October 8, 2025 • 43 mins
What if the leadership frameworks we’ve relied on for decades are fundamentally broken for today’s world? In this thought-provoking conversation, Pascal sits down with Jeffrey Rogers—his collaborator of nearly a decade—to explore how leaders can navigate sustained uncertainty and systematic disruption.
Jeffrey, an expert in organizational learning and futures thinking, challenges the “Good to Great” era of one-size-fits-all leadership models. Instead, he advocates for meta-learning: the ability to learn how to learn, adapt frameworks contextually, and build organizations that can transform repeatedly. They dive deep into the tension between efficiency and experimentation, why the “middle horizon” (5-10 years) is so hard to envision, and how generative AI fits into organizational learning—spoiler: it’s not the efficiency tool you think it is.
Key Topics Covered:
- Why best practices are dead and what replaces them in high-uncertainty environments
- The efficiency vs. learning paradox: how to balance execution with experimentation
- Generative AI’s real value: rapid prototyping, not scaling (and why that matters)
- The futures cone and how to think about multiple possible futures
- Practical advice for middle managers when leadership won’t listen
- Building learning systems that connect across your organization
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:07):
Disruption. I think there is a peril in
using generative AI tools as a crutch for imagination, in part
because that's they're not dreaming.
(00:28):
They're not imagining, they are recombining things with that
probabilistic engine, right? And so if you had the idea that
you were going to use a generative AI tool to help you
imagine a new future, there's a decent chance that you're going
to get on amalgam of futures that are in the training data
(00:51):
set. And in fact, you might even be
getting the ones that are in some sense, like most probable
have the highest representation in the training data set.
And so there's a good chance that if you're using AI to
envision alternative futures, those futures are actually going
to be ones that are really well represented in historical data
about the future. Hey, friend.
(01:14):
Welcome back to another episode of Disrupt Disruption.
Today we're featuring a very special guest, one I should have
had on this podcast years ago, my dear friend and collaborator
Jeffrey Rogers. Now, he doesn't really need an
introduction, but just in case, Jeffrey is the principal
(01:34):
learning and facilitation here at Radical and he truly wants
you to get involved. Jeffrey believes the future is
too important to be left to the futurists and his grand
professional goal is to empower more people to think, act, and
live as well as lead like the future is theirs to create.
Jeffrey's passionate about lifelong learning and designing
(01:56):
unique learning experiences thatbuild knowledge while also
building community. He's been a top rated
facilitator and moderator of executive education programs in
international summits at Singularity University since
2017, and has also been featuredas a guest lecturer and workshop
leader at the UC Berkeley Haas School of Business, the
Hassoplatner Institute of Designat Stanford, and the Empowerment
(02:19):
Summit at ECAL in Lausanne. Now you're in for a treat.
Let's get this party started. Jeffrey, it's an honor and a
pleasure to have you finally on the podcast.
And I'm very sorry, I guess thatwe haven't done this before
because you and I work together for what, 7 year, eight years,
nine years now. It depends on how you count, but
(02:43):
regardless, I'm thrilled to be here and speaking with you in a
somewhat more formal capacity, although I suspect it will
probably not be terribly formal,but we'll see where it goes.
Indeed. And let me kick us off.
I'd be just very curious to hearfrom your perspective maybe over
the last year or so, thinking about leadership and the
(03:04):
questions you get from our clients and people you work
with. Where do you see is currently
the big heat? What are people concerned about
when it comes to leadership, theorganizational development, the
way they run their businesses intoday's environment?
So I don't know that I have a particular unique take on this,
(03:24):
but it might be interesting to see where do we find overlap
here and maybe where do we find some points of differentiation.
But I'll start with the things I'm pretty sure are common.
I'm certainly hearing people talk more about what does it
mean to lead for and design for systematic disruption and
(03:46):
sustained uncertainty, right? That we're not seeing periodic
blips of disruptive change, but instead navigating that as like
a baseline condition, uncertainty is high and perhaps
rising. And also maybe more pressure to
(04:07):
really think about what that looks like at the macro level
where you have big uncertainty in geopolitics and how that
spills into perhaps the labor market and how that also touches
on the regulation or lack of regulation of certain
technologies. There's just a whole lot of
that. And I think a lot of leaders are
(04:29):
questioning whether their organizations are built for it,
whether their teams are built for it, and even whether they
themselves are built for it as. Leaders talking about this last
point, the question of like, howdo we lead?
Are we even ready to lead in this world?
Do we have the tools and the frameworks?
So you've been doing this work equally as long as I've been
(04:51):
doing it, maybe even longer. And be curious to hear, have you
seen a difference in the types of tools and frameworks you
bring to your clients, our clients maybe five years ago,
7-8, nine years ago when we wereboth at Singularity University
versus what we do today or you do today?
(05:13):
Yes, definitely. And I don't think it's, I don't
think it's limited to tool A versus tool BI think it's a
little bit more of a critical attitude toward and I welcome
this a critical attitude towardsthe ability to generalize tool A
across a huge range of contexts.And I actually think this is
(05:37):
good. I think that this is adaptive
and healthy that people are a little bit more skeptical about
being able to take a best practice, a thing that worked
really well for one leader in one organization and one
industry. Some of these like hero stories
of a visionary leader at X company and now say, OK, I just
(05:58):
need to know what he did and I can do that here as well.
And I think there's much less ofa belief in that kind of thing.
And I would generalize that to maybe a little more skepticism
around the idea of best practices that are transferable
and instead thinking about emergent practice, developing
practices, things that are goingto be iterated through
(06:20):
experimentation and learning so that we actually get what is
appropriate to my context at this point in time and not
necessarily a belief that's going to be fixed and durable
and something that we'll be ableto continue to rely on going
forward. Allow me to double click on
this. I think there's a really
interesting point. So I absolutely agree.
(06:40):
I think it feels to me we have moved on from the world of where
we all read go to great. And then we are all basically
like for the next year and a half until the next book comes
out. We're all good to great
followers. We all saw the right Ted Talk,
right? We all watched Simon Sinek, and
this thing is going to be endlessly translatable, yeah.
So with that being said, what doyou do as a leader though today
(07:04):
because you still, I think you still need to find insights,
tools, frameworks, learnings. How do you think about those in
terms of applying them? If I had to boil it down to
something pretty simple, I wouldsay just an agile approach to
learning and leadership. And this is something you and I
(07:25):
have talked about quite a bit, but having a test and learn
approach to the implementation of whatever framework it is that
you're using. There are lots of great
frameworks, many of which have alot to offer and things that are
applicable. But I believe that the
environment that today's leadersare operating in characterized
(07:48):
as it is by high degree of uncertainty and complexity and
malleability, but it is going tochange quite a bit.
I think it's nice to have a robust tool set and not be too
dogmatic or doctrinaire about weare committed to using this
thing here. This is our framework.
And instead saying we might pickand choose and combine or
(08:13):
recombine, remixing some of these things and always with the
idea that we're going to be oriented toward outcomes and
results and revisit the process.Sounds much harder for the
leader to do than the old way ofdoing things which was read go
to great implement be done. Yeah.
(08:36):
And I think that's probably trueof leadership and steering an
organization in general that in the very near term, you still do
have a discreet task. You need to do this thing.
Well in this environment, this context for this market, this
consumer, there's lots of stuff that we can use that we know
helps us do those discreet tasks, fulfill those near term
(08:57):
objectives. But I do think that there's a
little bit more of a, I'm not going to say it's a different
challenge, but maybe a growing emphasis and awareness on the
importance of organizational learning.
And people have certainly been writing about this since the
90s, that the ultimate competitive advantage is
(09:19):
organizations that learn to learn quickly and can do it
again. That is a repeatable skill.
You could boil this down even tolearning transformation as a
repeatable skill. That would be the ultimate level
of this and being able to execute that again and again as
(09:41):
needed to maintain relevance over the long term.
