Slow Productivity: The Lost Art of Accomplishment Without Burnout by Cal Newport
Published in March 2024
The best explanation I’ve heard about how generative AI works came from a talk given by Cal Newport, part of a series of talks he gave as a summer 2023 Montgomery Fellow on my campus. If you have not read Newport’s April 2023 New Yorker piece, from which he drew for his talk “What Kind of Mind Does ChatGPT Have?,” I highly recommend it. (His Aug. 12 New Yorker essay “What if A.I. Doesn’t Get Much Better Than This?” is also worth reading.)
The reason I start with Newport’s AI writing in a discussion about his latest (excellent) book, Slow Productivity, is to predict (or lobby for) his next book. It is impossible to think about the future of knowledge work without thinking about AI, just as I kept thinking about AI while reading Slow Productivity.
For fans of Newport’s books, essays and podcasts, the arguments in Slow Productivity will be familiar. Newport (a CS professor at Georgetown) argues in Slow Productivity that the key to building a sustainable and impactful knowledge work career is to:
- Do fewer things
- Work at a natural pace
- Obsess over quality
He laments the devolution of knowledge work into the theater of performative busyness, pseudo-productivity and presenteeism. Noting that daily/weekly productivity is difficult to gauge in most arenas of knowledge work, professionals end up falling back on “visible activity” as a signal of meaningful contribution.
As Newport notes, the plague of jittery busyness worsened with the pandemic, as we all descended into (and never quite emerged from) a Zoom apocalypse.
Suppose the visible measure of productivity is to have your Outlook calendar completely booked with back-to-back Zoom meetings and the friction to invite and accept these meetings is as low as never leaving your laptop. In that case, everyone will be on Zoom all the time. (Look at your Outlook calendar and tell us if this sounds familiar.)
Is there a better way? Can we take the lessons from the 1980s slow food movement (as coined by activist and journalist Carlo Petrini) and apply them to knowledge work?
Newport tells fun anecdotes of highly productive individuals who traded daily busyness for creating a lifetime body of work. Stories of these exemplars of contemplation in Slow Productivity range across Galileo, Jewel, John McPhee, Marie Curie, Lin-Manuel Miranda and Alanis Morissette.
The knock on Newport’s arguments has always been that slow productivity is less a workplace strategy than a marker of privilege. Any knowledge worker with the autonomy to cut down on their task lists and set independent goals is by definition occupying a privileged position in whatever organizational hierarchy or caste system they are situated.
In higher education, I have many colleagues who would benefit from adopting the precepts of Slow Productivity, blocking off time from Zoom meetings to focus on long-term strategic goals. However, most of my colleagues have little choice in when deliverables must be delivered, meetings must occur and services and support must be provided.
Rather than relitigating these old arguments, a better path forward for Newport’s next book would be to explore how generative AI, if utilized correctly, might democratize the benefits of slow productivity to the nontenured.
Can we envision a future where AI tools offer academic staff the same level of autonomy and flexibility as tenure-track faculty?
As a professor, Newport can look around his place of work and apply his expertise in both knowledge work and AI to thinking about the future of higher education as a workplace.
What are you reading?
