HBS Managing the Future of Work
Jul 8, 2026 · 42 min · 12 segments
We present Joe Fuller’s 2025 conversation with the Georgetown computer scientist, New Yorker contributor, and longtime student of knowledge work.
Joe FullerHost[upbeat music] Coming at the issues of knowledge work from this technological lens, I understood in a maybe a unique way the challenges that knowledge workers were beginning to have around the notion of productivity.
There was this real fatigue, there was a real frustration with this term productivity.
And I said, "The problem is our definition of productivity in knowledge work was never really that sensical." We basically just said as, like, a, a fallback solution or a heuristic as knowledge work became a big field, "Let's just use activity, visible activity as a proxy for useful effort."
[upbeat music] Knowledge work has always lacked well-defined measures of productivity.
The rush to adopt generative AI and the battle over remote work have only added to that challenge.
Underneath it all, as anyone engaged in or managing it knows, knowledge work is hampered by distractions, inefficiencies, and competing demands on time.
I'm your host, Harvard Business School professor and non-resident senior fellow with the American Enterprise Institute, Joe Fuller.
It's my pleasure to welcome back to the podcast Cal Newport, Georgetown University professor of computer science, author, and contributor to The New Yorker magazine.
Cal is a longtime observer of the world of work and the impact of digital technology.
In his latest book, Flow Productivity: The Lost Art of Accomplishment Without Burnout, Cal renews his critique of the cult of busyness.
He advocates a sustainable approach that prioritizes deep focus, long-term impact, and higher quality output.
We'll talk about how email and social media conspire to derail us and how to structure work to limit digital interruptions and promote manageable workflows.
We'll consider why AI, like the World Wide Web in its early stages, has yet to serve up killer apps and boost productivity despite rapid technological advances.
Also, amid concerns that AI might exacerbate inequality, Cal argues that the technology has the potential to increase access to good jobs.
Finally, we'll explore the tension between regulation and innovation, as well as how technology, used wisely, can help restore work-life balance.
Uh, Cal, I don't know, uh, how many of our listeners will be familiar with your, your historical work, books like Deep Work and, and Digital Minimization.
Um, maybe you could just give, uh, our listeners a, a brief background about your research, uh, before we turn to the question of your recent book, Flow Productivity.
Well, if we go all the way back, I'm trained as a computer scientist, from a, you know, tenured full professor of computer science at Georgetown, and my specialty is algorithms.
I mean, this is what I actually trained to do, was to study the theory of distributed algorithms, which is just as arcane as it actually sounds.
[upbeat music] Coming at the issues of knowledge work from this technological lens, I understood in a maybe a unique way the challenges that knowledge workers were beginning to have around the notion of productivity.
There was this real fatigue, there was a real frustration with this term productivity.
And I said, "The problem is our definition of productivity in knowledge work was never really that sensical." We basically just said as, like, a, a fallback solution or a heuristic as knowledge work became a big field, "Let's just use activity, visible activity as a proxy for useful effort."
[upbeat music] Knowledge work has always lacked well-defined measures of productivity.
The rush to adopt generative AI and the battle over remote work have only added to that challenge.
Underneath it all, as anyone engaged in or managing it knows, knowledge work is hampered by distractions, inefficiencies, and competing demands on time.
I'm your host, Harvard Business School professor and non-resident senior fellow with the American Enterprise Institute, Joe Fuller.
It's my pleasure to welcome back to the podcast Cal Newport, Georgetown University professor of computer science, author, and contributor to The New Yorker magazine.
Cal is a longtime observer of the world of work and the impact of digital technology.
In his latest book, Flow Productivity: The Lost Art of Accomplishment Without Burnout, Cal renews his critique of the cult of busyness.
He advocates a sustainable approach that prioritizes deep focus, long-term impact, and higher quality output.
We'll talk about how email and social media conspire to derail us and how to structure work to limit digital interruptions and promote manageable workflows.
We'll consider why AI, like the World Wide Web in its early stages, has yet to serve up killer apps and boost productivity despite rapid technological advances.
Also, amid concerns that AI might exacerbate inequality, Cal argues that the technology has the potential to increase access to good jobs.
Finally, we'll explore the tension between regulation and innovation, as well as how technology, used wisely, can help restore work-life balance.
Uh, Cal, I don't know, uh, how many of our listeners will be familiar with your, your historical work, books like Deep Work and, and Digital Minimization.
Um, maybe you could just give, uh, our listeners a, a brief background about your research, uh, before we turn to the question of your recent book, Flow Productivity.
Well, if we go all the way back, I'm trained as a computer scientist, from a, you know, tenured full professor of computer science at Georgetown, and my specialty is algorithms.
I mean, this is what I actually trained to do, was to study the theory of distributed algorithms, which is just as arcane as it actually sounds.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.