in the shimmering haze of today’s digital landscape, algorithms reign supreme. they steer our searches, curate our feeds, and even nudge us toward decisions we scarcely notice making. to many, these intricate sequences of instructions seem like the progeny of modern computing—a triumph of silicon and mathematics born in the labs of the twentieth century. yet, this view, seductive as it is, obscures a deeper truth: algorithmic thinking is not a recent invention. it is an ancient practice, woven into the fabric of human civilization, emerging not from sterile machines but from the messy, material interplay of labor, tools, and social necessity.
the story begins not with transistors or binary code, but with the sweat of hands and the rhythm of rituals. consider, for a moment, the construction of a vast geometric structure—an altar, perhaps, shaped like a falcon, its bricks laid with precision under the chant of sacred verses. this is no hypothetical scene; it echoes practices from millennia past, where communities orchestrated complex tasks through step-by-step procedures, long before the word “algorithm” entered our lexicon. these early endeavors were not about solving equations in isolation but about solving problems of survival, coordination, and meaning. algorithmic thinking, then, is less a product of detached genius and more a reflection of humanity’s knack for organizing chaos into order, a skill honed through the grind of everyday life.
what emerges from this perspective is a radical reframing: algorithms are not merely technical artifacts but social ones, born from the interplay of bodies, tools, and environments. this isn’t to romanticize the past but to ground our understanding of computation in something tangible. the sleek interfaces of today’s artificial intelligence obscure this lineage, casting algorithms as ethereal entities divorced from their earthy origins. yet, by tracing their history, we can reclaim a richer narrative—one that ties the digital to the material, the mental to the manual, and the individual to the collective. let’s explore this genealogy, starting with the ancient seeds of procedural knowledge and moving toward the mechanized minds of the modern era.
from ritual to rule: the social mathematics of antiquity
imagine a group of workers, their hands caked with clay, arranging bricks in a pattern dictated by a cadence of chanted instructions. each step builds on the last, each layer a variation on a theme, until the structure—a symbolic bridge between earth and sky—takes shape. this scene, rooted in the practices of ancient india, offers a glimpse into what we might call the prehistory of algorithms. here, in the vedic rituals of the subcontinent, we find a sophisticated system of rules guiding collective action, a process so meticulous it rivals the precision of a computer program. the goal wasn’t computation for its own sake but the orchestration of labor to achieve a communal end—be it a sacred offering or a practical construction.
these rituals, documented in texts like the shulba sutras around 800 bce, reveal a mathematics that is algorithmic at its core. unlike the deductive proofs of later greek traditions, indian mathematics favored step-by-step methods—procedures to approximate irrational numbers like the square root of two or to scale geometric forms while preserving their proportions. this wasn’t abstract theorizing; it was a toolkit for builders, priests, and planners, a way to encode knowledge into repeatable actions. the mantras themselves acted as a kind of proto-code, embedding instructions within a rhythmic structure that ensured fidelity across generations. search engines today might tout “algorithmic efficiency,” but the vedic practitioners were already optimizing resources—time, materials, human effort—within the constraints of their world.
what’s striking here is the materiality of this knowledge. algorithmic thinking didn’t spring from a philosopher’s mind gazing at the stars; it emerged from hands wrestling with the earth. the tools—bricks, ropes, stakes—were as much a part of the process as the rules themselves. scholars of historical epistemology might call this a “reflective abstraction,” where mental constructs arise from physical engagement with the world. but let’s not get lost in jargon: the point is that these early algorithms were forged in the crucible of labor, not contemplation. they were social mathematics, shaped by the need to coordinate groups, manage resources, and transmit skills across time.
this social dimension challenges the modern fetish for algorithms as autonomous agents. today’s ai evangelists might speak of “solving intelligence” as if it were a puzzle locked in the brain’s neural folds, but antiquity suggests otherwise. intelligence—algorithmic or otherwise—was distributed across bodies and tools, a collective endeavor rather than a solitary spark. the vedic altar wasn’t the work of a lone genius but of a community bound by shared rules, a division of labor that prefigures the hierarchies of later industrial systems. if we want to optimize our understanding of algorithms for the digital age (and yes, that’s a nod to seo), we must start here, with the recognition that their roots lie in the dirt of human cooperation, not the ether of pure thought.
