>> Justin Cranshaw: Alright, yeah, thanks everybody for coming. We’re really excited to bring Greg
here. I’ve been reading Greg’s work for awhile. He writes a number of really smart pieces on a kind of
broad range of topics from cities to sharing economy, to future work, all kinds of things that we’re
interested in, especially at Fuse where I’m from.
Greg has lots of titles. I’m going to kind of read some of the things he does because I’ll forget them
otherwise. He is a non-resident senior fellow at the Atlantic Council’s Strategic Foresight Initiative. He’s
a scholar at the New York, NYU’s Rudin Center for Transportation Policy and Management. He’s a senior
fellow at the World Policy Institute.
He writes in a number of different places such as Fast Company. You can probably read lots of his stuff
online. I’m sure you’ve read him and know him. Anyway I’m excited to have him speak. He’s talking
about Engineering Serendipity which is his next book to come out soon hopefully.
>> Greg Lindsay: Soonish.
>> Justin Cranshaw: Thanks.
>> Greg Lindsay: Thank you. I guess I should wait. Do we know if this is being taped yet? I should
probably wait for the final all clear on that.
>> Justin Cranshaw: I think you’re good to go.
>> Greg Lindsay: Is it live?
>> Justine Cranshaw: Yeah.
>> Greg Lindsay: Okay, great. Alright, I say you’ve got the screen. Thank you, Justin. Thank you all for
coming. It’s a pleasure to be here. I’m going to say speaking of Engineering Serendipity I would say I
think Justin and I know each other through Twitter friends. Then we had a phone call. Then I was
coming out here.
Now I’m here in front of you. To me it’s real interesting in the sense of again like having written a book
about sort of air travel in cities, and you know business travel. It’s always interesting to me to see how
electronic media continually spawns face to face encounters, which is sort of a theme of my talk.
But yeah, so I’m currently working on a book with the title of Engineering Serendipity. I am particularly
really interested in the notion of you know how do we discover unknown unknowns? Which of course,
the famous question associated with this man, Donald Rumsfeld during the run up to the Iraq War.
Rumsfeld probably in the first or maybe second graph of his obituary will be this quote from I believe
was his February twelfth two thousand two press conference. Just to read it out loud for those of you at
home. “Reports that say something hasn’t happened are always interest to me, because as we know,
there are known knowns; there are things we know we know. We also know there are known
unknowns; that is to say we know there are some things we do not know. But there are also unknown
unknowns – the ones we don’t know we don’t know. And if one looks throughout the history of our
country and other free countries, it is the latter category that tend to be the difficult ones.”
In the journalism business we call this foreshadowing. That’s how that worked out. I would add one
more category if we were actually to graph this out on a two by two. The category he doesn’t touch
upon. I think is also a particularly wicked problem for any large organization such as Microsoft or
anyone else, which is the known unknown. The things that we know, sorry did I already cover that one.
They’re, yeah, known as the unknown knowns is what I meant to say.
The unknown knowns which is the things that we don’t know that we know, this of course is the famous
statement if only HP knew what HP knows. You can see what happened to HP because it didn’t know
what it knew.
But to me it’s sort of the notion of you know how do we actually discover unknown unknowns? How do
we actually find these things? How do you search for something when you don’t know what it is? You
don’t know who can help you find it. This is of course the problem is the complexity of doing organized
work looking for emergent outcomes.
Sort of a counter example of that is sort of what happens when you follow the impulse to try to actually
organize everything rationally and follow it all the way down. This is a molecule called Darapladib. This
is a molecule that was developed by GlaxoSmithKline. This was their potential wonder heart drug that
failed horribly in clinical trials last year or almost two years ago.
Darapladib you know affected a single enzyme that was meant to control the buildup of plaque in
arteries. It was meant to be an anti-cholesterol drug. It worked well enough in pigs. But when it got to
humans not only did it lack efficacy. But it also had the terrible side effect of producing abnormal odors.
You can see they weren’t exactly pleasant. My favorite is abnormal skin odor.
It raised the whole question there of the notion would you willingly take a drug that you don’t actually
know if it’s going to affect your heart disease or not. But you do know it produces really terrible smells.
That’s why it had a much higher than normal actually dropout rate. Disgusted patients stopped taking it.
To me what’s interesting about Darapladib is that as you know there are of course many, many drugs
that fail to come to market. I’ll talk about that in a moment. But Darapladib was originally developed by
human genome sciences which was the for-profit arm of Craig Venter’s research, almost over twenty
years ago which lead the human genome project.
William Hazeltine was the doctor who promoted this. He promoted this vision that you know that we
would map the entire human genome. We would pluck only the genes that matter. We would
understand only the structures that mattered. We would have the sort of very organized hierarchical
approach. There would never be any serendipity in drug discovery ever again.
The classic you know examples of Penicillin and all of these advances in heart disease. You know in
cancer and everything else would become a thing of the past. For example, you know Lipitor the best
selling Statin drug. One of the pivotal moments in the development of Lipitor was the fact that a
Japanese researcher discovered that he had a colony of dogs that had abnormally high heart disease.
He had to actually you know this became sort of pivotal in building the animal model of Lipitor.
But with genetic research you know we would never have to do this again. But instead of course you
know of this organized model of the chromosomes I use this just as an illustrative approach. You know
we know that there’s all sorts of junk DNA, as it’s called, which of course turns out to have incredible
deep order affects in terms of how it affects gene expression, which no one has been able to actually
sort of figure it out in an orderly expression.
That’s why Darapladib failed. That’s why you know so much of the promise of genomic medicine hasn’t
really turned out. You know, so I showed this image. Anyone know what this is? This is a tree farm in
Germany. This is one of the sort of classy examples of you know of an orderly system. It makes sense
from above. It’s highly legible. You can look down from ten thousand feet and you understand it’s a
tree farm. You understand how it’s structured.
This is how William Hazeltine thought we would look at the gene. Of course this also is a farm. This is a
Polyculture farm in New Guinea. Of course what’s interesting to me is that this is a very you know
ordered hierarchical system. It’s very understandable how it works. It’s very scalable until of course the
trees get blight and then they die by the tens of thousands. As the first generation of tree culture did in
Germany and Bavaria in the nineteenth century.
Then this is a farm as well. But this is a Polycultural farm. It’s extremely difficult to understand. It’s
highly illegible. But it’s very resilient. It basically produces all sorts of crops. It is able to respond to
environmental conditions. It’s just that you know if you come at it from outside being one of these
farmers it’s very difficult to understand.
The reason I use these two examples is because they come from a very influential book in urban circles
and beyond the Sociologist at Yale, James C. Scott, Seeing Like A State, with the great subtitle, How
Certain Schemes to Improve the Human Condition Have Failed. Scott you know uses this notion of what
he calls Legible vs. Illegible systems. Legible ones are like the tree farm. You can look at it from the
outside. You understand how it’s ordered. You can react to it. Illegible ones are the ones that have an
internal order that’s very difficult to understand.
He lays out all sorts of examples of Legible vs. Illegible systems. Some of these are mine as well but the
notion of a Closed system vs. an Open one. Illegible systems can take all sorts of input to produce
unknown emergent behaviors. Companies vs. Cities, we’ll get into this a little bit later. But you know a
company of course is a very legible system. The actual hierarchy and organization of labor versus sort of
the messy social structures of cities. Cathedral vs. Bazaar is of course based on Eric Raymond’s essay
about open source and sort of approaches to software development. Robert Moses vs. Jane Jacobs in
the Master Planner of the City, very top down and how he wanted to build infrastructure versus Jane
Jacobs in discussing emergent complex systems of the city. The last one to me is introducing Purpose vs.
Generative. You know a company of course can have goals, ideals, culture. City is much harder and that
of course, well and legible systems are generative like cities which of course you know are more
resilient, more unbounded. But it’s much harder to guide them down a particular path.
The reason I think we need more serendipity, we need more illegible generative systems is because the
approach that we’ve taken to science and innovation is actually failing. This is from a paper that came
out in 2010 by Jose Lobo and Deborah Strumsky of Arizona State University. They are Sociologists, and
Joseph Tainter who is the Anthropologist who wrote The Collapse of Complex Systems.
Tainter you know described his work the notion of decreasing returns to complexity. This is sort of work
they did where they looked at patents. You can see here for example the number of patents per
inventory has been falling over time for the last forty years. While the average size of patenting teams is
going up. Rather than a way of getting better in innovation we’re actually getting worse. Where we’ve
plucked the low hanging fruit he argues. We’re throwing more and more resources and more and more
money at actually sort of diminishing returns.
