-- Intro --
Chris Moriarty
Hello and welcome to the Workplace Geeks your regular deep dive into the world of wonderful workplace academia and the brilliant brains behind it. I'm Chris Moriarty.
Ian Ellison
And I'm Ian Ellison.
Chris Moriarty
And we're simply your north star on this epic voyage through the choppy waters of all things, workplace research. Now, before we get into today's interview, we just wanted to give a quick nod to our lovely listeners who have left us some lovely comments about our two-parter on neurodiversity Ian, what did folks say to us?
Ian Ellison
Well, Becky Turner at Claremont thanked us for highlighting such an important topic and suggested a team listened with her colleagues. And what a cracking idea a Workplace Geekslistening club and Hugh Allcock Greene said about the Josh episode in particular, I listened to this on the commute this morning and one of the best podcasts I have ever listened to. I kept nodding in agreement, I must have looked odd.
Chris Moriarty
And that brings us to our regular reminder to get in touch with us about your thoughts wherever you are in the world. And on that note, hello to our listeners in Canada. Hello to our listener in Kenya, and hello to our listener in the Netherlands. Hi guys. Again, we're just picking countries we'd love to visit. So just you know book us for an event or something like that, that'd be cracking. The easiest and most popular way to do this on LinkedIn is to just search for Workplace Geeksusing the #workplacegeeks that's #workplacegeeks, dropping us an email on hello@workplacegeeks.org or signing up to our newsletter for which all the information is available at workplacegeeks.org. That is the end of the admin now folks in a break from tradition, rather than ask Ian to reveal who we have on today's episode. I'm gonna do it. Why? Because it's been a passion project of mine for a few episodes now. Do you remember this?
Thank you very much. Now you mentioned Humanize there. That's of course, the organisation led by Ben Waber. And we are desperate to get Ben Waber on the on the show. So maybe we need to start a little competition, some sort of six degrees of separation exercise or how can we get to Ben Waber, but we've dropped him an email, but we know how busy he is. So, he hasn't got back to us yet. But if you know him personally, just get your phone out. Now drop him a WhatsApp say hey, look, Ben, you're being called out in the Workplace Geeks podcast. They want you on time to get in touch.
Yes, we found him and you're about to find out why we are so keen to get him on the show, Ian give the people a bit of background on Dr. Ben Waber.
Ian Ellison
So, Ben is co-founder and president of people and organisational analytics company Humanize and the work that underpins Humanize was born out of the MIT Media Lab over in the US and a very famous Professor Alex or Sandy Pentland. So, Ben is the author of a book called people analytics. And as he mentioned, during the interview, he still teaches at MIT alongside his commercial work. So, this is very much his field of passion and expertise. He's also got a new book coming out next year, which he mentioned at the very end of the interview. The working title for this is blackjack society. But it does sound like it's still work in progress. So, Chris, the last thing I'm going to say about Ben is actually a bit of a listener advice. Really, Ben is incredibly astute, but he also speaks phenomenally quickly. It won't take long to get your ear in, but it's really worth paying close attention even listen twice because there is so much in there. And also, don't forget to check the show notes. We drop goodies in there every episode for you to take your learning deeper.
Chris Moriarty
I would also advocate listen to this episode twice, not least it will get our stats right up if we just double everyone's listening. So, download it twice. Help us get our stats up. That's the most important thing learning come second. And if Ben wasn't enough, we have a very, very special guest for the reflection section. The Wanderer has returned. But for now, buckle up. Because here's Dr. Waber.
-- Interview --
Chris Moriarty
Ben, welcome to the Workplace Geeks podcast before we dive into today's episode and to your work. Could you give the listeners a little bit of background on Mr. Ben Waber.
Ben Waber
So Ben Waber, I am one of the co-founders and the president of Humanize , which is a workplace analytics company that spun off of our PhD research back in MIT were I am still a visiting scientist, I still teach the executive People Analytics course there, you know, our work and what we do as a company is really around trying to use data about how our happens to understand macro-level organisational outcomes, right really understanding using data from things like email chat, meaning data, but also sensor data about the real world to understand in an individual level, but at a team divisional organisational level, how our teams collaborating, how are people working, and how does that relate? performance, employee attrition, that sort of thing, you know, and at this point, we probably have, by far the largest multi-platform data set or workplace interaction the world, which enables you to do some pretty interesting things, which, hopefully, we can, we can talk about
Chris Moriarty
Some of these data points you've mentioned there, people will be very familiar with, right, you know, we understand we have emails and use all that stuff. But there's a couple of things that you sort of touched on there. And Ian and I both know you from, the past that we know a little bit about your work, there's two sort of areas that I think are really interesting for people listening, there's looking at metadata and what we mean by that, and how far that extends, and all the rest of it. But also, these, the socio-Vectric badges, which is, in itself is quite an innovation. So, if you can talk to us a little bit about those two before we dive into how they've been used in the study, we're about to cover.
Ben Waber
As you were saying, there's essentially two classes of data that we've looked at that before we started looking at them had received very little attention, that there's much more obvious in this space now. So one is, as you were saying, sort of metadata from these systems that we use just to work, you know, for things like email, you can think of metadata is, you know, person A, since Person B, an email at like, some time with some subject line. Now importantly, the way that we deal with it, and this goes back to our time at MIT, our hypothesis was that things like content mattered, right? Like, what do I email you about matters? Or what do we talk to you about matters, that say it has no effect, but it has a very small lift in predictive power when you look at organisations.
And here's the intuition behind it. Imagine that the executive team only communicates with marketing once a month, it doesn't really matter what they communicate about that one time, the bigger issues they barely communicate. And just what we would consistently see, at literally every company we looked at is that those effects dominate, at large scale, and even that large scale, so you get over a couple dozen people, those effects dominate. And so, what that means is that looking at the structure of these interactions, and then accumulating them across these different tools, right, so you can think of calendar in a similar way, you know, who do you have a meeting with? How long are those meetings, those sorts of things? Even, you know, person A, is it individual? Like it actually doesn't matter? Like, what is that person doing is the things that predict, you know, how likely is a team or the organisation to the milestone, how likely are people to leave the company, it's not about that individual, it's about how that individual fits into these larger social structures, and how those change over time. And so really, what we're trying to pull out of these different systems are things like, we're not looking at names or email addresses, or the subject line, things like that. It's really just these patterns of interactions and, you know, mash together, but again, digital communication, as we all know, that is only part of the puzzle.
