Compressing Idea to Outcome (I2O): WWT Co-Founder and CEO Jim Kavanaugh on Why AI Coding Assistants Could Reshape the Enterprise

AI-powered coding assistants are rapidly compressing the time it takes to build software and delivering a competitive advantage that can lead to transformational outcomes. But according to WWT Co-Founder and CEO Jim Kavanaugh, the bigger shift is organizational: companies must rethink leadership, data strategy and workforce skills to compete in an AI-native world. In this episode of the AI Proving Ground Podcast, Jim shares his vision for how coding assistants are emerging as one of the most practical and disruptive use cases in enterprise AI.

More about this week's guest:

Jim Kavanaugh is globally recognized as an innovative business executive and is renowned for his inclusive, people-first leadership style. His emphasis on promoting a healthy, vibrant workplace has anchored WWT's award-winning culture, while his savvy business acumen has propelled the company's substantial growth. Jim leads WWT's 18-person executive team and focuses on technology innovation—for both WWT and its customers and partners—long-term planning, strategic acquisitions, financial performance, employee development and workplace culture. Under his leadership, WWT has grown into a multi-billion-dollar company with approximately 12,000 employees globally that is a great place to work for all and is a key business enabler to 80+ of the Fortune 100 companies.

The AI Proving Ground Podcast leverages the deep AI technical and business expertise from within World Wide Technology's one-of-a-kind AI Proving Ground, which provides unrivaled access to the world's leading AI technologies. This unique lab environment accelerates your ability to learn about, test, train and implement AI solutions. 

Learn more about WWT's AI Proving Ground.

The AI Proving Ground is a composable lab environment that features the latest high-performance infrastructure and reference architectures from the world's leading AI companies, such as NVIDIA, Cisco, Dell, F5, AMD, Intel and others.

Developed within our Advanced Technology Center (ATC), this one-of-a-kind lab environment empowers IT teams to evaluate and test AI infrastructure, software and solutions for efficacy, scalability and flexibility — all under one roof. The AI Proving Ground provides visibility into data flows across the entire development pipeline, enabling more informed decision-making while safeguarding production environments. 

Transcript

How AI Coding Assistants Are Transforming Enterprise Software

SPEAKER_00 0:00

AI coding assistants are rapidly changing how quickly companies can build, launch, and meet the needs of their customers, compressing innovation cycles from quarters into weeks or even days. And that shift is forcing executives to rethink productivity, capital efficiency, and how work actually scales. Today's conversation, which features WWT co-founder and CEO Jim Kavanaugh, looks at this transformational opportunity through the executive lens, how AI coding assistants, agentic AI workflows, and enterprise security guardrails are reshaping the economics of software and ultimately the speed of business itself. Quick note, this conversation naturally unfolded in two distinct directions, so we're releasing it as a two-part series.

The Breakthrough Behind AI Coding Tools

SPEAKER_00 0:44

In part one, we're focusing on the operational inflection point created by AI coding assistants, how they're accelerating software creation, reshaping enterprise productivity, and forcing leaders to rethink execution speed and competitive advantage. Part two, which we'll release tomorrow, shifts from technology to leadership, how executives guide organizations through AI-driven change, balance speed with governance, and help teams adapt to new ways of working in an agentic AI era. Together, the two conversations tell a single story of how AI changes the economics of the enterprise and then how leadership must evolve to keep pace. So let's jump in. Excellent. Thank you so much for taking your time to be here. I want to jump right in, very specific AI use case, but one that has very broad application, and that's AI native engineering, things like coding assistants, application builders, autonomous agents. We've been bullish on this subject, and you've built this somewhat as the great AI unlock. I'm curious, dive into that kind of nomenclature. Why do you see this as such an opportunity right now and not just kind of another wave of tooling?