So for someone who's listening, and I believe this fits nicely
into you talk a lot about meta learning, a term I started to
adapt after I I learned meta learning from you as a leader,
as a listener who's like currently hearing this, what
(10:01):
would be a very practical approach from your perspective
on how do you go about this? Because I think it sounds great.
And I do believe a lot of peoplejust look at this and they're
like, I don't know where to start.
Sure. The good news is that I think
probably a lot of organizations have learning practices embedded
(10:22):
in some of their different systems and their different
teams and their different business units and their
different functions. And I think the opportunity is
actually scaling those up acrossthe organization or weaving them
more deeply and systematically into the culture or actually
connecting these kind of isolated learning systems at a
higher level so that they learn to talk to each other.
(10:45):
There are there are a lot of methodologies that actually have
learning and learning goals at their core.
Agile, again, being one of them.Like that whole idea of we don't
know exactly how to get where we're going, but we're going to
learn our way in that direction and we're going to refine our
vision of the end state even as we learn.
That's great. But as you and I have discussed
(11:05):
before, that's something that often only exists within certain
teams, certain units, certain functions.
But recognizing the value of that kind of approach of a
learning oriented approach givesus an opportunity to think about
how do we how do we do somethinglike that in a different
environment. There are there are all sorts of
(11:26):
opportunities like that. There's the opportunity to
examine our KPIs and metrics andget away from just performance
indicators that are maybe oriented toward revenue growth,
what have you, And instead thinkabout learning metrics.
How many experiments are we running?
Do we actually have processes and incentives in place to
(11:48):
support and reward experimentation to drive
learning or not? And a lot of this is learning
how to learn more effectively. And there's plenty to be read
and reviewed about how to learn and what kinds of systems and
strategies actually promote learning.
(12:08):
It's a different thing to implement those and and scale
them. But a lot of the stuff is out
there, and some of it I think isalready fortunately embedded in
parts of the organization. And I think there's an
opportunity to find those brightspots and amplify them and then
start to connect them. In this context, how do you
reconcile the seemingly eternal conflict between learning feels
(12:35):
like you need to slow down a little bit versus the necessity
or perceived necessity to speed up?
As in, we talk a lot about core edge as like a duality.
It feels to me like learning andexecuting is also a little bit
of a duality. And how do you, as an
organization, which is under stress these days, how do you
(12:57):
balance this out? Has a big question that I don't
know that I have a definitive answer to.
If I did, I would probably be turning on the money faucet
instead of appearing on this podcast.
But I do have some ideas. I think we can also refine that
a little bit where the polarity or duality there is not just
learning an execution, it's learning an efficiency, right?
(13:19):
We don't do things efficiently that we're trying to learn.
When you're trying to learn to ride the bike, you're not going
to ride the bike very fast. When you're trying to learn to
do a new thing, you're going to be making a lot of mistakes.
That's how we learn. And that necessarily means that
it's going to be inefficient andit's also not going to be a
(13:40):
thing that you even would want to do at scale.
You don't want to be making lotsof mistakes at scale.
You scale it after you gain efficiency, but you're only
going to get there through the learning part.
So I think the challenge, which the flip side of that is the
opportunity is can we find a wayto continue to do the things we
need to do efficiently at scale,do those things well, while also
(14:04):
being open to the possibility that there is a different way or
a new thing that we need to learn and beginning to explore
that while we're still doing theother thing.
And a lot of that I think actually comes down to
leadership being able to take the Longview and to understand
(14:25):
that for everything there is a season, right?
And the the time of stability and making all the money is not
going to last forever. And I think most leaders in most
industries now are even ones who've been pretty stable for a
pretty long time. I feel like there was a growing
awareness that those times will not last indefinitely.
(14:46):
And so we can either be proactive about exploring the
new, the uncertain, or we can wait until we get pushed into
it. And I think one of those is
definitely going to be more adaptive.
In this context, how do you think about AI?
Do you specifically in the learning organizational learning
(15:08):
organizations as learning entities, do you see AI as a
boon, as a true helper, as an enabler?
I'm so tempted here when you sayhow do you think about AI to
say, I try not to, hey, I try tolet everybody else do it.
I feel like there's so many people thinking so many thoughts
about AI right now. I don't know if they even need
(15:29):
mine, but I might have somethingto offer here.
And it might just be this that Ibelieve that some of the AI
tools. And are you talking specifically
about generative AI right now like LLMS question?
My friend, yes, as we just had the this is inside baseball for
(15:51):
everybody who's listening, Jeffrey and I just had a longer
conversation about the fact thateverybody conflates AI into a
singular thing, which is generative AI, when in reality
AI is actually much more like machine learning and so on.
Yes. So let's talk about generative
AI because everybody loves to talk about it and we just
learned that what infrastructureinvestments for the next 5 years
going to be 2.9 trillion U.S. dollars or some crazy number?
(16:12):
Yes, let's talk generative AI. Yeah.
So I was hoping you would say exactly that and make that
clarification because I think it's relevant to what we're
talking about here. I think some of the other kinds
of AI tools and systems that we're not talking about here are
actually the ones that allow us to scale what we have learned if
(16:32):
that's where we build efficiencies now.
I believe that generative AI is not great for efficiency, in
part because of the challenge ofreliability, right?
But I think it can be really useful for rapid
experimentation, for exploration, that we can try a
(16:54):
lot of things. We we can throw a lot of ideas
at the wall and do a lot of prototyping.
In a sense, I think that generative AI can allow us to be
extremely agile as long as we'recareful to recognize the
limitations and don't try to scale the efficiencies there
(17:17):
because we have that reliabilityproblem and reproducibility
problem. So in my mind, we might be able
to use some of the generative AItools to explore the uncertain
and work towards they're the new.
And then once we have identifiedand learned the thing we need to
figure out, how do we build thatinto a reliable, scalable system
(17:39):
that is going to enable us to achieve the efficiencies at
scale that actually you can build your business around?
I have a question around this. You and I run a bunch of
workshops together. We just recently did one where
we openly admitted essentially for a particular exercise where
someone was supposed to or a team was challenged with
(18:02):
envisioning a. Future World.
In a particular scenario, to useAI, to use generative AI to
actually help them essentially build out this world created.
I personally struggle a little bit of this.
Like AJAI is amazing at this. At the same time, I'm always
asking myself, how much do you actually learn?
How much do you actually really build the muscle and stretch
(18:25):
yourself into, for example, futuristic thinking like?
World. Actual world.
Building versus just having the AI do the thing for you and then
have the word slop there, which might be very nice and might be
very accurate. I'm curious to hear like how do
you actually feel about that as the like the use of AI in the
(18:47):
learning in our learning journeys?
So there's a, there's an awful lot going on in there, and I
think it's worth unpacking and maybe disambiguating some of
these things 'cause there might be some interesting stuff at the
bottom if we do some digging. And I think that I think there
(19:10):
is a peril in using generative AI tools as a crutch for
imagination, in part because that's they're not dreaming,
they're not imagining, they are recombining things with that
(19:32):
probabilistic engine, right? And so if you had the idea that
you were going to use a generative AI tool to help you
imagine a new future, there's a decent chance that you're going
to get on amalgam of futures that are in the training data
set. And in fact, you might even be
getting the ones that are in some sense like most probable
(19:57):
have the highest representation in the training data set.
And so there's a good chance that if you're using AI to
envision. Alternative futures, those
futures are actually going to beones that are really well
represented in historical data about the future.
And I would question the utility, creativity, uniqueness
(20:18):
of historical representations ofthe future.
