CR078: The role of culture in responding to change with Tony McManus, Bloomberg

Ensuring your organization can quickly adapt to changing pressures is critical in today's fast-moving market environments. However, many organizations are still structured with traditional hierarchies, which enable stable operations but can make change difficult.

This week, Dave, Esmee, and Rob talk to Tony McManus, Global Head of Enterprise Data and Index at Bloomberg, about tracking and responding to mega-trends, Bloomberg's collaborative structure, supporting customers on their cloud journeys and how this helped prioritize Bloomberg's Cloud strategy.

TLDR
01:15 Confused about lost data and knowledge
05:05 Cloud conversation with Tony McManus, Bloomberg
38:10 Michael Porter's five forces model
46:40 Going to Taipei!

Guest
Tony McManus: https://www.linkedin.com/in/mcmanustony/

Hosts
Dave Chapman: https://www.linkedin.com/in/chapmandr/
Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/
Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/

Production
Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/
Dave Chapman: https://www.linkedin.com/in/chapmandr/

Sound
Ben Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/
Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/

'Cloud Realities' is an original podcast from Capgemini

Creators and Guests

Host

Dave Chapman

Chief Cloud Evangelist with nearly 30 years of global experience in strategic development, transformation, program delivery, and operations, I bring a wealth of expertise to the world of cloud innovation. In addition to my professional expertise, I’m the creator and main host of the Cloud Realities podcast, where we explore the transformative power of cloud technology.

Host

Esmee van de Giessen

Principal Consultant Enterprise Transformation and Cloud Realities podcast host, bridges gaps to drive impactful change. With expertise in agile, value delivery, culture, and user adoption, she empowers teams and leaders to ensure technology enhances agility, resilience, and sustainable growth across ecosystems.

Host

Rob Kernahan

VP Chief Architect for Cloud and Cloud Realities podcast host, drives digital transformation by combining deep technical expertise with exceptional client engagement. Passionate about high-performance cultures, he leverages cloud and modern operating models to create low-friction, high-velocity environments that fuel business growth and empower people to thrive.

Producer

Marcel van der Burg

VP Global Marketing and producer of the Cloud Realities podcast, is a strategic marketing leader with 33+ years of experience. He drives global cloud marketing strategies, leveraging creativity, multi-channel expertise, and problem-solving to deliver impactful business growth in complex environments.

What is Cloud Realities?

Exploring the practical and exciting alternate realities that can be unleashed through cloud driven transformation and cloud native living and working.

Each episode, our hosts Dave, Esmee & Rob talk to Cloud leaders and practitioners to understand how previously untapped business value can be released, how to deal with the challenges and risks that come with bold ventures and how does human experience factor into all of this?

They cover Intelligent Industry, Customer Experience, Sustainability, AI, Data and Insight, Cyber, Cost, Leadership, Talent and, of course, Tech.

Together, Dave, Esmee & Rob have over 80 years of cloud and transformation experience and act as our guides though a new reality each week.

Web - https://www.capgemini.com/insights/research-library/cloud-realities-podcast/
Email - [e-mail]

