Designing AI Interfaces – Louise Macfadyen

Computation: Designing for the Processing and Generation Phase On July 20, 1969, during the final descent of Apollo 11 toward the Moon, a warning alarm lit up the astronauts’ display: 1202. Unfortunately, no one in the cockpit knew what 1202 meant; the computer didn’t appear to be crashing, but other than that, it was a mystery.

The alarm indicated something was wrong. The 1202 alarm turned out to be a low-priority overload warning. The onboard computer was shedding nonessential tasks to stay focused on landing-critical ones—an intentional feature that hadn’t been fully thought through. From an engineering perspective, this was an elegant act of triage, not a malfunction. But to the astronauts, it looked like a potentially fatal glitch. They had no way to see how the system was allocating its computational resources, or why some functions were dropped while others were preserved.

The system was working under strain, but because that logic was invisible, it became a source of confusion and near abort. Encountering an error in AI can feel much the same. Many parts of the process feel confusing, locked behind occult signifiers, much like the 1202 alarm. Understanding this middle layer—how AI systems process, triage, and generate outputs—is essential for building interfaces that balance the abilities of AI with the real human needs of its users.

Once the input is submitted, the user expects an answer, but before an AI can respond, it has to compute. That might sound obvious, but in practice, this process (sometimes referred to as inference) is the least visible, least understood part of the AI interaction pipeline.

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Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 979-8-341-63982-9 [LSI] OceanofPDF.com Foreword One of the most persistent myths in AI right now is that design doesn’t matter when the AI is powerful enough.

The argument goes something like this: if the model is smart enough, it doesn’t need a good interface. Users will just talk to it, it’ll understand them, and everyone goes home happy. You can tell it what you want, and it does it! UI is seen as old-fashioned, just like Windows 95. I’ve been arguing against this for years, not because I’m a designer defending my turf (although let’s be real, I am), but because the argument is empirically wrong.

And now, thankfully, Louise Macfadyen has written the book that proves it. The book you’re holding makes a case I find deeply satisfying, not because it vindicates a position I already held (although, again, it does), but because it goes further than my ranting and arrives somewhere much more useful and practical. Macfadyen takes a deceptively simple framework (input- computation-output) and shows that different species of potential failure haunt each stage.

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