{"id":177939,"date":"2026-01-14T14:29:20","date_gmt":"2026-01-14T14:29:20","guid":{"rendered":"https:\/\/ktromedia.com\/?p=177939"},"modified":"2026-01-14T14:29:20","modified_gmt":"2026-01-14T14:29:20","slug":"understanding-model-drift-data-decay","status":"publish","type":"post","link":"http:\/\/ktromedia.com\/?p=177939","title":{"rendered":"Understanding Model Drift & Data Decay"},"content":{"rendered":"
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One of the most common explanations for inaccurate AI output is that it\u2019s 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.<\/p>\n

It\u2019s 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<\/a> each year.<\/p>\n

Even if it were true that AI efficacy is simply a problem of scale, the above would be cause for concern\u2014but it isn\u2019t. In fact, this argument represents a fundamental misunderstanding of what AI is and how it actually works.<\/p>\n

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

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. <\/p>\n

Employing AI effectively and ethically requires clarity on how it functions, where it goes wrong, what it can actually accomplish, and what it can\u2019t.<\/p>\n

Why All Machine Learning AIs in the Real World Experience Model Drift<\/h2>\n

When most people think about AI, they think about Large Language Models (LLMs), which use statistical analysis to predict language patterns. All of OpenAI\u2019s GPT models, along with Google\u2019s Gemini and X\u2019s Grok, fall into this category.<\/p>\n

There are other types of AI models as well. Examples include:<\/p>\n

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<\/a>.<\/p>\n

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.<\/p>\n

Different Causes of Model Drift (with Real-Life Examples)<\/h2>\n

Most model drift can be attributed to one of two distinct problems<\/a> that render its training data obsolete:<\/p>\n

Concept Drift<\/h3>\n

This occurs when new concepts in the real world fundamentally change the relationship between a model\u2019s input and output data.<\/p>\n

Let\u2019s 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 \u201cScam Likely\u201d or \u201cSpam Risk\u201d on the Caller ID and know not to answer.<\/p>\n

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\u2019t trained to recognize.<\/p>\n

In this case, the distribution of the model\u2019s input data might still be accurate, but the assumptions it makes based on the patterns it identifies are no longer valid.<\/p>\n

Data Drift<\/h3>\n

This occurs when the distribution of a model\u2019s 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.<\/p>\n

For example, phone sales teams making legitimate business calls may use AI-assisted call deliverability platforms<\/a> 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.<\/p>\n

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\u2019s 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.<\/p>\n

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.<\/p>\n

What About AI Models that Don\u2019t Rely on Machine Learning?<\/h2>\n

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.<\/p>\n

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<\/a> (\u201cif A, then B\u201d, \u201cif A but then C, D\u201d, etc.).<\/p>\n

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<\/em> make in any given game of chess, these decision trees were also explicit<\/em> (i.e., they had clearly defined limits). <\/p>\n

The computer responded directly to clear user inputs rather than identifying and extrapolating trends in data.<\/p>\n

How Data Decay Can Accelerate Model Drift<\/h2>\n

Data decay <\/strong>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.<\/p>\n

Data decay can occur in a wide range of forms<\/a>. It might look like:<\/p>\n