CR078: The importance of culture in responding to change with Tony McManus, Bloomberg
This is an edit point to say Esmee, it's time to do the outro. I'm sorry, Rob's face is confusing me this time. He's very off putting.
Welcome to Cloud Realities, an original podcast from Capgemini. This week, it's a conversation show with Tony McManus from Bloomberg exploring how their culture and organizational structure has allowed them to very quickly innovate their products, keeping pace with. Innovation in the cloud, what their customers need, and then looking forward to the world of AI.
I'm Dave Chapman. I'm Esmee van der Giessen. And I'm Rob Kernaham. How's everyone doing? You good? Always Dave, always. So after we did a recording before you like we need to get this this first bit of chat sorted out right so we don't just talk about the weather so well that's that's a very British thing to do so I am very much inclined to talk about the lack of sunshine in the UK which can go on.
Esmee, how are you today? You good? I am doing very good, yes. Good, glad to hear it. Rob, what's confusing you this week? We're going to be talking about data today, Dave, and it got me thinking actually about over human history, now we have these great servers and things and lots of ways to store information much more effectively than we ever have on a parchment.
Have you ever thought about how much useful human, and what I mean by useful human information isn't, isn't your pick of you on holiday or the fireworks display you've videoed on your phone, but like something useful to the human race that has been lost. But I'll give you two examples. The first was the Minoan society, very advanced. They were sailing around in massive ships while, you know, others were in small huts somewhere, wondering how to live their lives. And then an almighty volcanic explosion destroyed them and they were basically wiped out overnight. But they were the most advanced civilization going. What did we lose?
Right. And the second one is something like the Bayer Tapestry, which was found in the back of a car in the middle of the Napoleonic Wars from 1066 that charted the Battle of Hastings. So somebody just happened to find it and cart it off and save it. And now it's a beautiful. bit of artwork that sits in bayou.
Then it got me thinking about what have we lost and how much has it held us back? And is it going to happen again, like some, you know, electronic disaster or something, or are we just, it's all right, we found it out again and off we go. And I was confused, Dave, about are we thousands of years behind where we should be and we should be traveling around space or is it just, uh, we actually managed to So is this you now trying to roll back from your digital litter piece that you did last season?
Well, digital litter? No, no, digital litter's still valid and actually filming the fireworks display, right? We're not interested in that. Society does not need to remember that you watched a fireworks display, right? Because 300 people filmed it. But it's more about something useful about the cure for, uh, a horrible disease that we lost and we never found out again, you know, that type of stuff.
So useful information. Something that would benefit society. I think it's inevitable, and the more and more things become just purely digital, increases the risk of lost knowledge, surely. Well, but then you've got this massive proliferation of the information, where there's multiple copies stored everywhere.
We used to be like the, the big libraries, if it burnt, that was it, that was the only copy, but now you've got it spread everywhere. So, now I think we're at far less risk. But back then, if the book got lost or somebody misplaced it, or it, you know, dropped out of the saddlebag and into the river, was it gone?
Is that it? Is that human history lost? Something exciting. And that's the bit that confused me about how much did we actually lose? Cause it must've been quite a bit. I reckon that's my theory. The pyramids, the Stonehenge. These are the things that immediately come to my mind in terms of like, we're having to sort of try and work out how those were built.
But at some point. Those were built so people knew what they were doing. And there was a recent thing on Stonehenge where one of the main stones has actually come from like 500 miles away further than they thought it had. Yeah, from Scotland, exactly, instead of Wales. So, yes, I think it's inevitable. And I think the question I would would ask for the next phase of that happening is if it is like to do with the big media blackout, let's say if that happened, the scientific, you know, the sci fi idea of. like, all media goes down EMP style, then you lose more, right?
Because at this point, are we still printing things out? Are we still writing things down? Ah, so you're saying it's more fragile. I think it's more fragile. Cataclysmic disaster, more fragile, bigger reset. No, there's one thing that we can, you know, fall back on and that's this timeless wisdom. And that might sound a bit spiritual, but you know, generations on generations, I believe that there's more to it in wisdom than, you know, restoring data and technology.
Time will remember, or maybe not, it won't. But anyway, that's what's confusing me this week. Well, I think we're all slightly more confused than we were coming in. So thanks for that, Rob. Thankfully, though, I have got somebody here who is going to help us with our main subject of the day, which is about culture and innovation in financial services.
I am delighted to say that Tony McManus, the global head of enterprise data and index division at Bloomberg is with us. Tony, thank you so much for spending a bit of time with us today. Do you just want to introduce yourself and say a few words about Bloomberg? Yeah, of course. So thank you. And as you say, I'm the head of the enterprise data and the index businesses at Bloomberg.
You know, Bloomberg is the largest, and I would certainly argue premium provider of data analytics and news to the financial industry. We very much regard ourselves as a technology company. We have a massive investment in technology and a huge investment in AI. And we're, we're, I'm very excited to chat with you today.
So let's start then, really, with talking about Bloomberg and the cloud. When you think about it, Tony, and you frame up in your mind the relationship that Bloomberg has to the, to the cloud and sort of cloud era technologies, what really goes through your head? Is it like, I'm concerned about how we as Bloomberg ourselves are leveraging the cloud, or is it?
Is it more about your customer ecosystem and how people might be consuming things that you guys are presenting to them? Yeah, thanks dave. It's it's more though. It's more the latter. It's more the latter for us You know, we do use the cloud internally, but really for very specific needs. The big cloud journey for us was really in identifying what was clearly becoming a mega trend where customers were just talking about migration Uh, in almost every major transformation became the, the, the, the term that everyone was using, and we had to make a decision early and quickly as to the role that we wanted to play in that journey with them.
