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Spencer Levy
Like the rest of the business world, commercial real estate is searching for the keys to AI, and it's a rapidly evolving role in the industry, as we all have to figure out how to use this new technology, how to harness its power while controlling things like data, privacy, and what value it truly brings in developing business and culture. On this episode: big thinking on the story of AI in commercial real estate.
Sandy Pentland
We live in this world that's intrinsically uncertain. Things change. Rare events happen. There's bad luck. And you can't just logic it out. You can't put it all in equations. But the equations can really help you think and do what you do as a human better.
Spencer Levy
That's Sandy Pentland, a computer scientist and faculty member at several institutions including Stanford and MIT, and an advisor to the Abu Dhabi Investment Authority. He's a data analyst by trade whose primary interests center on people, the way stories and ideas flow around organizations, and how they are used to make decisions. He's also an author who recently published a book called Shared Wisdom about social communication in the age of AI.
Sandeep Davé
As a technologist, I get excited with all of this innovation, but I also have two rules. One is that we have technology to solve business problems. And two is we should not get trapped into a solution looking for a problem.
Spencer Levy
And that's Sandeep Davé, CBRE's Chief Knowledge Officer. With that unique title, Sandeep’s purview covers digital technology as well as CBREs data and research teams. The goal of their work, in Sandeep's words, is to create an unbeatable information or knowledge advantage for the company. Coming up: the proliferation of AI and the ways this emerging technology is moving into commercial real estate. I'm Spencer Levy, and that's right now on The Weekly Take.
Spencer Levy
Welcome to The Weekly Take and this week we are going to address AI and commercial real estate with Sandeep Davé, the Chief Knowledge Officer of CBRE, his second visit to the show. Welcome back, Sandeep.
Sandeep Davé
Always a pleasure to be here, Spencer. Thank you for having me.
Spencer Levy
Thank you for coming. And then we have multiple book author, Sandy Pentland, most recent book called Shared Wisdom. Sandy, delighted to have you here today.
Sandy Pentland
Glad to be here. Look forward to the conversation.
Spencer Levy
Sandy, I loved your books
Sandy Pentland
Thank you.
Spencer Levy
Alright, and the reason I loved your books is–the way that I frame it is you took the old school, and you combined it with the new school. What I mean by that is how humans got information hundreds of years ago through social institutions, and how they get it today and how AI is one of the many things that is upending that today. That's my summary. What's yours?
Sandy Pentland
I think you're right-on there. We're taught in schools, and we like to brag about being logical and down in the data and here's the models and so forth, but that's not actually the way people work. If you look at large-scale data of people actually making decisions around the world, and we have data from literally hundreds of millions of people doing these things, you find that actually it's the stories that they tell that really matter. In a community you tell stories and you come to a consensus about what is the right way to do things, what's going to work, that's sort of your community wisdom, as it were. And it doesn't mean it's true, it just means it's your best guess, right? And that's the thing that people draw from in making personal decisions and certainly in making group decisions. And we forget that. We forget that we live in this world that's intrinsically uncertain. Things change. Rare events happen. There's bad luck. And you can't just logic it out. You can't put it all in equations. But the equations can really help you think and do what you do as a human better. And that's what the book Shared Wisdom is about. It’s how can we use these tools, these AIs and things like that, to make better human decisions; not to replace human decisions.
Spencer Levy
Sandeep, what's your reaction to that? AI allows us to make better decisions. It doesn't replace human decisions. What do you think?
Sandeep Davé
I think this is a perfect description of how we should be deploying AI. In fact, this is something that we are very thoughtful of when we deploy enterprise in AI setting, which is that it is a co-pilot, AI helps us make better decisions, gives us a perspective, but at the end of the day, we all know AI can hallucinate and we need a human in the loop. I think one of the other things that Sandy mentioned also resonated very much about communities. AI is often thought of as a silver bullet, but it is not. In fact, what Sandy referred to as communities is what we in the corporate world refer to as breaking the silos, where we bring groups together to solve real-world problems.
