Large language models like GPT, Claude, and Gemini have captured the public imagination with their ability to write code, answer questions, and generate human-like text. However, despite the impressive demonstrations and marketing claims about “artificial intelligence,” these models are fundamentally designed to produce average outputs. Understanding why reveals important limitations about what we can reasonably expect from current AI technology.
How Language Models Actually Work
At their core, large language models are sophisticated prediction engines trained on massive datasets of human-created text. During training, they learn statistical patterns about which words and phrases typically follow others in different contexts. When generating text, they’re essentially asking: “Given this input, what would be the most statistically likely continuation based on my training data?”
This process is inherently averaging. The model doesn’t have novel insights or creative breakthroughs – it recombines patterns it has seen before in ways that match the statistical distribution of its training data. When you ask an LLM to write a story, it produces something that reads like the average of all stories it was trained on, weighted by similarity to your prompt.
The Mathematics of Mediocrity
The training process reinforces this averaging behavior. Models are optimized to minimize prediction error across their entire training set. This means they get better at producing outputs that would be “correct” or “acceptable” for the typical example in their training data, not for exceptional or groundbreaking examples.
Consider code generation: an LLM will produce code that looks like the average of similar code snippets it has seen. This often results in functional but unremarkable solutions. The model has learned patterns from millions of developers, but it gravitates toward the most common approaches rather than the most elegant or innovative ones.
This is mathematically inevitable. When you optimize for the lowest average error across a large dataset, you’re optimizing for outputs that work well for typical cases, not exceptional ones.
Training Data Shapes Everything
The quality of an LLM’s output is fundamentally limited by its training data. If the model was trained primarily on Stack Overflow answers, blog posts, and documentation, it will produce outputs that resemble average Stack Overflow answers and blog posts. Breakthrough research papers, innovative solutions, and genuinely novel ideas represent a tiny fraction of most training datasets.
Even worse, training data often includes a lot of mediocre content. For every elegant algorithm implementation online, there are dozens of quick-and-dirty solutions. For every insightful essay, there are hundreds of generic blog posts. The model learns from all of this, and the sheer volume of average content dominates the signal.
This creates a regression toward the mean. Even if groundbreaking content exists in the training data, it gets diluted by the overwhelming volume of ordinary content.
The Innovation Gap
True innovation often comes from combining ideas in unexpected ways, challenging fundamental assumptions, or making intuitive leaps not derived from existing patterns. These processes require understanding context, having genuine insights about problems, and sometimes deliberately breaking established patterns.
LLMs can’t do this because they don’t actually understand the content they’re processing. They manipulate statistical patterns in text, but they don’t have beliefs, intentions, or genuine comprehension. They can’t recognize when a conventional approach is fundamentally flawed or when a problem requires thinking outside established paradigms.
Consider major programming breakthroughs: object-oriented programming, functional programming paradigms, or revolutionary algorithms like MapReduce. These innovations required humans to question existing approaches and imagine entirely new ways of solving problems. An LLM trained on pre-OOP code couldn’t have invented object-oriented programming – it would just produce more procedural code that looked like its training examples.
Why “Creativity” Is Just Sophisticated Averaging
When LLMs appear to be creative, they’re usually combining elements from their training data in ways that seem novel to humans but follow predictable statistical patterns. The model might mix the style of one author with the subject matter of another, or combine programming concepts from different domains, but it’s still operating within the bounds of what it has seen before.
This can produce outputs that seem creative to us, especially the unexpected combinations. However, this is fundamentally different from human creativity, which can involve genuine insight, intentional rule-breaking, and the ability to imagine solutions that don’t exist in any training data.
The Plateau Effect
As LLMs get larger and are trained on more data, they become better at producing average outputs, but they don’t become more innovative. Adding more training data and parameters helps models capture more patterns and produce more sophisticated averaging, but it doesn’t fundamentally change their inability to transcend their training distribution.
This suggests we’re approaching a plateau where LLMs will get incrementally better at producing human-like average content, but won’t suddenly develop the ability to make genuine breakthroughs or innovations.
Practical Implications for Developers
Understanding these limitations helps set realistic expectations for how developers can use LLMs effectively:
What LLMs are good at:
- Generating boilerplate code that follows established patterns
- Explaining common programming concepts
- Suggesting conventional solutions to well-understood problems
- Refactoring code to follow standard practices
What LLMs struggle with:
- Solving novel problems that require innovative approaches
- Designing new architectures or paradigms
- Debugging complex issues that require deep system understanding
- Making strategic technical decisions that require business context
The Value of Average
This doesn’t mean LLMs are useless – average outputs are often exactly what you need. Most programming tasks don’t require groundbreaking innovation. They require solid, conventional solutions that work reliably. Most writing doesn’t need to be revolutionary; it needs to be clear and informative.
LLMs excel at generating competent, average outputs quickly. This can be incredibly valuable for routine tasks, but it’s important to understand that you’re getting sophisticated averaging, not artificial intelligence in the science fiction sense.
Moving Beyond the Hype
The current discourse around AI often conflates impressive averaging with genuine intelligence. LLMs can produce outputs that seem intelligent because they’re very good at mimicking patterns they’ve learned, but this is fundamentally different from understanding, insight, or innovation.
Recognizing this helps us use these tools more effectively. Instead of expecting breakthrough insights, we can leverage their ability to quickly generate competent, average solutions and then apply human creativity and judgment to refine, improve, and innovate beyond what the model can produce.
The most effective approach combines LLM capabilities with human insight: use the model to quickly generate conventional solutions, then apply human creativity to identify limitations, explore alternatives, and push beyond the boundaries of what’s been done before.
Large language models are powerful tools for generating average outputs at scale. Understanding this limitation – rather than viewing it as a flaw – helps us use them appropriately and avoid the disappointment that comes from expecting more than sophisticated averaging.