AI has become as a deeply polarizing issue on the left, with many people having concerns regarding its reliance on unauthorized training data, displacement of workers, lack of creativity, and environmental costs. I'm going to argue that while these critiques warrant attention, they overlook the broader systemic context. As Marxists, our focus should not be on rejecting technological advancement but on challenging the capitalist framework that shapes its use. By reframing the debate, we can recognize AI’s potential as a tool for democratizing creativity and accelerating the contradictions inherent in capitalism.
Marxists have never opposed technological progress in principle. From the Industrial Revolution to the digital age, we have understood that technological shifts necessarily proletarianize labor by reshaping modes of production. AI is no exception. What distinguishes it is its capacity to automate aspects of cognitive and creative tasks such as writing, coding, and illustration that were once considered uniquely human. This disruption is neither unprecedented nor inherently negative. Automation under capitalism displaces workers, yes, but our critique must target the system that weaponizes progress against the workers as opposed to the tools themselves. Resisting AI on these grounds mistakes symptoms such as job loss for the root problem of capitalist exploitation.
Democratization Versus Corporate Capture
The ethical objection to AI training on copyrighted material holds superficial validity, but only within capitalism’s warped logic. Intellectual property laws exist to concentrate ownership and profit in the hands of corporations, not to protect individual artists. Disney’s ruthless copyright enforcement, for instance, sharply contrasts with its own history of mining public-domain stories. Meanwhile, OpenAI scraping data at scale, it exposes the hypocrisy of a system that privileges corporate IP hoarding over collective cultural wealth. Large corporations can ignore copyright without being held to account while regular people cannot. In practice, copyright helps capitalists far more than it help individual artists. Attacking AI for “theft” inadvertently legitimizes the very IP regimes that alienate artists from their work. Should a proletarian writer begrudge the use of their words to build a tool that, in better hands, could empower millions? The true conflict lies not in AI’s training methods but in who controls its outputs.
Open-source AI models, when decoupled from profit motives, democratize creativity in unprecedented ways. They enable a nurse to visualize a protest poster, a factory worker to draft a union newsletter, or a tenant to simulate rent-strike scenarios. This is no different from fanfiction writers reimagining Star Wars or street artists riffing on Warhol. It's just collective culture remixing itself, as it always has. The threat arises when corporations monopolize these tools to replace paid labor with automated profit engines. But the paradox here is that boycotting AI in grassroots spaces does nothing to hinder corporate adoption. It only surrenders a potent tool to the enemy. Why deny ourselves the capacity to create, organize, and imagine more freely, while Amazon and Meta invest billions to weaponize that same capacity against us?
Opposing AI for its misuse under capitalism is both futile and counterproductive. Creativity critiques confuse corporate mass-production with the experimental joy of an individual sketching ideas via tools like Stable Diffusion. Our task is not to police personal use but to fight for collective ownership. We should demand public AI infrastructure to ensure that this technology is not hoarded by a handful of corporations. Surrendering it to capital ensures defeat while reclaiming it might just expand our arsenal for the fights ahead.
Creativity as Human Intent, Not Tool Output
The claim that AI “lacks creativity” misunderstands both technology and the nature of art itself. Creativity is not an inherent quality of tools — it is the product of human intention. A camera cannot compose a photograph; it is the photographer who chooses the angle, the light, the moment. Similarly, generative AI does not conjure ideas from the void. It is an instrument wielded by humans to translate their vision into reality. Debating whether AI is “creative” is as meaningless as debating whether a paintbrush dreams of landscapes. The tool is inert; the artist is alive.
AI has no more volition than a camera. When I photograph a bird in a park, the artistry does not lie in the shutter button I press or the aperture I adjust, but in the years I’ve spent honing my eye to recognize the interplay of light and shadow, anticipating the tilt of a wing, sensing the split-second harmony of motion and stillness. These are the skills that allow me to capture images such as this:
Hand my camera to a novice, and it is unlikely they would produce anything interesting with it. Generative AI operates the same way. Anyone can type “epic space battle” into a prompt, but without an understanding of color theory, narrative tension, or cultural symbolism, the result is generic noise. This is what we refer to as AI slop. The true labor resides in the human ability to curate and refine, to transform raw output into something resonant.
AI tools like ComfyUI are already being used by artists to collaborate and bring their visions to life, particularly for smaller studios. These tools streamline the workflow, allowing for a faster transition from the initial sketch to a polished final product. They also facilitate an iterative and dynamic creative process, encouraging experimentation and leading to unexpected, innovative results. Far from replacing artists, AI expands their creative potential, enabling smaller teams to tackle more ambitious projects.