And especially at MIT. Even as we started collecting that data, we had a, you know, strong hypothesis that face-to-face interaction mattered a lot. And actually, some of the work done in my research group at MIT, before I joined was about trying to use sensors to quantify those interactions in more limited settings and things like salary negotiation. But so, then what we did is started to take that to organisations for long periods of time. I mean, he's actually it was just again, to be totally honest, it's not like we had some super brilliant insight about doing this. It was randomly a professor from Sloan from business school, came by our lobby, he, you know, seen the kind of work we were doing. And he said, well, you know, I'm actually collecting a lot of data at a bank in Germany, giving them daily surveys, monthly surveys about, you know, their engagement, performance, all these other things, but I bet that you know, face to face interaction, and also things like email matter a lot for the performance. And so, we thought that sounded interesting.
So, we started planning this in late 2006. The really the idea was that, okay, well, we need some way to figure out who is likely talking to each other. And that's a combination of proximity from wireless radio, sort of like Bluetooth, but also looking at, like, who are you facing? And you can use infrared for that. So, like on a remote control, but then also using voice data, the idea that even if we're facing each other, the question is, are we communicating? And then at the time, we also try to get aspects of that communication, like who's dominating the conversation, those sorts of things. As well as things like accelerometer data to look at posture, look, at mimicry, there's a whole number of things you can look at. So, we built these things at MIT, we built, you know, over the years, we've built 1000s of them. But we're able to do those with very high degrees of accuracy, get data on face-to-face interaction, who talks to who and how often we are that sort of thing. And what we would see is that face-to-face interaction was just orders of magnitude more predictive of outcomes than the things like email data.
So, at MIT, we were doing this first couple 1000 people, then hundreds, and then finally 1000s. Again, importantly, we didn't collect names or email addresses. We didn't give individual data to companies, we still do this right, we still add great data. Because again, the point is not what is person a two to three on Tuesdays, what is the what are these larger patterns, but that that is maybe not so brief intro to?
Ian Ellison
Well, so this is a good challenge, right? Because what I'm going to try and do for my brain and Chris's brain, and our listeners is I'm just going to sort of recap that with a few key things. Because I think there's some lovely things to point out as we go Ben said So you are a doctoral, and then a postdoc student at MIT in the early 2000s. And you get approached by somebody from the business school, Sloan, who says, I'm doing this stuff with an organisation, but I think you might be able to help me so lovely. We've got two areas of interest, if we smashed them together, what amazing this can happen. So, you know that there's great information in the work data of people at organisations just using stuff like email, and meetings and stuff.
And it's impossible not to now think about what Microsoft is doing with 365. And with Viva and stuff, based upon what you were saying there. But we got to remember, this is almost 20 years ago, right? So, you know that there's great metadata there. But you also think that because work doesn't just happen in inboxes, as much as some people pretend it does, work happens in physical space. So, what is the relationship of people in space with each other, their social networks? And how do we bring those two things together to understand more, so you develop these what's called sociometric badges. And those sociometric badges allow you through a bunch of different sensors, all of which live in smartphones. But again, this is 20 years ago. So, you create essentially a Smart Access Control past that hangs around your neck. Now that access control past data, combined with the organisation of data, allows the first name for your company with socio-metric solutions. didn't roll off the tongue the way Humanize does?
Ben Waber
Yeah, it no it didn't it this Humanize is it certainly better. So yeah.
Ian Ellison
So, this is essentially both the product and the insight solution that Humanize brings to the world, which is when you combine those two things at an aggregated level, so we're not talking about what you are doing with me, or what Chris is doing with James or whatever. It's the aggregated interactions across company, you can bring that to life to understand high-level impacts like productivity, and the sorts of things that are helping and hindering them. So, you can get to the Holy Grail, which is how do we unlock better performance? For organisations.
Ben Waber
There's this fundamental problem. And this has really always been true of the fact that we are all individuals. And so, it's almost impossible for us to have perspective on especially really large organisations. And I say really large, when really when you start to cross, you know, the Dunbar number of 150 people, sort of the group of people you can reasonably know, it essentially means you don't really know what's going on. Right? Now, you might have a vague sense of it. But you know, I think this is one of the fundamental problems of just work in management is that even me as an individual? I can say, for example, well, I like working from home, I feel better, which I'm not saying you're lying. Like that could be totally true. The question, of course, is, well, what is the impact of that? On everyone, you work with. With you an individual, like, don't know that? Because we're people? Like, that's just not how it works.
In a similar way? Is that true for even the people you work with? What about like, all these other teams that work in different ways and have different kinds of work? Is that like, you don't know that? In a similar way? Even if you're an executive of a company? Okay. Like you might believe that a certain thing is a problem. But does the engineering team like actually ever talk to sales? Like you don't know that? And if you roll out some new training program, does it actually change how people work? Right, like, unclear? And so, the idea is that, yes, like, those things are measurable, and they can be made salient. And the idea is not just blindly using these behavioural metrics to make decisions, but it's really combining that with, you know, people ultimately know way more than any algorithm possibly could about the context of a specific team, work product, whatever. It's combining that though, with these more macro-level insights to say, well, it looks like here are the problems.
Ian Ellison
All the way through a conversation so far, you've kind of naturally now drop in caveats about oh, and by the way, this isn't data which identifies you. So, it's clear. And there are news reports featuring you on YouTube of people sort of almost going, is this surveillance at work? Is this the future of work? You have had to wrestle with this thorny problem, all the way through the history of this technology, and almost be ahead of both regulation and people's perceptions of Big Brother. So, before we get into the paper, Ben, would you just sort of give us your take on that angle and how you deal with it, because the ethics of this and the data governance of this is absolutely fascinating.
Ben Waber
And I will say my, my view on this has certainly evolved over time. Obviously, we first started doing this at MIT, where when you're doing a research study at an academic institution, you have to go through an internal review board and then there's all sorts of ethics checks you do there, which primarily in the social sciences, and again, include this under there really revolve around okay, you keep data private, you make sure you don't give individual data to If you're doing something in organisation, you don't give that individual data out. And so, we had to go through that process. But also, we found like that that was actually what was important for the organisations anyway. So how do you do that in an unethical way? Well, you first have to do a lot of communication beforehand. You have to, you know, to be opt in, right? And then, if people don't want to participate, it should also possibly be unclear if they're not participating. Well, how do you do that? We can give people fake badges that like look the same. Know one except them would know, it's not collecting data, all those things, there's all sorts of things like that can do, and which we did.
