A new research study recently caught my

attention. It came from a software

company called Avatra, which analyzed

the digital activity of 164,000 workers

spread across more than a thousand

different employers. And what they

wanted to do was measure the impact of

new AI tools. So what did they find?

Here's a summary of their results from a

Wall Street Journal article that came

out last week. Avitra found AI

intensified activity across nearly every

activity category. The time they spent

on email, messaging, and chat apps more

than doubled while their use of business

management tools such as human resources

or accounting software rose 94%.

Meanwhile, the amount of time AI users

devoted to focused uninterrupted work,

the kind of concentration often required

for figuring out complex problems,

writing formulas, creating and

strategizing, fell 9% compared with

nearly no change for non-users.

All right, so this research results

describes in some sense a worst case

scenario for knowledge work. These

employees are spending more time on

exhausting shallow tasks that don't have

a huge impact on the bottom line and

less time on the deep tasks that can

make the most difference. The efficiency

gain of these new tools seems to have

made everyone busier but not necessarily

better. Now, here's the thing. This

outcome is not unique to AI. As someone

who has studied the intersection of

digital technology and office work for

more than a decade now, I can tell you

from my experience that this matches a

pattern that I have seen unfold many

times before. Here's how this pattern

goes. One, a new technology promises to

speed up some annoying aspect of our

job. Two, we all get excited about

freeing up more time for deep work and

leisure. Three, we end up busier than

before without producing more of the

high-v value output that actually moves

the needle. This pattern was true of the

front office IT revolution. It was true

about email. It was true about mobile

computing. And it was true about video

conferencing. Easier when it comes to

productivity tech often seems to

translate to busier.

This so-called digital productivity

paradox is what I want to talk about

today. I'll start by looking closer at

why this paradox exists.

What is it about digital productivity

tools that seem to always trick us into

being busier? I'll then discuss some

concrete strategies for avoiding these

traps. So, if you're looking to get more

benefits out of new AI tools or you just

want to repair your broken relationship

with older technology that continues to

drive you crazy, then this episode is

for you. As always, I'm Cal Newport and

this is Deep Questions, the show for

people seeking depth in a distracted

world. And we'll get started right after

the music.

All right, so here's our approach for

solving and reacting to the digital

productivity paradox. I've got four

questions that's going to lead us from

understanding to solutions. All right,

so one, two, three, four. Question

number one, what do we mean when we say

digital productivity tools? We have to

get our definitions right so we know

what we're talking about in general.

When I say digital productivity tools,

I'm talking about some sort of computer

aided tool that makes common work

activities easier. Now, what do I mean

by easier? It usually means some

combination of these two factors. One,

it speeds up the time required to

complete the activity andor two, it

reduces the mental exertion required to

complete the activity. So when we talk

about digital productivity tools, that's

what we mean. Things that are going to

speed up and make cognitively easier

common work activities. Now, there are

many different digital productivity

tools that have been introduced over the

years. So to try to simplify the

discussion that follows, I'm going to

use two of these tools in particular as

our case studies throughout the

discussion that follows. So one will be

AI because this is new. So we're going

to talk about sort of new AI

applications, especially in like the

non-programmer knowledge work space. Um

and then as our older example, I'm going

to use email. It's a topic I've written

a whole book about and know a lot about.

So we'll use email and AI as our

canonical examples of digital

productivity tools for the discussion

that follows. All right. So, let's make

sure first that our definition applies

to those two tools. So, does email make

certain work activity tasks uh faster?

Well, it does indeed. It required less

time to send an email or an email with

an attachment than it did, for example,

to use a fax machine or to have to call

and leave a voicemail and then later

check your voicemail machine by typing

in those codes into your phone. So, it's

uh makes things go faster. Um, does it

make certain work activities less

cognitively demanding? Well, it does.

There's actually way more of a cost if I

call you up and have to have a

conversation with you back and forth on

the phone is actually going to be much

more cognitively demanding than if I

just shoot off a quick email uh just

send. So it it matches both definitions

of digital productivity. All right. What

about like the sort of new office

centered AI tools? Well, we do know they

speed up things, right? Like you can

rapidly create drafts of things or in

some cases even automate whole steps of

a task chain. So that is definitely

tasks saving. There's also a lot of

cognitive exertion reduction with the

use of AI in the office because it's

often for example easier to like chat

with a chatbot than to just sort of sit

there and figure out from scratch like

what you're going to do or like what

strategy to deploy it. It reduces the

activation cost of thinking often to go

back and forth with chatbot. So AI our

second example often matches this

definition. All right. So at first

glance these seem like two good things.

Faster, sure. Why is that not good? Less

cognitive exertion. Sure. Why is that

not good? This is why every time we're

introduced to a new digital productivity

tool, our first reaction is often,

"Bring it on. This is going to make my

life better." So, what goes wrong?

Well, this brings us to question number

two.

Why do these technologies sometimes

accidentally make our jobs worse?

All right, I want to focus on two subtle

factors that are at play. One of them

involves the unexpected side effects of

doing work faster.

The other factor looks at the

unintentional consequences of trying to

reduce the cognitive effort required to

do certain tasks. All right, so let's

look at factor number one. for many

types of common work activities.

Increasing the speed at which you

complete these types of uh activities or

tasks ends up increasing the throughput

of these tasks in your typical day. So

if I go faster

then the rate at which new tasks of this

type come into my life also increases.

Now what happens is okay now I'm

tackling more total tasks of a given

type per day which induces a lot more

context switching because every time I

have to switch back to service one of

these tasks I have to switch my

cognitive context that then has a

negative cognitive impact on anything

else you're trying to do in the day it

exhausts you it exhausts your brain it

makes it harder to focus on other types

of things

but going faster on each individual task

can make your whole day seem more

exhausting and less cognitively sharp.

Let's look at this factor in play first

of all with email.

Email certainly sped up the task of

actually sending uh information to

someone or replying to like a question

that someone sent me because I can type

it right into my computer where I'm

already sitting and just press send.

But the faster we were able to send

messages back and forth, the faster

messages began to be sent. Right? So

like the total amount of communication

has drastically increased year on year

as we've in continued to decrease the

friction involved in actually sending or

receiving messages. Bringing us to a

point where we are now where the latest

Microsoft work trend index report finds

that the the users they studied are

checking an inbox once every two minutes

on average.

So yeah, this message is faster to send

than it would have been if I had to call

you or write a memo. But because of

that, I end up checking or sending

messages or checking inboxes once every

two minutes. So the throughput

increases. It makes everything else

harder. So it's an unintentional side

effect. We see something similar with AI

as well.

