The ongoing dispute between OpenAI and Elon Musk has been framed, at least superficially, as a legal and personal confrontation—a founder challenging the trajectory of an institution he helped to create. The superficiality lies in the tendency to treat proceedings before a US federal court—and any potential jury verdict—as dispositive in the narrow legal sense of conclusively settling a matter. Such outcomes, however, are better understood within what Michel Foucault would call a dispositif: a broader apparatus of power and knowledge in which legal judgments function not as endpoints but as moments within an evolving field of institutional and discursive forces. In other words, a different sequencing before the court, or a variation in how the case was constructed, could just as plausibly have yielded an antithetical result. Headlines that cast the episode as a win or loss for either side therefore risk mistaking procedural resolution for substantive closure, obscuring the more consequential reality: this is not simply a conflict over contracts or corporate governance, but a struggle over the meaning of “openness” in an era in which it is both under assault and structurally difficult to sustain.

At stake is the evolution of artificial intelligence from an aspirational public good into a capital-intensive strategic infrastructure. Musk’s critique rests on the claim that OpenAI has departed from its original nonprofit, open ethos. OpenAI, for its part, has implicitly argued that the scale, cost, and risks associated with developing frontier AI systems require new institutional forms—hybrid models that combine mission with market discipline, and openness with controlled deployment. The disagreement, then, is less about whether OpenAI has changed than about how that change should be interpreted.

A useful contrast can be found in the Human Genome Project, often cited as a model of large-scale scientific openness. Completed in 2003, the project made a deliberate commitment to keeping genomic data in the public domain, resisting efforts to privatise genetic sequences. Yet even there, openness was not uncontested. The publicly funded initiative operated alongside private ventures, most notably Celera Genomics, which sought to commercialise genomic data through proprietary databases. The eventual outcome—a largely open genomic commons—was not the natural state of scientific progress but the product of political decisions, institutional coordination, and sustained public investment.

The comparison is instructive precisely because it reveals what has changed. The Human Genome Project unfolded in a context in which governments could mobilise resources at scale and in which the benefits of openness could be institutionalised through public funding. By contrast, contemporary AI development is driven by computational demands and competitive dynamics that far exceed the capacity of any single public institution. The infrastructure required to train advanced models—massive data centres, specialised chips, and continuous iteration—has shifted the centre of gravity towards private actors. Under such conditions, openness is no longer a default aspiration but a contested and often costly choice.

This transformation can also be parsed through what Fredric Jameson famously described as postmodernism, or the cultural logic of late capitalism, in which concepts that once functioned as ethical or intellectual commitments are progressively absorbed into the circuitry of capital. In this sense, “openness” no longer operates as a stable normative principle but as a flexible signifier, rearticulated according to institutional position and market strategy. What appears as a disagreement over fidelity to founding ideals is thus also a symptom of a broader condition in which even the language of the commons is recoded within competitive economic structures.

This shift signals a broader reconfiguration of the political economy of knowledge production. In earlier eras, the state functioned as the primary underwriter of large-scale scientific endeavours, enabling forms of openness that were insulated, at least partially, from market imperatives. Today, however, the locus of innovation has migrated towards corporate ecosystems in which intellectual property, platform control, and first-mover advantage shape the trajectory of research. The result is a hybrid regime in which public rhetoric continues to invoke the language of the commons, even as the underlying structures increasingly resemble those of proprietary capitalism.

The legal dispute between Elon Musk and OpenAI thus operates as a proxy for a deeper structural tension. Musk’s position gestures towards a more decentralised and transparent trajectory for AI development, even as his own venture, xAI, competes within the same high-stakes ecosystem. OpenAI’s alignment with partners such as Microsoft reflects the opposite pull: towards consolidation, managed deployment, and integration into existing technological and economic hierarchies. Neither position stands outside the system it critiques. Both are embedded in a competitive landscape in which scale and control increasingly determine viability.

OpenAI CEO Sam Altman seen during the trial over the future direction and governance of OpenAI PHOTO: REUTERS

The future of AI governance will not be determined by technical capability alone, nor secured through regulatory design in any straightforward sense. It will instead be shaped by how a small set of foundational terms—“open,” “safe,” and “aligned”—are continuously defined, contested, and operationalised by the institutions that now govern computation at scale.

What makes the conflict particularly instructive is that it cannot be resolved at the level of legal judgment alone. Courts may adjudicate specific claims—about contractual obligations, fiduciary duties, or representations of intent—but they cannot definitively settle what “openness” was meant to signify at the project’s inception. That question is not purely legal; it is historical, philosophical, and strategic. Each side reconstructs the past in order to legitimise its present position.