This is well known in pharmaceuticals. There’s been papers about this published that coins the phrase
Eroom’s Law, which is of course Moore’s Law in reverse. In PhRMA this is the fact that we still produce
or the FDA still approves about the same number of drugs every year and has since the nineteen sixties.
It’s just that the billions of dollars spent per drug goes up over time. We’re actually you know we have a
red queen problem, so to speak. We’re running in place to produce the same results.
It raises the question of you know our hierarchical top down structures we’re going to pursue this gene
for this drug. The best way to go or are there other ways of sort of trying to tackle this problem. It goes
beyond just PhRMA. You can see here you know like Tainter in their paper look at all sorts of scientific
processes that show the sort of decreasing returns of productivity over time. They also sort of show the
sort of general collapse in productivity relative to GDP and R&D spending, sort of arguing, that we’re
going through this sort of crisis of complexity when it comes to scientific research.
To me there’s all sorts of interesting you know all sorts of interesting possible expressions of illegibility
in research. As a counter to that I want to show this is an aerial image of Paris Juco. This is the main
Parisian complex of the University of Pierre and Marie Currie, the biggest medical research complex in
France.
What’s interesting about UPMC’s main campus is they went through a natural experiment for fifteen
years, between nineteen ninety-seven and two thousand twelve. Where due to asbestos removal every
single research lab got picked up and dropped at random either within the campus or at some of the
satellite campuses. An enterprising academic who’s now at MIT Sloan, Christian Catalini recognized that
this was a really interesting idea of testing how much physical proximity matters in teams. How you can
figure teams and research together.
He went back and he looked at fifty-five thousands papers published between nineteen sixty-five and
two thousand twelve. He looked at the teams that had previously crossed disciplines and had
collaborated on papers. That had been separated physically in the complex which stretches for miles. If
you try to walk from one side of campus to the other you’ll quickly find that you’re losing a ton of time
in your day to do so.
Then he looked, so as a sort of an after effect he looked at teams that were suddenly randomly moved
together by asbestos removal. What he found is when these teams were moved together through shear
chance in a closer proximity. They started publishing three to five times as many papers. The papers
they published were three to five times more likely to get cited in various journals. They were basically
doing you know much better work at, almost at random when they had been moved together.
He also found, this is interesting because this only really happened in cross disciplinary teams. Teams
that were in the same discipline but suddenly got shoved together started producing markedly worse
work. His hypothesis was when you’re in the same discipline you know you quickly go through the
lower hanging fruit. If you’re spending more time together you’re producing worse fruit because you’re
testing all these bad ideas.
But for the teams that were in different disciplines that had less time to actually communicate. They
were serendipitously moved together suddenly they could test out all these hypothesis that they never
had before. To me this is sort of interesting because it shows the power of proximity. It shows the fact
that communication patterns are defined by how closely we interact with each other.
This is something I’ll talk a bit about, Tom Allen sort of working this out in the nineteen seventies at MIT.
But it also shows the fact that you know which to me is interesting is that. You know even an institution
like this which is highly devoted to producing you know cross disciplinarian innovated research, could do
no better job than to actually move teams around at random by asbestos removal.
When I actually confronted the university with this they of course pushed back on this. They gave me
many, many reasons about why they had gotten better over the last few years about these sorts of
things. Then when you talk to the researchers at the end after they tell you it’s because we changed our
funding models. It’s because we’ve had all these different conferences. At the end they tell you yeah
and like I just ran into this person. I needed a robotics person. I ran into him at the café you know down
the street. I hadn’t seen him in twenty years. Or someone like you know we’re building a whole sort of
center where we can actually mix these disciplines in space.
They will defend the sort of orderly approach. But then at the bottom it gets down to the fact that there
are all these serendipitous encounters that were happing at a micro-level. Serendipity, this whole
notion of this word serendipity of course is you know the notion of a happy accident, right.
The most famous examples of serendipity that pop up in science and research are things like Fleming
and you know in Penicillin. There’s a great book that came out, Richard Myers, or maybe I have the
name wrong. But anyway there’s been several books devoted to looking at the whole history of science.
You know entire swats of industrial chemistry and organic chemistry have come as the result of various
lab accidents and other things. The ability to recognize this moment, that some anomalous data has
occurred and your ability to actually seize the advantage.
The word of course serendipity comes from this letter from Horace Walpole in seventeen fifty-four
describing the Three Princes of Serendip which today is Sri Lanka. “We’re always making discoveries, by
accidents and sagacity, of things they were not in quest of.” To me this is a really interesting quote to go
back to the basics of this. Because everyone seizes upon the accident you know the popular description
of serendipity is a happy accident. You know something you were not expecting that leads to a happy
outcome.
But he was emphasizing this notion of sagacity as well. That there is this prepared mind. The ability to
understand the moment when it happens and be able to actually take advantage of it, or perhaps to
create more of these moments, which is sort of the subject of this talk.
What I want to propose here is sort of you know three part thing we’ll get into here. Is that serendipity
is not magic. Serendipity actually comes up a lot as people’s favorite word in the English language. I
think those people that experience serendipity as sort of a magical force when something just happens
to you out of the cosmos. I want to argue that’s not true, that we can actually do a better job of actually
creating emergent encounters and ideas. We can create better conditions for serendipity to occur.
The second you know this is sort of where good ideas come from. That the really breakthrough ideas,
the really innovative stuff is not something where you can sit down and have a meeting. Say we’re
going to pursue this course of action at least not at the beginning. It’s going to become out of these you
know possibilities. These sort of building blocks that in ways we could never sort of choose from a
strategic top down version.
Then the third you know coming out of the Eroom’s Law it’s absolutely necessary. This notion that you
know we’ve done an incredible job with the corporation as we know it. In terms of organizing people
and very smart people to pursue these avenues of research. But there’s tremendous more possibilities
out there. I think we need to like figure out oblique strategies to start organizing work in different ways.
I want to talk about this briefly in sort of four different structures, the serendipity view. How you can
you know personally improve your faculty of understanding serendipity, then looking at sort of these
other skills of the office, the city, and the network. How can we you know make changes in these
various environments to increase serendipitous possibilities?
We’ll start with you. There have been a handful of psychologists; James Lally is Australian who’s one
who really tried to actually model out the psychological process of serendipity. What does it look and
feel like when you actually go through it?
You know so they’ve laid out, there’s Stefan Mockery. I forget what London University he’s attached to.
There are a couple of people who’ve built models. But this is the Lally Model and I like this one the most
in terms of its simplicity. About you know walking through having what Louis Pasteur called the
prepared mind of course. You know chance favors the prepared mind. The ability to actually have deep
domain knowledge in what you know and then have enough you know the sort of T-shape skill set to be
able to actually take your domain knowledge and apply it into other fields.
This is something that happens a lot in industrial design for example. There’s a lot of papers written
about the ideal model where they actually have domain experts in various fields. The way they actually
deal with clients is they actually take someone for example who’s well versed in healthcare and adapts it
to extreme sports or something like that, or vice versa. They’re able to basically cross pollinate through
these prepared minds.
Then of course there’s you know the unexpected event. You know in the notion of Flemings you know
Petrie dishes. It was a fact of course that he purposely kept them dirty. Then he found the various mold
that was killing off, you know killing off his bacteria. This happens in various fields. You know what is
that moment that produces anomalous data?
Robert Merton the Sociologist has a great saying for this. That you know the real sound of scientific
discovery is not someone saying Eureka. It’s someone saying huh, that data looks weird. Then
understanding that this is not you know a mistake. But something that’s actually opening up a different
inquiry, allow you to open up a different theory. That’s sort of you know what Merton’s talking about
here, E plus one. You know the ability to actually recognize that this is a potentially serendipitous
moment.
In discussing this with Lally, this is really the individual and sort of organizational faculty to not move
along. The moment you end up with seemingly wrong data. The ability to actually hang back, suspend
judgment. Sort of probe the circumstances of it and start to understand whether this is actually a sort of
an opening here. This of course requires not, this requires you know mentally you’re prepared to do
this. Also you know you actually have the space within the organization to do it.
The second one then is you know is seizing the moment. This one’s a little fuzzier in the Lally thing. You
know the notion of choosing the appropriate action and what that can be. You know, how do you
actually sort of recognize? You know who are the potential collaborators? Who can help you find this?
Doing the sort of pattern recognition and there are people of course who have this skill.
I’ve talked about this with Joi Ito who now runs the MIT Media Lab. If you know Joi or know his work
this is really what he does in the world. He is the master connector and master of pattern recognition.
The ability to bring together you know inter-disciplinary teams and assemble things in new orders. One
of the things that Joi brings up which I think is interesting is that this is a skill that of course develops
with age. Pattern recognition is something that we get better at over time, particularly as our domain
knowledge increases.
