August 27, 2025 • 38 mins
What happens when startups like Figma outpace industry giants like Adobe? In this episode of The Innovation Show, Aidan McCullen sits down with bestselling author Sangeet Paul Choudary (Platform Revolution, Reshuffle) to explore how AI, cloud technology, and mindset shifts are reshaping the future of work, design, and competition.
Discover why incumbents often fail—not from lack of innovation, but from failing to unlearn old architectures. Using the case of Figma vs. Adobe, we unpack how element-based design, governance-led workflows, and AI-first thinkingare redefining industries from design to law to construction.
Key takeaways:
-
Why adapting mindsets matters more than adopting tools
-
How AI is unbundling knowledge work and rewriting career paths
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The importance of governance in enterprise-wide collaboration
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Why the forgetting curve is as critical as the learning curve
Perfect for innovators, leaders, and professionals navigating the knowledge economy, this episode is a roadmap for thriving in a world of structural uncertainty where AI and cloud-native startups are rewriting the rules.
00:00 Introduction and Sponsor Message
00:17 The Hammer and the Nail: Misreading New Technology
00:28 Adobe vs. Figma: A Case Study in Innovation
01:03 The Importance of Mindset in Technological Shifts
01:20 Guest Introduction: Sanjeet Paul Choudary
01:49 Figma vs. Adobe: A Deep Dive
04:07 Technological Shifts and Organizational Change
05:57 The Impact of AI on Work and Careers
17:00 Governance vs. Execution in Modern Enterprises
18:22 The Future of Competition and Industry Structure
21:40 The Need for Unlearning in the Age of AI
24:34 Preparing for the Future: Skills and Careers
36:54 Conclusion and Final Thoughts
38:05 Closing Remarks and Sponsor Message
📚 Resources & Links
• Reshuffle: Who Wins When AI Reshapes the Knowledge Economy
→ https://amzn.to/3HWwj1I
• Platform Revolution
→ https://amzn.to/4mBq2aQ
• Sangeet’s Substack
→ https://substack.com/@platforms/posts
• Aidan’s show notes
→ https://thethursdaythought.substack.com
🎙️ Listen now to learn how to reshuffle your thinking, your organization, and your career for the AI-driven future.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Before we start today's episode, I wantto thank our sponsor, Kyndryl, and indeed,
the Kyndryl Institute, where you can findarticles on ai, the future of work, the
future of payments, and so, so much more.
You can find Kyndryl atww.Kyndryl.com/institute.
To a man with a hammer,Everything looks like a nail.
(00:20):
That's a quote often attributedto Mark Twain, and it captures
why incumbents so often misreadthe impact of new technology.
Take Adobe, for example, it isrightly loaded as a great innovator,
even a pioneer for moving to thecloud, well before most others did.
Yet, while it changed its businessmodel, it didn't reshuffle its mindset,
(00:41):
it carried over the architecturefrom the desktop era to the cloud.
By contrast, a more mentallynimble startup with no
baggage, no mental baggage.
Figma reimagined design itself.
It shifted the unit of work from the fileto the element reshaping, collaboration,
coordination, and ultimately thestructure of the entire industry.
(01:02):
This is the bigger lesson.
Incumbents often mistake for adaptation.
in the age of Bolting new technologyonto old architectures won't be enough.
The winners will be those whorebuild or reshuffle their
mindset around AI makes possible.
Our guest today has spent hiscareer unpacking these shifts.
(01:24):
He is the bestselling authorof Platform Revolution.
I have it here beside me, a book thatI've had a long time on my shelf.
He's been on my list for a long time.
Also, he's written platform scale andmost recently his book Reshuffle Who Wins
when AI Restocks the Knowledge Economy.
Sanjeet Paul Choudary,welcome to the show.
(01:46):
Thank you, Aiden.
Absolute pleasure to be here.
we're gonna talk particularly about thatamazing case of Figma, and particularly
Figma versus Adobe, which I find sofascinating that Adobe, I've written
about them, you've written about them.
They were great innovators and they wereinnovators in adopting a new technology.
we see so often, they fail tothen to make the shift to the new
(02:09):
technology with a new one or areshuffling of the landscape comes in.
But before we introduce the players,Figma and Adobe in particular, let's
start with the framing of the challengethat I hope I set up in the introduction
startups or new entrant beat incumbents,not because of a new technology or
even speed, but it's this mindset.
(02:29):
It's there.
Adaptation rather than adoptionthat they failed with so often.
That's absolutely right.
When we move to a new technology,when a new technology comes in.
There are fundamentally new architecturalproperties of the new technology.
So the way desktop software usedto work is very different from the
(02:50):
way cloud hosted software works.
And the way traditional softwareworks is very different from the
way an AI first solution would work.
And we use these terms cloud first,AI first, very, very loosely.
But behind this, there is a fundamentalchange in the nature of the solution
architecture that you can create.
(03:11):
A very simple example, if I takeyou know, the shift from desktop
to cloud was that you could see theshift as purely a shift in channel.
You could see the shift as purely ashift in how you deliver your solution
or how you monetize your solution.
Instead of selling one-timelicenses, you go to subscription.
Now that's very much adopting the cloud.
But not really re-architectingyour business around what
(03:34):
the cloud makes possible.
And I think that's, one of the keyissues that we see when we look
at how Adobe moved to the cloudand how Figma moved to the cloud.
Figma rearchitected its business aroundthe architectural properties of the cloud.
So I'll pause there and I'm happyto go deeper into that, but it's
really understanding that the newtechnology offers you fundamentally
(03:55):
new ways to rearchitect the logicof how a certain industry works.
We've seen that with the cloud.
We see, we are seeing that with aiand that's really the opportunity
whenever technology shifts happen.
Of the places I saw this firsthand,Sanjeet was in digital when, media
companies moved from analog to digitaland they tried to cram or force the
(04:17):
old way onto the new architectureand again, failed spectacularly
and failing to adopt that mindset.
Before we started, I wanted to help peoplesee their business or indeed their career
through the lens of this case study.
And in reshuffle, you explainedthe transformation effects of
technology and how they play outacross the entire system of work.
(04:39):
And I was gonna share a figurefrom the book and indeed from your
substack that I'll also put in theshow notes, which we'll cover in
depth in due course, like I said.
But I wanted to share this becausepeople will see this will affect them.
And in the book and in your substack,you share how a legal firm's system
of work has to change becauseof the effect of new technology.
(05:00):
So be ideal for people who watch usto have a look at this on YouTube.
If you're listening, I'll share thisin the show notes because you have to
really see this to take you through it.
But let's have a bit of empathy,Sangeet, for those people who
are just listening to us as well.
I'll share this on the screen now.
So the idea of this framework is thatwhen we think about a new technology,
(05:21):
we very often think about its effectonly at the level of individual
tasks, but actually the effects ofthe technology play out at the level
of tasks which sit inside workflows.
And so workflows get reorientedworkflows set inside organizations.
So organizational systems how weorganize has to change in response.
And finally, all of thisactually gets shaped.
(05:43):
By the fact that organizationscompete within a certain logic, and
if the technology changes that logic.
Organizations have to change, notbecause the tasks are changing,
but because the fundamental logicof competition itself is changing.
So take a simple example of alegal firm today with AI coming in.
(06:04):
One of the key things that we see withAI coming in is that what used to be
scarce traditionally the access to skills,the ability to apply knowledge, work to
solve specific problems, a lot of that.
Application oriented, synthesizedknowledge is now available in a more
commoditized fashion through AI models.
(06:25):
So you don't need to invest allof those resources and skilling
and training and hiring people.
You can access that with a set ofwell-crafted prompts, if you will.
I don't wanna dumb it down toomuch, but the point is that.
What used to be scarce in the past is nowavailable without the associated scarcity.
So what that essentially meansis that the logic of competition
(06:46):
changes because competition isstructured around what is scarce.
And so.
You need to look for howthe system changes when this
logic of competition changes.
D does work move away fromservices companies to in-house.
Does it lead to the creation offundamentally new, more entrepreneurial
companies that subsidize whattraditionally lawyers used to charge
(07:08):
for and monetize somewhere else?
So you need to think abouthow the logic of competition
is changing across the board.
And in response to that, think aboutwhat that means for your organization.
What kinds of skills, what kinds ofcapabilities need are still needed
to compete in this new landscape.
And that then changes how you organizetasks and workflows internally.
