charlotte in North Carolina. He’s been investigating NFTs since 2015 and has been creating them for brands and non-profit organizations since 2021.
