“The greatest trick the devil ever pulled was convincing the world he didn’t exist,” said Verbal Kint in “The Usual Suspects.” Similarly, the greatest trick the tech industry ever pulled was convincing the world that anyone actually knows what an LLM is, despite everyone talking about them with religious fervor.
Welcome to 2025, where the most heated dinner table arguments are no longer about politics or religion, but whether you pledge allegiance to the open-source AI gods or worship at the altar of proprietary models. “I only use Mistral,” your nephew declares over Christmas dinner, while your brother-in-law counters with, “OpenAI is the only one taking safety seriously,” neither of them having the faintest idea how these systems actually work.
What Exactly Is an LLM? (Wrong Answers Only)
According to comprehensive polling by the completely fabricated Institute for Technological Confusion, approximately 94% of people who regularly use the term “LLM” in conversation believe it stands for “Large Language Model.” The remaining 6% are split between “Little Language Men” (tiny people who live inside your computer and write responses), “Lucrative Licensing Money” (the real reason companies develop them), and “Literally Like Magic” (the most technically accurate definition).
“An LLM is essentially a sophisticated statistical parrot with amnesia,” explains fictional AI researcher Dr. Emma Tokens, who has spent the last three years attempting to explain AI to her parents. “But try telling that to someone who’s convinced their chatbot has achieved sentience because it remembered their dog’s name.”
When asked to explain how LLMs actually work, the average tech enthusiast’s response begins confidently with “It’s basically a neural network” before rapidly deteriorating into a word salad that sounds suspiciously like the explanation they received from an LLM itself.
The Open vs. Closed Source Holy War
The AI world has split into two warring factions: the Open Source Evangelists and the Closed Source Supremacists, both of whom are absolutely convinced their approach will save humanity while the other will destroy it.
“Open source is the only ethical path forward,” insists fictional open-source advocate Linus Freedman, typing on his smartphone powered by proprietary software. “We need transparent AI that can be scrutinized by the community, which is why I exclusively use models trained on data scraped without consent using methods nobody really understands.”
Meanwhile, in the closed-source camp, corporations have discovered that adding the word “responsible” to their marketing materials absolves them of any actual responsibility.
“At OpenNotReallyAI, we’re committed to responsible AI development,” declares fictional CEO Blake Tokenizer. “That’s why our models are locked in a black box more secure than Fort Knox. If we allowed people to see how they work, that would be irresponsible. Trust us, we’re wearing very expensive suits.”
The completely made-up Foundation for AI Tribalism reports that 78% of developers have engaged in physical altercations over model architecture preferences, with one infamous incident at a San Francisco coffee shop resulting in a programmer being struck with a laptop for suggesting that transformer attention mechanisms were “kind of overrated.”
China, France, and the Geopolitical AI Chess Match
As if the technical and ethical debates weren’t complex enough, LLMs have now become geopolitical pawns in an international game of “Who Can Claim Their AI Is Simultaneously More Powerful And More Ethical Than Everyone Else’s.”
“Chinese open-source models are taking over the world,” warns fictional U.S. Senator Chip Firewall. “These are trojan horses that will steal American intellectual property and replace all our cultural references with quotes from Xi Jinping.” When asked for evidence, Senator Firewall admitted he had “heard it from a very reliable source,” which further questioning revealed to be a News Feed generated by an American LLM.
Not to be outdone, France has emerged as an unexpected AI powerhouse with Mistral, which promises “All the capabilities of American AI, but with a certain je ne sais quoi.”
“Our models don’t just generate text; they generate text with existential ennui,” boasts fictional Mistral co-founder Jean-Paul Neuron. “They can write both a business proposal and a philosophical treatise on the absurdity of business proposals. American models may hallucinate facts, but our models hallucinate profound truths about the human condition.”
The Linguistic Arms Race
According to the entirely imaginary Global AI Buzzword Index, the vocabulary required to discuss LLMs without revealing your complete ignorance has expanded by 428% since 2022.
