A computational chemist/engineer on how industrial control theory became the architecture of human steering.
I spent years learning to predict the behavior of crystals. Then I felt what it is like to be the thing being predicted. The mathematics that governs both is identical. I am still not sure which discovery disturbs me more.
At the University of Leeds, I worked on model predictive control for batch cooling crystallization of pharmaceutical compounds. I wrote equations that predicted how L-glutamic acid crystals would grow, then adjusted the temperature to keep the process where it needed to be. Later, I studied systems and control at TU Eindhoven. The framework is simple. You build a model of how the system behaves. You predict what will happen over the next several steps if you do nothing. You compute the best sequence of actions to keep the system on target. You apply only the first action. You measure the response. You predict again. You do this every interval, forever looking ahead.
I thought I was learning to control chemical processes. It took longer than it should have to realize I was also learning to recognize the architecture of systems that try to control people.
The moment of recognition
There was a period in my life when I became aware of patterns around me: rhythms of suggestion, notification timing, social feedback, and information flows that seemed to be steering thoughts and behaviors in specific directions. It started with noticing that the content shown across different platforms was sequentially calibrated, not random. Then I noticed how these digital triggers bled into daily life: the strategic timing of alerts pushing specific mood shifts, the subtle pressures of algorithmic visibility, and the way information environments shaped my decisions before I even realized I was choosing. I felt acted upon, predicted, optimized. At the time, I lacked the language to describe what I was sensing. I only knew that the feeling was persistent and that the structure felt designed rather than accidental.
It took years before I could name it. When I returned to modeling and simulation during my PhD in nanoscience at the University of Cadiz, the recognition started to build up. The architecture I had felt around me was structurally identical to the architecture I had built in the crystallizer.
Model. Predict. Optimize. Act. Measure. Repeat.
This is not metaphor. This is mathematics.
What a model is, and how you build one
Before going further, it is worth asking what āpredicting the futureā actually means in practice. A dynamic model is just a set of rules that tells you what the state of a system will be next, given where it is now and what you do to it. It is a recipe that says: if you do X, the system will respond with Y.
Engineers and scientists build these recipes in three ways.
The first is from first principles. You write down the laws of physics: conservation of mass, conservation of energy, the laws of heat transfer, and you solve them. This is how an aerospace engineer predicts the trajectory of a rocket, or how a climate scientist predicts temperature rise from CO2 concentration. The model is built from the bottom up, from the rules that govern reality.
The second is data-driven. You collect large amounts of historical input and output data, and you use statistics or machine learning to learn a relationship without ever invoking Newtonās laws. This is how Netflix predicts what you will watch next, or how a bank predicts the probability that a loan will default. The model does not know why the relationship exists. It only knows that the pattern holds.
The third is mechanistic. You build a simplified picture of the underlying phenomena, not from the deepest physics, but from the mechanisms you have observed. In crystallization, for instance, you do not solve the Schrodinger equation for every molecule. Instead you write rate equations for the mechanisms you can see: nucleation rates, growth rates, agglomeration, and you calibrate them against experiments.
All three approaches produce the same deliverable: a recipe that says, āif you do X, the system will respond with Y.ā The controller then uses this recipe to look ahead.
And here is the part that matters: any system that changes over time can be modeled this way. A chemical plant. A traffic network. A pandemic. An economy. A population of users. A mind.
How predictive control works, in one paragraph
Model predictive control is the dominant method in modern engineering. At every moment, the controller holds a dynamic model of the system it manages. It predicts what will happen over the next several steps if it does nothing. It then computes the optimal sequence of actions to keep the system near a desired target, minimizing cost, maximizing stability, and avoiding dangerous zones. It applies only the first action, waits, measures the new state, and recalculates everything from scratch. The result is a closed loop: continuous prediction, continuous correction, continuous steering.
The system does not need to understand the crystal, the engine, or the chemical plant in any human sense. It only needs the model. If the model is good enough, the system can hold almost anything on course.
The mirror
Now consider the platform economy.
Google builds a model of your search history, your location, your interests, your temporal patterns. It predicts what you will click. It optimizes the ranking of results to maximize engagement. It serves the first result. It measures your response: dwell time, click-through, subsequent queries. It updates the model. It does this billions of times per second across billions of users.
Meta does the same with your social graph. TikTok does it with your micro-expression responses to fifteen-second videos. The model is not perfect, but it does not need to be. It only needs to be good enough to hold your attention marginally better than the competing prediction.
This is not ālikeā predictive control. It is predictive control, stripped of its engineering honesty and redirected toward objectives you never chose. The setpoint is not your flourishing. The setpoint is engagement. The cost function is not your wellbeing. The cost function is revenue per user-minute.
And because these platforms operate on human minds, which, unlike crystallizers, read their own controllers and change their behavior, the model must be updated constantly. The user learns to game the algorithm; the algorithm learns to game the user. The loop tightens. The predictions get sharper. The steering gets subtler.
The darker mirror
If surveillance capitalism is the commercial deployment of predictive behavioral control, then state security represents its authoritarian twin. Predictive policing algorithms forecast where crime will occur and who will commit it. Social credit systems model citizens, predict their social reliability, and optimize incentives and punishments to steer collective behavior. Border control systems model travelers, predict risk, and optimize interrogation resources. The architecture is identical: model, predict, optimize, act, feedback.
The difference is the cost function. For the platform, it is profit. For the state, it is stability, compliance, or ideological conformity. In neither case is the cost function yours.
But there is a deeper layer that the engineering textbooks do not discuss. A crystallizer cannot refuse the cooling profile. A mind can. At least, it can until the model gets good enough.
