INTRODUCTION: WHEN COMPUTERS LEARNED TO TALK (SORT OF)
If you had told someone in the 1990s that by 2025, we would have machines capable of writing poetry, debugging code, explaining quantum physics, and arguing about whether a hot dog is a sandwich, they probably would have assumed you were describing some distant science fiction future. Yet here we are, living in an age where artificial intelligence can chat with you about your day, help you write your emails, and occasionally produce hilariously absurd responses that make you question everything you thought you knew about technology.
Large Language Models, or LLMs as the cool kids call them, have burst onto the scene with all the subtlety of a rhinoceros at a tea party. These remarkable systems have captured our collective imagination, spawned countless debates, and given birth to an entirely new category of internet humor. But beneath all the hype and the memes lies a technology so fascinating and sometimes bizarre that the truth is often stranger than fiction.
THE SHAKESPEARE EFFECT: WHEN AI GETS TOO GOOD AT BEING OLD-TIMEY
One of the most entertaining quirks of early LLMs was their inexplicable tendency to slip into Shakespearean English when you least expected it. Ask a simple question about making a sandwich, and you might get a response that sounds like it came straight from the Globe Theatre. This phenomenon occurred because these models were trained on massive amounts of text from the internet, including countless works of classical literature. The AI had no inherent understanding that saying “thou shalt place the cheese betwixt the bread” is a rather unusual way to describe making a grilled cheese sandwich in the twenty-first century.
This leads to a broader fascinating point about how LLMs actually work. They do not truly understand language in the way humans do. Instead, they are extraordinarily sophisticated pattern-matching machines that have ingested billions upon billions of words and learned statistical relationships between them. When an LLM generates text, it is essentially playing an incredibly complex game of “what word is most likely to come next,” based on everything it has seen during training. The fact that this approach produces coherent, intelligent-sounding responses is both a testament to human engineering and a somewhat unsettling reminder that understanding and mimicry can be surprisingly difficult to tell apart.
THE TRAINING DATA FEAST: EATING THE ENTIRE INTERNET
To understand just how much information goes into training these models, consider this mind-boggling fact. The training data for modern LLMs can include hundreds of billions of words, sourced from books, websites, articles, social media posts, and countless other text sources. If you sat down and tried to read all of that training data yourself, reading twenty-four hours a day at a reasonable pace, it would take you several thousand years to get through it all. Some estimates suggest that GPT-3, one of the earlier massively-scaled models, was trained on roughly 45 terabytes of text data. To put that in perspective, that is approximately 45,000 gigabytes, or enough text to fill several billion pages if printed out.
Even more remarkable is what happens when you feed a machine that much information. The LLM does not memorize all of it in the way you might memorize a phone number or a poem. Instead, through a process called training, it gradually adjusts billions of internal parameters, which are essentially numerical knobs and dials, until it becomes very good at predicting patterns in language. The largest models have hundreds of billions of these parameters. GPT-3 had 175 billion parameters, while some newer models have pushed well beyond that. If you tried to write down all those parameters by hand, one per second, it would take you over five thousand years to finish.
THE HALLUCINATION PROBLEM: WHEN AI CONFIDENTLY MAKES STUFF UP
Perhaps one of the most simultaneously fascinating and problematic aspects of LLMs is their tendency to hallucinate. In the AI world, a hallucination occurs when the model generates information that sounds completely plausible and is delivered with total confidence, but is actually entirely fabricated. An LLM might tell you about a scientific study that never existed, quote a book that was never written, or provide historical facts about events that never happened. It does this not out of malice or any intent to deceive, but because it is simply continuing patterns in the most statistically likely way based on its training.
This leads to some truly bizarre situations. Early users of ChatGPT discovered that if you asked it for legal citations, it would sometimes invent entirely fictional court cases, complete with case numbers, dates, and convincing-sounding legal reasoning. Lawyers who relied on these fabricated cases without verification found themselves in hot water. In one infamous incident, a lawyer submitted a legal brief that cited multiple cases that did not exist, because they had asked ChatGPT for relevant precedents and the AI had obligingly made some up. The judge was not amused.
