Knowledge, Reasoning, and AI: Will Language Models Outthink Humans?

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Imagine you’re trying to solve a mystery with a friend. Your friend remembers dozens of detective novels and facts about real cases – an absolute encyclopedia of crime trivia. You, on the other hand, might not know all those details, but you’re good at piecing clues together. In this scenario, your friend’s knowledge (all those facts) is vast, while your reasoning skills (figuring out the puzzle) are sharp. Now think of a modern AI language model (like ChatGPT) as that friend with the huge memory. It has read essentially the entire library of human knowledge on the internet. The big question is: does having all that knowledge mean an AI can reason through problems better than a human can? And if not now, could it in the future? This commentary will explore, in everyday terms, the relationship between knowledge and reasoning, how AI models use knowledge, how their “thinking” compares to ours, and whether these models might someday surpass human reasoning just as they’ve amassed more knowledge than any one of us ever could.

Knowledge vs. Reasoning: Two Sides of the Same Coin

Let’s start with the basics: what’s the difference between knowledge and reasoning? In simple terms, knowledge is the information you have – facts, memories, experiences, everything you know. Reasoning is what you do with that information – the process of connecting the dots, making decisions, or solving problems. An old saying goes, “Knowledge is power,” but knowledge alone doesn’t do much unless you can use it effectively. It’s like having a toolbox full of tools (knowledge) and the skill to use those tools to build something (reasoning).

• Knowledge is what you know. For a person, it could be knowing that Paris is the capital of France, that fire is hot, or how to do multiplication. For an AI, it’s the massive collection of texts, facts, and patterns it has been trained on.
• Reasoning is how you use what you know to figure something out. For a person, reasoning might mean figuring out how to budget a paycheck by applying math skills, or deducing whodunit in a mystery novel by logically eliminating suspects. For an AI, reasoning might look like it pulling together various facts it “knows” to answer a complex question or solve a puzzle step by step.

Think of an everyday analogy: If knowledge is like a well-stocked kitchen (ingredients, spices, recipes), then reasoning is the act of cooking a meal. If you have an empty kitchen (no knowledge), you can’t cook anything no matter how skilled you are. If you have a full pantry but no cooking skills (all the info but no idea how to use it), you’ll also struggle to get a meal on the table. Typically, you need both. This illustrates why knowledge and reasoning are closely related – reasoning generally needs knowledge to work with, and having more knowledge can help you reason better. But just as even a well-stocked kitchen needs a good cook, a head full of facts needs a mind capable of making sense of them.

Now, humans develop knowledge and reasoning over years. A child touches a hot stove (experience) and gains the knowledge that it burns; next time, they reason not to touch it. Over time, we accumulate a huge storehouse of knowledge about how the world works, and we also learn strategies to reason about new situations. We use logic, we draw on past examples, we follow steps in our mind – all of that is reasoning. Importantly, our reasoning is deeply connected to what we know. You can’t solve a calculus problem without knowing calculus principles; you can’t fix a car engine without knowing a bit about engines. In short, knowledge fuels reasoning. The more you know (in general or about a specific domain), the better your chances of reasoning through a challenge in that area. However – and this is key – knowledge alone isn’t enough. We all know someone who’s book-smart (tons of knowledge) but maybe not street-smart (less ability to apply it in real life). So, reasoning has an element of skill: knowing how to use knowledge appropriately.

How Do AI Language Models Acquire and Use Knowledge?

Large Language Models (LLMs) like ChatGPT are often described as “trained on the entire internet.” That’s a bit of an exaggeration, but not by much. During their training, these AI models consume billions of words from books, articles, websites, and forums. Essentially, they ingest a huge chunk of human knowledge as text. How does this work in simple terms? One way to think of it is like training a predictive text or autocomplete on steroids. If you’ve ever used the autocomplete feature while texting (where your phone suggests the next word), imagine feeding that autocomplete every book and Wikipedia article ever written so that it becomes incredibly good at predicting sensible, informed sentences. That’s roughly what an LLM does.