And we continue to do commercially when we were still using badges. And again, I think all that's important for, you know, whole variety of reasons. I guess what is especially evolved on my view of it, though, is that I assumed for a long time that okay, well, the fact that we're not collecting content, because content can also be sensitive, of course, and we saw very quickly, that was not very predictive of anything. So, there's not really good reasons to look at that, you know, in the fact that weren't giving individual data to companies, every data point you'd ever show me, they were averages, there was always more than three people in a group together, see, like, okay, mathematically, you can't identify people. So, we're like, okay, well, that's enough that that means we're clear.
Chris Moriarty
So, this is the paper from about going on for 13 years ago, 2010, you're sort of working on this stuff. So, it's titled productivity through coffee breaks, changing social networks by changing brake structure. I'm gonna, I'm gonna go out on a limb here, I guess, you know, some people might have like, looked at this, and almost downplayed it in their head with these guys looking at coffee breaks for you know, we're trying to do big management things. And these guys are messing around with coffee breaks, and all the rest of it. But tell us a bit about how this paper came about. You've told us how the work you do came about, but how did this specific paper come about? Who were the different players in the CO writers? Why did they come together? Just give us give us the background. So, this paper before it became a thing?
Ben Waber
Yeah. So, this group, this is sort of a group that we made the badges together, we all did our PhDs together, I mean, all of us founded human rights together as well. So, this is sort of like the core group, we've written other papers with, you know, other collaborators as well. But this is really the core group. So, while we were all doing our PhDs, one thing, that was challenging because we were going to real companies and collecting data on work patterns, and we were trying to relate those two outcomes, right? And as you all know, again, I've learned this very quickly that, especially for information workers, outcome data is extremely poor. So, you know, we wanted environments, at least at that point, we're like, okay, we really need to figure out what are we have so much data we can look at here, and we want to try to examine what are the things that are likely related to performance. So, we're really fortunate at MIT, because our lab was sponsored by companies a lot like hundreds of companies. And so we were, we had a pick of many companies that wanted to work with us to have us look at aspects of their workforce.
And so, you know, looking at a call centre, we thought was an ideal situation, because they get their first other one of the most managed workplaces, measured workplaces, in in the world, right? I mean, for decades, in terms of like, are people on the phone? Are they not? There's all sorts of data that was already there. And it's also interesting because they've also been, presumably, well, people assume they were highly optimised, right? Because they were so highly measured, they've been very optimised to increase the amount of time people spent on the phone. There's all these training things, they have hard metrics of performance. But you know, this, this, this bank thought something weird is going on, because they have sort of call centres actually all over the world that are essentially managed in exactly the same way. And people go through the same training program, similar employee demographics, but different call centres had very different performance, which, but yes, you could, of course, posit that individual skills make a difference, which of course they do. But they've tried to look at all those things, and really couldn't find something consistent there. And so, they assumed that, alright, the culture of these teams and call centres was something to do with that. But so how should we measure that? And that's where we come in, we say, okay, well, we'll look at, you know, we'll use badges, we'll use other datasets, we'll take a look at interaction within these call centres.
So again, it's essentially in the first phase, we were able to replicate what we had seen before, in terms of people with more cohesive teams. So, the people that you talked to talk a lot to each other, they were completing calls a lot more quickly than people, the less cohesive teams. And so, the fact that you see these cohesive networks tend to lead to higher performance in terms of like people completing calls more quickly. That seems kind of weird. But there's, I mean, there's decades of research in social science that these more cohesive networks lead to much higher levels of trust. And why is that? Well, it's probably because then if you lie to me, then all of our friends find out and then we all cut you out of the group at a basic level, right? So, the idea that if I give you a tip, or if I don't give you a tip, everyone finds out we cut you out of the group, and then your performance kind of lower. So, it sort of theoretically makes sense.
And again, this is not like new theory, right? Even the people ran this call centres like they sort of intellectually knew that was the case. They just didn't have data to show like, is this actually going on? But we wanted to see all right, can we manipulate it? And so, then when you looked at the data, use we have simple questions, all right, people's brains are normally staggered. So again, a call centre if the objective was to maximise uptime maximise the number of people on the phone in a given call centre at any given time. The reason that is the case is sort of this historical aberration, it's because if you go back to like the 60s, you might have only had a call centre with like, 100 people, which meant that if you had 20, people take a break the same time, you actually couldn't handle the calls coming in. Again, of course, in modern call centres, we have 1000s of people, even a single call centre, having a team of 20 people that deal with a single product, take a break, like doesn't really matter, you could shift the load to other teams that just had not been optimised for again, we found enough local optima. And so, you just didn't do that.
But occasionally, people would have breaks at the same time, sometimes their lunch breaks would overlap by about 15 minutes. And that's what about 80 plus percent of the interaction was happening, not surprisingly, right? That's when people had free time. And so, when you looked at you said, okay, well, people already take breaks. What if you just gave people on teams breaks at the same time, right? Because likely they will talk more to their teammates, and likely that will increase cohesion. Right? And you don't even have to give them more breaks, right? You can just do that. So, this is free, right? Like, yes, it requires shifting the load to other teams. But again, that was always technically possible, right? They just had no data to suggest that will be better than their current optimum. So, we rolled that out. And then we come back months later and collect data again, right? And again, of course, over this whole-time performance data is being collected, but we collect using the badges, they don't face-face interaction. And so again, not surprisingly, we saw that yes, cohesion goes up very significantly, in the groups when you changed, you know, the break structure again, and the other groups no change, not shocking. You see stress go down very significantly. And you saw that in surveys, as well as there's voice analysis, you can do also validating that, but finally, you saw performance go up by well over 20%. In quantitative, again, this is a quantitative metric. This is a hard metric.
And I think we were all surprised at the magnitude of that effect. Again, imagine going to a company and saying we're just gonna change when people take breaks when Ivan gave them new breaks, and we're gonna increase performance by 20%. I mean, the normal reaction was, do you think of something of that kind of magnitude, you say, well, we're gonna have to completely re-engineer the company. This results showed what we would consistently see across our research, and even today is that if you can find these social levers that people are responsive to, and you can ask them the right way, you get really big results.
So, it was just, again, it was a really great and also attrition went down over time, very significantly, again, goes down to like 20%. And these, I think, might have been 20%. But still, like, very significant reductions in attrition. I think that's what not only the result itself was very positive. But I did this, this experiment, in particular, made me even more positive that this kind of technology and approach can have a really positive impact on work for most people, because of the ability to point out these really poor work and management practices in a way that actually lifts all this.
Ian Ellison
So, what you've got there, you've got the kind of the old school-received wisdom about how to manage call centres based upon essentially previous technologies and previous scales of operation. And they just become pervasive. And that essentially, without even realising it, holds an organisation back. And then the other thing to the role of sociometric badges beyond the organisational metadata, even if it's high-level performance data, it's like arrangements of people in time and space makes a difference.
