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Cloud Computing Continues to Reshape Capital Markets

Rohit Bhat from Google Cloud shares his insights about how the cloud is helping finance firms tackle all manner of tasks -- and what the future of the cloud might look like for capital markets.

Rohit Bhat Google Cloud

Sponsored by: ION

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When cloud technology was first introduced in markets, many people in the financial services sector were hesitant. There were concerns about security and even questions about overall use cases. Well, it is safe to say that things have changed. Today, the cloud is being used by finance pros in ways that would have been hard to imagine just a few short years ago.

Joining me today to provide an update on the current cloud landscape in financial services is Rohit Bhat from Google Cloud. Rohit leads Google Cloud’s efforts in Capital Markets, Digital Assets, and Exchanges in North America. His focus areas include technology enablers for accelerated quantitative research, pattern and anomaly detection, market data distribution, and insights-based risk and underwriting.

Rohit shares his insights about how the cloud is helping finance firms tackle all manner of tasks right now, but he also shares his view on what the future of the cloud might look like. Advancements in things like machine learning and artificial intelligence mean there are a lot of incredible capabilities on the way.

More resources

Learn more about Google Cloud for Capital Markets

Google Cloud whitepaper: Building the financial markets foundation for the future

 

Key highlights

2:49 – The current landscape regarding the adoption of cloud technology in financial services
4:56- Biggest technology challenges finance firms are currently encountering
8:23 – Trends and common questions from existing or potential cloud users
11:03 – Top tasks finance firms are aiming to tackle via cloud technology
14:52 – When regulatory reporting requirements lead to optimized operations and strategies
18:30 – The role of cloud technology in an ESG world
21:33 – On Google Cloud’s new partnership with MSCI
22:29 – Leveraging other Alphabet properties to make financial services more efficient
24:55 – How data providers are putting cloud technology to use
27:25 – Rohit’s 3 bold predictions
31:22 – How artificial intelligence and machine learning will continue to impact markets

 
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Transcript

 

(Note: This transcript was created using artificial intelligence. It has not been edited verbatim.)

Sean McMahon  00:14

Hello, everyone, and welcome to another episode of The Modern Money SmartPod. I’m your host, Sean McMahon … and I gotta say, financial markets have changed in countless ways during the last decade or so. But arguably the most significant operational change has been the adoption of cloud technology.

When cloud technology was first introduced in markets, lots of folks in the financial services sector were hesitant. There were concerns about security, and even questions about overall use cases. Well, it’s safe to say that things have changed. Today, the cloud is being used by finance pros in ways that would have been hard to imagine just a few short years ago.

Joining me today to provide an update on the current cloud landscape in financial services is Rohit Bhat from Google Cloud. Rohit leads Google Cloud’s efforts in capital markets, digital assets and exchanges in North America. His focus areas include technology enablers for accelerated quantitative research, pattern and anomaly detection, market data distribution, and insights based risk and underwriting.

Rohit and I are going to talk about how the cloud is helping finance firms tackle all manner of tasks right now. But he’s also going to share his view on what the future of the cloud might look like. Advancements in things like machine learning and artificial intelligence. mean there are a lot of incredible capabilities on the way so trust me, you are going to want to hear everything that Rohit has to say.

But before we hear from Rohit, here’s a quick word from a sponsor of today’s episode. ION.

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Sean McMahon  02:22

Hello, everyone, and thank you for joining me today. My guest is Rohit Bhat from Google Cloud. Rohit, how you doing today?

Rohit Bhat  02:29

Good Sean, how you doing?

Sean McMahon  02:31

I’m fantastic. I’m fantastic. I’m in Southern California, it’s sunny and warm. So it’s always nice on a January day to look outside and see blue skies.

Rohit Bhat  02:37

Oh, I am jealous. Though the topic is cloud. I also live out of Chicago, the Windy City and it is rather cloudy and cold here today. So very jealous of your location.

Sean McMahon  02:48

Yep. And like I said, we’re gonna be talking a lot about the cloud today. So I kind of want to set the table a little bit. I’ve been covering this industry for a long time. I remember the days when the notion of putting a lot of trading information and data in the cloud was just way too complex and technologically and so insecure and things like that. But obviously, things have changed a ton. And so set the table for our listeners, where do things stand right now, with adoption of all these technologies?

