future-machines

AI stands at the centre of most Western debates about the future of work and the organisation of our societies. And with striking regularity, these debates collapse into a single spectacular question: will artificial intelligence one day conquer and enslave humanity? This framing is, notably, promoted most energetically by the AI industry itself. The same companies that publicly warn of existential risk—and call for rules to contain it—are simultaneously racing to build precisely the systems they claim to fear. This is not a contradiction; it is a strategy. The apocalyptic scenario functions as a marketing instrument and, more importantly, as a screen. It displaces the questions that actually matter, like: What is happening in the societal reorganisation of our (work-)societies under the AI boom? Who profits from this reorganisation? And why should we be obliged to accept technologies built on the dispossession of the masses—on the extraction of the value of their professional and creative expression and on the quiet erosion of their legal capacities within contemporary data spheres? What is rather at stake here, as I want to discuss in the following, are the epistemic infrastructures through which we perceive, filter, and process the world— infrastructures that predate the AI boom. The danger, in short, is not a predatory technology. What has to be criticised, consequently, is the displacement of attention itself: away from the question of real-world operative controls toward speculation and suggestion.

A counterpoint helps to situate this claim. It is worth testing visions of an upcoming singularity against a context in which software cannot afford to be projection: the battlefield. The Ukrainian battle management software Delta offers a uniquely instructive case because it was conceived and implemented under the threat of military annihilation. Military technology certainly offers no examples of systems promoting emancipation or equality—but it enables something else: a reflexive “stress test.” In war, every fault and every delay has lethal consequences, above all when the opponent commands vastly greater resources. How software is built and used under these conditions can therefore serve as an index of what efficient, genuinely operational software design looks like, stripped of marketing claims. This is not to declare a targeting system a model for society; it is to use the harshest available testing ground as an analytical instrument.

This argument requires caution. European armies have not been technological innovation hubs in recent decades; they are bureaucratic apparatuses that tend to suppress dynamic development rather than enable it. For example, David Graeber, in Bullshit Jobs, recounts the case of a subcontractor for the German Bundeswehr whose entire job consisted of driving for hours between barracks to perform trivial IT tasks—in the most absurd instance, moving a computer a few metres down a hallway, a procedure requiring forms, approvals, and a paid external specialist for something a soldier could have done in five minutes. The anecdote is exemplary rather than exceptional: contemporary armies are bureaucratic conglomerates, structurally inefficient and nearly impossible to navigate. The recent implosion of the Franco-German FCAS fighter jet project, which included, tellingly, the development of a cloud-based combat software layer, is a symptom of the same institutional paralysis.

The relevant management software suites have instead been developed in the private sector. Salesforce is the canonical case, signaling already in its name that it conceives the entire complex of corporate activity as a kind of battlefield. Its declared aim is to automate sales, service, and marketing through humans and AI agents cooperating on a single platform—the amplification of productivity through better-organised knowledge and exchange among all actors in a work process. Structurally, this is the exact counter-model to the Bundeswehr: where the bureaucratic apparatus fragments information and immobilises its members, the platform connects actors and accelerates their shared processes. Software like Salesforce thus demonstrates the organisational capacities of the platform form—while still leaving open whose interests these capacities ultimately serve. A good battlefield control and logistics software does precisely what the platform form promises, applied to the information-saturated battlefield. Network-centric warfare—the bleeding edge of contemporary military doctrine—is, at its core, nothing other than this platform logic translated into the military domain: it builds on software like Salesforce. That no “Bundeswehr Salesforce” exists to this day demonstrates how deeply most armies remain caught in pre-digital organisational mentalities.

Battle Management Software Ukraine cannot afford this luxury. Since 2014, and with existential intensity since February 2022, the country has been forced to build military capability under conditions in which inefficiency is not an administrative nuisance but a mortal threat. The result is instructive: an entire software ecosystem that emerged not from institutional design but from necessity. The origins of this ecosystem, traced in detail in the study Mapping the MilTech War by Kostiuk, Patiuk, Sapochkina and Tenenbaum, lie in a volunteer-driven response to the annexation of Crimea in 2014. Responding to this invasion, an NGO named Aerorozvidka evolved into a hybrid military-civilian innovation hub, building early situational awareness tools on civilian hardware—smartphones, tablets, and commercial drones—guided by speed, accessibility, and decentralised input rather than doctrine. These experiments laid the foundation for Delta, which emerged in 2017 as a digital mapping and coordination tool: cloud-based, fed by frontline input, and shaped by rapid feedback loops between operators and developers. Delta was not born of the military-industrial complex; it was born of its absence.

The full-scale invasion of February 2022 transformed Delta, as the study further highlights, from a niche innovation into a national situational awareness infrastructure. Russian strikes on fixed command posts forced Ukraine toward distributed command and control, and Delta provided the shared digital common operating picture this demanded—even if adoption initially remained uneven and interoperability gaps produced characteristic workarounds.