Ben Waber
You know, the ability to put numbers on those things and to say, right, like when the watercooler is here, within this proximity people, here's how likely they are to interact. Again, what impact does that have? I would just argue that all these things are management tools, right? That I can say that when you give people a break, at the same time, it makes it more likely that these things will happen. doesn't guarantee it? It does not? That's a tool, right? And so, a lot of times in a huge amount of management is to try to engineer work to happen a certain way. And you know, but people mostly do it through formal methods. They're like, you report to this person, we're gonna have meetings at this time. And I'm not saying these tools are useless like they do serve a very important purpose. But I'd argue, again, instead of being to your point, space and time survey, arguably more important purpose. And we've just seen this reinforced over time.
I mean, I think that's what actually surprised me most about even research was my actual assumption, just be totally clear. I assumed that it space didn't matter at all didn't matter. That was my assumption. And it was just I remember very vividly in the first company; we ever collected data with a badges and just the spatial effect was just beating us over the head. I mean, it's not shocking, right? People sit next to each other, they talk a lot to each other, which like, yes, like we understand this, but then the degree to which that effect matters, and then dominates everything else. And so, what I hope is that people recognise that yes, these things are tools and space is I would argue probably the most powerful tool that management has to very quickly change how people work. But again, if you change it once, and then never change it, again, you're not using the tool, and again, but to do that effectively, I would argue that you do need quantitative data, because otherwise, you can't possibly know, with enough velocity, how things are changing and what's effective and ineffective, right?
If you can only collect surveys once a year is their level? Again, I'm not saying you don't do that, absolutely, people should do that. But that gives you a very slow different kind of qualitative view on what's going on. And that really needs to be combined with this much faster-paced data they can give you tools on Alright; how does this quantitatively change things? Right, that enables you to iterate. And so, I do hope that, and this is still certainly not caught on at all. But this more experimental mindset to management, I think is fundamentally correct. And is where things will eventually move. But it's going to it's going to take time, because that is not how work happens or management happens today.
Chris Moriarty
There's so much about this paper, I enjoyed reading apart from anything. I've worked in a call centre. Now it wasn't a it wasn't a big call centre farm is about 20 of us. And it's for a small company. But you're right. I mean, it was customer support, it was people coming up complaining, it wasn't as constant complaints. So, we weren't one of those that we get battered all the time. But it was it was constant.
And it was just a few things that kind of stuck out to me. I think the first one was the measure of productivity. And that, you know, we could do a whole podcast series, never mind, another episode on the P word. Because you were talking about reduction of call time is what a company values, whatever. I've always said this about productivity, what do you value? What do you want to achieve? And then we're going to look at something and do that because I would be sitting again, I'd actually prefer something like call resolution, because we've, we’ve solved it.
Ben Waber
We actually have those metrics as well. And they all showed the same thing. It was just nicer to use it average handle time, it shows the same effect. I would argue there are other ways they could have looked at it that were not individual over time, we did not get into it in the paper. I also don't think I exactly had the perspective on how one could do that. At the time, I think I have much better ideas now about how to do that.
Chris Moriarty
So, the other thing in this paper, from my call centre days that I found really interesting. And this is definitely a UK versus America thing was the cubicle. And it just got me thinking that I spent a lot of time that call centre and we had partitions as was duergar. Back in the early noughties, we had politicians, but it was kind of those sloppy ones. So, the like were all four desks facing into each other. You could sort of see my face on the angle, you could just see like a whole one of my eyes and a little bit of another eye. I mean, I'm demonstrating this on a podcast, I understand that this is terrible podcasting, right? But one thing that came from that is that in between calls, I could really hear the other call, I could hear what they were saying. And it just got me thinking about that whole knowledge transfer bit as well.
Because what I obviously think and yes, the breaks showing that social cohesion and stuff like that. But equally, it'll be fascinating to see what the impact of those moving from cubicles, which I can imagine quite a depressing kind of environment to something that felt like you were connected with your people that you were breaking with in between times as well. I don't know whether you've done any work like that since but it's that really stuck out to me that I just didn't want to work in a cubicle. That's was the thing that kind of immediately struck me.
Ben Waber
Yeah, no, we've done a lot of work on that over time as well, maybe not surprisingly, and again, consistently see sort of effects. I think similar to what you're describing, in that there's really fascinating things were like the length of desks matters was like as people start to get farther apart, then they're less likely to communicate, which is not but again, the degree to which it matters. It's so weird, because like, you start to get beyond like a desk could be like 20 centimetres longer, which like, doesn't matter. But like it does, it actually makes people less likely to communicate. And again, like you see these things. So, you see, like teams who are at I know it's one I forget exactly which study site this was.
But there's some teams that sat at these longer tables, and some that actually were at cubicles where they were closer together and the cubicles, I don't think if I recall correctly, they weren't like tall partitions either in a similar way, like what you're describing, and the people who are at these more tightly clustered desk or just would have much tighter networks and much more likely to communicate, again, despite like seemingly trivial distances, in terms of difference, right? So those things do 100% matter. What you also see, though, is that these effects is the spatial effects. They keep occurring at different scales. And importantly, it doesn't just happen with face-to-face communication, you see the exact same thing with email and chat, which to me doesn't. Like it makes sense. Now I understand why. But at first, I was just like, that doesn't make sense. I can email anybody, but like you don't.
Ian Ellison
Yeah so, what we got from this study is a little market in the evolution of both Humanize and in your insights and you're thinking as a group of founders, but what we've got from 2010 is a is a write up of a small group of people and spotting a spatial and time change, rearrange the structure so that people are together to get have an opportunity to be together on breaks. And that improves well-being, and it improves organisational performance is the basic upshot of it and then that becomes a replicable thing which can actually small changes that you wouldn't even necessarily spot without your sort of insights matter.
Now, what you said during that was space matters, cohesion matters. It impacts on collaboration. And I'm now pretending to be that managing director of an organisation that absolutely hated the pandemic because I had to accept the people working from home. And I don't like that. And the one thing that I want to do now, because I believe the best work gets done together, I believe that the watercooler moment is the thing that makes everything sparkle. But the counter to that is all of this great performance that has happened beyond these organisational spaces. So, what I'm sort of working up to sort of saying, Ben is, what did Humanize learn, because of the pandemic? And beyond the pandemic? What have you got to say about hybrid? And how do we step on from some of your pre-pandemic insights? Because this is the stuff that Microsoft get really excited about? Because it's amazing for Microsoft to have a hybrid world, right? Because, because their products are superb for it. And who's benefited more than teams do, you know, I mean, and zoom. But when you look at that study, and when you look at your book, there's this real importance of space. So how do we translate into the virtual world as well?