I like this notion that rather than you know the classic scientific field where of course you know you lose
your edge once you get a little bit older. This raises the question of being able to actually spot patterns
between fields.
Then the third one, this one amplify effects. This is really where social networks start to come into play.
You know once of course you have this event. Once you recognize you know that it’s a possible avenue
of serendipity from there. You know how do you actually sort of bring it to the right people now that
you’ve identified the pattern? Who are the resources? Who are the collaborators? How do you bring
together the people that amplify it through your network effects?
That’s where we start to get in the notion of the office which is where we take it from the individual to
the organizational level. We all know that space matters, right. You know this is an example of an
attempt in the nineteen sixties to build an illegible office environment. One that would allow the people
using it to modify their space as necessary to either do highly intense focused work. Or maneuver it to
be more social.
Its creator, Robert Propst of Herman Miller, call it the Action Office and the Action Office two. Of course
it went from this into this you know. I love this; this is a still from TRON, one of my favorite movies as a
kid. You know they’re ENCOMs; endless fields of cubicles which of course is apt considering that John
Dillinger in the film had destroyed a Flynn’s highly creative culture and replaced it with the MCP. It’s a
dead way of computing.
But of course you know there were models the whole time. You know while most of corporate America
shoved people in cubicles then tried to sort of glean as much productivity out of them as possible.
There were examples like you know AT&T’s Bell Labs. Or IBM’s, Thomas J. Watson Research Center in
Yorktown Heights, which today has the most patents of any building in the world on an annual basis, all
their sort of basic research being done there.
Of course you know the Bell Labs is famous for the fact that you know they pursued a whole model
where they took multi-disciplinary teams. Of you know theoreticians and metallurgists. They put them
in the same spaces. They separated the labs from the actual offices. Flowed people through the
cafeteria and they built really what you could argue as sort of first purposely serendipitous workspace.
That was designed to actually create as much mental overlap in the fields.
Of course you know you get the laser, the satellite, all sorts of famous examples out of their labs. The
other great example of this from the literature is Building twenty at MIT. You know of course the
famous wood structure that was built during World War two was always meant, gazuntite, to be a
temporary structure. Of course this is where they put the leftovers that didn’t fit into other
departments, so Noam Chomsky’s Linguistic Group ended here. This is where the RAD Lab was,
etcetera, etcetera.
My favorite thing about this building before it was torn down. Is the fact that they screwed up the
numbering system. People were apparently often lost wandering its corridors, bumping into each other
in the halls, and producing these conversations. You know there’s interviews today where people like
Tom Allen and others say this was really the womb of MIT’s creativity.
It really followed this model which I borrowed from Frank Duffy and Stewart Brand. Duffy was the
architect who ran DGW which is the world’s perhaps most influential space design firm. What I love
about this diagram it’s become very influential in software cultures, but not so much in architecture.
It’s simply the whole notion that you know that buildings like various legacy systems are like
programming evolve at different speeds, right. You know the stuff, the actual things of how people
move around in space you know evolves much more quickly than the actual structure of the skin of the
building.
What to me is interesting about this is that you know when it comes to actual office design no one really
seems to understand this. A year ago for example I was here in Seattle visiting NBBJ which I think I have
some images coming up. NBBJ you know developed the first version of the new Googleplex which was
cancelled in favor of the most recent version. They also developed a new headquarters from Samsung
which I’ll show you.
But you know they were showing me you know for example the fact that they had all of these different
light models. You know they showed me the five hundred different plans they could calculate to where
they could put all these people at Samsung. Then when it was done they would do that. They would
put all those people into one plan it would never changes over ten years.
It just struck me as sort of crazy that you know we know in this environment. We read all this
management literature about you know the ever quickening pace of competition, the fact that teams
need to be as fluid as possible. We still work in environments that are often very, very resistant to
change.
There have been various attempts to actually build alternatives to this. Here’s Stata Center at MIT
which was Frank Gehry’s attempt to actually try to codify the magic of Building twenty. Some people
think it’s good. Stewart Brand famously called it an abortion. He thought it was a terrible way of trying
to codify those concepts.
Now in Silicon Valley you know of course like this is where these ideas have come to root. Where you
know Google and Facebook are really trying to you know to create these environments where they can
have the maximum velocity of people colliding in space and time.
This building is open now. I could have changed the slide and showed you the mature version of it. But I
just thought it was fascinating that you know that Mark Zuckerberg went to Frank Gehry, told him that
he wanted to build an office that was essentially three thousand people on a single plain. With all the
desks mounted on casters for maximum re-configurability in there which I think is interesting on a
couple of levels.
One when I talk to architects about this, this basically shows that Mark Zuckerberg had absolutely no
faith in Frank Gehry’s ability to actually design an environment to enhance Facebook. He basically told
him to build this big box and then get out of the way and put a garden on the rooftop. Two, you know
this sort of trying to take to this notion of. To its extreme about the importance of having one in the
same sort of sight line the ability to actually collide with each other.
Google has acquired some large sites in Los Angeles, for example the former Howard Hughes aircraft
hangars. Where, they’re going to turn into office space, almost the same size, thousands of people
operating in one space. It raises the question of you know will these people actually coalesce into a sort
of single sort of work group. Or will it you know splinter into all these smaller groups. I guess we’re
going to find out.
For example, other attempts to actually build you know a serendipitous workspaces. This is the
Samsung Headquarters, North America Headquarters that NBBJ is developing. Their approach here is to
actually sort of you know create these public places at every couple of levels. Where people can come
out behind their screens and mix in various ways. Sit there and you know code on your laptop in a nice
little park.
To me what’s interesting about it is like the impulse is correct. But do we actually know if it will work.
Again, you know they can actually you know do these various renderings about how light will flow
through the space, or how you might configure people. But they don’t actually know how people will
interact in space and time.
To me the next level question is you know what can we actually do to bring people out and actually mix
more? Of course you know there’s of course a strong inertia to not do this. I was asking Justin before
how many buildings he moves through in a given day. I guess the answer is you know two more or less.
This is Sam Lessin. He’s a former Facebook executive who after he left you know gave this quote. His
wife, Jessica Lessin runs a website called The Information. He wrote an essay for it discussing like now
the fact that he was footloose and fancy free in San Francisco outside of the Facebook bubble. He was
actually horrified to discover that moving around a city takes time and energy.
Now you know now he has all these little interruptions here. He wants to go back inside the bubble
which I think is very interesting. Because now we’re actually starting to see companies are starting to
evolve beyond just the notion of one campus, right.
For years of course Microsoft really was the model for this. You guys are at ground zero I think of the
modern tech campus. But I’m really interested in starting to see other corporate attempts to start
bringing together more diverse groupings of people. Not just employees within their own firms.
The current, so slightly a vanguard here, this is an office in Grand Rapids Michigan called GRid seventy.
This is basically a down town office complex where the six local billion dollar companies. Steelcase one
of the world’s largest furniture manufactures. Wolverine Sporting Goods which makes Hush Puppies
and Top-Siders. Healthcare company, a grocery store chain, fields of widely divergent industries have all
decided they’d put their creative and design teams into the same space.
To me this is interesting because in a sense of it’s basically arguing that rather than learning, trying to
learn faster from colleagues who may be in different areas of the company but draw the same paycheck.
Perhaps it would better if you actually put them in the same, put yourself in the same space with
someone who has an analogous role. But is actually in a different industry or is actually in different
structure here. Perhaps you’d learn faster if someone who is somewhat similar to you but also diverse
in the sense that they were not inside your company.
You know there’s been no great breakthrough that’s come out of GRid seventy. But these various firms
are sharing research, they’re sharing tactics. To me it’s the whole notion of you know it’s not just about
discovering penicillin or discovering the next billion dollar product. But it’s a question of you know can
we actually use workspace to accelerate learning, to accelerate the ability to actually. You know
accelerate your ability to actually discover new things and to assimilate research faster if you’re in an
environment where you’re talking to more people.
I show this image because this is the skunk works room at GRid seventy which is an oxymoron. Because
this is the room where if you have a meeting there you’ve sort of expressly invited anybody else in the
space to come into your meeting and critique it. They’re trying to from that. Yes, Harold, you have a
question?
>>: It’s somewhat related and you may have this coming up, I don’t know. But the notion of co-working
spaces where you have a bunch of different organizations.