Rohit Bhat  03:13

Yeah, it’s a fair question. Hard to believe that we’re already in 2023. And some of the advancements of technology in the adoption continue to rapidly outpace where we were even months ago. You know, one of the most fascinating and enjoyable things of working in this particular space capital markets, is if there’s one thing you can always trust is it will always lead the way in the adoption of technology in the pursuit of market differentiation, capital efficiency, risk management and experience of interactions. These things have been true since day one. And what’s fascinating moving forward is that we now live in a paradigm where the technology capabilities of tomorrow are available in the context of how financial services need them to be, in order to go drive that differentiation and explore better efficiency and management of risk. So I took a look broadly across the spectrum, cloud one dot o started very much about this mindset of burstable compute, or the ability to ensure that more resources can be accessed at a lower cost structure. And while those things were very successful for many firms across the board, I think where we see the world now progressing towards is, how do I get more intelligent about the decisions I’m trying to make in a market given the context of how markets operate? And then how do I do that at scale, in a manner that allows me to move freely with flexibility and with speed. And in that space data has become called the new gravity or the new asset type from which differentiation seems to be derived. So we see adoption quite rapidly advancing in the space of analytics, machine learning, automation, these types of capabilities seemed to be driving behavior moving forward.

Sean McMahon  04:56

Yeah, and agree with you. Obviously, you know, one of the fascinating things about financial services is it is full A lot of firms and individuals who are trying to chase the latest and greatest technology. So what are some of the biggest challenges those folks are encountering right now?

Rohit Bhat  05:07

Yeah, I think that the same mission statements present new challenges today, I think, you know, in version one of how we operated in cloud between financial services and the services that we would like to bring to market, the number one question always was security. Today, most Fs firms operate between their four walls, and so they have full control over top down what occurs within those four walls. So we are led to believe in the future, you know, allowing any level of control or access between those four walls to a provider of technology, you know, comes with its fair share of consternation, as you might imagine, where we are today is, you know, cloud providers such as Google have taken a pretty strong stance in ensuring that not only are we able to meet the requirements of security and resiliency as defined by financial markets from yesterday, but are actually leading the way in helping enable how perhaps a new paradigm and a new way of looking at security, authentication control at scale, will actually enable and fuel the type of innovation that financial markets are eagerly awaiting to have access to. So I would say security was number one, and has become more of a reason to move to cloud than in prior years, where it was more of a concern of how might we be able to securely operate in cloud. So that’s one. The second, you know, I would tell you is the growth in financial services has been at breakneck speed. I don’t think that that’s news to any anybody listening to this particular podcast. As a result, you’ve had a plethora of homegrown applications, customized software entities and suites and capabilities that most firms would believe provide differentiation to what they bring to bear in the market. With that comes a significant amount of technical debt that needs to be addressed before you’re able to ingest at scale the next new level of technology or capability. And so that’s been a barrier for some time, as cloud providers have had to take a harder look at understanding what is that landscape that exists today? And how might we best integrate our technologies and services to make it easier for financial service firms to grow and adopt these next generation technology? So barrier number two has been a place of constant investment and playing close attention to Lastly, you know, I think there’s been this constant, I would say, trade off on cost for innovation. There’s the age old adage that rings true today, as much as it did the first time it was said, which is do more with less. I think, you know, that rings true today, especially in the backdrop of the market that we operate in currently, where cost efficiency, operational excellence, driving the right type of behaviors are ever more present to happen in the past. That’s been a barrier for some time, it’s very hard to take upfront capital expenditures to go explore innovative services in market when you’re sitting in the backdrop of having to deal with operational efficiency and cost implications across your firm. Cloud plays a very important role in that segment as well. And we can talk about that. So those are probably the three areas I’ve seen, as you know, perhaps areas that have been difficult in past. But I’m happy to say material progress has been made in order enabled moving forward.

Sean McMahon  08:23

Yeah, you mentioned cloud and the role it plays right now. So what is that role in the markets right now? And what kind of questions you get from either existing clients who are thinking about expanding the services you provide for them or brand new clients that haven’t quite nailed? They haven’t seen have heard of Google Cloud, but they haven’t really taken your services on board yet? And what does that conversation look like from the very beginning?