Ben Waber
So, this is something that we were even a Humanize d, we're going on a transition, I sort of hinted at this earlier, even pre-pandemic were, as we were deploying it larger and larger scales. I mean, we think starting in like 2018, we're at the point where we deploy globally across every single employee at a bunch of fortune 100 companies, which have hundreds of 1000 people, we could not do that with the badges and maintain, from an ethical perspective, we rolled out, we would have had to force people to wear them. And then if you don't do that, then you can't, you know, get compliance to a high enough level where it matters.
So essentially, we were doing over time is shifting more and more towards just using existing corporate data anyway, some of that was still badge and badge out from buildings and internal sensing data mean, actually, in 2019, we got to a point where we were deployed broadly enough, where at least for information workers at large companies, we have a globally representative dataset, I can say that with a very high degree of competence. Yeah, at this point, we have a large data set in the world on those sort of things across platforms. I guess what was interesting is that pre-pandemic, there was, I mean, tons of work, you know, research done on remote work of pre-pandemic, and it was fairly consistent, right? There's meta-analyses on it done pre-pandemic, where what happens people work remotely? And their networks are smaller performance, lower all these things in general, right?
Now, how does it different than then the pandemic? Well, one is that not everyone was doing it, which matters a lot. There's a whole variety of reasons why you would think that wouldn't transfer to the pandemic right? Now, then we started to observe during the pandemic, the you know, during times in different regions, when people were almost exclusively working remotely. Again, you saw some things that we wouldn't have expected and some that we did expect, right? So, on the not expect, which can make sense now. But people strong ties dramatically increased over the course of pandemic, getting it makes sense, you know, you need to talk to certain people to do your work, I'm not going to bump into them or equivalent people in the office. So, I have to schedule meetings them makes total sense.
The flip side, again, thing you would expect that you observed is weak ties dramatically decreased. This is something also incidentally, you sort of hinted at this, this isn't just work we've done, actually, folks at Microsoft on the exact same thing. I mean, our results match up almost perfectly. Right? So yeah, so you see those things? So why does that matter? Well, weak ties are interesting, because actually, we also did surveys, we asked people like, okay, how do you feel like you communicate with people, and people assume that nothing really changed for them, and they just communicate better than they did before. People don't notice these things. Well, you don't really notice weak ties because they're a very small percentage of our time. You know, typically these ties the way we calculated so like a longer discussion, or we do it, but basically, it's like people, you spend the equivalent of five to 15 minutes within one-on-one communication a week. So, imagine, like, you're in one for person one-hour meeting with this person a week, like, that's sort of what we're talking about. So, like, you know, them, like work perfectly with them, but not like really, they're not on your team or anything, right?
So, if you instead of meeting with them once a week, it's once every two weeks, like you don't feel that at all. But that means that you know, information flow is half as quick as it was before. Right? Like it has a huge impact. Oh, one other one that's interesting is communication was way less hierarchical, when people work remotely, communication with people, other layers of the hierarchy went up by things like 23% is quite significant. Which again, why would that happen? Well, again, the social signals that are in offices, like executive sit on a different floor in a private office, there's all these things saying don't talk to them. If you're over slack, you were teams, that doesn't exist, right? So, I'd argue that's probably in general a good thing, although again, what does that tell you? That should tell you that remote work is also a management tool. Do people need to do more focus work? Do they need to have more time with closed eyes? remote work is better than in the office. Or it will be more likely to create that.
If you need to have people bump into folks on other teams they don't normally talk to, you know, again, if you need them, you know, if executives need to focus in more for whatever reason, like then the office is going to be much better for those things in general like these are generalities. Are there exceptions? Absolutely. There are exceptions, right? That's in general, what happens. So again, I always advocate that like, yes, that's how you should probably form your hypothesis, what you should do is you should test that all these things when you come to what executives think about this, where I spend a huge amount of my time is with, you know, because we have the data from all these companies. And typically, we have a lot of CEOs who say I want people back in the office five days a week, and they say you peel back, why do you want that?
You know, will they use some reasons that are just indefensible, which is like, that's how I did things? Would you like, well, Were you successful? Because you do that? Or in spite of that? It's actually unclear, right? Unclear. But we typically when you peel back beyond that, you know, and sometimes like, oh, well, my buddy did it. And they're successful. You're like, well, that doesn't tell me anything, again, in a similar way. But again, if we just think of these, okay, let's think of them as management tools. That's it, they make certain behaviour more likely, certain behaviour, less likely. Okay, but what does it also mean? It means that his work changes, then our use of them has changed, but that's our hypothesis. And so, we're gonna test it. And if it doesn't work, we'll try something else.
Now, again, everybody's going to be happy with every manager decision now. But reasonable people see that, and they say, okay, I understand, do it right. And that enables you to move in a sort of speed that I think is appropriate. Now, no one's doing that. Right. We see what everyone's doing. What is everyone doing? Three days a week? Why is it three days? Well, the CEO wants everyone in five days a week, but we did a survey. And we found out if we did that, like 40%, people would leave. And people say they want to come in one day a week well, threes in between one and five. I mean, that is when like that is actually, we know this, that is actually the discipline which is which is indefensible, right? But that's what they do. So, there are better ways to do it. But to get also the days in the week doesn't matter. Right? So, like, when people saying come in whatever day you want, well, that is a waste of everyone's time.
Ian Ellison
Deal with Nick Bloom on this, because he's like, if you're in you're in together, right? Because it's about cohesion.
Ben Waber
Right. But if we're saying the primary purpose of an office is to create connections that can't be done, easily remotely, then then certainly that's what it's for. Beyond that, I am very bullish on the possibility of using things like events, to supplement or even maybe replace office space. Um, we just couldn't do them during the pandemic, you know, but I am, I do think that's an interesting thing to sort of support this creation of new ties, it probably cost the same.
Ian Ellison
So instead of a property budget, you have an event budget, and that's that. So, firms like automatic to run WordPress, that's exactly what they do. Right?
Ben Waber
And so, I think that is that is plausible. Now again, I still want you to measure it. I know they don't measure it. And again, if you, if you twist my arm, we say okay, you can either be remote or in person should you be in person to be in person, right? Because there's a survivorship bias on the remote side, right? People always point to like Git lab or like automatic, I mean, automatic got like, what, 50 people, so it doesn't tell me very much. Everyone points to Git lab, you're like GitLab is one company, if literally, you can only tell me that one company has gotten to like over 1000 people and work remotely. That probably means is like they got lucky, basically, right? Is what it tells you, which is fine. It's fine to be lucky, but don't pretend you just copy what they do. And you'll get the same result like that is unlikely to happen because the forest littered with dead companies.