SPEAKER_01 2:02

Yeah, it's something that I absolutely believe the coding assistance, the coding platforms is where we are right now. It is the biggest unlock for AI. Other than when we're talking about AI infrastructure being built out, that's different when you're looking at the hyperscalers building out. We're doing a lot of things working very, very collaboratively with NVIDIA on neo clouds and enterprise organizations, hyperscalers. So that that piece is growing, growing like crazy in the market. And then you just look at the CapEx spend that the big hyperscalers just projected for this year. So that's you know, just uh a huge piece of our business and the market and a huge opportunity in itself. When it comes to actually models and AI use cases, this is where I believe that these AI coding platforms and IDEs, ADI, A, you know, AI coding assistance, software platforms, et cetera, they are going to be the ones that are the most significant unlock for organizations. And the reason I say that is these models keep getting better and better and better. And these coding platforms, if you think about what really drives organizations today, it's software. And you think about all the software that's out there today that are driving mobile applications, back office enterprises, you know, if you can take that kind of capability that is very global, it is ubiquitous, it's in every organization, you know, large companies, small companies, innovative startups. So the ability to actually write code with incredible speed, you know, in incredible innovation, and to do it at scale, it just changes everything. And you know, when you talk about you know transforming organizations and creating real competition, because you know, part of the challenge is the AI, you know, coding platforms to actually be able to integrate them into large enterprises today. There's a lot of tech debt. Sure. There's a lot of software, there's databases, there's you know, structured data, unstructured data. And to

The Real Barrier: Enterprise Data Chaos

SPEAKER_01 4:28

enable like these coding platforms, somebody has to come in and be able to look at that, you know, call it ecosystem of tech debt and the complexity and where the data is. They got to figure out how to bring it together and then use these AI coding assistants and platforms to actually drive innovation, to drive efficiency, to drive scale. That's not easy. And so, as much as these coding platforms and assistants are just incredible, they're game-changing, just the innovations that are coming out, you know, almost every day is mind-boggling. But they need smart people like developers today, need to move up and kind of be thinking about writing less code and thinking about more of how are they more of an architect and an and a designer and connecting the dots to actually deploy these capabilities. So I'm incredibly excited about what we're doing internally with these AI coding platforms and assistants, and then also what we're doing externally working with our customers.

SPEAKER_00 5:36

Yeah. So I mean, fantastic insight there. We could take that in so many different directions, just to kind of close off on where the market's at right now. You know, you have a clear vision of where this can go. We've started to see uh, you know, progress internally here at WWT, but what are you hearing from, you know, external of WWT peers or you know, clients that we work with? Are they as clear-eyed on this exciting opportunity or is there convincing to do, or are they just thinking to themselves, how are we actually going to make this

Why Most Companies Aren’t Ready for AI Yet

SPEAKER_00 6:05

work?

SPEAKER_01 6:05

Yeah, I think you you have all the above. I I think we have, you know, some customers today that see the opportunity and and understand how big it is, but need help getting there. Yeah. And they need help understanding, you know, what tools do what, you know, they keep leapfrogging each other. How do we use them? How do we train up our teams? You have other organizations that are not sure. They're kind of hesitant. It's like, what's really going on in the market? How real are these tools? Are they really driving transformation? And so I think you have a number of different customers and partners and and and just you know, big part of the ecosystem that are at different levels, I would say, of understanding and different levels of of I would say competency in regards to where the tools are, what they can do, and how impactful they can be.

Why AI Transformation Starts With Leadership

SPEAKER_00 7:13

How does this change your role and other senior leaders, their management style, if at all? If if we're gonna be allowing not just traditional coders to do, you know, to do more AI-powered coding, what types of ripple effect does that have in terms of leadership and driving an entire company forward?

