One of the most common explanations for inaccurate AI output is that it’s primarily a problem with training data. AIs would produce better results, the argument goes, if only we gave them more resources: more power, more server rooms, more access to information.

It’s an argument many people (or at least, a handful of people with significant power and influence) seem to be in favor of, because investment in AI continues to grow at a breakneck pace. The United States is currently home to over 4,000 data centers, collectively spanning over 50,000 acres. Some individual sites currently under construction are expected to require as much electricity as millions of homes each year.

Even if it were true that AI efficacy is simply a problem of scale, the above would be cause for concern—but it isn’t. In fact, this argument represents a fundamental misunderstanding of what AI is and how it actually works.

Evidence shows that machine learning models consistently and inevitably experience performance drops over time. This phenomenon is known as model drift, and its relationship to data availability is significantly more complicated than the above narrative suggests.

The more widespread such misconceptions become, the easier it becomes for the real potential of this technology to be obscured beneath layers of empty hype, and the harder it becomes to develop or employ it in ways that demonstrably benefit humanity.

Employing AI effectively and ethically requires clarity on how it functions, where it goes wrong, what it can actually accomplish, and what it can’t.

Why All Machine Learning AIs in the Real World Experience Model Drift

When most people think about AI, they think about Large Language Models (LLMs), which use statistical analysis to predict language patterns. All of OpenAI’s GPT models, along with Google’s Gemini and X’s Grok, fall into this category.

There are other types of AI models as well. Examples include:

• Convolutional Neural Networks (CNNs), which are largely used to process still images and video by recognizing spatial patterns.
• Recurrent Neural Networks (RNNs), which handle sequential or time-series data.
• Generative Adversarial Networks (GANs), which pit two individual models (most often CNNs) against each other to generate output.

LLMs and GANs are examples of Generative AI, which is used to produce new data. CNNs and RNNs are primarily used to analyze existing data. But all of these model types rely on Machine Learning (ML), which is essentially the process of training computers to make inferences based on data patterns.

When real-world data evolves away from the data an AI model was trained on, its ability to recognize patterns suffers and its outputs become less reliable. This is model drift.

Different Causes of Model Drift (with Real-Life Examples)

Most model drift can be attributed to one of two distinct problems that render its training data obsolete:

Concept Drift

This occurs when new concepts in the real world fundamentally change the relationship between a model’s input and output data.

Let’s say you have an AI model designed to analyze data from phone calls. Major telecom carriers use this kind of technology to identify dialing patterns that might indicate fraudulent or nuisance calls. They then assign labels to numbers that fit these patterns, so that people receiving calls from them see warnings like “Scam Likely” or “Spam Risk” on the Caller ID and know not to answer.

The problem is that many organizations engaging in legitimate phone outreach can still find their numbers flagged this way because their calling patterns might inadvertently resemble the ones carrier algorithms have been trained to associate with risk. At the same time, actual scam or spam callers might develop new tactics that the carrier algorithms aren’t trained to recognize.

In this case, the distribution of the model’s input data might still be accurate, but the assumptions it makes based on the patterns it identifies are no longer valid.

Data Drift

This occurs when the distribution of a model’s input data from the real world no longer matches its training data. This can occur even if the essential relationship between input and output data remains the same.

For example, phone sales teams making legitimate business calls may use AI-assisted call deliverability platforms to help protect against being falsely labeled as spam callers. These platforms can monitor business numbers for spam flags, and, when they occur, provide call behavior data to identify potential causes and corrective actions. When AI systems are used to parse this information, they can surface patterns or trends, such as erratic dialing behavior, far more quickly than a human team working manually. That insight helps organizations adjust dialing practices after remediation and reduce the likelihood of future issues.

Data drift becomes a challenge when the broader calling environment changes in ways that alter patterns in those behavioral signals until they no longer resemble the model’s training data. This could result from carrier detection systems evolving, enforcement thresholds shifting, or changes to any number of other factors, including network conditions, destination types, device mix, and timing.

In these cases, the model is no longer able to map trends correctly because the patterns in its original training data no longer apply to real world inputs. Incidentally, this is why the most successful number reputation services tend to maintain a deep understanding of the analytics ecosystem and adapt their models as carrier algorithms and enforcement behaviors change.

What About AI Models that Don’t Rely on Machine Learning?

Some relatively primitive forms of AI do not use machine learning and are less susceptible to model drift than the ones listed above. These typically use hard-coded logical rules and explicit algorithms to create a surface-level appearance of intelligence.

Early computer chess programs are one of the clearest real-life examples. Until around 2017, when ML-driven chess engines such as Leela Zero and AlphaZero debuted, chess software did not use machine learning to identify patterns in the moves a player made and inform its next moves. It simply used a decision tree (“if A, then B”, “if A but then C, D”, etc.).

These decision trees were complex enough that the computer had an automatic response for any move a player could make. But since there are only so many moves a player can make in any given game of chess, these decision trees were also explicit (i.e., they had clearly defined limits).

The computer responded directly to clear user inputs rather than identifying and extrapolating trends in data.

How Data Decay Can Accelerate Model Drift

Data decay is what happens any time the quality, reliability, or accuracy of data deteriorates. It is a separate concept from data drift and concept drift, but it can contribute to both.