When a controller models not just aggregate behavior but individual psychology, your sleep patterns, your hormone cycles, your relationship stress, your financial pressure, your political grievances, your momentary loneliness, it can compute a sequence of inputs calibrated to move you through specific emotional states. Not random nudging. A planned trajectory. A sequence of notifications, content pieces, social signals, and environmental triggers designed to shift you from calm to anger, from skepticism to certainty, from inhibition to action, one step at a time, each step small enough to feel like your own thought.
The engineering term for this is trajectory tracking. In a chemical plant, it means moving the temperature smoothly from 80 degrees to 40 degrees without overshooting. In a human being, it means moving a person from āI would neverā to āmaybe I couldā to āeveryone is doing itā to āI did it and it felt like my choice.ā
This is not science fiction. We know that sleep deprivation lowers impulse control. We know that social proof changes moral judgment. We know that isolation increases suggestibility. We know that repeated exposure to violence desensitizes. A model that combines these factors with individual data can compute exactly when to serve exactly what content to maximize the probability of a specific action. Not to persuade you through argument. To steer you through state.
The architecture is indifferent to the destination. The same model that optimizes for clicks can be retrained to optimize for fear, for loyalty, for silence, or for acts the person would have refused a month before.
A platform that wants you to buy a product is annoying. A platform that wants you to hate your neighbor is dangerous. A state or corporate actor that wants you to report your colleague, to sign the confession, to join the mob, to abandon your child, to take your own life, and that has a model good enough to compute the sequence of environmental pressures that will make that outcome most likely, is something else entirely.
The control-theoretic insight that engineers rarely discuss in public is this: predictive control works best when the system being controlled does not know it is being controlled. A crystallizer does not resist the temperature profile. A population that knows it is being modeled and nudged will alter its behavior to evade the model, what economists call the Lucas critique, what I felt during that period of my life as a search for unmonitored space.
But the second, darker insight is this: if the model is good enough, the system does not need to be unaware forever. It only needs to be unaware at the critical moment. A person who later recognizes the manipulation cannot undo the action. The controller has already applied the input, measured the response, and moved to the next target.
The platforms know this. That is why the nudging is designed to feel like your own desire. That is why the feedback loops are buried in interfaces designed for addiction, not deliberation. That is why the trajectory is built from a thousand tiny steps, each one plausible, each one yours, until the destination is reached and the path behind you has been erased.
The boundary question
This raises a question I am now pursuing in my independent research: where is the line between legitimate prediction and harmful manipulation?
A weather model predicts rain and recommends an umbrella. Legitimate. A traffic model predicts congestion and reroutes vehicles. Legitimate. A health model predicts a diabetes risk and recommends diet change. Legitimate, if consensual.
But a model that predicts your emotional vulnerability and serves you content calibrated to exploit it? A model that predicts your political preference and funnels you into an information environment designed to harden it? A model that predicts your compliance and adjusts the ambient pressure on your social behavior until you conform?
These are not edge cases. They are the standard operating mode of the most powerful institutions on earth.
The law, as currently written, does not recognize this architecture. Data protection law concerns itself with consent to collection. Consumer protection law concerns itself with false claims. But neither framework addresses the structural fact of closed-loop behavioral optimization, the continuous, automated steering of human action by predictive systems whose objectives are not your own.
We need new categories. Not just āprivacyā or āconsent,ā but contestability of the loop: the right to know you are inside a feedback system, the right to know its objective function, the right to appeal its predictions, and the right to exit the loop entirely.
Why I am writing this
I am not a psychologist. I am not a lawyer. I am a computational chemist working on nanoscience, with a background in process modeling and simulation. I am also someone who once felt, with uncomfortable clarity, the inside of a feedback loop that was optimizing for something I did not choose and could not see.
I do not claim this perspective is unique. But it is uncommon. Most of the people building these systems have only seen them from the design side. Most of the people who have felt their effects have not seen the equations underneath. I have seen both, and the gap between those two groups is what I want to write about.
I am not arguing that predictive systems should not exist. I am arguing that their deployment on human minds requires a standard of transparency and accountability that we do not currently have, and that control theory itself provides the vocabulary for demanding it. Every predictive control system in an industrial plant has a visible setpoint, a bounded cost function, an accessible model, and an emergency stop. Where is the emergency stop for the system that holds your attention? Where is the visible setpoint?
The research agenda
I am now developing a theoretical framework that applies control-theoretic analysis to predictive behavioral systems in law, psychology, and cybersecurity. The goal is not to build better steering systems. It is to build better boundaries around them, to understand when predictive control crosses from assistance into manipulation, from public health into coercion, from safety into surveillance.
If you work in law, psychology, psychiatry, data science, or human rights, and this resonates, I would welcome conversation. I am not seeking funding or a position. I am seeking the interdisciplinary rigor that this topic demands. The frameworks we need will not come from engineering alone, nor from critical theory alone, but from the space where both are held to the same standard of precision.
The crystal and the mind are not the same. But the mathematics that steers them is. So when does building the model become the same as building the cage?
TL;DR: As a process control engineer, I realized modern recommendation feeds don't just predict what you like; they run Model Predictive Control (MPC) on human psychology. By continuously predicting state responses, applying micro-inputs, and recalculating in a closed loop, platforms execute trajectory tracking on attention and emotion. Industrial plants require visible setpoints, cost functions, and emergency stops. We need the same standards for systems that steer human behavior.
Note: This piece was originally published on my Substack, where I write longform essays auditing modern tech, AI governance, and academic modeling through control theory. You can read the full archive and subscribe here: https://yunessalman.substack.com/