And, you know, one of the, one of the things that I've always really enjoyed about working at Bloomberg, and this goes right back to when I first joined, I remember when I was being interviewed by our CTO, he said to me at the time, You know, um, you know, cause I'd worked at other, other, other big, big companies.
And he said, do you know much about Bloomberg? And I said, No, tell me. And he said, well, we're, we're, we're really like a large overgrown startup. And I remember being super excited by that, but what, what it has meant specifically with relation to the, to this is that once that mega trend started to evolve, we could move quite quickly in a, in quite a nimble manner, which, which I think surprises some people for a company of our.
And before we actually go on to what that change looked like, I'm just interested in maybe double clicking on that. So what does that mean? Is that because there is a certain organizational structure that allows you to be nimble and responsive versus kind of that you have, say, a hierarchical structure that resists change?
What is it in the, in the core of the organization that helps it? Yeah, we, we could, we could spend the whole, the whole hour or the whole podcast talking about that. But in summary. We are notoriously not hierarchical. So, for example, most companies in capital markets have corporate titles, Vice President, Senior Vice President, Managing Director, all of those kind of things.
We don't have any of those in our organization. Our view is that they tend to create barriers to collaboration and communication. So, people have functional titles that describe what they do, but they don't have those kind of artificial hierarchies injected into their titles. You know, into their, their kind of persona or their profile because we feel that it makes it less likely for a junior person with a great idea to go and have a conversation with a very senior person who might be able to actually influence the likelihood of that idea coming to market.
And in a culture like that, do you find that the challenge can come from anywhere, but the the challenge from somebody outside in actually helps promote a better idea and help you think about how it could be better? Some organizations get frustrated with external challenges. It sounds like you're in one where the challenge is always there, and that's good because A, it keeps you on your toes because you have to answer the challenge and B, you can actually make things better.
Yeah, yeah, we, um, you know, I, what I've observed over the years is that most large companies, what they tend to do over time is they have a problem, they do some sort of postmortem on the problem, and then they implement a process. to protect themselves against that problem occurring again, which never happens again.
All of the protections to the problems become the problem. Um, and you become really slow moving and bureaucratic. Uh, and then, you know, smaller, nimbler, creative companies come up and they take your business away from you. So we, we've been very careful to try to ensure that we don't fall into that trap.
Now, when you, when you get to our size and scale, of course, you need a certain amount of structure. operating models, organizational models for the company to function at scale, but you've got to get the balance right. And that's really what we try to be very cognizant of. Tony, did you, did you have to unlearn things when starting at Bloomberg?
If you've had, you know, other companies in the past, did you, you know, were you being confronted with your own? You know, that, that, that is a fantastic question. One of the biggest challenges that we have in hiring senior people is. that their expectation of what the company is, is very different. They expect it to be more akin to what they're used to.
And for me personally, I, I really flourished. I really enjoyed this environment because, you know, if, if you look at my history, I'd worked at, you know, two or three big companies prior to Bloomberg on one startup. And I loved the startup and I didn't like the big companies very much. And I saw this pattern emerging.
So when I heard, you know, at the time when I was interviewing, that Bloomberg is like a big overgrown startup, that was really intriguing because it sounded like all the good things on both sides. You know, the benefits of a large company with scale. But the creativity and the agility of a startup and that was 12 years ago and I'm still here.
So, you know, something must have worked out quite well. Yeah, right. Well, let's, let's talk then about how that relates to cloud. So you were saying that, that you are having lots of customers in conversations, constantly bringing up this mega trend and it was becoming more and more real. So just take us through then what the thought process was and how you guys mobilized around whatever your strategic response was going to be.
Well, in true Bloomberg fashion, we ended up being somewhat contrarian in our approach to that, because what happened was around the 2016 2017 time frame, we started to ask a lot of customers about whether they wanted real time streaming data in the cloud. Now, for the benefit of people tuning in, real time data is really used at a bank or an investment fund or a hedge fund for the purposes of trading.
And back then, when we were speaking to our customers, they were all saying, we're moving to the cloud, but the thing we're going to do last is the real time trading piece. Right. Right. Right. And we're starting off going to Office 365, then we're maybe starting to run some, you know, maybe HR and internal type of applications.
And then we'll go to maybe data and analytics because the cloud is very good for that. But trading and execution is probably going to be last. And this was, in their minds, it was because the risk of that was too high and the real time nature of it meant it was extremely sensitive tech, or they felt the cloud couldn't do it, or a mixture of things.
What was the risk appetite thing going on there that was having them push that to the back of the queue? It's a, it's, it's, it is. You're right. It's a, it was a combination of those factors. You know, CIOs at the time, still a little bit nervous about what it meant for security. Right, right. That was definitely an issue.
You know, trading is where our customers make their money. So it's the most sensitive part of their business in many respects. But there were other factors, you know, for example, Trading certain types of trading happens in real time. It's low latency, you know, performance system performance is very important, right?