>> Greg Lindsay: Yes, co-working up in I think two slides. This is the next iteration of this. This is AT&T’s
Foundry in Palo Alto. They’ve built a whole sort of chain of research centers like this. It’s sort of similar
to GRid seventy in the sense of this is an environment where they have sponsors from their sort of you
know value chain partners. Erickson sponsors the one in Palo Alto and they also bring in startups in
there to basically try to bring ideas into the space and work with AT&T’s own engineers.
You try to create this sort of you know this multiple cultures occupying the same space. This, you know
this is the best success that came out of the Foundry in Palo Alto was a company called Intocell. Those
of us who have AT&T IPhones, you remember it was only a few years where it’s basically impossible to
make a phone call unless it lasted thirty seconds before dropping. That was the company that they
brought in that increased the viability of their call network that allowed you to actually make a phone
call that could last more than two minutes. Cisco eventually bought it for five hundred million dollars as
well which sort of shows the importance of getting them in the same space and sort of working on the
tech.
Then to Harold’s point earlier then the next model up is sort of the rise of the co-working space. Justin
and I were discussing this. Co-working a few years ago which is the notion of working in the same space
along side people you don’t work with, the notion being alone together working in proximity. A few
years ago this was sort of a fringe notion. This was just for freelancers. It was very much a sort of Bay
area sort of alternative approach to work.
Today wework now has twenty-three thousand members in eighteen different cities. You know we’re
starting to actually see the sort of re-aggregation of new work spaces where you have a lot of different
people with a lot of different backgrounds. A lot of different objectives occupying the same space which
in theory will create these hyper fertile environments where you know you might actually discover
someone at the coffee machine. Who actually has the solution to your problem.
The question is you know can you find the right person at the right time? But you know it’s the notion
of we can start actually mixing these people in physical space and actually increase the communication
pattern in a way that are a lot more. They have a lot more search in them I guess you could say than just
you know working in the sort of static office environment.
Of course you know the next level up from that what the co-working space is trying to mimic is the
notion of the city which is the next level here. Cities are really interesting. There’s been some research
in the last couple of years from the Santa Fe Institute. This has been of course probligated by a lot of
different media outlets.
This is, the papers written by Geoffrey West and Luis Bettencourt who are both Physicists who looked at
cities. They looked at the fact that cities are not like other or not like natural organisms. They get better
as they get bigger in certain circumstances whether that’s patents or wages, or all these sorts of things.
Also it can be dysfunctional, disease, and crime.
Basically what they discovered is that cities are special. They, unlike organisms which scale sublinearly
where you are born, you mature, you plateau, and you die. Cities exhibit sublinearly behavior. Where
as long as you can keep feeding in the inputs they basically can keep growing forever and they keep
scaling up forever.
They appear to be immortal. They appear to you know have no actual unbounded size. I mean Tokyo is
effectively thirty million people because of its infrastructure. The whole notion of what is a city, right.
This has become one of the whole questions that goes through urbanism. Is it a machine for living? Like
[indiscernible] discussed or is it an organic thing like Patrick Geddes talked about?
Bettencourt has a whole theory which I think sounds right which is the fact that cities are social reactors.
Cities are where we take social networks. Real world social networks and compress them in space and
time. Where the best cities become these hyper-dense sort of structures that throw off instead of you
know heat and light. They’re throwing off all the positive effects they saw. Instead of you know
compressing hydrogen you’re compressing people at the edges of their social networks.
In this regard, this is my whole theory here. Is that serendipity really; in cities is how this works. Is that
serendipity is actually the point where these overlapping social networks fuse at the edges in
unpredictable ways. In terms of accelerating serendipity here, it’s just a question of can we create
structures in the city or the workplace, or using technology to actually accelerate this sort of rate of
fusion.
One of the things I think about. This is you know a conversation that came up or we were discussing this
before hand of the notion of slums. You know as someone who writes about cities whose passion really
is the city. I think it’s really interesting to see some of the discussions about you know what is it about
slums that makes them so interesting? You know where Ed Glazer would tell you that slums will save
the world for example.
To me what’s really interesting is when we look at slum economies versus what we have in western
cities is that the space is hyper utilized. We can see this, what the Mumbai based design collective CRIT
calls the blur. There’s a blurring of public and private space. There’s a blurring over various functions
where people are running all these micro businesses on the street. There’s intense relentless
entrepreneurialism that goes on in these places.
We can argue you know about whether this is you know a positive thing or a thing that comes out of
hopelessness because of a lack of a safety net. But to me it’s really interesting because a lot of the
behaviors exhibited in slums. The notion of what another design collective Erb’s calls the Tool House
where you have homes and factories and multiple functions put together.
We’re starting to see these same typologies start to re-emerge in the United States in the form of the
sharing colony. We’re starting to see this intensification of space. To me, it again raises the question of
the city of you know can we actually do better examples of urban design, where we can create more
diverse heterogeneous environments. That can accelerate serendipity by accelerating the various uses
that are going on inside of them.
I bring up this because you know because in the case of CRIT when they’ve talked about this notion of
the blur and transactional capacities of cities. The ability, the really healthy cities are not you know are
not the ones with the most gleaming skyscrapers. They’re also not the ones with the most slums.
This is a graph that they laid out when you know you can imagine at one extreme. This is the, I believe
this is the [indiscernible] private residence tower in Mumbai. You know one extreme you could image a
city that has all the resources in the world. Essentially, it’s like New York right now where we’re building
skyscrapers that exist purely as empty condos. At the other extreme you’d have hopeless slums. You
know where they have no resources but pure entrepreneurialism.
The ideal city for engineering serendipity or productivity would be here at this point. You know where
you have this sort of nice overlap of resources and transactional capacities of good blur. To me it’s
interesting. Because the typology they picked, old labor housing is exactly what Jane Jacobs talks about
in The Death and Life of American Cities, where she talks about the fact that new ideas require old
buildings.
It is basically Building twenty; it is basically the sort of buildings where we can play with them. But are
not too expensive where you can test out ideas, these sorts of heterogeneous dense environments,
where you can have a lot of people mixing in space and time.
I’m interested in zero, you know at the city level are there different ways we can start rethinking the
city, or ways of activating space that we don’t do now? I was in Australia a couple of months ago. This
is a project there called Renew Newcastle. Newcastle Australia is sort of the Toledo Ohio of Australia.
It’s a former rust belt steel town that fell on hard times.
It had a completely dead downtown in two thousand eight. Then an arts festival organizer came in
there and realized that you could come up with a whole new way of taking these empty buildings, which
were all owned by someone. It’s just that no one was interested in basically renting them out because
of the various tax implications and costs involved.
Then he found all these entrepreneurs and artisans and basically got a way to offer them the space for
free on a very temporary basis. What he did was he took you know these sort of you know empty
buildings that was impossible to rent. He found the people who wanted to rent these spaces. But
found it impossible to get the money to do a ten year lease and match the two together to create all this
energy. They didn’t change any space. They didn’t build any buildings. They didn’t change any
infrastructure. They just found a way to put people together and basically jumpstarted the Newcastle
economy.
We started to see other structures in cities to do this too. Of ways of reclaiming you know sort of dead
spaces in cities. This is Parklets in San Francisco that came out ten years ago. Where literally you take a
parking space and then you actually build a temporary park there. Use it as sort of way of urban
attractors.
There’s a whole sort of subgenre now in urbanism circles called tactical urbanism where you basically
find very low cost, temporary ways of intervening in space to get more people out in there. I think this is
a really promising area of like how do we actually engineer the cities for more serendipity?
Then we start to get into the sort of digital overlay, right. AirBnB is potentially I think more
transformative a service than it is. AirBnB seems to be interested in basically wanting to become a hotel
chain when it grows up. With you know, one with no assets. But this notion of space as a service, right,
that suddenly we can look into all of these unused spaces around the cities. Find ways of actually filling
them is to me really powerful.
Earlier I mentioned Frank Duffy, for example, the Architect at DGW. His firm has found that at any given
moment forty percent of skyscrapers, forty percent of offices are underutilized space. Those people are
elsewhere. They’re either working from home. They’re working from somewhere else.
But we have all of these sorts of conference rooms that are sitting empty at any given moment for
example. You know and so the question becomes you know, is there a way that we can actually start
welcoming these people into spaces? Can we actually increase the utilization of our offices? In doing so
increase serendipity by finding people we wouldn’t have found otherwise.
You know there’s a few attempts to actually sort of do this for real. This is Tony Hsieh in Las Vegas.
Tony has taken everything that I’ve sort of talked about here and taken it to heart and is trying to sort of
build it here. You know its Zappos Headquarters in the former City Hall. You know he’s welcomed
people into the lobby as sort of a co-working space. He’s trying to create his own sort of new creative
class company town by sort of spreading the company into the surrounding area.