SPEAKER_01 7:34

Yeah, I you know, I just had a conversation with a large, you know, a Fortune 500 company. I won't say the name, but it it is very transformational on numerous levels. It's one, the technology is changing dramatically, as we talked about, from the large language models to the coding capabilities of the models, to the IDEs, to, you know, the individuals that are using them. So from a technical standpoint, it's it's a big transformation, and it needs to be viewed as that. And just in regards to how you train and upskill your entire organization, what kind of vision are you providing as a chief technology officer, the chief information officer? But the same applies, and it's even probably more important that the CEO is driving a vision around this AI journey and how transformative AI is today and is going to continue to be moving forward, and really putting into play not just words and a communication, but methodologies and approaches in regards to how they are going to engage the line of business owners, the presidents, the VPs of HR, finance, line of business, sales, marketing, operations, manufacturing, RD, you look at all those different areas. So it's really, really important for the CEO to have a vision and to have a plan and a methodology that they're actually putting into play that aligns with the chief technology officer and a chief information officer in regards to how you're bringing this to life because these worlds are merging and converging in regards to how like these AI coding assistants apply to transform and call it 2x, 3x, 5x, the capabilities of an IT organization. And then the same applies for the CEO connecting with the chief technology officer and the IT organization in regards to how are they aligning that to creating new differentiated product offerings? How are they looking at that in regards to how they go to market? How are they looking at how they drive efficiencies in different operational areas of the business, whether it's manufacturing, it's HR, it's recruitment. So this is a very, very comprehensive play that I believe should be viewed as all-encompassing, and it should be coming from the CEO, aligned with the CTO, with a pragmatic methodology and approach in regards to how you're going to drive that throughout the organization.

SPEAKER_00 10:16

So you're saying that it's not even just a conversation about speed and throughput here. This is also a conversation about a totally new way to bring new types of software into the organization or out into the market, things that we weren't even thinking about before, that we can now get to. Is that

From No-Code to AI Agents

SPEAKER_00 10:34

right? Yeah.

SPEAKER_01 10:34

It it you know, it is a combination of really thinking about how you design this from really aggregating organization, organizing all the data in the company, which is probably one of the most significant challenges that every organization has. Tech debt's one, data is probably the most significant. And the organizations that lean in to really getting good at aggregating, organizing their data, making sure that they they build the different models, they write the APIs into the different data sources, they create their data lakes, they create their meta layers, their data, their semantic layers of data. As they do that and they get better and better, the data will get richer and richer, and the models will be more and more capable of doing more and more innovative things. They will be better at problem solving as you're able to bring the data together. So there is a multiplier effect in regards to how these things come together. And then I would say circling back to how this impacts an organization, and we talk about this internally, that this isn't just this isn't a technical thing, just you know, with our developers or data scientists. If you think about as we talked about the stack, you know, this will involve the coding and using these coding capabilities for our data scientists, for data consultants, for our full stack developers, our front-end developers, UX design, pre and rapid prototyping, all the way up to the low code, no code. So you think about this is gonna start, you know, at the bottom to the top, and you're gonna think about individuals that have no coding capabilities whatsoever. When they

Will AI Disrupt the SaaS Model?

SPEAKER_01 12:20

understand what you can do when you build the architecture and the infrastructure around these AI models, they're gonna be able to deliver capabilities where they actually look like they're a developer because the infrastructure was set up properly. And then you're gonna have the kind of the techno business folks that are more of the low-code kind of people. So this is a uh again, a very complete and comprehensive view of the organization, and everybody is and needs to be involved in this AI transformation and vision.

SPEAKER_00 12:54

Yeah. I mean, is your intent that that extends? I mean, you mentioned some specific roles from coders to data scientists and things like that. Is your intent that like even you know, folks like me who don't have a real technical background, you know, just to show my cards, you know, more of a journalistic background, like you want me to be for lack of a better term, vibe coding?