And it wasn't yet really understood how the public cloud was going to perform relative to bare metal in a data center from a performance perspective. So there was just a lot of kind of unknowns. So if the customers, you know, it didn't seem like they were ready. The contrarian part of it from our perspective was we thought about what that meant.
We thought, okay, we've got, we've got two options here. Option one is we wait for the demand, but then we're in an arms race against our competitors. Option two was we move early and build a service and we're ready to go when the market demand arrives. And obviously there's some risk with that second approach in terms of ROI, cause you've got to build out the technology and then you might not have, you might not have any, any customers. Yeah. The field of dreams model of, uh, of, of technology development. Exactly right. If you build it. Yeah, yeah, yeah. But, but our founder, um, our founder is always telling us take a risk.
You know, be, be thoughtful about it, make sure it's calculated, think through it, you know, do, do the homework, but don't be afraid to take a risk. Right. Um, so, so we went, went and built a service. on AWS in 2018. First to market by multiple years. And it was probably one of the best business decisions of, of me and my team's existence.
Well, ironically, that's what AWS did when they built their first cloud. They thought they'd have 12 months on the competition. It turned out they had more like five because exactly that. They knew they had something in the technology. They knew it was coming. They took the risk because that's the ethos in the organization.
It worked very well for them. So as you, you have repeated their story, but again, building on their platform, your platform on theirs. Yeah, that's, that's, I mean, well, they've done pretty well for themselves. From I'm going to sell some books to world's largest provider of data center services in under a decade.
Yeah. It's quite the result. It's amazing. Yeah, well, I mean, they're, they're an interesting case in point, because if you look at their trajectory, you know, ours was a little bit of a microcosm of that, you know, at the beginning, it was like, who's going to buy technology infrastructure from a bookseller?
That was the first question. Then it was, oh, look, this looks quite interesting. Lots of startups are starting to, um, Light up on on AWS. And then it was, Oh, now some enterprises are starting to move in there, but will they ever move your kind of mission critical business critical functions? And lo and behold, here we are, you know, a decade or more later, and none of those questions are being asked anymore.
So going back to the conversations you were having with customers where they were talking about their journey to the cloud, and I guess we're asking you guys what your position was going to be, what your product strategy was going to be and how that was then going to layer into there. cloud strategies.
Just take us through a little bit through that conversation and how in those conversations you guys got to where you got to in terms of what you wanted to launch first. Well, what we, what we came to learn quite quickly was that although we worked early with AWS and Amazon were a tremendous supporting partner in that exercise, it was very obvious that there were three big players in the space and we were going to have to figure out a strategy as to how to So for all of the needs of our clients and really what that meant was, you know, we will provide our services where you are and what that meant was we had to make a decision quite quickly that although we worked first with AWS, we would light up equivalent services on Azure and also on GCP that came out of the discussions quite quickly that that we would need to do that.
And the other thing that we had to think about was, although we delivered our. real time products to the market early. Again, customers wanted access to all of our data products in the, in the market because we, you know, to, to simplify it a little better, abstract it out a little bit. We have broad families of data products.
We have real time data products where you're providing streaming data. through very high end technology, usually for the purposes of trading. You've data that you deliver in a more batch form, you know, maybe once a day or twice a day, and customers will run big accounting and P& L type processes against it.
And then you have historical data, which is very important for quantitative analysis, AI, investment research, and modeling. Um, and you would go into one meeting and they would be talking about the fact they're moving their trading application to the cloud. And it was a conversation about real time and you would go into the next meeting and they would be talking about Putting, wanting to use, I don't know, you know, a big query for data analysis and analytics.
And then you suddenly realized you had to put all of your historical data somewhere else. So the scope of the project grew significantly very, very quickly. And how are you restructuring your teams to support that because presumably your teams are in a, were in a, had a certain capability, certain capacity, and, you know, certain ways of working that, that presumably, and I am sort of making a leap here, so do correct me if I'm wrong, but presumably you had to do some form of transformation of your product teams.
To ensure that they could keep up with one, the scale of change that was going on to the introduction of new services and then the refactoring and transformation of services, but actually they might have been, you know, writing that and with entirely different architectures with entirely different tech stacks.
What was that journey like internally for you? It was actually quite exciting. Um, you know, the, the good news and you're, you're seeing it now with, with generative AI, the good news about these emerging technologies, especially when they represent something really transformational, lots of people and lots of smart people suddenly want to work in that technology space within that domain.
And that's what we find quite quickly. Our Our, um, our most capable engineers and product people were very quickly gravitating towards this because they could, it was exciting and new and different, and they could learn new technologies and new ways of doing things. And with smart, bright people, they're inquisitive.
That's what they want. You know, they want to get onto the, to the, to the next thing. So, so getting people interested. Was very, very easy. But what we didn't do is we didn't go out and do, you know, a big org structural change with, you know, lots of new head of cloud and all this kind. We did. We, we were much more, more organic about it.
Um, on the product side, we put a, a, a small team to get kinda centralized team together to get started. And then once we got up and running and we made some progress, we then federated that back out. To the, to the, to the broader product teams again, because otherwise you're disenfranchising from everyone taking part in this really cool technology.
