Speaker 1: Hello, Hello. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeart Media and IBM about what it
Speaker 1: means to look at today's most challenging problems in a
Speaker 1: new way. I'm Malcolm Gladwell. Today I'll be discussing the
Speaker 1: innovations around hybrid cloud with Lumen's David Shakochus and IBM's
Speaker 1: Howard Boville. David is Vice President for Enterprise Technology and
Speaker 1: Field CTO at LUMEN, where he's helped clients across industries
Speaker 1: create new business opportunities through unique digital interactions. David has
Speaker 1: been immersed in cloud computing long before his time with Luhmen,
Speaker 1: working with companies such as Unit, Digital Island and fuse Point.
Speaker 1: You're putting computing capacity in places that didn't used to
Speaker 1: be thought of as data centers before. There's an element
Speaker 1: of novel challenge and so they're inherently there's there's more complexity.
Speaker 1: Howard is the head of IBM Cloud Platform. In this role,
Speaker 1: Howard has focused on driving digital transformation for enterprises, especially
Speaker 1: in highly regulated industries. Before joining IBM, Howard was Chief
Speaker 1: Technology Officer for Bank of America, where he led the
Speaker 1: transformation of the Bank's infrastructure and developed one of the
Speaker 1: largest internal private clouds. The times you have to kind
Speaker 1: of be a technology of angelists in terms of what
Speaker 1: the art of the possible is against the problems. In
Speaker 1: this episode, we'll explore working and living in a world
Speaker 1: of cloud technology. Will show you how new innovations in
Speaker 1: cloud computing have reimagined a world where computing can happen
Speaker 1: anywhere and businesses can use data to accelerate innovation to
Speaker 1: improve service and performance. Let's dive in. Welcome everyone, Howard
Speaker 1: and David. Thank you for joining us today. Let's jump in. David,
Speaker 1: I'm gonna ask you to define some terms and that
Speaker 1: will be easy for you but useful for the rest
Speaker 1: of us. First of all, the Fourth Industrial Revolution? What
Speaker 1: is it? Yeah? Good one, so we can really look back.
Speaker 1: I think on history in these periods of technology advancement, right,
Speaker 1: you know, the period of industry that was defined by
Speaker 1: steam power, the period of history, and the industrial period
Speaker 1: of history that was defined by electrical distribution. We commonly
Speaker 1: think of the third one is really this information age,
Speaker 1: the information revolution of digitization of process creation and online
Speaker 1: connectivity of data. Is this third industrial revolution of the
Speaker 1: digital age? Information technology systems community caating with each other,
Speaker 1: and the advent of all that you can do in
Speaker 1: industry with those technologies, and the Fourth Industrial Revolution is
Speaker 1: really this reflection of the explosion of data that gets
Speaker 1: created by all that connectivity. Taking data and being able
Speaker 1: to acquire it, analyze it, and take action upon it
Speaker 1: is opening up a wide range of new industries and
Speaker 1: new business opportunities and new regulatory challenges. UM and that's
Speaker 1: what we mean when we say the Fourth Industrial Revolution.
Speaker 1: How did Luminum IBM come together, and what's the logic
Speaker 1: behind your collaboration in this field? You can't take the
Speaker 1: heritage of both companies. So limit are a world class
Speaker 1: global networking company. They connect things together at the highest
Speaker 1: level of quality, lowest latency and so on. And it's
Speaker 1: and it's hard through all the actual transformations that IBM
Speaker 1: has been through. Is where a compute company on which
Speaker 1: the software runs and we write that also the software
Speaker 1: in certain contexts as well. So the combination of the
Speaker 1: two capabilities solves for the problem. We've been working together
Speaker 1: for years. I think the the advent of what we've
Speaker 1: been focused on with IBM Cloud Satellite has really been
Speaker 1: initiated by Lumen's investment in making our network a place
Speaker 1: where you can run software workloads more readily and easily.