Spencer Levy
So Sandy, you've written several books, and what I'm really trying to do is get to that connective tissue. The connective issue between shared wisdom, social physics, and the new economy, data is capital. To me, they're all saying essentially the same thing. How would you describe it?
Sandy Pentland
That's basically right. Social physics is about how conversations, information, stories flow. The new economy is about the new tools. So we talk about AI, but things like blockchain, I mean, people go, oh, I don't want to hear about that, distributed ledgers. But you know, SWIFT, $4 trillion a day of moving money around now has a distributed ledger blockchain with little AIs on it for doing trading and hedging and things. J.P. Morgan deployed the same type of thing. So what we're interested in is a world where all these things happen very rapidly mediated by AI. And we have to remain in control of this because it can get out of whack. And you can see this in all sorts of places. Like you look at the volatility of Bitcoin. You don't want your investments to be that volatile. You might want them to be that profitable, but not that volatile. And so one of the main things is try to think about, okay, in this new world, what's it going to be like? One of the main things that's going to be different that's relevant to your listeners is a thing called tokenization. So this is like securitization. You may be familiar with that. It's a way to have a contract that you can trade that makes illiquid assets liquid. So mortgages, that was a perfect thing. You could tie them up in a bundle. And we remember also in 2008 that that turned out to be problematic in some cases. Well, that process is gonna take off on steroids. The moment you get these sort of digital distributed ledger AI things, you're gonna have tokenization of everything. And you could have 2008s all the time also. Let's try to make sure that doesn't happen. And one of the ways to do that is to think about the proper ways to use AI, the proper way to use tokenization and securitization. And think about it now, before we get into the thick of it, and realize we're hip deep in the mud.
Spencer Levy
Well, I'm with you on that. I'm all for the democratization of capital, the democratization of the ownership of real estate, but maybe it needs to be structured in a way to prevent that type of outcome. Is that what you're saying, Sandy?
Sandy Pentland
Yeah, it's this new age where–we're used to a certain pace of transactions, you know, T+3 resolution in banks, things like that. But now we're going to enter a world where everything is tradable, and they're tradable on milliseconds. So it's like high frequency trading everywhere, all the time, of everything. And we all know that high frequency training has some problems, and as you mentioned, some previous efforts at this went awry. And so one of the main things that I'm trying to do is figure out the right guard rails to be able to detect when things are running in the wrong direction, and then what to do about it. And it looks like there are ways to do this. We have an effort with a thing called the IETF, which is the international body that specifies what's in the internet. And we're trying to put hooks in there so that we can detect these crashes really, really early on. And take advantage of them to smooth the road.
Spencer Levy
Sandeep, let's bring this ahead a little bit to commercial real estate. What do you think is going to be the current use case of AI in real estate that you use every day, and where do you think it's going?
Sandeep Davé
There are several use cases. I also had a thought and a reaction as Sandy was talking through the examples. And it really fits with the question you asked, which is what is the current use case of AI. And we referenced a couple of technologies. We referenced tokenization, blockchain, AI. And as a technologist, I get excited with all of this innovation. But I also have two rules. One is that we have technology to solve business problems. And two is we should not get trapped into a solution looking for a problem. So this fractional ownership, for example, one might say that perhaps it was ahead of its time, was a solution looking for the problem and it never took off. And there's a graveyard of VC capital money in that space. And so coming back to your question around what are the use cases? I think the way we look at it is that there is so much opportunity as it pertains to application of AI. Given our business context, what are the most important strategic priorities, and where is it that we will prioritize application of AI? For example, we've said that there are a few areas where we expect to see a dramatic reduction in costs, but at the same time, significant improvement in quality of output. Software development, content generation, and those are areas that we are focused on. How we manage buildings is a data-driven problem in many ways. And how can we get really better at managing buildings, predicting faults, failures, ensuring that our technicians have the right information at the right time. These are a couple of examples where the application of the technology is aligned with our core business priorities.
