How will generative AI impact work? And why are smaller companies adopting generative AI more than enterprises. Generative AI is almost literally exploding: there are so many possibilities. But how is it changing work and business?
Recently GBK Collective, a consultancy founded by top academics at Wharton, studied 672 businesses in the US with annual sales over $50 million. In this TechFirst we’re chatting with 2 of the authors to get a sneak peek into what they learned:
- Dr. Stefano Puntoni, Professor of Marketing at The Wharton School and Co-Director of AI at Wharton
- Jeremy Korst, former Microsoft and T-Mobile exec, now President of GBK Collective
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Transcript: generative AI, work, and business … what’s changing?
(note: this is automatically generated and may have errors)
John:
Hello and welcome to TechFirst. My name is John Koetsier. Generative AI, we know, I mean it’s obvious, is almost literally exploding. There’s so many possibilities, new tools coming up every single day. How is it changing our everyday work, the businesses we work at? We’re going to dive into that.
Recently, GBK Collective, a consultancy founded by top academics at Wharton, studied 672 businesses in the U.S. with annual sales over $50 million where you get a sneak peek into what they learned.
Joining us today are two of the authors, Dr. Stefano Puntoni, Professor of Marketing at The Wharton School and Co-Director of AI at Wharton. We’re going to dive into why he holds both those roles. That’s pretty cool.
And we also have Jeremy Korst, who’s a former Microsoft and T-Mobile exec. I believe he led the rollout of Windows 10. So just a tiny little role. No big deal. Former Microsoft and T-Mobile exec, now president of GBK Collective. Welcome both of you.
Stefano:
Thank you for having us.
Jeremy:
Good to be here. Hi John.
John:
Really pumped to have you guys. Stefano, I said it right in the intro. Got to dive into that. You’re Professor of Marketing and you’re Co-director of AI. That sounds pretty unique to talk about that.
Stefano:
I am a behavioral scientist, so the kind of work I do, I really study individual behavior and I’m studying consumers. How do people make decisions, how users interact with technology, what kind of experience they have as they play around with the stuff that we have today. And that’s changing consumption in many, many ways. And over time I’ve basically got more and more interested in the topic of automation and AI and so this year we are setting up this new center called AI at Workman, pulling together everything, AI across the workman school, every department involved. We have a fantastic client for researchers projects and I think it’s super exciting opportunity to be able to do that.
John:
Very, very cool. Jeremy, let’s go super high level with the report. Give us a top level. What did you learn?
Jeremy:
Yeah, what we wanted to do… First of all, at GBK, one of the things we get to do a lot is work with top academics and industry leaders like Stefano and some of his colleagues. And so we’ve been engaged and talking about some of the predictions about the use of AI at work and other factors. Essentially what we saw missing was some work on what’s actually happening today?
And so we engaged in the study and ended up interviewing over 600 business decision makers from a range industries and functions in this study. And what we found is that there’s a range of options, but overall, well more than half of these business decision makers are using AI and some type of the context at various levels of frequency. And we were frankly a bit surprised at the level of adoption in some areas.
John:
I think the real big question right away and then I want to dive into some of the aspects of the study. What confirmed your suspicions and what changed some of your ideas, the industries that are using it the most, the least and some of the implications of that.
But I want to ask the really big question that’s on my mind and I think it’s on a lot of people’s minds right up front, what is work going to look like in five or 10 years? And I’m going to contextualize that a little bit and I want both of you to speak on that, but the thing that comes to mind when we think about generative AI chat, GPT, Siri, Alexa, that sort of stuff.
Maybe it’s just my generation, maybe it’s just me, I don’t know. I think sort of Ironman Jarvis, right? And here’s this dude and he’s got this Jarvis, this almost sentient AI turns out to be basically sentient does almost all the work. He just tells him what he wants and it does all the heavy lifting. Is that where we’re going? What’s the future look like with the generative AI stuff that you’re looking at right now? Maybe Stefano you start.
Stefano:
If I had a very strong and competent answer to that question, I probably would not be a professor I would assume, but all I can say is that we’ve never had something like generative ai. If you think about automation in the past and even very recent past, it’s been about making things easier and helping us bring time and capacity to do thinking, removing routine tasks, removing manual tasks and letting us free to do say moving from blue collar jobs to white collar jobs and increasingly thinking more about how do you serve customers, how do you improve things and taking maybe spending less time on repetitive things like paperwork, stuff like that. And that thanks to technology, but this technology is different in that it automates higher order cognitive functions. So now it’s syncing and it is not thinking in a very basic way like summing numbers in an Excel file that’s syncing too of some sort.