He’s basically invested in startups. Tempted them to move to Las Vegas trying to create this whole new
simulacrum of San Francisco there, which he hopes will actually help his employees of Zappos. By
helping them learn faster. He’s basically arguing that moving to downtown Las Vegas will make you
smarter through exposure to all the people there.
It’s a completely open ended question whether it will work. As it is right now you know they’ve sort of
run out of money as it is. Or they’ve committed all the money they have. They’ve sort of reached this
equilibrium stage that’s not optimal. They’re trying to figure out ways you know how can we actually
use more space to actually attract more people to it and go from there? I’m not sure the praying mantis
from Burning Man will help all that much. But that’s what they’re trying to do.
The final scale of this which I think all of you will find the most interesting is the notion of the network.
This is really the possibilities that exist in real world social networks. This is a network diagram of a
large, I believe, EU based healthcare company. This was diagramed by Ronald Burt who’s a Professor at
the University of Chicago’s Booth School of Business.
Burt has been writing about for almost forty years about what he calls Structural Holes. His argument of
course is that any given social network particularly in organizations there are gaps, right. There are not
evenly distributed networks. There are people who will span gaps in the organization.
For example in this diagram if I recall correctly, here’s the bulk of the firm geographically in the EU. I
believe this is North America here. This is Asia. There’s several other sort of sub-units by function here.
This is my favorite part. This is R&D, completely cut off from the organization. They knew it. There’s
actually in the paper he wrote on this the R&D people were very curious to see what he would find
when he graphed this out by hand.
But what this map shows is this is a two hundred and fifty-six people here showing the upper leadership
of this organization. Looking at, basically here the orange ones are the top executives. The yellow ones
are sort of seen as the heirs apparent. This is part of, they’re all members of a program designed to
identify the next generation of top talent in this company.
What Burt found is that the most pivotal people. That the ones who are actually spanning the gaps,
number 818 and number 96 were incredibly important people in the organization. Who had not been
recognized as such formally. When he presented this, his findings to the top leadership of the firm they
very, very quickly rushed these two people into the program, because they had been basically
overlooked.
To me this is really interesting. I think he has other slides but I don’t show them. But they were actually
fairly marginal figures in the actual formal hierarchies. The point about Burt which I find really
interesting is of course is that we know that you know despite the formal hierarchy of any given
organization. The way product groups are managed, etcetera. There are of course informal networks
that define how work actually gets done here at Microsoft and anywhere else.
The question is you know can we actually build apps to actually find them. I’m showing you SCRUFF.
This is a gay dating app that a friend of mine uses. We went out actually in New York actually using it as
a sort of dowsing rod to see if we could find people around us. The reason I show this is because gay
dating apps like Grinder and SCRUFF which led to Tinder which is my next slide here where some of the
very first approaches of actually trying to find social networks in space and time. Actually proximity
based mobile dating apps.
Yeah, here’s Tinder which is of course the one that really proved the breakthrough of this. The notion
of, you know that there are buried possibilities in space all around us. It’s just a question of can we
actually find them using technology to actual accelerate our discovery of social networks. Then plug the
structural holes that Burt found.
Tinder of course just uses straight proximity. It’s actually sort of primitive when you think about it, but
addictively gamifying. But now there’s a whole new generation of apps that are starting to point to this
notion of. Well can we actually start doing analysis of these networks and then bring them to our
attention? You know it’s interesting because then it creates a trusted intermediary. Where you know
the app can do semantic analysis and suggest maybe you should go on a date with person. You can
always double check it against a friend.
This is an app called Hinge. Hinge is a dating app that basically looks at your friends of friends on
Facebook and suggests good candidates to you. You have a friend in common but you don’t actually
know who what person is.
You know it’s interesting because then it creates a trusted intermediary where you know the app can do
semantic analysis. Suggest maybe you should go on a date with person. You can always double check it
against a friend.
In a not a perfect world these are actually used in tandem. A friend of mine met his girlfriend in
December on Tinder. They moved in together in February. Then one day he’d started to realize that all
of her friends where sending him Facebook requests. He asked his girlfriend what is this about. Why
are all your friends stalking me? She’s like don’t worry about it. They just want access to you for their
Hinge accounts because he was fresh meat in their dating pool. This gets more and more interesting
and more powerful I think. Those are using just basic sort of Facebook social graph stuff.
The question becomes for me is you know can we actually build a finer grained more granular social
network that has a lot more context in it. That can actually produce value for us beyond do you want to
date this person?
This is a company that I wrote about last spring called Relationship Science. They built a proprietary
social network of four million people or so, essentially, the financial one percent. They hired eight
hundred people mostly based in India. They raised a hundred and twenty million dollars. What they did
was they sourced tens of thousands of public databases. To basically build really fine grained social
profiles of all these people.
What you use it for is you pay three thousand dollars per seat per year. You match your social
connections through LinkedIn or your address book, or whatever. Then what it shows you is whenever
you want to get someone like Stephen A. Schwarzman of Blackstone I believe. It’s going to show you all
three hundred pathways. It’s going to show you how you are connected here.
They’re color coded which you can then mouse over. I believe I forget if these are all red or orange. It’s
coded you know red shows you a very strong connection, orange is intermediate, yellow is very weak. I
believe that’s probably the yellow one there.
But basically it shows you through these hidden pathways through where you went to school. Did you
invest together in the same funds? Are your spouse’s on the same boards? All of these sorts of possible
outcomes that were never, you could find them but you could never surface them all very quickly
before.
They basically produced this sort of engine that would allow you very quickly to understand you know
how to find the serendipity that would lead you to this person. What I find really interesting about and
an opportunity they missed. Is they actually developed their own facial recognition algorithms so they
could scan party photos through it. They could see who knew each other on the social circuit.
Well, you know to me one path they could have done or we could imagine very easily. Is you take this
and you reverse engineer that so if you wear Google Glass or some future wearable device. You could
easily have a facial recognition match of some one. Then suddenly it starts showing you all the
pathways of how you’re connected to that person. Now, suddenly in a world there’s no longer any
strangers in that field of view. But just simply a question of you know how do I know them and how can
I approach them.
Then of course you know there’s other sort of various technology being developed to like look at social
media activity. This is a company called Rexter who’s actually in an incubator program here at Microsoft
right now at the Southlake Union Campus. This is basically developed where it goes in and it looks at
your Gmail. It looks at your Voice over IP calls. It looks at our other social media and actually builds a
sort of activity graph. To see who are generating the most value for you based on the number of
communications you have.
You know if you, what it aspires to be is essentially ways for your social network. You know if you are
startup CEO and you’re trying to raise money right now. Rexter can basically lurk in the background;
monitor all your social media traffic. Then essentially propose recommendations to you, call this person
first depending on what your objectives are.
Again, it can sort of you know basically passively monitor all the sort of communications. Sort of build
out you know an engineered sort of serendipity. These kinds of models go back almost twenty years.
There was a company in nineteen ninety-seven called Task Systems that was trying to do this with just
sort of pure email sniffing. Their clients were PhRMA companies like Johnson and Johnson, or Military
Defense contractors like Northrop Grumman.
You know essentially it was a black box. If you were stuck on a major problem you could query it looking
for an expert. It would go to that person and you know it would say so and so is looking for your help.
Do you want to help them? If they did you know it would connect the two of you. What Johnson and
Johnson found is often those people were down the hall from each other. There was all this buried
expertise in the dark matter they couldn’t find.
Then this starts to get really, really interesting with sort a next generation of wearables. This is a
Sociometric Badges developed at MIT at Sandy Pentland’s group commercialized by a company called
Humanize run by Ben Waber. Who was one of Sandy Pentland’s Ph.D. students.
This is a sensor badge. I wore this for a week and a half at Fast Company recently. I found you forget
you’re wearing it pretty quickly. But others did not. But what it is you know it’s a sensor package. It’s
got a microphone in there. It doesn’t actually record what you’re saying. But it listens to your vocal
intonations and who you’re speaking to. It has infra-red so it can identify exactly who you’re speaking to
if they’re wearing the badge, an accelerometer to see where you go, then the low energy Bluetooth as
well to accentuate all that data.
What you can use it for is that overtime you can collect all this data to actually see how people actually
speak in meetings. You can see who’s taking the most time speaking. How are they actually taking turns
doing so? Is someone dominating the conversation? What is the level of interactivity? One of the apps
that’s actually come out of this is what they call the meeting monitor. Where basically if you’re talking
too much it shuts you up so other people can actually talk instead. All these sorts of nudging that
happens in that.
But I’m particularly more interested from a serendipity standpoint. Of you know starting to understand
the social networks inside organizations in real time. You know, so one of his clients, Bank of America.