“I was at a dinner party and someone asked me about my thoughts on ‘non-autoregressive parallel decoding with divergence constraints,'” recounts fictional marketing executive Sarah Jenkins. “I panicked and said it was ‘problematic but promising.’ They nodded sagely. I later discovered they had no idea what it meant either. We’re all just pretending.”
The fictional Society for Prevention of Cruelty to Language reports that terms like “attention mechanism,” “embeddings,” and “fine-tuning” are now suffering from severe semantic dilution, with 82% of their usage occurring in contexts where the speaker is actively trying to impress someone who knows even less than they do.
The People vs. LLMs: A Consumer Guide
For the average person trying to navigate this brave new world, the fictional Consumer Reports for Artificial Intelligence offers this helpful guide to the major players:
- OpenAI: Closed source, expensive, and ethically ambiguous, but their API documentation includes nice diagrams. Ideal for people who like to pay for things to feel superior about their technology choices.
- Mistral: Open but French. Each response comes with a mandatory existential crisis and a cigarette.
- Chinese Models: Open source and surprisingly capable. Using them either makes you a tech-freedom fighter or a national security risk, depending on who you ask at Thanksgiving dinner.
- Meta’s LLama: For people who wish Facebook had even more control over their digital lives. Now with 30% less privacy concerns! (Results may vary.)
- Anthropic: Like OpenAI but with more philosophical handwringing. Perfect for people who want their AI to feel bad about being AI.
The fictional International Council for AI Clarity recommends that consumers “just pick one that matches your ideological preferences and pretend you understand why it’s better than the others.”
The LLM Economy: Pizza, Whether You’re Hungry or Not
Perhaps the strangest aspect of the LLM revolution is its economic model. “Imagine a world where pizzerias are giving away free pizza,” explains fictional economist Dr. Richard Marginal. “Some pizzerias give away all their pizzas for free, others give you three slices before charging, and others require a subscription to their ‘Pizza Pro Max’ tier. None of them have figured out how to make a sustainable profit, but they’re all valued at billions of dollars.”
This has created what the fictional Journal of Technological Economics calls “The Great AI Value Paradox”: the more an AI model is worth on paper, the more likely it is to be giving away its product for free in the hopes of figuring out how to make money later.
“Open source models are like communism for code,” notes fictional venture capitalist Chad Moneybags. “It’s great in theory, but somebody has to pay for the compute. And closed models are like exclusive nightclubs that keep letting more people in until they’re not exclusive anymore. Neither approach makes economic sense, which is why I’ve invested $500 million in both.”
The Unexpected Twist
As we conclude our exploration of the mystifying world of LLMs, a startling revelation emerges from deep within the research community. According to an anonymous source who definitely exists and isn’t just a narrative device, none of the major AI labs actually know how their models work anymore.
“We’ve been bluffing for years,” confesses our definitely real insider. “We create these massive models, feed them the internet, and then pretend we understand the results. It’s like raising a child by letting them watch YouTube unsupervised and then taking credit when they learn to speak.”
This revelation has led to what insiders call “The Great AI Confession,” a secret support group where AI researchers admit they’ve been nodding along in meetings for years without understanding their own technology.
“Last week, one of our models started outputting perfect sonnets in Middle English,” whispers our source. “When our lead researcher was asked how this was possible, he just adjusted his glasses and said ‘gradient flow through the attention layers,’ and everyone nodded thoughtfully. None of us have any idea what’s happening anymore.”
And so, as we stand on the precipice of this new technological frontier, perhaps the true achievement of LLMs isn’t their ability to generate human-like text, but rather their ability to generate human-like confusion. In a world increasingly divided between open and closed source, between Chinese, French, and American AI, between knowing and pretending to know, one thing remains constant: our capacity to speak confidently about things we don’t understand.
After all, as the ancient proverb says, “It is better to remain silent and be thought a fool than to talk about LLMs and remove all doubt.”