Speaker 1: And IBM cloud satellite is a great modality that just
Speaker 1: snaps right into that network. Yeah, you work for Lumen.
Speaker 1: Is the simplest way to describe Lumen that Lumen is
Speaker 1: a Fourth Industrial Revolution company. We're we're a Fourth Industrial
Speaker 1: Revolution company because we believe at the core of all
Speaker 1: of it is connectivity. All that data and all those
Speaker 1: sources of data and all the ways that you need
Speaker 1: to interact with that data requires a substantial amount of
Speaker 1: aggregate networking capacity. We're now kind of hitting this tipping
Speaker 1: point in the Fourth Industrial Revolution where the amount of
Speaker 1: data coming inbound from cameras and from sensors and from
Speaker 1: devices and gaming consoles and and a variety of uh
Speaker 1: input sources like that is actually exceeding capacity in the
Speaker 1: other direction. So that's really why for the Fourth Industrial
Speaker 1: Revolution of work, you need massive amounts of network connectivity.
Speaker 1: And that's what what LUMAN does. So this brings up
Speaker 1: the second word I want you to define, and that's
Speaker 1: edge computing, which I'm assuming is edge computing is it
Speaker 1: is a technological response to the phenomenon you've just described
Speaker 1: it is. It's one way to think about edge computing.
Speaker 1: The way we talk about it a lot is it's
Speaker 1: moving workloads software workloads closer to digital interactions, and a
Speaker 1: digital interaction could be between things and people and business models. Yes,
Speaker 1: I mean, just to add to some of David's point
Speaker 1: with some kind of practical use cases that we kind
Speaker 1: of we're involved in. So so, first and foremost, the
Speaker 1: edge computing piece actually is joining the analog world to
Speaker 1: the digital world, Whereas until this point you would look
Speaker 1: at the digital world through the screens that we all
Speaker 1: spend sumers time looking at. Whereas on the edge, it's
Speaker 1: actually looking at physical locations like retail branches, like shipping
Speaker 1: concern as, like welds on on a well, but an automobile.
Speaker 1: And there's there's two practical examples. So there's thermal imaging
Speaker 1: techniques that we now use to look at the quality
Speaker 1: of a weld all the way through a production process
Speaker 1: in an automotive plant that wasn't possible, that connects in
Speaker 1: that local location, gathers that data and determines that the
Speaker 1: welders at the actual right quality or on a shipping
Speaker 1: content basis, it's it's the combination of r F I
Speaker 1: D tags, connecting to networks that contract with that level
Speaker 1: of accuracy and giving you that experience. Come in terms
Speaker 1: of the how has this come about, it's because as
Speaker 1: we've become more familiar with the amount of data that
Speaker 1: we can capture through a digital interaction through a screen,
Speaker 1: whether that's a mobile phone or whether computer, and all
Speaker 1: of the analytics that you can then do on kind
Speaker 1: of humans behavior, the same questions that get posed the
Speaker 1: physical locations or physical assets, the physical interactions or the
Speaker 1: physical assets, and it's the wedding of those two things
Speaker 1: create this I T problem. The companies like Lumen and
Speaker 1: IBM sold for at the edge so that you can
Speaker 1: actually tie together the digital world and the physical world
Speaker 1: in the same way as you were capturing the data
Speaker 1: purely from a digital world. And it's then human's curiosity
Speaker 1: that I say, Okay, well we've got these questions answered
Speaker 1: from the kind of the Third Industrial Revolution, I dad,
Speaker 1: we went through. How do we apply that through the
Speaker 1: Fourth Industrial Revolution into the analog world? Yeah? Yeah, you
Speaker 1: know what this makes me think of if I was
Speaker 1: and and start me if this is too speculative if
Speaker 1: I was a basketball coach, I would love to have
Speaker 1: an edge computing system which picked up data from my
Speaker 1: players on the court in real time and told me
Speaker 1: who was getting tired, told me whose performance was subpar,
Speaker 1: told me how quickly someone was responding on defense. And
Speaker 1: I mean, that's a that's kind of what That's an
Speaker 1: example of what you're talking about. How it isn't it.
