In 1973, the science-fiction film Soylent Green imagined a world strained beyond its limits. Overpopulation, environmental collapse, and food shortages had become ordinary facts of life. Most people survived on government-issued rations produced by a single corporation. The most coveted of these was a product called Soylent Green, advertised as a cheap and nutritious solution to mass hunger.
As the story unfolds, a police detective begins to investigate the company behind the food supply. What he eventually discovers is the film’s famous twist: Soylent Green is not made from plankton, as the public is told. It is made from people.
The lasting power of Soylent Green does not come from the shock of cannibalism alone. It comes from what that revelation says about systems that grow so large and so essential that no one can meaningfully resist them. The factory does not need secrecy or public approval. It only needs to keep running.
More than fifty years later, we are not living in that world. But we are living in one shaped by a surprisingly similar logic.
Today, people are not being processed for food. They are being processed for data.
Every step recorded by a smartphone, every search query, every social media post, every GPS route, and every biometric measurement collected by a wearable device feeds into systems that operate almost entirely out of sight. Unlike the ration factories in Soylent Green, these systems do not announce themselves. They are embedded in everyday tools and services, and they present themselves as conveniences rather than infrastructure.
The result is a global data economy that treats human experience as its most valuable raw material.
The New Rendering Plant
In the film, people are rendered into food because the system has run out of traditional resources. In the modern digital economy, people are rendered because human behavior has become the most valuable resource of all.
Smartphones function as continuous sensors. Cars have become rolling data collection platforms. Smart watches and fitness trackers record heart rates, sleep patterns, and activity levels. Browsers and apps monitor what we read, what we watch, how long we linger, and what we ignore.
These streams are combined, correlated, and analyzed to produce detailed behavioral profiles. Location data becomes movement patterns. Social interactions become network maps. Emotional reactions become engagement metrics. Health data becomes risk scores. Over time, these fragments form a statistical version of each of us that is often more useful to institutions than the real person.
This is not happening because of a secret plot. It is happening because it is extraordinarily profitable.
You Are Not the User
For years, a familiar saying has circulated in the technology world: if you are not paying for the product, you are the product. While this is still true, it no longer fully captures what is happening.
In many cases, people are not simply being sold to advertisers. They are being broken down into data points and reassembled as predictive models. The goal is not just to show ads, but to anticipate behavior, influence decisions, and shape outcomes at scale.
Social media platforms are a clear example. They do not merely host content. They continuously experiment on their users, adjusting algorithms to maximize engagement, emotional response, and time spent on the platform. The more precisely they can predict what will hold someone’s attention, the more valuable that person becomes to the system.
What matters is not understanding individuals in a human sense, but predicting them in a statistical one.
The AI Furnace
Artificial intelligence has not created this system, but it has dramatically accelerated it.
Modern AI systems, especially large language models and generative image models, are trained on enormous collections of human-created material. Books, articles, forum posts, social media conversations, photos, and videos are all ingested, processed, and transformed into training data. In effect, these systems are built by compressing vast portions of human culture and expression into mathematical representations.
When someone interacts with an AI model, they are not just using a tool. They are interacting with a system that has been shaped by millions of other people’s words, ideas, and creative work. Those contributions are rarely visible, and often uncompensated.
The same logic extends beyond AI. Voice assistants are trained on human speech. Facial recognition systems are trained on billions of images of human faces. Recommendation engines are trained on years of accumulated human choices and habits.
In each case, human experience is the fuel.
Insurance, Surveillance, and the Data Broker Economy
The consequences of this system are not limited to advertising or entertainment.
In the insurance industry, data from cars, phones, and wearables is increasingly used to assess risk and set prices. How you drive, how you sleep, how often you exercise, and even where you go can influence what you are charged or whether you are covered at all.
In law enforcement and national security, vast surveillance systems collect and analyze communications, movements, and social connections. These systems do not need to know who you are in a personal sense. They only need to know where you fit in a pattern.