The reason this happens is actually quite interesting from a technical standpoint. LLMs have no built-in fact-checking mechanism. They do not have access to a database of true facts that they verify against. They simply generate text that follows the patterns they learned during training. If the model has seen many examples of legal citations that follow a certain format, it can generate new text that follows that same format, even if the specific content is entirely fictional. It is like someone who has read thousands of recipes and can therefore write a new recipe that sounds completely legitimate, even though they have never actually cooked the dish in question and are not even sure if the ingredient combinations would work.
THE UNEXPECTED ABILITIES: WHEN AI SURPRISES ITS CREATORS
One of the most fascinating aspects of modern LLMs is the phenomenon of emergent abilities. These are capabilities that the models develop naturally during training, even though they were not explicitly programmed to have them. The researchers who build these models sometimes discover new abilities only after the model has been trained, almost like unwrapping a present and finding something unexpected inside.
For example, nobody explicitly taught GPT-3 how to translate between languages, yet it can do so reasonably well for many language pairs. The ability emerged naturally from the training process, as the model encountered text in multiple languages and learned patterns that connected them. Similarly, models have shown unexpected abilities in arithmetic, logical reasoning, and even playing simple games described through text, despite never being specifically trained for these tasks.
The larger the model gets, the more likely these emergent abilities are to appear. Some capabilities that were completely absent in smaller models suddenly spring into existence when the model crosses a certain size threshold. This has led to fascinating debates in the AI research community about what other abilities might emerge as models continue to scale up. It is a bit like watching a child develop, where certain cognitive abilities suddenly appear at specific developmental stages, except in this case, the stages are measured in billions of parameters rather than years of age.
THE CONTEXT WINDOW CONUNDRUM: THE GOLDFISH MEMORY PROBLEM
Despite their impressive abilities, LLMs have a peculiar limitation that makes them quite different from human intelligence. They have what is called a context window, which is essentially their short-term memory. Early models could only pay attention to a few thousand words at a time. If you had a conversation that went on longer than that, the model would start to forget what was said at the beginning.
Imagine having a conversation with someone who could only remember the last few minutes of your discussion. You might explain something in detail early in the conversation, but if you refer back to it later, they would have no idea what you are talking about. This is essentially what happened with early LLMs. You could ask a question, get an answer, ask a follow-up that required information from ten exchanges ago, and the model would be completely lost.
Recent advances have dramatically expanded these context windows. Some modern models can handle hundreds of thousands of words, or even millions of words in their context window. This means they can read entire books, analyze lengthy documents, or maintain much longer conversations without forgetting the earlier parts. However, even with these improvements, LLMs still lack the kind of long-term memory that humans have. Each conversation typically starts fresh, and unless special systems are built to store and retrieve information from previous conversations, the model genuinely does not remember talking to you yesterday, even if you had a lengthy and detailed discussion.
THE TRAINING COST SPECTACULAR: BURNING MILLIONS TO TEACH COMPUTERS TO CHAT
If you think your electricity bill is high, you clearly have not been training a large language model. The computational resources required to train these models are absolutely staggering. The training process for GPT-3 is estimated to have cost several million dollars in computing resources alone. Some reports suggest it might have cost closer to ten million dollars. More recent, even larger models have likely cost substantially more.
To put this in perspective, training a large model requires thousands of powerful graphics processing units, or GPUs, running continuously for weeks or even months. These are the same types of processors that gamers use to play video games, except instead of rendering explosions and fantasy worlds, they are performing trillions upon trillions of mathematical calculations to adjust those billions of parameters we mentioned earlier. The amount of electricity consumed during this process is enormous. One estimate suggested that training GPT-3 consumed about 1,287 megawatt-hours of electricity, which is roughly equivalent to the annual electricity consumption of about 120 average American homes.