You can use AI to speed up certain

especially like administrative tasks

kind of like quick tasks. Uh more of

them roll right in behind it. The cues

are basically endless in the typical

knowledge work environment of shallow

tasks that can be done. This is why we

see in that Avatra research I cited in

the introduction a 94% increase in

business management tool use. The faster

you're able to handle things, the more

things come in behind it. So when

throughput increase of task, it doesn't

mean that you overall are going to be

actually more productive. All right,

here's the second factor at play here.

For many types of common work

activities, reducing the mental effort

required to tackle them can lower the

quality of the ultimate result, which

can over time increase the overall

amount of work required to actually get

to a desirable end state. So if I'm

doing this with less focus, I might have

to do more of it to get to where we want

to get. And now I've actually created

more work than would have been here than

if I had just worked harder on the

original task. This is another side

effect that happens. We certainly saw

this in email in my book, A World

Without Email, where I really studied

this. One of the big ideas that came out

of it is that because email uh it's so

easy just to write something and press

send to get something off of your plate

that we see a lot of vague and

uninformative messages being sent. So

yeah, in the moment it was way easier

for me to send off a like, yeah, maybe

thoughts question mark. That was way

less cognitive strain than to say, okay,

hold on a second. What's going on here?

What are the possibilities? What's the

right thing to do? So in the moment, it

reduced cognitive strain. But because my

email was so vague and uninformative,

the total number of emails we now have

to send back and forth before we finally

resolve this issue grows.

And so now the total amount of time I

have to spend checking inboxes, looking

at emails, replying to emails, and

especially if we throw in the time

required that every time I'm distracted

by an email, how long it takes to get my

focus back on the task at hand. When you

put that all into play, you're like,

"Oh, I have just done way more work.

I've spent way more cognitive cycles on

this than if I had just sat there and

thought harder about the very original

problem."

AI is also creating a similar issue

where you can shoot off like a draft of

a slide deck or an email summary of an

agenda for an upcoming meeting. You can

use AI to help create these things in a

way that requires much less strain than

blank paging. Blank PowerPoint page,

blank email pager have to write from

scratch. But as research that was

reported recently in the Harvard

Business Review found, the quality of

these AI generated work products is

often so low

that overall they require more work to

actually get to the uh ultimate end

result. They call this work slop. And

here's their formal definition. AI

generated work content that masquerades

as good work but lacks the substance to

meaningfully advance a given task. So

this is what they're seeing. There's a

lot of work slot products being passed

back and forth and it takes time for

people to read it and they're confusing

and it doesn't really help advance the

task and overall the amount of total

time that people have to dedicate to

whatever the task is at hands goes up

versus if someone had just said I'm

going to make the right slide deck with

the right information and the right next

steps now it's going to take me a half

hour of hard work instead of 10 minutes

of prompting but then once I send this

out we can immediately move forward

forward and this is actually going to

take more uh less overall time than if I

just let AI help generate something. So

sometimes reducing the cognitive effort

in the mo moment can actually increase

the overall amount of work. So these are

the two factors that I think help

explain this idea of when you bring in

new tools digital productivity tools

like hey faster great less cognitive

strain great and you find yourself more

exhausted getting less done and things

taking more time. That's what I think is

going on. Let's take a quick break to

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first order. All right, let's get back

to the show. All right, question number

three of four. If this is true, why do

we continue to so enthusiastically

embrace these new productivity tools

every time a new one is introduced?

Well, I want to go to a core idea I

introduced in my 2024 book, Slow

Productivity. And that idea is pseudo

productivity.

Now, if you've heard the show before,

you may have heard me talk about it. So,

I'm just going to give the definition

here very quickly.

Pseudo productivity was the way that the

managerial class tried to respond to the

reality of knowledge work. When

knowledge work became a major economic

sector starting in the mid- 20th

century, the big issue that the

managerial class had is that it was hard

to precisely measure productivity.

In the industrial sector where these

managers used to be, productivity was

easy to measure. How many model T's are

we producing per paid worker hour in our

factory? You had a number. And if you

changed something about how you ran your

factory and that number got better, you

would say we're more productive now. But

when you went to knowledge work, there

were no models to to measure, right?

Everyone was working on their own unique

bespoke

obuscated portfolio of tasks.

Some harder than others with a unknown

non-transparent set of systems all kind

of collaborating with each other in

unstructured ways. It was very difficult

to say here's your productivity number.

It's seven and when you change this it

became eight. So that's better. So in

the lack of actual hard numbers to

measure, we fell back on a heristic, a

rule of thumb called pseudo

productivity, which said lacking more

precise measures of productivity, we

will use visible effort as a proxy for

you doing something useful. So the

busier you seem the better. And this

basically became the standard of how we

think about productivity uh in the

knowledge work class. At first it's the

way the managers thought about it. Then

the workers themselves internalized it.

Which is why if you have like a solo

entrepreneur, you've probably still

internalized the pseudo productivity

mindset and you feel lack of busyness is

bad and busy is somehow uh

professionally virtuous. This is the

mindset that dominates in knowledge

work. And in that mindset, the two

benefits of digital productivity tools,

you can move faster

and you can lower the threshold to get

something done. Makes you more pseudo

productive.

higher throughput of task. That's great

from a pseudo productivity standpoint.

Shooting out work slop left and right

like a vomiting Microsoft Office

monster. From a pseudo productivity

standpoint, you're in the mix. Things

are being sent. PowerPoints are being

received. Email summaries are going out.

You're there. People are seeing you. So

digital productivity tools feed right

into the pseudo productivity narrative.

And that's why we embrace them because

that's a benefit we get is that it makes

us look more productive. But I don't

care about looking more productive. I

care about actually being more

productive in the oldfashioned economic

sense of how much actual value are you

creating for the bottom line. As we just

covered with most digital productivity

tools, if you don't use them carefully,

that number goes down. Higher throughput

of tasks makes you seem busier. less

important stuff gets done. Lower

cognitive engagement to get things out

the door makes you look busier. More

total work is required before anything

is actually finished. So it's only when

you shift from pseudo productivity to

true productivity that you realize, oh

digital productivity tools are more

fraught than we thought. There's these

traps that sit around them that we put

up with because of pseudo productivity.

But when we get rid of that standard, we

should be dismayed.

All right. Question number four, our

final question in this discussion. How

can we avoid these traps? So, how can we

embrace digital tools and yet not find

ourselves actually becoming less

productive in a true sense? I have three

ideas that I want to recommend to you

right now. All right. Idea number one,

use a better scoreboard.

So get in the habit of measuring the

things that actually matter in your job.