And one of the things that people talk a lot in futures
thinking and conversations about, like the futural
imagination, is that very often it is colonized by the past,
right? That just as we as humans very
often have a tendency to imaginefutures that look like the past
(20:42):
because of our past experience, right, That there's a strong
hold there. I think there's something that
is in some ways analogous, but maybe even more limiting if you
are using a system that not onlyhas a training data set that
kind of prescribes limitations, but also is working with this
probabilistic pattern recognition thing that dictates
(21:05):
the output to a certain extent. So I would argue that the better
utility perhaps for a generativeAI in helping to envision
futures would be maybe illustrating some of the futures
that you have imagined or might be, hey, I have a version of the
future now. I would like you to take this as
(21:27):
an input and give me 17 or 1700 modifications of this thing.
It's really good for that kind of thing.
I don't believe that it is as adept at coming up with
something that is genuinely weird or genuinely unpre.
It's not supposed to come up with things that are
(21:48):
unprecedented. That's not the design.
So I think that we adopt it for that kind of tool at our or for
that kind of purpose at our peril and recognizing what does
it do well? It's many slightly
differentiated versions of a thing that's already documented.
Awesome. What does it not do so well?
(22:09):
Come up with something totally new.
What does it not do so well? Come up with something that
actually needs to be accurate. What might it do?
Come up with something where accuracy doesn't matter, but we
want to come up with it quickly or we want to be able to
illustrate it, those types of things.
So I think it certainly has a role to play.
Interesting. Let me take a little bit of a
(22:29):
detour here because I'm very curious about something you very
well read, which I really appreciate about you.
We have lots of conversations about my mind wanders and I tend
to gravitate towards certain just ideas which I then start to
explore. So currently for me, this is the
whole notion around anti fragility.
So like revisiting a lot of Nassim Taleb's work and like
(22:50):
figuring out like, what does it actually mean in today's
environment? I'd be curious to hear.
And I think we actually never talked about this, where your
current Romney ruminations took you or take you.
Yeah, I probably also have some things that I come back to a
whole lot, and that my origin story is that I was supposed to
(23:12):
be a historian originally. That didn't totally work out.
I actually don't know if I've ever told you this, but this is
an interesting digression Duringmy wayward Graduate School days
where I was trying to figure outwhat on earth I was doing.
I didn't come up with a lot of good answers.
I at one point came to one of mypotential PhD advisors, and he
(23:32):
wanted to know what it was that I was going to be focusing on
when I got around to writing a dissertation.
And I said, I don't really know which was the honest answer, but
I have a thing I've been thinking about.
And at the time, what I was interested in was perhaps
getting into the history of conspiracy theories and
conspiratorial thinking in the United States.
(23:52):
And he was like, you wouldn't make an idea.
You can't do your dissertation. You can't do a PhD on that.
You don't do killing. Today it's such a great idea.
This was a long time ago, but inretrospect I might have had a
good hunch there. Oh yeah, talk about visionary,
my friend. But so some of the things that I
come back to a lot and this doesconnect to history.
(24:13):
I am very interested in contingency.
I am interested in the decisionsthat we made or did not make in
the past to get to the present, right, that this was once one of
many possible futures that we ultimately could wind up with.
And here we are. And we are here because we
(24:35):
didn't go down a bunch of other possible paths.
And in the same way, now, of course, we have a lot of
decisions that we might make that will ultimately shape
whatever future we do or don't wind up with.
And so with that, I continue to come back to some pretty similar
(24:59):
models and frameworks and writings and thinkers.
And I'm a huge fan of the futures cone.
I guess Joseph Boros is probablythe one who kind of popularized
the version that we see a lot. He always credits Herman Kahn
and Norbert Wiener for coming upwith kind of the predecessor of
(25:19):
that. And this is also one of the
things that I'm interested in iswhere do we get our where do we
get our ideas about the future, about what it might look like?
Where do we get our models for understanding how people think
about the future? Do we have the right models?
Are there better ones that wouldenable different kinds of
futures thinking? I come back periodically to some
(25:42):
of the stuff around complexity. I'm really interested in the
relationship between complex environments and the kind of
alignment that we can achieve through story and value of
narratives for holding things together in complexity and
uncertainty. And then all of that, of course,
ties back to how we think about the futures that we're trying to
(26:06):
enable or that we're trying to prevent some, those are probably
some of the kind of the stars and the constellation that I
keep finding my eyes drawn back to over and over again.
Interesting in this context, maybe I've got a good point on
this. What just came up for me was the
interesting challenge we have ingenerally in leadership
(26:30):
regardless in which domain between.
On one hand, we're very focused on the here and now and we have
developed tools to allow us to do this better in a, in a highly
uncertain environment, largely agility, right, trying things
out. We just talked about learning,
etcetera, which for me has a hasan interesting component, which
(26:53):
is you, it's a bit like walking through a dark room and you're
just like, like one step in front of the other.
You'll get very carefully, but you don't actually know where
you're going and you just like hope that you get to the right,
right exit. On the flip side of that, I
think you know, at least some people have this ability to
really see or envision a future long term, whatever it is.
(27:17):
The in between in our like threetime horizon model, that time
horizon 2 feels like extremely messy and it feels extremely
hard for people to envision. I find it fascinating is that,
you know, when you and I work with clients, they can all
envision, you know what whateverlike next quarter and next year
might look like. They can all envision what they
(27:37):
can dream about, what the far distance future look like self
like flying cars, like robots who are like doing all our
laundry and so on. What they really struggle with
is this weird in between, which kind of looks still a lot like
today, but is dramatically different like the 10 years.
And I'd be curious like from your perspective, how do you go
(28:00):
about this? How do you think about, how do
you get to people to think aboutthe in between the five 1015
year time frames? At first, I would be careful
with the time horizons, right? And this is a conversation you
and I have definitely had beforeand I know you agree with me on
(28:21):
this, which is why I don't mind pushing when we say 15 years, 20
years for the organization to bethinking 15 or 20 years out at
this point, I think is that's a virtual impossibility, right?
The organizations that are thinking 20 years out are
governments, churches, previously institutes of higher
learning. I think they're recognizing that
(28:42):
they can't do that anymore. They can't take that for
granted. They certainly want to have an
eye toward that long term future, but they also recognize
that they can't take things for granted in the way that they
used to. But for the typical firm, I
think that's an insane timeline.I think long term for the firm
(29:03):
is 5 to 10. We've even talked about our
mutual friend John Hagel with his Zoom out, Zoom in framework,
which I continue to find incredibly useful.
I actually think it's quite goodfor this sort of thing.
John, when he wrote about that initially, I think the time
horizon for the Zoom out was 10 to 20 years and that revisiting
(29:25):
that framework 10 or 15 years into its usage, he said the one
thing he would change is that hewould shrink that time horizon.
That said, in my mind, we can work on those different
timelines, but we have to be realistic with ourselves and
honest with ourselves and ultimately comfortable
(29:47):
recognizing which things we can know with certainty versus the
things that we can envision withclarity while recognizing
uncertainty, right? When we think about that long
term, there are things that we know that this is the macro
forces, the mega trends, stuff like that, like the aging up of
(30:09):
different demographics and probably deepening climate
crisis. These are things that they are
not going to change. We can see the trajectory.
It is slow in coming, but we know we'll get there.
There are other things that I think we could probably put in
that category as well. At this point, despite some of
the changes in economic support and incentives in the United
(30:30):
States, I think the energy transition globally, that is a
thing that is happening. We can see what the next wave of
manufacturing is going to look like globally.
We can imagine some of the change in industries and inputs
and the drivers of industries. I think that stuff you, you have
a pretty good idea on the 10 year timeline, what that looks
(30:51):
like. But as you start to bring it
closer to the organizational future at that time horizon,
there's going to be greater uncertainty.
There's going to be a lot of uncertainty.
And in my mind, just recognizingand differentiating between the
(31:12):
things that we actually can probably safely assume versus
the things that we're going a little bit more out on a limb.