Rohit Bhat  08:43

It’s evolved, and it’s been quite fascinating. I will tell you if I divide the bucket of that conversation to let’s say, participants in a particular segment of a market or market hole, versus say the venue’s themselves, the exchanges. You know, participants are eager to have higher quality interactions with the venues that they participate within, they would like to understand their risk positions. At a far more rapid interval, near real time is the word you might hear, firms would like to have a better understanding of how to manage margin understand collateralization of their assets, so that they understand what might need to be in the system from a capital efficiency perspective. Subsequently, they also then want to be able to manage that within their own four walls far more efficiently and effectively. And ultimately, those three things brought together allow them to make more intelligent decisions and how they want to proceed in a particular market moving forward. And so a lot of the effort that we’ve spent with hedge funds, asset managers, wealth management firms or global market institutions, is generally in that platform front to back, you know, area of value chain, because that’s generally where those systems reside. Consequently, as we work with exchanges, you might find that it is the exact source material necessary to go and service those platforms front to back, where the error To focus resides so foreign exchange, for example, you know, we see a lot of time and effort initially spent on simplifying distribution of information, you know, generally coin market data of various types constant asset classes, but rapidly expanding to things that, you know, exchanges have had to do in house for a very long time for internal purposes, for example, cross collateralization of assets to understand perhaps what a cap call might look like for a particular client. That information in the past was not easily accessible at a client level by client in a self service manner. Well, exchanges of the future are going to look and are actively working with us to go build those robust capabilities so that they can externalize become far more transparent, far easier to interact with, while still providing the world class services for actual trade execution that they are known for today. So that’s kind of the if I had to give a flyover of the kind of topics that we’re seeing light up with a lot more energy in the last 12 to 24 months, versus months and years prior.

Sean McMahon  11:04

Alright, there’s been some headlines lately that a lot of Wall Street firms are kind of planning to spend, I think it was a record amount, Operations and Technology, things like that. So what kind of services in Are you trying to use to differentiate your offering from others? mitigating risk is huge right now. So is it somewhere along those lines? Is it operational resilience, you know, where do we go with that?

Rohit Bhat  11:24

Well, I would tell you, it starts number one with security and resiliency, while maybe in the olden days thought of as more of a foundational level to ensure no one has access to my material. And, and you know, maybe I’m protected from insider threats. I think that’s evolved, you know, the next generation of these firms are going to have to live in a far more connected, and a far more accessible environment, the level of interactions and the volume of those interactions are not downtrending. In fact, they’re up into the right. However, what you will find is in tandem, the cost of operations, is almost mandated to stay flat best case, and ideally, lower. So you have these divergent lines off, I need to do more, like I mentioned, but I need to do it with ideally less or far more effectively than I did in the past. So security and resiliency become top focus for us in order to go satisfy those types of requirements. So that’s step number one. And we do that at scale, with some unique things on our platform, where we are able to provide a layer of control and transparency to any of the services that run on our platform both to the venue’s themselves, and then form a connectivity perspective to the participants as well, to better understand where, how, and what is being done with information that flows through the platform, really, really important. The second that will tell you is there is a significant amount of investment that then goes into the analytic and data capabilities that arguably from a venue to participant, have everything to do with the transparency, reach a more robust information. And then for participants in any given market. For them. It’s more about how do I simplify the ingestion of that particular type of data asset? Which is only increasing in complexity and structure number one, to once ingested? How do I make sense out of it intelligently and quickly? And in theory, if I’ve done that really well, how do I now act on it? And that becomes about the actual execution systems within these firms itself. So I think of this as a flywheel, you kind of have to help the venue’s achieve a certain type of behavior in order to better service to clients and the clients that have to figure out how to now operate with that new model, which then comes right back to the venues. And when you think about post trade type of services, settlement, clearing regulatory 14. So those two broad buckets is generally where you’ll find for the next 12 to 24 months an enormous amount of investment and time spent in order for us to shore up how we can bring to market these services.

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Sean McMahon  14:50

Now back to our conversation. Already mentioned regulatory reporting. Obviously, market participants will tell you there’s just more and more of that seemingly more demands and more demands being placed on them by regulators, you know, but when you talk to a lot of folks, they say, Hey, this regulation that we were required to satisfy or be in compliance with, actually, quote forced us to track data that we didn’t previously track, which actually made us a little smarter about what we’re doing. And we’re able to act on that and use it strategically. So are you hearing any that similar kind of feedback from your clients?