People who attack gen AI on the grounds of it being “soulless” are recycling a tired pattern of gatekeeping. In the 1950s, programmers derided high-level languages like FORTRAN as “cheating,” insisting real coders wrote in assembly. They conflated suffering with sanctity, as if the drudgery of manual memory allocation were the essence of creativity. Today’s artists, threatened by AI, make the same error. Mastery of Photoshop brushes or oil paints is not what defines art, it's a technical skill developed for a particular medium. What really matters is the capacity to communicate ideas and emotions through a medium. Tools evolve, and human expression adapts in response. When photography first emerged, painters declared mechanical reproduction the death of art. Instead, it birthed new forms such as surrealism, abstraction, cinema that expanded what art could be.
The real distinction between a camera and generative AI is one of scope, not substance. A camera captures the world as it exists while AI visualizes worlds that could be. Yet both require a human to decide what matters. When I shot my bird photograph, the camera did not choose the park, the species, or the composition. Likewise, AI doesn’t decide whether a cyberpunk cityscape should feel dystopian or whimsical. That intent, the infusion of meaning, is irreplaceably human. Automation doesn’t erase creativity, all it does is redistribute labor. Just as calculators freed mathematicians from drudgery of arithmetic, AI lowers technical barriers for artists, shifting the focus to concept and critique.
The real anxiety over AI art is about the balance of power. When institutions equate skill with specific tools such as oil paint, Python, DSLR cameras, they privilege those with the time and resources to master them. Generative AI, for all its flaws, democratizes access. A factory worker can now illustrate their memoir and a teenager in Lagos can prototype a comic. Does this mean every output is “art”? No more than every Instagram snapshot is a Cartier-Bresson. But gatekeepers have always weaponized “authenticity” to exclude newcomers. The camera did not kill art. Assembly lines did not kill craftsmanship. And AI will not kill creativity. What it exposes is that much of what we associate with production of art is rooted in specific technical skills.
Finally, the “efficiency” objection to AI collapses under its own short-termism. Consider that just a couple of years ago, running a state-of-the-art model required data center full of GPUs burning through kilowatts of power. Today, DeepSeek model runs on a consumer grade desktop using mere 200 watts of power. This trajectory is predictable. Hardware optimizations, quantization, and open-source breakthroughs have slashed computational demands exponentially.
Critics cherry-pick peak resource use during AI’s infancy. Meanwhile, AI’s energy footprint per output unit plummets year-over-year. Training GPT-3 in 2020 consumed ~1,300 MWh; by 2023, similar models achieved comparable performance with 90% less power. This progress is the natural arc of technological maturation. There is every reason to expect that these trends will continue into the future.
Open Source or Oligarchy
To oppose AI as a technology is to miss the forest for the trees. The most important question is who will control these tools going forward. No amount of ethical hand-wringing will halt development of this technology. Corporations will chase AI for the same reason 19th-century factory owners relentlessly chased steam engines. Automation allows companies to cut costs, break labor leverage, and centralize power. Left to corporations, AI will become another privatized weapon to crush worker autonomy. However, if it is developed in the open then it has the potential to be a democratized tool to expand collective creativity.
We’ve seen this story before. The internet began with promises of decentralization, only to be co-opted by monopolies like Google and Meta, who transformed open protocols into walled gardens of surveillance. AI now stands at the same crossroads. If those with ethical concerns about AI abandon the technology, its development will inevitably be left solely to those without such scruples. The result will be proprietary models locked behind corporate APIs that are censored to appease shareholders, priced beyond public reach, and designed solely for profit. It's a future where Disney holds exclusive rights to generate "fairytale" imagery, and Amazon patents "dynamic storytelling" tools for its Prime franchises. This is the necessary outcome when technology remains under corporate control. Under capitalism, innovation always serves monopoly power as opposed to the interests of the public.
On the other hand, open-source AI offers a different path forward. Stable Diffusion’s leak in 2022 proved this: within months, artists, researchers, and collectives weaponized it for everything from union propaganda to indigenous language preservation. The technology itself is neutral, but its application becomes a tool of class warfare. To fight should be for public AI infrastructure, transparent models, community-driven training data, and worker-controlled governance. It's a fight for the means of cultural production. Not because we naively believe in “neutral tech,” but because we know the alternative is feudalistic control.
The backlash against AI art often fixates on nostalgia for pre-digital craftsmanship. But romanticizing the struggle of “the starving artist” only plays into capitalist myths. Under feudalism, scribes lamented the printing press; under industrialization, weavers smashed looms. Today’s artists face the same crossroads: adapt or be crushed. Adaptation doesn’t mean surrender, it means figuring out ways to organize effectively. One example of this model in action was when Hollywood writers used collective bargaining to demand AI guardrails in their 2023 contracts.