And I'm just, I think, I think making that distinction and
understanding that is useful. And of course, the closer you're
getting to the present, the moreyou're able to operate with
(31:34):
higher probabilities that you can probably gauge more
effectively and the more that you can ultimately be working
with data even. But it's a little bit of a
different, almost like a different way of knowing about
the far future versus the near term.
How much do you? If I answered your question.
Yeah, yeah. Yeah, talking about stuff.
I'd be curious. Again, it's like another thing
(31:57):
we we talk about but never really actually discussed.
Like, how much do you think the individual, how much do you
believe in the individual and the organization's ability to
shape the future? I think it depends on the
organization. Fair enough.
And which scope of the of futurewe're talking about.
(32:19):
There certainly are companies that you and I have worked with
that have enough market share and capital and scale that they
can do a lot to shape the futureof their industry.
Now that doesn't mean that they get to completely write it.
(32:42):
They're probably working within the scope of the possible and
working with the probable influencing probabilities.
And this is another thing that we often talk with our clients
about. The utility of envisioning
futures, plural, gives you the opportunity to identify your
(33:04):
preferred futures, your futures that you really would like to
steer away from and ask what youcould do today to influence the
respective probabilities of those possible futures.
Can we innovate in the directionof one to make it more likely?
Are there things that shape thatfuture that we can directly play
(33:25):
in to increase its probability and that I absolutely believe
in? And you can point to companies
that quite clearly are shaping the future at different levels,
some even at the very macro level and some much more micro.
Maybe it's the future of the community that they're located
(33:45):
in or the population that they directly serve, their consumers,
that kind of thing. But it's almost always going to
be negotiated, right? You're not the only stakeholder
in the ecosystem. In this context, how do you
respond? I get this question quite a bit
where if we work with more middle management and we talk
(34:06):
about the future, we talk about shaping the future, seeing the
future, anticipating a future, all this stuff.
I very frequently get this question of, but what do I do if
my management doesn't see it? They don't empower me, they
don't do it. What is your response to that?
Is this my initial response always.
And then I'm like I'm trying to count.
You know everything about the incumbents challenges.
(34:29):
Now your opportunity, after you've tried to pitch them on
addressing those challenges, your opportunity is to leave and
to build the organization that actually does the thing that
they won't let you do. Yeah, fair enough.
That that's one answer. I'm not saying that's the only
answer, the best answer. I think other possible answers
that still hold some value. Again, thinking about your local
(34:52):
scope of the possible and the local probabilities that you can
play with. Are there things that you can do
to open the aperture of consideration for your more
senior leadership? And this is not like a managing
up thing. It's a maybe helping them see
(35:12):
the urgency or maybe helping them understand on or appreciate
an underappreciated driver of change.
This gets a little bit into the whole inside out versus outside
in thinking thing. And it is quite possible that
one of the middle managers that we have worked with adopts a
little bit more of an outside inapproach and is maybe thinking a
(35:36):
little bit differently, maybe intouch with a different set of
signals or indicators that suggest a new set of
opportunities or maybe a threat that is not really on the radar
of the higher levels of the organization.
Because it's coming from an areathat they don't have a lot of
exposure to, that they're not incentivized to pay attention to
(35:58):
because it doesn't feel like it's their domain that they are
very deeply immersed in. And I think there's an
opportunity even for someone who's perhaps not at the most
senior level of the organizationto start talking about that
thing, start telling a little bit of a different story.
And we can even consider this aslike a form of kind of narrative
(36:19):
prototyping where we start a conversation around this.
And sometimes conversations themselves have a virality or an
exponentiality where other people start talking about this.
And then it becomes a conversation within the
organization. And other people are able to see
here is a story about how our firm could access a new
(36:40):
opportunity for growth, lean into a potential future, or also
how it could succumb to something that it has heretofore
ignored or downplayed as a potential threat to an
established way of doing. And then let me ask you kind of
if it's starting to get to the, to the wrap up of our
(37:01):
conversation, which I really enjoy.
What is your advice and probablylike your own practice in
staying up to date. I there's another question.
I get a lot and my ADHD brain asI, as I read a lot of like very
small pieces of information and consume them.
I'd be curious to hear what do you recommend for people to I'm
(37:24):
interested in the future. I'm interested in navigating
uncertainty. Where do I start and how do I
keep edit in a sustainable fashion?
Hang out with Pascal Finnette I I say that not just as a
shameless promo for my host hereand friend, but also I really do
(37:45):
believe deeply in surrounding yourself with people who are
going to hip you to new possibilities, sources of
information, ideas, signals, trends that are not necessarily
on your own radar. And I think the world is
sufficiently complex and also endlessly interesting that if
(38:07):
you're limited to only the things that you can pursue in
your spare time and only the scope of your own curiosity,
you're missing a lot of good shit.
And so I try to surround myself with people honestly, I try to
surround myself with people thatare that I think are smarter
than me. I try to surround myself with
people that I always find interesting and that I feel like
(38:28):
I will learn from. We have so many opportunities to
do this kind of thing too. Like I recognize that I have
been very fortunate to kind of luck into the network that I
have. But at this point in time, for
all the horrible things that I can say about the Internet and
the information ecosystem that we're swimming in, you have
access to an unbelievable range of ideas and thinkers and
(38:52):
researchers and newsletters and podcasts.
And like you, I subscribe to a lot of stuff.
And you can, you can sample widely and refine and curate
your inputs. And to the extent that you're
(39:13):
engaged with them, your algorithms to a certain extent,
you can tune to actually serve your interests and build your
capabilities. And I believe that we have more
than enough resources to developall of the skills that we've
been talking about here. A lot of it is just
intentionality and time and trade-offs.
(39:35):
What are you going to give up sothat you can pursue this thing?
What are you going to stop doing?
What are you going to put on theback burner so that you make
time for the new and actually invest in your own learning and
regard your future self as a thing that you are prototyping
all the time? Last question I have for you,
looking for you, looking a little bit into the future and
(39:57):
just like it connects a little bit to what we just talked about
in terms of like where do you get your information from?
How do you think about this? What are things, what are like
signals, trends, emerging, things you're seeing you're
currently keeping an eye on? As in what are you fascinated
by? Which is like early in its
infancy? I don't know that these things
(40:20):
are in their infancy, and I would neither claim to be
particularly attuned to nor say that you necessarily have to be
identifying the weakest signals.But some of the things that I'm
particularly interested in termsof how I think they're going to
shape my work and life and probably the kind of work that
(40:41):
we're doing in the years ahead actually are the kind of points
of interface between the organization, the organizational
leader and some of these macro uncertainties that we're talking
about. I believe that we might be
hitting a point in the development of generative AI and
(41:05):
LLMS where people start to question a little bit more of
the AGI by 202520272029 narrative and start to say, hey,
that's fine for the big AI companies and the Nvidia's to
keep pushing that timeline back and back.
(41:27):
But for organizations who are thinking about this stuff as
part of their operational tool set and making workforce
decisions and personnel choices and strategic choices based on
the timeline, they're going to have to get more critical about
it. And I think they're going to be
asking increasingly, hey, if this isn't going to get to a
(41:49):
level of superhuman, super reliable perfection where I can
cut my entire workforce and justrun the damn thing myself, how
do I actually, how do I actuallymake the most of what we have
where it perhaps stays messy andin that Gray area where
(42:10):
capability far outstrips reliability and we don't close
that gap. And the jagged frontier advances
more slowly and also has some things that it seems to just
have as pockets that are going to exist forever that are not
predictable and reliable. And I think they're going to be
some really interesting questions for organizational
leaders and organizational learning that they're going to
(42:31):
have to answer about that. I also think they're going to be
a lot of interesting questions about how do organizations and
leaders and communities and institutions navigate the, I
think probably prolonged uncertainty and even swinging
and oscillation of the politicalenvironment, particularly the
(42:55):
United States, but I think plenty of that's going on
globally as well. Jeffrey, on that note, we could
I could spend literally as and we had these sessions, hours and
hours talking about this stuff with you in the interest of time
and like to make this not a verylong podcast like a Rogan show
style, but keep it somewhat compact.