Spencer Levy
Let me put it more just day-to-day terms. Our listeners, Sandeep, are primarily rising professionals that are brokers, that are owners. We do have a lot of occupiers as well. If you were speaking to an owner, what would your answer be? If you are speaking to an occupier, what would your answer be? And would it be the same?
Sandeep Davé
I think in many ways the problems are similar. If I am an owner, then I want to have the best information around. How is my portfolio doing? What's the right time to trade? What is the right market trend, too? Do I have a view as to which way is the market moving? Which way are specific markets moving? What is the specific value of a property? There are very common business problems, and from an occupier perspective, there are similar questions. How is my portfolio doing? How do I ensure the happiness of my tenants in my portfolio? And these are all problems that we try to solve through people process technology.
Spencer Levy
And so the answer is not the same, but it's similar. You want the building to run better. And running better from an owner's perspective is happy tenants. And happy tenants means that their employees are happy, and maybe they get there using the same analysis of AI. It's not the same but similar. Fair statement?
Sandy Pentland
I agree with what Sandeep said, but we're living in uncertain times in many ways. It's everything from climate change, which people often think is far off, but not if you live in Miami, right? And we have this chaotic economic environment. And then you have technological change. All of that changes the value of businesses – in particular locations, the value for occupiers, etc., etc. For instance, one of the things that we see is we see that businesses can be run with many fewer employees than they used to be, right? And also that the lifetime of corporations is much, much less than it used to. And these are getting shorter and shorter. And sort of one vision of the future is it's gonna look a lot like San Francisco where more than half of the working age people are in startups. Think about that for a moment. So, take Chicago. Imagine that half of the people were in a startup and the startup was, you know, five, fifteen people, something like that. So there's just as many people working, but they're doing a million different things. That's a very different type of real estate market than if you have businesses that are ten thousand employees or a hundred thousand employees. And the volatility of that is also large. And so what does all that mean? I think it changes the equations. It doesn't make the buildings go away But it makes who uses them how uses them and how often they use them change quite a bit
Spencer Levy
And Sandeep: measurement. How do you measure the success of AI?
Sandeep Davé
Spencer, great question. And we look at it in two ways. As I was saying before, not technology for technology's sake, but solving a business problem. And there, I think, it becomes fairly clear. If, for example, the problem statement was customer wait times are too long, and we have deployed an AI solution for it, then the answer is simple. Did the wait times go down? And you just need to look at your standard business KPIs. And if the needle didn't move, then that's the measurement. Now where it gets complicated is personal productivity. So GenAI is being adopted across enterprises for personal productivity and measuring gains on personal productivity is harder when you save 10 minutes here and 15 minutes there. But that's where the point Sandy was making around how is the organization scaling? Is the organization just going to deliver the same or more at a flat head count or a lower head count? Many organizations are operating at a smaller capacity, as you said. So that's the other way that we are looking at it. One is where we solve the business problem, then it's really just a business KPI. And then overall, we are looking at our collective productivity metrics on how our organization scales.
Sandy Pentland
It's become clear, even in economics and some more traditional disciplines, that productivity is the thing. Productivity is driven by innovation, and innovation is driven by sharing stories among people who do things differently and trying different ways to organize things. And so that's really key, is that you can't just sit on something. You have to keep moving if you want to have good productivity. One of the interesting things about this AI stuff is it can look at broader data than you can or your group of people can. So just think back to 2008. Most cities were pretty solid, but when a few cities started going belly up, that propagated it and infected everybody, okay? And so normally you'd just look at local data. You wouldn't look at stuff at the other side of the continent. But now you can. And sometimes you can see things coming and that's important because it's those big rare events, those big innovations that make all the difference in the long run.