Artists hold leverage that they can wield if they organize strategically along material lines. What if illustrators unionized to mandate human oversight in AI-assisted comics? What if musicians demanded royalties each time their style trains a model? It’s the same solidarity that forced studios to credit VFX artists after decades of erasure.
Moralizing about AI’s “soullessness” is a dead end. Capitalists don’t care about souls, they care about surplus value. Every worker co-op training its own model, every indie game studio bypassing proprietary tools, every worker using open AI tools to have their voice heard chips away at corporate control. It’s materialist task of redistributing power. Marx didn’t weep for the cottage industries steam engines destroyed. He advocated for socialization of the means of production. The goal of stopping AI is not a realistic one, but we can ensure its dividends flow to the many, not the few.
The oligarchs aren’t debating AI ethics, they’re investing billions to own and control this technology. Our choice is to cower in nostalgia or fight to have a stake in our future. Every open-source model trained, every worker collective formed, every contract renegotiated is a step forward. AI won’t be stopped any more than the printing press and the internet before it. The machines aren’t the enemy. The owners are.
I will offer a few critiques.
The first is that "AI", which is really LLMs and more advanced CNNs, is oversold as a technology and does not deliver what is promised. It can make a 90% as good stocj image, sure, but it does not code well. For coding, at best, it functions as a template creator that often makes mistakes. And it does not do the actually important part of coding, which is conceptualizing and solving the challenges of the actual problem domain. This is because all it can really do is regurgitate patterns based on patternes inputs. There is not any actual understanding underneath it. In this sense, it is a parlor trick compared to what the concept of actual AI evokes. We are essentially in a tech bubble phase of overpromising and overinvestment by two camps of capital: one that buys the hype as a technology and wants to get ahead of "the curve" to cut costs (your example of automation under capitalism) and the cynical crowd that understands its limits but uses it anyways to discipline labor. Large companies already wanted to do layoffs, this just provides an excuse. And it's just that: an excuse, not really much of a variable cost capital saver for capitalists. Its best corporate use is by workers to help them craft diplomatic emails to unreasonable managers. Or maybe the 90%-as-good stock images thing.
The second critique is of the enclosure of "AI", which amounts to capitalist enclosure of publicly funded research combined with vast swaths of data from all sources. These large models require substantial up-front capital investment. They are like building a power plant: to democratize it, you need democratic control over their production, not just a free output that can run on consumer hardware. "AI" companies spend huge amounts on scraping data and training their models, two things that "the consumer" does not experience in use value and arguably does not see in value, either, as with other Silicon Valley tech schemes this bubble is fueled by financial capital pumping companies up in the hopes of getting in on monopolies later. Like with Uber being convenient and cheap for the first few years and now being much more expensive, these companies undercharge and take losses now in the hopes of gainjng "marketshare". The likely outcome if American "AI" took off in isolation would be 2-3 big companies controlling the entire process and selling an exoensive sunscription, its expense depending on whether they killed off the non-"AI" competition or not. For example, stock image generation prices would be kept below Getty stock image prices right up until they killed the actually-photos market, at which point they would charge whatever they wanted, having skipped ahead to the monopoly phase of pricing. And they would not make the models free or open source, they would use their positions to crush all such endeavors and make them illegal. The only hope there is a country like China, who could nationalize the production of these models and direct their production to be towards lower power and free/open end use. You can see this dynsmic already and the threat it poses for US "AI" companies in their response to DeepSeek: they are trying to get it and all Chinese "AI" banned under claims of national security and intellectual property. This is just their response to a lower power LLM produced by a private group, which highlights their mechanism of enclosure is the high capital barrier to entry. You mention this, but I want to emphasize that it is the entire game.
I do also want to note that part of the hype is itself disrespectful to artists. As it exists, most "AI" visual productions are a fun parlor trick but not actually good enough to even convince people they are part of, say, a narrative work you would want to personally enjoy, let alone pay for. There is a lot of "searching for the niche" where people are taking what it is already almost convincing at reproducing and trying to shoehorn it in simply because it's "AI" and cheaper than hiring artists, not because anyone actually prefers it (or doesn't notice it) aesthetically. We kind of all feel the vibes of an "AI" generated image, let alone movie. When they have to form a narrative structure with consistent character designs and a sense of place, with unique scenarios, it currently all falls apart outside of a few highly skilled individuals that can work these systems (even then you can always recognize it as "AI"). So in this context, think about what it means to artists when people around them say "AI" slop is just as good as their work, or good enough to do the job. I mean, we all know it isn't either of those things. 99% of the time it's slop and it doesn't make any interesting creative "decisions" (it literally can't). So it is really, in these cases, a way to demean the value of art and artists by exaggerating the value of "AI" "art" to equate them, even on the aesthetic output value.