But this is thinking in a way that we tend to until now basically to assume this is something only humans can do and actually it turned out not to be the case and so we don’t know whether this is going to go how much better it’s going to be. What we can tell already now is that this thing doesn’t have to get any better for it to be transformative. It’s already good enough to change lots and lots of things that we care about. So I think that’s something that I think everybody needs to realize that it’s not, the technology around the corner is into and so we need to learn what it can do and the big bottleneck is not going to be really, the technology is going to be understanding what to do with it, the deployment of the technology to improve workflows, to change the way we think about certain business processes and maybe re-engineering completely business functions. You can imagine you’ll take probably 20 years before we figure out what we can do for organizations.
Jeremy:
Yeah, I think John, I’m disagreeing with all that. Stefano says that, and by the way, aligned with our study that what we found in the study that is that those who actually have used or are using generative AI today have a really more positive view of its capabilities, whether that be an enhancing productivity et cetera, versus necessarily replacing human beings. And whether that’s cause or not right now in terms of who’s using it, that’s to be seen, but I think those of us who are using it learn on a thing is there’s real application today. You can see the promise of it. I’m using it my day-to-day work and life and so I think it’s really important for whether it’s people who are new to the workforce or those who are even long in their careers to really be experimenting with the technology learning is promised today because how we’re going to figure these things out.
The other thing I’ve seen is I’m also fairly involved in the early stage technology ecosystem as an advisor and board member of the several companies and we’re going through a typical hype cycle as well. I think what’s a little bit different of this hype cycle is the amount of adoption that’s already occurring, but the number of unique use cases and applications and companies that are being funded right now is pretty dramatic and I’ve got to believe that some of that it’s going to shake out overnight. And so I think a combination of what’s going on there as well as having real business users test and apply this in their day-to-day work that we’re going to get, we’re going to have a dramatic change. It’s going to be, in my opinion, less than five or 10 year horizon. It’s here and now
John:
You mentioned hype cycle and I want to hit Stefano on that in a moment, but it’s interesting and Jeremy, I’m going to stick with you for a second. When I’ve looked at young people in technology, and I’m going to try and draw parallel here between small companies at big companies, maybe it makes sense, maybe it doesn’t call me out, whatever you want to do. When I’ve seen young people use technology, they dive in. They’re not afraid to make mistakes, they’re not afraid to screw up. They find a way to make it work. When I see older people use technology, how does it work? I need some training, what do I do? They’re terrified of making a mistake. One of the things that you saw in your study is that smaller companies were diving right in, maybe they have less resources, maybe that’s because they need it, maybe they’re more open to change, they have less bureaucracy, who knows what’s going on there. And is there sort of a parallel there Jeremy?
Jeremy:
Yeah, I saw your work on generations aspect of adoption and we talked more things in the study. In fact, we saw the highest level of usage at work between those in their mid thirties, late, excuse me, to early forties and then a fairly precipitous drop off from there on. And like you said too, as well as in terms of company maturity and age or at least size, let’s use size as a proxy for asset. While our study focused on enterprises of $50 million in sales and above, we saw the most usage and adoption in the company that were the smaller of those size. So we found some interesting things that I’d say about technology adoption and usage overall that step on I’ll probably be following up on as it relates to things like just like you’re alluding to in terms of innovation and adopt of technologies by company size in particular.
John:
Super interesting and super interesting to keep learning as we guide farther into that. Stefano, I want to go back to you and talk about hype cycle. There is a tremendous hype cycle here and it’s always a little concerning because we’ve come out of so many hype cycles, there’s always a new hype cycle. We’ve come out of web three and crypto hype cycle. Some people will be pissed off at me for saying we’ve come out of it, but let’s be honest, we’ve come out of it. There’s other hype cycles. But you said one that you mentioned is Metaverse. You said this is not another Metaverse talk about that.