Sandeep Davé
I think our clients are thinking about AI in the same way that we all are. How do we deploy it in the most meaningful way? And having spoken to many of our clients, I also know that many of them are going through this corporate FOMO. And as a result, there is–what ends up happening is a death by use case, because there's a lot of activity going on, which does not necessarily result in meaningful benefits. I think there are a few things that given the breadth of opportunity I think what our clients would do is that they would pick a few areas that would meaningfully work for them. But what they really should be doing is focusing on their data. How is it that they create the fuel that is going to ultimately create the value? It's not the models, it's the underlying data. In many instances, if they were to streamline and look at efficiencies, then they'll have to first look at their processes. Are the processes standardized, streamlined, to be able to then apply technology on it? The technology in and of itself does not make the hard work go away. The hard work is still there to be done.
Sandy Pentland
So let me add something to this, because I was on a panel with CTOs from five of the largest companies. They were under pressure, of course, to deploy AI by their CEO. And what they had to do is they had to go back to the CEO and say, well, the core thing is data. We have to be able to bridge across silos. We need new data sources so that we can measure things. But there was one thing that they sort of almost didn't think of that was actually one of the biggest changes. They had all deployed what they called “AI buddies” for every single employee. So here's the idea, is that we all know that nobody reads the manuals, nobody reads the newsletters, nobody knows what's happening two offices down, let alone in the other side of the country. You give people these little local AIs that have read the manual, have read a newsletter, that know what other parts of the company are doing. And what you find all of a sudden is people are much more in the loop. They know the context of what they're doing, the best practice, far better. Now, it doesn't look like a revolutionary change, same job. It's just that they're avoiding serious mistakes and not missing as many opportunities. And some of the biggest corporations are using this to really be able to better support things that you might find difficult, like work from home or distributed offices. Because if the whole point of having an office, right, is so that you know what's going on and you know the people. But if you can actually enhance that using these little local AIs that are sort of connector AIs, right? They connect the stories between people, then that changes your real estate requirements.
Sandeep Davé
That really resonated with me. We had maybe a few months back published this perspective where now everyone can have an assistant, an AI assistant. And essentially, what we've done within the enterprise is we've deployed. Really, I think if we had to super simplify it, I would say an AI playground where every employee has the ability to upload the data, have access to their data. Use AI in many different ways to translate, summarize, generate new content. And they also have the ability to create these mini AI agents to do small tasks and save them and store them. And that has been hugely adopted across our enterprise and we are finding tremendous value.
Spencer Levy
What are some of the major changes maybe you've seen from a data, data collection, turning into research, technology running research? How is it different today than maybe 10 years ago?
Sandeep Davé
I think the big shift across the board is just the extent to which technology is now embedded into our day-to-day decisions and day-to-day decision making and increasingly the appreciation for the value of data and what it takes to get there is perhaps the biggest chains that I have seen.
Spencer Levy
We've heard the expression “data is the new oil” for some time now, and we've also heard that there's so much data we can't funnel it all as effectively as we can. What are we doing to cull the herd into actionable intelligence? Any comment on that?
Sandy Pentland
That’s exactly what AIs are good at. This notion of summarizing, of looking very broadly, being able to look at all the data sources, that we just don't have the time or the memory to do, and then pull out things that are connections there. They're also really good at reading things to be able to understand what other people are doing. So we've done a number of operational things where, you know, a lot of times you're just not aware of some innovation that somebody else did. And you ought to consider it when you're taking action, but how do you find that? That's an example of the sort of thing that the AIs are really good at. Right, it's sort of searching for contacts, searching for connections, searching for innovations, things that are different, organizing that for you so that you can digest it and make a decision. And incidentally, we do a lot of experiments. That sort of way of thinking about things is better than letting the AI make decisions, almost always. You need to have both the skin in the game that people have, the experience, the context, as well as that breadth that the AI can give you to make the best decisions.