And so the key point that I'm tryingto make over here is that whenever
(07:31):
there's a technological shift.
Start outside in.
Start by looking at what's changing interms of the nature of the competition.
so one of the reasons Iwanted to share That was that.
So much of the work we dotoday is knowledge work.
It's a knowledge economy, and youtell us in both your article and in
reshuffle that AI unbundle today'shuman dominant knowledge workflows
(07:52):
into tasks which can then be recombinedinto fundamentally new workflows.
And this is useful for the Figma casewhen we get there, but this changes
how work is structured itself, and howworkflows and organizations are organized
and how people and companies compete.
I thought that was just important forpeople who are genuinely concerned
(08:13):
and rightly so about their careers.
And indeed, as you say,careers will change.
They won't all be wiped out,but salaries will drastically
change because of this change.
And I, really feel that this is animportant shift that people need
to prepare for and , we'll go downthat rabbit hole in the future
episode when we get into reshuffle.
(08:33):
But again, it's useful before weget into the Figma case, because
you can see these shifts exactly.
In a business like Figma,versus Adobe in the same way
you look at your career today.
that's absolutely right.
So if I elaborate this in terms of howthis played out with Figma and Adobe
and that's very relevant in terms ofwhat's happening with AI today as well.
(08:55):
When adobe moved to the cloud.
Adobe was traditionally structured aroundthe logic of the file, so every project
was structured around a design file andAdobe's primary customer was the designer.
If something had to be done withthe rest of the team, the entire
file had to be moved across.
What Adobe did when it moved to the cloudwas it changed the delivery mechanism.
(09:18):
It made it easy to move the file around.
It it changed its revenue model,but fundamentally it was still
structured on the file dominant logic.
Now, the reason this is importantis because the cloud allowed a
fundamentally new architecture andAdobe was not able to adopt it.
Figma instead adopted it because it.
Did not have the architectural debtthat Adobe had already incurred.
(09:41):
It could reimagine designwork around the cloud.
And design work essentially is structuredaround how different elements of the
design file are arranged and organizedwith each other, and so Figma.
Architected itself around the elementas the atomic unit, A button align
a specific portion of that overallfile which served now as the atomic
(10:03):
unit around which work was organized.
Now, the reason this is importantis that in large companies you
don't just, create designs.
Those designs are partof larger workflows.
There are engineers, there aremarketers, there are other players
who need to work across all of this.
And there are governanceconsiderations that are important.
(10:25):
So for example, you might want a button tolook consistent across the entire company.
And so what this allowed Figma to do isit allowed Figma to set up enterprise
wide shared libraries of elementswhich could be reused across files.
So the first was the ability tostandardize what design looked
like enterprise wide, and that'sreally one of the key things that
(10:47):
Adobe would find very difficult toreplicate as long as it stayed stuck
in the file centric architecture.
The second thing that itallowed was permissioning and
governance at the element level.
So if you needed a certain elementto be consistent, but you could have
more flexibility on other elements.
You could tie governancenow at the element level.
So in general, Figma did not justmove to a new model by simply enabling
(11:13):
collaboration as is often seen.
Figma, what it really did was it enabledenterprise-wide governance and reusability
that was simply not possible with thetraditional file-based architecture.
So that's really the key youknow, shift that Figma achieved.
And because Adobe was structured aroundthe file, even though it moved to the
cloud, it adopted the cloud, it did notreimagine what design would look like
(11:39):
around the properties of the cloud.
And we see the same thing happen withAI today because, as you mentioned
one of the key things I talk about.
Is how AI allows a similarunbundling of knowledge work.
Figma unbundled the file into its elementsbecause the cloud allowed that AI today
unbundle a traditionally tacit knowledgebased work which could not be codified and
(12:02):
hence could not be broken out into steps.
It can now unbundle that Becausespecific forms of knowledge work
can now be achieved through models.
And so that allows us to thenfundamentally recombine and reorient new
ways in which workflows can be designed.
And that's the opportunity that's reallythere to create AI first companies today.
I just wanted to draw an analogythat you make also in the
(12:24):
article, which is the shift in.
For example, an album when we used tohave to buy an album, a physical album,
and this is very analogous to the Adobeshift that then that existed in the cloud.
I could buy just for example, a song fromthat album, which for some people they
believe the whole album is important,but for a lot of people, they just listen
(12:46):
to one or two songs from the album andmaybe bought the album because they had
to and didn't listen to the rest of it.
And that explosion or that unbundlingof the album as the entire file down to
the song and I could purchase that song.
Drove down the price in a huge way.
But it changed the ecosystemand then gave way to things like
Napster, for example, first, butthen iTunes and Spotify, et cetera.
(13:10):
Let's just draw that analogybecause I think that's really
helpful for people who are not.
I use Figma, I understand Figma, butmany people won't understand that
in a way when you use something likeAdobe, even in the cloud, you had to
have certain software to do, so youhad to understand how files worked.
You had to use Photoshop, forexample, which was a big massive
(13:32):
file, which meant your computer hadto be able to handle that as well.
So there was a whole ecosystemshift here that's quite important.
And once you get this, you'll understandwhy the shift was so dramatic.
there's a whole range of factors thatprevent the incumbent from moving over
to the new ecosystem architecture.
And it's not because the incumbentis slow or is risk averse.
(13:52):
All of those might be issues, buteven if the incumbent does not have
those cultural challenges, it's justarchitecturally prohibitive for the
incumbent to move in this direction.
So some of the points that youmentioned, which I talk about in the
article, the fact that this wholefile-based architecture was what
(14:13):
Adobe was centered around it assumedsignificant computing resources.
And that worked very well whenyou worked on a desktop model.
But what it prevented youfrom doing was having multiple
players across the workflow.
All be able to work on the samefile because all of them would
(14:34):
need to have similar computingresources to, to load those files.
The files or the interface itself was verymuch structured for design that is for
execution rather than for collaborationand for co-authoring, co execution.
And so moving that was another challenge.
To some extent, Adobe sorted.
The interface piece of it.
(14:55):
But that's again, something we see thatyou could, you know, make a similar
interface, but if the underlyingarchitecture is fundamentally
different, you can't achieve what'spossible with the new architecture.
There were other less obviousissues in the sense that.
When Adobe opens up its APIs and allowsother partners to integrate with the file,
it can do that at the level of a file.
(15:16):
So you can move files around, youcan change permissions, you can make
edits all at the level of a file.
Figma can open up APIs at the level ofan element, so you can have an external
tool make specific changes to a specificelement or make specific changes to the
governance of an element and the sharedlibrary all of which is not possible
(15:37):
with the Adobe based architecture.
So.
There are, as you mentioned, manydifferent shifts because of which
you simply cannot port your oldecosystem onto a new ecosystem.
You may actually have the same partners.
Adobe and Figma might havethe same partners, but the way
those partners work with you isfundamentally different because of
this difference in architecture.
One of the key shifts was theshift in the organization itself,
(16:00):
and I felt this was so importantfor people who work in innovation.
A lot of the listeners of thisshow, Sanjit work in innovation.
They're innovation workers,but their budgets lie with them
rather than at a governance level.
And that's a really key term.
And you write in the article in executionLED tools, software is paid for.
(16:20):
By the teams doing the work.
Designers, engineers, and marketers, forexample, for Figma, because the tools
help them complete specific tasks faster.
But in governance led systems value comesfrom managing consistency, control, and
coordination across the organization.
budget often shifts upward because thetool becomes strategic infrastructure
(16:41):
rather than just a productivity aid.
That is such a key point, and oftentimesthe success and actually death of a
project can lie there as well because nowit's front screen, they can see actually
where how much is being spent as well.
So it can have positives and negatives.
That is absolutely right.
I believe that the technologicalshifts towards cloud and now to AI
(17:04):
actually help us move more in thisdirection away from execution oriented
thinking and enterprises to moreof governance oriented thinking.
And you see this you know,with the Figma example.
In general, you see thisin many other industries.
A parallel would be just lookingat Autodesk and what's happening in
the construction industry because aconstruction workflow is even more
(17:26):
complex than a design workflow.
You have design, you haveplanning materials, and then
you have actual construction.
And Autodesk has been reallygood traditionally at.
Like Adobe creatingexecution based software.
But if you look at what Autodesk is doingnow, it's investing entirely in creating
governance layers across its software.
So what Autodesk Construction Cloud doesis it's really trying to do what Figma did
(17:50):
in design and create end-to-end governancelayers across all of its software.