SPEAKER_01 13:14

Yeah, I can jump on on that real quick and we can work through some of this, some of the details on you specifically as we go through the code. We don't need to do that. Yeah. No, but no, the uh on a very serious note, the answer is, you know, yes in a big way. And you know, without thinking through this, but you know, think about things that you can do relative like the podcast and think about maybe data and content that you organize. If you have certain streams of information that may be specific to worldwide that you want to gather on a real-time basis, if you are able to create an agent that literally like crawled real time through Atom, our one of our AI models that we have internally to give you insights into things that our customers and our partners are looking into, and then ask it to actually serve you up information instead of you going and looking for that information and doing that in a way that that agent is now working on your behalf to make your sm make yourself smarter, where you don't have to go out and activate that through a prompt. You actually have that serving it up. And so there's numerous ways that I think every individual, you know, as we build out that capability internally, that individuals like yourself that are I would consider kind of the no-coders, yeah, would be able to use these platforms and create agents and and leverage this kind of agentic architecture that we are creating that will create more and more capacity. And so you think it will create efficiencies for you, but also it can create, depending on how thoughtful you are and how you're thinking about how you're going to put to work some of the agents, how it can go out and create new ideas and new innovations for you for the podcast alone and other call it marketing and business development initiatives that we have.

SPEAKER_00 15:06

Yeah. I mean, that makes total sense. Things that apply and are applicable to my job, my role, not necessarily, you know, you hear vibe coding and you don't probably want me creating applications that are going to go out and be, you know, delivered to customers unless I skill up that way. But but we'll see. Let's

How Enterprises Should Buy AI Tools

SPEAKER_00 15:21

bring it back to you know, the enterprise here. There's a lot of other IT that is going on within organizations around the world. If software time is being compressed, what does that do to the SaaS market? Or what is maybe the better question is how should organizations think about their software that they're purchasing? Is it just is it going to be commoditized? Is that going to change cycles here? How do we think about you know that in the realm of coding assistance?

SPEAKER_01 15:47

Yeah, I think it's a it's a really fascinating space right now because you know, everything that comes out from one of the call it large, large language model providers almost seems like it disrupts every industry and market. And if you looked at, you know, five, 10 years ago, you know, everything was around SaaS. It's like if if you have a hardware infrastructure company, you need to move it into some type of SaaS model. You need to be migrating to SaaS. So now you go out and look at the market and you know, the SaaS companies are getting hammered. You know, their stock price is getting hammered. And a lot of it is around, you know, the belief that these large language models are gonna come out and they're going to take over their business. They're gonna start writing code that's gonna replace Salesforce or ServiceNow and you know, CrowdStrike, something anthropic just came out with something and disrupted a bit of that. And George Kurtz, the CEO of CrowdStrike, came back and, you know, had some comments and positioning of his own. I I think the market swings too dramatically too fast on these, you know, in these, these, these types of situations. I

Why AI Skills Must Evolve Fast

SPEAKER_01 17:06

don't believe that these SaaS companies, there will be SaaS companies that go away. Sure. And the model providers will build capability out. And, you know, how code is being developed and you know, how it's gonna be integrated is gonna continue to transform everything that we do. However, it's just not that easy to take, you know, ERP providers like SAP and Oracle and big, you know, platform providers like a Salesforce and a ServiceNow and just say that they're going to go away. I think personally you're gonna find, I don't know what their model is gonna look like, but you're gonna find them adopting, you know, a lot of these AI capabilities and building agents and agentic capabilities into their platforms. And if they don't, I think they will put themselves at risk. If they do, they may end up having, you know, a next generation of life and opportunity that will present themselves. So I think it is not gonna be, you know, just a sweeping change across the SaaS market. I think there's gonna be winners and there's gonna be losers. And I think that's gonna apply in a number of different areas as these models come out and continue to disrupt. But I I think it's a little naive just to like swing so fast and to to broad brush an entire kind of marketplace overnight on this. There's there's a lot more that has to be done there.

SPEAKER_00 18:40

I mean, we like to say we're in the early innings here uh as it relates to AI and what it's gonna do to the market. With what you just said, I mean, there's still organizations and IT buyers out there right now that are having to make decisions today. So is it just do what you can to remain flexible, or what is some of our guidance in terms of how to navigate while the game plays itself out?