This is just an example of the retail branch. He did an analysis of Bank of America’s Retail Branches to
sort of see the social networks there. Then map it to their actual real world KPIs. To see where was the
best performing social structures.
We’ll take a brief quiz here. This is from Ben’s slides. Which of these three banks do you think is the
highest performing bank depending on whatever their real world KPIs were? Who thinks it was Branch
number one, show of hands? Who thinks it was Branch number two? Who thinks it was Branch number
three? The majority thinks it was Branch number three. The answer was actually Branch number one
was the highest performing.
That’s because in the sort of literature of social network analysis. This has both a high degree of
cohesion and it’s including everyone within it. There’s good diversity of communication and really high
level of cohesion. Branch number three obviously has even higher levels of cohesion inside of it. But
there’s several people obviously who are totally minimalized. Perhaps they were recent arrivals or
something else. Then Branch number two is the worst performing where literally you can see that like
it’s essentially you know like a brain cut in half along the corpus callosum. The question I have for you is
can anyone explain why Branch number two is the most dysfunctional? What produced that sort of split
personality?
>>: Different floors.
>> Greg Lindsay: They were on different floors. This goes back to the sort of Tom Allen’s whole
discussions about how space and proximity matters and why sightlines matter so much.
To me what’s interesting about this is you could then take this data. You can start to actually intervene
in it. This is what a couple of handful of really farsighted companies are doing. Is that you know once
we can actually start using sensor data to collect social networks in real time and understand how work
is getting done.
We can then actually either, well we can do one of two things or both. We can either start actually
connecting people. We can intervene in the network and realize that you know what you guys should
actually be working on this. You’re working on similar projects. You have similar interests. Or you know
there’s this structural hole here that we want to plug.
Or the second thing is you can actually start rearranging physical space in real time. You can actually
start to see who’s actually doing work. Then you can actually start changing the office rather than
forcing them to actually fight proximity.
I’m just showing this, this is another example of some tech that’s being developed. This is by a friend of
mine at United Health Group who’s now going to run their own sort of internal, what they call people
analytics group. Again, this is sort of a doctors, sales, and marketing group here. Then they develop
their, once they’ve mapped this out they develop their own methodology involving workshop exercises
and physical space redesign. Where at the end of the process this is what the social network graph
looked like.
They went in and actually sort of mapped it to their own real world KPIs. When they were done they
found that the people who were involved in this had improved their. I don’t want to say productivity
because I don’t know exactly what the metric was, but their efficacy by fourteen percent. It makes a
tremendous difference in their ability to actually work and produce their actual outcomes.
With that I know it’s been somewhat a rambling talk. Hopefully we have some time for questions. But
the larger crux of my talk is that you know. We know that there’s all these potential latent connections
in the world. I’m really interested in the notion of you know what if the five or ten percent of
possibilities here. This unrecognized potential inside of Microsoft or in the city could be realized.
The basis of my talk was I was asked to give a talk to Blackberry a couple years ago. About you know
how mobile devices would change the city. I realized the killer app for me. What I would pay anything
for would be one that could tell me as I’m walking down the street what to say to this person walking
towards me. Something that could actually recognize off of our GPS and our data exhaust. Could do the
analysis in real time to understand if we had something in common and what that thing was. If so, what
would be the right moment of context that could actually unlock it? What would actually cause that
moment of serendipity to happen? That otherwise would never happen because of course the social
norms of cities or an office or anything else.
Yeah, so that, thank you very much for listening. Hopefully we can go from discussion from there.
Thank you and thank you for those in the Cloud.
[applause]
>>: [indiscernible]
>> Greg Lindsay: Question, yeah you were first.
>>: A lot of what structures there is for serendipity could also be closer to track from other kinds of
better interactions. Like I feel like sometimes serendipity is greater difference at stages of say product
development or you know.
>> Greg Lindsay: Yes.
>>: You know as social media that were developed for six years. At some point I got tired of interrupted
there are so many important issues for serendipitous connections that I had to work at home because I
just couldn’t concentrate in the office any more, right. Like do you have a sense, I mean when you’re
building these structures is there, do you have to go one way or another? Or there, I’m using examples
where serendipity can be turned on and off on demand. Like I said Facebook they do the whole building
for that. But I bet it’s great for something that may not be.
>> Greg Lindsay: That is a great question. Yeah, absolutely, so the, I mean so in the literature discussion
in what I read. Obviously and I’m going to spend all day reading all the literature. But you know the
classic is referred to as exploration and exploitation. Or I think it’s Sandy Pentland who refers to it as
the exploration engagement.
It’s basically these two modes, right, where you are in modes of discovery when you want to bring in
ideas. You know and you’re in the early stages of product innovation or anything else. You want
discovery. You want to do diversity of information. You want to find non-silo domain expertise and go
out there. Then at some point that flips and you need to go back and increase engagement where your
team then consolidates everything it knows. That’s when you do the heads down work.
This is something you know Ronald Burt’s is writing his most recent papers on this. He calls it Network
Oscillation. That the highest performers are people who actually you know their networks go through
phases like this, where they do exactly that sort of thing.
From a workspace design I think this is really interesting. I don’t know Harold can talk about this even
more than I can in a way about sort of approaches to this. I think this is why Steelcase for example has
now this whole notion of a pallet of places that offices should have seven or a million different
gradations of office space.
I personally think the most interesting way to do it is that you know I think companies should no longer
have an office. I think they should have entire portfolios of offices. Where you might actually go work
in a co-working space where everyone there has the social norm of we’re going to do heads down work
today. That’s all you do. You work in total silence. Versus you know, then others might be a place
where you go to basically network across industries. You could have you know a Microsoft consign of
building wide NDA and make a safe place.
Yeah, absolutely, there has to be this sort of oscillation process if you actually want to get products to
ship. Because otherwise you end up in like this permanent exploration mode which is the reason I have
not actually written this book after working on it for three years, because this is so much more fun.
Harold, yes.
>>: Awesome talk, thank you so much. I stumbled across one slide early on where you have the. You
know the open versus closed cathedral bizarre. Then once said purpose versus generative. In some of
the research we have done. Especially with [indiscernible], he talks about you know changing the
modes of communication when he was fighting the war. Going from a need to know to need not to
know is basically a maximum network communication structures into the Army.
He stresses the importance of creating that shared intent and purpose amongst the troops. When I saw
that the purpose versus generative, I love to kind of get a little bit more color from you.
>> Greg Lindsay: Interesting.
>>: Because when you look at serendipity within an organization I believe that purpose is still a very
important element. That can actually accelerate saying that this is discovery.
>> Greg Lindsay: Well yeah, I want to know. Well this is someone who’s not spending full time inside an
organization. I maybe under weighting it but I’m. I don’t know and that’s since I’m thinking of. I mean
you know I guess by purpose I mean a sense of setting strong strategic directions. Execution along
predefined goals and you know that sort of notion that heads down. I think, let’s put it this way, I think
organizations over privilege or are overly focused on the focus work.
Like the whole discussion about you know the ability I want to be able to put my heads down and do my
work. I think organizations are over weighted towards rewarding that. This is something Ben Waber
and I discuss all the time. Like I think for example in terms of generative, I don’t know these are maybe
two different things.
But so like one there’s that interpersonal level where Ben talks about the fact that if the soul value
created is making people ten percent better at their jobs around you because you’re not doing heads
down work. But because you’re sort of bouncing ideas off of them. That’s makes you tremendous and
more valuable than whatever designated work you do.
In the other sense I’m thinking of is just that notion of you know that you’re bound by this is what we do
here. Not invented here syndrome and things like that. I’m more interested in like the or at least I’m
more focused on companies. Google ten years ago was really much more like this than Google today.
That allowed you to basically generate new products in different categories and things like that.
>>: What is the purpose, this whole idea of like planning, like long term planning. I think that’s
[indiscernible].
>> Greg Lindsay: I think you might be right there. Yeah, I’m not thinking in the cultural sense. I’m
thinking more in the sense of a strategic direction sense. But yeah, you’re right it’s much more planned.
>>: [indiscernible].
>> Greg Lindsay: Sorry?
>>: It’s like instead of we’re trying to sell as much Coke as possible. We’re trying to solve the worlds
thirst problem. It’s like just elevating what you’re trying to do as a business to something that people
can rally around. It’s something that you can, like that’s what the purpose of a profit sort of theme
came in. Whereas I think your purpose versus emergent, or was it purpose versus…
>>: Generative.
>> Greg Lindsay: Generative.
>>: Generative, I think it’s, we know what we’re doing and this is it versus we’re going to discover
together what the right thing to do is.