(43:17):
Love the conversation, really enjoy the conversation.
So much fun to have you on the podcast finally.
And yeah, thank you so much for sharing your insights, being
here, working with me, being a friend of mine.
So yeah, it's great. It's been a, it's been a really
wonderful conversation. Thank you.
Pleasure was all mine man, thanks for having me.
I would love to continue it. Disruption.
Disruption. I think there is a peril in
using generative AI tools as a crutch for imagination, in part
because that's they're not dreaming.
(00:28):
They're not imagining, they are recombining things with that
probabilistic engine, right? And so if you had the idea that
you were going to use a generative AI tool to help you
imagine a new future, there's a decent chance that you're going
to get on amalgam of futures that are in the training data
(00:51):
set. And in fact, you might even be
getting the ones that are in some sense, like most probable
have the highest representation in the training data set.
And so there's a good chance that if you're using AI to
envision alternative futures, those futures are actually going
to be ones that are really well represented in historical data
about the future. Hey, friend.
(01:14):
Welcome back to another episode of Disrupt Disruption.
Today we're featuring a very special guest, one I should have
had on this podcast years ago, my dear friend and collaborator
Jeffrey Rogers. Now, he doesn't really need an
introduction, but just in case, Jeffrey is the principal
(01:34):
learning and facilitation here at Radical and he truly wants
you to get involved. Jeffrey believes the future is
too important to be left to the futurists and his grand
professional goal is to empower more people to think, act, and
live as well as lead like the future is theirs to create.
Jeffrey's passionate about lifelong learning and designing
(01:56):
unique learning experiences thatbuild knowledge while also
building community. He's been a top rated
facilitator and moderator of executive education programs in
international summits at Singularity University since
2017, and has also been featuredas a guest lecturer and workshop
leader at the UC Berkeley Haas School of Business, the
Hassoplatner Institute of Designat Stanford, and the Empowerment
(02:19):
Summit at ECAL in Lausanne. Now you're in for a treat.
Let's get this party started. Jeffrey, it's an honor and a
pleasure to have you finally on the podcast.
And I'm very sorry, I guess thatwe haven't done this before
because you and I work together for what, 7 year, eight years,
nine years now. It depends on how you count, but
(02:43):
regardless, I'm thrilled to be here and speaking with you in a
somewhat more formal capacity, although I suspect it will
probably not be terribly formal,but we'll see where it goes.
Indeed. And let me kick us off.
I'd be just very curious to hearfrom your perspective maybe over
the last year or so, thinking about leadership and the
(03:04):
questions you get from our clients and people you work
with. Where do you see is currently
the big heat? What are people concerned about
when it comes to leadership, theorganizational development, the
way they run their businesses intoday's environment?
So I don't know that I have a particular unique take on this,
(03:24):
but it might be interesting to see where do we find overlap
here and maybe where do we find some points of differentiation.
But I'll start with the things I'm pretty sure are common.
I'm certainly hearing people talk more about what does it
mean to lead for and design for systematic disruption and
(03:46):
sustained uncertainty, right? That we're not seeing periodic
blips of disruptive change, but instead navigating that as like
a baseline condition, uncertainty is high and perhaps
rising. And also maybe more pressure to
(04:07):
really think about what that looks like at the macro level
where you have big uncertainty in geopolitics and how that
spills into perhaps the labor market and how that also touches
on the regulation or lack of regulation of certain
technologies. There's just a whole lot of
that. And I think a lot of leaders are
(04:29):
questioning whether their organizations are built for it,
whether their teams are built for it, and even whether they
themselves are built for it as. Leaders talking about this last
point, the question of like, howdo we lead?
Are we even ready to lead in this world?
Do we have the tools and the frameworks?
So you've been doing this work equally as long as I've been
(04:51):
doing it, maybe even longer. And be curious to hear, have you
seen a difference in the types of tools and frameworks you
bring to your clients, our clients maybe five years ago,
7-8, nine years ago when we wereboth at Singularity University
versus what we do today or you do today?
(05:13):
Yes, definitely. And I don't think it's, I don't
think it's limited to tool A versus tool BI think it's a
little bit more of a critical attitude toward and I welcome
this a critical attitude towardsthe ability to generalize tool A
across a huge range of contexts.And I actually think this is
(05:37):
good. I think that this is adaptive
and healthy that people are a little bit more skeptical about
being able to take a best practice, a thing that worked
really well for one leader in one organization and one
industry. Some of these like hero stories
of a visionary leader at X company and now say, OK, I just
(05:58):
need to know what he did and I can do that here as well.
And I think there's much less ofa belief in that kind of thing.
And I would generalize that to maybe a little more skepticism
around the idea of best practices that are transferable
and instead thinking about emergent practice, developing
practices, things that are goingto be iterated through
(06:20):
experimentation and learning so that we actually get what is
appropriate to my context at this point in time and not
necessarily a belief that's going to be fixed and durable
and something that we'll be ableto continue to rely on going
forward. Allow me to double click on
this. I think there's a really
interesting point. So I absolutely agree.
(06:40):
I think it feels to me we have moved on from the world of where
we all read go to great. And then we are all basically
like for the next year and a half until the next book comes
out. We're all good to great
followers. We all saw the right Ted Talk,
right? We all watched Simon Sinek, and
this thing is going to be endlessly translatable, yeah.
So with that being said, what doyou do as a leader though today
(07:04):
because you still, I think you still need to find insights,
tools, frameworks, learnings. How do you think about those in
terms of applying them? If I had to boil it down to
something pretty simple, I wouldsay just an agile approach to
learning and leadership. And this is something you and I
(07:25):
have talked about quite a bit, but having a test and learn
approach to the implementation of whatever framework it is that
you're using. There are lots of great
frameworks, many of which have alot to offer and things that are
applicable. But I believe that the
environment that today's leadersare operating in characterized
(07:48):
as it is by high degree of uncertainty and complexity and
malleability, but it is going tochange quite a bit.
I think it's nice to have a robust tool set and not be too
dogmatic or doctrinaire about weare committed to using this
thing here. This is our framework.
And instead saying we might pickand choose and combine or
(08:13):
recombine, remixing some of these things and always with the
idea that we're going to be oriented toward outcomes and
results and revisit the process.Sounds much harder for the
leader to do than the old way ofdoing things which was read go
to great implement be done. Yeah.
(08:36):
And I think that's probably trueof leadership and steering an
organization in general that in the very near term, you still do
have a discreet task. You need to do this thing.
Well in this environment, this context for this market, this
consumer, there's lots of stuff that we can use that we know
helps us do those discreet tasks, fulfill those near term
(08:57):
objectives. But I do think that there's a
little bit more of a, I'm not going to say it's a different
challenge, but maybe a growing emphasis and awareness on the
importance of organizational learning.
And people have certainly been writing about this since the
90s, that the ultimate competitive advantage is
(09:19):
organizations that learn to learn quickly and can do it
again. That is a repeatable skill.
You could boil this down even tolearning transformation as a
repeatable skill. That would be the ultimate level
of this and being able to execute that again and again as
(09:41):
needed to maintain relevance over the long term.
So for someone who's listening, and I believe this fits nicely
into you talk a lot about meta learning, a term I started to
adapt after I I learned meta learning from you as a leader,
as a listener who's like currently hearing this, what
(10:01):
would be a very practical approach from your perspective
on how do you go about this? Because I think it sounds great.
And I do believe a lot of peoplejust look at this and they're
like, I don't know where to start.
Sure. The good news is that I think
probably a lot of organizations have learning practices embedded
(10:22):
in some of their different systems and their different
teams and their different business units and their
different functions. And I think the opportunity is
actually scaling those up acrossthe organization or weaving them
more deeply and systematically into the culture or actually
connecting these kind of isolated learning systems at a
higher level so that they learn to talk to each other.