SPEAKER_01 19:01

Yeah, I think you need to be eyes wide open on what you have today. And I, you know, I think an easy way to look at it is like any of these platforms and capabilities, SaaS providers, if you could eliminate them

Moving Faster Without Burning Out Teams

SPEAKER_01 19:15

and save the cost in your organization, you probably would. Sure. I doubt if that is the case for most of them at this point. That being said, there will be capabilities that come out of the large language models, depending on what they're specifically doing, what they're doing, that may, you know, obsolete or it may enhance some of these things. So I think you're you're just gonna have to really take the time to really understand what you're using today, where the models are and where they apply, and to be kind of projecting what you think is going to happen in the future around how these these models are going to work. And it's not clear at this point. What is clear that I would go back to is that the ability to write code and and to develop code uh with these tools and these platforms is just night and day. It is so transformational. And the models, even in the last 60 days, have gotten significantly better and impactful. And so the ability to actually go back and automate even some of your legacy kind of tech debt infrastructure is there, along with building, you know, the next generation new code and new capabilities, and then figuring out how does that work within the existing kind of SaaS providers that you're working with today. That that is, you know, that is going to be something I think is gonna be interesting to watch and see how it evolves as we go forward.

SPEAKER_00 20:46

Yeah. I mean, you're talking about the models evolving and providing new capabilities seemingly every day. It feels like, but there's a, I mean, there's a gap there, right? Like as the models get better, people have to evolve too, to stay up to pace with those, those models. There's probably some out there that are doing it quite well, but there's probably a lot that are struggling to keep up. Here internally, how are we helping, you know, those that do have their hands on these coding assistants right now keep up with the model releases that are getting better and better all the time?

SPEAKER_01 21:18

Yeah, I would, I would kind of put it this way.

AI Guardrails: Speed Without Chaos

SPEAKER_01 21:22

You know, generative AI has been around for several years now, a couple years now. And we at worldwide, you know, and from my level have been pushing hard that we are an AI-first company. And this is several years ago. And we looked at that in regards to how we're building out how we go to market, how we work with NVIDIA, how we work with you know, large language models and how we're helping organizations build out AI use cases, developing RAG models, and as that's continued to evolve, and then looking at, you know, as we talked earlier around the amazing and revolutionary information. impact that these AI coding platforms and assistants are having. So as that continues to evolve, you know, the it is even more important for organizations to be leaning in with this vision because it it is actually more important today. You if you are going to sit here and wait and see and the market is going to run past you. And you as a company and an organization, you are going to lose significant ground, you know, just just where you are. And so so right now it's it's it is truly amazing how fast all of this is coming and to stay on top of it, I guess where I was going before is that we were pushing this and from my level, all the leadership within the organization, I don't think we're pushing hard enough. And I'm going to be pushing and I think people already see it internally because even what we're doing internally and I think we are out on the forefront of being a thought leader and investing in upscaling our entire organization, both internal consumption and how we're going to market, I want to move even faster. And so you know last week we had, you know, call it on-site, off-site, you know, coding days and you know kind of innovation days around the coding platforms and IDEs. I thought it was eye-opening. And I think it was eye-opening to a lot of our organization, especially when, you know, we had, you know, 600 or so of our technical folks in there. And we'll do it even bigger and broader as we're going forward. But even those in there were seeing things that we're doing in different groups internally and for different customers that are literally eye-opening in regards to the capabilities and the magnitude of impact and the speed of problem solving, the speed of rapid prototyping, the speed of building and refactoring code is just something you would never really

What Past Tech Revolutions Teach Us About AI

SPEAKER_01 24:05

believe if it was two or three years ago. And even now, looking at what you can do when you have the right people that are trained up, skilled up on those capabilities and being able to apply them into specific areas, it's unbelievable. And so we're going to be doubling and tripling down on the effort internally and externally and I believe that the return is going to be significant.