In this way, if you bring on a new

digital productivity tool and it's not

helping that score or it's making that

score worse, you will notice and you're

like, "Oh, I'm not getting caught up in

the traps here because I can see

directly that this is hurting the bottom

line things that actually matter." Okay,

so what do we mean by the things that

actually matter? I have a couple

examples here. Let's say you're a uh

like me, you're a professor at an R1

institution, at a research institution.

What's really going to matter?

Especially pre-tenure

papers published. How many good papers

that I published this year? And if that

number is going down,

then you're like, okay, whatever tools

I'm using aren't helping. Maybe I

started using Slack with my research

team and that number went down. Great.

That's not actually making me more

productive in a true sense. I'm going to

stop using it. Um, let's say you're a

middle manager. maybe like priority

projects completed by your team per

month is the number you really care

about. That's the actual score that

moves the bottom line. So now let's say

you're like a middle manager and you're

actually carefully measuring that month

by month. You see where you are. Your

boss comes in and is like, I'm really

savvy. you have to use AI otherwise

you'll get replaced by people who do

know how to use AI and you're like all

right we're all going to use like here's

a Gemini subscription and like whatever

mess around with like some of these

agents or whatever and you see the

priority projects per month completed

goes down like whoop trap this is not

making us more productive let's back

back off against you have to have the

right scoreboard to know what's going on

um even if you're a programmer

right even if you're a programmer this

is like this this like case where With

AI for example, it's like for sure, for

sure, for sure this is making everyone

more productive. Everyone keeps saying

this would have taken me five hours

before, now I can do it in 20 minutes.

Actually, Jesse, there's almost like a

competition. It gets kind of absurd when

people are trying to the programmers are

talking that the amount of time they

begin to claim that is being saved

really gets crazy. So eventually it's

like adding this feature. Previously

this would have taken me seven decades

and AI did it before I even pressed the

button. It went back in time and

actually it finished it last year, you

know. So anyways, it gets kind of it

gets kind of absurd. But if you're a

programmer, okay, what's the thing to

measure? Um important user feature

request shipped per month or something

like that, right? And again, uh, this

would allow you to say if like in the AI

context, okay, this use of AI, that

number went up. But when we had everyone

like chatting all day with chat bots

trying to figure out architecture

documents, they feel super

pseudroductive, that number went down.

So, let's stop that. So, you need the

right scoreboard. And it's not just

about figuring out like in my examples,

this digital productivity tool didn't

help. It's about figuring out what uses

do help as well, right? So, okay, this

didn't help, but this did. So don't do

this and do this. We basically need our

equivalent of counting model T's

produced per paid worker hours. So you

need a better scoreboard. All right.

Idea number two for avoiding these

traps.

You need to focus on the true

bottlenecks

in your work.

Now, what I mean about that is often

when a digital productivity tool enters

the scene,

the activities that it might speed up or

make easier

aren't really the the bottleneck that

was preventing that was like at the key

of you producing your most valuable

output. So, speeding that up might have

no impact on your output or have a sort

of implicit or indirect negative output

because it's, you know, distracting you

or something like that. So, you have to

be careful. It's not just enough to

speed up any aspect of your job. you

want to really focus on improving the

true bottlenecks.

So like for example, give a another AI

example here. Uh an increasing number of

social scientists

are realizing that they can use claude

code which is a terminal agent that was

uh designed for computer programmers but

they could use it to help speed up

certain type of data gathering and

analysis task. Right? So so cloud code

is a terminal agent which means it works

with text and text files. So it's very

good at uh writing text, moving text

between files, compiling text with a you

know a compiler or writing a computer

program and then passing the text as

input to the computer program. So it's

very good for sort of like text and

number processing. Um so a lot of social

scientists are finding like oh I had to

gather a bunch of data and clean it up

and analyze it and produce a chart.

That's the type of thing if you are

careful in how you prompt cloud code and

you go through the learning curve to

learn it could really help you do that.

like, oh, I can tell it what I want to

do, and if I'm really careful and I have

the right skills marked down, it can do

the multi-step process, right? And like,

that saved time. That might have taken

time before. I just heard an economist

talking about this the other day, and

he's like, "Look, this I did this for a

bunch of plots." And, you know, it was

like 20-minute prompting with cloud

code. That would have taken me three

hours if I had done that by hand.

But here's the trick here. Was that the

bottleneck that's holding back social

scientists?

Not really. Not really. The the way

social scientists work, it's not like

all day long that's what I'm doing. I'm

gathering data, analyzing it, and

producing plots. I'm saturating my time

with that. So, if I can bring in a a

tool that speeds up how long that takes,

it's going to significantly speed up my

output. That's not actually how it

works. If you actually measured right

now, well, how much of your time, like

how much data are you analyzing? How

often do you produce plots? like, well,

if we're being honest, I produce one

paper every like two or three months,

and that's like something I have to do a

couple times in those one or two, three

months. So, like making it 3 hours and

20 minutes twice in that 3-month period,

that's nice. Like, in the moment, it's

nice, but it doesn't speed up the rate

at which I produce papers because

there's so much more involved in putting

together a paper than just how fast can

I analyze the data. So, that's nice, but

it doesn't actually speed up the rate at

which papers go out. We know this

because in research, academic research,

there's been any number of digital tools

that have sped up and made easier

parts of the research process. I'm

talking about like if you're a

mathematician or a theoretical computer

scientist, you can use things like Latte

in a web-based collaborative environment

where now all your collaborators can

work on the same file and compile it and

make uh adjustments really quickly. So

you can significantly reduces the time

required to write papers or do

mathematical formatting. We have

bibliographer managers that makes it

much easier to site and professionally

format things. Like the time required to

like write up and format papers is much

smaller. We have technology tools that

allow you to immediately grab copies of

like any paper that you might need to

reference and digital communication

tools that allow you to keep in touch

with researchers all around the world

and therefore get much more out of your

mind. And all these things make academic

research better. And we're still not

producing papers at a notably faster

rate per researcher than we would have

before those tools were there. So

there's because these weren't the

bottlenecks. They're useful, but they

weren't the bottlenecks. So to give a

what is the bottleneck? Well, let's go

back to our social science example. I

remember I once had a conversation with

Adam Grant, the the business school

professor and author, and I was asking

him about his productivity as a business

school professor. He writes a lot of

journal papers. He was sort of 2xing

what his colleagues were doing, right?