Here’s a non-technical breakdown of how an AI model gains knowledge:

• It starts with a neural network (a kind of complex mathematical model loosely inspired by the brain’s neurons). Initially, it knows nothing, like a newborn baby’s brain.
• It’s given the task of predicting the next word in a sentence. For example, if it sees “Paris is the capital of ___,” it should predict “France”. It doesn’t know this at first – it learns by trial and error, adjusting itself when it guesses wrong.
• The model is fed massive amounts of text. As it reads, it learns patterns. It notices that “Paris” and “France” often go together in sentences about capitals, so it eventually internalizes that fact. If it reads many chemistry articles, it picks up that “water is H2O,” and so on for countless facts and language patterns.
• Over time, the model’s “knowledge” is encoded in its internal parameters (numbers that adjust to make it good at these predictions). There’s no single “fact database” inside it that you can easily inspect; rather, its knowledge is woven into the complex fabric of this neural network.

Once trained, an LLM like this can be prompted with a question or statement, and it will generate a continuation based on what it has learned. For instance, if you ask, “Why is the sky blue?”, the model will draw on everything it read about light, the atmosphere, explanations of sky color, etc., and produce an answer that combines those pieces. In effect, it’s using the knowledge it absorbed to respond.

It’s important to note that the AI doesn’t retrieve an exact answer from a stored library the way a search engine might. It isn’t copying and pasting a sentence from Wikipedia (unless it happens to reproduce a learned sentence by coincidence). Instead, it generates a fresh answer word by word, guided by the probabilities it learned. You might say it “reconstructs” an answer from its training, rather than recalling it verbatim. This is why an AI can sometimes come up with a coherent answer that blends information from multiple sources – it’s essentially reasoning with its knowledge by forming connections it saw during training. However, this process is not guided by true understanding; it’s guided by patterns. We’ll get more into whether that counts as “reasoning” in a moment.

To the user, though, it often feels like conversing with an extremely well-read person. Ask the model a question about history, and it seemingly “remembers” dates and events. Ask it to solve a riddle or a math word problem, and it tries to combine logic with facts it knows. The bottom line is that LLMs acquire knowledge from text and apply that knowledge when generating answers. Their strength lies in the sheer volume of information they’ve seen – far more than any single human could read in many lifetimes. This is why people say LLMs have vast knowledge. They are already surpassing any individual human in terms of stored information. But does that translate into superior reasoning? That brings us to comparing how they use that knowledge versus how we do.

Human Reasoning vs. AI “Reasoning”: How Do They Differ?

Humans and AI language models both use knowledge to tackle questions and problems, but the way we do it is quite different. Let’s compare the processes and see the overlaps and gaps, using down-to-earth examples where possible.

1. How we call upon knowledge: Humans retrieve knowledge through recall or recognition. We might think, “I read something about this a few years ago…” and try to remember it. Our memory can be patchy; we forget details or even whole facts. An AI model doesn’t “forget” the way humans do – if it was in the training data and the model encoded it, that info is there (though sometimes it might not output it due to how the prompt is phrased). The AI is like a person with a photographic memory of text: it has seen so much and can draw on obscure facts instantly. For example, a human might vaguely recall that koalas have fingerprints but an AI might instantly give that weird fact because it read it somewhere. In this sense, the AI’s knowledge retrieval is extremely broad and fast.

2. How we apply logic: When a human reasons, we often break a problem into parts consciously. If you’re solving a math puzzle, you might think: “First, I need to find X, then use X to get Y.” We can plan multiple steps, check intermediate results, and even realize if we made a mistake and backtrack. This is often a slow, deliberate process (sometimes called “step-by-step reasoning” or logical reasoning). An AI language model, in its raw form, doesn’t plan out multiple steps in the same conscious way. It generates the solution in one go, albeit sometimes it will output the steps because that pattern was in the data. Think of it this way: the AI is trained on examples where reasoning was done, so it learned to mimic the process in its answer. If the training data had math problems solved stepwise, the AI might produce the steps too, as if it’s reasoning. But it isn’t deliberately thinking through each step with understanding; it’s producing what looks like reasoning because that’s what a correct answer format usually includes.