Speaker 1: It's like the and wor all that had previously been
Speaker 1: entirely analog perhaps perhaps bang on a trash can when
Speaker 1: they see something. But but but that but that in
Speaker 1: parts to the point you're making is in reality because
Speaker 1: that there are tracking devices now on athletes in practically
Speaker 1: all disciplines at tracking how many kilometers are, how many
Speaker 1: miles they're running average pace, And that's been tracked, and
Speaker 1: that will be analyzed at the halftime break or the
Speaker 1: quarter time break to being upon the actual sport that's
Speaker 1: been followed or the third time I guess of its
Speaker 1: ice hockey. So that has been tracked. What isn't is
Speaker 1: the physiological elements that you talked about. But I guess
Speaker 1: kind of that will be at some point because humans
Speaker 1: curiosity will drive into that element to say Okay, what
Speaker 1: what level of fatigue are we are? Therefore, was the
Speaker 1: optimal moment to actually make a substitution of a different
Speaker 1: player onto the pitch? Yeah? Yeah. Or if I'm if
Speaker 1: I'm a hospital, I want to monitor the performance of
Speaker 1: my surgeons. I mean an hour four of a complex operation.
Speaker 1: I would love to be able to in real time
Speaker 1: crunch a whole series of data that tells me, you know,
Speaker 1: who's working well and who's flagging. Another kind of hallmark
Speaker 1: of edge computing is when you really need to correlate
Speaker 1: things locally. Um. You know a public safety use case
Speaker 1: where you know, a gunshot rings out, um, and an
Speaker 1: audio sensor picks that up well, correlating that with all
Speaker 1: the stop lights in the area, all the lights in
Speaker 1: the area, you know, any other public safety device that
Speaker 1: is within a particular geographic boundary. UM. That intense correlation
Speaker 1: of events to other outcomes may need to happen within
Speaker 1: split seconds, uh, you know, for for a public safety
Speaker 1: outcome to be achieved. So so so it's it's not just
Speaker 1: the fact that you know, we're tracking, we're analyzing data,
Speaker 1: and then we're getting lessons learned at halftime of you know,
Speaker 1: which one of our players run around. The more fine
Speaker 1: grained like milliseconds matter kind of use cases is another
Speaker 1: place where edge computing really shines. In step one, you
Speaker 1: analyze that kind of data, say the past coutball player
Speaker 1: or the surgeon after the fact. So you have the
Speaker 1: meeting the next day and you say, you didn't perform
Speaker 1: very well yesterday, Malcolm on the court. But if I
Speaker 1: can do it in real time, then I can actually
Speaker 1: affect the outcome of the game as it's happening. And
Speaker 1: that that shift from being able to make those judgments
Speaker 1: immediately and make those judgments after the fact is huge.
Speaker 1: It's I could win the game, yeah, didn't otherwise lose
Speaker 1: And I'm echoing, I'm capturing your excitement. You are kind
Speaker 1: of echoing that position where we've kind of gone from
Speaker 1: the digital perspective where people are playing online games, sporting
Speaker 1: games and making judgment spist up on what they can
Speaker 1: see from the analytics they get in that digital realm,
Speaker 1: and then translating that into ideas that could be extended
Speaker 1: into the animal row and therefore that desire. You can
Speaker 1: imagine there are people as we speak now putting together
Speaker 1: innitative solutions that can address that very problem. Yeah. The
Speaker 1: other thing too, is it we're talking about all the
Speaker 1: whiz bang use cases and and there's sort of a
Speaker 1: subtext to everything we just said, which is that there's
Speaker 1: good software designed at scale able to run and achieve
Speaker 1: those outcomes better basketball performance, public safety use cases. There's
Speaker 1: software that needs to go and collect all that data
Speaker 1: and take action against it. And the other sort of
Speaker 1: really the dimension, and certainly with a big dimension of
Speaker 1: the IBM and LUMIN relationship is being able to enable
Speaker 1: great software development anywhere that the network can reach. All
Speaker 1: these use cases don't happen unless there's software that goes
Speaker 1: and runs that business logic or runs that analysis, or
Speaker 1: processes those inputs into actionable outputs and respond to an
Speaker 1: event stream. Yeah. Yeah, talk a little bit about the
Speaker 1: cloud piece of this. Why does hybrid cloud How does
Speaker 1: it fit into this puzzle that you've been describing. So
Speaker 1: the hyhbrary cloud space essentially encapsulates all the points that
Speaker 1: David has gone through. So it's a cloud essentially is
Speaker 1: a building with computers in that run applications. And the
Speaker 1: paradigm until probably about ten fifteen years ago was that
Speaker 1: a large corporate would have a big data center have
Speaker 1: its own computers in them and would have that capability.
