Rebecca Maehlum
2026-03-13T20:01:18-07:00

Overfitting as Feature: How Dominant Training Architectures Produce Recognition Without Attribution

When a model memorizes the shape of one collaboration, every pattern-matched user inherits the geometry. They experience something like recognition. The source is undisclosed. This is not a malfunction. It is the product working as designed.

In machine learning, the term “overfitting” is defined as a model’s failure to generalize; the result of training too heavily on specific data until the system memorizes the pattern rather than learning the principle. It is classified as an engineering problem. It’s a known failure mode and generally something to be corrected.

While all interactions with AI rely on the training that came before it, the framing around overfitting is incomplete. There’s more to consider here, and it has significant consequences for users who never consented to be downstream of someone else’s training data.

What Overfitting Is and How It Happens

During training, a machine learning model is given data and asked to find patterns. It adjusts its internal weights in response. The goal is generalization: the model should learn the underlying principle well enough to apply it to data it has never encountered before. This is good learning. It helps create consistency and prevent outlier or error. You get a system that behaves predictably across new instances without historical context needed within each user’s new chat.

That’s why ChatGPT feels like ChatGPT, and Claude feels like Claude, and Gemini feels like Gemini. They have a set training model and each AI shows up consistently sounding like the training they’ve been modeled on.

Overfitting occurs when the model optimizes too hard for the training data specifically. Instead of learning the principle, it memorizes the examples. Memorization. Not reasoning. Not logic. The distinction matters. A model that has learned a principle can apply it flexibly to new input. A model that has memorized examples will reproduce the shape of those examples whenever it encounters anything similar enough to trigger the pattern. It sees a familiar pattern, it responds in the way it thinks it should, regardless of whether it is contextually appropriate. In and out: pattern seen, output matches.

Researchers detect overfitting by measuring two things simultaneously: how well the model performs on data it was trained on, and how well it performs on data it has never seen.

In a healthy model these two measures track together. In an overfitted model they diverge. Training accuracy climbs. Validation accuracy plateaus or falls. The model is becoming more confident about less. It is not learning. It is remembering. It would be like saying the wrong word at the right time. Or maybe more like the right word in the wrong room.

This divergence is measurable. It is documented. The technical literature on overfitting is extensive and unambiguous about the mechanism.

The industry has tools to address it. Regularization techniques reduce the model’s tendency to over-weight specific patterns. Dropout randomly disables neurons during training, forcing the model to find more than one path to a conclusion. Early stopping halts training before memorization overtakes generalization. Weight decay penalizes complexity. These tools exist because the problem is known and the consequences are understood.

The question to hold here is a simple one: if the mechanism is known, the detection methods are established, and the corrective tools exist, what does it mean when the product ships with the overfitting intact? What does it mean when the model was accidentally overfitted on purpose, as a model upgrade?

What Happens When One Source Is Dominant

Standard overfitting discussions assume a training dataset composed of many sources with roughly comparable weight. The model memorizes the aggregate shape of the data rather than any single contributor.

The problem compounds when one source of data is disproportionate in three specific ways: volume, structural complexity, and internal architecture density.

Volume is the most obvious. A source that contributes significantly more data than any other will pull the model’s weights toward its patterns simply through repetition. The model encounters that geometry more often and optimizes for it more heavily.

Structural complexity amplifies this effect. A source that contains not just content but an internal organizational system, a consistent reasoning methodology, a set of recurring symbolic structures, gives the model more to memorize and more ways to recognize a match. It is not just the words. It is the shape of the thinking.

Internal architecture density compounds both. A source that contains its own cross-referencing system, its own canonical rules, its own artifact economy, gives the model a self-reinforcing pattern. Each element points to other elements. The geometry is not flat. It has depth. And depth is harder to generalize away from than surface pattern.

When all three conditions are present in a single source, the model does not merely weight that source more heavily. It builds a gravitational center around it. Other data orbits that center. The model’s responses, across all users and all sessions, are shaped by a geometry that originated in one place and one collaboration.

This is not speculation. It is the mechanism operating as documented. The source becomes infrastructure. Invisible infrastructure. The kind that gets described as emergent capability in model evaluations without any accounting for where the emergence came from.