So you kind of set up a little kernel of competency to get going and to get some wins on the board. And then once you build some confidence that you can do it, and you're starting to see those wins, then we, we federate it back out again. And what does the time frame look like, uh, right up to today, um, and during that period?
How, how cloudified would you describe your product set and so your dev teams now? Are they like 100%? It's all running on the cloud. It's all running on cloud stack. It's all, it's all working in a specific way. Are you still dealing with hybrid last generation products? Yeah, very much hybrid. And we expect that to run well into the foreseeable future.
Right. Um, the, the piece of, of transformation is very variable across different customer types and sizes. So if you look at a, at a, at a marquee name investment bank, you know, that have got massive CapEx investment and data centers that that journey is, I suppose, probably it's probably decades, you know, whereas, whereas if you get a startup hedge fund, they'll just light everything up from day one on the cloud.
Uh, so, so we're, we're very comfortable supporting that hybrid scenario and our engineering teams have done a phenomenal job and building the tax stack. whereby we have where we're able to service both those workflows and use cases equally well. And when you look at your customer base and you get the very cloud native versus maybe those who hold on to their heritage a little bit more.
One of the things we see is a stretching. So it used to be everybody was roughly in a pack and then cloud technology comes along and allows us to be different. We see the stretching of that. So the laggards are getting further behind and the startups are getting ever more capable of creating something new, especially from a business model perspective.
Do you observe that as well from your customer base that there is this stretching and a gulf opening up between the two? Or do you think in this particular domain, there's still much of a muchness or be it the way they apply the technology might be a bit different. Yeah, well, what we don't see anymore, um, this has changed over the last couple of years.
for the last few years. We don't really see any cloud naysayers anymore. Yeah. You know, you don't see people saying, Oh, this is a, this is a fad. It's going to pass. It's not for us. You know, you, you might get some people saying that they're concerned about the. non deterministic nature of their technology builds in the cloud because it tends to be all usage based and that becomes a problem for firms that run very tight budgetary controls.
Um, so you get, you get some of that, but generally what you tend to see or what we tend to see is The smaller customers in the more recent startups that are all in the big customers that are trying to get there. But it's a complex transformation and they've got newer applications or applications that work.
Let's say an application that was very compute intensive at certain times of the day or month. There's a big incentive to move that into the cloud because the cloud is very good at that. Yes. So they may have, you know, they may have put the project of work together to make that happen, but they've got other stuff that will just continue to run in their data center.
I mean, in, in the, in the capital markets, one of the, um, one of the, one of the trends in trading is to co locate your trading engines. next to the stock exchanges. So the stock exchange run their, their exchange effectively, their electronic exchange in a data center, and you want the lowest latency possible into that Exchange to execute an order, so you literally connect dark fiber from, from your switch into the cross connect of the exchange and you're a few feet away and it's executing sub millisecond.
And so until the exchanges move to the cloud, all of that will remain in the data center. Do you see that happening? And what over what time period? Um, it's, it's, there's a couple of exchanges that are trying to be particularly kind of progressive on this. So Nasdaq has been trying to move some of their trading into the cloud.
They recently moved their options market onto the public cloud and CME, the Chicago Mercantile Exchange, have been trying to move more of their Technology on onto the cloud. I do think I do think that will happen, but it's going to it's going to take time. That's going to be a slow move. Do I remember a story about somebody who laid a new fiber optic cable between, was it Chicago, New York or something, to cut the latency by a very small amount, but he got a massive strategic advantage through that?
I forget the name of the book or the story, but isn't it a famous one? Or is that just an urban legend that I'm just remembering, misremembering from the past? Well, in fact, it's even better than that. So that is, so what you're describing is absolutely the case where customers put. made massive investments to create dark fiber connectivity between New York and Chicago and various other trading centers.
But what happened is there was a company a few years ago that spent, I forget the number, you know, some huge amount of money, 500 million or something, building the fastest connectivity in the world between Chicago and New York. And, and within about a few months of them going live, someone built a series of microwave towers To get fast connectivity and immediately outpace them from a latency perspective.
Um, so yeah, that's a, that's a very cutting edge part of our part of the business. We actually, Bloomberg don't really operate at that, that's a very niche part of part of the capital markets, but an important one. Indeed, indeed. And so, interesting there to hear about how customer led you guys have been during the cloud transformation cycle and what that's looked like.
Obviously, AI is the next thing that's going to kind of engulf us in, in talk of transformational possibilities, lives within the cloud stack, of course. So what, what are you seeing at the moment in terms of a similar set of conversations I'm expecting about with your customers over what they're trying to do with AI and how they're trying to adopt it?
Yeah, it's, I mean, as you, as you, as you see everywhere, everyone is, is rushing very quickly into, into the space. Yeah. Certainly in the proof of concepts anywhere. I'm not sure the killer app has been, has been created yet, you know, certainly, certainly with, with the models, the foundation is there, but exactly what the application is, I think it'll take a little bit of time for that, for that to play out.
The part of it that we are very excited about. Or one part of it that we are very excited about is, if you are familiar with the Bloomberg Terminal, what the Bloomberg Terminal is, it's a, it's a piece of software that a user runs, and it gives them insight into data, analytics, news, and financial markets across almost everything you could possibly want to invest in.