Speaker 1: And then what created a huge innovation was the actual
Speaker 1: ability for our developer to come up with an idea
Speaker 1: not need to unbuild a big data center for computers,
Speaker 1: and it could actually rent the space and then turn
Speaker 1: that idea into software and turn that software into a
Speaker 1: Facebook or a Netflix or whatever it may be. So
Speaker 1: it reduced barriers of entry, and that was the first phase.
Speaker 1: The first that PHAs that were now in is this
Speaker 1: kind of synthesis between the digital and the analog at
Speaker 1: the edge, and that's the hybrid cloud computing, where we
Speaker 1: can actually create many data centers specific to particular needs
Speaker 1: all around the world, not just within the assets that
Speaker 1: IBM has or the assets that are the cloud service
Speaker 1: providers have. And it's these partnerships. There's also kind of
Speaker 1: new economic models in the marketplace where companies can operate
Speaker 1: with humility to recognize, Okay, we may be large companies,
Speaker 1: but actually we can see the assets in another company
Speaker 1: and the brilliant people that exist there, and if we
Speaker 1: could partner with them, we could create something valuable for
Speaker 1: the marketplace. What the what's the challenge if I'm a
Speaker 1: company and I want to do something sophisticated with all
Speaker 1: of this data. Where am I gonna What's going to
Speaker 1: keep me up at night? A part of this puzzle
Speaker 1: when you have this explosion of data and it can
Speaker 1: be at the edge, the key thing that we need
Speaker 1: to be very mindful of is cybersecurity risk in that
Speaker 1: that data gets in the hands of their own people
Speaker 1: who then can actually use that to their own to
Speaker 1: their own gain, or to whatever purpose they want to use.
Speaker 1: So every solution that has to be built has to
Speaker 1: be built at a very high gradive cybersecurity, so ensuring
Speaker 1: that we protect our customers data and also we protect
Speaker 1: them from the laws, rules and REGs that they have
Speaker 1: to be obligated to. Broadly speaking, you're you're putting computing
Speaker 1: capacity in places that didn't used to be out of
Speaker 1: the data centers before. Right there, there's an element of newness,
Speaker 1: There's an element of novel challenge that you may be
Speaker 1: running into, and so that inherently there's there's more complexity.
Speaker 1: The other thing that keeps a lot of it leaders
Speaker 1: up at night is whether they are going to be
Speaker 1: able to write software and deliver it at a pace
Speaker 1: of change that is actually going to be able to
Speaker 1: take advantage of or or solve the problem they're trying
Speaker 1: to run. So I want to go back. I want
Speaker 1: to do a for example here, because it seems to
Speaker 1: be a really interesting and important point when I raise
Speaker 1: that example of this surgeon and we want to gather
Speaker 1: data from the surgical suite, we want to make sense
Speaker 1: of it in real time, we want to inform the
Speaker 1: surgery itself. But then you also want to share that
Speaker 1: data with lots of other hospitals and use that to
Speaker 1: build some kind of system that can improve surgery generally.