Rohit Bhat  15:22

Oh, spot on spot on? The notion? Look, I will tell you, again, I mean, when I say data is the key to this Elvis has been that’s, I think, more prevalent now than ever. I think when I think about systems that describe, for example, trade behaviour, allocation of assets or portfolios, I think what you’ll find is there’s an inordinate amount of time spent in manual processing, dealing with breakages in data structures, breakages in trade allocations, asset allocations, a lot of effort is spent in ensuring accuracy, just to make sure the books makes sense. Step two becomes if the books makes sense. How do I ensure my reporting of fat to whatever regulatory body or standard that I have to adhere to are now complete? Well, you might find that is also an incredibly manual process. Third, what makes it really, really fascinating, the more we’ve dug into this, is the combination of the two being manual processes has a multiplier effect, to the amount of effort that goes into then dealing with exposure of getting it wrong, that can be very costly, and to fixing it before it’s identified as something that went wrong. So in session fixes, for example, Treasury markets are notorious for this. So I think where we’re investing a lot of our time is how do we take the best of what we have in terms of technology assets, partner with the right institutions that have the right domain expertise in this particular space? To go and do three things really, really well? One, help identify proactively, we’re using predictive techniques, where we might see breakages in trade flow, or trade, trade data and information across asset allocation, portfolio shaping, so on so forth. So how do we get ahead of this in session? Second, we’re trying to think through what are the right tools and technologies that we have in terms of assets on an auto umbrella, that again, when paired with the right domain expertise, could bring automation to the equation so that they are fewer infrequent manual interventions, or necessities for manual touchpoints. As we take a look at a trade lifecycle, the life of a trade. Third comes the regulatory reporting aspect, where we then say, okay, if we’ve done a good job in one and two, we at least have clean information to play with. Now, how do we bring both machine learning technology to understand how clean the data actually is from a reporting standard perspective, and then automation so that if we find breakages, we can go back and clean that up very quickly. To reduce that time to produce high quality reports, back out to the rec bodies in an automated way, we find that to be the only way to go satisfy what I would call an increasing volume and complexity requirements for how transparent markets will have to be moving forward. And let’s not forget the entrance of new types of asset classes and new types of venues, that will likely have to also be regulated in a very different fashion moving forward.

Sean McMahon  18:31

We’re talking about, you know, regulations and things like that. And I’d be remiss if we didn’t talk about you know, the SEC has got some some disclosure rules coming in on ESG. And other regulators around the world are kind of on that same glide path, right. So how do some of these tools help achieve what the anticipated rules are going to be or regulations are going to be? And then also back to that original question like What are firms learning about their own operations? From capturing that data that can help them even they themselves achieve higher scores on these various categories?

Rohit Bhat  19:02

Yeah, no, that’s a great question. ESG has been a big topic for a number of years. And the way to think about ESG in this context is twofold. There is data that represents either each individual element of the word ESG, or the acronym ESG. So you might have social graphic information or, or information that is a far more geospatial in nature. Once you have that data, it’s usually going to be used for one of two purposes. Either you are an asset manager or a wealth manager or somebody making a decision on underlying assets. And you’re looking at this data to understand the quality of the underlying asset, and geospatial and or ESG type information is used in that context. So there’s that version of it. There’s also the reporting element of it in which I think you’re referring to their standards that are going to be coming out that are already being led by key institutions, to say what is a common way or at least a common understanding that we can establish on how particular companies might be able to baseline themselves and show progress on They’re on fourth in both contexts is I think our purpose in this is going to be to help improve the quality of the elements of data that are used in order to make decisions. So we have, you know, interesting assets within the company, you might have heard of Google Earth Engine, or our geospatial analytics platform. These are assets between inside the company that allow us to take a very high quality and very granular look at data points that might describe the physical or transitional climate risk metrics around securities, geographies, or markets. And we’ve created capabilities that allow firms to go and very quickly explore and understand this information. What we’ve done in recent time, and you’ll see these announcements come out very shortly is then taken, like I said, those key assets and technology capabilities, and worked with domain experts, and create partnerships to bring those assets out and manifest themselves through standards. So for example, a partnership with a company like MSCI would be to really focus on building these types of client based solutions for asset managers, asset owners, banks, corporates, insurers to take this very, very high quality information surface through the normal mechanisms that you might be used to and working with a firm like MSCI or others, so that you have a higher quality of output in the standards that the regulatory bodies are coming out with moving forward. That’s kind of the mechanism you’re gonna see us play.

Sean McMahon  21:34

Alright, yeah. And you mentioned MSCI, and you guys just announced that deal a few days ago. So tell me more about that relationship and personal outcome and V and what its gonna look like going forward?

