AI outputs lack genuine novelty and rely illegally on copyrighted material, points to broader issues of assetization, privatization, and data colonialism in AI development.

Fundamental to such seizures is a lack of case law, which compounds existing confusion over the provenance of authorship, the role of human contributors in creating input data, and the degree to which generative AI outputs are original and transformative. A class action lawsuit filed against OpenAI in July 2023 rejects the idea that their ChatGPT model is capable of genuine novelty. ‘A large language model’s output is ... entirely and uniquely reliant on the material in its training dataset’, they reason.77 According to this view, OpenAI has made illegal use of copywritten material. The company’s response, in August 2023, situates the case around the science of inference. ‘As Plaintiffs acknow- ledge, the ability of digital software to [interpret user prompts and generate convincingly naturalistic text outputs] reflects a legitimate scientific advancement in the field of arti- ficial intelligence, which seeks to “simulate human reasoning and inference.”’78 This lawsuit captures aspects of the evolving character of animo nullius after the 2010s. Unlike in the mid-century, it is now corporations, alongside some researchers, who stand to benefit materially from equivocation over the perceived authority and intellectual genealogy that underwrites AI. OpenAI’s valuation fluctuates in line with this perception; at the time of going to press it is the most valuable start-up in America. Post-war IBM sought only outputs from AI, not its brand. Another difference is that debate over these scientific merits has shifted from labs and markets to courtrooms, too. That territories seized under terra nullius remain stolen today, centuries later, bears out how mis- interpretations of the past can calcify into law and prolonged settlement. Discerning publics – including judges, legislators and dispossessed authors – would be forgiven for assuming, naively, that the statistical ‘intelligence’ and ‘learning’ on display in post GPT-era systems derive from, say, the empirics of Darwin rather than, in my account, the contingent social order elaborated by Smith. What else would history suggest? Birch, who theorizes the rise of assetization in modern technoscience, characterizes this form of economic system as rentiership. Unlike with the valuation of a commodity, which is generally extinguished upon its consumption, ownership and control of an asset extend one’s rights to copies and derivatives, which adds scope for control and extraction. ‘No one can tell you how to wear a coat you have bought, but they can tell you how to use the copyrighted music or data they have sold you’, he writes.79 Animo nullius figures in rentiership economics because the value of an asset requires ‘active and ongoing organization, governance, and management ... it is not a passive process’.80 In my account, animo nullius animates AI’s value as an asset by granting those who invoke it an outwardly credible and durable (that is, historical) claim to the authority of a prestige metaphor. Data colonialism In my introduction, I cited Mejias and Couldry’s interest in how these emerging branches of capitalism intersect with data colonialism. I will end by offering three conjectural par- allels that emerge from my account. The first is an observable convergence between state and corporate interests over privatizing the costs of speculation (and initial seizures) amidst imperial expansion. State involvement is central to the durability of the extractive project outlined above because, per Birch, ‘assets are legal constructs, in that ownership and control rest on the state enforcement of property and control rights’.81 In Empire of Cotton, Sven Beckert argues that the commodification, processing and global trade of cotton illuminate the mechanics of historical colonialism and the Atlantic slave trade, wherein empires backed merchants and colonial corporations with military force to facilitate expansion.82 I suggest that the race to enclose the knowledge commons and assetize key analytical processes to the benefit of the global rentier class (and their respective nation states) helps to structure how data colonialism will shape capitalism in the era ahead. A second parallel, which puts tension on the first, is states’ backing of corporate self- governance and claims to the commons. State-backed corporate agency was decisive to the settler colonial project in ‘New England’, the land upon which, centuries later, AI would initially be developed at MIT. In that instance, the mechanics of self-governance in colonial corporations helped to prototype and, upon maturation, to stabilize and cohere America’s legislative independence from the British Empire.83 Possession via occupation succeeded wildly for the Puritans of New England, and for the Massachusetts Bay Company.84 Suggestive of a disanalogy with terra nullius, however, Birch notes that it is contract law rather than property rights that is central to the ownership of intangible assets such as machine-learning models.85 In any case, corporate self-governance is sure to remain an area of contestation as notions of sovereignty and power intersect with the global use of proprietary inference models across governments, universities, hospitals and other civic institutions. One final line of continuity concerns modes of capture. Analysing the relationship between terra nullius and historical colonialism, Buchan centres the role of subversive acts of ‘trafficking’, meaning the provision of gifts, favours and symbols of office that – as tokens of exchange – subtly coerced consent from Indigenous peoples that European colonists encountered to initiate trade relations. Over decades and centuries, these disingenuous relations reduced to modes of assimilation, subordination and then subjugation. While capture is already a concern in areas of AI critique, its relation to historiography and coloniality deserves particular attention.86 Indigenous scholars have shown how historiographical claims naturalize dispossession. Jean M. O’Brien’s Firsting and Lasting: Writing Indians out of Existence in New England, for instance, charts how colonizers narrativized Indigenous peoples as extinct in order to assert and mythologize their own sovereignty, ancestry and social order. Notably, Zoe Todd advances a similar critique of the history of science itself.87 Conclusion As with terra nullius, animo nullius threatens the commons with enclosure and private management. Both merit rebuke. In this paper, I have resituated histographies of AI in the his- tory of corporate colonial expansion to account for the fields’ imbrications in systems of power that prefigure its emergence in the mid-twentieth century. I have shown how, as far back as the 1950s, corporate efforts to incorporate digital media into mainstream knowledge practices (and, in doing so, to sell standardized computing infrastructures, from IBM to Microsoft) have intertwined with positivist interpretations of mind-as-computer metaphors as both literal and perpetually looming. Even when these overlaps were the products of opportunism by one side or the other, privatization appears to be the outcome. This speaks to the political economy within which AI was formed – significant elements of which are usually neglected by practitioners, historians and media commentary, however critical, on AI. For historians, the decision to characterize ‘AI’ as primarily a science of the mind, rather than a science of operations and adminis- tration, is, in my view, just that – a decision, and one that neglects the breadth of AI’s origins and helps blind us to its imbrications.