Stefano:
What I meant to say by that is not necessarily to be dismissive about Web three or the metaverse, but to say
A technology that has a lot, people recognize promise in it, but there isn’t a lot of substance yet and maybe that is around the corner, maybe not in the case of Metaverse. I think it’s been around the corner for 20 years let’s say, but maybe something next year will come and take everything for Storm. What I meant to say by this is not another metaverse is to say it’s already happening. Just like I said, adoption is already over 50% among the top execs with survey. You see business cases being really high on really what we did when we were designing the survey, we came up with a very long list of use cases, like the longest laundry list you can think of just to see what people would be doing with it. And my expectation was that you’d see a lot of variants where a lot of use cases would be kind of really and very low scores while others would be maybe higher.
But first all the use cases were in fact rather high. The lowest one was still over 50% and most of the use cases were in the seventies, eighties percent of respondents counting on going for it. So basically everybody and there were lots of them we’re talking about Jeremy, I think there were about 20 different use cases and exactly, so that really tells you something. It’s all one specific application for what type of business? It’s basically million different use cases for million different businesses. This has got to work now if it going to work for every use case for everybody I’m sure, no. Okay, and maybe we learn how to make them work there and we take some time. The technology will have to change, maybe companies will have to change, but certainly there’s going to be some very obvious low hanging fruits, but they’re already being picked.
So it’s not about next year, two year, three years, it’s this year. And on top of that, I think if you think about this technology, it’s mostly going to be an enterprise technology. So this is a technology that gets into the office and maybe some of the hypes that you’ve seen. Others were more consumer applications and maybe narrower applications and of course there also B2B application for the metaverse in crypto and so forth. But a lot of the action has been more on the consumer side, which I think tends to be showing these bigger hypes ups and downs maybe because there’s simply more voices and interest when it’s something that lots seem to people’s lives directly. While this one B2B, mostly it’s going to be enterprise application. Just imagine what Microsoft is going to be able to do with embedding large language models in all kind of productivity programs. So it’s great.
John:
That is amazing and it’s good that you bring up Microsoft because they are going full on here. Obviously they’re big investors in OpenAI, obviously they’ve got access to Chat GPT and everything that they’re doing there. Google is doing similar things in terms of their suite, their productivity suite, and I mean you are basically going to see in the very near future, if you’re not already on some of the early betas that hey, I’ll write that email for you. Hey, I’ll write that note for you, that marketing pitch or something like that. And I wonder, Stefano, if you could speculate a little bit where’s the competitive advantage going to be? I’ve done some of this myself and I’ve done some of this myself where I’ve almost co-written some things with an AI source. So there’s a lot of clients that I deal with in my personal practice and some things I want to write entirely myself, entirely my voice and I want my presentation or I want my blog post be exactly how I would write it. In other cases it’s a checkbox item and I’m not passionate about it and I need to be passionate about things that I really involve my soul in, if you want to put it that way. And so I’ll co-write it with AI and I wonder if the competitive advantage in the future is being able to meld the machine and the human in a way that really sinks and works Your thoughts?
Stefano:
Yeah, I think I totally agree. So if you think about deploying generative iron organizations, you have to worry about three elements. You’ve got the user, you’ve got the software, and you’ve got the integration process by which the two connect and do stuff. And if you think about we all compete for talent, so that’s always been the case. We try to buy the best people can get and maybe what talent means might be slightly changing as the skills needed to get a job in this new world of design might be slightly different. But essentially this, we’ve always been fighting for the systems. The software basically from the point of view of the user is a bit of a commodity, meaning there’s going to be five, six big models and everybody’s going to be using the same. Of course there’s going to be some value in data like fine tuning the networks and finding ways to carve out maybe some competitive advantage on that side, but essentially from the point of view of the users of models, commodities, everybody’s got access to the same ones.
So basically I think that the third element, the integration process is going to be where the competitive budget is going to come from. The companies are going to be able to learn fastest and best how to actually combine human artificial intelligence to maximize the collective intelligence as organization do the thing in the smartest way. I think that is going to be what’s going to get companies to win. So they’ve got to thinker, they’ve got to try. Every organization has got to care about this and get teams to experiment, try out things and see what it can do for you in your business, in your industry with your customers, how can you help? So nobody has a blueprint, nobody really knows. The only thing is just rushing ahead and try things out with some compliance and some support from whatever. Make sure that you do things and then just learn.