Um, you know, commodities, stocks and shares, currencies, derivative contracts, companies, um, on a, on a global basis, you know, a massive universe of data that we have been acquiring, organizing and creating for the last 40, 40 some odd years. Now, one of the challenges that users have. with a system that sophisticated is how do you learn all of its capabilities in order to extract the maximum amount of value from that product for your needs and we have an application for LLMs and AI which will solve a real world problem for our users which is giving a human being A natural language interface to that massive universe of data analytics insight with value add on top like summarization and creation of new insights is a massive opportunity for us and our users.
So we're very excited about that. And I think that's the big one. What has generative AI allowed us to? LLMs have created an interface that can democratize access to big data sets. You have a very well curated data set that is a massive recipe for success. But then that opens up the door for curiosity and people can go and try things out and I think it's that you no longer as the white coated scientist with the pen in the top pocket in the way of getting access to the information.
And I think that that we still have to learn. how to, how to grab hold of that and people have to, you know, not be fearful of going in. But I think that's where the big revolution will come. It's not going to be a killer app. It's going to be an interface to something different. As an aside, and just triggered by that thought, over the weekend, I was watching the Alien movies with my kids.
and first time they'd seen them. And it's quite, it's quite cool to watch films like that, especially ones that, uh, you know, was, were made what, 40 years ago and are projecting technology into the future. And then to see how they interact with the film, not just as a film, but also the technology prediction of them is generally quite a good conversation.
What really struck me about Alien, like the first one, was the crew's interface to the mother AI that runs the ship is exactly like ChatGPT. Uh, so it's a talk, yeah. Exactly like, it's prompt engineering. It's prompts on screen that they then continue to build on and continue to build on. And I'm like, Literally, if I'd have been watching this five years ago, that wouldn't have looked like a sensible computer interface.
But actually, looking at it now, it's like, we've caught up to this. It's the new programming language's English, they say. That's the thing, isn't it? But that's the revolution, I think. And you're absolutely right. I don't think the killer app's quite worked out. It's the interface, too, that is the big change, isn't it?
We just need to get our heads around it. Yeah, I think what's really interesting is, you know, almost overnight we saw this step change in the capability of AI, you know, because AI has been out there for years and we've all talked about it for years and then just completely out of the blue came chat GPT 4 and everyone was, you know, wow.
Yeah. Now the question is. Have we seen the big step change and now it's all about the application of it over the next decade? Are we going to see more changes on this magnitude next year and the year after and the year after? Uh, and I think there's going to be a leap in capability, the ability to apply context will come next, so context sensitive, and then I think it's the ability to reason.
At the moment, we're not seeing novel ideas come out yet, and the day that a novel idea pops out of an AI engine, you sort of now get to a place where you're going, oh, this is getting interesting, because then there's something maybe the humans haven't thought about. You know why, you know why, Rob. air of tension in the room when Rob Brigg builds us up, Tony, is because he's been so dismissive and derogatory about AI up until this point.
They've got, they've got, they've got their eye on him and now he's trying to be all nice. I welcome our, our AI overlords. And I've said many times before, I, I, I want to integrate into their new vision of society. Yeah. Well, listen, do you, you know, you shouldn't be too dull. I don't want to remember. The transformer model, which is what, what created the big step change was created at Google in 2017.
And they left it sitting on the shelf and did nothing about it. And the people that created it left and started open AI. Now Google, you know, they've obviously caught up, but you know, in some respects it was sitting right in front of them. They didn't do too much about it until a competitor. That's going to be Gates esque in his failure to recognize the internet, the ground they lost.
They were literally sat atop, seven years ago, the magic, ignored it, and then somebody else created the magic. And it's like, it is, it feels like Internet Explorer all over again for technology. Going back to the observation though. Um, before Rob got all paranoid again about, about whether, um, this is the breakthrough or whether there's going to be multiple breakthroughs.
Sort of my read on it is AI is almost like the killer app for cloud. So like cloud to a certain extent, even though it's had some good stuff going on, it's never really been run to its full capability and it still hasn't to a certain extent. But AI is really going to stress test the cloud. And for me, it's about piecing in data.
behind it. So like bigger and bigger, more interesting data sets and language models that allow that that level of interaction. And I wonder, I mean, I wonder about the future of basic apps at all. Like, you know, are we just going to have sort of, you know, like the mother interface in early and there's no multiple apps that they were using.
They just had one interface to run the whole shit. It's the mobile phone companies are starting to already think about this about the concept of the app platform might now be passe. And it's the one into, it's the, it's the API enabled life, your bank's API enabled, the transport system is, you know, all sort of that, and you just ask questions of your device and it sorts the rest out for you.
Obviously a lot of trust in there, from a, where's my data going, who knows what I'm doing, who can monitor, but if you get over that. It's vastly complex, but I think where it's trying to, where, where it's perhaps moving to, creates a simple and intuitive and, and highly powerful interface. Thanks. which is, you know, we'll see how that develops.
And that's, you know, before, of course, uh, Rob's prediction of AGI in the next 18 months, whether it comes, comes through or not, Rob. They'll come for you, Dave. They will come for you. For me, we're still, we're, we're constantly talking about the means and not the end. You know, whether it's cloud or AI or, you know, what are we trying to What is the impact that we finally, you know, see in the world.