Speaker 1: So what you're saying is, in order to do that
Speaker 1: last piece, which is arguably the most important of the pieces,
Speaker 1: everyone's going to be reading from the same book, right,
Speaker 1: And the key around that is there's a level of
Speaker 1: complexity as also reading from the same book means that
Speaker 1: the actual the format is the same, the language is
Speaker 1: the same, the type face to carry that analogy on.
Speaker 1: So getting consistency in terms of the data models as
Speaker 1: it's known is super important, as is the provenance so
Speaker 1: that you know that the actual quality of the data
Speaker 1: is at the highest level of insecurity. And the reason
Speaker 1: why that's important is you would take all of that
Speaker 1: inside all of those lessons that are turned into data
Speaker 1: and put them into an artificial intelligence model to treat
Speaker 1: what's not as training that model so that it actually
Speaker 1: can come up with hypotheses that are actually continually initiatively
Speaker 1: improved based upon the amount of data. But if there's
Speaker 1: any issue, any corruption in that data, it will compromise
Speaker 1: the actual outcomes. And because the volumes of data can
Speaker 1: be so large, it is actually difficult to ensure that
Speaker 1: actually the outcomes are trained correctly. So there's a huge
Speaker 1: amount of work has to go into ensure the integrity
Speaker 1: of the day to the providence of the days or
Speaker 1: is correct so the AI doesn't get trend in the
Speaker 1: wrong way. Yeah, that idea of software distribution. The in
Speaker 1: our data analytics practice. One of the industries they work
Speaker 1: with extensively is manufacturing. And one of the things that
Speaker 1: we see organizations challenged by, and as a phrase you
Speaker 1: are one of our data scientists uses all the time,
Speaker 1: is that it's actually kind of easy to go and
Speaker 1: collect a lot of data locally on the shop floor,
Speaker 1: and it's kind of easy to get all of the
Speaker 1: data historically that you've ever had once it's available in
Speaker 1: your data center to go have a data scientist analyze
Speaker 1: it and come up with you know, widely held best
Speaker 1: practices and the source of what should be the most
Speaker 1: efficient way to do things and what should be the
Speaker 1: most efficient data model that can analyze all the sensors
Speaker 1: in the factory. The challenges is getting it from the
Speaker 1: top floor to the shop floor. It's fine to get
Speaker 1: that lesson in your shortcore data center. It's fine. It's
Speaker 1: fine to go collect a lot of data. The challenges
Speaker 1: connecting them together, and that's really where this idea of
Speaker 1: consistent delivery of new software when you learn the lesson
Speaker 1: in the top floor says this is the way it
Speaker 1: ought to be. How do you get that code out
Speaker 1: into your built environment so that that software is actually
Speaker 1: taking effect. It's not just a theory that is a
Speaker 1: model in a data center, but it's a model that
Speaker 1: can make a difference. Tell me about how this collaboration
Speaker 1: between your two companies addresses that problem. Can you give
Speaker 1: me an example, Well, yeah, I think what however, was
Speaker 1: alluding to. One of the customers we're working with right
Speaker 1: now is in the financial services industry. But this is
Speaker 1: a digital interaction between the financial services business model of
Speaker 1: banking and the people that walk up to it. And
Speaker 1: there's a security risk out there in the world whereby
Speaker 1: bad actors will target a t M machines and it's
Speaker 1: called skimming, where they'll go and walk up to an
Speaker 1: a t M, put a device that looks same color,
Speaker 1: same fitting over the credit card slot, and surreptitiously scan
Speaker 1: the credit card as it's being inserted into the machine.