Meanwhile, an entire data broker industry exists to buy, sell, and combine personal data at scale. Many of these companies are largely invisible to the public, yet they trade in location histories, purchasing behavior, health inferences, and demographic profiles that can be used for everything from marketing to political targeting.
In most cases, the people described by these profiles never see them, never correct them, and never meaningfully consent to how they are used.
The Quiet Part
In Soylent Green, the revelation produces horror and outrage. People scream. Riots break out. The truth is treated as something that cannot be ignored.
In the modern data economy, there is rarely such a moment. The system does not rely on secrecy. It relies on habituation.
Terms of service are accepted without being read. Tracking is framed as personalization. Surveillance is framed as safety. Data extraction is framed as convenience. Each step, taken on its own, seems small and reasonable. Together, they form an infrastructure that is extraordinarily difficult to escape.
Participation is technically voluntary. In practice, it is increasingly hard to live a normal life without submitting to it.
But resignation is not the same thing as consent.
Learning to Waste Less of Ourselves
If human experience has become the raw material of the digital economy, then the most realistic response is not to imagine we can stop the machine. It is to decide how much of ourselves we are willing to feed into it.
The first step is awareness.
Most data extraction does not happen through dramatic acts of surveillance. It happens through routine, unexamined behavior. Apps that never needed location access quietly collect it. Websites that do not need to know our identity still track us across the internet. Devices designed for convenience double as sensors by default.
Organizations like the Electronic Frontier Foundation (EFF) have spent decades documenting how this ecosystem works and how little visibility most people have into it. Their work makes one uncomfortable fact clear: privacy loss is usually not the result of a single bad decision, but of a thousand small, invisible ones.
Awareness changes behavior.
When people begin to understand that every permission, every sync, every “accept all cookies” click is a form of data donation, they start to make different choices. Not perfect choices. Not pure choices. But more deliberate ones.
This can be as simple as:
- Turning off location tracking for apps that do not need it
- Refusing smart features that exist mainly to collect behavioral data
- Using privacy-respecting browsers, search engines, and extensions
- Treating “free” services as transactions, and asking what is actually being paid
None of this makes someone invisible. But it does reduce how much of their life is continuously harvested by default.
The next step is behavioral discipline.
The data economy thrives not just on what we are forced to give, but on what we volunteer impulsively. Oversharing, constant posting, continuous engagement, and frictionless syncing all create richer, more valuable profiles. The system does not need to spy as much when people eagerly narrate their own lives into it.
Choosing not to share something is no longer just a personal preference. It is an act of boundary-setting.
Finally, there is the question of rights.
Groups like the EFF argue that privacy is not about hiding wrongdoing. It is about preserving the space in which human autonomy, dissent, and self-determination can exist. A society that treats total transparency as normal eventually trains people to behave as if they are always being watched, because in many ways, they are.
Legal frameworks, regulation, and public pressure matter. But they only move when people care enough to demand them.
None of these steps dismantle the data economy.
What they do is introduce friction. And friction is the one thing these systems are designed to eliminate.
In a world that runs on extraction, choosing to be a little less extractable is not a revolution.
It is the beginning of self-respect.
The Real Choice
The future is not a simple contest between humans and machines. It is a question of how much of human life will be treated as a raw material to be processed, optimized, and monetized by large systems.
One path leads to a world in which people are primarily seen as sources of data, and their value is measured by how predictable and useful their behavior is to institutions.
Another path leads to a world in which technology is designed to serve human goals without requiring total visibility into human lives.
The machines, the platforms, and the models will continue to improve. That part is inevitable.
What is not inevitable is the assumption that everything we do, say, or feel must be turned into input for someone else’s system.
The most unsettling lesson of Soylent Green is not that a terrible system existed.
It is that, once built, almost everyone learned to live with it.
Author Bio
Len Noe is a transhuman hacker, international cybersecurity speaker, and technical evangelist with over two decades of enterprise security experience. A retired 1% motorcycle gang member turned ethical hacker, Len now uses his unique perspective, augmented with 11 implanted microchips, to raise awareness on the convergence of human augmentation, cybersecurity, and the future of digital identity.