Large language models are not a neutral technology. They are a new front in a centuries-old war over the means of production — this time waged inside the digital commons. Codeberg’s recent votes, banning the training of LLMs on hosted code and restricting “vibe-coded” projects, appear at first glance to be a defensive posture by a small free-software host. In reality, they are a frontline skirmish in a much larger class conflict: the capitalist enclosure of the last remaining spaces of non-commodified human collaboration.

At its core, the LLM industry is a massive apparatus of extraction. The models do not create; they reconstitute. They ingest code, text, images — the accumulated social labor of millions of developers, writers, and artists — and refashion it into something that appears novel but is statistically derivative. The real labor that went into writing that code, debating that bug fix, documenting that API, all performed largely outside the wage relation, is appropriated without consent, compensation, or even acknowledgment. This is primitive accumulation in its digital form: the seizure of a commons that was built through collective, voluntary effort, and its transformation into private capital.

The costs of this extraction are socialized with ruthless efficiency. Codeberg’s infrastructure groans under the weight of AI crawlers that treat every page, every commit, every issue variant as raw material for training sets. These crawlers offer nothing in return; they merely consume. The database queries they trigger degrade service for everyone else, forcing human administrators — themselves waged or volunteer workers — to spend their time building defensive walls instead of improving the platform. This is the classic capitalist externality: profit is privatized, while the wear and tear on the shared infrastructure is borne by the community.

And then there is the hardware. Codeberg notes that SSDs that once cost €700 now cost €3,700 — a more than fivefold increase — and are often out of stock entirely. The cause is directly traceable to the insatiable appetite of data centers built to train and deploy LLMs. Here we witness a physical manifestation of the contradiction at the heart of capital accumulation: the drive to amass ever-larger fixed capital (servers, GPUs, storage) to extract surplus value from data, which in turn bids up the cost of that very hardware, making it harder for non-profit, cooperative, and small-scale operators to exist. The digital commons is being priced out of its own material substrate. What was once a relatively cheap means of digital production (a server, some drives) becomes a luxury item, accessible only to the largest cloud firms that have locked in supply contracts. The result is a rapid concentration of digital infrastructure in the hands of a few monopolies — Amazon, Google, Microsoft — who then sell back compute, storage, and AI services as a rental. This is the classic rentier-capitalist playbook, now applied to the very foundations of the internet.

The environmental toll is not a bug; it is a feature of a system that must constantly expand its material throughput to sustain profitability. Codeberg points out that data centers in Frankfurt already consume 40% of the local electricity, and the industry lobbies to burn fossil fuels to meet demand, while communities face rising water and electricity costs. In Marxist terms, this is the metabolic rift between capital and nature: the relentless drive to valorize value at the expense of both ecological stability and the living conditions of working people. The LLM boom accelerates the rift by orders of magnitude, transforming the digital realm from a relatively low-impact sector into a ravenous consumer of resources, all for the purpose of generating plausible text that could have been written by a human with a fraction of the energy.

The attack on collaboration is particularly instructive. Free software has always been a social product — code that is written, reviewed, improved, and maintained through human relationships and shared knowledge. LLMs short-circuit this process by enabling “vibe coding”: the rapid generation of single-use software by individuals who bypass the community entirely. The result is a flood of code that is “not written by anyone” and “not maintained by anyone,” yet consumes CI/CD minutes, storage space, and maintainer attention. Codeberg’s observation that such projects often consume more resources than large community projects, while having virtually no users, lays bare the commodity fetishism at work: the appearance of abundant software production masks the destruction of the social relations that make real, lasting software possible. The code, stripped of its human context, becomes a mere ghost of value — use-value for a single person, but exchange-value for none, because it was never embedded in a community that could sustain it.

This process also represents a novel form of labor degradation. Maintainers, who already perform a vast amount of unpaid, often invisible work to keep the digital commons alive, are now burdened with reviewing AI-generated contributions that look plausible but are often subtly wrong. The time spent vetting this algorithmic output is dead labor that produces no value for the commons; it is a direct drain on the collective’s capacity. At the same time, the pervasive presence of LLM-generated text erodes trust: experienced contributors are accused of using AI when they didn’t, while others learn to disguise their use of AI to avoid detection. The social fabric of the commons — built on mutual recognition of skill, effort, and good faith — is being poisoned. This is a classic divide-and-rule tactic, whether intended or not. When workers cannot trust each other, they cannot organize. When maintainers are overwhelmed by synthetic noise, they burn out and retreat. The space for genuine collective deliberation shrinks.

The “license laundering” issue, noted by Codeberg, exposes a deeper contradiction in intellectual property law. Copyleft licenses were designed to protect the commons by ensuring that modifications are shared back under the same terms. LLMs, however, are black boxes: they train on copyleft code, but the model’s output is not considered a derivative work by current legal standards. Thus, the reciprocity requirement is nullified. The code is laundered through the model’s weights, emerging as “new” content that can be incorporated into proprietary products with no obligation to share back. This is not a loophole — it is a structural feature of how capital treats information under its rule. The tendency is always to absorb the commons into the circuit of accumulation, to convert gift into commodity, to dissolve communal obligations in the acid of property rights. The copyleft movement was a defensive bulwark against this tendency; LLMs are the latest and most powerful solvent yet devised.

Finally, the social consequences beyond Codeberg cannot be ignored. Rising hardware costs push digital tools out of reach for low-income users, NGOs, local cooperatives, and grassroots organizations. Personal computing, once a democratizing force, becomes a luxury. Those who cannot afford capable devices are pushed into cloud-dependent terminals, where they pay subscriptions for services that were once locally run. The “digital divide” is not a mere inequality; it is a class differentiation mechanism that restratifies society along lines of ownership and rent. The capitalist class builds a digital world where you own nothing and pay for everything; the FLOSS commons offered an alternative model based on mutual aid and shared ownership. The LLM industry, by accelerating the concentration of compute and storage, threatens to obliterate that alternative.

What Codeberg has done — declaring its infrastructure off-limits to training and discouraging AI-generated projects that consume resources without contributing to the community — is not a technophobic reaction. It is a defensive act of class struggle from within the digital commons, an attempt to draw a line around a sphere of production that remains governed by use-value and human need rather than exchange-value and profit. It is a recognition that the commons is not a free reservoir for capital’s extraction machine, but a social achievement that must be protected by those who built it.

The broader lesson is that no technology is socially neutral. The shape of LLMs — centralized, resource-intensive, trained on stolen labor, deployed to eliminate skilled work and concentrate power — is not an accident. It is the result of an economic system that prioritizes the accumulation of private wealth above all else. The fight over who controls artificial intelligence is, at its root, a fight over who controls the means of computation, who appropriates the surplus of collective intelligence, and whether human collaboration can continue to exist outside the market. Codeberg’s small stand is a reminder that even now, alternatives are possible — but they must be actively, and collectively, defended.