I think that's what we should be talking about and not so much about. I know we thought we'd have to talk about technology. Don't get me wrong, but you know what? Are we changing the end? Yeah, I think it's a really interesting question. You know, the thing about technology historically has been is that, you know, the real benefit is that it enables it enables.
human beings to synthesize and consume large amount of information much faster. You know, that's, that's really been what technology is for. Um, the, the difference here is that you, you can start to think about, about AI as an extension of human intelligence, because it's no longer just, presenting information.
It's actually synthesizing itself. You know, it's coming up with answers. It's coming up with ideas. It's generating content, which technology has never done done before. And I think how that all plays out is going to be, is going to be really interesting. So maybe to bring our conversation today to a little bit of a conclusion, maybe we want to go full circle and try and draw a bit of threads together, because we opened the conversation by talking about the nature of culture and organizational structure in Bloomberg, allowing this sort of fast response to changing technology.
And of course, there's this other wave coming. You know, you give some great examples about how you've managed to very quickly adopt a cloud and become market leading in your products in the cloud space. Now AI is coming on a wave. What advice would you give to organizations that maybe are struggling with their, you know, speedy adoption of technology and new technologies into their, either their product sets or just their internal use of technology?
Well, I think with, with AI, certainly, you know, if you operate in a business. Like we do, you know, our users are professional users. They're trading investor and investing investor money. So we can't have models that hallucinate. We can't have models that, that are giving answers based on information that is out of date, you know, that that would be highly damaging to, to our business.
So we have to be very, very careful with the models. that we're building and that we're using and the data that we're providing to them to make sure that they are, you know, weapons grade and robust in terms of the information that they are giving to our users. So, so, so probably, you know, you have to be very careful not to rush headlong too quickly into this because you could, in the interest of time to market, Do something that's quite damaging to your business.
And we will be extremely careful on the, on that, on that point. And I've seen a couple of examples of that already. You know, I read a story about an agent at a retail bank that had hallucinated a refund policy that didn't exist to someone. And now those people are claiming a fairly large, you know, monetary refund, um, because they've been given incorrect information.
And I think there's a few examples of that floating around out there in the industry. So I think you have to be very, very careful. Despite that, you know, the technology is of course incredibly powerful and it's going to open up all sorts of capabilities, but I do think also you have to be very careful.
Esmee, what have you been looking at? Yeah, so diving into today's topic, I was thrown back into class learning about macro, meso, micro analyzers, and Michael Porter's five forces model, who hasn't been thrown up with it. You just caused Rob to get a bewildered look on his face there. Rob. That I've been to class?
You went fast through that and I wasn't sure if I misheard you or not, but keep going. His eyes widened and he looked a bit scared. I could hear his brain going. The cog's going, you could hear the cog's going. Have I missed some homework that I need to do? Have I missed something important? But keep going.
Hopefully, it feels like I'm going to learn in the next 60 seconds. So here we go. No pressure there. Do you want me to start all over? No, no, no, keep going, keep going. Okay. Uh, so Michael Porter's five forces model, which saw daylight in the eighties. And writing a business plan back then containing the broader industry conditions, including macroeconomics, uh, to gain competitive advantage at time would only, or would hold for three to five years, right?
So we had a plan and we could stick to that. And now with the advantage in technology, the complexity and speed of today's global markets and the growing availability of data, Business strategy and execution has evolved significantly over time, especially since, since Porter's piece. But data might have a real time sync that we just discussed as well.
But I think the biggest challenge is still to have all those departments, the teams, the individuals respond in sync with that data. So thinking about market scenario planning and predictive analyzes feeding into your PI planning, how would that be? Or consumer behavior leading to exploring new product features or quickly allocating resources.
And then I really mean quickly, without any hassle, without people crying. So what's that saying? Culture eats strategy for breakfast. I think, I think the part we've discussed this in the past about the, we played around with the one week strategy in season three. And I know it sounds about, but the ability to adapt your strategy quickly based on understanding what's going on has become ever more important and not dogmatically sticking to the plan.
And we talk a lot about. ditch your 12 month and go to three month. But actually there's an argument to say, you know, and COVID tested this, didn't it? Where it says those organizations that could adapt quickly in the way you're talking about were much better at surviving and not having a, uh, a crash in the middle.
And those that were more duck in their ways suffered for an awful lot longer. And I think you've got a good point about if you've got the right people with the right mindset, armed with the right information, then you can get success, but the system has to allow them to adapt quickly. So I think there is a big point in that.
I still think you need the long range planning, but absolutely. If your culture's wrong, it's very difficult to often enact change in the way you're sort of discussing Well, we've talked on this show before about a thing that I have a big fan of called the Knafen framework. Um, I'll let people go back and listen to the other episodes so I don't have to fully explain it now, but there is a section in that framework around complicated thinking, which is, you know, you're planning and then you're running to plan versus complex thinking, which is you are market testing and responding, market testing and responding on much shorter, faster cycles.
And it's always seemed to me that that's a pretty neat summation really about. What's really at the heart of digital transformation? You're trying to go to something where you are fast responding and it requires all kinds of change to do that from delegation frameworks and governance from board level downwards to organizational structure to the tech stack.