To summarize: "AI" has silicon valley financial capital dynamics that threaten any open/democratic use, it is oversold, and overselling it does harm to those whose jobs and interests are meant to be displaced per techbro marketing.
With that said, there are entirely valid use cases and situations where it performs very well, usually when highly constrained by heuristic modsls that only use it for a subset of aspects. For example, text-to-speech that relies on "AI" for speech generation and only part of translation but not all of it. In that sense it operates like a traditional technology, though still with high cost capital inputs. Though in this same case, there are many slop examples out there, usually when too much is offloaded to the "AI" models, like auto-translation devices for tourists (they do NOT work well).
My experience with coding models has been quite positive of late. In particular, I find DeepSeek does a great job in this regard. As a concrete example, I had some mp3s with corrupted Cyrillic id3 tags that I wanted to fix. I got DeepSeek to write a node script that would read the mp3, figure out the encoding, and update the tags. It's over a 100 lines of code and it worked out of the box. It's nothing groundbreaking to be sure, but it's a task that I needed to get done, and probably would've taken me a couple of hours of looking up libraries for reading id3 tags, figuring out how to fix encodings, etc. I've also had great success throwing sample data from JSON endpoints at it to generate UI scaffolding, writing SQL queries, and doing many other common tasks. I find it's a particularly great tool for working with languages I'm not well versed in where I know what I want to do conceptually, but don't know the specific syntax and conventions in the language. The LLM doesn't need to conceptualize and solve big challenges, that's the part I'm happy doing. What the LLM does is save me a lot of time writing boilerplate, and doing menial work that's boring.
Regarding the second critique, I would argue that's precisely why it's important for this technology to be developed in the open instead of it being shunned. For example, efforts like petals allow models to be run in distributed fashion and this approach can be used for training large models as well. We can leverage a network of many individual machines in a decentralized fashion to build and train large scale models without needing big corps and their data centres. Meanwhile, new techniques such as the approach DeepSeek pioneered further bring down the cost of training making the whole process more accessible.
I expect that we'll see plenty of new and genuinely useful things being done with AI going forward as is the case with every new technology. It's been rapidly and visibly improving in just past year alone, and we simply don't know what the plateau is going to be where further improvements start to be difficult to do. While corps are busy on focusing on figuring out ways to monetize this tech, I think far more interesting uses will come out from people playing with it for the sake of having fun and trying things.
I think you make some good points on the more sobering realities of where AI is at. However, on this part I have to disagree:
From everything I've seen, while some might wish for this to be true, it isn't in reality. Both anecdote and tests done would indicate that people can't necessarily tell the two apart and sometimes, the lower rating is specifically when they know something is AI, not because of anything inherent to the image. AI images do tend to have certain tells if you look closely, but they are not always obvious at a glance and even someone who spends a lot of time around AI images can be fooled (and as models get better, the tells tend to become more subtle). Perhaps experts in the field would be harder to fool, I'm not as sure about that, but at that point, we're getting into the same theme as blockbluster movie shlock vs. indie art project, and people can start sounding kind of snobbish when they're talking down to the masses like they lack the proper taste and are too easily entertained. As it is, image generation AI is a sort of remix of human art, so it shouldn't be surprising if a remix will sometimes be close enough to skilled works and the artistic principles behind them that people can enjoy a remix too. (Side note: "remix" is probably not an accurate enough machine learning term - I'm just trying to get at the idea of AI training on human art, while not exactly reproducing what it was trained on, but still retaining conceptual elements of it.)
I have only seen a few examples where people couldn't tell and it was for niche cases like portraits of people they don't know and low resolution landscapes. I'd be interested to see better examples.
I don't really have references on hand, so instead I'll try something on the fly and see what happens. Kind of a little game that some people do, to see how they do at telling the difference. This is not me saying that this is all-defining for the argument (maybe you will tell what is what just fine, after all), but we'll see how it goes.
At a glance, which of these do you think is AI? One, both, or neither, and which is what.
"Anime girls" is one of their other niches, lol.
Both look "AI" to me, honestly. They both fuck up their hands and have non-stroke, non-pen-looking accents and outlines despite being watercolor + pen outlining style.
Lol yeah, there's a lot of anime gen. As far as I know (unless the artist used AI in its creation and did not disclose it), the one on the right is drawn by a human. The one on the left is AI-generated using the one on the right via NovelAI's Vibe Transfer tool to mimic some of the essence of it.
BTW, many AI detection tools aren't that reliable, especially for text, so I wouldn't count on them to mean much, but this one does seem to be fairly reliable for image gen (they have a demo here): https://thehive.ai/demos/ai-generated-content-detection (and in this case, it does sync up - it rates the left as high confidence AI and right as high confidence human... I think it has some way to detect the special pattern of "noise" that AI images produce, which is where it gets its accuracy).