And I, I believe that'sa, a secular shift that.
Is happening across industries becauseboth the fact that the cloud modularizes
computing capabilities, it unbundle,compute, storage, et cetera, from
infrastructure and ai, unbundle knowledgework from the underlying skills.
(18:14):
And because of that, all ofthese can be recombined into
fundamentally new workflows.
That's why governancebecomes so important.
There's three major shiftsyou talk about here.
We've talked about the first one, whichis work, and in the Adobe case, the shift
that Figma made to the cloud, but alsothe element based architecture broke
Adobe's control over the unit of work.
(18:35):
The second was organization wherethe value shifted from power shifted
power from execution to governance.
And the third is industry.
And this is where it eroded.
Adobe's closed loop power.
the industry structure.
I'd love to just lean into that alittle bit because again, if people
understand this as a pattern, notparticularly a case study, but a pattern
(18:56):
that you cover in your books and coverin particularly in reshuffle, they can
understand how this is gonna affect them.
Yeah.
And, and I think you know, toyour point, exactly the third
point is the least understood, butit's actually the most important.
And that's what I talk about when Isay the rules of competition change.
So if you look at Adobe or you lookat Autodesk, both of them have sort of
(19:18):
built quote unquote kingdoms around theirfile-based traditional execution software.
And so even though they realizethe logic of governance is
becoming important, they cannot.
Abandon their traditional softwarekingdoms, and so they try to lock in
governance with their existing products.
(19:39):
What we instead see startups do isbecause they don't have existing
execution oriented products, they canreimagine a governance first solution.
So when you imagine a governance firstsolution, you kind of break this lock in.
So if you look at Adobe or Autodesk,both of them work on the model of.
Works with Adobe or works with Autodesk.
So they create an ecosystem, butit's, it's more of tools that
(20:02):
work best with Adobe or Autodesk.
It's not a fundamentally open loopecosystem where no matter where your
work gets done, we'll still govern it.
But the opportunity in, in bothof these workflows is really
to create these fundamentallynew governance layer startups.
And we are seeing thathappening in both industries.
Figma is clearly a winner.
Canva is another example.
(20:23):
But even in the construction side,we have companies like build outs
et cetera, coming in, which are tooagnostic and creating tool agnostic
governance layers, so that to yourpoint, the shift happens at every level.
You don't just serve execution at thetask level you serve every stakeholder
in the workflow and you govern across it.
(20:44):
And hence that changes how youthink about things organizationally.
But most importantly, it changes howclosed or open the ecosystem can be.
Because for governance orientedworkflows to work well, you need a
fundamentally more open ecosystem.
You work with all theseFortune 500 companies.
Of the things is.
It's not just about learning,but it's about unlearning.
(21:05):
So they found a way that works.
It's that way has been successful.
The more successful it is, theharder it is to let go of that way.
That works, hence the case of Adobe,and that's a cycle that's persistent.
And what I find so fascinating, whyI do the show genuinely, is because.
These are templates of disruptionthat you see time and time again.
(21:28):
And when you cover enough of them, youjust see the pattern, the patterns there.
And again, another quoteattributed to Twain is history
doesn't repeat, but it rhymes.
Right.
that's what we see time and time again.
I'm sure your sick telling companiesabout this, but it's not just
about the learning curve, butit's about the forgetting curve.
(21:49):
I think the forgetting curve,you absolutely nailed it.
That's more important than evertoday, and it's going to become
progressively more important.
And I write in about this in reshufflein terms of why that's happening.
The reason this idea ofunlearning is so important.
Is because in the industrial age,especially the post-war age from
1950 to say the early two thousands,most companies largely struggled with
(22:13):
what we call operational uncertainty.
So demand goes up and down thecyclicality their supply chain shocks,
these are all operational, uncertain.
There are uncertaintieswithin a fixed structure.
Today, what most companies strugglewith is structural uncertainty.
The structure ofindustries is up for grabs.
Your competitors do not look thesame as they did in the past.
(22:35):
Industry boundaries blur yourthe vectors on which products
compete change all the time.
What used to be monetized cansuddenly be subsidized, Google Maps
doing what it did to Tom, Tom orInstagram doing what it did to Kodak.
And so the real point over here is thatwe are in a world especially over the last
(22:57):
20 years where structural uncertainty.
Is accelerating.
And the dominant way to respondto operational uncertainty was
to just create better buffers.
So if your supply chainbreaks down, create a buffer.
If demand is cyclical, create a buffer.
So there were ways to respondto operational uncertainty,
which would smoothen it down.
Structural uncertainty.
(23:18):
You can't respond toit by creating buffers.
You have to unlearn whatused to work in the past.
You have to start again.
What's the atomic unit of what's changing?
We talked about the atomic unit of work,but really what are the fundamental
parameters of what's changing?
You need to really think about thatat that level and then reimagine
what value and you know, a powerfulposition in your industry would
(23:40):
look like given these new realities.
So.
In general for executives, I would saythat it's really important to ensure
that you're not responding to structuraluncertainty with operational tools.
We've seen this repeatedly happen.
Yahoo is a classic example.
The structure of the internet was movingfrom pages that were difficult to produce
(24:01):
to anybody can produce pages, and soeditorial curation could no longer work.
Algorithmic curation had to work.
So Yahoo invested heavily in Agile.
It pioneered scrum in many ways.
And so a lot of operational toolsthat create operational agility
were there at Yahoo, but they wereconfronted with structural uncertainty.
So you can't address structuraluncertainty with operational tools.
(24:24):
You have to unlearn.
You have to figure out what'sfundamentally changing, and then you have
to really reimagine what your businesslooks like with that new reality.
on a business level so I'm a leaderof an organization, or maybe I'm
somebody who has been tasked withchanging the future of the organization.
(24:45):
Oftentimes, as you say, the first thingthey'll do is lean into technology.
maybe get people tooledup in ai, for example.
At the moment, let's geteverybody on an AI course.
One of the things I get fromunderstanding your work and indeed
reading the books that I do is itstarts first with a mental shift, an
agility, a mental agility shift in theorganization, and a willingness to learn.
(25:09):
I, and I think that I'd love you toshare your thoughts on that people
get stuck because they're experts.
And that expertise trap leads themthem down the path that we've seen, for
example, here with Adobe versus Figma.
Yeah, that's absolutely right becausewhen, when you are confronted with
what I've called here, structuraluncertainty expertise is actually a trap
(25:31):
because when the structure is changing,the rules of the game are changing.
And so you cannot bestuck being an expert.
You could.
Survive.
I mean, there are companies thathave survived over time, but they
no longer command the premium thatthey do because they are still
stuck being experts in an old frame.
And so expertise that is resistant tolearning is actually a liability in
(25:53):
a period of structural uncertainty.
We, you know, we, we, we see this needfor, i, I, I would say that we need
to advocate this need for unlearning,relearning at both ends of the spectrum.
So there are a lot ofcompanies that say, you know.
Let's focus on the customer.
Let's follow the customer.
(26:14):
Let's understand what theirneeds are, and we can never fail.
We'll figure it out.
And that's part of it.
I think you can absolutely nevermove your eye away from the customer.
You need to be very clear aboutwhat problem you're trying to solve
and how you go about solving it.
But at the same time, you also needto really rethink what tools and
capabilities are available to you.
In order to solve those problems,and you need to have that supply side
(26:37):
lens that everything we know about ourindustry could fundamentally change
tomorrow, or is changing at the moment.
So you can't simply layer on newtechnologies onto old ways of
thinking about your capabilities.
You have to reimagine what capabilitieswhat new capabilities are made available
because of a, a technological shift.
, Very much what Figma did withseeing the cloud and seeing what
(27:00):
was possible and hence what previousconstraints could be the least.
You no longer needed to have highlypowerful computers to run the software.
You could run the software onthe cloud, and that fundamentally
changed who could be your customer.
And so you really need to bring thatunlearning to capability, this swell
and not dismiss what's possiblewith the shift that's happening.
(27:20):
On a different levelthan on a personal level.
So organizations we're seeing this wherethere's either a freeze on new hires,
so lower level hires because of ai,people are now using AI instead of that.
Or there's a letting goof people in favor of ai.
But in doing so, one of the fearsI would have is that you're not
(27:41):
bringing in that new mindset.
And I was thinking about howFigma it's not even that.