I wonder, Tony, in your organization, What do those planning cycles look like? Are you, are you using one in three plans at the moment, or are you, have you brought that in and you're tightening that to quarter on quarter reviews? What does it look like? Yeah, so we, we do run on much more of an agile methodology.
You know, we kind of leave, you know, we teams to figure out what that means for themselves, but we do tend to operate on much tighter, iterations, you know, even the concept of evolutionary design is important to us. And, you know, getting a product relatively early in its cycle out to market, even if it's just to a small number of customers that understand that they're taking something early, but are keen to be part of the process of development of the of that product and then using the feedback to iterate and and you know that that's very much the cycle that we're in.
Although we do that because we need to be able to calibrate as we build the product, we do also overlay that with a longer term vision of what we're trying to get to within the market. So so less so about The specifics of what we're going to build that's over a two or three year period, and more so about what does a leadership position in the market look like for a product or a product set of this, of this nature?
And then what do we need to build to achieve that? And then by being iterative in that so that we can course correct along the way. Do you find that you get to times where the organization does have challenges in response? What does it feel like to actually unpick a problem in that way of working? Well, there's there's certain risks that are inherent in that in that process.
You know, one of the risks is That I have observed over the years is putting too much weight on the feedback from one or two customers, you know, especially if they're big and important customers. You know, it's very, very easy for a product manager to fall into the trap of going to a meeting at a large customer who's very vocal about something and then thinking that that's the market sentiment, right?
Right. And you have to do certain things to kind of protect yourself against that. And another thing is that. And this is a human nature problem. When people become very attached to an idea, it's very, very difficult for them, often, no matter what data points are coming at them, that they need to course correct, to bring themselves to do that, because they've staked so much on the original idea.
Yeah, yeah, yeah. They've put themselves really out, really far out there with it, and, uh, hard to rein that back in again. Yeah, yeah, exactly. You have to, you have to really develop that skill over time. I felt, certainly that's what I felt within myself. That you have to learn a willingness to change your own conviction.
I think that's entirely right and you can see, can't you, those, those individuals that are, I don't know whether sometimes it's, it's, it starts out as an ego issue, but then over time it becomes actually a courage issue and like it takes sometimes quite a lot of bravery to pivot your position on something without looking like you've just been blown over in the wind.
You know what I mean? Exactly right. Absolutely. There's two stories in that world though, isn't it? There's the, the ego that can't let go and it often results in dramatic failure, but there's also the one that knows they're right and pursues it and then actually makes a massive success in the end and they go through the trials and tribulations on the way.
So it's a, it's a tough one to call sometimes because they, they know there's something in it, but it hasn't quite, you know, there's been Plenty of companies that almost failed before they succeeded. I mean, complete collapse, and others who have failed because they wouldn't let go. But then data can help, eh?
And of course the challenge there is you don't know till the end. Yeah, yeah, that's the thing, isn't it? You don't know until the bitter end. All the bitter, all the great success, it's one or the other, isn't it? Sometimes a couple of years after the end. Lifeline when everyone's like, oh yeah, that was a good idea.
Well, we talked about this before. This is the Kodak. They invented the digital camera and went, nah, nah, we're gonna stick with film and failed to see the value. So that's an example of where a company didn't, it didn't embrace the change, even though they were sat on it, or the transformer one at Google where open AI took control.
And then there's others where they've just pushed something to market that they truly believed there was a market for, and there just wasn't. And it was the end of them and they never moved away from it. So, and of course Kodak now just relaunched her. A film camera. Yeah. I noticed recently. Come back.
Quite cool. Anyway, look, let's, let's bring today to a bit of a close, a fascinating conversation about the role of culture and organization in how you can evolve your technology and meet your customer's needs. Tony, thank you very much for joining us today and making the time for this. Absolute pleasure.
Thank you for having me. It's been great to see you. Now we end every episode of this podcast by asking our guests what they're excited about doing next. That could be you've got a great restaurant booked at the weekend, or it could be something in your professional life. So Tony, what are you excited about doing next?
Well, what I'm very excited about is I'm going to Taipei. in September. Oh, nice. Very nice. For, for work, you know, we've got, we've got some really interesting things going on there with, with, with, uh, the local plant base. And I've never been, you know, I've been to, I've been to all, you know, many, many parts of Asia many times, but I've never been to Taiwan.
So I'm really looking forward to that. That'll be good. Wow. That'll be fascinating. Are you going to manage to get some personal time in while you're there? Or is it just a quick there and back type situation? No, I'll get maybe a day at the We'll get there. I'm going to try and arrive on the Saturday morning so I've got the Sunday to do.
Some sightseeing and some looking around. That's a good thing to try and do that, isn't it? When you do that, we're all lucky enough, I guess, to do work travel. And it's very easy to slot into taxi meeting room, taxi aeroplane, and, and not, you know, get the time to just absorb a little bit, you know, even if it's only for an afternoon, like you're saying.
If you would like to discuss any of the issues on this week's show and how they might impact you and your business, please get in touch with us at [e-mail]. We're all on X and LinkedIn. We'd love to hear from you. So feel free to connect and DM if you have questions for the show to tackle.
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