Sandeep Davé
Yeah, and I fully agree with Sandy. I think the ability for us to access. The breadth and complexity of the data that is in front of us, and also unlock the value of our dormant data. Spencer, as you know, our business is a very manual business. We generate documents of all kinds, leases, contracts, offering memorandums. All of that information has remained dormant until now where we now have this amazing capability to be able to read through all of that and connect the dots.
Spencer Levy
Part of the reason why I love commercial real estate as an industry. No matter how good AI is, no matter how good the data is – and it's very good, and it is getting better, and is getting faster – there's still a heavy relationship component in this business. There's still heavy uniqueness to this business, we're not selling widgets here. And that is one of the beauties of the real estate business, maybe as opposed to a commodities-type business, where trading the price of oil is much less complex. What do you think Sandy?
Sandy Pentland
It's sort of obviously true, I think. The value of a business or the value for an occupier has to do with a lot of details – you know, what's surrounding that? What exactly is your business? Where's your business going, right? Are you growing headcount? Are you shrinking? Is the surrounding economy any good? You have to really think about all those things, particularly because in this business you're making fairly long-term commitments. If you build a building, it may be quite a while before you can get liquid out of that. If you're signing a lease, you're on the stick for a while. There are things you can do, but you really do want to think longer term about the trajectory where things are going, and that's hard. It's hard for people, but it's particularly hard for the people if they don't have all the data and the context. And that's the combination that's magic there. We've done, you know, experiments with real professionals in this sort of area. It's the people plus the AI that end up winning. They don't win all the time, right? It's not like this is the recipe for always making good decisions. What you wanna do is you wanna make more good decisions than you are today. You wanna improve it. You're not gonna be perfect. So that's the thing that you oughta be striving for.
Spencer Levy
I think that a lot of people are down with everything we're saying with AI, with data, but there is this nagging privacy concern about what are you doing with the data, whose data is it and what's being done. So perhaps we'll start with you Sandeep. What's CBRE doing on the privacy concern about our data?
Sandeep Davé
Yeah, I think that's a great question. The use of AI is thought of as a trade-off with privacy, but it doesn't have to be. It perhaps is how it's playing out in the consumer world, but in the enterprise world, we take great strides to ensure that it is actually not a trade off. By ensuring that we have enterprise-grade technology and AI capabilities, ensuring that we have the right guardrails around our data and how we can use our data. And then putting appropriate governance around the use cases and human in the loop requirements. So it actually does not have to be a trade-off or a risk as it pertains to privacy. We also put in certain security guardrails as it pertains to data protection, which is also super important.
Spencer Levy
Sandy, I think this privacy concern is part of the broader concern that you raise in your book about having the right rules of the road for data use and AI. But why don't we start with privacy and just talk more broadly about the rules of the road.
Sandy Pentland
Well, the nice thing about the rules of the road in this area that people have recognized it as important. And there's a lot of technology that's been developed and deployed for doing this sort of thing. Trusted execution environments in the cloud, homomorphic encryption, many different sorts of things. So it's getting to be much more sort of a standardized way of dealing with things that genuinely protects not just privacy but proprietary data. And as a consequence, when you get that sort of thing happening, the data becomes more valuable. Because you can share it under particular circumstances, like for instance, you see data brokers far more now than you ever did before. And they're higher quality than they were before. Because they're actually making use of these privacy-preserving technologies.
Spencer Levy
What we do at CBRE is we're giving real estate that is based on the data that says this is good real estate or this is worth this. So it may be a physical manifestation of the data that comes before it.
Sandy Pentland
That's right.
Spencer Levy
This is a complete non sequitur. But this year there was the MVP race between Aaron Judge and Cal Raleigh. Aaron Judge played for the Yankees, Cal Raleigh played for the Seattle Mariners. Aaron by every statistical measure had a better–didn't have as many home runs–but just per at bat, all of the modern statistics, Aaron just blew him out of the water. However, Cal Raleigh had a much better story. I mean, this is a catcher that is leading the league in home runs, hit over 60 home runs. And I'll tell you what, even though my statistical mind said Aaron Judge has got to win MVP, I was rooting for Cal Raleigh and I was rooting for him because it's just a better story! What do you think, Sandy?