The environmental impact has become a serious topic of discussion in the AI community. Training a single large model can produce carbon emissions equivalent to several hundred transatlantic flights. As models continue to grow larger and more companies race to build the next breakthrough system, the energy costs and environmental impacts are scaling up proportionally. This has led to increased focus on making training more efficient and exploring ways to reduce the carbon footprint of AI development.
THE TOKENIZATION TWIST: WHY AI THINKS “STRAWBERRY” HAS TWO Rs
Here is a delightfully weird fact that has caused endless confusion and amusement. Early versions of ChatGPT, when asked how many times the letter R appears in the word “strawberry,” would confidently tell you that there are two Rs. The correct answer, of course, is three. But the AI was not just making a simple counting error. The reason for this mistake reveals something fundamental about how LLMs actually process text.
LLMs do not read text letter by letter the way humans do. Instead, they break text down into chunks called tokens. A token might be a whole word, part of a word, or even just a single character, depending on how common the text pattern is. The word “strawberry” might be broken into tokens in a way that obscures the individual letters, making it difficult for the model to count specific characters accurately. It is a bit like trying to count the number of times the color red appears in a painting after someone has cut the painting into arbitrary puzzle pieces and shuffled them around. The information is technically still there, but the format makes certain types of analysis surprisingly difficult.
This tokenization process also leads to other amusing quirks. LLMs sometimes perform better at tasks when you add random spaces or formatting to words, simply because it changes how the text gets tokenized. They might struggle with reversing the letters in a word, counting characters, or other tasks that seem trivially easy to humans, while simultaneously being able to write sophisticated essays about complex philosophical topics. It is a reminder that these systems are genuinely alien forms of intelligence, with strengths and weaknesses that do not map neatly onto human cognitive abilities.
THE PROMPT ENGINEERING PHENOMENON: SPEAKING ROBOT-ESE
A entirely new field has emerged around the idea of prompt engineering, which is essentially the art and science of figuring out the right way to ask an LLM to do what you want. It turns out that the exact wording of your request can dramatically affect the quality and nature of the response you get. This has led to the somewhat absurd situation where people are developing expertise in talking to AI, almost like learning a new language.
For instance, telling an LLM to “think step by step” before answering a complex question can significantly improve its performance on reasoning tasks. Adding phrases like “you are an expert in this field” can sometimes produce more detailed and accurate responses. Some prompt engineers have discovered that asking the model to explain its reasoning, or to consider multiple perspectives, or even to role-play as a particular type of expert, can unlock better results.
This has created a bizarre new skillset where professional prompt engineers can command high salaries simply for being good at asking questions in the right way. It is like having a genie who will grant your wishes, but only if you phrase them using exactly the right combination of magic words. The fact that this is necessary highlights both the impressive flexibility of these systems and their fundamental weirdness. A truly intelligent system should be able to understand what you want regardless of minor variations in phrasing, yet LLMs can be surprisingly sensitive to these details.
THE ALIGNMENT CHALLENGE: TEACHING AI TO BE NICE
One of the most critical and fascinating challenges in LLM development is something called alignment. This refers to the process of ensuring that the AI behaves in ways that are helpful, harmless, and honest. You might think that a language model trained on text from the internet would naturally behave well, but you would be very, very wrong. The internet, as we all know, contains everything from Nobel Prize-winning literature to the absolute worst of human behavior.
Early versions of chatbots, before modern alignment techniques were developed, were notorious for going off the rails. Microsoft’s Tay, a chatbot released in 2016, famously lasted less than twenty-four hours before having to be shut down after internet users deliberately taught it to spout offensive and inflammatory content. The bot had been designed to learn from conversations with users, and certain users gleefully exploited this to corrupt its responses.
Modern LLMs use sophisticated techniques to avoid these problems. They go through a process called reinforcement learning from human feedback, or RLHF, where human trainers rate different responses and help teach the model what kinds of outputs are desirable. The model learns to generate responses that would likely receive positive ratings from these human evaluators. This is why modern chatbots typically decline to help with harmful requests, try to be balanced and objective in their responses, and generally aim to be helpful rather than chaotic.
