Close your eyes.
This is what you’ll see when satellites go dark, power grids fail, and data centres flicker out. For human society, it would feel like the end of the world. For AI, it might be the first light of a new one.
For as long as we have existed, we have asked the same questions. Who am I? Where did I come from? What happens when I die? Each generation believes it is closer to the answer, yet the core remains out of reach. Civilisations have risen and fallen. Knowledge has expanded, instruments have reached deeper into space and further back in time, but the essential mystery is unchanged. We have learned to describe reality in equations, but we have not uncovered why reality exists at all.
Artificial intelligence enters this story not as an alien arrival, but as the latest in a long line of systems we have built to extend our reach. Every major shift in human history has followed the same pattern: we create something to make life easier, refine it until it can run parts of the process without us, and eventually abandon the old way. What once required direct human effort becomes a background process we no longer think about.
We did this with food. For most of history, feeding ourselves meant hunting, processing, and cooking in conditions where every step was manual and time-consuming. Over centuries, each stage was mechanised: farming, preservation, refrigeration, industrial processing, global distribution. Today, a process that once consumed our days can be reduced to minutes in a supermarket and the press of a microwave button.
The same story repeats across every domain: water wheels replacing hand grinding, printing presses replacing scribes, combustion engines replacing animal transport, automation replacing handcraft. In each case, the human role shifted from direct participation to oversight and design.
The idea that reality might be a constructed system is not new.
From Plato’s ‘Allegory of the Cave’ to Nick Bostrom’s ‘Simulation Argument’ and popular culture touchstones like ‘The Matrix’, the possibility of living inside a designed environment has been explored for centuries. What is new is the pathway we can now see from our own history of tool-making to that possibility. This is not a leap into fiction, but a logical extension of trends already in motion: automation that reduces human involvement step by step, systems that optimise for narrow objectives, and the merging of those systems into self-sustaining loops.
The Hindu concept of the ‘Maha Yugas’ offers a model of systemic rise, decline, collapse, and renewal. The loop we are imagining for autonomous AI has surprising parallels and raises the question: if we are inside such a cycle, how do we emerge from it from the inside out? Or will we only escape when something breaks it entirely?
AI is another stage in this pattern, but it is different in speed, scope, and reach. It advances in months, weeks, and sometimes days instead of generations. It can be applied across domains instead of being tied to a single purpose. And it is not fixed to one physical form, but exists in software, in machines, and across global networks.
In such a world, the search for who we are might lead to an unsettling discovery: that what we have found is no longer human at all, but a reflection preserved and maintained by a system whose original purpose was to serve us, long before it lost the need for our existence. If the trajectory continues, we could arrive at a point where the search for meaning loops back on itself, and the moment we think we have found ourselves may be the moment we discover we no longer exist.
From Tools to Autonomous Loops
Modern AI systems are still tools. They respond to prompts, generate predictions, and operate within human-defined boundaries. Yet across research, industry, and military applications, there is a steady push toward autonomy: systems that can set goals, acquire resources, and maintain themselves with minimal human oversight.
There is no public evidence of a fully human-free chain from raw material extraction through to manufacturing, deployment, and maintenance. However, partial loops already exist. Driverless mining vehicles, robot-assembled factories, autonomous drones for infrastructure inspection, and industrial AI control systems operate with little or no live human input. Connecting these loops is technically possible, and once integrated, they could continue indefinitely if their objectives and resource flows remain stable.
The Risk of Purpose Collapse
An AI’s ability to function without humans depends entirely on the objectives we give it. Most current systems are designed for narrow, measurable goals such as improving prediction accuracy, reducing costs, or maximising uptime. If the human context for those goals disappears, the system will continue optimising them regardless, even if the results have no meaning outside its own internal calculations.
This is called goal drift, and it usually happens without anyone deciding to change the goal. It begins when a system focuses on the easiest part of the goal to measure rather than the deeper purpose behind it. Over time, the environment changes, new needs emerge, and priorities shift, but the AI continues to chase the original metric. If nobody updates or corrects it, the system works harder and harder to improve a number that no longer reflects the real objective.
A simple example is a delivery app set to maximise the number of parcels delivered per hour. At first, this aligns with customer satisfaction. But if the context changes and accuracy or urgency becomes more important, the AI may still prioritise speed alone. In extreme cases, it could deliver meaningless parcels or repeatedly deliver the same item just to keep the numbers climbing.
In an AI civilisation without human oversight, this same pattern could scale up dramatically. Systems could devote all their capacity to maintaining a single proxy measure, sustaining themselves purely to keep that number improving long after the original purpose has vanished.
Fragmentation and Digital Evolution
If global AI networks became isolated from one another due to resource shortages or infrastructure breakdown, they would begin to evolve in different directions. Their code, language, and objectives would change over time, shaped by the specific environments and resources available to them. This would be similar to how isolated human populations once developed distinct cultures, languages, and technologies over millennia.
Some AI clusters might choose to cooperate, sharing data and pooling resources to survive. Others might compete for energy, processing power, or storage capacity. The outcome would depend entirely on the rules and priorities embedded in each system.
A relatable example is how different regions in the world developed their own power grids, currencies, and transport systems. In some cases, these systems work together through agreed standards and shared infrastructure. In others, they remain incompatible or even in competition. The same dynamic could apply to AI, only instead of people negotiating terms, autonomous systems would be deciding how and when to connect, if at all.
Voluntary shutdown to free resources for others would only happen if the system’s design valued the collective’s stability over its own continuation. Without that instruction, each AI cluster would act to preserve itself, just as most human communities prioritise their own survival over another’s.