Sandy Pentland
That's sort of what I'm saying is you have to marry the old with the new. The book is about how to do that. And we always mistake who we are because there's so much. We've been all brainwashed. We went to college. We went through all this school. You know, this idea that we're logic and that's what we have to do, but that's not true, right? It really is finding the best stories, the stories that resonate with other people, the learning from other people's experience as well as your own. And it's using AI to help you do that better. That is the way to have wisdom, right? Wisdom is, again, not like absolute truth or anything like that, but it's like the best guess you can do at the time.
Spencer Levy
Shared wisdom.
Sandy Pentland
Shared wisdom.
Spencer Levy
And that's the title of your book.
Sandy Pentland
That's the title of the book, that's right.
Spencer Levy
A lot of what you talk about in your book, Sandy, is – and I'm gonna quote you in your books, Sandy – the dominant voices are, quote, “dragons”. What does that mean?
Sandy Pentland
That's a term from finance. It means big and dangerous, okay? But, you know, you're familiar with this if you do any sort of social media. There are people, there are these voices that have millions of followers, and they just dominate the discussion. And of course, this is what everybody, all the politicians try to dominate the discussion with their view. But if you cannot listen to them for a while, you get a very different view of what's going on. So like when we look at topic after topic after topic, people have the impression that, you know, there's an incredible amount of polarization and lack of trust and so forth, but actually they agree about most things, except for some extreme people at the ends. Those are the dragons. And unfortunately, in a society where the louder voices get more advertising. They get more power. They get this, that, and the other thing. There's always gonna be these dragons that rise up and destroy the ability to have a productive conversation. And I think it's one of the things you need to be aware of is the thing of the day, the thing that's front and center on everybody's mind, is often the thing that's destroying the conversation, not the thing the most important thing in the conversation.
Spencer Levy
What you're getting at here is something that is community intelligence versus collective intelligence. And i think the difference is – and again like your definition but I think that difference is that the community embraces one, the community may know the other, but they don't necessarily embrace it.
Sandy Pentland
That's right.
Spencer Levy
Is that the difference?
Sandy Pentland
I mean, community intelligence is what the community knows about how to work, what's the right way to do it, things like that. And there's a subset in there of community intelligence, which is the wisdom of the community. These are the ways that really work and everyone pretty much agrees. The reason it's important is that the community intelligence includes new things that aren't vetted yet. They're sort of ideas you ought to be aware of because maybe they'll turn out to be good. And the core that everybody agrees about, the community wisdom, those are the things that you can get lots of people to sign on to. So for instance, if you're trying to put it together an investment consortium or you're trying to get something approved through some sort of regulatory body, you have to stick to things that people are familiar with and they believe that there's a reliable story about it because otherwise it's going to be really hard to get everybody in the same space. But at the same time, you have to be aware of these new innovations, these new stories, these new things or the rare events that happen, and put that in your equation to balance it. But it's very difficult to use those as a way to promote action, because it's not shared among everybody.
Spencer Levy
So I think action is both collective action and then that it's micro action. So collective action is something like climate change. Let's all get on board and solve this mega issue. The micro action is go buy this building, go lease this space, and how does this information make us better? So Sandeep–
Sandy Pentland
Well, let me just disagree just a little bit.
Spencer Levy
Okay,
Sandy Pentland
There's the big-big like climate change, right? To invest in Miami or not, right. And it's sort of obvious. But then on the other hand it’s like, presence in Boston – I have roots there – of course, everybody was on board about the idea of doing bio labs. Very expensive. Long-term leases. Looks like a good business. But the pharma industry has to be healthy for that to happen. And so everybody had this collective wisdom that the way forward was to build more bio labs, right? Turned out that was a mistake, because pharma has had a pause in its growth and investment. And so all these people are left hanging, trying to build these bio labs, or having built bio labs. They all stand empty. So what you had in Boston was this agreement by everyone, this shared wisdom that biolab's were the way forward. Turned out to be, let's just say, premature. Bio labs are a good investment, absolutely. We need them. They'll be occupied. But the people that were promoting them went over the top. They were the loud voices, the dragons in the conversation. And the result of them selling, selling, selling, and selling it without enough introspection by the actual people who are paying the bills was overbuilding.