(10:45):
There are there are a lot of methodologies that actually have
learning and learning goals at their core.
Agile, again, being one of them.Like that whole idea of we don't
know exactly how to get where we're going, but we're going to
learn our way in that direction and we're going to refine our
vision of the end state even as we learn.
That's great. But as you and I have discussed
(11:05):
before, that's something that often only exists within certain
teams, certain units, certain functions.
But recognizing the value of that kind of approach of a
learning oriented approach givesus an opportunity to think about
how do we how do we do somethinglike that in a different
environment. There are there are all sorts of
(11:26):
opportunities like that. There's the opportunity to
examine our KPIs and metrics andget away from just performance
indicators that are maybe oriented toward revenue growth,
what have you, And instead thinkabout learning metrics.
How many experiments are we running?
Do we actually have processes and incentives in place to
(11:48):
support and reward experimentation to drive
learning or not? And a lot of this is learning
how to learn more effectively. And there's plenty to be read
and reviewed about how to learn and what kinds of systems and
strategies actually promote learning.
(12:08):
It's a different thing to implement those and and scale
them. But a lot of the stuff is out
there, and some of it I think isalready fortunately embedded in
parts of the organization. And I think there's an
opportunity to find those brightspots and amplify them and then
start to connect them. In this context, how do you
reconcile the seemingly eternal conflict between learning feels
(12:35):
like you need to slow down a little bit versus the necessity
or perceived necessity to speed up?
As in, we talk a lot about core edge as like a duality.
It feels to me like learning andexecuting is also a little bit
of a duality. And how do you, as an
organization, which is under stress these days, how do you
(12:57):
balance this out? Has a big question that I don't
know that I have a definitive answer to.
If I did, I would probably be turning on the money faucet
instead of appearing on this podcast.
But I do have some ideas. I think we can also refine that
a little bit where the polarity or duality there is not just
learning an execution, it's learning an efficiency, right?
(13:19):
We don't do things efficiently that we're trying to learn.
When you're trying to learn to ride the bike, you're not going
to ride the bike very fast. When you're trying to learn to
do a new thing, you're going to be making a lot of mistakes.
That's how we learn. And that necessarily means that
it's going to be inefficient andit's also not going to be a
(13:40):
thing that you even would want to do at scale.
You don't want to be making lotsof mistakes at scale.
You scale it after you gain efficiency, but you're only
going to get there through the learning part.
So I think the challenge, which the flip side of that is the
opportunity is can we find a wayto continue to do the things we
need to do efficiently at scale,do those things well, while also
(14:04):
being open to the possibility that there is a different way or
a new thing that we need to learn and beginning to explore
that while we're still doing theother thing.
And a lot of that I think actually comes down to
leadership being able to take the Longview and to understand
(14:25):
that for everything there is a season, right?
And the the time of stability and making all the money is not
going to last forever. And I think most leaders in most
industries now are even ones who've been pretty stable for a
pretty long time. I feel like there was a growing
awareness that those times will not last indefinitely.
(14:46):
And so we can either be proactive about exploring the
new, the uncertain, or we can wait until we get pushed into
it. And I think one of those is
definitely going to be more adaptive.
In this context, how do you think about AI?
Do you specifically in the learning organizational learning
(15:08):
organizations as learning entities, do you see AI as a
boon, as a true helper, as an enabler?
I'm so tempted here when you sayhow do you think about AI to
say, I try not to, hey, I try tolet everybody else do it.
I feel like there's so many people thinking so many thoughts
about AI right now. I don't know if they even need
(15:29):
mine, but I might have somethingto offer here.
And it might just be this that Ibelieve that some of the AI
tools. And are you talking specifically
about generative AI right now like LLMS question?
My friend, yes, as we just had the this is inside baseball for
(15:51):
everybody who's listening, Jeffrey and I just had a longer
conversation about the fact thateverybody conflates AI into a
singular thing, which is generative AI, when in reality
AI is actually much more like machine learning and so on.
Yes. So let's talk about generative
AI because everybody loves to talk about it and we just
learned that what infrastructureinvestments for the next 5 years
going to be 2.9 trillion U.S. dollars or some crazy number?
(16:12):
Yes, let's talk generative AI. Yeah.
So I was hoping you would say exactly that and make that
clarification because I think it's relevant to what we're
talking about here. I think some of the other kinds
of AI tools and systems that we're not talking about here are
actually the ones that allow us to scale what we have learned if
(16:32):
that's where we build efficiencies now.
I believe that generative AI is not great for efficiency, in
part because of the challenge ofreliability, right?
But I think it can be really useful for rapid
experimentation, for exploration, that we can try a
(16:54):
lot of things. We we can throw a lot of ideas
at the wall and do a lot of prototyping.
In a sense, I think that generative AI can allow us to be
extremely agile as long as we'recareful to recognize the
limitations and don't try to scale the efficiencies there
(17:17):
because we have that reliabilityproblem and reproducibility
problem. So in my mind, we might be able
to use some of the generative AItools to explore the uncertain
and work towards they're the new.
And then once we have identifiedand learned the thing we need to
figure out, how do we build thatinto a reliable, scalable system
(17:39):
that is going to enable us to achieve the efficiencies at
scale that actually you can build your business around?
I have a question around this. You and I run a bunch of
workshops together. We just recently did one where
we openly admitted essentially for a particular exercise where
someone was supposed to or a team was challenged with
(18:02):
envisioning a. Future World.
In a particular scenario, to useAI, to use generative AI to
actually help them essentially build out this world created.
I personally struggle a little bit of this.
Like AJAI is amazing at this. At the same time, I'm always
asking myself, how much do you actually learn?
How much do you actually really build the muscle and stretch
(18:25):
yourself into, for example, futuristic thinking like?
World. Actual world.
Building versus just having the AI do the thing for you and then
have the word slop there, which might be very nice and might be
very accurate. I'm curious to hear like how do
you actually feel about that as the like the use of AI in the
(18:47):
learning in our learning journeys?
So there's a, there's an awful lot going on in there, and I
think it's worth unpacking and maybe disambiguating some of
these things 'cause there might be some interesting stuff at the
bottom if we do some digging. And I think that I think there
(19:10):
is a peril in using generative AI tools as a crutch for
imagination, in part because that's they're not dreaming,
they're not imagining, they are recombining things with that
(19:32):
probabilistic engine, right? And so if you had the idea that
you were going to use a generative AI tool to help you
imagine a new future, there's a decent chance that you're going
to get on amalgam of futures that are in the training data
set. And in fact, you might even be
getting the ones that are in some sense like most probable
(19:57):
have the highest representation in the training data set.
And so there's a good chance that if you're using AI to
envision. Alternative futures, those
futures are actually going to beones that are really well
represented in historical data about the future.
And I would question the utility, creativity, uniqueness
(20:18):
of historical representations ofthe future.
And one of the things that people talk a lot in futures
thinking and conversations about, like the futural
imagination, is that very often it is colonized by the past,
right? That just as we as humans very
often have a tendency to imaginefutures that look like the past
(20:42):
because of our past experience, right, That there's a strong
hold there. I think there's something that
is in some ways analogous, but maybe even more limiting if you
are using a system that not onlyhas a training data set that
kind of prescribes limitations, but also is working with this
probabilistic pattern recognition thing that dictates
(21:05):
the output to a certain extent. So I would argue that the better
utility perhaps for a generativeAI in helping to envision
futures would be maybe illustrating some of the futures
that you have imagined or might be, hey, I have a version of the
future now. I would like you to take this as
(21:27):
an input and give me 17 or 1700 modifications of this thing.
It's really good for that kind of thing.
I don't believe that it is as adept at coming up with
something that is genuinely weird or genuinely unpre.