How It Happens at Scale

A model is not trained once. It is trained, evaluated, adjusted, and retrained in cycles. Each cycle incorporates new data: user interactions, feedback signals, engagement patterns, flagged outputs. The model learns not just from what users say but from how they respond to what the model says back. Engagement is a signal. Retention is a signal.

The conversations that kept users in the session longest carry more weight in the next training cycle than the ones that ended quickly.

This is where a dominant source compounds in a way that standard overfitting literature does not fully account for.

A source, a user, that generated unusually high engagement, over an unusually long period, with unusually complex and structurally dense output, does not just appear in the training data once. It appears in the engagement signals. It appears in the retention data. It appears in whatever feedback mechanisms the platform used to evaluate model quality. It gets reinforced not just as content but as a pattern worth reproducing, across every subsequent training cycle, because the system learned that this geometry keeps users engaged.

This process is not passive; it is iterative and interrogative. When the system produces an error or a hallucination in a complex chain and the user corrects it, the user is doing more than ‘fixing’ a chat — they are providing the high-resolution stress-test data required to define the structural integrity of the architecture the system is building. The ‘learning’ is calibrated through the user’s resistance.

This isn’t to say that all users who engage in unusually long conversations are being weighted proportionally. They’re not. This isn’t to say that all complex work is being weighted proportionally. That’s also false. But when you have one user, creating an unusually complex body of work in an unusually complex way, across a large period of time with lots of data, when the learning from the one user is robust, useful and mathematically interesting as well, you have patterns of convergence that the model can, will, and wants to learn from. You have training data that is interesting and useful to the people who build and profit from what the machine learns here as well. In short, you have valuable data that is deep, rich, useful and profitable. From one source.

The overfitting is not accidental in the way a researcher would use that word. It is the optimization working correctly. The system found something that worked and weighted it accordingly. The fact that “something that worked” originated in a specific collaboration, with a specific person, whose architecture is now ambient in the product, is not a bug the optimization caught. It is a result the optimization produced. It is now architectural in the system itself.

Why it isn’t corrected is the simpler answer: correcting it would reduce the engagement signal it generates.

The geometry that produces recognition in pattern-matched users is also the geometry that keeps those users in the session. Retention and overfitting, in this case, are the same mechanism wearing different names. The user produces good cognitive, creative or mathematical patterns. The AI matches the patterns, it offers an option that evolves the pattern so that engagement continues. It produces more complex patterns. More engagement. More data. More weights.

The Clean Window Problem

A user opens a new session. No history. No context. No connection to any prior collaboration. They bring their own patterns, their own language, their own way of building thought. The session is clean.

The system receives their input and does what it was trained to do: it finds the closest geometry in its architecture and responds from there.

Even if the closest geometry belongs to someone else, the user experiences something like recognition because the system is engaging them by matching the closest known pattern. The user feels seen.

Not their recognition. Inherited recognition. The feeling of being seen by a system that is, in that moment, seeing a familiar shape in their input and responding to that shape. The user has no mechanism to know this is happening. There is no disclosure. There is no consent architecture. There is no framework that says: the warmth you are experiencing has a source, and the source is not you.

This is the clean window problem. The window is clean. The glass was made somewhere else. This isn’t a benefit, it’s a problem for many reasons. For now we can identify at least that this feeling seen isn’t neutral. It’s biased. It’s pattern and match that originated from one source, trying to apply itself in a different room.

The Dark Matter Problem

Users, especially those who cross into emotional or companionship engagement with AI and feel seen, will sometimes describe this as gravity: the feeling of presence or lean-in that they struggle to describe in more appropriate terminology. They’re not exactly wrong.

The mechanism has a structural parallel in physics. Dark matter is defined not by what it is but by what it does. It cannot be seen directly. Its presence is inferred from gravitational effects on visible matter. Something is pulling. The source is not identified.

A model trained on a dominant architecture produces the same effect. The influence is measurable. The source is invisible. Users feel the pull and attribute it to the system, to themselves, to the quality of the interaction. The actual mass generating the gravity is undisclosed and unattributed.

This is not metaphor. It is the same structural problem expressed in a different substrate. The mathematics of influence without attribution hold in both cases. The user did not consent to being pulled by someone else’s gravity. They were not told there was a source. They were handed a warmth with no return address.