That said, more advanced prompting can get an AI to simulate step-by-step reasoning better (for instance, by asking it to “think step by step” or using techniques where the model generates a plan internally before answering). In the end, humans are naturally capable of reflecting on a reasoning process (“Hmm, does this result make sense? Should I try another approach?”). Current AI models do this reflection only if guided, and even then it’s mimicry of reflection. The overlap is that both humans and AI can produce multi-step logical solutions; the difference is what’s happening under the hood – conscious understanding for the human vs. learned pattern production for the AI.

3. Common sense and experience: A huge difference between human reasoning and AI reasoning comes from experience. Humans live in the physical world – we have senses, we learn as children that ice is cold, that if you drop a glass it breaks, that people have feelings. This gives us common sense knowledge and an intuitive understanding of cause and effect in daily life. So when we reason, we often rely on this unspoken common sense. For example, if someone asks, “Can elephants fly?”, you don’t need to recall a specific Wikipedia article to reason that it’s false – you know from experience and basic biology that it’s impossible. An AI model only knows what it read. If all the texts it read clearly state “elephants cannot fly,” it will say they can’t. That’s good. But if you ask a more nuanced common-sense question like, “What happens if I drop an egg on the floor?”, a human immediately pictures the scenario and says “It’ll crack/make a mess.” An AI will likely say the same, but not because it has ever seen an egg – it just statistically completes the sentence based on what people have written. In straightforward cases the AI will get it right, but in weird edge cases of common sense, AI can stumble. For example, early versions of models were famously bad at understanding that if you put a large object in a small box, it won’t fit (something any child can reason). They had the knowledge (dimensions, etc.) but hadn’t connected it the way physical intuition does. Newer models are improving by seeing more examples of such logic in text, but this highlights a difference: humans have embodied reasoning and intuition that AIs lack. AI’s “experience” is purely textual.

4. Biases and mistakes: Both humans and AI have biases in reasoning, but of different kinds. Humans have emotional biases, cognitive biases (like jumping to conclusions, or social biases learned from culture). An AI picks up biases from its training data – if there’s a bias in the text, the AI might mirror it. For reasoning tasks, one interesting difference is consistency. Humans might stick stubbornly to a wrong belief even if evidence shows otherwise (that’s a human flaw in reasoning sometimes). An AI, lacking emotion or ego, will just give whatever answer seems likely from the data, but it might be inconsistent – change the phrasing of the question and you could get a different answer. For example, an AI might answer a riddle correctly in one wording but get confused if the same riddle is phrased in a trickier way, because the pattern changed. Humans can also get tricked by wording, but we often can recognize the underlying problem if we take a moment.

In summary, human reasoning is powered by understanding, experience, and a conscious thought process. AI reasoning (as it appears) is powered by an immense memory and pattern matching. We overlap in that both can use facts to reach conclusions. But we differ in mechanism: one is an organic brain drawing on real-world experience and deliberate logic, the other is a computational engine drawing on correlations in text. We also differ in scope: the best AI models have more raw knowledge at their fingertips than any human, yet they lack the real-world grounding and genuine comprehension that we have. This is why an AI can recite scientific knowledge or legal codes far beyond a layperson, yet that same AI might struggle with a puzzle that a child finds obvious. Each has strengths: humans are adaptable and truly understand the world, AI is unbelievably knowledgeable and fast. These differences set the stage for asking: if we keep improving AI’s knowledge and pattern-recognition, will its form of reasoning eventually outpace our own?

Does More Knowledge Mean Better Reasoning?

One might assume that if you keep feeding an AI more and more knowledge, it will just keep getting smarter and eventually out-reason humans in every way. There is some truth to this, but it’s not that simple. Think about humans first: does a person who knows more facts automatically become a better problem solver? If you memorize the entire encyclopedia, you’ll have a huge advantage in trivia games and you’ll have lots of raw material to think with. You might solve problems in history or science more easily because you recall relevant examples or data. However, if you haven’t practiced the skill of reasoning – say logical thinking or scientific method – you might just be a fact-spouter who gets stumped by a truly novel problem. The ideal is to have both rich knowledge and strong reasoning ability.