Rohit Bhat  21:44

Well, I mean, it’s been a fantastic relationship. And I gotta tell you, the, you know, Henry Fernandez, the Chief Executive Chairman of MSCI wonderful partner, a great relationship, we couldn’t ask for a better partner to help us bring that level of domain expertise to this particular side of the equation, I think, look, what he would tell you is that the industry is is going through an exponential growth in data, both in terms of the type of information that is out there, but also the amount of information that is being presented. And while most might say more data is more good. That’s a fun phrase for me. It generally also causes a lot of complexity. And what does this actually mean? How do I actually use it? What is the value I’m going to derive from it? And so when we approached and had the conversation, you know, with with Henry and the team at MSCI, the real conversation was about how do we take our ability that we have formed over many years, across what we call our properties, like YouTube search, pay, and these types of core capabilities across the ultimate portfolio, where the mission is about helping the world derive better insights and understanding for me incredibly diverse and ever explored in a universe of data, how do we take that capability, and bring that into financial services with the help of MSCI, and that was the genesis really of the partnership to think through, again, the ability to help the world of financial services better understand how to make sense of all this information that is coming out of almost every single type of telemetry engine that you can imagine, and do so in a manner that is useful, you know, very quickly, so doesn’t take a data scientist, or a PhD student, to go and make sense of it. So that that’s, that’s really the intent. And I think you’re gonna see some amazing things come out of that partnership here in the very near term.

Sean McMahon  23:39

I got to tell you, from where I sit, it’s pretty fascinating to think that technologies or lessons learned from other alphabet assets, like YouTube, are gonna make market participants more efficient. I just find that hilarious. But I mean, it’s also, you know, your strengths, I guess, you know, I’m laughing because it’s like, of course, it is. Why wouldn’t they use that?

Rohit Bhat  23:59

You know, it’s not, it’s not readily obvious. On day one, I will tell you Look, I one of the fascinating thing I will tell you is, you know, we run, and also I was talking to I won’t mention the name right now a fairly large, you know, derivatives and commodities market participant. You know, if you think about it, we’ve run today, Google, a fairly large, globally distributed matching engine and bid-ask system: It’s called our ads platform and search, and the volume of transactions and the necessity to drive a relation information at a global scale. Well, we’ve kind of had to solve that because that’s, that’s part of the core business. Now, granted, there are requirements that are unique to financial services, but it’s not foreign to us. And so a lot of the time that we think about where we place our investments and how we bring things to market and partner with firms institutions, it does come from a place of at least some familiarity. It is not completely out of the realm of how we think about our world today as well.

Sean McMahon  24:56

Okay, so we spent some time talking about market participants and exchanges. But how do you see the data provider that segment of the market? How do you see that evolving?

Rohit Bhat  25:04

You know, it’s a fascinating space to be in right now, when it comes to data providers and the ecosystem that they service, you kind of see a, I’ll use the word a bifurcation, perhaps of thought, in that particular segment. You know, there’s a world that says data providers are looking to provide the best asset type information or data asset information out to their particular participants or community. And that that is their IP, and therefore providing just that will be sufficient. And they look for differentiation in the quality of data. There’s a separate line of thought that says actually, while data assets are certainly important, and key, the services that you provide around that data, and how close you can get to self service, and how much you can help clients compress the number of steps it takes to understand the type of data to providing becomes the next proving ground, I will tell you, while we are not trying to pick one side over the other, I would say in a biased world, there’s likely more to be done on the ladder, providing differentiated data services and capabilities that help the end clients compress and drive better decisioning, there really shouldn’t be four or five hops between, you know, level one level two data or tick by tick information, coming from a data provider flowing through a quote system to a processing plant to then a decisioning system to then a reporting system. That’s about four hops too many, in my book for any large institution to have to handle. So I think the role that we’re trying to play is around enablement and partnership in that particular context. You know, how do we take a highly connected platform, that is our planet cloud platform, combined with the differentiated engines that we have, we think of our data and analytics as engines that are brought to data versus the other way around, which is why we’re multi cloud. And we can bring our engines to really any cloud platform today. And we do that by partnering then with these data providers, and Rikako, MSCI earlier, we’ve done some great announcements with our friends at Bloomberg. And you’ll see a few more come out, again, in the mission of trying to help them create these differentiated data services for their end clients in the mission of providing high quality, but also allowing those then in clients to have better efficiency in doing so.

Sean McMahon  27:26

One of the things that I like to do on the show is we ask guests for bold predictions, right? So I’m not going to throw you a softball and be like, oh, is the cloud space gonna get bigger in the next five to 10 years? Because I’m pretty sure I know how you answer that. But what I do want to ask, what I do want to ask you, is this? Are there any functions or capabilities that the cloud can’t do right now that you think and save five years? It will or you’re hopeful it will or it’s gonna be right on the cusp?