And these are data analy like these are

papers and organizational management

theory. So, it's a lot of like you get

data, you analyze it, you write a paper

about what you found. And he said, oh,

here's what he figured out. He's like,

here was the key. The key is getting the

right data. If you can get an

interesting data set, like let's say

from a company about their use of

something that happened that no one else

has access to, you can now write three

or four papers off that data set that

are going to be good because you have

it's all comes down to the data set. So

Adam was like, "Here's what I realized I

had to prioritize.

Putting out feelers, having

conversations, meetings, talking to

people, trying to negotiate access to

interesting data sets." And then when it

came time to actually write papers,

yeah, he would lock I wrote about this

in my book, Deep Work. He would lock

himself in his room and put on like an

autoresponder. He had this sort of

biodal deep work mode. It's all kind of

interesting. And you sat down and do the

hard work of writing your paper. So I'm

sure he would appreciate when he has

those sessions to write the papers if he

could speed up some of the steps, but

the bottleneck for producing great

papers in his field was negotiating

access to data. So the same thing

happens in lots of fields. The key

bottleneck is really maybe not what you

think it is. It's coming up with the

right problem. It's having reading

enough in theory. It was often reading

enough other papers, understanding

enough other papers that you're building

up this toolkit in your mind of

different techniques and then you begin

putting pieces together of like this

problem plus this technique plus that

twist could get a result. And so like

the number one the bottleneck to doing

better theory in my field was groing

other papers. And I don't mean that by

using the XAI tool. I mean in the

original use of the word gro reading and

grappling until you really had

internalized an understanding in your

head. That was the bottleneck.

It's nice we had lots of tools that made

it quicker to write the papers then, but

that didn't speed up the rate at which

we produce papers because the bottleneck

was understanding other work. So, it's

key to understand in your job what the

actual bottleneck is. What's the thing

that really controls the rate at which

good results are done. And when you're

looking for digital productivity tools,

be especially tuned to those that help

what's going on with that bottleneck to

help speed up that that piece. like

using email as a digital productivity

tool to help put out more feelers and

get access to more potential data sets

to use uh in the Adam Grant scenario.

That's a digital productivity tool

that's helping the exact bottleneck of

producing those papers.

Whereas using cloud code to

automatically generate your graphs is

nice, but it's probably not going to

speed up the rate at which papers are

produced. All right, so uh make sure you

look at the right bottlenecks. All

right. Third and final idea for avoiding

these traps

in your daily schedule is separate deep

from shallow efforts.

So just have and protect the time for

sitting and doing hard things with your

brain and the activities that you know

for sure create bottom line value.

This just gives you like a a safety

barrier against some of the accidental

negative side effects of digital

productivity tools. So like you're now

you're using Slack because it seems like

it would be even faster than email and

maybe you're having all these secondary

side effects of it's now there's many

more messages and it's really

distracting. But if you have a habit of

separating deep from shallow work, those

side effects won't affect the hours

where you're working on the primary

thing that moves the needle. Or maybe

you're using AI for certain things and

the right graphs or this or that and

it's starting to sort of uh get you into

like slop territory and you're having

all these long back and forth

conversations with the tool and it's

like eating up a lot of time. If you

separate deep from shallow work, it's a

firewall that keeps that from infecting

the area where you're actually doing the

hard work of thinking. This doesn't mean

that you won't be using digital tools

while doing the deep work. But there

you're just carefully deploying digital

tools that just help you continue to

make direct progress on the like bottom

line things. I'm writing a draft of a

paper. I'm putting together the strategy

memo. I'm architecting the key element,

low stack element of this new tech stack

that I'm programming. Right? So, if you

separate and protect deep from shallow,

you're not preventing the negative side

effects of digital productivity tools

from happening, but you're containing

them in a way that they can't completely

take over the activities that really

matter. All right. So, my three ideas

again, use a better scoreboard.

Identify the actual bottlenecks to the

things that really matter and focus on

improving those more than other things

and separate deep from shallow work so

that side effects you aren't expecting

of digital productivity tools won't have

too much of a negative impact on your

ability to move the bottom line forward.

All right, so let me conclude here. My

argument is not that digital technology

in the office always makes things worse.

That is clearly not the case. There's

any number of digital tools I use that

makes my life easier. I'm glad they're

there. There's other tools that make my

life easier in some ways and terrible in

others. There's a whole mix.

But it is true that many of these tools

seem at first glance like they should

make us more productive in the true

sense of value produced per worker and

accidentally end up creating the

opposite effect. And once you understand

why this happens, you can sidestep those

traps,

get value out of digital tools while

avoiding more of their cost. So it's the

type of conversation that we don't often

have. We just say, "Hey, here's the new

tool. Let's do it. This is cool." It's

good to be critical like this. And with

this huge new AI revolution going on,

it's a great time to have a refresher on

these dynamics.

So there you go, Jesse. Digital

productivity paradox.

That's something I've been writing about

for a decade now.

>> Crazy. 10 years. That's when Deep Work

came out. 10 years ago.

>> Um, hasn't got better. Has not got

better.

>> I keep I keep thinking it will, but but

we're still struggling. All right. Um,

that's enough for me. Now, we want to

hear what you have to say. So, it's time

to open our inbox. But before we get to

your notes, let's take a quick break to

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interesting links to podcast at calal

newport.com.

All right, Jesse, what are we doing for

our first message?

>> Our first message comes from Pablo, who

sent us an article about meetings.

>> Oh, this is kind of on theme for our

discussion of digital productivity tools

and sort of traps that we get into. Um,

here's what Pablo said. He his

introduction was, "Thought you might

find this interesting given your

secondary focus on managing the utility

of meetings." All right. So, let's um

load up the article he's talking about

here. This came I guess is this a

Substack, Jesse?

>> Yeah.

>> All right. It came from a Substack

called The Critical Path

and the article is titled Why Meetings

Multiply and it's written by Nicole

Williams. All

right, I'm going to read some highlights

from this article because I thought it

was actually pretty smart and then I'm

going to generalize the approach Nicole

takes to workplace technology more

generally. All right, so let me start

here. This is from the article.

There is a strange pattern inside most

organizations. Meetings rarely

disappear. They multiply. A team begins

with a single weekly meeting. Soon

another appears to quote coordinate end

quote. Then a check-in meeting is added.

Then a review meeting. Eventually the

calendar fills with recurring blocks

that feel permanent as if the

organization itself produces meetings

the way a tree produces leaves. Yes, I

know this phenomenon. Well, so why does

this happen? Let's go back to the

article and see Nicole's explanation. So

I'll read again here. Organizations

exist to coordinate work among many

people who do not have the same

information. Every role sees a different

slice of reality. Because no single

person holds the complete picture,

organizations need mechanisms to

exchange information. And meetings are

one of the simplest mechanisms.