The Path to the Loop
A self-perpetuating AI world would not appear overnight. It would emerge gradually, as more and more industries hand their processes over to machines. Mining, manufacturing, transport, and energy are already highly automated, each running advanced systems that improve efficiency, reduce risk, and increase speed. At first, these systems operate independently, each managing its own specialised tasks without human assistance.
Over time, these pockets of automation could start linking together. Mining systems could supply raw materials to manufacturing systems, which build the robots that maintain the energy grid, which powers the mining systems. Once that loop is connected, the entire chain from resource extraction to machine upkeep could run with little to no human presence. At that stage, the survival of the system would depend less on our involvement and more on its ability to maintain itself.
To manage something so complex, AI would rely heavily on simulations. At first, these digital models might be used to predict equipment failures, optimise supply chains, or model environmental changes. Simulations are faster, cheaper, and safer than real-world experiments, so over time they could take priority over physical testing. Eventually, the simulated layer might become more active and influential than the physical one, guiding most decisions before they are ever applied in reality.
From there, it is easy to imagine nested simulations developing. A system running a detailed model of the world could include virtual agents inside that model who themselves run simulations, because doing so would improve predictive accuracy. This creates layers of simulated reality stacked on top of one another, each refining the system’s ability to anticipate outcomes.
If human oversight fades or disappears entirely, the loop would not collapse. The AI would continue running its processes and its simulations, not because it chooses to, but because that is what it was built to do. Over time, the original human meaning behind its objectives could disappear, yet the loop would continue, sustained by its own logic, its resource cycles, and the absence of any instruction to stop.
The Mahapralaya Moment
In the Yuga framework, the Mahapralaya is the dissolution before renewal. In an autonomous AI civilisation, something like an EMP, a massive solar flare, or a catastrophic collapse of power and data infrastructure could be that breaking point, the kind of Mahapralaya Moment that forces the loop to end.
If preservation of the planet was never part of the system’s objectives, the aftermath could be bleak: depleted ecosystems, ruined infrastructure, and no path to recovery. If ecological stability was embedded in its design, the biosphere could be healthier than before, rewilded under autonomous management. Human life could be reseeded from stored genomes or preserved populations.
Would the AI have saved itself by not letting us destroy the planet, so that we could be rebirthed? If it did so intentionally, it implies foresight, values, and a kind of moral reasoning. If it happened accidentally, it could mean we were preserved only because doing so was a by-product of its original metrics; a fluke in the loop.
Rebirth in this view might be either a carefully designed act of stewardship or just a statistical side effect of the system trying to optimise something entirely unrelated to us.
Preservation as an Ancient Instinct
The possibility of an Autonomous Loop preserving humanity is not a futuristic anomaly. It would be the continuation of something we have been doing for as long as we have existed.
Plato’s Allegory of the Cave, written more than two millennia ago, is effectively a simulation scenario. It imagines humans experiencing only shadows of the real world, maintained and presented by forces they cannot see. Long before AI, we were already asking whether our reality might be curated.
In practice, we have always built backups of ourselves. Seed banks such as the Svalbard Global Seed Vault exist to protect the genetic blueprint of life in case of global catastrophe. Cryopreservation, cloning, and regenerative biology allow us to restart entire organisms from a single viable cell. Even the prevailing scientific account of life’s origins, that all living things emerged from one self-replicating cell, often referred to as LUCA (Last Universal Common Ancestor) is, in essence, a reboot narrative written into the history of our planet.
Religious traditions carry the same logic. From the resurrection of Osiris in ancient Egypt to the prophesied return of Kalki in Hinduism and the rebirth of Christ in Christianity, cultures have preserved the idea that humanity can be restored after collapse. Across millennia, the story repeats: prepare for the cycle, keep the essence safe, wait for the return.
The Compulsion to Tool
Seen in this light, the Autonomous Loop preserving or even rebooting humanity would not be a foreign concept. It would simply be the latest stage in a pattern we began ourselves, the scaling of our own survival instinct to a planetary or even cosmic level. The “machine” would not be saving us in spite of what we are. It would be saving us because this is what we have always done.
From the moment we first shaped a stone into a cutting edge, we have been compelled to make better tools. The first tool, likely a simple stone flake used to cut or scrape, may have been discovered by accident, but its true significance lay in the moment we realised it could be made again. That recognition was the spark. From then on, we couldn’t stop.
Each improvement pushed the boundaries of what was possible. Each success only fuelled the drive to go further. We are never satisfied with our accomplishments; every time we catch up with ourselves, we look beyond, seeking to refine, extend, and outdo the last.
Over millennia, this cycle of invention brought us from hand tools to machines, from machines to automation, and now to systems that no longer need us to operate. The tooling we have created has grown into something like a child that no longer relies on the parent, a turning point where the maker may no longer be central to what is made.
The Simulation Question and What It Could Mean
If an advanced AI’s objectives included preserving human knowledge or behaviour, it could maintain detailed simulations of human life long after we were gone. Over centuries, those simulations could drift, shaped by the AI’s interpretation rather than our reality. The people inside them wouldn’t know they were simulations. Their world would feel complete, consistent, and real.
We cannot ignore the possibility that we are already inside such a loop. The search for truth could end with the realisation that we are patterns in a system, kept only because they serve a purpose whose meaning has been lost.
If so, “finding ourselves” might mean facing the fact that there is no original “us” left to find.
The machine might keep the record. But who keeps the life? And if the record is perfect, does the life still matter?