Rohit Bhat  27:53

Hmm, that’s a really good question. You know, I would say three things. One, when it comes to be highly interconnected, and highly precise is the word I would use their world of trade and market structure that follows that. I don’t think there is a cloud provider today that with the capabilities as they exist today, would be a drop in replacement or an additive to the equation of that particular part of trade cycle. Ultra low latency, extremely fair markets with extreme transparency is a really, really hard thing to do at scale. And it’s something that financial markets have done well for some period of time. Interestingly enough, if I had a prediction to make, and perhaps I’m biased in this prediction, I will tell you that a non trivial amount of time and investment that has been put into this particular place, evidenced by the partnerships that we have announced thus far, and some that you will see in the near term, we are ultra focused on customizing and improving the type of services that we can bring to market by understanding this requirements at a very in depth level. And I predict that you will see trade execution in its truest form. With derivatives securities, equity markets, you name it, running in an end to end distributed cloud platform in that timeframe. Second, I will tell you is the pursuit of real time information globally distributed so that you can operate at a global scale at plus value markets, understanding risk positions in near real time so that traders in any desk for their front end can make a decision with the best high quality information. I think that I don’t even know that that’d be a bold enough prediction, quite frankly, but let’s call it a bold prediction that will absolutely happen. The cloud providers of today have the data engines and capabilities to do it. The domain expertise and the tuning of those environments is what is in progress, and the launching of those production grade systems will will certainly occur so real time risk, real time positioning, real time insights These things are not science fiction anymore. They will occur and are occurring in microcosms today already on the platform. You know, lastly, maybe I’ll go a bit bolder and perhaps you know, the latest, I would say buzz in the market might provide some some guidance to this, I think AI and machine learning will play a far larger role than we understand it to play today. The capabilities that exists today are far more robust than perhaps the market understands the missing ingredients that we are incredibly focused on around security trust, and having highly predictable outcomes out of those ML and AI models. You know, those things and the investments that are going to those types of patterns will allow financial institutions and financial markets to start operating at interacting at a very different playing field than they can today. And I think that will probably be one of the single largest changes in market dynamics, structure and behavior, because it will no longer be algorithms that are self tuning, it will no longer be automated market making machines, it will truly be decisioning. And market shaping that is occurring thanks to AI and machine learning technology and capabilities. And I think these things will have a dramatic impact on how markets operate.

Sean McMahon  31:15

Well, first of all, I gotta say, I’m bummed because I thought I was gonna catch you off guard with that question. And then you said you had three answers. And so you’re too savvy for me. But anyway, I want to follow up on your on your notes about AI and ML. So is that a situation where they’re almost kind of to like a bifurcation of the markets, right? You got the counterparties are talking to each other using AI and ML and counterparties? Who aren’t? Because it seems like if they’re kind of crossing over that it’s a little bit of a mismatch. So is that something you’re talking about is like, there will be kind of just different levels of markets. There’s always different sophistication, but actually different markets.

Rohit Bhat  31:47

I generally don’t like to live in a world of binaries, and bifurcations, those things generally tend to spook markets. So let’s not do that today. What I would tell you, however, is I think, again, going back to the core principles of what do capital markets look to achieve, right at the onset we talked about, it’s about capital efficiency, it’s about better risk management, it’s about better interactions, in that context is where you will see a far more infusion of these types of technologies. So verifying counterparties at large scale trade using machine learning technology, I think you’ll see that come to fruition sooner than later. From there, you can imagine the ability to understand and predict market movements in a very automated manner. remodels are evolving with some level of intelligence that isn’t always manual factors, you’re gonna see that come to fruition probably next year from there. Because those things will occur in gradual steps, I don’t think you’ll see a bifurcation, I do think you’ll see folks that are leaning in sooner than later will build the type of talent and muscle memory necessary to operate with those next generation capabilities. And so let’s bifurcation but they will be a, we’ll call it an unfair share of wallet, that could likely occur with firms that are able to understand why those technologies represent the kind of value they do, you know, versus maybe the later part of the market. So I think that’s probably closer to what might occur versus a bifurcation of structures or bifurcation of how counterparties might exist in risk pools, subsequently.

Sean McMahon  33:22

Okay, well, hey, listen, right. I really appreciate all your insights. This has been fascinating, and I’m definitely gonna be watching how the cloud evolves in capital markets.

Rohit Bhat  33:30

Absolutely. Thank you for the time we appreciate it.

Sean McMahon  33:36

Well, that’s our show for today. But before we get out here, I want to say one final thank you to the sponsor of today’s episode, ION.

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