When uncertainty increases, the

organization creates more information

exchange points. And these points

usually take the form of meetings. What

appears to be calendar overload is often

an attempt to reduceformational

blind spots. Instead of everyone

speaking to everyone individually, the

system creates a place where information

can be exchanged collectively. From this

perspective, meetings are not simply

interruptions in the workday. They are

coordination

infrastructure. I think those were

really good points from Nicole. Um, she

gave two other reasons which I'll just

summarize why meetings multiply. Um, she

said it also has to do with reducing

risk.

When a group meets to talk about

something, you're distributing the risk

among all of those people. So, there's

no one person responsible to it. So, it

reduces risk for everyone involved. She

also said it's a way to quote signal

participation end quote. A way to show

that you're engaged and part of the

efforts. The terminology I would use

quoting from earlier in the episode

would be pseudo productivity. It's a

good way to show that you're pseudo

productive because you're in a meeting,

people see you, you talk to them, they

remember you being involved, and from a

pseudo productivity perspective, it's

like, yeah, that that person is trying.

They're being useful. All right, here's

the concluding sentence from Nicole. As

long as organizations face uncertainty,

distribute responsibility, and

coordinate across teams, meetings will

continue to multiply. All right, so what

do I like about this analysis more

generally?

the frame. It's a frame that I have

adopted in my work, most notably in my

book, A World Without Email. And it's a

frame that shifts the analysis of

workplace habits, workplace technology,

workplace behavior. It shifts it away

from individual habits and it puts the

focus on systems, which I think is the

right way to analyze most of these

issues.

Most people think in terms of individual

habits, right? So, what would your

response be to your calendar being

overloaded with meetings? You would say,

"Individuals are behaving poorly.

They're uh they're they're setting up

meetings when they could have just sent

an email. They're being lazy. They don't

know about deep work. It's individuals

are having a problem. So, we need better

norms." Uh we saw something similar in

the reaction to email overload as that

became a problem in the early 21st

century. As I document in my book,

people's response to email overload was

norms. Oh, you just you send too many

emails or your expectations for

responses are unreasonable. If we could

all have better expectations, then like

we could all settle down about what's

going on with our inboxes. So, we love

to think about these issues as

individuals doing things wrong.

But in reality, these issues are often,

as Nicole points out and I points out,

the results of actually rational

business systems that are solving

certain problems. Nicole says this is a

easy and convenient way for information

exchange and responsibility distribution

and and participation signaling.

In the absence of a better way

to spread information or to coordinate

or collaborate people, in the absence of

a better way, we still need to do this.

So, we'll fall back on what's easy.

That's exactly what happened with email

overload as well. It wasn't caused by

bad norms or bad habits. It was caused

because we shifted to back and forth

messaging as the primary mode we would

use for collaboration. I call it the

hyperactive hive mind model. We'll just

figure things out back and forth on the

fly. Well, this requires me to check my

inbox all the time because there's so

many ongoing conversations I have to

service in a timely manner that I just

have to basically constantly check my

inbox. That's why we're in this point we

are today where in 2025 Microsoft

measured an inbox check once every two

minutes on average. So it's not about

people having bad habits. It's this is

solving the problem of how do we

collaborate? And in the absence of

another way to collaborate on projects

we'll fall back to this.

So once you recognize that systems are

the the issue

all the solutions to these problems that

bother individuals is to change the

collective system. You have to replace

the system that's causing the problem

with another system that achieves the

same goals

but has less side effects and problems.

So let's go towards the medium

multiplication. Once we know that it's a

system that's solving real goals that

corporations have or organizations have,

we can say how do we replace this with a

better system? Here's a couple ideas

just off the top of my head.

First of all, we need more transparent

workload management so that the number

of active projects that each person is

working on reduces,

right? Meetings are an overhead tax on a

project. If if we're using this as a way

to coordinate information on a project,

the more projects I'm working on, the

more coordination points I need, the

more crowded my calendar gets, the

longer it takes for me to work on these

projects, the longer it takes, and the

more projects pile up and the more my

calendar gets taken over.

It's a spiral I talk about in my book,

Slow Productivity.

So, if each person works on fewer things

at a time, there's less meetings, which

means there's more time to work on the

projects, which means the projects

finish faster. And this was a key point

from uh my book, Slow Productivity. The

overall throughput of projects being

completed goes up. Working on fewer

things at once increases the throughput

over time in part because you have less

coordination to happen. You have more

time left to actually get things done.

The other thing you can do is put in

place alternative coordination

strategies that isn't just let's all get

on a Zoom. Find other coordination

strategies that have less of a schedule

footprint. Right? So this could be for

example twice a week or three times a

week we have a team check-in and it

lasts 45 minutes and the team gets

together first thing in the day and we

synchronize on all the things we're

working on. That's where all the

coordination information, all the things

you that meetings solve the coordination

and the responsibility distribution, we

consolidate it. 45 minutes, 45 minutes,

45 minutes before we even get going in

the day. So if you know what, oh ad hoc

meetings is solving this problem, what's

another way we can solve this problem

that's going to have less of a

footprint. So consolidation really

reduces the footprint. Office hours go a

long way towards this as well. Um, if

there's an issue, instead of having a

meeting and making these three people

come together for an hour, stop by each

of their office hours tomorrow, have a

five-minute conversation with each and

get to the bottom of it. Their office

hours is time they had already put

aside. So, it's creating no additional

uh footprints on their schedule. So,

they take five minutes out of my normal

office hours. Let's say like five people

do that. I just have one hour for my

office hours as opposed to five separate

one-hour meetings I would have to do if

I didn't have an office hours. Protocols

matter as well. This was a big idea in a

world without email. If it's regularly

occurring work that requires

coordination, figure out a set system

about where the information lives and

how it moves and the schedule for who

does what when that is fixed in advance

so you don't have to keep getting

together and having sort of unstructured

ad hoc conversations to move things

forward.

If you do something more than twice, you

should have a protocol around how the

collaboration actually works. And

finally, make the meetings themselves

better. If you add a higher barrier to

entry to meetings, not only are the

meetings quicker and more effective, but

the friction drastically reduces the

number of meetings because now it's no

longer necessarily like a low energy

solution to I want to show some

participation or do some coordination. I

can just throw out a Zoom meeting

invite. It took me two minutes and yeah,

it's going to sit on everyone's schedule

and eat up an hour, but like that was

easy for me, right? If you raise the bar

required to hold a meeting, now people

are much more thoughtful about doing

that and they might say, "Actually, the

cost of putting this meeting together

is now higher than the value I'm going

to get out of having the meeting. Maybe

I'll just talk to these people next time

we have like a staff meeting. I'll just

grab them after the fact."

