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🤿 malleable software

🤿 malleable software

insights from a design engineer at Notion

Ridd
November 14, 2025

AI is fundamentally shifting the way we think about digital products and the core deliverables that we're bringing to the table as designers.

So I asked Geoffrey Litt (Design Engineer at Notion) to share his vision for the future of malleable software and AI system design.

He’s dedicated much of his career to this topic so his insights are fascinating. Some highlights:

The keys to effective system design
Geoffrey’s workflow for “working like a surgeon”
Why too many products become “nightmare bicycles”
The principles of malleable software vs. disposable software
Why version control is the most important AI interaction problem
Inside Geoffrey’s malleable software experiments at Ink & Switch
+ a lot more

Listen on Youtube, Spotify, Apple, or wherever you get your podcasts 👇

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🔑 KEY TAKEAWAYS

Are we approaching a platform shift?

This line from Geoffrey stuck with me 👇

“A lot of the things that I care about around malleable software aren't things that any one application can easily solve. There are more platform level things that really require a fundamentally new way to think about software.

And those shifts don't happen very often. They last a decade or more…

I think we're in a time of such volatility right now with AI kind of forcing everyone to reimagine how things work, that there's actually going to be a serious platform shift over the next few years.”

Geoffrey Litt

That line “fundamentally new way to think about software” isn’t hyperbole.

In our interview Geoffrey teases out ideas that challenge nearly every constant we associate with digital products and the practice of design.

Some questions I still have on my mind:

what if anyone can mold software over time as they use it?
what does it look like for designers to be responsible for pattern systems rather than fixed UI?
how much of “malleable” software is also “disposable"?
what patterns need to be created to enable effective version control in a much wider array of AI experiences?
what does it look like to effectively expose the system when designing AI products?

This episode will get you thinking so I hope you enjoy it as much as I did :)