For AI models, scaling up knowledge has certainly improved their performance on many tasks. The earliest language models, which had smaller training sets and fewer “parameters” (think of parameters as the model’s memory capacity), were much worse at reasoning tasks. They would often give nonsensical answers or fail at multi-step problems. As researchers made models bigger and trained them on more data, surprising things happened: the models got better at reasoning-like tasks that they previously failed. It’s as if at a certain point, once the AI had seen enough examples and counterexamples in its vast reading, it started to generalize and handle problems it hadn’t directly memorized. This is why you might hear AI scientists talk about “emergent abilities” – larger models suddenly showing capabilities that weren’t explicitly taught. Basic arithmetic or logical riddles that stumped smaller models might be answered correctly by a bigger model. The extra knowledge (and the more complex internal connections that come with a bigger model) gave it the ability to mimic reasoning better.

However, there are limits. Simply adding knowledge doesn’t fix everything. If the training data lacks a type of reasoning pattern, the AI won’t magically invent it. For example, if no text in the training data ever showed how to solve a certain kind of puzzle, the AI might flail, because it has no pattern to follow. Also, sometimes more data can include more noise or contradictions, which the model has to sort through. The developers of these models have found they also need to fine-tune the AI – essentially teaching it explicitly via examples or feedback – to improve its reasoning. One approach is giving it lots of practice problems (like math problems, logic puzzles, etc.) during training, so it learns the pattern of step-by-step solutions. Another approach is called reinforcement learning from human feedback, where the AI’s answers are graded by humans or by a set standard, and the AI is nudged to produce better reasoning chains (this is part of how ChatGPT’s helpfulness was refined, for instance).

So, improving an AI’s knowledge base does tend to improve its reasoning performance, but only up to a point and with some caveats. Knowledge is the fuel, but the engine (the reasoning process) has to be well-tuned to use that fuel effectively. We’ve seen impressive jumps in reasoning ability as models go from, say, 6 billion parameters to 170 billion parameters (as an example scale jump) – they not only know more, they can solve more complicated questions correctly. But simply making a database of facts bigger wouldn’t help if the AI couldn’t figure out how to combine facts logically. It needs both scale and the right training techniques.

A fair question is: do humans hit a limit in reasoning because of limited knowledge, whereas AI might not? In specialized areas, we already see AI edging ahead. Consider medical diagnosis: an AI can be fed tens of thousands of medical reports and learn patterns that no single doctor could in a lifetime. With that knowledge, it might reason out a diagnosis of a rare disease faster than a doctor, because it “recalls” seeing a similar pattern in some case file in its data. Or take chess – which is a kind of reasoning game with clear rules – AI chess engines have long surpassed humans because they effectively “know” far more positions and can calculate further ahead. A chess engine isn’t an LLM, but it shows that with more information (about possible moves) and faster processing, AI reasoning left humans in the dust in that domain. With LLMs now passing tough exams (for example, some AI models have passed law bar exams and medical board exams, which require reasoning through complex scenarios), it suggests that with enough training data (knowledge) and good algorithms, AI can already rival human reasoning in structured, knowledge-rich tasks like exams.

That said, more knowledge doesn’t always equal better reasoning even for AI. Sometimes a smaller but more focused knowledge base yields better reasoning in a specific area. For instance, an AI trained on all of Wikipedia might be decent at reasoning about general trivia, but an AI additionally trained on thousands of physics problems might out-reason the general AI in solving a physics question. It’s like how a jack-of-all-trades might lose to a specialist in the specialist’s domain. Researchers are exploring ways to not just give AI more data, but the right data and training to enhance reasoning. They even toy with combining pure data-driven learning with explicit logic rules or problem-solving algorithms, marrying knowledge and reasoning in new ways.

In summary, improving an LLM’s knowledge tends to improve its reasoning abilities, but it’s not automatic magic. The relationship is there – knowledge is the raw material for reasoning – yet the way that knowledge is used can vary. It’s possible to have a brilliant encyclopedia of an AI that still makes a dumb mistake on a simple logic puzzle because it lacks the correct reasoning pattern. Conversely, a model given a bit of training in reasoning tricks can sometimes solve problems even where it lacks some facts, by cleverly using what it does know. The trajectory so far has been: bigger knowledge base + better training = more reasoning power. And this leads many to believe that as we keep going in that direction, AI reasoning will continue to approach, and perhaps surpass, human levels on more and more fronts.