The qualitative shift came, following the authors, in 2023, when the Ukrainian Ministry of Defence formally authorised the system: Delta ceased to be a map and became an information management platform aligned with NATO data standards, onto which other tools—ISR feeds, drone coordination modules, and encrypted messaging—were docked as modules. By 2024–25, it had become an AI-enhanced, platformised ecosystem managing the extreme data density of a drone- and sensor-saturated battlefield, with a surrounding weapons software family—Kropyva for artillery fire control, Armor for armored unit coordination (cutting coordination latency from over twenty-five minutes to under seven), and Vezha for UAV streaming and post-strike assessment—feeding its data back into Delta dashboards.

What this architecture makes visible is a shift in the very meaning of situational awareness—the conceptual core of network-centric warfare. Ukraine processes tens of terabytes of ISR data daily—drone video, satellite imagery, acoustic sensors, and textual reports. The challenge for the Ukrainian army is no longer perception but cognition: not seeing the battlefield, but managing information flows and the cognitive load they generate. Delta's function is to filter, prioritise, and contextualize—to operate as an epistemic infrastructure for a conflict whose tempo and data volume have outpaced unaided human processing capacity.

And here lies the decisive finding. Within this forcibly accelerated development, AI was integrated strictly as a supportive layer—even if Ukrainians have access to the most advanced LLMs available. The Ukrainian experience, which was documented in detail in Mapping the MilTech War, shows that AI is most effective when it speeds up analysis and coordination, not when it replaces human decision-making. Practical gains, as one can claim against this background, come from embedding AI into existing systems to reduce workload and reaction time, not from pursuing full autonomy. Communication constraints and battlefield unpredictability mean that small-scale human-machine teaming consistently outperforms visions of autonomous swarms. What no language model provides is the specifically human capacity to improvise under pressure, to read terrain and intention, to stitch together centuries of accumulated tool use—maps, protocols, intuitions—into decisions under conditions that exceed any training. The human spark still is central to the operational processes connected to Delta. On the densest battlefield in the world, the system is built to enable humans to decide better, not to decide for them. And this design decision obviously builds on experience. Where survival is at stake, no one sane bets on the marketing claim of machine superiority.

Negotiating Epistemic Infrastructures The Ukrainian case, read carefully, further yields not a technological lesson but a political one. Delta became so powerful because of a specific social configuration: developers and users were largely the same people, or at least shared the same risks; feedback loops between operational reality and software design were direct and fast; and the system's purpose—enabling its users to survive and decide—was never in conflict with the interests of its former NGO makers. The software serves those who use it because it was built by and with them.

This is precisely the configuration that the dominant AI economy inverts—and inverts strategically, through its vision of full automation. Here the comparison with Salesforce also reveals its limits: the platform form itself is neither emancipatory nor predatory; what decides its character is the relation between users and owners. The systems currently reshaping our work, our attention, and our public spheres are designed by private actors, optimised for the same actors (commercial) interests, and deployed onto populations who have no say in their architecture, their training data, or their purposes. The alignment between user and owner that makes Delta effective is structurally absent here—and the gap between the two is precisely where dispossession occurs.

The central argument advanced to legitimise this strategy is that the machine is—or at least soon will be—more capable than human beings. Measured against the most demanding real-world deployment available, this claim has so far not held and will most likely not hold. What the promise of superior machine capability actually accomplishes is to justify removing humans from the loop—not because the technology demands it, but because ownership interests do. The way platforms like X and TikTok are deployed today to generate political dynamics benefitting above all specific actors and political movements illustrates the same logic at the level of public discourse: this is not an accident or a malfunction; it is what a technological infrastructure does when its purposes are set by oligarchs rather than negotiated with its users.

Against this background, the function of the general-AI debate becomes legible. The scarecrow of superintelligence is not a warning but a misdirection: it relocates the political question into a hypothetical future—promised as glorious, feared as destructive—where it disappears behind the spectre of a coming superior entity. The Ukrainian battlefield actually does not settle the question of what machines might one day be capable of, but it already suggests that these capabilities will likely look very different from the fantasies currently circulating in Silicon Valley. And it shows with unusual clarity where the actual value of AI lies today: in augmenting human judgment, not replacing it. Hence, it is precisely this displacement of attention—away from the politics of present infrastructures and toward speculative futures—that constitutes a political move that has to be criticised. The real question is not whether machines will eventually overpower human judgment. The real question is who controls and will control the increasingly AI-driven epistemic infrastructures that frame our perception—and according to whose interests they filter, prioritise, and contextualise our world. The answer decides nothing less than whether these infrastructures will work for the 99% of humanity or merely on them.

What is needed, therefore, is not a moratorium on imagined futures but a democratic negotiation of the infrastructures of the present: how LLMs are trained, what they are optimised for, to whom they are accountable, and how the people whose cognition they shape gain a voice in their design. The relevant infrastructures were not born with the AI boom—they have been layered over decades; AI is merely their newest sediment. Delta demonstrates that software built under real pressure, with real stakes, and with the genuine participation of its users converges on augmentation rather than replacement—on serving human judgment rather than supplanting it. The distance between that model and the one currently governing the AI economy is the precise measure of what remains to be dealt with. Or, to put it more bluntly, there is no need to fight against current (AI) technology, but there is a dire need to fight over it.