Somebody from Adobe left andbuilt Figma because they wouldn't
listen to them inside Adobe.
Kind of like we saw withZoom and Cisco, with Eric
Yuan
Right.
many times case studies of that wherethe frustrated engineer or the frustrated
executive goes, okay, screw you guys.
(28:01):
I'm gonna do it myselfand builds a company.
But by not bringing in new blood.
You're not bringing in newmindsets and new capabilities.
Like I'm sure somebody in Figma seesthe world the way they've built the
world rather than the way Adobe hadbuilt the world because they come in
at a different level in time and indoing so, and then not hiring those
(28:23):
new blood or those new capabilities orthose new mindsets, you don't inject
the new DNA into an organization.
I'd love to you to sharesome thoughts on that.
I think that's certainly a challengeand it's not a challenge at, just
at the level of entry level folks,but it's a challenge across the
board when we are in an environment.
(28:44):
Where.
Your ability to solve customerproblems are no longer limited
to your traditional capabilities.
You could be getting capabilitiesfrom other industries today.
Insure insurers need to bereally good at understanding ai.
And if you think about traditionalinsurance, they may not have really
had access to those capabilities.
And, and similarly, if you're building acar today, you're no longer building it.
(29:07):
On the traditional gas based model, you,you need to understand self-driving.
You need to understand electrificationyou need to understand connected vehicles.
And so this convergence makes it allthe more important that you constantly
look for fresh blood who is notstuck within the assumptions of your
traditional industrial ways of working.
It, it requires that you.
(29:30):
Think about even internally structuringyour teams so that teams are not
specialist teams as much as they'reworking on subsystems, where multiple
spec specializations work together havetheir ideas bounced off, and get to
learn from each other and innovate at theintersection of, of those capabilities.
And so.
A lot of you know, innovation potentialreally lies not in eking out more
(29:55):
from what already you do well, butin really bringing the intersection
of vastly different capabilitiestogether and the skills behind it to,
to create something fundamentally new.
And so I absolutely agree withthe point that you need to.
Strategically think aboutwhat you do about your talent.
It's not AI replacing talent.
(30:15):
You need to think about AI toreplace your competitiveness.
You need to reimagineyour competitiveness.
And then given the new logic ofcompetition, think about what's the
right talent and the right capabilities.
As long as you keep thinkingabout AI versus talent, you're,
you're sort of you know, shufflingdeck chairs on the Titanic.
And on an individual level.
Then finally, so I hintinside of the descriptions.
(30:40):
AI won't necessarily get rid of yourcareer, but it will affect your salary.
That was one of the points that youmake and actually it's one, I think you
wrote this in one of your substack aswell, and I'll link to your substack.
I'll link to these individuals articlesas well as the books as well, Sanjeet.
But I'd love you to share on a highlevel what you mean by that and
how we can prepare for that shift.
(31:02):
Yeah, absolutely.
And this is a very central thesisof my book Reshuffle that today
we think about AI in terms ofautomation or and augmentation.
We think of AI as taking our jobor making us better at our job.
And exactly your point,AI won't take your job.
But someone using AI will is sortof couched in that same logic.
(31:23):
But what all of this assumes is thatthere are only two ways that the
effect of AI will play out and thatyour job will largely remain the same.
You either continue to do itor somebody else who learns the
tools better starts doing it.
What this fails to account for is thatthere are two fundamentally different
(31:44):
shifts that happen when AI comes in.
You learn to use AI and yet you mightpotentially lose out if you don't
understand how these two shifts happen.
The first is about your pricing power.
So your pricing power istraditionally being tied to certain
skills that you've acquired.
If those skills.
Get automated and replaced by machines.
(32:04):
That's a different issue.
We understand the problem with thatand we see that as a threat, but
even if you start using AI and youget better at doing what you do.
What ends up happening is you'll seea flattening of skills across the
industry because people with lesserskills will now be able to perform more.
Now that they have they'recomplemented by ai.
And there, there are tons ofresearch studies that have proven
(32:27):
this out with every generation oftechnology, but especially with ai,
that there's a flattening of skills.
And what that then does is thatit increases the supply base of
potential competitors to your job.
Who would do, who wouldbring the same skills?
So if you're really just relying onskills and relying on AI to get better
at delivering what you were doing,then you're getting into a place where
(32:48):
your skill premium will gradually getflattened because many others would
have access to the same thing as well.
The second thing is about agency,and we see this in the case of
the Uber driver, I call this.
A distinction between about thealgorithm and below the algorithm jobs.
So an Uber data scientist versusan Uber driver, an Uber data
scientist is very much a capitalistbecause he owns stock in Uber.
(33:09):
He builds algorithms that thenorchestrate how these drivers work.
But the drivers have absolutely no agency.
They can't choose you know,the nature of the ride.
They can't choose the pricing of it.
Even if they get a five star, thatdoes not allow them to charge more.
The pricing is fixed.
So in many ways, the agency isbrought down, and today we see this
(33:30):
with Uber and delivery workers.
Tomorrow we may see this with otherthings, nursing staff, caregivers.
We are seeing algorithmicscoring and coordination
happening increasingly in work.
So these are the twoeffects that the more.
You rely on technology, the more youcould open yourself to being commoditized,
the more you get commoditized, as Uberdrivers did when they started using Google
(33:50):
Maps, anybody could, navigate the city.
Now, the more you get commoditized, themore algorithms can start managing, , your
career trajectory and your outcomes.
And so you need to thinkabout value and agency.
, Which brings us to the question,, which is your main question.
How do you think aboutyour job in this scenario?
So.
You really need to, again, as I said,don't just focus on your job as it
(34:12):
is today and the tasks within it.
Those jobs make sense in today's system.
If the system changes, if they, ifthe nature of competition changes.
And hence, if organizations shift how theywant to structure skills and capabilities.
You'll have to think about what arescarce skills and capabilities in
this new system, and then think about.
Setting up or rebundlingyour job around that.
(34:35):
So just like, your point aboutalbums, when albums got unbundled
into, individual music files, youhad Napster, but then you, eventually
had Spotify where they were rebundledinto playlists and, , the rebundling.
Was about the new system because inthe new system, attention was scarce.
And so it was really a consumer-centricrebundling rather than a producer centric
(34:56):
or artist centric or label centric,, bundling that used to be in the past.
So think about rebundling your job,what is scarce in the new system?
Where does the new system break down?
What are the key risks, andwhat are the broken pieces that
need to be brought together?
So think about.
Where scarcities risk and coordination,emerge, or issues around these
(35:17):
emerge in the new system and rethinkwhat your job would look like.
And I'll just give, a couple of veryquick examples, to illustrate this.
One example is that ofthe anesthesiologist.
So an anesthesiologist todayuses a variety of machines.
And every task that he doescan be done by a machine.
But somebody has to be accountable for therisk in the operating room in real time to
(35:38):
ensure that whoever is, being administeredthat anesthesia is, administered
exactly the right dosage in real time.
And so the anesthesiologist is paidfor assuming the risk in that system.
Think about what new risks emerge inthe new system in which you are working.
That's the first example I'd say.
And a second example, you know, is just.
(36:00):
Think about, the example ofwhat is typically called a rev
ops role, revenue operations.
This role did not exist 15 years back, andthe reason this role came up was because
SaaS tool started emerging in the early2010s and every part of the customer
journey, the execution could be improved.
So you could have really goodcustomer support, really good sales,
really good marketing, but nobodywas managing the end-to-end customer
(36:23):
journey and revenue across it.
And that lack of coordination wheneverybody's executing, the coordination
breaks down, the accountability breaksdown, the governance breaks down.
That's where the new jobs emerge.
And that's why, rev ops, even though it'saway from the customer, it's a backend
role, but it's the most powerful rolewhen it comes to serving the customer.
So I'll just leave those two examples.
(36:44):
Think about scarcity.
Risk and coordination failures,that's where your value is.
And of course, leaning intothe forgetting curve as well
as the learning curve as well.
It's been an absolutepleasure talking to you.
For people who wanna find you, I am gonnalink extensively to all those articles
and indeed your substack in the books.
Where's the best place to find you?
Sanjeet.
I think substack is the right place.
(37:05):
So you have, the substack iscalled platforms do substack.com.
, I write on it almost every week.
And, now that the new book is out,a lot of my posts are follow ups to
how to apply some of those ideas.
And you can look for reshuffle on Amazon.