John:
I love that sum compliance and the people who are in banking and the people who are in healthcare and they’re going to have big issues, but they need it, especially healthcare. We need a lot of AI in healthcare to deliver outcome. Jeremy, I want you to continue the thoughts. Asana was talking about therapy because he’s right. We’re going to have these big models, we’re going to take ’em in. I think there’s going to be some value in verticalizing them for specific industries, but I also wonder, and I’m getting ahead of ourselves right now because it’s going beyond maybe a couple years where we are right now, I also wonder to what extent companies will own kind of their AI in a sense. And clearly it’s going to be built by somebody, but they want to have it sandboxed. They want to have, and companies have always struggled with continuity, continuity of process as people change continuity of knowledge as people shift outer roles and leave the company. And how do you see, will a company have an AI that kind of manages its knowledge and processes and will that grow over time getting very sci-fi here?
Jeremy:
Yeah, so I think along the lines of both you were talking, when we look at the enterprise use cases, which I agree are going to be the primary drivers of overall adoption and use of this, the concerns we’ve seen in other studies and heard from our clients as well as in our own study were very relevant things like clients, whether it’s it ownership, you saw what I B M did this week announcing that they were going to unveil more data around actually what IP they’re training their AI against. We have concerns about privacy of both proprietary company information as well as PII for customers. All of those things lend themselves to some of these big players that understand enterprise and be it Microsoft or Google to be able to create the winning solution in this space. I I’ve spent half of my life in B2B and half in B2B in terms of marketing and product management. And I agree with you John. I do think that where we will see some opportunity for vendors is in more special purpose and industry or use case or jobs to be done application and make this more, whether it’s on a compliance side within like HIPAA and et cetera, or make it applicable to various areas of those industries.
John:
It is interesting, Salesforce is another company in this space and I recently was at Dreamforce and I saw some of their things. They literally put artificial general intelligence on one of their slides, literally put, and there’s a strong belief, mark Benioff, c e o, strong belief that the companies with the best AI will win in the future. And that is really interesting. We’ve always talked about human talent, can you get the best human talent Now, can you also pair that with the best artificial talent, artificial intelligence, super interesting. We can’t go into all the aspects there, but it is kind of mind expending, mind blowing to think about what that might look like in 5, 10, 15, even 20 years. Who knows. Jeremy, if you look at the study you did, what confirms on your suspicions going in and maybe what contradicted some of your preconceived ideas?
Jeremy:
Well we saw, I do study innovation option just in general from my academic background and some of the work I’ve done. And so when I saw some things like we’ve already talked about perhaps smaller, more nimble enterprises being earlier on the adoption curve, validated some things that I may have guessed other things like when we look at in terms of industry, technology and finance, and it was also earlier on the adoption curve, not that surprising. A surprising thing and perhaps because we didn’t look at the legal department specifically was that the application of large language models to a legal profession ranked one of the lower, in fact the lowest on the 20th use cases we had, although Likedo said it was over 50%, that was been a, and then just generally also in terms of functional, we found that things like marketing and sales were a little bit, let’s say later on the adoption curve compared to some other functions like technology, which of course the technology team and the procurement team not being, that’s amazing. Going back to what I said initially where I was a bit surprised at just seeing that 58% of business decision makers across functions have been using AI at some level, large language models, generative AI within their work. And so I would’ve lost that bet.
John:
Interesting. On the legal stuff, that’s also super interesting because we know that there’s some AI lawyers out there. I mean there’s some basic stuff that’s been around for some years helping me get out tickets and everything, but there’s some significant AI being built for law. I guess they just haven’t figured out the billable hours model yet. They figure that out.
Jeremy:
No, I’ve got to believe I spend a decent amount of my time in contracts negotiations. I think those of us who have engaged in that to understand that the logic and language, there’s an opportunity to have that more standardized and perhaps have our friends in the legal on higher value tasks. So that was a surprise I think to be studied a little bit more. But I personally believe that’s going to be right up there in terms of
Stefano:
The scenarios that would be applied.
John:
I sure hope so. Imagine studying for eight years, 10 years, whatever. You become a lawyer. It’s wonderful. It’s amazing. And you read contracts for a living, I mean we need a machine here. Absolutely. Stefano, one of the things Jeremy just said is that sales and marketing was not using AI as much or to the same degree as some other industries. You are in the marketing space at Wharton teaching about that and the AI space. What’s going on here? Certainly in the ad world, we’ve seen a lot of ai. We’ve seen Google and Facebook meta, I should say other big platforms. Start building generative AI ad units. Tell us what you want, tell us where you want people to go. We’ll build an ad unit, we’ll build 50 million ad units and give them to individual people. We’ve started to see stuff like that. We are seeing a lot of AI and targeting in optimization for ad networks. Why is there this gap in on the marketing side and the sales side
Stefano:
As a marketing professor teaching AI? I suppose I should be to blame, I don’t know, but
John:
We get a couple years.