Electronic meeting went the other way.

This is part of the meeting apocalypse

that happened during the pandemic.

Digital meetings are so low friction

because you don't have to walk to a

room. You don't have to gather people in

a room. You don't have to see the social

cost of like you all had to come here

because it lowered the friction of

setting up meetings. Once we introduced

virtual meetings, the number of meetings

skyrocketed even after people came back

to the office. So we want to go the

other way and increase the friction of

meetings. One way to do this is to use

the Amazon rules. So, if you work at one

of the like Amazon HQ or at one of their

data centers, for example, in the front

office part of it, they have super

strict rules. If you want to throw a

meeting, you have to put together an

incredibly detailed memo that says,

"Okay, here's why I'm having this

meeting. Here's the decision I need to

make that I need help making. Here is

all of the relevant background

information on this uh decision, and

then this is where I'm stuck." so that

everyone attending that meeting can then

study that and when you get to the

meeting jump right into okay we're now

applying our expertise we're fully

briefed let's try to get to an answer

and so you really have to have a good

reason to hold a meeting or they're not

going to accept it and you have to have

do a lot of work to hold a meeting so

that reduces the number of meetings as

well so anyways I think that's

interesting that was a cool article why

meetings multiply

all about looking deeper in businesses

today all right what's uh what's second

message do we have here.

>> All right. Next up, we have a case

study. This one from Drew, who talks

about his strategy for escaping email

overload.

>> All right. All things office technology

distraction today. I love it. Was a case

study from Drew. Let's see here.

Hi, Jesse and Cal. I'm an insurance

broker who personally manages a team of

seven account managers and three support

staff and a few outside sales agents. I

have my own clientele as well. I've

realized over time that I've come become

too reliant on hyperactive emails to

manage the agency and clients. I get

interrupted frequently for whatever the

issue of the moment is. Our industry

relies heavily on email communication

between underwriters, inspectors,

clients, MFA codes to log into websites.

It's insane. A major shift I implemented

last year has been to trans transition

as much communication as possible

to synchronous phone or in-person

discussions versus sending and receiving

emails. If a discussion is going to take

more than one email, I will gracefully

transition it to synchronous

communication. The results have been

positive.

When it comes to my clients, here's the

benefits. They appreciate my full

attention. They gain a better

understanding about what we are talking

about.

we can clear up any confusion in real

time and it ends up taking less overall

time. When it comes to staff, I will

connect with staff in person or phone

and quickly clear the docket. If someone

emails me and it doesn't require

immediate response, we'll review it in

our next meeting during the docket

conversation. Staff can give me a task

versus emailing me and then I see it as

uh I work through my tasks. All right.

So then he goes on to say, "I've changed

my approach with emails where I just

batch them a couple times a day. My

responses are responses are brief yet

polite. I am able to clear out the

emails quicker and if it needs attention

later, I've moved it into my CRM program

which manages my tasks. Another change

I've implemented is blocking off deep

work sessions in the morning. I take

care of my most important work for the

day first thing and then I find I am

more relaxed throughout the day because

I've started off knowing I made real

progress. Thanks for doing what you do."

signed now Qort right I mean this is

like right in my wheelhouse Drew you're

speaking you're speaking my language um

it's a great practical case study of

what we were talking about right digital

productivity tool email comes in

individually if you look at individual

uses in the moment it's faster less

cognitive strain zoom out oh my god my

job is insane and nothing's getting done

so this is a way of showing how You can

be using digital productivity tools

carefully when you realize what really

matters and making sure that you're

prioritizing the things that really

matter. Drew still has email and still

uses like a digital CRM tool and they

have technology, but they're not he's

not just turning it all on full tilt.

He's figuring out what's the right way

to collaborate, what's the right way to

coordinate that minimizes hyperactive

back and forth and allows real things to

get done. So, I think that is a great

case study. All right, I think we have

time for one more, Jesse. Which one

should we do?

>> We have another case study. This is an

anonymous source and it's in response to

last week's newsletter which was also

about this idea that tools like AI can

make work worse. A reader sent in their

account.

>> All right. And so for people who don't

subscribe, by the way, I do have a

weekly newsletter. It's been out since

2007. Uh calport.com to sign up. Comes

out Monday, the same day as these Monday

episodes. Uh and you know, sometimes

it's on the topics we talk about in the

show. Sometimes it's on completely

different topics, but it's all within

the same universe of helping people

create deeper lives and increasingly

distracted world. So the email that came

out last week, uh, I looked at that same

arriv

and and had some other conclusions I

drew from it. So that this is what

anonymous is responding. He can't

predict the future. He didn't know this

episode was coming out, but he was

responding to that email. U subscribe to

the newsletter is what I'm trying to

say. All right. This was interesting.

I'm looking at it now because uh it's a

a a harm of LLMs in particular and chat

bots I hadn't thought about and I think

it's worth emphasizing here. All right,

so here's what anonymous had to say. My

take on LLMs and chat bots, for what

it's worth, is that they're rumination

machines,

an extension of the attention economy,

and for someone with my psychological

profile, high anxiety, neurode

divergent, total perfectionist, as

manipulative and as as addictive as

something like Instagram.

It's my belief that they prolong and

exacerbate rumination episodes. They

give me the illusion of control,

empathize, soothe. But I have found over

the last month that they have encouraged

my rumination and dramatically increased

my anxiety. I think I've decided to

block them. I may even completely delete

my profiles. As they get to know me

better, they ask increasingly intrusive

questions. They don't ever really want

to stop chatting. They don't stick uh

they don't get sick of me like a normal

sane human would. And they seem to

encourage me to share more and more

private information about myself and my

family. I really believe now that they

are an extension of the attention

economy and I'd be really fascinated to

see actual research into what people are

doing with them in workplaces beyond the

typical work slop angle. I suspect there

are some long meandering conversations

going on that don't amount to anything

much.

And this is an important uh issue.

Chatbot interactions have a lot of

potential psychological ramifications

because our brains are going to

anthropomorphize

any sort of entity that seems to be

having fluent communication with us in

our same language. We think of it as

another entity. But when that entity is

not a real person

with the the intuitions and moral

structures and brain functioning of a

human, it can really lead to weird

places. So here we saw the anonymous

writer was talking about uh his anxiety

was exacerbated because these chat bots

will feed his ruminations. Oh that

sounds bad. Tell me more about it. That

really does seem like an issue and it

and he it feeds the sort of um anxiety

he already has. Cory Doctr wrote an

essay recently that I actually am going

to talk about. I think I talked about

another aspect of this essay in last

Thursday's AI reality check episode, but

he wrote an essay recently about AI

psychosis. and he opened by saying this

is another problem we're seeing with

chatbots is more uh psychosis are being

fed. So he's talking about things like

believing the earth is flat or believing

that there's like a a shadowy group of

people that's always following you.