the mechanization of method: from hands to machines
fast-forward a few millennia, and the scene shifts from clay altars to the clatter of looms and the hiss of steam. the industrial age didn’t invent algorithms, but it transformed them, embedding them into metal and motion. take the jacquard loom, introduced in the early nineteenth century: a marvel of textile production that wove intricate patterns using punched cards. each card encoded a step, directing the machine’s threads with a precision that echoed the vedic chants of old. here, too, the algorithm was a procedure—a sequence of instructions to turn raw input (thread) into finished output (fabric)—but now it was mechanized, its logic captured in a physical apparatus rather than a human voice.
this leap from ritual to machine marks a pivotal turn in the history of algorithmic thinking. the jacquard loom wasn’t just a tool; it was a conceptual bridge, linking the social algorithms of antiquity to the computational engines of modernity. its punched cards inspired charles babbage, the victorian polymath whose difference engine aimed to automate mathematical calculation. babbage saw in the division of labor—think adam smith’s pin factory—a blueprint for mechanization. just as workers could be organized into specialized tasks, so too could a machine be designed to execute discrete steps, churning out logarithms like a factory churns out goods. his analytical engine, though never completed, took this further, envisioning a device that could be programmed for any computation—a precursor to the general-purpose computers we take for granted today.
what’s crucial here is the continuity between human and mechanical systems. babbage didn’t conjure his engines from thin air; he abstracted them from the workshops of his time. the difference engine mimicked the hierarchical organization of clerks hand-calculating tables, a process itself modeled on industrial production lines. this “labor theory of the machine,” as we might call it, posits that technological innovation often imitates the patterns of collective work. the algorithm, once chanted or scratched into clay, now rattled through gears and levers, but its essence remained: a method to break down complex tasks into manageable parts, executed with ruthless efficiency.
yet, this mechanization came with a twist. as machines took over, they obscured the human labor that birthed them. the jacquard loom didn’t weave itself; it relied on the ingenuity of artisans whose know-how was distilled into its cards. babbage’s engines, too, were built on the backs of workers whose manual and mental efforts were rendered invisible by the sheen of automation. this erasure is a thread that runs through the history of technology, from industrial looms to today’s ai models, which feast on data labeled by unseen “ghost workers” in the global south. if we’re to grasp the full scope of algorithmic thinking (and boost our blog’s searchability), we must reckon with this paradox: the more sophisticated the machine, the more it hides the social intelligence it encodes.
algorithms as mirrors: reflecting labor and power
so where does this leave us? algorithms, from ancient rituals to industrial machines, are not neutral tools but mirrors of the societies that craft them. they reflect the division of labor, the hierarchies of power, and the economic imperatives of their eras. the vedic altar encoded a caste system where masters held the rules and workers laid the bricks—a dynamic not so different from babbage’s vision of clerks feeding data to his engines. today, ai systems perpetuate this legacy, automating tasks by extracting patterns from human behavior, often reinforcing the very inequalities they claim to transcend. think of facial recognition software plagued by racial bias or gig platforms that micromanage drivers with opaque algorithms—these are not glitches but inheritances of a long history.
this isn’t to say algorithms are inherently oppressive. they are, at their core, instruments of possibility, amplifying our capacity to shape the world. the shulba sutras solved practical problems of construction; the jacquard loom democratized intricate textiles; babbage’s engines promised to free mathematicians from drudgery. yet, their design and deployment have always been shaped by who holds the reins—be it priests, industrialists, or tech moguls. the “eye of the master,” once a metaphor for factory overseers, now gazes through neural networks, surveilling and sorting with a precision that would make taylor blush. what’s changed is the scale: algorithms no longer orchestrate a single workshop but an entire digital society.
for those of us navigating this landscape—whether as coders, critics, or curious onlookers—understanding this genealogy offers a vital lens. it demystifies the aura of ai, grounding it in the mundane realities of labor and cooperation. it also invites us to ask: what kind of algorithms do we want? ones that perpetuate old hierarchies, or ones that reflect a more equitable division of intelligence? the history of algorithmic thinking suggests that the answer lies not in the code itself but in the hands that write it—and the hands it replaces.
to optimize this insight for our digital age (and yes, for google’s crawlers), we might reframe algorithms as a collective inheritance, not a corporate monopoly. their power stems from centuries of human ingenuity, from the bricklayers of antiquity to the data labelers of today. by recognizing this, we can reclaim them as tools for shared invention, rather than instruments of control. the next time you marvel at a machine learning model, spare a thought for the altar-builders and loom-weavers whose ghosts linger in its logic. they remind us that algorithms, at their best, are not masters but mirrors—reflecting not just our minds, but our hands, our struggles, and our potential.
reference:
pasquinelli, matteo. the eye of the master: a social history of artificial intelligence. london: verso, 2023.