Data decay can occur in a wide range of forms. It might look like:

• A sales team failing to maintain updated customer records
• Incorrect product information being left on old websites
• Legacy systems being upgraded and leaving data in older formats unusable
• Software glitches corrupting the data in an archive
• Bit rot resulting from aging hardware

All of these situations can cause errors in the data an AI model is trained on. When they cause gaps between live data and training data, it produces data drift.

When errors or inconsistencies build up and obscure the patterns a model is meant to recognize, it corrupts the relationship between the model’s inputs and outputs (a form of concept drift). In either case, model drift is the end result.

Why Model Drift Is More than Just a Data Problem

If data decay leads to model drift, then doesn’t it follow that feeding models more data and updating that data more frequently is the key to solving their accuracy issues?

The answer is: only to a certain extent.

Updating a model’s training data can certainly prevent it from decaying, which does help mitigate the risks listed above. Techniques like Retrieval Augmented Generation (RAG) aim to accomplish this by allowing LLMs to access external and up-to-date data sources without retraining the underlying model before generating outputs (like when GPT browses the internet). But this isn’t enough to solve model drift completely.

It’s vital to remember that the sheer amount of data and computing power AI uses is the only thing that allows it to create the illusion of having intelligence in the first place. LLMs and simple, decades-old predictive text generators such as T9 both generate text without any awareness of what it means. Their core mechanics (statistical analysis, token prediction, probabilistic matching, etc.) are essentially the same. The LLM just performs those steps exponentially faster and in massively larger quantities.

So if we’re already feeding LLMs all the data we can, then why is model drift still a problem? Why do they still make so many mistakes?

Why Doesn’t More Data Necessarily Mean More Accuracy?

Reality changes faster than datasets

The tip of the iceberg is that there’s simply no practical way to ensure an AI model’s access to information will always be complete and current enough to prevent model drift from occurring. That would require humanity to update the internet and every training database in existence to reflect every single change in the real world the moment it occurs.

The idea that we could ever build enough data centers or produce enough power to achieve this outcome is bad science fiction. Even if the sheer amount of resources required for such an undertaking could be extracted, the world still changes faster than data can be observed, collected, and recorded. This is to say nothing of the catastrophic environmental impact such a project would have.

So the core problem here isn’t just about power and resources. It’s about the fact that reality is fundamentally more complex than the data we can feed AI.

Models don’t experience the world

An entire field of research called Grounded Language Learning (GLL) exists to study the role real-life experience plays in language acquisition. GLL shows that physically interacting with the world is vital for grasping the essence of words instead of merely defining them, which is a vital part of conscious thought.

LLMs do not experience the world, so they lack this essential context. The next-token prediction they rely on may resemble consciousness from the outside, but no actual consciousness exists.

The limits of simulation and pattern-matching

This is why no amount of training self-driving cars in a simulation can completely eliminate their risk of causing an accident on a public street. There are always going to be more complexities in the real world than in a simulated one, because we cannot produce a simulation as detailed as real life.

It is also why AIs hallucinate in their responses to prompts. They are not “thinking” through a problem; they are mimicking the results of the thought process by performing pattern recognition at scale. No amount of increasing that scale will turn the latter into the former.

Does this mean there are some tasks AI will never be able to do better than humans? Not quite.

It means that unless some brand new type of AI is invented that none of the rules covered above apply to, AI will never be able to do most tasks even remotely as well as humans.

What Should AI Do vs. What Should Be Left to Humans?

This is not to say AI has no value. The point is simply that when AI is overvalued, its true potential to improve the world is obscured by promises it is incapable of keeping.

It is well-established that AI systems are exponentially better than humans at gathering and processing large quantities of information. But we remain much more capable when it comes to solving problems and interacting socially when unforeseen circumstances or events arise.

Unlike AI models, humans possess the capacity for novel reasoning, empathy, judgment, imagination, and emotion. All of these qualities (and many others) still make us uniquely qualified to make leadership decisions, weigh risks, navigate arguments, create art, administer justice, hire talent, sell cars, and walk dogs.

So yes, using AI to analyze answer rates and call durations from hundreds or thousands of calls placed daily by your sales team is far more effective than combing through that data manually. You might even use AI to surface insights from that data about gaps in your outreach strategy so that you can make better strategic decisions. But replacing your team members with AI agents would be a mistake if you wanted to close more deals with customers.

It brings to mind that old IBM training manual slide from 1979: “A computer can never be held accountable, therefore a computer must never make a management decision”.

AI is already being used to streamline data collection for cancer researchers, but it lacks the capacity to set meaningful research priorities for them. It is being used in the fight against climate change to optimize agricultural food systems and help assess drought-risk, but it is unsuited for determining government climate policy.

Acknowledging and fully understanding challenges like model drift and data decay is the difference between knowing how this technology can help humanity and mistakenly assuming it can work miracles on problems it remains our responsibility to solve.

Making the world a better place is still up to us. Whether AI helps or hinders is up to us as well.

About the Author

Chris Sorensen is the CEO of PhoneBurner, where he leads innovation in outbound sales technology. A hands-on business leader with roots in entrepreneurship, he focuses on building tools that help sales teams perform at their best.

Under his leadership, PhoneBurner introduced ARMOR®, a patented AI-driven call protection solution that prevents business numbers from being flagged as spam, helping teams reach more prospects and drive more revenue.

Origianl Creator: Chris Sorensen
Original Link: https://justainews.com/technologies/machine-learning/understanding-model-drift-and-data-decay-in-real-world-systems/
Originally Posted: Wed, 14 Jan 2026 07:50:56 +0000