It's not supposed to come up with things that are
(21:48):
unprecedented. That's not the design.
So I think that we adopt it for that kind of tool at our or for
that kind of purpose at our peril and recognizing what does
it do well? It's many slightly
differentiated versions of a thing that's already documented.
Awesome. What does it not do so well?
(22:09):
Come up with something totally new.
What does it not do so well? Come up with something that
actually needs to be accurate. What might it do?
Come up with something where accuracy doesn't matter, but we
want to come up with it quickly or we want to be able to
illustrate it, those types of things.
So I think it certainly has a role to play.
Interesting. Let me take a little bit of a
(22:29):
detour here because I'm very curious about something you very
well read, which I really appreciate about you.
We have lots of conversations about my mind wanders and I tend
to gravitate towards certain just ideas which I then start to
explore. So currently for me, this is the
whole notion around anti fragility.
So like revisiting a lot of Nassim Taleb's work and like
(22:50):
figuring out like, what does it actually mean in today's
environment? I'd be curious to hear.
And I think we actually never talked about this, where your
current Romney ruminations took you or take you.
Yeah, I probably also have some things that I come back to a
whole lot, and that my origin story is that I was supposed to
(23:12):
be a historian originally. That didn't totally work out.
I actually don't know if I've ever told you this, but this is
an interesting digression Duringmy wayward Graduate School days
where I was trying to figure outwhat on earth I was doing.
I didn't come up with a lot of good answers.
I at one point came to one of mypotential PhD advisors, and he
(23:32):
wanted to know what it was that I was going to be focusing on
when I got around to writing a dissertation.
And I said, I don't really know which was the honest answer, but
I have a thing I've been thinking about.
And at the time, what I was interested in was perhaps
getting into the history of conspiracy theories and
conspiratorial thinking in the United States.
(23:52):
And he was like, you wouldn't make an idea.
You can't do your dissertation. You can't do a PhD on that.
You don't do killing. Today it's such a great idea.
This was a long time ago, but inretrospect I might have had a
good hunch there. Oh yeah, talk about visionary,
my friend. But so some of the things that I
come back to a lot and this doesconnect to history.
(24:13):
I am very interested in contingency.
I am interested in the decisionsthat we made or did not make in
the past to get to the present, right, that this was once one of
many possible futures that we ultimately could wind up with.
And here we are. And we are here because we
(24:35):
didn't go down a bunch of other possible paths.
And in the same way, now, of course, we have a lot of
decisions that we might make that will ultimately shape
whatever future we do or don't wind up with.
And so with that, I continue to come back to some pretty similar
(24:59):
models and frameworks and writings and thinkers.
And I'm a huge fan of the futures cone.
I guess Joseph Boros is probablythe one who kind of popularized
the version that we see a lot. He always credits Herman Kahn
and Norbert Wiener for coming upwith kind of the predecessor of
(25:19):
that. And this is also one of the
things that I'm interested in iswhere do we get our where do we
get our ideas about the future, about what it might look like?
Where do we get our models for understanding how people think
about the future? Do we have the right models?
Are there better ones that wouldenable different kinds of
futures thinking? I come back periodically to some
(25:42):
of the stuff around complexity. I'm really interested in the
relationship between complex environments and the kind of
alignment that we can achieve through story and value of
narratives for holding things together in complexity and
uncertainty. And then all of that, of course,
ties back to how we think about the futures that we're trying to
(26:06):
enable or that we're trying to prevent some, those are probably
some of the kind of the stars and the constellation that I
keep finding my eyes drawn back to over and over again.
Interesting in this context, maybe I've got a good point on
this. What just came up for me was the
interesting challenge we have ingenerally in leadership
(26:30):
regardless in which domain between.
On one hand, we're very focused on the here and now and we have
developed tools to allow us to do this better in a, in a highly
uncertain environment, largely agility, right, trying things
out. We just talked about learning,
etcetera, which for me has a hasan interesting component, which
(26:53):
is you, it's a bit like walking through a dark room and you're
just like, like one step in front of the other.
You'll get very carefully, but you don't actually know where
you're going and you just like hope that you get to the right,
right exit. On the flip side of that, I
think you know, at least some people have this ability to
really see or envision a future long term, whatever it is.
(27:17):
The in between in our like threetime horizon model, that time
horizon 2 feels like extremely messy and it feels extremely
hard for people to envision. I find it fascinating is that,
you know, when you and I work with clients, they can all
envision, you know what whateverlike next quarter and next year
might look like. They can all envision what they
(27:37):
can dream about, what the far distance future look like self
like flying cars, like robots who are like doing all our
laundry and so on. What they really struggle with
is this weird in between, which kind of looks still a lot like
today, but is dramatically different like the 10 years.
And I'd be curious like from your perspective, how do you go
(28:00):
about this? How do you think about, how do
you get to people to think aboutthe in between the five 1015
year time frames? At first, I would be careful
with the time horizons, right? And this is a conversation you
and I have definitely had beforeand I know you agree with me on
(28:21):
this, which is why I don't mind pushing when we say 15 years, 20
years for the organization to bethinking 15 or 20 years out at
this point, I think is that's a virtual impossibility, right?
The organizations that are thinking 20 years out are
governments, churches, previously institutes of higher
learning. I think they're recognizing that
(28:42):
they can't do that anymore. They can't take that for
granted. They certainly want to have an
eye toward that long term future, but they also recognize
that they can't take things for granted in the way that they
used to. But for the typical firm, I
think that's an insane timeline.I think long term for the firm
(29:03):
is 5 to 10. We've even talked about our
mutual friend John Hagel with his Zoom out, Zoom in framework,
which I continue to find incredibly useful.
I actually think it's quite goodfor this sort of thing.
John, when he wrote about that initially, I think the time
horizon for the Zoom out was 10 to 20 years and that revisiting
(29:25):
that framework 10 or 15 years into its usage, he said the one
thing he would change is that hewould shrink that time horizon.
That said, in my mind, we can work on those different
timelines, but we have to be realistic with ourselves and
honest with ourselves and ultimately comfortable
(29:47):
recognizing which things we can know with certainty versus the
things that we can envision withclarity while recognizing
uncertainty, right? When we think about that long
term, there are things that we know that this is the macro
forces, the mega trends, stuff like that, like the aging up of
(30:09):
different demographics and probably deepening climate
crisis. These are things that they are
not going to change. We can see the trajectory.
It is slow in coming, but we know we'll get there.
There are other things that I think we could probably put in
that category as well. At this point, despite some of
the changes in economic support and incentives in the United
(30:30):
States, I think the energy transition globally, that is a
thing that is happening. We can see what the next wave of
manufacturing is going to look like globally.
We can imagine some of the change in industries and inputs
and the drivers of industries. I think that stuff you, you have
a pretty good idea on the 10 year timeline, what that looks
(30:51):
like. But as you start to bring it
closer to the organizational future at that time horizon,
there's going to be greater uncertainty.
There's going to be a lot of uncertainty.
And in my mind, just recognizingand differentiating between the
(31:12):
things that we actually can probably safely assume versus
the things that we're going a little bit more out on a limb.
And I'm just, I think, I think making that distinction and
understanding that is useful. And of course, the closer you're
getting to the present, the moreyou're able to operate with
(31:34):
higher probabilities that you can probably gauge more
effectively and the more that you can ultimately be working
with data even. But it's a little bit of a
different, almost like a different way of knowing about
the far future versus the near term.
How much do you? If I answered your question.
Yeah, yeah. Yeah, talking about stuff.
I'd be curious. Again, it's like another thing
(31:57):
we we talk about but never really actually discussed.
Like, how much do you think the individual, how much do you
believe in the individual and the organization's ability to
shape the future? I think it depends on the
organization. Fair enough.
And which scope of the of futurewe're talking about.