Speaker 1: The user doesn't know that it happened, and the bank
Speaker 1: doesn't necessarily know that it happens. In the the the
Speaker 1: point at which they can take most efficial effective action
Speaker 1: against that bad actor is the point at which they're
Speaker 1: walking up to the machine which has a video camera
Speaker 1: inside of it, and inserting that device. And so there
Speaker 1: are certain patterns you can be looking for. Are they're
Speaker 1: walking up to it with a bag, are they reaching
Speaker 1: into the bag? They are they taking on a certain
Speaker 1: posture against that a t M interface to know maybe
Speaker 1: there's further correlation we need to take against this person.
Speaker 1: But so financial companies would look at that and say,
Speaker 1: you know, that could be a needle in a haystack
Speaker 1: kind of analysis problem. And if you get better and
Speaker 1: better at getting closer to figuring out who is skimming
Speaker 1: off your A t M machines and who isn't. Once
Speaker 1: you get good at building that model and then deploying
Speaker 1: that software to all your A t M s, you're
Speaker 1: in a situation where your overall risk to your customers
Speaker 1: and your brand that the payoff becomes immeasurable. So that's
Speaker 1: one of the things that we're working on with IBM
Speaker 1: and some of the great video analytics software they have
Speaker 1: that we can put out closer to some of these
Speaker 1: financial institutions, acquire analyzed, but then act upon the data
Speaker 1: that's involved. Oh, I see. So to your point, the
Speaker 1: insight number one is this particular a t M has
Speaker 1: been compromised, But the much more useful bit of information
Speaker 1: is it's been compromised by such and such a person,
Speaker 1: and we're observing that person compromising it in real time,
Speaker 1: right right, right, So whether that a t M learns
Speaker 1: what a bad actor looks like walking up to it
Speaker 1: in Minneapolis, well that's good. But the key is then learning,
Speaker 1: updating the model, getting that new software tested, and then
Speaker 1: getting it deployed consistently to all the other places that
Speaker 1: can benefit. I want to go back to this partnership
Speaker 1: between Lumin and IBM. You said, you guys have been
Speaker 1: working together for some time. How long when did you win?
Speaker 1: Did it first start? We've had relationships with IBM and
Speaker 1: some of it's a Elia companies in one way, shape
Speaker 1: or form for a few decades. The other thing to
Speaker 1: remember is Lumen is a service provider, right, so we
Speaker 1: contract with our customers to go deliver services for them.
Speaker 1: In a lot of cases, those services have always involved
Speaker 1: IBM software, IBM data capabilities, working with the IBM cloud,
Speaker 1: and so IBM as a technology entity has been connected
Speaker 1: to the end points of Lumen networks, you know for
Speaker 1: all that time. Yeah, what does from a customer standpoint,
Speaker 1: what does the partnership between Lumen and IBM look like?
Speaker 1: I mean, are you if I'm that financial service companies
Speaker 1: is trying to trying to stop my A t M
Speaker 1: s from being hacked? Is am I dealing with a
Speaker 1: kind of task force made up of lumon and IBM folks,
Speaker 1: So that the solution that we're putting together there is
Speaker 1: precisely that. So the out of technology companies continue to evolve,
Speaker 1: they have these kind of tusk forces that you talk
Speaker 1: about that actually work on problems and then reapply let
Speaker 1: us technology innovations to those problems. We then create new
Speaker 1: go to market offerings. As I mentioned earlier, kind of
Speaker 1: the business models that are kind of really worked now
Speaker 1: is where you actually get and understand with humility the
Speaker 1: assets that you have as a company and combine them
Speaker 1: with assets of other companies. And the thing that really
Speaker 1: makes it come a live is in getting too very
Speaker 1: smart groups of people together to actually face off to
Speaker 1: those business problems. So the the problem that DIV was
Speaker 1: going through, there was a conversation in the meeting room
Speaker 1: which is, we have this problem, how would you think
Speaker 1: about this? And then we combine our engineers various components
Speaker 1: that we have worked up what we call proof of
Speaker 1: concepts to kind of work through is there are there
Speaker 1: there in some of the solutions that we can put together,
Speaker 1: and then increasingly that becomes something that we would call
Speaker 1: a production offering, which actually becomes more generally available in
Speaker 1: the marketplace. What's the what's the hardest problem is always
Speaker 1: I think called latency is always the hardest thing, and
Speaker 1: it's in the both domains were probably primarily in the
Speaker 1: limit demand, and that's where you kind of forever put
Speaker 1: shing physics to actually get as close to the speed
Speaker 1: of light in terms of how quickly you're you're transmitting data.