But the other thing that we see consistently data and actually no, there's a paper, one of our collaborators in Harper's Harvard Business School, looked at our data, and has written a paper on this on time zones. So, the question, of course, is people say, oh, well, you if you're working remotely, you can be anywhere. And that is demonstrably false. Right? So, one thing that we've seen in our data, and actually using daylight savings time in the US as an exogenous shock on this. So, the question is, if I'm like an hour, like for every hour away from you, I am, how much less likely to communicate? And of course, his normally conflated with a whole variety of issues, because you'd say, okay, well, normally our team in Boston does this sort of work, and they don't need to work as much with this team in San Francisco. So, like, it makes sense, they won't communicate as much fine.
But Daylight-Saving Time is great because the teams are the same, but you change by an hour how far away they are. And essentially, you're able to show is that that hour makes them 9% less likely to communicate, right, which is a lot, that is a lot for one hour. And so, you get interesting. And so again, actually, if you look at the date over like, you know, not with those kinds of controls, but you see over long distances, you get dramatically less likely to communicate as people get more geographically separated by time zone, again by time zone, not distance time zone, which gives you interesting results like that, for me in Boston. It's actually assuming language isn't an issue, easier for me to work with, like someone in Peru than on the West Coast, which is interesting, but I don't think companies or individuals think strategically about those things.
Chris Moriarty
It kind of feels to me that what you're saying about organisations, and you know, if we use the general kind of idea that organisations want people to work better for whatever reason, right? We want people to work better, what you're saying, throughout this whole conversation, I think the paper from a decade ago the work you're doing now your frustrations, you clearly got a lot of pent-up frustration there glad Ben that we've been able to offer you a platform to get rid of those frustrations. Absolutely.
But it kind of feels to me that the reason all of this is so difficult, the reason there's so many books, so many papers, so many projects, so many research studies, is there's so many things that can impact it on an individual collective team organisation basis, there are so many things that can change what happens with work that either organisations just put the fingers in their ears, as you know, it's kind of the Henry Ford approach where he talked about marketing, he said, I know half my advertising budget works, I just don't know which half right? If I just keep doing what I'm doing, things are generally working, right? And we might just say, you know what that is as good as it gets. But it feels to me that you're an advocate of a more scientific approach to workplace performance. And not I mean, that kind of is obvious to say to somebody who's a scientist, right?
Ben Waber
Sides of the argument, of course, problematic term, we can Yes.
Chris Moriarty
But yes, but I've always when people talk about that, I think people say data and tech and things right. And I've always I've said this to him before now, when I hear people talk about scientific approach, I think back to my like in your world high school, but you know, Senior School, for me that kind of science projects, where we would have a control test tube that we did nothing to that one, we put that liquid in that one, we put that liquid in, we'd only change the liquid, everything else would change the same so that we can isolate the reaction of each thing. But we don't do that in workplaces, because quite often, we change loads of stuff at the same time. And we don't know which one works.
Ben Waber
But what I would say is people copy each other like that is what management is today. And I include workplaces in this as well. Everyone just copies each other. They don't even again, don't even argue sort of Chris, as you were speaking there's this step above that, which is what is it OB saying or whatever the research says on average is true doing that it's what's true, on average, it could be different for a certain company. And so again, what I advocate is just even if you don't believe it, that's fine. Just test it. So, this is sort of I think this is the fundamental problem with management. And again, I include workplace in this as well, right? Because I think that people in corporate real estate, and I think people in executive positions in general are far too complacent with that. We're just gonna keep doing what we did. And we make a lot of money, which means we know we're doing which, again, like I actually that's unclear if you know what you're doing, right?
So yeah, the CEOs are paid ungodly amounts of money. And it's assumed that that's because they know like their better way better than everyone else, right, basically never tested except is a great example. And I do have a new book coming out where this is one of the examples which we know about in 2011, 3g capital bought Burger King. And three years later, they let that CEO go, and they put a 32-year-old associate from 3g capital, who had only managed a team of five before then they made him CEO of Burger King, they paid him $7,000 a year, which is a lot of money, a lot of money, okay, but compare that to Jack in the Box. So, I don't know if you're familiar with Jack in the Box across the pond, it's another fast-food firm. Right? But you're not familiar with it at the time. They were the same size, Burger King, any way you slice it revenue, estimated market cap employee base all these things, right?
But their CEO was getting paid $30 million a year, after 2014 Burger King wipe the floor with Jack in the Box, right? And that's how you get paid a bunch more. So, the story isn't about the Burger King CEO. It's about okay, if you're the board, a Jack in the Box, and you're paying someone 20 times more than like some guy, you better be damn sure that they're 20 times better than just some random person. And clearly, they're one. And so really this is the issue is that everyone assumes that this is how you have to do things. But I think as soon as we start using some data to show actually the emperor has no clothes.
I think this has a lot of implications, but also for workplace because workplaces in all these things are subject to such trends and people copy each other. And I think it's so corrosive. I think we don't learn anything. We waste time money. We make workplaces worse for like everybody. And that I think the opportunities, not the data solves all of this. Of course, it doesn't. But it enables us to at least agree on here's reality, here's what matters. And then we can start moving forward to start to change these cultures of how these things work.
Ian Ellison
Isn't the first time we've spoken then we spoke back in 2017 When you were over keynoting in the UK, but I remember you saying that, that you thought there was almost like a 10-year lead time with new. I don't know whether it's specifically new technology or new approaches to organisational stuff. And it was like a bedding in period of 10 years. And so, when we spoke in 2017 you were kind of going this is new stuff to the world's not new to MIT. It's not new to us. We're doing this for bloomin' ages, right? But to the world this kind of analytics is new, and it will take 10 years, so I sort of mentally started the clock, then I thought, right 2027 I wonder where now, here we are post-pandemic 2023. Microsoft is scraping the living bejesus out of all of our use data and telling us stuff. We've got data analytics books, we've got people analytics groups on LinkedIn, how far down that dial of progress to getting genuine data-level people analytical insight into organisations, and how much work have we got left to do?
