AI was supposed to make us more productive, which was supposed
to. Mean working less.
Yeah, a new study from UC Berkeley put researchers inside
a tech company for eight months and found, well, the complete
opposite. People using AI tools worked
more, not less. And to be honest, this mirrors
exactly. My experience and our.
Company's experience, too The study found that people took on
broader responsibilities, the line between work and rest.
Blurred. Very.
Fast and filled up every single. Minute with.
More and more tasks nobody told them to.
The tools just made it easy enough that stopping felt like a
waste of time. Today I'm covering exactly what
researchers found, why this pattern has repeated with every
major labour saving technology from the past 100 years, and
what sociologist theory of social acceleration.
Tells us about. Why Productivity Tools just
never seem to produce that spare.
Leisure time that they seem to promise.
This February, 20262 researchers at
Berkeley's High School of Business.
Published a piece in Harvard Business Review with the title
that. Well, it tells.
You a lot of things it says AI doesn't reduce work, it
intensifies it. They'd spent eight months inside
a. 200. Person tech company.
Watching how people actually use.
AI tools in their day-to-day work.
So not in a lab, not a self reported study.
They were on site twice a week from April to December 2025.
And they did more than. 40 like in depth interviews across
engineering and product. Design research operations.
And they found. 3 distinct patterns.
Of work intensification. And all three of them are
actually voluntary and nobody was being forced to do more.
So let me run through those. The first was task expansion,
the second. Was.
Blurred boundaries and the third was compulsive multitasking.
So for the first task expansion they called it.
Basically, they found that product.
Managers started writing code. Researchers took on engineering.
Work People suddenly started absorbing tasks that would have
definitely been handed off. To another person in their.
Team or just never attempted because.
AI just made it feel possible. And the downstream effect of
this? Wasn't efficiency.
It was actually. Just load.
Redistribution, removing some bottlenecks.
And. Redistributing it across teams,
engineers found. Themselves becoming as well the
researcher. Described and as most people
working with AI coding quality inspectors.
For what could be described as a.
Sometimes fantastic, but often junior colleague as.
I said you had product. Managers starting to write code
themselves. In the product function, we're
already starting to see product manage pick up smaller bugs or
tasks. The same with designers.
Why would a designer? Design things just in Figma.
When you could be in core code with access to the code based.
On your local machine and start. To.
Create things as well that could actually just be merged there
and then. We've seen this happen,
everyone. Across our product.
Organization is now in an Ida developer environment basically,
so suddenly everyone in the team has access.
To the entire code base, both to talk to to.
Investigate ideas and problems and theories to plan work.
But also to just. Start fixing things and getting
stuck in. And if people have the right
system set. Up what our company has suddenly
anyone can start to produce something so yeah, that task
expansion certainly means that people are just.
Doing more work. That they previously didn't do.
And this brings us on to the second area of work
intensification that AI brought which was blurred boundaries so
because. Prompting an AI.
Is just so simple and easy it literally takes seconds it feels
like nothing work. Started to easily seep into a
lunch break. You could go on your phone.
I do this all the time and just. Quickly voice note Claude.
Because you know it's important for the next task and you want
to make sure that Claude is working whilst you rest and eat.
I'm terrible for this, whether I'm with friends with whom
having lunch, whether I'm in a meeting.
I'm thinking about what the next.
Prompt is or. The next thing is that I need
this different set of AIS to go and do for me and so very
easily. I have 456 tasks going on and
I'm doing them and managing them at all times of the day.
So yeah, the research found that workers.
Realized, often in hindsight, that.
Downtime no longer. Provided that.
Same sense of. Recovery.
And as a result, work fell even less bounded and a bit.
More ambient, something that could.
Always be. Advanced a little.
Bit further with just a little bit of effort.
So again, that was another. Way that AI.
Intensified work. And this comes on to the third.
Way that AI intensified work. Which was?
Compulsive multitasking. Suddenly they found that workers
are running multiple. AI processes just at the same
time, you know, keeping several threads.
Alive at once. Reviving you know certain tasks
because the AI could just handle them in the background.
And in this research? Study, they actually said, and I
quote this. Created a rhythm where the both
the human and the. Machine were constantly in
motion and I think one engineer captured this fantastically in
this paper. You had a.
Thought that maybe. Oh, because you could be more.
Predictive with AI. Then you could save some time
and you could. Therefore work less.
But then really. You don't work less, you just
work the same amount. Or even more.
And our team often jokes about this Barry, the CEO who who, you
know, programs every single day, often jokes that it's made him
tireder than ever before in his entire career.
And I just said I'm terrible for this.
Just suddenly multitasking all the time with different tasks.
And it could even be personal tasks as well and personal
projects I'm making sure I've got on the go.
And you know, just naturally you suddenly have increased
expectations to. Produce more work.
And as a result of those expectations rising.
There's no real. Productivity game because you
fill that spare time doing more to meet that expectation.
So yeah, these three patterns created a self reinforcing cycle
and the the research report talks about this.
Essentially AI accelerated. Certain tasks which raised
expectations for speed, not through certainly explicit
demands, but just what became visible.
And normalized in everyday. Work, you know, higher speed
made work is more reliant on AI increased reliance.
