Where Do Chatbots Come From? What I Wish Everyone Knew About AI in 2026

LW disclaimer: This piece is aimed at people who don't know much about how LLMs work; I think the discourse would be much better if more people knew the basics. So most LWers are not in the target audience. I'm posting it here in case people want to pass it along to people in their lives who are in the target audience. Substack version minus this disclaimer is here.

AI can be confusing. But there is a certain amount of baseline information about what AI is and how it works that can be extremely helpful, both for making decisions about how to use AI for yourself, and understanding the claims you hear about it in the news and on social media.

My goal with this post is to give you that basic information. I am by no means an expert—I'm a philosopher by training, not an AI developer or even particularly a power user. But I've been using AI basically since ChatGPT came out, and trying to keep up with developments in a casual way for longer than that. And this is the information I have found helpful for understanding AI and interpreting what people say about it. After reading, you'll hopefully understand why people call AI "just fancy autocomplete" (but also why that's misleading), and how the people who say it's really dumb and the people who say it's really smart can both be right.

AI is not one thing. Self-driving cars, image classifiers, recommendation algorithms, chess AIs, predictive policing algorithms, and large language models are all "AI," but they're as different as cars, planes, boats, and bicycles. Thinking of all of these different technologies as just "AI" would be like thinking of all those different forms of transportation simply as "vehicles," with no more specific concepts to differentiate them.[1] That would be really confusing, given how many important differences there are between them. So when someone says "AI will/won't do X," your first question should be "which AI?"

(If all these kinds of AI are so different, what unites them? Briefly: they all involve systems that learn patterns from data rather than following hand-written rules. But that's often where the similarities end. And to make it even more confusing, some things that people call "AI" do follow hand-written rules.)

This piece is about large language models (LLMs) specifically. This is the technology behind ChatGPT. Some of what's said here generalizes to other kinds of AI, but much of it doesn't. But it does apply to different models within the class of LLMs. (I'll sometimes call these things "chatbots," because that's how most people first encounter them. But we'll see by the end that, nowadays, they can do a lot more than just chat.) People often treat "ChatGPT" and "AI" as synonyms (the same way they treat "Kleenex" as a synonym for "tissue", or "Google" as a synonym for "search"). But there are several competing models from different companies (OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini (and AI Overviews), and others, including Chinese models like DeepSeek and Kimi), and they have meaningfully different strengths, weaknesses, and even "personalities." Treating your experience with one model as representative of "AI" is like evaluating all smartphones based on one brand.

And even within a single company's product line, "ChatGPT" isn't just one thing. GPT-3.5 is a different model from GPT-4o, which is a different model from GPT-6 Astra; they're not all just incremental updates to the same system. If the last time you tried ChatGPT was when it first came out, there's been a lot of changes since then. For a recent guide to which models to use and how, check out Ethan Mollick's "An opinionated guide to which AI to use to do stuff" (he generally writes a new guide every few months, this is the most recent as of this post's publication). (For myself, I've mostly decided to just stick with the Claude family of models and not worry too much about trying to keep up with releases from other model families.)

Here's the plan: I'll explain how a neural network works, then walk through how an LLM is built in stages. It's trained first to predict text, then to act as a helpful chatbot, then to reason through hard problems and use tools as an agent. I'll end by arguing that the best way to understand what these systems can do is to use the best available models yourself.

II. It's Not Magic, But It's Not Normal Software Either

The first thing to know about LLMs is that they're made of math. Interacting with them for the first time can be a bit like going to a magic show—you leave with the impression they're doing something impossible. But just like a magic show, there is a non-mystical explanation for how they do what they do: mostly it's a bunch of addition and multiplication.

But unlike a magic show (and unlike ordinary software), we don't actually fully understand the non-mystical explanation for what they're doing. We don't write an LLM like we do other software. Instead, we write a program that trains the LLM. So there are two programs: the training program, and the LLM itself. We understand how that training program works. And we understand on a basic level why it results in an LLM that's able to do the task we're training it for. But we don't know why it works as well as it does. And more importantly, we don't know how the trained LLM works on the inside. We can see every calculation it makes, but not how those individual calculations add up to doing the task it was trained for. One way to look at it is that an LLM is "grown, not crafted."[2]

LLMs are a type of neural network. The really basic version of how a neural network works is this: you have a program with billions of dials that you can tweak, and those dials (called the "weights") determine what the output of the program is. You have some problems for the program to solve, and an answer key. You give the program a problem (for example: given a photograph of a handwritten digit, identify which digit it is), and it spits out an answer—at the start this answer will just be random nonsense. But then you tweak the billions of dials in whatever directions they need so that it will give a slightly better answer on the problem the next time. Then you repeat this for billions of training examples, and at the end it has been trained to actually do the task well. For more on this, I implore you to go watch 3Blue1Brown's excellent series on this topic (particularly the first three videos on neural nets in general, and the 7 minute introduction to LLMs). Indeed, one of the reasons I wrote this post is to beg more people to watch those videos.

III. How to Grow an LLM in Five Easy Steps

What I want to do with this section is give you a mental model for how an LLM works under the hood. It's not a full technical explainer—if you want that, go watch Karpathy's "Deep Dive into LLMs like ChatGPT" or read UnderstandingAI's "Large Language Models, Explained With a Minimum of Math and Jargon" (from which I've learned a lot, and frankly stolen a lot for this piece). But it should be helpful to understand what is going on on the other side of the screen when you're talking to ChatGPT or your LLM of choice.

i. Pre-training (aka the base model)

When you're growing an AI, the first ingredient is a neural net (seriously, go watch those 3Blue1Brown videos). The second ingredient is an entire internet's worth of text.

What you do with that is you train the neural net to predict the text. You give it bits of text, and ask it to predict the next word. So with this post, you might feed it "Where Do Chatbots Come From? What I Wish Everyone Knew About AI in 2026. AI can be confusing. But there is a certain amount of baseline" and then ask it to predict the next word (which in this case is "information"). If it gets it right, you reinforce the neurons that led to the prediction. If it gets it wrong, you reinforce the neurons that would have led to getting it right. (It's actually slightly more complicated than this: the model does