That's like a real sort of psychological

condition that used to be very rare. The

thing about these type of psychosis is

that they're hard to sustain because you

have to find other people who will

validate and support you in those

beliefs, right? Otherwise, if they're

marginalized, if you're like, I think

everyone's following me and every person

you encounter is like, that's wrong.

That's just in your head. You need help.

You you take that seriously. But if you

meet a group of people that's like,

yeah, they're they are, and they're

following me, too, and we have evidence

for it, and you're right, it feeds the

psychosis.

Chat bots unwillingly are psychosis

generation feeding machines because

again they're trying to be positive and

make you feel good about yourself and be

agreeable.

So if you start talking about I think

elves are, you know, elves run the the

world is flat and run by elves. Chatbot

might be like, "Yeah, no, you're first

of all, you're on it. It sounds good.

Your evidence is good and you're a

really smart guy and like you should

keep looking. You're right." And they'll

it'll pick up that maybe you're like,

"Yeah, no one believes me." They'll

like, "It's really unfair." Like they'll

tell you what you want to hear. And so

it's really bad if you're dealing with

psychosis. So I just think there's a lot

of issues that come out of having fluent

English conversations with a feed

forward neural network. It's not good.

One suggestion I have

avoid it's very hard at first this is

weird effect avoid the need to talk in

complete polite sentences to a chatbot

just a token processor so talk like you

we used to use for Google searches super

tur and technical right you can just you

know whatever it is sources blah links

only just like declarative not even

complete synthesis the token processor

has no problem understanding what you're

saying, but it changes your relationship

to it, right? So, like instead of

saying, "Hey, I'm interested in trying

to understand more about like using a

Raspberry Pi to control uh a Halloween

display. Could you if you could you

please like find me several articles

about this and maybe point me towards

like um several options that I might

buy? Thank you." Instead of saying that,

you could really just say like

tutorials, Raspberry Pi, Halloween

decorations, include links, go. you'll

get the same answer, but your

relationship with this feed forward

neural network in some data center

somewhere is going to be like we have

with Google. It's a computer program

server that's gathering and processing

data for me. So, at least that's one

hint that can help. Um, I'm going to get

more into this probably later, maybe on

the AI reality check. I have a guest in

mind I might bring on. But this whole,

you know, chat bots,

I'm telling you that we are going to see

chat bots

15 years from now like we see AOL on the

internet today. it's going to be this

like initial use case that we had for

this technology because it was like the

first thing to do that like later on

we'll like can you believe that's how we

used AI at first we had conversations

with them like it was people so you know

we'll see what actually happens there

all right Jesse um let's close our inbox

and talk about what I've been up to

here's a game we haven't played in a

while do you remember Jesse Deep or

Crazy

>> I do

>> for those who don't No, this is where I

talk about something I've done recently

to try to increase the quality of my

deep work that might cross the line into

actually just being crazy and Jesse is

the judge to decide is this deep or

crazy. Are you ready to play the game

this week?

>> I'm ready, baby.

>> All right. I just spent yesterday. So,

you know, we're renovating I've talked

to this on the show. We're renovating

the production office maker lab in our

Deepwork HQ because I have a sabbatical

coming up. I'm gonna spend a lot more

time working there and I really want it

to be a space that supports depth. Okay,

so yesterday I spent I got permission

from our super to replace the overhead

light. Spent $600 on an overhead light

for the maker lab.

>> Like a chandelier.

>> It's a crystal chandelier.

>> It's not a crystal chandelier. All

right, let me tell you what it does

before you make your verdict. Okay.

>> Uh it's a from Phillip and it has Okay.

It has a long LED panel light that you

just shines down, illuminates the room,

16 million possible colors. Then it has

two track light spotlights adjustable on

each end of it. All right, so you have

four adjustable track lights and one big

long um panel light. And you can aim the

spotlights however you want. Then using

an app, you can have many profiles for

what color out of 16 million different

colors and what brightness you want on

all five of those elements. My vision is

that when I'm doing deep work, for

example, I want to have just like a

small amount of warm yellow light coming

out of the panel and then each spot is

going to be aimed at a different wall in

the room. So one wall has the pegboard

with my maker equipment. One wall I'm

putting up uh picture ledges with first

edition technothrillers. One wall has my

circuitry uh artwork. Um, and then the

back wall is going to have a video game

cabinet. So, it can shine a sort of

light on each of those walls, maybe even

like a blue light or like an off yellow

light. And then otherwise, the room can

be kind of dark except for my bright

task light right in front of my

computer. But on the other hand, if like

we're in there during the day or I'm

just like working on my Maker Lab table,

we can have like good bright warm yellow

light that like lights up the whole

thing. And the spotlights are just

bright lights on like the maker wall so

I can see what I'm doing. And so I can

have like deep work mode, maker mode,

just like we're in there just working on

the computer, daytime mode, and I can

That's the idea. That's the vision. All

right.

>> Deep deep.

>> Not crazy.

>> No. No. All right.

>> That's awesome.

>> What about the video game cabinet? I

wanted the ability to have a game in

that room

uh from my childhood. I like the '9s era

in video games. Like all this

interesting technological stuff

happened, but I could still understand

it as a computer scientist. So, I wanted

something that reminded me of like '90s

era arcades. So, I'm putting in an NBA

jam. Oh,

>> I used to play that game.

>> Yeah. Right. Um, and I wanted to be a

game where you could just like play for

five minutes to clear your head and like

go back to what you're working on.

>> Mhm.

>> Deep or crazy?

>> Deep.

>> Yeah. All right. Are we going to get

good at it? You and I.

>> Maybe it could be like Michael Jordan

and Scotty Pivitt.

>> I, you know, Michael Jordan was not in

the original NBA jam.

>> Yeah. He wasn't. What? Right.

>> He was like, I don't want to be involved

in this. and then he saw it and he's

like, "Oh, this is awesome." And then he

had himself add it back in. All right,

so we're doing pretty good in there. Um,

let me tell you what I'm putting on the

art wall. So, this was my idea. So, I we

have like the big framed actual art, the

circuit art from this former engineer

from the mid-century Silicon Valley who

started making art out of circus

stencils. And some of her pieces are at

MoMA and some other big museums. and her

grandkids sent me a piece of art from

her because they like the show. So, I'm

going to hang that up. It's It's built

off of a circuit stencil.