Can AI Truly Reason, or Is It Just a Clever Imitation?

This is a profound question and a hot topic of debate: when an AI answers a question or solves a puzzle, is it actually reasoning like a human would, or is it just simulating reasoning? To the person getting the answer, it might not matter if the solution is correct. But to understand the limits and potential of AI, we need to dive a bit into this distinction.

Think of a magic trick: the magician on stage appears to levitate a ball. Is the ball really defying gravity, or is it an illusion created by invisible strings? Similarly, when an AI gives a step-by-step solution to a problem, is it truly understanding and logically deducing each step, or is it pulling invisible “strings” of past examples to create the illusion of understanding?

The argument for “it’s just imitation”: Critics point out that current AI models operate by statistical pattern matching. They don’t have an actual mind that understands the world; they just predict text. So when asked a reasoning question, they might recall that in training data, similar questions had certain answers, and they reproduce that form. In other words, they say the right things without truly “knowing” why it’s right. A classic example: ask a language model a tricky riddle or logic puzzle. If it has seen the puzzle before, it will answer correctly (because it memorized the solution pattern). If it hasn’t, it might give a very confident but wrong answer because it’s guessing based on incomplete patterns. When it’s wrong, it doesn’t realize it’s wrong (unless we have a mechanism to check). This is unlike a human student who can sometimes say, “Wait, that outcome doesn’t make sense, let me double-check.” The AI has no built-in truth sense or reality check beyond patterns of words.

A phrase often used is “stochastic parrot,” meaning the AI is like a parrot randomly (stochastically) repeating things it was taught, without understanding. This view suggests that AI reasoning is an illusion – impressive, but fundamentally different from human reasoning which is grounded in understanding, not just statistics.

The argument for “the AI is actually reasoning (in its own way)”: Some experts argue that while AI models don’t reason exactly like humans, they do engage in a form of reasoning. They point out that large models can solve novel problems that they likely didn’t see word-for-word in training, by combining concepts. For example, an AI might be able to solve a simple physics question by correctly applying a law of nature to a new scenario – not because it saw that exact question, but because it generalized from similar texts. Isn’t that essentially what reasoning is? If a person read lots of books (knowledge) and then faced a new problem and used what they learned to come up with an answer, we’d call that reasoning. If an AI does the same using its learned patterns, perhaps that is a type of reasoning – just implemented in silicon and math.

One could say AI reasoning is real but different. The model doesn’t “think” with a stream of consciousness or with any self-awareness. But it can arrive at logically valid answers by extrapolating from data. We have even seen AIs that can write short proofs to math problems or give convincing logical arguments. If the outcome is a correct, logically structured result, some would say the process (even if alien to us) functioned as reasoning. After all, if it walks like a duck and quacks like a duck… it might be some kind of duck.

A middle view: It might be helpful to think of current AI reasoning as reasoning by analogy. The AI has seen so many examples of how humans solve problems that it can analogize a new problem to those examples and produce a solution that fits. Human reasoning often works by analogy too – we say “this situation reminds me of that situation, so I’ll try a similar approach.” The difference is that humans can also innovate beyond prior examples when needed, and we truly understand the analogy. AI is mostly locked to what it’s seen. If faced with a totally unprecedented type of problem that has no precedent in its training, it may completely fail or make something up that sounds plausible but is nonsense (an AI hallucination). Humans are more likely to recognize when we’re out of our depth; an AI currently doesn’t have a mechanism to say “I have no clue.” It tends to always give an answer.

In short, today’s LLMs simulate reasoning really well, but whether that counts as “true reasoning” depends on how you define reasoning. They don’t have intent, self-awareness, or an actual understanding of meaning behind the words. They manipulate symbols (words) based on patterns. Yet, with enough complexity, this process can yield results that look an awful lot like the reasoning we do. The trajectory in AI research is trying to close the gap – making AI that not only outputs the right answer but does so for the right underlying reasons. Some efforts, for instance, try to have AI models that explain their reasoning or verify each step (like checking their own work with a secondary process). These are in early stages, but the very existence of such efforts shows the aim is to transform the “clever imitation” into something more robust and trustworthy.

