Making Time for Thinking

“Human beings, it seems, are at their best when immersed deeply in something challenging.” — Cal Newport

As we move toward thinking together about a system of interrelated software, the conceptual load can become overwhelming. The question I’m asked most often is “what about the cognitive load?” People feel exhausted, as if they can never do enough, and constantly frustrated by the rising demand for ever-more skill development. How do we know when there’s just too much complexity?

I don’t know the answer because it depends on the circumstances. I do know that, to some extent, we need to stop trying to “keep up”. Our linear approaches don’t scale to manage complexity. We can’t control everything and we don’t need to control everything. Power and control can be nice to have but what we need is … time to think.

“I don’t have time.” I hear this – and feel it myself – all the time. I don’t have [expletive] time to learn, think, consider, read, build new things … unless those activities meet deadlines. Unless they mow my lawn and take my kids to school. We’ve been taught that keeping a relentless, mechanized daily schedule is the path to becoming a top performer.

Spoiler alert: it isn’t. Top performers generate new ideas. They create things, publish, consider and explore. They take risks; they create knowledge. They deliver things we didn’t know we needed that help us see what was missing. They help us swim in the ocean of ideas without drowning, shining light on new approaches.

I confess, I sometimes delight in race-driven productivity. Delivering tickets faster off the backlog. Studying over the weekend to develop expertise. Track more hours, contribute to tech communities. When I’m super productive, I feel dependable, valuable and important to others. When I work many hours, I feel financially secure, even though my work ethic has not made me immune to market vicissitudes.

Working hard is a good skill to learn. To a great extent, life is planting seeds, watering them, harvesting the plants then starting all over again. The challenge with our mechanistic view is: It’s not your garden you’re planting. You are so busy cultivating other people’s thinking, you don’t have time for your own. This is exhausting.

Science is creative work and like all creatives, as complexity increases, knowledge workers need time to think.

Cal Newport calls this “deep work”, uninterrupted time to think deeply, where the primary goal is not “meeting expectations”. Nobody can ask you for your deep work because nobody else knows exactly what you’ll create.

For example, you want to recommend a change. Deep work is thinking about, researching, exploring, modeling and constructing what you’ll recommend. It’s creating an artifact that reflects sound thinking and articulates an opportunity to improve what you seek to improve. You are generating ideas, not simply shaping and sharing external expectations.

Reading books that expand your thinking. Modeling a problem you don’t yet understand. Learning how relationship patterns work in systems and applying that pattern thinking to your situation. Writing a blog post about how those patterns can be understood and improved. Teaching others – we often don’t understand something deeply until we try to explain it. Designing an algorithm; building a service using those patterns to create more efficiency across a system.

There are so many options. The true measure is your experience:

Are you uninterrupted by distractions?
Are you listening to your own mind?
Are you generating something that didn’t exist before while simultaneously figuring out the shape and structure of that thing? (For example, you aren’t just writing a blog post, you are also improving your writing style, deciding what length and format expresses the ideas best, and figuring out how to nourish and support your process.)
Is the thinking challenging? Does it stretch you, do you understand something differently when you are done?

There might be some rote learning involved. If I don’t know Python, I might do a Udemy lesson every day. But I’m not only “learning Python”. I’m applying it to some challenge I’ve decided to take on, like building a service that demonstrates a solution to a business-critical problem.

As we move from software to systems, subtly but intentionally, we move towards seeing gaps and filling them with our thinking. We create synergy, listening to our intelligence as if it’s a good friend.

Setting aside one hour a day for generative thinking can significantly improve your quality of life. And shift you, a bit, out from under the relentless weight of “time”.

Deep Work: Rules for Focused Success in a Distracted World

by Cal Newport

Deep work “allows you to quickly master complicated information and produce better results in less time.” The book focuses less on getting things done and more on cultivating craftsmanship..

Read

Deep Work: Rules for Focused Success in a Distracted World

by Cal Newport

Deep work “allows you to quickly master complicated information and produce better results in less time.” The book focuses less on getting things done and more on cultivating craftsmanship..

Read