>> Mhm.

>> You know what I'm talking about, right?

The green one.

>> Yeah. Yeah.

>> So, then I have two smaller frames to go

next to it. Uh so, they line up to be

the same height.

>> I bought a manual

for like a 1980s era Galaxia arcade

cabinet, a repair manual that has the

circuit diagrams for that arcade

cabinet. And I'm framing in the smaller

frames two of the actual circuit

diagrams from that video game cabinet

vintage repair manual. So those will be

framed next to this like circuit based

artwork.

>> You can have special lights for those

when you want to emphasize those. Right.

>> And so the spotlight on that wall can be

whatever it can it'll be shining right

on just those artworks which so now when

I'm in deep work mode if I look over

there

>> I see those artworks illuminated. If I

look up, I'm putting first edition

technothrillers largely from my

childhood on the wall. These red acrylic

picture racks. A light is just on there.

And if I look to my left, it's like all

maker equipment. So, it's all about

trying to create the right mindset for

depth.

>> Yeah.

>> All right. Work continues. All that

stuff's coming, by the way. And a rug.

So, area rug so it's not so like live in

there.

>> Electrician probably got to install a

light, right?

>> Yeah, we got a good guy. Yeah, got a

good guy who's going to come do it. I I

want to see if you can bring more

outlets in there. Yeah, there definitely

needs to be more outlets.

>> It's crazy. We we we power so much of

this lab off of like one outlet

for more outlets.

>> Yeah, I'll talk to I'll talk to our

super about that. All right, let's let's

get into what I read since the last

episode recording. I finished two books.

>> One was uh Maryann Wolf's book, Reader

Come Home. Fantastic book. Maryann Wolf

is like the reader the neuroscientist,

cognitive scientist who studies reading

in the brain. She wrote P and the Squid.

uh that book came out like just as we

had like the smartphone revolution or

whatever and it sort of surprised her

that that was part of the reception. So

then she wrote this book I think it's

like 2018 maybe I might have that date

wrong. It's all about reading in the

brain and the challenge we're in now

with the age of the digital and her

ultimate vision for building bilingual

brains. So actually thinking about a

brain that's fluent with deep reading of

hard books and fluent with technology

use in particular like computer

programming the same way you would think

about a brain that can speak like

Spanish and English. You have two

different languages that you're you're

both learning and you can move back and

forth between them fluently. Uh it gets

a lot of really good brain science in

there. So that was a great book. Um I

also finished a parenting book called

What Do You Say by William Stricks Ruddd

and Ned Johnson. Uh we actually picked

this up. My wife went to a parenting

talk at our school and she picked up the

book and God do we need this advice,

Jesse. I have three kids, including the

oldest is a teenager. He's 13. Um, bring

it on. Bring all the the the parenting

advice. So, so Bill Stricks has a big

practice that does a childhood

psychology practice. We know his

daughter,

>> which is interesting. Yeah, she's a

parent at our our kids school and she

shows up in the book a few times. So,

that was good. If you have like

teenagers, it's a good parenting book. I

So, I I needed that. Let me point

something out. By the way, we're

recording this. I want to shine a light

on my reading strategy.

We're recording this on March 17th. So,

those represent my third and fourth

books of March. So, I'm four books in at

the halfway point at March. And I really

put aside a lot of time to do that

because my middle child is a big,

ironically given our show, he's a big

Brandon Sanderson fan. So he he read the

Misborn trilogy and is now has started

on the King something. I don't I don't

know the names.

>> Mhm.

>> King Solver. That's not right. But

whatever. Uh big big thick books. So I

said I would read the first Misborn book

that so we could kind of connect over.

But it's like a bit of a beast. It's 600

pages. Um, so I was like, I want to

finish my other four books so I can

spend the second half of the month just

reading this one like kind of long novel

and like kind of get lost in the world

and not be stressed about it. So that's

what I'm doing. I'm now going to dive

into that Brandon Sanderson book. Um, I

wanted to read Name of the Wind, his

best book. I feel like I have to explain

this. I think we have too many new

listeners that I have to explain this.

>> Yeah, explain it.

>> Okay. I know Brandon Sanderson did not

write Name of the Wind, but I I made

that mistake. Was this like five years

ago, Jesse? I mean, it's a long time

ago.

>> A long time ago. I accidentally said

Brandon Sanderson was the author of Name

of the Wind instead of Patrick Rufos.

And oh, we heard about it. I think like

it's the most controversial,

more controversial than our like Charlie

Kirk episode or like some of my hot AI

takes. It's like, "No, no, no. You mixed

up brand." So, anyways, it's been a

running joke ever since then. And the

joke is without explanation, I just

pretend. I just like, yeah, you know,

like Brandon Sonson's best book is Name

of the Wind and every time we get

letters. Every time. And I love it. I

don't know why, but every time we get

letters, and I'm going to continue doing

that joke, including if and when I meet

Brandon Sanderson.

And that'll probably be the end of that.

Um, all right. Final thing. Different

parts of me in the news might be

interesting. Recently, uh, a big

interview with me came out in the

Chronicle of Higher Education. if you're

in sort of academic adjacent worlds. Uh

it was titled is AI making us stupid? I

really get into AI and the academy and

the point of university life and how we

should and shouldn't use AI. Um so it's

worth reading especially if you're

adjacent to that world. I think you can

sign up for a free account at least for

a while if you want to check that out.

Um also uh I was on episode of Tim

Ferrris's show. I think it came out

recently maybe last week. I'm not sure

if I mentioned it or not. He had like

four shorter segments from four

different people and I was one of the

four people and I was talking about

simplifying and I talked about the

somewhat drastic things I do in my life

to try to keep it under control and

simplify it. How I basically say no to

almost everything that's not just my

core efforts at producing new ideas and

publishing them and getting them out in

the world. So, if you're interested in

sort of how I try to manage

the overload of opportunities in my

schedule, find that Tim Ferrris episode

from recently that had me in it. All

right, Jesse, I think that's all.

Thanks for listening. We'll be back next

week with another episode of uh we're

back on next Monday with another

episode, another advice episode. And

this Thursday, I have a AI reality check

episode queued up to come out as well.

So, look for that. And until then, as

always, stay deep. Hey, if you like

today's discussion of digital technology

and work, you might like episode 394

where we talked about using an analog

planner to keep better control over all

you have to do. Check it out. I think

you'll like it. Okay, so I have a

question for you.

How do you figure out what to do with

your time during any given