(32:19):
There certainly are companies that you and I have worked with
that have enough market share and capital and scale that they
can do a lot to shape the futureof their industry.
Now that doesn't mean that they get to completely write it.
(32:42):
They're probably working within the scope of the possible and
working with the probable influencing probabilities.
And this is another thing that we often talk with our clients
about. The utility of envisioning
futures, plural, gives you the opportunity to identify your
(33:04):
preferred futures, your futures that you really would like to
steer away from and ask what youcould do today to influence the
respective probabilities of those possible futures.
Can we innovate in the directionof one to make it more likely?
Are there things that shape thatfuture that we can directly play
(33:25):
in to increase its probability and that I absolutely believe
in? And you can point to companies
that quite clearly are shaping the future at different levels,
some even at the very macro level and some much more micro.
Maybe it's the future of the community that they're located
(33:45):
in or the population that they directly serve, their consumers,
that kind of thing. But it's almost always going to
be negotiated, right? You're not the only stakeholder
in the ecosystem. In this context, how do you
respond? I get this question quite a bit
where if we work with more middle management and we talk
(34:06):
about the future, we talk about shaping the future, seeing the
future, anticipating a future, all this stuff.
I very frequently get this question of, but what do I do if
my management doesn't see it? They don't empower me, they
don't do it. What is your response to that?
Is this my initial response always.
And then I'm like I'm trying to count.
You know everything about the incumbents challenges.
(34:29):
Now your opportunity, after you've tried to pitch them on
addressing those challenges, your opportunity is to leave and
to build the organization that actually does the thing that
they won't let you do. Yeah, fair enough.
That that's one answer. I'm not saying that's the only
answer, the best answer. I think other possible answers
that still hold some value. Again, thinking about your local
(34:52):
scope of the possible and the local probabilities that you can
play with. Are there things that you can do
to open the aperture of consideration for your more
senior leadership? And this is not like a managing
up thing. It's a maybe helping them see
(35:12):
the urgency or maybe helping them understand on or appreciate
an underappreciated driver of change.
This gets a little bit into the whole inside out versus outside
in thinking thing. And it is quite possible that
one of the middle managers that we have worked with adopts a
little bit more of an outside inapproach and is maybe thinking a
(35:36):
little bit differently, maybe intouch with a different set of
signals or indicators that suggest a new set of
opportunities or maybe a threat that is not really on the radar
of the higher levels of the organization.
Because it's coming from an areathat they don't have a lot of
exposure to, that they're not incentivized to pay attention to
(35:58):
because it doesn't feel like it's their domain that they are
very deeply immersed in. And I think there's an
opportunity even for someone who's perhaps not at the most
senior level of the organizationto start talking about that
thing, start telling a little bit of a different story.
And we can even consider this aslike a form of kind of narrative
(36:19):
prototyping where we start a conversation around this.
And sometimes conversations themselves have a virality or an
exponentiality where other people start talking about this.
And then it becomes a conversation within the
organization. And other people are able to see
here is a story about how our firm could access a new
(36:40):
opportunity for growth, lean into a potential future, or also
how it could succumb to something that it has heretofore
ignored or downplayed as a potential threat to an
established way of doing. And then let me ask you kind of
if it's starting to get to the, to the wrap up of our
(37:01):
conversation, which I really enjoy.
What is your advice and probablylike your own practice in
staying up to date. I there's another question.
I get a lot and my ADHD brain asI, as I read a lot of like very
small pieces of information and consume them.
I'd be curious to hear what do you recommend for people to I'm
(37:24):
interested in the future. I'm interested in navigating
uncertainty. Where do I start and how do I
keep edit in a sustainable fashion?
Hang out with Pascal Finnette I I say that not just as a
shameless promo for my host hereand friend, but also I really do
(37:45):
believe deeply in surrounding yourself with people who are
going to hip you to new possibilities, sources of
information, ideas, signals, trends that are not necessarily
on your own radar. And I think the world is
sufficiently complex and also endlessly interesting that if
(38:07):
you're limited to only the things that you can pursue in
your spare time and only the scope of your own curiosity,
you're missing a lot of good shit.
And so I try to surround myself with people honestly, I try to
surround myself with people thatare that I think are smarter
than me. I try to surround myself with
people that I always find interesting and that I feel like
(38:28):
I will learn from. We have so many opportunities to
do this kind of thing too. Like I recognize that I have
been very fortunate to kind of luck into the network that I
have. But at this point in time, for
all the horrible things that I can say about the Internet and
the information ecosystem that we're swimming in, you have
access to an unbelievable range of ideas and thinkers and
(38:52):
researchers and newsletters and podcasts.
And like you, I subscribe to a lot of stuff.
And you can, you can sample widely and refine and curate
your inputs. And to the extent that you're
(39:13):
engaged with them, your algorithms to a certain extent,
you can tune to actually serve your interests and build your
capabilities. And I believe that we have more
than enough resources to developall of the skills that we've
been talking about here. A lot of it is just
intentionality and time and trade-offs.
(39:35):
What are you going to give up sothat you can pursue this thing?
What are you going to stop doing?
What are you going to put on theback burner so that you make
time for the new and actually invest in your own learning and
regard your future self as a thing that you are prototyping
all the time? Last question I have for you,
looking for you, looking a little bit into the future and
(39:57):
just like it connects a little bit to what we just talked about
in terms of like where do you get your information from?
How do you think about this? What are things, what are like
signals, trends, emerging, things you're seeing you're
currently keeping an eye on? As in what are you fascinated
by? Which is like early in its
infancy? I don't know that these things
(40:20):
are in their infancy, and I would neither claim to be
particularly attuned to nor say that you necessarily have to be
identifying the weakest signals.But some of the things that I'm
particularly interested in termsof how I think they're going to
shape my work and life and probably the kind of work that
(40:41):
we're doing in the years ahead actually are the kind of points
of interface between the organization, the organizational
leader and some of these macro uncertainties that we're talking
about. I believe that we might be
hitting a point in the development of generative AI and
(41:05):
LLMS where people start to question a little bit more of
the AGI by 202520272029 narrative and start to say, hey,
that's fine for the big AI companies and the Nvidia's to
keep pushing that timeline back and back.
(41:27):
But for organizations who are thinking about this stuff as
part of their operational tool set and making workforce
decisions and personnel choices and strategic choices based on
the timeline, they're going to have to get more critical about
it. And I think they're going to be
asking increasingly, hey, if this isn't going to get to a
(41:49):
level of superhuman, super reliable perfection where I can
cut my entire workforce and justrun the damn thing myself, how
do I actually, how do I actuallymake the most of what we have
where it perhaps stays messy andin that Gray area where
(42:10):
capability far outstrips reliability and we don't close
that gap. And the jagged frontier advances
more slowly and also has some things that it seems to just
have as pockets that are going to exist forever that are not
predictable and reliable. And I think they're going to be
some really interesting questions for organizational
leaders and organizational learning that they're going to
(42:31):
have to answer about that. I also think they're going to be
a lot of interesting questions about how do organizations and
leaders and communities and institutions navigate the, I
think probably prolonged uncertainty and even swinging
and oscillation of the politicalenvironment, particularly the
(42:55):
United States, but I think plenty of that's going on
globally as well. Jeffrey, on that note, we could
I could spend literally as and we had these sessions, hours and
hours talking about this stuff with you in the interest of time
and like to make this not a verylong podcast like a Rogan show
style, but keep it somewhat compact.
(43:17):
Love the conversation, really enjoy the conversation.
So much fun to have you on the podcast finally.
And yeah, thank you so much for sharing your insights, being
here, working with me, being a friend of mine.
So yeah, it's great. It's been a, it's been a really
wonderful conversation. Thank you.
Pleasure was all mine man, thanks for having me.
I would love to continue it. Disruption.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Las Culturistas with Matt Rogers and Bowen Yang
Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.