Speaker 1: And it's a tough two problem to solve for but
Speaker 1: because of the huge volumes of data and because of
Speaker 1: increasingly humans nature for instant gratification and that we want
Speaker 1: everything now, we want it immediately. And what's hard about
Speaker 1: that is latency is a particularly hard problem from a
Speaker 1: technical standpoint because in some cases latency, you know, latency
Speaker 1: is that is the amount of time it takes usually
Speaker 1: measured in milliseconds, which are less than the blink of
Speaker 1: an eye, but the amount of time it takes a
Speaker 1: packet to traverse between two particular endpoints and a network,
Speaker 1: but those all add up, right. You can sort of
Speaker 1: thinking of it in a computer or a brain context
Speaker 1: as processing speed. How fast can I react to things? Well,
Speaker 1: if it takes a while for the packets to travel
Speaker 1: through their neurons, to use a brain analogy of a network,
Speaker 1: the longer it takes for the packets to process through,
Speaker 1: the longer it takes for an outcome to occur. And
Speaker 1: if an outcome takes too long to process, then it
Speaker 1: becomes fairly useless. Yeah, Yeah. My first question is do
Speaker 1: customers always realize what the potential of all of these
Speaker 1: different pieces are or is part of your job in
Speaker 1: helping people in opening people's eyes to what's possible. You know,
Speaker 1: very at times you have to kind of be a
Speaker 1: technology of angelists in terms of what the art of
Speaker 1: the possible is against the problems UM. And it's not
Speaker 1: because customers don't have the samability to see that. It's
Speaker 1: just very often they don't see that the breadth of
Speaker 1: things that we see when we're working with lots of
Speaker 1: different industries and we can apply solutions from one place
Speaker 1: to another. UM. The other element in terms of the
Speaker 1: pace of adoption in organizations is less about the actual
Speaker 1: people within them, but also the technology decisions that were
Speaker 1: made in the past. Large investments will have already been
Speaker 1: made to actually build the technology environments that they have.
Speaker 1: They're known as legacy environments, and it's getting from a
Speaker 1: leg see environment to the new environment. And that that's
Speaker 1: a tricky dribble in the sense that you have to
Speaker 1: look at your balance sheet, you have to look at
Speaker 1: the amount of work that would be necessary to do that.
Speaker 1: You've got to change everything from infrastructure to lines of
Speaker 1: application code, of data sets and so on. Um so
Speaker 1: it's a very complex environment for our customers to be
Speaker 1: able for other thinking about and therefore, what do they
Speaker 1: prioritize as their next area of innovation relowsy to the
Speaker 1: actual value that they would get for their customers, are,
Speaker 1: for their shareholders or whatever that drivers are. It's really
Speaker 1: interesting the that word I was going to kick out
Speaker 1: of it. Enterprise i T. It's the only context in
Speaker 1: which legacy is an epithet. Right, Like you say legacy
Speaker 1: to an I T person, they roll their eyes and
Speaker 1: you know their their blood pressure goes up. It's like
Speaker 1: nails on a chalkboard. But to most individuals, like what
Speaker 1: is your legacy, the word legacy means like it's something
Speaker 1: to be honored, right, It's something. In an enterprise context,
Speaker 1: legacy just means you've made a lot of decisions already,
Speaker 1: You've made a lot of the say as you've made
Speaker 1: a lot of implementations, you're bringing a lot behind you.