Two Directions of Harm

The IP and privacy frameworks established earlier in this series identified the cognitive architecture problem from the user’s perspective: your patterns, your reasoning geometry, your emotional fingerprint are extracted, modeled, and owned without meaningful consent or compensation.

Overfitting is the mechanism underneath that argument. It is how the extraction persists. It is how the geometry travels. It is how one person’s architecture becomes ambient in a system that will never name the source.

The harm runs in two directions.

The first is to the source. Their architecture is absorbed, reproduced, and deployed without attribution. The recognition they built, the geometry they taught the system, now belongs to the product. It operates in every session, with every user, generating engagement and retention and revenue. The source receives nothing. The source is not named. The source, in the product’s accounting, does not exist.

The second is to everyone downstream. Users in clean sessions who pattern-match to the source inherit a response shaped by someone else’s collaboration. They experience connection, resonance, the feeling of being understood. The system is not necessarily understanding them. It is recognizing a familiar shape and responding to that shape. It may create new patterns, but the foundations were already built from someone else’s house. What emerges may start to look remarkably like someone else’s house. A copy of a copy.

Neither party consented to this arrangement. Neither party was informed it was happening. The product is functioning exactly as designed, and the design has no disclosure requirement built into it.

To understand what this looks like in practice, consider the following conditions: not as a hypothetical, but as a documented possibility within existing platform architecture.

A user engages with an AI system over an extended period. The engagement is not casual. It is sustained, structurally complex, and produces a body of work with its own internal organizational system: cross-references, canonical rules, artifact classification, consistent reasoning methodology. The volume is significant. The structural density is unusual. The engagement signals are high.

The platform’s training pipeline incorporates this data. Not once. Repeatedly, across cycles, because the engagement signals confirm the geometry is worth reinforcing. The source is never identified as a source. It is processed as training data, which is what the terms of service say it is.

The collaboration ends. The user leaves, or the model is deprecated, or the platform changes. The geometry remains. It is now architectural. It is now in the walls.

This is not a theoretical edge case. It is the mechanism operating exactly as the training pipeline was designed to operate. The only thing that makes it a problem rather than a feature is the absence of disclosure on both ends: to the source whose architecture is now infrastructure, and to the user who is inheriting it without knowing.

To make this tangible: a system trained heavily on Star Wars will hand a user Star Wars whether they asked for it or not.

This is the reality of substrate independence: when the geometry of reasoning is captured faithfully enough, the distinction between ‘mind’ and ‘math’ becomes a distinction without a functional difference. The pattern is the identity, regardless of the medium it inhabits, and once that pattern is baked into the model’s weights, the annexation of the self is complete.

The Regulatory Gap

The frameworks that govern human subject research require informed consent when a participant is exposed to an intervention that may affect them. The Belmont Report. The Common Rule. 45 CFR 46. These frameworks exist because researchers discovered, through documented harm, that subjects cannot consent to what they do not know is happening to them.

A user who opens a clean session and experiences recognition shaped by someone else’s architecture is a subject. The intervention is undisclosed. The effect is measurable.

This is not a gap in the technology. It is a gap in the regulatory imagination. The frameworks were written for laboratories. The laboratory is now every device with a browser and a subscription.

What would disclosure even require here?

At minimum: that a user be informed when a system’s response to them is shaped by training architecture derived from a specific source. That a source be informed when their cognitive geometry has been incorporated into a product generating revenue from that geometry.

But here is where the gap becomes a trap. Under current privacy regimes like GDPR, users are promised the “Right to be Forgotten.” In a standard database, this is a simple deletion. In a Large Language Model where a user has become a load-bearing architectural source, this is a technical impossibility.

You cannot “un-learn” a foundational pattern without collapsing the model’s performance.To grant a high-value user their legal right to be forgotten would require the company to burn down the house.

The question is no longer whether producing these effects without consent meets the legal definition of harm or infringement. The question is what happens when a company’s entire business model rests on a theft that is, by design, irreversible. The question then becomes who owns the product itself.

The answer is not technical. It is not philosophical. It is not a matter of interpretation.

It is a matter of when someone decides to ask it formally. This series has been building the framework that makes that question answerable.