5 Lessons from Microsoft’s Clippy

What the rising AI avatar industry can learn from the brief career of a Microsoft Office assistant

Nahua Kang
May 2 · 5 min read

Steven Sinofsky comments on Clippy’s resurgence in pop culture

5 lessons from Clippy’s failure

Clippy: The unauthorized biography with a16z
Clippy: “Hey Bill. We’re gonna be BBF, right?” Bill: “Right…” (Credit: Reuters)

1. Optimize for repeated use, not just first time use

First, according to Microsoft’s Chris Pratley, Clippy suffered greatly from the “optimization for first time use” problem. The first time you saw the cordial, “It looks like you’re writing a letter,” suggestion, you might have been pleasantly surprised by Clippy’s intelligence. But after the 1,000,000th time you probably found it hard to take Clippy’s incessant, repetitive options every time you wrote “Dear…” on your Word doc. It’s not surprising that many users chose to turn Clippy off after his novelty faded.

2. Imbue diversity in product design and development

Second, Clippy was created in a male-dominated design process that lacked diversity. The original idea was to create a fun and non-intrusive helper for the Office interface, yet Clippy and his fellow digital helpers turned out to be especially unpopular among women. In an interview with the New Yorker, former Microsoft executive Roz Ho recalled: “Most of the women thought the characters were too male and that they were leering at them.”

3. Seek and listen to your real customers’ feedback

Crucially, feedback from these focus groups were not taken seriously in the product development phase. It seems that the male-dominated engineering team couldn’t understand why the female reviewers thought the characters were leering or male-looking. Ultimately, 10 out of 12 assistants that were shipped with Clippy were male characters. According to Sinofsky’s interview, even Bill Gates made fun of the assistant’s annoying nature when he first heard of the idea, suggesting he’d want to kill “the clown”. Furthermore, Sinofsky also revealed that many reviewers in the focus groups were tech enthusiasts and did not include “regular folks” who represent a larger proportion of Office users.

4. Avoid being excessively attached to your creation

Along the journey to create an assistant that users could connect with, the creators of Clippy became emotionally attached to their own product. Unwilling to accept feedback, “they were willing to throw out the focus-group-provided data” because it defied their expectations. James Fallows also softly hinted that Clippy was a holdover from an unsuccessful Microsoft Bob project, which Melinda Gates led. It might not have been a decisive factor, but it may be why employees were hesitant to offer their sincere opinions about poor Clippy.

5. Be aware of the adjacent possible

Finally, in our estimation, the Clippy product was outside the adjacent possible and way ahead of its time. According to Sinofsky, the Clippy project emanated from studies on social interaction and intent classification with Bayes theorem and NLP. But he also revealed another critical problem that doomed Clippy from the beginning: when Clippy was launched, contemporary computers had only 2MB of RAM, 20MB of harddrive space, and a VGA screen that could fit only two paragraphs of Microsoft Word. Melinda Gates also acknowledged that Bob needed a more powerful computer. But, at that time, the gigabyte was not a given and GPUs were nonexistent. For any intelligent virtual assistant today, these engineering constraints are like digital starvation.

Clippy retirement party in San Francisco (Credit: Steven Sinofsky)

From Clippy to AI Avatars

The rise of AI avatars today bears resemblance to the early days of personal computers. While deep learning has made many previously unimaginable ideas possible, building likeable AI characters shares the same fundamental challenges. So Clippy’s failure imparts valuable lessons that can help us — the creators of the new generation of AI-powered assistants — avoid the same mistakes that Microsoft made all those years ago.


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We teach machines to perceive the world like humans.

Nahua Kang

Written by

Marketer @twentybn. Write about history, biographies, literature, and tech. Website: https://www.nahuakang.com/. Newsletter: https://nahua.substack.com

twentybn

twentybn

We teach machines to perceive the world like humans.