>> Greg Lindsay: Yes, or just recognize the notion that yes there’s, this intrinsic focus is on. You know
there needs to be an intrinsic value to waste and the wasted time involved. I mean I guess I think is
there’s a paradox obviously in any sort of given organization which is that we know that. You know even
on a personal level great breakthroughs, discoveries, all these sorts of new things are completely
unplanned and don’t actually conform to product timelines. But what we must manage if everything
has an ROI it actually must conform to that.
They’re completely across purposes. Actually there’s a former, I believe it’s a former Microsoft
executive wrote a great paper on this called The Serendipity Economy. Like sort of teasing out the
implications of this which is the fact that like there is a shadow serendipity economy where actions
today have emergent value far out. There’s absolutely no way in current management structures that
we can actually accommodate that. You know, how do we grapple with the cognitive dissidents
involved for that?
>>: Yeah, the theme that we see coming out of all this research is moving from efficiency of process to
effectiveness of outcomes. Where it’s, you’re looking at the result you’ve got.
>> Greg Lindsay: Yeah, or like you know the Hagel and Seely Brown notion of scalable efficiencies versus
scalable learning. I mean that’s another way of framing it too. Like the notion of how do you simply
create I would call the serendipitous environment. But how do you create environments where you can
basically accelerate your ability to learn through diversity and all these other things versus just to focus
on processes like he said, yeah.
>>: We have a bunch of people at Microsoft working on the social graph or the office graph. You know
looking at you know all these kinds of similar…
>> Greg Lindsay: What did you learn because I’d love some more [indiscernible]?
>>: No we’re not quite there yet. But, yeah, there’s teams that are kind of fairly new. Like the deep
data teams, whatever they’re called. But essentially looking at all the different signals and how we
work. Whether it’s using mail, meetings, Link. I don’t know if they take physical sensor stuff.
But I mean how, what’s the dark side to all of this stuff? I mean getting this really right seems like a
fundamental task...
>> Greg Lindsay: Yeah, I know it’s, obviously, well I was discussing. What was it? I was talking to one
startup out in Silicon Valley, or in San Francisco called Human which is you know basically trying to
replace your contacts app on your phone. By bringing in you know all sorts of other contextual
information so you can start with this.
You know they were saying for example, I think I have to look in my notes. But they were saying like you
know the, when people, when it doesn’t work people have a negative reaction that’s six times stronger
than the positive reaction. You know all this sort of stuff you know when it doesn’t work it feels
annoying or creepy, the creep factor high.
I feel like when it does work it will feel like magic, right, when you discover that one thing that happens.
This happened to me for example like on, I was using Swarm. I’m like, Justin and I were joking. I’m like
one of the last people who uses Swarm in America. In Chicago last week I was using it checking into the
Trump International Hotel right on the Chicago River. We were having drinks and one of the other
Swarm users I follow, John Tolvell the former IBM exec and CTO of Chicago. Was literally checking at the
restaurant across the street which I had noticed the name of the restaurant earlier. I could text him and
he dropped by for drinks.
You know that was a really coulagy way of actually doing serendipity. It was really powerful. It actually
you know one of the people were present was someone he had to have a meeting with later at one
point. Yeah, in terms of actually sort of doing the office, the dark side obviously is like. You know is all
sorts of creepiness factor, stuff like that. Also you know the possibility of, in an organization of top
down management that’s invasive and creepy.
I mean Ben Waber for example with his badges, let’s back up to that. I mean you know Ben was
explaining to us at Fast Company. I’m sure you know this but nobody else does. But like you know like
his company you know you actually have to sign contracts with Ben where you will not abuse it. Like his
company will sue you if you abuse it in ways that they don’t like.
That safeguard is going to go away very, very quickly when large scale organizations build their own
people [indiscernible] group. They build knockoff sensors and then they can do anything they want. To
me it’s a question of like the abuses.
I guess the most vital abuse of this. The worse abuse is that this kind of technology will be used once
again as fast food companies are using it to basically eek productivity out of you, in the most abusive
destructive ways because they’re managing for this very legible top down way of doing it.
The real value you can get out of this. Going back to what I talked about what Ben Waber and I believe
is the notion of like yeah we can actually start to see real value happening here. How do you actually
design a workspace that is not about squeezing as many people into a small amount of space as
possible? But is actually about rearranging it so people can actually work more productively and
encounter each other, and create these new things out of it.
The sensor data can be used to actually do that. Like no architect can actually justify that if we do this
layout this way it will lead to all these great outcomes and we can’t tell you what they are. This points
to how that might be done. But, yeah, that to me is like the obvious one. The companies will basically
use this to invade privacy and will lead to all this sort of suboptimal outcomes. Then of course there’s all
this sort of NSA implications, about you know large scale privacy violation stuff.
>>: How would this look like? I was intrigued by your slide with the one offer research where you map
the Bell Company or whatever. Do you know of examples where companies are actually rewarding
people? It sounds like they were putting these guys into like Exxon, radar, management categories. Are
there companies out there that actually use some of that data to promote people or give them…
>> Greg Lindsay: That’s a good question. I haven’t found it though. I mean this is like very early. I mean
there’s, the same question in terms…
>>: I think [indiscernible] talked about it like a few years ago in as far as actually doing it.
>> Greg Lindsay: Really.
>>: But, yeah, he was claiming he would actually give BP level bonuses to people. That was the thing
that he was launching chatter who are the uber network [indiscernible] in your organization. But I don’t
know if he actually did it.
>> Greg Lindsay: See obviously that maybe like that might over optimize for who is the most networking
company, like you could easily make mistakes in that direction. I mean the last slides I showed like
United Health Group. That’s the sort of thing where I don’t know if they’re doing promotional level
stuff. But like, but that is the notion of like you know of helping people increase performance and those
sorts of things. I do think it’ll become part of that.
I’m trying to think of some other examples that I’ve come across. But nothing springs to mind in terms
of like, yeah, actually linking and tying into performance, like it just strikes as five minutes into the
future.
I know for example like, do you know David Thompson at Boeing & Ingelheim? He runs a sort of people
analytics group. They don’t call it that there. But they came up with the whole thing called lunch
roulette. Where it’s basically like a randomizer where you just basically meet people in the org. They’re
trying to test out ways doing sort of noninvasive email traffic scanning to figure out. You know who’s
doing this sort of thing and how you might reward them. But no, there’s, we’re still a little bit away
from this. Yeah.
>>: This actually, this slide brings up the point. I’ve heard about research where they said that within
organizations when they do re-org they see a bump in productivity. Then if they go back a year later and
re-org back to the same structure they had they see yet the same bump in productivity.
If that is actually valid, how do we know that the bump that they’re seeing, whatever their
[indiscernible] guys are here, are valid gains. Or just that you know the reaction that people have to
change.
>> Greg Lindsay: Well there is, that’s a really interesting question. I mean like, because part of that
argument is like Hawthorne effect stuff, where like people know they’re being observed. They increase
productivity in that regard.
I mean I would argue partly based on what I’ve read about network oscillation that you know maybe we
want to stretch the timeframe out further. Just the question of after a year in this configuration you
learn everything you need to know from those people. Therefore, you go back to the old configuration
which then feels new again. Then suddenly you know you’re basically sharing what you’ve learned over
that year. Then you know you’re increasing effectiveness that way.
I guess my take away is a notion of like maybe we should be doing more reconfigurations. I mean, or
maybe simply we should be reconfiguring all the time in real time and figuring out, or close to real time
based on how work is evolving.
>>: [indiscernible]
>> Greg Lindsay: I know; I know this is very easy for an outsider to say, right. I mean this is sort of an
independent of politics. But it point to the fact that there’s a lot of like potential energy in the system
that we haven’t actually tapped.
But that raises an interesting question about whether this is like Hawthorne effects. But, I don’t know, I
like to think like, yeah. This is the sort of thing that comes up in co-working spaces, right. When it
comes to, and I’ve seen this and felt this that you know you go into a co-working space. You quickly
learn everyone who’s there. If there’s possibilities of collaboration or interesting work you exhaust it
pretty quickly. Like the weak ties are pretty apparent. Then you’re there or you move on.
From like an exploration perspective I found in co-working spaces. Like it helps to surf across spaces and
meet multiple communities. Then go back and focus. You know that might be one of the things there
we’re like after a year you’ve absorbed everything you’re going to learn from those people in that time
or enough. Then you go back to the other configuration.
>>: I know a guy who manages his career that way. He’s says you know I learned the most the first two
years of the job. He just changes job not matter what. That works for him.