You can look at my website,platform thinking labs.com.
These are three different places.
And for people who are interestedin reshuffle, I'm gonna cover it
(37:26):
in extensively in the new year withSanjeet, one of the big problems
for the show, for people thatkeep sending me recommendations
of people to have on the show.
The show is booked out for fouryears, so it's difficult to find when
a new book comes along like this.
And I really want to cover, it'sdifficult to find an opportunity to do so.
And indeed, I'm gonna find spacein the new year to cover Reshuffle
'cause it's an essential book forthis massive shift we're seeing
(37:49):
on every level of society as well.
It's been a pleasure talking to yourauthor of the platform Revolution
and reshuffle, like I said.
Sanjit.
Paul Chaudry, thank you for joining us.
Thank you, Aiden.
It's been a pleasure.
Thanks as always to our sponsor, Kyndryl.
You can find Kyndryl andthe Kyndryl Institute at
(38:14):
where you can find a plethora of articlesand content about the future of work,
the future of technology, the futureof payments, and so much more written
by many guests from the Innovation showand many other experts in their fields.
You can find all that atww.Kyndryl.com/institute.
Before we start today's episode, I wantto thank our sponsor, Kyndryl, and indeed,
the Kyndryl Institute, where you can findarticles on ai, the future of work, the
future of payments, and so, so much more.
You can find Kyndryl atww.Kyndryl.com/institute.
To a man with a hammer,Everything looks like a nail.
(00:20):
That's a quote often attributedto Mark Twain, and it captures
why incumbents so often misreadthe impact of new technology.
Take Adobe, for example, it isrightly loaded as a great innovator,
even a pioneer for moving to thecloud, well before most others did.
Yet, while it changed its businessmodel, it didn't reshuffle its mindset,
(00:41):
it carried over the architecturefrom the desktop era to the cloud.
By contrast, a more mentallynimble startup with no
baggage, no mental baggage.
Figma reimagined design itself.
It shifted the unit of work from the fileto the element reshaping, collaboration,
coordination, and ultimately thestructure of the entire industry.
(01:02):
This is the bigger lesson.
Incumbents often mistake for adaptation.
in the age of Bolting new technologyonto old architectures won't be enough.
The winners will be those whorebuild or reshuffle their
mindset around AI makes possible.
Our guest today has spent hiscareer unpacking these shifts.
(01:24):
He is the bestselling authorof Platform Revolution.
I have it here beside me, a book thatI've had a long time on my shelf.
He's been on my list for a long time.
Also, he's written platform scale andmost recently his book Reshuffle Who Wins
when AI Restocks the Knowledge Economy.
Sanjeet Paul Choudary,welcome to the show.
(01:46):
Thank you, Aiden.
Absolute pleasure to be here.
we're gonna talk particularly about thatamazing case of Figma, and particularly
Figma versus Adobe, which I find sofascinating that Adobe, I've written
about them, you've written about them.
They were great innovators and they wereinnovators in adopting a new technology.
we see so often, they fail tothen to make the shift to the new
(02:09):
technology with a new one or areshuffling of the landscape comes in.
But before we introduce the players,Figma and Adobe in particular, let's
start with the framing of the challengethat I hope I set up in the introduction
startups or new entrant beat incumbents,not because of a new technology or
even speed, but it's this mindset.
(02:29):
It's there.
Adaptation rather than adoptionthat they failed with so often.
That's absolutely right.
When we move to a new technology,when a new technology comes in.
There are fundamentally new architecturalproperties of the new technology.
So the way desktop software usedto work is very different from the
(02:50):
way cloud hosted software works.
And the way traditional softwareworks is very different from the
way an AI first solution would work.
And we use these terms cloud first,AI first, very, very loosely.
But behind this, there is a fundamentalchange in the nature of the solution
architecture that you can create.
(03:11):
A very simple example, if I takeyou know, the shift from desktop
to cloud was that you could see theshift as purely a shift in channel.
You could see the shift as purely ashift in how you deliver your solution
or how you monetize your solution.
Instead of selling one-timelicenses, you go to subscription.
Now that's very much adopting the cloud.
But not really re-architectingyour business around what
(03:34):
the cloud makes possible.
And I think that's, one of the keyissues that we see when we look
at how Adobe moved to the cloudand how Figma moved to the cloud.
Figma rearchitected its business aroundthe architectural properties of the cloud.
So I'll pause there and I'm happyto go deeper into that, but it's
really understanding that the newtechnology offers you fundamentally
(03:55):
new ways to rearchitect the logicof how a certain industry works.
We've seen that with the cloud.
We see, we are seeing that with aiand that's really the opportunity
whenever technology shifts happen.
Of the places I saw this firsthand,Sanjeet was in digital when, media
companies moved from analog to digitaland they tried to cram or force the
(04:17):
old way onto the new architectureand again, failed spectacularly
and failing to adopt that mindset.
Before we started, I wanted to help peoplesee their business or indeed their career
through the lens of this case study.
And in reshuffle, you explainedthe transformation effects of
technology and how they play outacross the entire system of work.
(04:39):
And I was gonna share a figurefrom the book and indeed from your
substack that I'll also put in theshow notes, which we'll cover in
depth in due course, like I said.
But I wanted to share this becausepeople will see this will affect them.
And in the book and in your substack,you share how a legal firm's system
of work has to change becauseof the effect of new technology.
(05:00):
So be ideal for people who watch usto have a look at this on YouTube.
If you're listening, I'll share thisin the show notes because you have to
really see this to take you through it.
But let's have a bit of empathy,Sangeet, for those people who
are just listening to us as well.
I'll share this on the screen now.
So the idea of this framework is thatwhen we think about a new technology,
(05:21):
we very often think about its effectonly at the level of individual
tasks, but actually the effects ofthe technology play out at the level
of tasks which sit inside workflows.
And so workflows get reorientedworkflows set inside organizations.
So organizational systems how weorganize has to change in response.
And finally, all of thisactually gets shaped.
(05:43):
By the fact that organizationscompete within a certain logic, and
if the technology changes that logic.
Organizations have to change, notbecause the tasks are changing,
but because the fundamental logicof competition itself is changing.
So take a simple example of alegal firm today with AI coming in.
(06:04):
One of the key things that we see withAI coming in is that what used to be
scarce traditionally the access to skills,the ability to apply knowledge, work to
solve specific problems, a lot of that.
Application oriented, synthesizedknowledge is now available in a more
commoditized fashion through AI models.
(06:25):
So you don't need to invest allof those resources and skilling
and training and hiring people.
You can access that with a set ofwell-crafted prompts, if you will.
I don't wanna dumb it down toomuch, but the point is that.
What used to be scarce in the past is nowavailable without the associated scarcity.
So what that essentially meansis that the logic of competition
(06:46):
changes because competition isstructured around what is scarce.
And so.
You need to look for howthe system changes when this
logic of competition changes.
D does work move away fromservices companies to in-house.
Does it lead to the creation offundamentally new, more entrepreneurial
companies that subsidize whattraditionally lawyers used to charge
(07:08):
for and monetize somewhere else?
So you need to think abouthow the logic of competition
is changing across the board.
And in response to that, think aboutwhat that means for your organization.
What kinds of skills, what kinds ofcapabilities need are still needed
to compete in this new landscape.
And that then changes how you organizetasks and workflows internally.
And so the key point that I'm tryingto make over here is that whenever
(07:31):
there's a technological shift.
Start outside in.
Start by looking at what's changing interms of the nature of the competition.
so one of the reasons Iwanted to share That was that.
So much of the work we dotoday is knowledge work.
It's a knowledge economy, and youtell us in both your article and in
reshuffle that AI unbundle today'shuman dominant knowledge workflows
(07:52):
into tasks which can then be recombinedinto fundamentally new workflows.
And this is useful for the Figma casewhen we get there, but this changes
how work is structured itself, and howworkflows and organizations are organized
and how people and companies compete.
I thought that was just important forpeople who are genuinely concerned
(08:13):
and rightly so about their careers.
And indeed, as you say,careers will change.
They won't all be wiped out,but salaries will drastically
change because of this change.
And I, really feel that this is animportant shift that people need
to prepare for and , we'll go downthat rabbit hole in the future
episode when we get into reshuffle.
(08:33):
But again, it's useful before weget into the Figma case, because
you can see these shifts exactly.
In a business like Figma,versus Adobe in the same way
you look at your career today.
that's absolutely right.