SPEAKER_00 24:31

Yeah I like when you say like you know just jump in, just start to play around with these tools or and and if you don't you you're at risk of of falling behind or becoming obsolete. It's not it's moved beyond any type of like threatening statement and it's just kind of like you know coaching advice and on on how to succeed. But I am I am curious you're talking about doubling down tripling down how do you manage an entire organization to go that fast? You're talking about I'm happy with how fast we've gone but it's definitely not fast enough. So how do you how do you push them to keep going faster and faster knowing then at some point there's you know there's a red line.

SPEAKER_01 25:10

Yeah I you know I do want to be conscious that I'm not you know pushing people to a point of you know exhaustion or just you know where it's you know something that is counterproductive. But I also believe that you know there are times like this that they just don't come around very often. And you've got to see the opportunity and you got to be willing to do whatever it takes to take advantage of that opportunity. And that's where we are today. And so when I look at the organization I look at each individual I'm saying you know it's up to me and then it's up to you as an individual to do everything that you can to put you in the you individually in the best spot that you can be to take advantage of this opportunity. And oh by the way if you don't you're going to put yourself at risk. So this applies for me at the CEO level it provides the leaders of the organization and all employees across the board we all need to be responsible for our own kind of ambition and and and willingness and desire to learn these these new capabilities eyes wide open and be a lifelong learner. And I think if we do that's where we're going to get this kind of multiplier effect of capability because when you're around smart people that are learning and you you create that kind of ecosystem and that multiplier effect everybody gets smarter and you build on each other. And that's what I'm seeing at worldwide and I look at it from a ground floor a very deep technical standpoint but it also applies from the no-coders and the low coders that this is applying up and down you know the entire organization and it is something that is very universal. This this is something that is applying you know in our personal life and it's something that is absolutely you know impacting our business life. And my view is you can look at it as a threat or you can look at

Hyperscalers, CapEx, and the New AI Economy

SPEAKER_01 27:16

it with excitement and as an opportunity to do some unbelievably innovative things. And I believe that there are things that we we haven't talked and we we can you know uh we can talk about some of the things around security and governance which is incredibly important both from a business standpoint and we have that built into kind of our methodology and approach and architectures when we do things internally and when we do them with our customers on the development side and infrastructure build around AI. But I would say also just the things that AI I believe are going to do in regards to solving like incredibly challenging problems around healthcare, genomics, cancer research you know, it's the the impact I think is going to be beyond what we can even imagine. And so much of this if you think about it it's a big part of it is like aggregating all the data and being able to have these models go and create trends and be able to challenge the models and some of these agentic architectures to be looking for things and finding things that we just couldn't find you know as humans human beings. And so the the the opportunity there is amazing and the opportunity to actually build models within your organization and and innovate and create new capabilities, product offerings I think is another one that is really exciting. So I truly believe you can look at this opportunity as wow I'm I'm really nervous scared and it's a threat or you can look at it as something I'm really excited about and I'm going to lean into and get the most out of I'm glad you brought up security and governance I wasn't going to bring this up unless you did you obviously want to move fast.

SPEAKER_00 29:11

You have you see a very wide opportunity and you have lots of ambition with what this can accomplish so I'm curious how you're balancing security and governance from your standpoint or maybe the executive team's standpoint or do you do you have teams I'm sure that are funneling up to you that are saying here's what we need to be keeping in mind or be cautious of or how do you balance that that risk and put the governance in place or is it kind of getting back to hey if we slow down here for one second we'll move faster later or how do you it is a bit of that.

SPEAKER_01 29:43

And I would say that you know governance and security is absolutely integrated into everything that we're doing and how you do things and where you go, I would say very aggressive will depend on different use cases. It's like we're not going to go experiment with personal you know company financial information or personal individual you