Ben Waber
We still have a significant amount of work to do I actually in Asia, and I'm actually fairly impressed that I don't think I was that far off. I mean, people analytics roles are like a second fastest growing position on LinkedIn at this point, which is insane to think about that this didn't exist before. So that's interesting. I think we're at the point right now where most companies believe they need this, but they don't exactly know why what I thought in the past, even when I started Humanize was like, why did I even start the company? Because I thought that the biggest barrier towards getting to this point was a lack of skills and technology. And that still is a barrier. I think the biggest barrier at this point is that this is not how companies have been managed and have people worked in the past. And so now we're talking about cultural change, which is hard because we're talking about people. But this is actually why I think that a lot of the work going on with the investment community. But this idea that investors are starting to use people-related metrics to derive their executive compensation at investment decisions. I think that is what is ultimately going to push this to a point where the field will be totally mature.
-- Reflection Section --
Chris Moriarty
Well, that was Ben Waber. And it's time for the reflection session. And like Gandalf, coming over the hill at the Battle of Helm's Deep at the last moment, our Saviour, we are joined ladies and gentlemen, By Dr. James Pinder. James, welcome back to the reflection section. How have you been?
James Pinder
Very well, it's nice. Nice to be back.
Chris Moriarty
Have you missed this?
Ian Ellison
He always misses us, Chris,
James Pinder
I've been looking forward to this for weeks.
Chris Moriarty
So, you, you've had a chance to listen to Ben, it would be unfair to invite you back and get you back into the fold and not allow you to go further. So just tell us what you thought of Ben and the work that he's been doing. What is it that you think listeners should be really taken away from that conversation?
James Pinder
The thing that stood out to me, I guess it was the sort of golden thread through the conversation, which was the often we talk about the impact of working practices and the way we work on organisational outcomes, which is clearly important. But if you think of it as a three-stage process, it's the stuff that happens that influences work and the way people work. And often that's kind of not overlooked but taken for granted. So, it's those interventions that impact how people work interventions in terms of changes, whether that's cultural workspace or technology, and the impact they have on work. And I guess, is this tent? Isn't its management and art? Or is it a science?
And yeah, often that sort of those changes either taken for granted or their guests Ben suggested is it that people just copying what other people are doing? Or actually just based on gut feel, or, you know, other that influence how we make decisions, you know, this is the way I used to do it sort of thing. So, I thought that was really interesting, because often the focus is on the end part of that flow, rather than that beginning part of the flow. And also, I guess, linked with that is some part gender discussion. He talked about decades-old theory. And what's interesting for me is theories that have been around decades that may be proven or improved in inverted commas. How new data and new techniques can start shining a new light or putting a different perspective on things that we've talked about for decades, but maybe haven't had the evidence, or we haven't had good evidence to prove or disprove. So, they were the two things that I guess stood out for me.
Ian Ellison
Could I just ask just for my own understanding, when you said there's like a three-step process, right, the beginning of that bit? What did you mean? What are the three steps? What are the three elements you were thinking about?
James Pinder
Doesn't necessarily need to be three separate, I was almost thinking make an intervention that influences how people do their jobs, how they work, which has impacts on organisational outcomes. Now whether you draw it as three stages is kind of irrelevant, but it could be two stages with arrow coming in from the side. But the point is, we're pulling these levers aren't we these venues to management tools, and as you said, they're not guaranteed to deliver if it's not pull this lever and you get result A, but increases the certainty of it or uncertainty of a particular thing, or people working in a different way.
Chris Moriarty
The kind of message throughout that whole thing now from Ben and I think I kind of mentioned that when I took a trip down memory lane into my science class as a teenager is that I think people are desperate for the thing. They want the thing and just be able to do it and then move on with their lives right but to your point about whether management is a science or an out, I guess what you're talking about there is we have to constantly look at how things are working, do something, then observe what happens as a result of doing the thing, decide whether that's preferable or not. And if it's not go back and try something different. If it is, learn from it and keep that process going. It's almost like a you were talking about stages and arrows, but it's kind of like there's a loop, and then you move on, I think I knew there'd be a word for it, iterative.
James Pinder
I'd put on the notes that I've made while I was listening to his test and learn that sort of mindset of not becoming so wedded to something that you can't turn back or try something else. And I guess looking at with a consultancy, hat on that really chimed with me. So that for me was like the big one and the sort of things linked to that.
Ian Ellison
What I've heard from you both is management actually is both science and art in organisations. I think the hardest bit is doing the hard work to be open to learning, like you said, James, creating opportunities to test and learn. But actually, the sticking power of going with that, because it's easy just to decide something and then crack on. Even if it's the wrong thing. It's far harder to be in a perpetual state of learning and improvement. And there's loads of stuff over the years, you know, when you said, iterative, I thought Deming cycles, and then we're a hop, skip and a jump from continuous improvement departments. And yet, there's been endless ways that organisations have tried to crack this. But I think there's something in what you've said about how new science and data techniques, and now enabling old theories to be tested, reopened, and re-explored.
James Pinder
One that occurred to me as I was listening to Ben, and the sort of period that he's been doing this work is just the improvements in natural language processing. And actually, what they might now add to some of the early data that Ben collected the content as he described it, which wasn't very predictive. But now actually, if you could do more with that content, would that actually provide insights that you wouldn't have got 20 years ago, or 15 years ago, even five years ago, maybe you talked about this decade-long period for new ideas sort of become widely adopted, there's a lot of things at play, there isn't there's techniques developing, there's more players coming into a field and therefore putting more brainpower into and developing new ways of looking things.
There's lots of different things that sort of drive that adoption isn't there. There's the fact that people like copying what other people do and look around. So, you can see why these things do take time to sort of become embedded, but also part is technology developing as well, at the same time, isn't it?
Chris Moriarty
You know that kind of reminds me of the cold cases, thing, you know, that DNA techniques, you know, they go back to a case in the 80s. And go, well, actually, we can, we can check this now, because we've got powerful data technology, or we've got new scientific methods that allow us to extract DNA in a way that we could never do it before. So, kind of forensic workplace management, there's a spin-off TV series about that, like going back to the 70s to a case study and find out why it didn't work.
James Pinder
Can I contact Netflix about that, Chris?
Chris Moriarty
Yeah, the I'm just going to draw out the presentation and pitch it.
James Pinder
They’ll snatch your hands off, Chris,
Chris Moriarty
Ian what was your big takeaway from Ben's conversation? For that to work, we require you to come off mute.
Ian Ellison
I didn't mean to do that. There's a new button appeared at the bottom about marking clips so that you can give yourself little pointers.
Chris Moriarty
So, are you telling me that you weren't listening to James and you instead of playing around with the technology?
Ian Ellison
No, I wanted to check that you could hear me properly without asking and interrupting. And then I managed to hit the wrong button.
James Pinder
He was testing and learning. That’s what he was doing.