So if I elaborate this in terms of howthis played out with Figma and Adobe
and that's very relevant in terms ofwhat's happening with AI today as well.
(08:55):
When adobe moved to the cloud.
Adobe was traditionally structured aroundthe logic of the file, so every project
was structured around a design file andAdobe's primary customer was the designer.
If something had to be done withthe rest of the team, the entire
file had to be moved across.
What Adobe did when it moved to the cloudwas it changed the delivery mechanism.
(09:18):
It made it easy to move the file around.
It it changed its revenue model,but fundamentally it was still
structured on the file dominant logic.
Now, the reason this is importantis because the cloud allowed a
fundamentally new architecture andAdobe was not able to adopt it.
Figma instead adopted it because it.
Did not have the architectural debtthat Adobe had already incurred.
(09:41):
It could reimagine designwork around the cloud.
And design work essentially is structuredaround how different elements of the
design file are arranged and organizedwith each other, and so Figma.
Architected itself around the elementas the atomic unit, A button align
a specific portion of that overallfile which served now as the atomic
(10:03):
unit around which work was organized.
Now, the reason this is importantis that in large companies you
don't just, create designs.
Those designs are partof larger workflows.
There are engineers, there aremarketers, there are other players
who need to work across all of this.
And there are governanceconsiderations that are important.
(10:25):
So for example, you might want a button tolook consistent across the entire company.
And so what this allowed Figma to do isit allowed Figma to set up enterprise
wide shared libraries of elementswhich could be reused across files.
So the first was the ability tostandardize what design looked
like enterprise wide, and that'sreally one of the key things that
(10:47):
Adobe would find very difficult toreplicate as long as it stayed stuck
in the file centric architecture.
The second thing that itallowed was permissioning and
governance at the element level.
So if you needed a certain elementto be consistent, but you could have
more flexibility on other elements.
You could tie governancenow at the element level.
So in general, Figma did not justmove to a new model by simply enabling
(11:13):
collaboration as is often seen.
Figma, what it really did was it enabledenterprise-wide governance and reusability
that was simply not possible with thetraditional file-based architecture.
So that's really the key youknow, shift that Figma achieved.
And because Adobe was structured aroundthe file, even though it moved to the
cloud, it adopted the cloud, it did notreimagine what design would look like
(11:39):
around the properties of the cloud.
And we see the same thing happen withAI today because, as you mentioned
one of the key things I talk about.
Is how AI allows a similarunbundling of knowledge work.
Figma unbundled the file into its elementsbecause the cloud allowed that AI today
unbundle a traditionally tacit knowledgebased work which could not be codified and
(12:02):
hence could not be broken out into steps.
It can now unbundle that Becausespecific forms of knowledge work
can now be achieved through models.
And so that allows us to thenfundamentally recombine and reorient new
ways in which workflows can be designed.
And that's the opportunity that's reallythere to create AI first companies today.
I just wanted to draw an analogythat you make also in the
(12:24):
article, which is the shift in.
For example, an album when we used tohave to buy an album, a physical album,
and this is very analogous to the Adobeshift that then that existed in the cloud.
I could buy just for example, a song fromthat album, which for some people they
believe the whole album is important,but for a lot of people, they just listen
(12:46):
to one or two songs from the album andmaybe bought the album because they had
to and didn't listen to the rest of it.
And that explosion or that unbundlingof the album as the entire file down to
the song and I could purchase that song.
Drove down the price in a huge way.
But it changed the ecosystemand then gave way to things like
Napster, for example, first, butthen iTunes and Spotify, et cetera.
(13:10):
Let's just draw that analogybecause I think that's really
helpful for people who are not.
I use Figma, I understand Figma, butmany people won't understand that
in a way when you use something likeAdobe, even in the cloud, you had to
have certain software to do, so youhad to understand how files worked.
You had to use Photoshop, forexample, which was a big massive
(13:32):
file, which meant your computer hadto be able to handle that as well.
So there was a whole ecosystemshift here that's quite important.
And once you get this, you'll understandwhy the shift was so dramatic.
there's a whole range of factors thatprevent the incumbent from moving over
to the new ecosystem architecture.
And it's not because the incumbentis slow or is risk averse.
(13:52):
All of those might be issues, buteven if the incumbent does not have
those cultural challenges, it's justarchitecturally prohibitive for the
incumbent to move in this direction.
So some of the points that youmentioned, which I talk about in the
article, the fact that this wholefile-based architecture was what
(14:13):
Adobe was centered around it assumedsignificant computing resources.
And that worked very well whenyou worked on a desktop model.
But what it prevented youfrom doing was having multiple
players across the workflow.
All be able to work on the samefile because all of them would
(14:34):
need to have similar computingresources to, to load those files.
The files or the interface itself was verymuch structured for design that is for
execution rather than for collaborationand for co-authoring, co execution.
And so moving that was another challenge.
To some extent, Adobe sorted.
The interface piece of it.
(14:55):
But that's again, something we see thatyou could, you know, make a similar
interface, but if the underlyingarchitecture is fundamentally
different, you can't achieve what'spossible with the new architecture.
There were other less obviousissues in the sense that.
When Adobe opens up its APIs and allowsother partners to integrate with the file,
it can do that at the level of a file.
(15:16):
So you can move files around, youcan change permissions, you can make
edits all at the level of a file.
Figma can open up APIs at the level ofan element, so you can have an external
tool make specific changes to a specificelement or make specific changes to the
governance of an element and the sharedlibrary all of which is not possible
(15:37):
with the Adobe based architecture.
So.
There are, as you mentioned, manydifferent shifts because of which
you simply cannot port your oldecosystem onto a new ecosystem.
You may actually have the same partners.
Adobe and Figma might havethe same partners, but the way
those partners work with you isfundamentally different because of
this difference in architecture.
One of the key shifts was theshift in the organization itself,
(16:00):
and I felt this was so importantfor people who work in innovation.
A lot of the listeners of thisshow, Sanjit work in innovation.
They're innovation workers,but their budgets lie with them
rather than at a governance level.
And that's a really key term.
And you write in the article in executionLED tools, software is paid for.
(16:20):
By the teams doing the work.
Designers, engineers, and marketers, forexample, for Figma, because the tools
help them complete specific tasks faster.
But in governance led systems value comesfrom managing consistency, control, and
coordination across the organization.
budget often shifts upward because thetool becomes strategic infrastructure
(16:41):
rather than just a productivity aid.
That is such a key point, and oftentimesthe success and actually death of a
project can lie there as well because nowit's front screen, they can see actually
where how much is being spent as well.
So it can have positives and negatives.
That is absolutely right.
I believe that the technologicalshifts towards cloud and now to AI
(17:04):
actually help us move more in thisdirection away from execution oriented
thinking and enterprises to moreof governance oriented thinking.
And you see this you know,with the Figma example.
In general, you see thisin many other industries.
A parallel would be just lookingat Autodesk and what's happening in
the construction industry because aconstruction workflow is even more
(17:26):
complex than a design workflow.
You have design, you haveplanning materials, and then
you have actual construction.
And Autodesk has been reallygood traditionally at.
Like Adobe creatingexecution based software.
But if you look at what Autodesk is doingnow, it's investing entirely in creating
governance layers across its software.
So what Autodesk Construction Cloud doesis it's really trying to do what Figma did
(17:50):
in design and create end-to-end governancelayers across all of its software.
And I, I believe that'sa, a secular shift that.
Is happening across industries becauseboth the fact that the cloud modularizes
computing capabilities, it unbundle,compute, storage, et cetera, from
infrastructure and ai, unbundle knowledgework from the underlying skills.
(18:14):
And because of that, all ofthese can be recombined into
fundamentally new workflows.
That's why governancebecomes so important.
There's three major shiftsyou talk about here.
We've talked about the first one, whichis work, and in the Adobe case, the shift
that Figma made to the cloud, but alsothe element based architecture broke
Adobe's control over the unit of work.
(18:35):
The second was organization wherethe value shifted from power shifted
power from execution to governance.
And the third is industry.
And this is where it eroded.
Adobe's closed loop power.
the industry structure.
I'd love to just lean into that alittle bit because again, if people
understand this as a pattern, notparticularly a case study, but a pattern
(18:56):
that you cover in your books and coverin particularly in reshuffle, they can
understand how this is gonna affect them.
Yeah.
And, and I think you know, toyour point, exactly the third
point is the least understood, butit's actually the most important.