What AI Means for the Next Decade of Software

SPEAKER_01 30:09

know information of the company that those are things that we're not going to go and experiment with. You know other areas that don't have the sensitivity are going to be areas that we may do things more aggressively more experimentally. And then I would also say around it that it is very important both from a data, you know a data architecture standpoint, software development standpoint when you're talking about these AA coding platforms and coding assistants, that you are very thoughtful around the design. It's not speed is not just a factor of moving fast, it's moving smart, smartly and and it's moving in a in a way that you have guardrails and when you do have intentional and thoughtful guardrails on software development projects and leveraging coding platforms and you have a methodology and approach of how you're going to use them, you can move with speed. If you just go and run and just start without being thoughtful about what you are asking that platform or that AI model and coding platform to do, it will go do a lot of work but you may sound it send it down a rat hole that it's writing a bunch of code or solving problems and it's creating more problems downstream. So so how you actually design the thought process, how you create the guardrails and the governance around the data and how the models are being used and the and the and the software is being written is very important. And if you do it the right way and you're thoughtful about it, you can move faster and do it in a very governed and secure way.

SPEAKER_00 31:50

Yeah. I mean those don't sound like necessarily like you know the the guardrail conversation doesn't sound like innovative thought those are all lessons learned over time. And you you've been around the block you've been the internet age dot com cloud digital you know whatever do you find yourself pulling from lessons learned over those kind of unique eras that you're applying right now in AI?

SPEAKER_01 32:10

Yeah there you know it would be it would be foolish for you know for anybody as you know as we become older more experienced however you want to frame it set around the block. Yes yes well we're you know we're hoping uh these large language models come up with the fountain of youth also uh they'll we'll be serving that up in uh bottles here shortly so but no I I I think you know when you when you think about you know you you you think about that and how you're gonna use the models and use them thoughtfully and then you think about you know some of the history of things that we have collectively been through whether it's the dot com days and you know at times you know there's a lot of comparisons between the dot-com days and the big dot com bubble bust to to to AI and where we are today. I think there's things that look similar but like financially and the dynamics I think are very very different. A lot of the companies that were funded and that were growing with you know large valuations relative to that time were companies that most of them didn't make any money and the business models were very much in question. When you look at where the majority of the capex money is going you know today where it's coming from the you know if you look at it I I think it was looking at the data that 10 years ago the 10 largest capex spending companies were more like some of the large telecommunications companies some of the pharma companies car manufacturers Saudi Rampco was in there I believe you you have that it was about you know $900 billion. You look at you know our or no it was just over I think it was around $200 billion at that point where I'm seeing now you look at the top 10 today you look at the top two are spending almost what's equivalent to the top 10 before so if you look at like a Google and you know you look at Amazon today they're both $85 to almost $200 billion in capex spend the day you're looking at some of the most profitable companies look at Google you look at Meta you look at Amazon you look at Microsoft these are companies that are the largest capex spenders. They're also by the way the largest capex companies in the world they got the largest market cap excuse me they get largest market cap but they're largest capex spenders and they generate a boatload you know of profit every year in free cash flow. So you you look at those and then you look at Nvidia which just came out with their numbers that their numbers are off the charts and one of the most profitable companies in the world. So and you know still growing at a huge clip with about almost a $5 trillion dollar market cap. So the the numbers of the companies that are doing a lot of the funding are are companies that are you know really smart they see where the market's going they are incredibly well funded and they're not making these big investments because they don't think that the models are going to keep maturing and producing. So so I think we're in a different space. It's always smart to pay attention and see if all of a sudden debt's being used in a significant way and companies get leverage to leverage but if you look at the companies I just described their leverage isn't even one-to-one from a you know a debt uh profitability standpoint so so I think it's it's a different world we're in and it is going to be interesting to see how the models continue to produce as we go forward. And my view is you're just seeing the very beginnings of them and there's going to be more innovations that are going to be coming out that I think we all need to be just paying very very close attention to.

SPEAKER_00 36:09

Okay thanks to Jim for joining AI coding assistants are changing more than how software gets written they're changing how quickly organizations can think, build and compete. But technology acceleration is only half the story the harder question is what leadership does next how companies move fast without losing alignment, culture or control. And that's where we'll pick up tomorrow in part two. So thanks for listening to this episode of the AI Proving Ground podcast. We'll see you next time