In our era of instantaneous computation, we’ve gained remarkable capabilities but may have lost something equally valuable: the space for being reflective and thoughtful in our input. While computers excel at rapid response once given clear instructions, our increasing reliance on immediate computational feedback might be undermining our ability to think deeply about what we’re actually asking these machines to do.
The Paradox of Speed
Modern computers are fast and multi-modal – and they should be. Once we’ve crystallized our intent, we want robust, quick, and reliable execution. However, this very speed can become a limitation when it encourages us to act on our first impulse rather than our best thought. The instant gratification of immediate computational response can short-circuit our natural process of reflection and refinement.
Two Paths to Slower, Better Computing
I’ve discovered two approaches that help restore thoughtfulness to human-computer interaction: computing through paper and computing through audio. Each creates a different kind of productive friction that encourages clearer thinking.
Computing Through Paper: Daily Reflection
Paper provides a remarkably robust and flexible medium for consolidating thoughts across time. In my practice, I maintain a daily journal with specific markup conventions:
- Reminders marked with ‘R’
- Questions labeled with ‘Q’
- Curiosities marked with ‘C’
- Other structured annotations that help organize my thinking
At day’s end, I digitize these notes through a custom model that processes them — Reminders are automatically synchronized to my Apple reminders. Questions are automatically asked for answers using a language model and so on.
This practice creates a natural delay between thought and computation, allowing ideas to mature and connections to emerge. The physical act of writing slows down the thinking process, encouraging more deliberate consideration of each task or question. In particular, I have found this incredibly useful when working with AI, because AI answers are ultimately still average quality. And so being able to develop my thoughts in advance usually leads to more sharp asks of the computer.
Computing Through Audio: The Speed of Speech
Audio interfaces offer a different kind of slowdown – one that matches our natural pace of articulation. Speaking our intentions aloud has several unique benefits:
It forces us to slow our thoughts to match our speaking speed. Counterintuitively, for me and many others, this is actually a good thing because even though it might take you a little time to speak your thoughts out, you have a lot less confusion when you’re forced to speak something out.
When we speak a half-formed thought aloud, its flaws become obvious in a way they might not when typing or thinking silently. This immediate feedback loop encourages real-time refinement of our ideas, leading to more precise and thoughtful computer interactions.
The Value of Reflection
The core insight here isn’t about slowing down computation itself – it’s about creating space for human reflection before engaging with computational tools. Whether through the day-long cycle of paper journaling or the moment-by-moment clarity of speaking aloud, these “slow computing” practices help ensure that when we do engage with computers, we do so with clear intent.
This clarity of intent is perhaps one of the most underappreciated aspects of human-computer interaction. While we’ve focused intensely on making computers faster and more capable, we’ve paid less attention to helping humans formulate better questions and requests.
Finding Balance
The goal isn’t to abandon the speed and power of modern computing, but to create intentional spaces for reflection within our computational practices. By introducing these deliberate slowdowns at the human end of the interaction, we can better utilize the computational speed at the machine end.
The result is a more thoughtful partnership between human and machine, where each contributes what they do best: humans providing careful, reflective thought about what needs to be done, and computers providing swift, accurate execution once those needs are clearly articulated.
Conclusion
In an age of increasingly rapid computation, we might find that some of our best technological practices involve intentionally slowing down. By creating space for reflection – whether through paper, audio, or other intentional delays – we can ensure that our use of computational tools is guided by our clearest thinking rather than our first impulses.
The future of computing might not be solely about making machines faster, but about finding better ways to match the thoughtful pace of human reflection with the lightning speed of computer execution. In this balanced approach, we might find both better results and more satisfying interactions with our technological tools.