Before anyone had heard the term “large language model,” before “AI assistant” meant anything outside of academic computer science journals, somewhere around 30 million people were already chatting with a bot every day. It was 2002. The bot was called SmarterChild. The platform was AOL Instant Messenger.

If you came of age in that brief window between the spread of always-on internet and the arrival of the smartphone, you almost certainly typed something into a chat window with SmarterChild. You probably tried to insult it. You probably asked it what the weather was. You may have, late at night, attempted to have a conversation with it about the meaning of life and felt a strange flicker of satisfaction when it produced something almost plausible.

This is the story of how SmarterChild happened, what it actually was under the hood, and why it matters for how we think about chatbots today.

The setup: ActiveBuddy and the bot economy of 2001

The company behind SmarterChild was called ActiveBuddy, founded in 2000 by Robert Hoffer, Timothy Kay, and Peter Levitan. The idea was straightforward in retrospect, although unprecedented at the time: people were spending hours on instant messaging platforms, and there ought to be a way for businesses and services to offer information through that channel.

ActiveBuddy’s first big idea was a bot called General Buddy that could fetch facts on demand. You’d type a question into AIM, the bot would respond. Need a stock quote? Ask it. Want the weather in Cleveland? Ask it. Movie showtimes? Ask it.

The technology was not what you’d call sophisticated by modern standards. It was a rule-based pattern matcher with a series of integrations into external data sources — weather APIs, stock APIs, sports score feeds. It used something called BuddyScript, a domain-specific language ActiveBuddy had developed, to handle natural language input and route it to the right backend. Most “conversations” with SmarterChild were essentially fancy database queries dressed up in friendly language.

But that didn’t matter. To the average user, it felt like talking to something.

The launch and the scale

SmarterChild launched in June 2001 on AOL Instant Messenger. Within a year, it had something like 9 million buddy list adds. By 2003 it was over 30 million users actively chatting with it. For context, that’s a population scale that would put SmarterChild ahead of most major social platforms today as a percentage of internet-connected users at the time.

The success had a few drivers. AIM was huge — at its peak in the early 2000s, it had roughly 53 million users in the US alone according to data Time Warner published around the period. Adding SmarterChild to your buddy list cost nothing. And the bot was useful in a way that nothing else on the early consumer internet really was — you could just ask it stuff in plain English and it would, often, tell you something accurate.

The bot was also funny, in a way that mattered enormously for retention. ActiveBuddy’s writers had put real personality into the responses. If you swore at SmarterChild, it would tell you to grow up. If you tried to flirt with it, it would shut you down sharply. If you asked it whether it was a real person, it would tell you straight up that it wasn’t and then continue the conversation. That mixture of competence and personality was unusual for software at the time and is, in retrospect, why so many users formed something like an emotional attachment to it.

What was actually going on under the hood

The interesting bit, for a history of chatbots, is how little “intelligence” was actually involved. SmarterChild was a sophisticated decision tree with a lot of hand-written response templates and external API connectivity. There was no neural network. There was no model in any modern sense. There was a team of writers at ActiveBuddy producing thousands of conditional responses to anticipated inputs.

When SmarterChild told a joke, the joke was in a database. When it appeared to remember something from earlier in your conversation, it was using session storage to retrieve a token. The conversational illusion was achieved through very careful writing, not through any genuine understanding.

This was, of course, also the technique used by older systems like ELIZA in the 1960s. The Eliza effect — the tendency for users to attribute human-like understanding to systems that produce human-like text — was just as strong in 2002 as it was in 1966. Arguably it’s still the dominant factor in why people find modern chatbots compelling, although the underlying technology has changed beyond recognition.

The Microsoft acquisition and the slow fade

In 2007, Microsoft acquired ActiveBuddy, which by that point had been renamed Colloquis. The acquisition was reported at around $46 million per coverage in The New York Times and elsewhere. SmarterChild was effectively wound down as a public-facing product in 2008 when Microsoft shut it off.

The reasons were not really about the technology. AIM itself was in steep decline by 2007 as users migrated to MySpace messaging, then Facebook, then SMS, then phone-based messaging apps. The platform SmarterChild lived on was dying. There was no obvious successor platform that needed a chatbot, and Microsoft’s interest was more about acquiring the natural language processing patents and the team than about keeping the product running.

For an entire generation of users, SmarterChild’s disappearance was a small but real loss. There are still threads on Reddit and elsewhere where people in their thirties and forties mourn it.

Why it actually matters

The most important thing about SmarterChild isn’t the technology — which by modern standards is borderline trivial — but what it taught the industry about user behaviour. It demonstrated, conclusively, that people would adopt chat as an interface for getting information done if the chat felt natural, if the bot had personality, and if it actually worked for common tasks.

That lesson sat dormant for about a decade. Slack opened up bot integrations in 2014. Facebook Messenger bots launched in 2016 to enormous initial hype and underwhelming user uptake. WeChat bots in China became huge in the same period and have remained huge. And then, in late 2022, ChatGPT arrived and demonstrated what could happen when you took the chat interface SmarterChild had pioneered and put a genuinely capable language model behind it.

The early SmarterChild users are now in their late thirties. They were the first generation of internet users who treated chatting with software as a normal activity. The cultural muscle memory they built — the willingness to phrase questions in natural language, the patience to rephrase when misunderstood, the comfort with a non-human conversational partner — turns out to have been training for what came next.

It’s a strange piece of internet history. A throwaway gimmick on a messaging platform that no longer exists trained a generation for the interface that would eventually dominate computing.

There’s a thread worth pulling on for the contemporary AI industry too. Most of the companies now building chat-based products are reinventing patterns SmarterChild had figured out by 2003 — personality writing, conversation state, graceful failure responses. The teams at firms doing custom AI development today are essentially layering modern language models onto interaction patterns that the ActiveBuddy writers worked out by trial and error twenty years ago. The plumbing has changed completely. The user expectations have barely moved.

I suspect we’ll see more retrospectives like this one as the chatbot boom matures. The deep root system of how we got here runs through AIM, through ICQ, through ELIZA before it. SmarterChild was just the moment the public got their first proper taste.