And that's what I talk about when Isay the rules of competition change.
So if you look at Adobe or you lookat Autodesk, both of them have sort of
(19:18):
built quote unquote kingdoms around theirfile-based traditional execution software.
And so even though they realizethe logic of governance is
becoming important, they cannot.
Abandon their traditional softwarekingdoms, and so they try to lock in
governance with their existing products.
(19:39):
What we instead see startups do isbecause they don't have existing
execution oriented products, they canreimagine a governance first solution.
So when you imagine a governance firstsolution, you kind of break this lock in.
So if you look at Adobe or Autodesk,both of them work on the model of.
Works with Adobe or works with Autodesk.
So they create an ecosystem, butit's, it's more of tools that
(20:02):
work best with Adobe or Autodesk.
It's not a fundamentally open loopecosystem where no matter where your
work gets done, we'll still govern it.
But the opportunity in, in bothof these workflows is really
to create these fundamentallynew governance layer startups.
And we are seeing thathappening in both industries.
Figma is clearly a winner.
Canva is another example.
(20:23):
But even in the construction side,we have companies like build outs
et cetera, coming in, which are tooagnostic and creating tool agnostic
governance layers, so that to yourpoint, the shift happens at every level.
You don't just serve execution at thetask level you serve every stakeholder
in the workflow and you govern across it.
(20:44):
And hence that changes how youthink about things organizationally.
But most importantly, it changes howclosed or open the ecosystem can be.
Because for governance orientedworkflows to work well, you need a
fundamentally more open ecosystem.
You work with all theseFortune 500 companies.
Of the things is.
It's not just about learning,but it's about unlearning.
(21:05):
So they found a way that works.
It's that way has been successful.
The more successful it is, theharder it is to let go of that way.
That works, hence the case of Adobe,and that's a cycle that's persistent.
And what I find so fascinating, whyI do the show genuinely, is because.
These are templates of disruptionthat you see time and time again.
(21:28):
And when you cover enough of them, youjust see the pattern, the patterns there.
And again, another quoteattributed to Twain is history
doesn't repeat, but it rhymes.
Right.
that's what we see time and time again.
I'm sure your sick telling companiesabout this, but it's not just
about the learning curve, butit's about the forgetting curve.
(21:49):
I think the forgetting curve,you absolutely nailed it.
That's more important than evertoday, and it's going to become
progressively more important.
And I write in about this in reshufflein terms of why that's happening.
The reason this idea ofunlearning is so important.
Is because in the industrial age,especially the post-war age from
1950 to say the early two thousands,most companies largely struggled with
(22:13):
what we call operational uncertainty.
So demand goes up and down thecyclicality their supply chain shocks,
these are all operational, uncertain.
There are uncertaintieswithin a fixed structure.
Today, what most companies strugglewith is structural uncertainty.
The structure ofindustries is up for grabs.
Your competitors do not look thesame as they did in the past.
(22:35):
Industry boundaries blur yourthe vectors on which products
compete change all the time.
What used to be monetized cansuddenly be subsidized, Google Maps
doing what it did to Tom, Tom orInstagram doing what it did to Kodak.
And so the real point over here is thatwe are in a world especially over the last
(22:57):
20 years where structural uncertainty.
Is accelerating.
And the dominant way to respondto operational uncertainty was
to just create better buffers.
So if your supply chainbreaks down, create a buffer.
If demand is cyclical, create a buffer.
So there were ways to respondto operational uncertainty,
which would smoothen it down.
Structural uncertainty.
(23:18):
You can't respond toit by creating buffers.
You have to unlearn whatused to work in the past.
You have to start again.
What's the atomic unit of what's changing?
We talked about the atomic unit of work,but really what are the fundamental
parameters of what's changing?
You need to really think about thatat that level and then reimagine
what value and you know, a powerfulposition in your industry would
(23:40):
look like given these new realities.
So.
In general for executives, I would saythat it's really important to ensure
that you're not responding to structuraluncertainty with operational tools.
We've seen this repeatedly happen.
Yahoo is a classic example.
The structure of the internet was movingfrom pages that were difficult to produce
(24:01):
to anybody can produce pages, and soeditorial curation could no longer work.
Algorithmic curation had to work.
So Yahoo invested heavily in Agile.
It pioneered scrum in many ways.
And so a lot of operational toolsthat create operational agility
were there at Yahoo, but they wereconfronted with structural uncertainty.
So you can't address structuraluncertainty with operational tools.
(24:24):
You have to unlearn.
You have to figure out what'sfundamentally changing, and then you have
to really reimagine what your businesslooks like with that new reality.
on a business level so I'm a leaderof an organization, or maybe I'm
somebody who has been tasked withchanging the future of the organization.
(24:45):
Oftentimes, as you say, the first thingthey'll do is lean into technology.
maybe get people tooledup in ai, for example.
At the moment, let's geteverybody on an AI course.
One of the things I get fromunderstanding your work and indeed
reading the books that I do is itstarts first with a mental shift, an
agility, a mental agility shift in theorganization, and a willingness to learn.
(25:09):
I, and I think that I'd love you toshare your thoughts on that people
get stuck because they're experts.
And that expertise trap leads themthem down the path that we've seen, for
example, here with Adobe versus Figma.
Yeah, that's absolutely right becausewhen, when you are confronted with
what I've called here, structuraluncertainty expertise is actually a trap
(25:31):
because when the structure is changing,the rules of the game are changing.
And so you cannot bestuck being an expert.
You could.
Survive.
I mean, there are companies thathave survived over time, but they
no longer command the premium thatthey do because they are still
stuck being experts in an old frame.
And so expertise that is resistant tolearning is actually a liability in
(25:53):
a period of structural uncertainty.
We, you know, we, we, we see this needfor, i, I, I would say that we need
to advocate this need for unlearning,relearning at both ends of the spectrum.
So there are a lot ofcompanies that say, you know.
Let's focus on the customer.
Let's follow the customer.
(26:14):
Let's understand what theirneeds are, and we can never fail.
We'll figure it out.
And that's part of it.
I think you can absolutely nevermove your eye away from the customer.
You need to be very clear aboutwhat problem you're trying to solve
and how you go about solving it.
But at the same time, you also needto really rethink what tools and
capabilities are available to you.
In order to solve those problems,and you need to have that supply side
(26:37):
lens that everything we know about ourindustry could fundamentally change
tomorrow, or is changing at the moment.
So you can't simply layer on newtechnologies onto old ways of
thinking about your capabilities.
You have to reimagine what capabilitieswhat new capabilities are made available
because of a, a technological shift.
, Very much what Figma did withseeing the cloud and seeing what
(27:00):
was possible and hence what previousconstraints could be the least.
You no longer needed to have highlypowerful computers to run the software.
You could run the software onthe cloud, and that fundamentally
changed who could be your customer.
And so you really need to bring thatunlearning to capability, this swell
and not dismiss what's possiblewith the shift that's happening.
(27:20):
On a different levelthan on a personal level.
So organizations we're seeing this wherethere's either a freeze on new hires,
so lower level hires because of ai,people are now using AI instead of that.
Or there's a letting goof people in favor of ai.
But in doing so, one of the fearsI would have is that you're not
(27:41):
bringing in that new mindset.
And I was thinking about howFigma it's not even that.
Somebody from Adobe left andbuilt Figma because they wouldn't
listen to them inside Adobe.
Kind of like we saw withZoom and Cisco, with Eric
Yuan
Right.
many times case studies of that wherethe frustrated engineer or the frustrated
executive goes, okay, screw you guys.
(28:01):
I'm gonna do it myselfand builds a company.
But by not bringing in new blood.
You're not bringing in newmindsets and new capabilities.
Like I'm sure somebody in Figma seesthe world the way they've built the
world rather than the way Adobe hadbuilt the world because they come in
at a different level in time and indoing so, and then not hiring those
(28:23):
new blood or those new capabilities orthose new mindsets, you don't inject
the new DNA into an organization.
I'd love to you to sharesome thoughts on that.
I think that's certainly a challengeand it's not a challenge at, just
at the level of entry level folks,but it's a challenge across the
board when we are in an environment.
(28:44):
Where.
Your ability to solve customerproblems are no longer limited
to your traditional capabilities.
You could be getting capabilitiesfrom other industries today.
Insure insurers need to bereally good at understanding ai.
And if you think about traditionalinsurance, they may not have really
had access to those capabilities.