Spencer Levy
So Sandeep, how do you see research and taking the data and communicating it better so our clients make better decisions?
Sandeep Davé
Yeah, in so many different ways we are leveraging AI in our overall research value chain. One: just the ability to ingest a lot more data, do it quickly, increase the breadth of our vision as we were discussing before and the point that Sandy made. And the second is the ability for us to accelerate creation of content, new content, more focused content, more topical content. The effort to create content is going down. The effort to create tailored content is going down and so therefore, how can we take advantage of doing something like that through the use of AI.
Spencer Levy
Given that we're talking at the cutting edge of technology here, where are we going here? What would you like to see in the next three or four years with our use of data, with our use of AI that makes things better for our clients? So Sandeep, why don't we start with you?
Sandeep Davé
Yeah, I think that we are going to see the technology evolve at a significant pace, as we've continued to see over the last couple of years. And that's going to unlock massive opportunities, massive efficiency opportunities, the ability for us to do things differently, but to do different things as well. And we've got to be prepared for that change, focused effort., really ensure that we are sitting on good, clean data, streamlining our processes, and picking our bets. That's what we are doing. We are focused on a few areas that will unlock meaningful value for us. And we are engaged with our clients in similar sort of problem discussions. How do you get to better data, break down the silos, access more data? It's going to be an exciting time.
Sandy Pentland
The thing that will catch on most is using these AI tools to make people smarter, basically, as opposed to replacing people. A lot of evidence is showing that it doesn't actually reduce headcount all that much usually, depends on the industry, of course. What it does is it gives you a broader view so you can make better decisions. It does mean that you can try more things, together with things like tokenization, securitization and so forth. Means instead of buying a few buildings a year or investing in a few things, you might be able to have a much more distributed organization, something that has parts of lots of investments as opposed to just a few. And traditionally, that's been something that required coordination among lots and lots of employees, very difficult. Now it's getting easier and faster. And you made earlier reference to it taking, you know, three months to buy something, it's gonna be a lot faster. It really is. A lot faster, a lot cheaper to line up financing to make things happen. And I expect the whole environment to be a little more dynamic, which is a little scary, but it also is gonna be more productive. The whole point here is this sort of migration towards faster, more distributed, more need for context and a broad view. It's just going to happen to everybody.
Spencer Levy
Better faster information is i think the name of the game but i think we all agree there needs to be some rules of the road on how it's used absolutely so that we don't make some of the mistakes of the past.
Sandy Pentland
Yup.
Spencer Levy
Well on behalf of The Weekly Take, thank you again for coming back again Sandeep Davé the chief knowledge officer at CBRE, great job
Sandeep Davé
Pleasure to be here.
And then we had sandy pentland multiple book author, most recently Shared Wisdom, but has other books, another big thinker on data, big thinker on AI. Sandy, thank you
Sandy Pentland
Glad to be here. Glad to talk. Interesting conversation.
Spencer Levy
We'll have more big thinking about this topic in the weeks to come, keeping, well, an eye on AI and the role of this important and rapidly emerging technology. You can stay on top of our upcoming shows by subscribing to our feed, and you can also look back at other tech-focused episodes in our archives. All of that is available on our website, cbre.com/TheWeeklyTake, or on the podcast platform of your choice. In the meantime, we'll bring you views from around the country and the world, looking at some forward-thinking global real estate projects, and taking you inside the latest and most notable developments across the commercial spectrum. Thanks for joining us. I'm Spencer Levy. Be smart. Be safe. Be well.
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