Stefano:
You’re okay. I did think it was a big concerning and also surprising to me because if you, set aside for a moment, generative AI. Look at the last 10, 15 years of technology in business and you ask question, which business function has been affected the most? I’d say that there’s a good case for marketing. Think about any of the four Ps of marketing or product, price, distribution, communication, basically everywhere. Automation and AI have been crucial tech advertising. So just the shift to digital advertising and the other network which we’re just saying think about functional market research and be able to national language processing and customer sentiment analysis and all that you can do online with algorithms and the list goes on. And so you expect marketing to be by this point, to be pretty much on top of new tech. And then you think about this particular new tech, right?
So there’s been, for example, to me, and I don’t want to sound controversial, but to me there’s been an enormous amount of marketeers going crazy over things like crypto and the metaverse and now we’re find this server generative AI that actually they’re lagging behind on everything. I’m thinking you guys, come on. This is the technology that is going to affect the most of your job because it’s about content creation. This is about marketing, it’s about making stuff, social media posts, about advertising copywriting, it’s about websites, it’s about sales support materials, it’s about value proposition and that kind of stuff. I mean this is what marketers do and basically gen AI can do that to help you do it. So the fact that they seem to be the function that lies most behind, for me, that’s concerning. I’m expecting it’ll change, but I wasn’t expecting. That’s one of the surprises for me.
Jeremy:
I am not going to dive too deeply right now into what that means for the types of people who are in marketing, but, well John, here’s one of the things. If I take the work that we do with marketing officers and their colleagues and large brands, I think one of the concerns I have about the adoption of technology and the function is any belief that this is going to replace some of the needed strategy those brands have that I can outsource all of my content creation, et cetera in using technology. It needs to have a strategy. When you’re prompting it, when you’re giving it that feedback, ask it for a result or content back, it will give you content back. Now whether that is going to be targeted to the appropriate audience with the appropriate word, that all still takes a tremendous amount of experience and strategy from the marketing leader. And so I think that’s going to be the key is those brands and those leaders who really do, are able to articulate their strategies in a way that can be used in a prompt to create the appropriate content is going to be, they’re going to find amazing, powerful use out of this. Otherwise it’s garbage and garbage out.
Stefano:
If I can add to that, I think one way to frame even beyond market in general, one way that I think about what generative AI is doing is thinking about, okay, what is innovation? Typically we define innovation as new and useful. Okay, and now you could say that with generative AI the cost of new has gone to zero. You can create content at the tip of a finger, and so the cost of new is basically zero, but the cost of useful hasn’t gone down because you figure that out and it’s genetic.
I may be able to help with that too, but that’s exactly, I think what the general was pointing to. The fact that it still takes a lot of expertise to see 1 million different slogans or social media posts or nice images for whatever product and figure out which one is the right one. So in a way, we are becoming, we are no longer sort of, we are moving from being artists to being art critics, from being the people to the people who curate it, the people who understand where the value lies. When you see something, a lot of people see the same thing and I see something value so that I think about what generative AI is going to do to people.
John:
I really like that thought. I really like that idea and it makes a ton of sense. I recently, I’m a subscriber to chat GPT OpenAI and I recently got the dolly three update, so I can use that sometimes I’m weeks behind everybody else getting all the cool new features on Chat GPT. I’m like, Hey, roll it out to me. I’m here, but I got this one making a second day or something. That was cool. Of course I said, make me a cool space station, futuristic orbiting Saturn. And there is not a shortage of images like that.
It’s interesting, the correlation to attention and information. We’ve exploded the amount of information I’m talking pre-AI, pre generative AI and attention span. The amount of available human attention in the world hasn’t really gone up, hasn’t really scaled, at least not per person. And now we’re just opening the floodgates on more that we can create more that we can build easily, amazingly, incredibly. So being an art critic and being a curator and finding the right things and using them at the right time, that’s super interesting. It’s relevant to what we talked about earlier, which is how are we working together with generative AI? How are we generating the most impact and the most competitive advantage? I want to end there and ask you guys both to put on your wizard hat, look into your crystal ball and look out two, three years for us and reimagine the world. Work with the generative AI tools that we will build, that we will have available. What does it look like? Who wants to take first crack?