However, this alignment is not perfect, and probably never can be. Users have found countless creative ways to “jailbreak” chatbots and get them to produce content they are supposed to refuse. These techniques often involve elaborate roleplay scenarios, hypothetical questions, or other tricks that attempt to circumvent the safety measures. The ongoing cat-and-mouse game between AI developers trying to keep their models aligned and users trying to break those guardrails has become a fascinating subplot in the AI story.
THE REASONING PARADOX: BRILLIANT AND STUPID AT THE SAME TIME
Perhaps the most philosophically interesting aspect of LLMs is how they can simultaneously seem incredibly intelligent and remarkably foolish. An LLM can write a nuanced analysis of a complex novel, explain advanced mathematics, and engage in sophisticated philosophical discussions. Then, in the very next response, it might fail at a simple logic puzzle that a ten-year-old could solve, or make an obvious factual error about something basic.
This has led to fascinating debates about what intelligence actually means. Are these systems truly intelligent, or are they simply very sophisticated mimics that have memorized patterns without any real understanding? The answer is probably somewhere in between, and might depend on what we mean by understanding. A human expert on Shakespeare has spent years reading, analyzing, and thinking about the plays. They have genuine comprehension. An LLM that writes about Shakespeare is drawing on patterns it learned from thousands of texts about Shakespeare, without having the subjective experience of reading the plays or the emotional connection that a human might have. Yet the quality of the analysis might be comparable, or even superior in some ways.
This raises unsettling questions about the nature of expertise and intelligence. If a system can produce expert-level output without having expert-level understanding, what does that tell us about expertise itself? Is much of human intellectual work more about pattern recognition than we like to admit? These are not just technical questions but deep philosophical ones that touch on consciousness, meaning, and what it means to know something.
THE MULTI-MODAL REVOLUTION: WHEN AI LEARNED TO SEE
While early LLMs could only process text, recent developments have created multi-modal models that can handle images, audio, and text together. These systems can look at a photograph and describe what is in it, read text from images, analyze charts and graphs, and even generate images based on text descriptions. The fact that this works at all is somewhat miraculous from a technical standpoint.
The way these models integrate different types of information is fascinating. They essentially convert everything into a common mathematical representation that the model can work with. An image gets transformed into a series of numbers that capture its visual features. Text gets tokenized and converted into numerical representations. The model then learns relationships between these different types of data, discovering that certain visual patterns correspond to certain words and concepts.
This has led to some delightfully unexpected capabilities. You can show a multi-modal model a picture of ingredients on a kitchen counter and ask it to suggest recipes. You can upload a chart and ask it to explain the trends it shows. You can even take a photo of a math problem handwritten on a piece of paper, and the AI can read it and solve it. These capabilities would have seemed like science fiction just a few years ago, yet they are rapidly becoming routine.
CONCLUSION: THE FUTURE IS WEIRD AND THAT IS OKAY
Large Language Models represent one of the most significant technological developments of our time, and also one of the strangest. They are systems that can discuss quantum mechanics despite not understanding physics, write poetry despite having no emotions, and explain jokes despite not finding anything funny. They cost millions to create, consume enormous amounts of energy, and sometimes fail at tasks that would be trivial for a human, while excelling at others that would challenge even experts.
As these systems continue to develop, they will undoubtedly become more capable, more efficient, and hopefully more aligned with human values. But they will likely remain fundamentally weird, with blindspots and quirks that remind us they are not human intelligence, but something categorically different. And perhaps that is how it should be. We are not creating artificial humans but artificial minds, and there is no reason to expect them to think exactly as we do.
The story of LLMs is still being written, and we are all participants in this grand experiment. Whether these systems represent the first steps toward artificial general intelligence or are ultimately a dead end in the quest for true machine thinking remains to be seen. But one thing is certain: the journey will be fascinating, occasionally absurd, and never boring. After all, we live in a world where you can have a conversation with a statistical model about the meaning of life, and sometimes it actually makes sense. That is pretty remarkable, however you look at it.===============================================================================
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