To understand the stakes more fully, one must situate this dispute within the emerging architecture of AI as infrastructure. Unlike earlier digital technologies, advanced AI systems do not merely enable applications; they function as foundational layers upon which entire economic sectors are being reorganised. From finance and logistics to healthcare and education, AI models are becoming embedded in decision-making processes at scale. Control over these models therefore confers not just market advantage but structural power—an ability to shape the conditions under which knowledge is produced, accessed, and applied.

This infrastructural turn introduces new forms of dependency and asymmetry. Firms and governments alike increasingly rely on a small number of providers for access to advanced AI capabilities, raising concerns about concentration and systemic risk. The analogy to energy markets is not entirely misplaced: just as control over oil and gas once defined geopolitical leverage, control over computational resources and model architectures is beginning to define the contours of technological sovereignty. In this environment, openness is constrained not only by economic incentives but also by strategic considerations, including national security and geopolitical competition.

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Elon Musk’s lawyer Marc Toberoff addresses the media outside the Oakland federal courthouse during the trial over OpenAI’s conversion to a for-profit organisation on May 18, 2026. Photo: Reuters

The labour dimension of this transformation is equally significant. The development and deployment of AI systems rely on vast, often invisible networks of human labour—from data labelling and content moderation to engineering and infrastructure maintenance. Much of this labour is geographically dispersed and unevenly compensated, reflecting broader patterns of global inequality. The political economy of AI thus extends beyond questions of ownership and control to encompass the conditions under which value is extracted and distributed. Openness, in this context, cannot be disentangled from questions of labour, exploitation, and the global division of technological work.

At the same time, the data that fuels AI systems raises its own set of political-economic questions. Large language models are trained on vast corpora of text, much of it produced without explicit consent or compensation. This has sparked growing debate over data rights, intellectual property, and the boundaries of fair use. If data is the raw material of AI, then the governance of data becomes central to the governance of AI itself. Here again, the language of openness collides with competing claims over ownership, privacy, and value.

Regulation enters this landscape as both a constraint and an enabler. Governments are increasingly seeking to shape the development of AI through rules on safety, transparency, and competition. Yet regulatory frameworks often lag behind technological change, and they are themselves shaped by the lobbying power of major firms. The risk is that regulation may entrench existing advantages rather than democratise access, thereby reinforcing the very concentrations of power it seeks to mitigate. The political economy of AI is therefore not simply a matter of market dynamics but also of institutional design and political contestation.

In this sense, the dispute between Elon Musk and OpenAI illustrates a recurring pattern in technological transformation. Founding ideals, articulated under conditions of relative uncertainty and low capital intensity, are reinterpreted as systems scale and the stakes rise. What appears as betrayal to some becomes adaptation to others. The shift from openness to controlled access is not unique to AI; it echoes earlier transitions in the history of the internet and platform economies. In the case of AI, however, the stakes are considerably higher, given the technology’s potential to reshape economic structures, information ecosystems, and political power.

The future of AI governance will not be determined by technical capability alone, nor secured through regulatory design in any straightforward sense. It will instead be shaped by how a small set of foundational terms—“open,” “safe,” and “aligned”—are continuously defined, contested, and operationalised by the institutions that now govern computation at scale. These are not neutral descriptors but instruments of alignment and legitimation, whose meanings shift as they move between corporate strategy, regulatory discourse, and geopolitical competition.

The dispute between OpenAI and Elon Musk is therefore less a legal episode than a diagnostic of this broader condition. It reveals a political economy in which meaning itself has become unstable: no longer anchored in shared normative reference points, but continually reconstituted through shifting institutional positions and infrastructural constraints. What emerges is a regime of free-floating semantics, in which the key vocabulary of AI governance no longer stabilises practice but instead travels with it—recast, reweighted, and redeployed as strategic circumstances demand. In this sense, the struggle over artificial intelligence is also a struggle over language: a contest in which even the language of the commons is drawn into systems of competitive differentiation and control.

Whether or not any actor prevails in court, the deeper dynamic will persist. “Openness”, once treated as an originating ideal of the field, has become an object of ongoing negotiation—its content contingent, its boundaries elastic, and its function increasingly embedded within a wider architecture of power and computation. The question, then, is no longer who wins this dispute. It is whether any vocabulary remains capable of anchoring collective judgment at all—or whether the language through which AI is governed is itself already the terrain on which capture is complete.

Dr. Faridul Alam, a former academic, writes from New York City.