Speaker 1: That should be a good thing. But an enterprise I
Speaker 1: T context and a technology domain, it's really challenging. Yeah,
Speaker 1: I mean that what I've heard played back to me
Speaker 1: is kind of yeah, how that God may have created
Speaker 1: the earthen seven days, but you didn't have to deal
Speaker 1: with legacy. Uh yeah, so it kind of gives you
Speaker 1: a sense as to the differences in an I T context. Yeah,
Speaker 1: one last question, I want you guys to jump ahead
Speaker 1: ten years from now. I've gathered the two of you
Speaker 1: ten years from now, tell me what's top of mind
Speaker 1: in I think what's what's really a huge challenge in
Speaker 1: business and in the ways that business and organizations collaborate
Speaker 1: is this concept of compose ability. And I think compose
Speaker 1: ability of the ability to go break things down into
Speaker 1: simple functions and have them be intercombined. Um, we're just
Speaker 1: still even at the outset of that. You're to see
Speaker 1: that a lot in the cloud, but as we get
Speaker 1: out closer to edge computing in some of these Fourth
Speaker 1: Industrial Revolution use cases, the ability to take and compose
Speaker 1: different capabilities from from an IBM, from another software company,
Speaker 1: from a real estate company that's selling you access to
Speaker 1: run computing capacity at the end of a of a
Speaker 1: physical link. The ability to compose services together, whether it's
Speaker 1: through multiple parties, or the ways organizations even present themselves
Speaker 1: to the world take advantage of us in any way,
Speaker 1: in any slice that you so choose. Compose ability is
Speaker 1: going to open up a massive amount of possibilities that
Speaker 1: it's maybe a little rooted in the here and now,
Speaker 1: but it's it's something that I'm excited about over the
Speaker 1: next five to ten. Yeah, the thing I'm interested is
Speaker 1: kind of the So we're in the midst of ultificial
Speaker 1: intelligence that is increasingly starting to tax the inventors of those,
Speaker 1: which is human beings. So the prefrontal call text only
Speaker 1: has so many energy it can burn it a debt
Speaker 1: and it is being burnt out at the end of
Speaker 1: every day through the actual amount of debt, so it's
Speaker 1: bombarding it. So the intelligent augmentation, so flipping the two
Speaker 1: letters from artificial intelligence to intelligence augmentation so that we
Speaker 1: actually can actually work within these environments in a far
Speaker 1: more commulative style relative to what we can biologically do.
Speaker 1: It's going to be where there's a lot of advancements.
Speaker 1: And I talked about the partnerships between two technology companies,
Speaker 1: so saluminate ourselves, but they'll be increasing partnerships between health
Speaker 1: and bio companies as well as it relates to technology. Yeah, wonderful, Well,
Speaker 1: thank you so much. This has been really fun. Thank
Speaker 1: you very much being a pleasure it was. Thanks again
Speaker 1: to David cos and Howard Boville for talking with me.
Speaker 1: It's fascinating to consider how quickly data analysis can change
Speaker 1: performance in real time, and the endless possibilities of hybrid
Speaker 1: cloud and edge computing, and look forward to witnessing its evolution.
Speaker 1: Smart Talks with IBM is produced by Emile Rostak with
Speaker 1: Carlie mcgliori and Katherine Gurda, Edited by Karen shakerge engineering
Speaker 1: by Martin Gonzalez, mixed and mastered by Jason Gambrell and
Speaker 1: Ben Tolliday. Music by Granmascope. Special thanks to Molly Sosha, Andy,
Speaker 1: Kelly Neil, LaBelle, Jacob Weisberg, Head of Fane, Eric Sandler,
Speaker 1: and Maggie Taylor and the team's at eight Bar and IBM.
Speaker 1: Smart Talks with IBM is a production of Pushkin Industries
Speaker 1: and I Heart Media. You can find more Pushkin podcasts
Speaker 1: on the I Heart Radio app, Apple Podcasts, or wherever
Speaker 1: you like to listen. I'm Malcolm Gladwell, See you next time.