LLMs Aren't AI, But They're Getting Better at Pretending

Where We Are and Where We're Going

In the world of animal intelligence, crows are fascinating creatures. They solve complex puzzles, craft tools, and even hold what appears to be "funerals" for their dead. Meanwhile, parrots can mimic human speech with incredible accuracy, sometimes even seeming to use words in context, but they're ultimately repeating patterns they've observed. This distinction perfectly illustrates where we are with Large Language Models (LLMs) like ChatGPT, Claude, and Gemini.

The Architecture of Mimicry

To understand why LLMs are more parrot than crow, we need to look under the hood. These models are built on a foundation of mathematical patterns called neural networks—but perhaps "pattern networks" would be a more accurate term. Imagine a vast web of interconnected nodes, each holding a tiny piece of information about language patterns. These connections are weighted, meaning some patterns are considered more important than others.

During training, these weights are adjusted billions of times as the model processes vast amounts of text. It's similar to how a parrot might learn that "hello" is more commonly said when someone enters a room than when they leave. The model isn't learning meaning; it's learning statistical relationships between patterns of tokens (pieces of words or characters).

From Training to Prediction

The training process itself is remarkably mechanical. The model starts by making random guesses about what words should come next in a sequence. Initially, it performs about as well as a parrot that's just heard its first human word. But through a process called backpropagation, the model adjusts its internal weights based on how wrong its guesses were. Over time, these adjustments lead to increasingly accurate predictions.

This is fundamentally different from how a crow learns. When a crow solves a problem, it's developing an understanding of cause and effect. When an LLM makes a prediction, it's more like a supremely sophisticated version of your phone's autocomplete, it's just much better at considering a broader context when making its predictions.

Prediction vs. Intelligence: The Core Distinction

At their core, LLMs are probability engines, more like a parrot with an infinitely good memory than a crow with problem-solving abilities. They generate the most likely next word in a sequence based on statistical relationships learned from vast amounts of training data. Just as a parrot might learn to say "wanna cracker" when it's hungry without understanding the concept of hunger, LLMs lack:

• True comprehension – They process text without grasping meaning, like a parrot reciting Shakespeare
• Causal reasoning – A crow understands that pushing a stone into the water makes the water level rise to reach a floating treat; an LLM only knows these events often appear together in text
• Intent or agency – They respond reactively, like a parrot, rather than planning proactively like a crow building a tool for future use

The Mathematics of Mimicry

The predictive nature of LLMs is best understood through their token prediction process. When you input a prompt, the model calculates probability distributions across its entire vocabulary for what should come next. This isn't intelligence; it's advanced statistics.

And here's where we encounter one of the most delightful ironies in artificial intelligence: LLMs, whose very existence depends on sophisticated mathematical operations and statistical modeling, often struggle with basic arithmetic. A model that can effortlessly calculate the probability distributions across billions of parameters might confidently tell you that 7 × 8 = 54. It's like having a quantum physicist who can't balance their checkbook.

This mathematical paradox perfectly illustrates the difference between pattern matching and true understanding. The model's neural networks perform countless mathematical operations to predict the next token, yet it can't reliably perform the kind of mathematical reasoning that a young student can master. While its internal architecture is processing advanced linear algebra and calculus at a massive scale, the model itself might stumble over middle school math problems.

Consider how differently this works from actual reasoning. When a crow wants to reach food floating in water, it understands several key concepts: water levels rise when objects are added; higher water levels make floating objects more reachable; therefore, adding stones to water can help reach the food. An LLM, by contrast, is just calculating that in similar textual contexts, discussions of floating food are often followed by descriptions of stones being dropped in water—all while potentially mishandling the simple arithmetic of how many stones were used.

The Illusion of Reasoning: Sophisticated Mimicry

Recent advances in models like DeepSeek and OpenAI's latest offerings seem to demonstrate reasoning capabilities but look closer. While a crow might reason through a novel problem, like figuring out how to bend a wire into a hook to fetch food, LLMs are using:

• Chain-of-thought prompting (following pre-learned steps, not actual problem-solving)
• Self-consistency techniques (like a parrot that's learned multiple phrases for the same situation)
• Tool integration (matching patterns about which tools worked before, not understanding why they work)

Blurring the Lines with Tool Use

Here's where things get interesting. Modern LLMs are being integrated with external tools, APIs, and reasoning frameworks. It's as if we're giving our sophisticated parrot a Swiss army knife and a user manual. The parrot still doesn't understand the tools the way a crow does, but it's getting remarkably good at matching the right tool to the right situation based on observed patterns.

Early LLMs were notoriously unreliable with mathematics, often generating answers that would make a first-grade teacher wince. However, through sophisticated pattern recognition and tool integration, they've been programmed to identify mathematical patterns and delegate calculations to specialized functions. While this might appear as cognitive growth, it's more akin to a parrot being trained to ring a specific bell when it encounters numbers. The model isn't actually developing mathematical ability, it's matching patterns to determine when to defer to a calculator.

Perhaps this tool use does make our LLM more crow-like after all... Though it's less like a single clever crow and more like a committee of specialists in a trench coat, each handling their specific task when the pattern-matching algorithm summons them. The integration of these tools represents a significant advancement in capability but not necessarily in intelligence. When an LLM calls an external API or performs a calculation, it's doing so because its statistical weights indicate this is the appropriate next action, not because it understands the underlying need for that action. The Future: Perception vs. Reality

As these systems evolve, they're becoming hybrid creatures—still fundamentally pattern-matching like parrots but with an increasingly convincing facade of crow-like problem-solving ability. The introduction of multimodal models that can process images, audio, and text simultaneously makes the mimicry even more impressive, but the fundamental limitation remains: they're still pattern-matching engines, just across more types of patterns.

The Takeaway

The next time you're amazed by an LLM's capabilities, remember the parrot and the crow. One can flawlessly recite complex phrases and use them appropriately, while the other can solve novel problems and understand cause and effect. LLMs are undoubtedly the most sophisticated "parrots" we've ever created—and that's incredibly useful! But they're not crows yet.

The real question isn't whether LLMs are "true" AI but rather how we can best use these incredibly sophisticated pattern-matching systems while understanding their fundamental limitations. After all, a parrot's ability to mimic human speech can be remarkably useful, even if it doesn't understand what it's saying. The key is knowing the difference and knowing when it matters.

The future of AI might not lie in making our parrots more sophisticated but in figuring out how to give them some of the crow's genuine problem-solving abilities. Until then, we should appreciate these models for what they are: remarkable achievements in pattern recognition and statistical prediction rather than true artificial intelligence. And for goodness' sake, don't let them make our decisions. Otherwise, we're all going to be eating crackers!