Ian Ellison
Anyway, James has already kind of talked about the two things that I was pondering as I re-listened. One for me was this sort of the timescale piece to all of this. And the second bit was you mentioned, the word you use with certainty and uncertainty, James, but what Ben was actually talking about was datasets where you could make predictions from them. Because what this is really, about when we're talking about statistical analysis, is being able to say with a degree of certainty or less certainty how much something is likely to be the case, based upon what we found previously.
And I think people forget that. I think what your brain does, as you're reading findings, like this is going, you know, A plus B causes C or A plus B equals C or something like that. And actually, you almost need to pause and go, well, actually, this is about knowing that Humanize have observed this phenomenon in so many places now that they can comfortably suggest that it is likely to be the case in your place as well, if they see that pattern, maybe to folks who deal with huge datasets, and to do analyses like Ben and Humanize . Maybe that's given, but I don't think it's a given in everyday organisational thinking. Because I think that probably links to the fact that we're so busy looking for silver bullets to improve things so that we can move on and do other stuff that's on our to-do list.
Chris Moriarty
Ben talked about that when he was talking about averages wasn't he was sort of saying that you know, what the study is telling you is on average in that study? That is what happened. But it is not what will happen if you do it.
Ian Ellison
So, you have to be able to the testing and learning. Yeah, exactly, definitely. So, the other bit, which I’ll sort of swing back to you now, then is this idea of timescale. I mean, I spoke to Ben in 2017. And I referenced that towards the end of the conversation we were talking about this 10-year kind of bedding in for this way of thinking into the industry and into organisations at the same time as speaking to Ben in 2017. I'd also spoken to Kirsten Zeiler. Another previous guest of workplacegeeks, and I sort of treat them in some respects as a bit of a pair in my thinking and where I sort of box theory in my little brain shell because, you know, they're both interested in social network analysis from different perspectives. Kerstin's is always more in a space syntax direction, but the theory for this sort of stuff began James was it the 60s Is it Hillier and Hanson? kind of stuff?
Yeah, which is a very particular branch, ultimately, of workspace theory. And so, when I was thinking about timescales, I was thinking about exactly as you said, James, these original theories, which have been explored in different ways, particularly by University College London over the years. And then here comes Humanize through MIT 20 years ago, with Sandy Pentland, and his research lab, of which Ben was a member of and all of his co-founders have Humanize all playing with these ideas but doing something different with them finding different ways to map the social networks. And then 20 years later, this becoming a real organisational valuables thing to the tune that did you notice that bit? He said, right at the end. People analytics roles are the second most What did he say second, fastest growing.
Chris Moriarty
Second fastest-growing job title or job title on LinkedIn
Ian Ellison
Absolutely which really signifies and then he said, and actually what's really going to drive it is the ESG approach to organisational value. So, when stakeholders start calling for better ways to understand and treat people because it's part of what makes an organisation of valuable, not just profit and shareholder return, all of a sudden, this thing truly tips. So, I just thought the timescales that I mean, that's the 50-year timescale, right? From the beginning of ideas about social network analysis, I may be wrong about my origin time, but something like that to now. And that's kind of mind-blowing when you start then adding the big data analysis to it.
Chris Moriarty
It's interesting that you mentioned Kirsten, because I thought about Kirsten as well, James, when you were talking about new technologies, going back, and explaining something that previously we might not have been able to explain. And I think it's just exciting to see lots of studies like this come out. Now, it kind of leads me on to what I was thinking about off the back of Ben's conversation. And it's the running theme in this podcast, which is this relationship between academic study and practice and commercial activity and drivers. Right. And I think we've in the past talked about it, maybe me personally, as a tension, you know that you've got this group over here doing that, and the commercial people over here doing this, and never the twain shall meet. But when Ben was talking about the fact that this paper and the co-authors were the people that ultimately went on to found Humanize , and then become the sort of commercial entity that went on and did more stuff, it just got my brain ticking along about something that I remember seeing my MBA, which is this symbiotic relationship that you can create between commercial practice and an academic practice.
And it can go the wrong way, commercial drivers will fund certain research in a certain direction. But this is a really nice example of where research, they said, Well hold on a minute, this is really valuable, this is really interesting, has then become a commercial entity. And lots of the insights that Ben was talking about is as a result of the commercial activity, not as a result of the early stuff, which would have been like a couple of projects here, and maybe a project there may be an expansion of a project here. It's actually through the commercial vehicle, that they've been able to take that practice, large scale across territories, really sort of gather more and more and more data. And I just think it's just nice to see, I guess, I don't know if I've got any sort of reflection on it, particularly other than it's just nice to see when the two things work nicely together. You know, and we are through Ben's insights in this particular episode. benefiting from that, that kind of model.
Ian Ellison
It must have been an incredibly optimistic and possibility-fuelled Jedi to be at MIT at that point, working on something which died out is purely research and just realising that can mercial opportunity, it must have also been a challenge because let's be honest, Ben is quite a brainiac. Right? That realisation that if you pivot, arguably your entire career here, unless they went into it with a hunch that this was going to become something hugely commercial, my guess is that thinking unfolded. And then you have this whole new opportunity in front of you, which is absolutely incredible.
Chris Moriarty
On that note, what I hope is that Ben, Sandy, the gang all have a really grainy photo of them all in the lab together, arms around each other, like something out of a Hollywood film, you know, it's just pinned up in the corner. And maybe in the final scene, we just glanced to remember in the good old days.
Ian Ellison
Could I just fact-check myself Because certainly, Kirsten will be writing in and correcting me if I don't do this here. So, Bill Hillier, and Julian Hanson, the social logic of space 1984, not the 60s and the 70s, although I'm sure they were thinking about it earlier than the massive tome of a book came out. But yes, that's the timescale to this from social network analysis to where we are now.
Chris Moriarty
James, we're going to leave it there, because it's quite short reflection section here. No offense intended that we've cut your one short, but what plans you got for the rest of the day? If you got any medieval trades? You get to go and learn this afternoon? Yeah, of course. Yeah. How is your blacksmithing coming along? Have you done any more? Blacksmithing?
James Pinder
Not recently? No, no, I need to get back into
Chris Moriarty
What so you made a poker last time. If you were gonna go out and make something new. What is what do you do blacksmith sit down and go, gor, you know what I really want to make next I really want to make a gate handle. Like, what is it that you're going to do next?
James Pinder
I'll ever think and the next time I'm on. I'll let you know.
Chris Moriarty
That is far too much tension for our listeners to be waiting to hear what you're going to blacksmith next. Well, look, thank you very much for joining us. We hope it's not too long before you join us again. Speak to you next time.
-- Outro --
Chris Moriarty
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Until then, speak to you next time.