[laughter]
>> Greg Lindsay: That’s possible. I mean there’s got to, that’s the sort of thing. We don’t know what
the actual optimal structure is. Like I would say probably flipping every year is sub-optimal. Probably
staying the whole time is sub-optimal.
>>: Well sometimes the optimal structure is changing structure every couple years.
>> Greg Lindsay: Yes, that I…
>>: Or what it is.
>> Greg Lindsay: I would actually agree with that. So would probably most management consultants.
This is, I can feel my consulting career blossoming.
[laughter]
Well there is, I mean there has been some interesting papers written on you know sort of borrowing
from like the structural hole concept. You know there have been some interesting papers written on
like. Where they’ve done theoretical models where what if you try to optimize everybody. What if
everybody tried to optimize to sit on a structural hole?
Quickly, like it achieves like a maximal lattice. Like, yeah, it turns out it actually increases, it would
increase the epicocity of people by a couple of percent. You know and then that would change. I mean
there is something to be said for you know constantly tinkering with the configuration. Yes.
>>: I have a question about measuring serendipity. The examples that you gave they’re either
measuring serendipity from the perspective of some like second order effects, like a number of patents
or something like that. Or they’re something like looking at some structural properties that could lead
to serendipity.
>> Greg Lindsay: Yeah.
>>: But not actually serendipity.
>> Greg Lindsay: Of course.
>>: Have you observed any studies or have you come across anything there looking at like for example
your meeting in Chicago trying to observe actual serendipitous…
>>: I think it’s what they call [indiscernible] synchronize the perfect time.
>> Greg Lindsay: There is, well yeah, but even then like it doesn’t show the exact actual object medium.
This is partly why you’d need, so yeah, so the answer is, no there isn’t, like you would need constant real
time granular, contextual data down to whatever.
For example, some of the office spaces, I remember this at a. Do you know Kerstin Sailer, remember
talking to her? Kerstin Sailer’s an Architect at University College London Bartlett. Where they
developed this whole method of mapping space called space syntax. It was a really interesting way of
seeing how people move through environments and communicate into them. She’s the one who
studies offices more intensely than anyone else.
They’ve done some really interesting research on showing what they call a global versus local
communication pattern. They’ve studied R&D centers for example. It shows that like the best
performing ones. I think they’re a little less scientific than Ben Waber stuff.
But it shows that one were not only can you hang out in the lab together and talk very closely. But also
people who are going by passed an open door can see you, interact with you. It has different levels of
interaction. When I asked her about like we’re trying to use Ben Waber’s sensor data to map that, she’s
like it still wouldn’t be enough to actually measure serendipity in real time, you need to know the
gesture. You need to know who popped there head and when. Being able to actually see how I talk to
you almost sight to sight would be the only way to really measure that. At some level there’s just…
>>: Could you use structures like in your graphic study where you’re like a chip embedded in some
environment.
>> Greg Lindsay: Well, you can do it that way and people have done those kinds of studies. But that
sort of thing the argument about Ben Waber’s stuff for sensors is the fact that like survey data is never
as good. People assign all sorts of meetings. Also, like you know there’s a, you know people have biases
against a lot of sort of collaboration.
There’s a great paper that was done at Arizona State using Ben Waber’s badges. Where they asked
people when you feel the most creative. People were biased to say it’s when I’m sitting there with focus
and my head down. Well actually they used email traffic data to see when they were communicating.
They used the badges to see when they were moving. They also used surveys so people could say when
they felt most creative. Of course the paper found the opposite. People who felt most creative on the
days where they were basically roaming around, partly because I’m sure it was actual embody cognition
where they were moving and that probably helped. Then the people they were talking to.
I don’t know, so yeah, so part of it is like yeah. That’s why I love the term engineering serendipity
because it’s an oxymoron, right. Because, yeah, at some level you’ll never actually figure it out. You can
only sort of increase the conditions. There has been a word coined for that by the way. Stowboy has
popularized the phrase coincidencity for the actual conditions of the system. I’m not sure I want to go
that far and try to popularize that more. Other questions, yes.
>>: A lot of these examples have shown the importance for geographical proximity for engineering
serendipity.
>> Greg Lindsay: Yes.
>>: But I’m struck by how successful [indiscernible] open source [indiscernible] are the people that work
hundreds of miles away and might never have face to face interaction. How are they able to overcome
those challenges?
>> Greg Lindsay: That’s a good question. I haven’t studied that one as much. I don’t really know the
properties of how serendipitous those systems are, but like, obviously that is a completely other way to
organize work.
Yeah, I don’t know. I keep coming back to like a base, I just have read, either I have read the subset of
literature which confirms my bias. Or it’s true but like the notion like the most effective teams and most
effective collaborations have high degrees of face to face. Obviously that doesn’t mean that like the
Jason Fried’s of the world, you know with Basecamp. The fact that he doesn’t do meetings and doesn’t
do contiguousness, can’t lead to effective organizations.
But like you know I mean I’m struck by the example. I don’t know, especially those organizations are
much better at actually. I don’t know in an open source environment it’s really hard. Because I was
going to say I think you know for example the whole discussion about Telework and the evolution of
telepresence systems. Like I think that’s particularly great for an organization like Microsoft in the sense
that like you can use that to maintain weak ties or strong to weak ties in the organization while you go
out and actually explore face to face in different environments, or meet with different offices.
I think that’s how it will be used most powerfully. But I couldn’t tell you like why it works in sort of open
source systems. That’s something that I’ll have to look into. Yeah, like GitHub and others, like I know all
the great examples there but I didn’t study the dynamics as much. Yeah.
>>: What do think of tools like Slack [indiscernible] context?
>> Greg Lindsay: See Slack, I don’t know Slack is interesting. I haven’t used Slack as much. Like Asoni to
me. I’ve talked to the Asoni guys which is sort of similar to that. Like I don’t know I worry too much
they start to over formalize a little bit. You know in their attempt to deal with the informality of
organizations they make it too formal.
I don’t think Slack falls into that problem. But like this has been, pardon me, my distrust of holacracy
that Zappos is trying to do, like that whole experiment. Like to me like for example like holacracy and I
guess you know digital attempts to emulate it if Slack and others fit into that. But like holacracy starts in
the proposition of okay we want to create an emergent way of working that isn’t as rigid as email and
formal systems. We want to acknowledge that but then holacracy basically tries to solve it by over
formalizing massively where every single communication must conform to a template to do that.
It’s, I don’t know strikes as making it even worse. I mean obviously I don’t think emails the best way of
doing work. But I don’t know I’d be curious about your thoughts. What do you think of Slack or IBM
Verse, or Asoni, or all these new tools?
>>: Well I think we have this dream that we need tools that can scale from informal to formal. I mean
you talk about what is the spectrum of the palette of spaces. I think in an ideal world we want tools that
can of map to that. They have very low areas of entry in the informal stage. But then you have all the
richness that you need when you work on complex projects, which we do.
I don’t think the future is a bunch of lightweight apps on mobile devices. But there, I don’t know many
tools that can actually do that today. I think that’s kind of the journey that we’re on.
>> Greg Lindsay: But it does strike me like it sort of leads again to the sort of notion of like you know it
will actually increase your ability to do face to face work you know in more diverse areas. You know as
those tools get stronger and you know workflow becomes more and more virtual and more powerful. It
frees us up beyond you know, I mean the reason offices were invented, right. Was to basically build
factories for paperwork, like you know it raises the question, it allows us to be more configurable in
space.
>>: When you listed the Slack apps they recognize that people working together is critical to their
success. They actually say the best organizations use a bunch of like desktop PCs. They’re not all
running around on Smartphones. They want that mix. We’ll see where that goes.
>>: In my experience there’s no substitute for having a team that adds a high degree of trust built. That
just comes through face to face and just going through a lot together. Once you’ve achieved that then
the tools sort of falls by the wayside. It doesn’t matter email, IM, Slack, whatever. You use it to support
the momentum of the team. That changes where you are on the project cycle.
>> Greg Lindsay: Yeah.
>>: But it’s getting to that point where you have that high trust culture in the first place, I think. That’s
the challenge for most teams.
>> Greg Lindsay: Yeah, and that’s the reason there’s no substitute for face to face.
>>: Yeah, exactly.
>> Greg Lindsay: Yeah, well any other questions?
>> Justin Cranshaw: Great.
>> Greg Lindsay: Alright, well thank you all so much.
>>: Thank you.
>> Greg Lindsay: Hopefully that cohered into a talk. Thank you.
>>: When will we see the book?
>> Justin Cranshaw: Well, I can contractually owe the Knight Foundation a manuscript by the end of this
year. But, yes Benji if you’re watching this I will have it to you by December thirteenth.
[laughter]