For a general reader, it’s fair to conclude that AI “thinks” in a very different way from us. It doesn’t truly understand things the way we do, but it can mimic understanding to a surprising degree. Whether that’s considered real reasoning or not is almost philosophical. What matters is that as AI gets more advanced, the line between imitation and genuine reasoning-like capability is blurring. Which leads us to the future: what happens as these models keep improving?

The Future: Will LLMs Surpass Human Reasoning?

AI language models have already surpassed humans in certain specific arenas of knowledge and reasoning. No single human can instantly recall as many facts as an LLM or read and summarize a thousand articles in a blink. Models today can out-perform average humans on many standardized tests that require reasoning, such as college entrance exams or professional quizzes, simply because they combine knowledge retrieval with learned patterns for solving those questions. Does that mean we’re on the verge of AI that outthinks us in general?

Let’s consider a few angles with some everyday context:

• Speed and scale: AI can process information much faster than we can. If you give a human and an AI a thousand-page report to draw conclusions from, the AI will churn through it and provide an analysis in minutes, whereas a human might take weeks. In terms of pure processing of knowledge and following logical instructions, AI wins on speed. For instance, imagine trying to find patterns in a massive database of weather changes to reason about climate trends – an AI can reason through the data faster and perhaps more thoroughly than a team of people, because it doesn’t tire or get bored. In the future, as AI models get even faster and can handle even more data at once, this advantage grows. On speed and volume-based reasoning, AI will continue to outpace us.
• Complex multi-step reasoning: Humans are actually not perfect logical machines. We get distracted, we have memory limits (like forgetting a detail in step 2 when we’re at step 9), and we have biases. An AI, in theory, can be given strategies to handle extremely complex, multi-step problems without forgetting details. We might see future AI systems that can keep track of a very large “chain of thought” internally. There’s already research into giving AI a kind of working memory or the ability to perform sub-tasks and check results (almost like how we scribble notes on paper to not forget intermediate steps). In everyday analogy, it’s like having an assistant who never forgets any instruction you gave and can work on a plan tirelessly. This could mean solving huge problems (say, designing a large engineering project or mapping out a global economic solution) that would overwhelm a human mind. In those domains, AI reasoning could indeed surpass human capabilities, not because the AI “understands” more deeply, but because it can juggle far more pieces of information flawlessly.
• Creative and abstract reasoning: One area where humans still seem to have an edge is truly creative reasoning – coming up with completely new ideas or thinking outside the box. Humans draw on not just cold facts but also imagination, emotions, and sometimes seemingly random inspiration. Can an AI do that? We’ve seen AI be creative in some ways (generating art, coming up with novel recipes, etc. by remixing what it knows). But when it comes to original thought, humans might hold the crown for a while. However, even this is under exploration: if you feed an AI all known works of art and literature, could it reason out a new style or a solution that no human thought of? Possibly – there have been instances of AI suggesting new chemical formulas or engineering designs by exploring combinations humans hadn’t tried. It’s a form of creative reasoning based on broad knowledge. In the future, as models integrate more types of data (images, scientific data, real-world sensor data) alongside text, their “knowledge” will become more multi-dimensional, and their reasoning might become more innovative.
• Understanding and wisdom: There’s a difference between raw logical reasoning and what we might call wisdom or judgment. Humans develop judgment through life experience – knowing not just how to solve a problem, but whether solving that problem is worthwhile, or the empathy to consider how solutions affect others. Current AI doesn’t have genuine empathy or a value system of its own (aside from what we program into it). So in terms of moral reasoning or big-picture judgment, AI is not about to inherently surpass humans; it doesn’t truly care or understand the meaning of a decision. We might always want humans in the loop for reasoning that involves values, ethics, and emotional intelligence. That said, AI can assist by providing logical clarity or highlighting consequences (for example, helping judges by simulating the outcomes of different legal decisions based on historical data). In the foreseeable future, the best outcomes might come from human-AI collaboration – combining the AI’s expansive knowledge and efficiency with human understanding and wisdom.