And, and similarly, if you're building acar today, you're no longer building it.
(29:07):
On the traditional gas based model, you,you need to understand self-driving.
You need to understand electrificationyou need to understand connected vehicles.
And so this convergence makes it allthe more important that you constantly
look for fresh blood who is notstuck within the assumptions of your
traditional industrial ways of working.
It, it requires that you.
(29:30):
Think about even internally structuringyour teams so that teams are not
specialist teams as much as they'reworking on subsystems, where multiple
spec specializations work together havetheir ideas bounced off, and get to
learn from each other and innovate at theintersection of, of those capabilities.
And so.
A lot of you know, innovation potentialreally lies not in eking out more
(29:55):
from what already you do well, butin really bringing the intersection
of vastly different capabilitiestogether and the skills behind it to,
to create something fundamentally new.
And so I absolutely agree withthe point that you need to.
Strategically think aboutwhat you do about your talent.
It's not AI replacing talent.
(30:15):
You need to think about AI toreplace your competitiveness.
You need to reimagineyour competitiveness.
And then given the new logic ofcompetition, think about what's the
right talent and the right capabilities.
As long as you keep thinkingabout AI versus talent, you're,
you're sort of you know, shufflingdeck chairs on the Titanic.
And on an individual level.
Then finally, so I hintinside of the descriptions.
(30:40):
AI won't necessarily get rid of yourcareer, but it will affect your salary.
That was one of the points that youmake and actually it's one, I think you
wrote this in one of your substack aswell, and I'll link to your substack.
I'll link to these individuals articlesas well as the books as well, Sanjeet.
But I'd love you to share on a highlevel what you mean by that and
how we can prepare for that shift.
(31:02):
Yeah, absolutely.
And this is a very central thesisof my book Reshuffle that today
we think about AI in terms ofautomation or and augmentation.
We think of AI as taking our jobor making us better at our job.
And exactly your point,AI won't take your job.
But someone using AI will is sortof couched in that same logic.
(31:23):
But what all of this assumes is thatthere are only two ways that the
effect of AI will play out and thatyour job will largely remain the same.
You either continue to do itor somebody else who learns the
tools better starts doing it.
What this fails to account for is thatthere are two fundamentally different
(31:44):
shifts that happen when AI comes in.
You learn to use AI and yet you mightpotentially lose out if you don't
understand how these two shifts happen.
The first is about your pricing power.
So your pricing power istraditionally being tied to certain
skills that you've acquired.
If those skills.
Get automated and replaced by machines.
(32:04):
That's a different issue.
We understand the problem with thatand we see that as a threat, but
even if you start using AI and youget better at doing what you do.
What ends up happening is you'll seea flattening of skills across the
industry because people with lesserskills will now be able to perform more.
Now that they have they'recomplemented by ai.
And there, there are tons ofresearch studies that have proven
(32:27):
this out with every generation oftechnology, but especially with ai,
that there's a flattening of skills.
And what that then does is thatit increases the supply base of
potential competitors to your job.
Who would do, who wouldbring the same skills?
So if you're really just relying onskills and relying on AI to get better
at delivering what you were doing,then you're getting into a place where
(32:48):
your skill premium will gradually getflattened because many others would
have access to the same thing as well.
The second thing is about agency,and we see this in the case of
the Uber driver, I call this.
A distinction between about thealgorithm and below the algorithm jobs.
So an Uber data scientist versusan Uber driver, an Uber data
scientist is very much a capitalistbecause he owns stock in Uber.
(33:09):
He builds algorithms that thenorchestrate how these drivers work.
But the drivers have absolutely no agency.
They can't choose you know,the nature of the ride.
They can't choose the pricing of it.
Even if they get a five star, thatdoes not allow them to charge more.
The pricing is fixed.
So in many ways, the agency isbrought down, and today we see this
(33:30):
with Uber and delivery workers.
Tomorrow we may see this with otherthings, nursing staff, caregivers.
We are seeing algorithmicscoring and coordination
happening increasingly in work.
So these are the twoeffects that the more.
You rely on technology, the more youcould open yourself to being commoditized,
the more you get commoditized, as Uberdrivers did when they started using Google
(33:50):
Maps, anybody could, navigate the city.
Now, the more you get commoditized, themore algorithms can start managing, , your
career trajectory and your outcomes.
And so you need to thinkabout value and agency.
, Which brings us to the question,, which is your main question.
How do you think aboutyour job in this scenario?
So.
You really need to, again, as I said,don't just focus on your job as it
(34:12):
is today and the tasks within it.
Those jobs make sense in today's system.
If the system changes, if they, ifthe nature of competition changes.
And hence, if organizations shift how theywant to structure skills and capabilities.
You'll have to think about what arescarce skills and capabilities in
this new system, and then think about.
Setting up or rebundlingyour job around that.
(34:35):
So just like, your point aboutalbums, when albums got unbundled
into, individual music files, youhad Napster, but then you, eventually
had Spotify where they were rebundledinto playlists and, , the rebundling.
Was about the new system because inthe new system, attention was scarce.
And so it was really a consumer-centricrebundling rather than a producer centric
(34:56):
or artist centric or label centric,, bundling that used to be in the past.
So think about rebundling your job,what is scarce in the new system?
Where does the new system break down?
What are the key risks, andwhat are the broken pieces that
need to be brought together?
So think about.
Where scarcities risk and coordination,emerge, or issues around these
(35:17):
emerge in the new system and rethinkwhat your job would look like.
And I'll just give, a couple of veryquick examples, to illustrate this.
One example is that ofthe anesthesiologist.
So an anesthesiologist todayuses a variety of machines.
And every task that he doescan be done by a machine.
But somebody has to be accountable for therisk in the operating room in real time to
(35:38):
ensure that whoever is, being administeredthat anesthesia is, administered
exactly the right dosage in real time.
And so the anesthesiologist is paidfor assuming the risk in that system.
Think about what new risks emerge inthe new system in which you are working.
That's the first example I'd say.
And a second example, you know, is just.
(36:00):
Think about, the example ofwhat is typically called a rev
ops role, revenue operations.
This role did not exist 15 years back, andthe reason this role came up was because
SaaS tool started emerging in the early2010s and every part of the customer
journey, the execution could be improved.
So you could have really goodcustomer support, really good sales,
really good marketing, but nobodywas managing the end-to-end customer
(36:23):
journey and revenue across it.
And that lack of coordination wheneverybody's executing, the coordination
breaks down, the accountability breaksdown, the governance breaks down.
That's where the new jobs emerge.
And that's why, rev ops, even though it'saway from the customer, it's a backend
role, but it's the most powerful rolewhen it comes to serving the customer.
So I'll just leave those two examples.
(36:44):
Think about scarcity.
Risk and coordination failures,that's where your value is.
And of course, leaning intothe forgetting curve as well
as the learning curve as well.
It's been an absolutepleasure talking to you.
For people who wanna find you, I am gonnalink extensively to all those articles
and indeed your substack in the books.
Where's the best place to find you?
Sanjeet.
I think substack is the right place.
(37:05):
So you have, the substack iscalled platforms do substack.com.
, I write on it almost every week.
And, now that the new book is out,a lot of my posts are follow ups to
how to apply some of those ideas.
And you can look for reshuffle on Amazon.
You can look at my website,platform thinking labs.com.
These are three different places.
And for people who are interestedin reshuffle, I'm gonna cover it
(37:26):
in extensively in the new year withSanjeet, one of the big problems
for the show, for people thatkeep sending me recommendations
of people to have on the show.
The show is booked out for fouryears, so it's difficult to find when
a new book comes along like this.
And I really want to cover, it'sdifficult to find an opportunity to do so.
And indeed, I'm gonna find spacein the new year to cover Reshuffle
'cause it's an essential book forthis massive shift we're seeing
(37:49):
on every level of society as well.
It's been a pleasure talking to yourauthor of the platform Revolution
and reshuffle, like I said.
Sanjit.
Paul Chaudry, thank you for joining us.
Thank you, Aiden.
It's been a pleasure.
Thanks as always to our sponsor, Kyndryl.
You can find Kyndryl andthe Kyndryl Institute at
(38:14):
where you can find a plethora of articlesand content about the future of work,
the future of technology, the futureof payments, and so much more written
by many guests from the Innovation showand many other experts in their fields.
You can find all that atww.Kyndryl.com/institute.
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