On AI, widen the scope of what? Therefore, people attempted to
do, you know, taking on task from another person's job role
and as a result of that. Wider scope.
The total volume of work Therefore, suddenly.
Increased and then the cycle continues with nobody really at
the controls. So yeah, increased capability
leads to increased output, which leads to increased expectations,
which then increase the amount of pressure to expand and
improve further. So essentially self regulation
failed. You know, self regulation of all
of us individuals has completely failed and.
The researchers were quite explicit.
About why they think this happened.
And they put it. Down to three reasons, which is
#1 the intensification felt really rewarding, you know?
Workers described. Momentum and the ability to
expand capability. Or the thrill of suddenly.
Being able to do things they've never done.
Before I I've. Certainly felt that as a huge
driver. Second, because the work was
voluntary. Neither the workers nor their
managers. Recognized it as.
Overwork and 3rd as colleagues expanded their output informal.
Expectations also reset. So what was once, you know,
extra effort became just, you know, the minimum standard of
expectation for performance. And I think for those that have
gone really far. Into using AI within teams
structuring everything around AI and how to get the best out of
AI like our entire way of working at our company is
stricture around AI you're probably.
Going to resonate with a lot of what I'm saying.
It's described exactly what's happened at Mindset.
Our output is insane and. What you.
Might expect from a single person over weeks a year ago.
I have an expectation to produce in days.
Because I know it's. Possible if you're.
Using AI properly again, back to that schools discussion last
week and it's really interesting I mean for those that listen
every single week, any opportunity to look back in time
and trying to understand, you know, why does this feel like it
keeps happening because it isn't new.
So there's a really interesting history to this and a really
interesting theory about why us as humans cannot just be
satisfied with that increased output and suddenly have leisure
time to go enjoy with our family and and our hobbies and and do
things that are nice. Because yeah, this isn't new.
And this pattern has repeated with almost.
Every technology sold as a time saver.
For over 100 years in the 1920's, the average American
woman spent roughly 60 hours a week on housework.
So cooking, cleaning, washing, fixing things.
And so by the 50s there was. Now a washing machine, a vacuum
cleaner, refrigerator and a gas stove in most people's homes.
And so the difference. Between the 1920s spending 60
hours a week and the 50s with all that new technology to make
things easier. You know, what do we what do we
think the. Housewife spent on cleaning per
week. It's about 60 hours a week
again, and you can look at other technologies for the
spreadsheet. 20 hours of manual calculation suddenly done in 15
minutes. Well.
What was the outcome? Accounting clerks of people
doing accounting fell by 400,000 people when it was invented, yet
counters and auditors rose by over 600,000 with e-mail.
Handful of memos. Per day.
Became 100. And 21 emails and 11 hours of
work a week for cars, a researcher found.
That in 1994, humans spent roughly one hour.
Per day. Commuting regardless of era
culture. And even technology.
So basically. As cows got better, we traveled.
For further inside them. Because they were.
Better and it was easier to travel.
The point being the. Mechanism is always.
The same so technology gets better and reduces the cost per
unit of that activity. So it makes it easier to do that
thing yet. As a result, the volume of that
activity just. Increases, so we do more of it
and we just consume the savings in time and effort that we would
have gained regardless of the technology.
So that new volume just becomes the baseline.
And there's a lot of theories behind this.
There's a well. Known thinker who?
Explains how this happens perfectly called Hartman.
Rosa, a German sociologist, and it gives you a really
interesting perspective on not just this discussion.
But why the world often in modern?
Times just feels quite chaotic. You know Rosa.
'S core argument, which was laid out in the.
Book. Social acceleration, is that
modern? Societies are caught in what he.
Describes a triple acceleration, so you get.
Technology acceleration. So things just get faster.
Acceleration of social change. So institutions, norms and
expectations shift really fast as a consequence.
And the acceleration of the pace of life.
Despite all the time saving technology, individuals just
feel more time pressure and the critical insight I think is that
these three just feed into each other, into a loop that has no
natural stopping point. And which is the just the story
of? Capitalism, Rosa actually called
this the process of dynamic stabilization, which is
essentially a fancy word for the modern institutions and
companies and economies can only maintain their stability by
continually trying to grow and accelerate and go faster.
And standing still means you're falling behind.
Just like the bicycle. You have to just constantly keep
paddling, otherwise you're just going to fall over.
So what? What's?
Really interesting about this is and what does.
What does it explain why everything continuity still
feels the same no matter how much cool technology comes
about? Well.
Rosa called that. Standstill, which is when
everything is in constant motion but nothing changes.
So the act of constantly being in motion means that nothing can
change. Technology shift, fashion
changes, work gets busier. And yet the basic.
Experience of. Falling or feeling like you're
behind, Feeling stretched, feeling like things are never
enough. That just stays the same because
the treadmill is only getting faster.
And I think that Berkeley study is a case.
Study of this playing out. On the level.
Of individual workers in AI. But the more they did, the more.
That was expected and you know, this expectation becomes the new
flaw and that's why. AI so far.
Doesn't save us time, it makes us work more.
Anyway, I hope you enjoyed the episode and I'll see you next
week.