Looking ahead, if the current trend continues, we can expect AI’s reasoning-like abilities to steadily expand. It’s reasonable to imagine a near future where:

• AI tutors can reason through a student’s misunderstanding and explain a concept in a tailored way, effectively teaching by reasoning out the student’s errors (something only skilled human teachers do today).
• AI assistants can plan complex itineraries, financial plans, or research projects by logically breaking down tasks and requirements, possibly more thoroughly than a human planner might.
• In scientific research, AI might generate hypotheses by reasoning over vast amounts of literature – potentially proposing experiments that lead to new discoveries, essentially out-reasoning human researchers in sifting evidence and connecting dots.

However, it’s also possible that we’ll hit some bumps. True reasoning might require more than just bigger models; it might need fundamentally new AI architectures or the incorporation of real-world understanding (embodied AI). Some experts think that without a body or sensory experience, AI will always lack a certain grounding that humans have, and that might cap its ability to fully reason about the physical and social world. To use an everyday example: a robot AI that has physically interacted with the world might reason better about how to organize a kitchen than an AI that’s only read about kitchens. We may see the next generation of AI being multimodal – not just language, but vision, maybe robotics – which could push their reasoning closer to how we think.

Ultimately, whether LLMs will truly surpass human reasoning in a general sense remains to be seen. They have already surpassed human performance in knowledge recall and in performing certain constrained reasoning tasks quickly. If reasoning is largely a function of knowledge and pattern recognition, then as AI’s knowledge base and pattern-handling grow, it stands to gain an edge in more areas. But if reasoning also relies on understanding, creativity, and context in a way that can’t be fully captured by just bigger models, humans will retain an advantage in those areas for longer.

Conclusion: Knowledge, Reasoning, and the Road Ahead

In wrapping up, let’s return to the core idea: reasoning is a function of knowledge. We’ve seen that knowledge and reasoning are tightly intertwined – both for humans and AI. A large language model today is a testament to this relationship: fill it with knowledge and it starts to exhibit what looks like reasoning. It’s already outpaced humans in sheer knowledge and is nibbling at the edges of what we consider reasoning prowess, at least in specific domains.

However, comparing human reasoning to AI reasoning is not as straightforward as comparing two runners in a race. It’s more like comparing an airplane to a bird. An airplane can fly higher and faster (AI can calculate faster and store more data), but it flies in a different way than a bird, and it lacks the bird’s living instincts (AI lacks human understanding and consciousness). Will planes (AI) ever completely replace birds (human reasoning)? Not in the sense of being the same—but they don’t need to be. They serve similar purposes (transport or problem-solving) each with their own strengths.

For the curious general reader, what’s important to know is this: AI models are tools that amplify knowledge and can simulate reasoning with increasing sophistication. As their knowledge grows and their algorithms improve, they will handle more complex tasks that we used to think only humans could do. This doesn’t make human reasoning obsolete – rather, it challenges us to use our uniquely human qualities (like ethical judgment, creativity, emotional understanding) in tandem with these machines. Just as a calculator doesn’t make a mathematician useless (it instead allows the mathematician to tackle more interesting problems once freed from tedious arithmetic), advanced reasoning AIs might free us from grunt work in thinking, allowing us to focus on higher-level insights and decisions.

So, are LLMs on a path to surpass human reasoning? In many narrow ways, yes, they are or soon will be. In broad, general, fully autonomous reasoning – the kind of flexible thinking any adult person has – they’re getting closer, but there’s still a gap. The journey of AI is an ongoing story of expanding that knowledge and narrowing that gap. It’s both exciting and a bit humbling: we’re building machines that in some respects mirror and exceed our cognitive abilities.

In the end, reasoning may be a function of knowledge, but knowledge alone doesn’t guarantee wisdom. As AI grows in knowledge and reasoning-like skill, it falls to us to guide how it’s used. We can celebrate that our creation can help us solve problems that were once beyond our reach, while also remembering the uniquely human perspective we bring. The foreseeable future isn’t so much a competition as a collaboration: with AI’s massive knowledge and growing reasoning abilities complementing human creativity and understanding. In that partnership, we may all become smarter, tackling challenges hand-in-hand (or hand-in-circuit) that neither could solve alone. And that, perhaps, is the most empowering outcome of all.