Stefano:
Okay, I’ll say something I have to say, I am not going to make a prediction. I’m going to make a wish. I’m going to tell you what I hope it will happen. I dunno that it will happen. But what I hope that will happen is two things. First that we get AI, right? Meaning if I look at other technologies being rolled out in the market at scale, at speed global in the past few years, I don’t think we’ve always done a great job and social media comes to mind to me like you generated enormous externalities for people and I hope we do better this time because I don’t think we can afford to do a bad job at rolling it out. That’s the first thought. The second thought is there’s lots of discussion about human replacement. The fact that this technology is essentially now automating our brains and therefore is making really humans to some extent obsolete.
That’s a fear, that’s a dystopian kind of scenario. And my hope is that I see so much potential good with this technology and I’m hoping that instead of human replacement, we’ll see human flourishing. But instead of feeling useless, you’ll feel empowered, you’ll feel inspired and you’ll feel able to actually do what you do best rather than feeling that you’re no longer needed. So that’s my hope, and I think I’m optimistic about a lot of things in my own experience using it. I see what it can do. I feel it can save me a lot of time. Even better can be my aspiring partner, can be my friend, helping me think sort of. And I’m really looking forward to the things getting better and me learning how to do it. And I hope there will be many people like that. But certainly what I would like just to argue and I make this quote to my students and I talk about the parles, the leader of the ancient Greece, Athens, golden age athletes. And he said this thing about politics, the famous quotes, and I think it can be repurposed for AI. And his quote was, even if you don’t paraphrasing, but even if you don’t take an interest in politics, politics take an interest in you. And I think
You may or may not be the tech person, you may not be interested in this. I see a lot of people are just not very interesting in learning, figuring out, trying it out, trying to work out the scenarios, the implications. And that’s fine. Everybody has their own interest, but I don’t think we can afford not to care about this because it’s going to affect everybody
John:
And the crystal ball shifts to Jeremy.
Jeremy:
Yeah, by now I should be smart enough to go before Stefan, not after. But I liked the pivot in terms of hope. I think as well, a couple hopes that I have East on what I’ve seen thus far in some of the research
We’ve done and others have done. And one is I think we’ve all been part of organizations and groups where some people better communicators than others, but everybody has something to offer. And I think one of the hopes I have for AI is going to allow some of those ideas to be more effectively articulate, especially we can put them into action. So perhaps those who don’t write us every day, like those of us on this today or came from a liberal arts background who have that capability hopefully to that this is their co-pilot that allows them to be more articulate and get ideas up front. That’s one I think is going, I hope happens. Number two is we saw this in some of the predictions and hopes that people responded to the survey said is that allowing teams to do higher value work, we really allowing humans to do more of the human aspect of the job, whether it’s connect with other people, connecting with customers, connecting with others in the organization versus doing the more routine work that perhaps we can all outsource to this helpful agent over time. So those are my two hopes.
John:
It’s super interesting. I want to thank both of you for some of your thoughts and insights and maybe cap it off with a couple things that you’ve kind of just spurred in my own mind. The quote from Steve Jobs about what a computer is comes to mind and he was talking about versus tv, which he was not a big fan of, but he said that the computer is a bicycle for the brain. In other words, an extender, an amplifier, an empowerer. It’s interesting that we’ve seen in the past decade that companies have required fewer employees to generate massive impact. That really came to light when Instagram was sold for what, a couple billion dollars and it had 17 employees, right? And it’s pretty old now, but you see that again now. I talked to somebody recently on the TechFirst podcast and they said, hey ChatGPT is my CMO chat. GPT is my CFO and other things. That’s perhaps an exaggeration, but there’s some reality there. And Jeremy, you just mentioned you don’t write much. Well, you can now. Maybe you don’t write code, maybe you can now. And maybe what an individual or a small group or team or smaller company can do is be bigger than they are. And that’ll be interesting to see how that impacts the world of work, help people and business in the future. I want to thank both of you for taking this time and sharing your thoughts.
Jeremy:
Thank you. It’s been fun.
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