Microsoft’s 18% Revenue Growth YoY, Meta’s AI Investments Weighing On Q2 Profits

30 Jul 2026 · 37 min · 18 chapters

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In short

The episode covers two earnings stories—Meta and Microsoft—and then shifts to agentic AI as a “platform transition.” Topic: Meta Q2 results and AI-driven product/engagement investments; Microsoft Q2 results with Azure acceleration and Copilot monetization; Dave McJanet on how enterprises should manage AI agents.

Guests

Ron Josie (Citi, Managing Director/Senior Internet Analyst) focuses on Meta. Rishi Jaluri (RBC Capital Markets, Managing Director, Software Equity Research) focuses on Microsoft. Dave McJanet (CEO/co-founder of Dome Systems; former HashiCorp CEO) discusses agentic AI transition lessons from cloud.

Key claims

Meta’s ad business is strong (28% revenue growth; decelerating from 33%); free cash flow is down to <10% YoY; engagement is rising (Instagram time spent + double digits; Facebook video time +9%); AI improvements and ranking/recommendations are driving usage, but ROI timing is debated. Microsoft’s Azure growth accelerated to 43%; Azure revenue crossed $100B; Copilot monetization is improving; Microsoft stays free-cash-flow positive despite higher CapEx; compute overcapacity risk is mitigated via third-party capacity deals. McJanet argues agentic AI will be heterogeneous (models and platforms), take a decade+, and needs enterprise “platform controls” to prevent runaway agent costs.

Notable examples

Meta’s Instagram DAUs (2B), Threads MAUs (500M), business agents launched in early June with ~1M SMBs; Microsoft’s multimodality approach (mix of frontier and open models) and Office Copilot engagement “on par with Outlook”; Dome Systems’ proposed control points: broker models, constrain tools, and register agents as code with budget-based routing.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

Tap a time to open that second in VO

Meta and Microsoft Results Overview

0:45 to 1:27

Discussing Meta and Microsoft's quarterly results and stock market reactions.

“Today on the show, we are unpacking both Meta and Microsoft's quarterly results.”

Insights on Meta's Second Quarter

1:27 to 3:02

Analyst Ron Josie shares insights on Meta's revenue growth and challenges.

“Meta reported second quarter results last night.”

Engagement Metrics and Product Strategy

3:02 to 4:23

Exploration of engagement metrics and Meta's product strategy moving forward.

“where we should start seeing just more coming out here in the next couple of months and quarters.”

Meta's Future in Enterprise

4:23 to 5:50

Discussion on Meta's potential in enterprise sales and product innovation.

“when all these ad tools are getting better, as you said.”

Threads and New Product Launches

5:50 to 7:29

Analyzing the success of Threads and potential new products from Meta.

“You know, if there's if there's a coding tool, I mean, is that where you see this story going?”

Free Cash Flow and Profitability Concerns

7:29 to 10:53

Examining Meta's cash flow situation and investor reactions to profitability.

“could argue we're just in the early days of seeing that return on investment from a revenue perspective.”

Meta's Investment Strategy

10:53 to 14:14

Ron Josie discusses Meta's current investment strategy and future outlook.

“That's how we look about, look at the company.”

Microsoft's Azure Growth

14:27 to 15:26

Discussion on Microsoft's strong earnings report and Azure's growth.

“The company actually told us for once how big Azure is, crossing$100 billion in revenue.”

Modeling Cloud Business Sizes

15:26 to 18:06

Insights into the growth of Azure, AWS, and Google Cloud and their future.

“You know, ahead of the earnings, I had raised my CapEx forecast.”

Microsoft's Data Center Strategy

18:06 to 19:48

Microsoft's CapEx plans and data center capacity management are examined.

“And so you do expect that Google will be as big as Azure and maybe even AWS one day?”
Show all 18 chapters

Open Source vs. Closed Source Dynamics

19:48 to 21:48

The balance between open-source and closed-source models in AI is analyzed.

“Their goal is to bring all the third-party capacity back in-house.”

Customer Feedback on Copilot

21:48 to 23:50

Customer feedback regarding Microsoft's Copilot has improved significantly.

“Well, and it sort of gets to, it feels like from a customer interest point of view, I feel like Seth and Adela wants to say open source is the future.”

The Future of Xbox

23:50 to 25:11

Discussion on the challenges and future potential of Xbox in gaming.

“It's going to show up in the engagement metrics.”

Cloud Business Convergence Timeline

25:11 to 27:07

Exploration of when Azure might converge with AWS in size and competition.

“And Rishi, I just want to go back very quickly before I let you go.”

Understanding Platform Transitions

28:04 to 30:12

Learn about the impact of platform transitions on software and business models.

“to be honest, some of the companies we did prior to that, which have followed this notion of platform transitions.”

AI Transition Insights

30:12 to 31:45

Explore the complexities and expected duration of the AI transition in tech.

“I mean, I wonder as you went through that transition and managed that transition, I wonder what surprised you as you went through it for for many decades.”

Heterogeneity and AI Models

31:45 to 34:00

Discover the variety of AI models and their implications for businesses.

“And I think that is a truism that was true of the cloud translation and is true of the AI one.”

Managing AI Costs with Platform Controls

34:00 to 36:28

Understand strategies for controlling costs in AI deployment at enterprises.

“And then ultimately, ops security and finance said, stop, enough.”
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Transcript

Automatic transcript. May contain errors.

0:13Welcome, everyone, to The Information's TITV. My name is Akash Pasricha. It is Thursday, July 30th. Before we get to today's show, I want to flag a new story our newsroom just published on TSMC. The information's Channer Liu reported that the company is developing a new advanced chip packaging technology that would rival what Intel has right now and is yet another sign of chip making becoming the battleground of the AI boom. That is posted right now on our website. I encourage you to check it out. Today on the show, we are unpacking both Meta and Microsoft's quarterly results. Our co-executive editor, Martin Pierce, called it a tale of two companies in his column last night.

0:59The stock market certainly thinks so. Microsoft is up. Meta is down. We're going to chat with two analysts about their thoughts on the results and where both companies' AI buildouts are at. We're then going to have a conversation with former CEO of HashiCorp, Dave McJanet. I'm going to ask him about how the agentic AI transition is similar, but also distinct from the cloud transition that he built his career on. It's going to be a great show, so let's get right on into it. Meta reported second quarter results last night. Revenue growth decelerated to 28%, down from 33 % last quarter. Free cash flow was less than a tenth of what it was a year ago.

1:39So I want to bring on Ron Josie, Managing Director, Senior Internet Analyst at Citi for his thoughts. Ron, welcome to the show. It's great to have you here. Thanks, Kosh. Good to be here. So what was the headline from Meta's results for you? Look, the headline is the ad business continues to do incredibly well, right? 28 % growth on a revenue number that's incredibly high tells you they're seeing the demand. I think you're seeing the reaction of the stock for a variety of reasons. One is 3Q guidance came in relatively in line with expectations. In other words, numbers didn't really move up that much.

2:11You also had some people maybe expecting a little bit more. There's no doubt our view here is that the online ad industry is actually quite healthy. You saw that in U.S.-based revenue growth for Meta, around 30-plus percent. But maybe there is a little bit of issues from a targeting and personalization perspective in Europe going forward that brought their 3Q guidance lower. But really, the big debate is, okay, we are now a year into Meta Superintelligence Labs. We are spending hundreds of billions of dollars on CapEx and compute infrastructure. And frankly, I thought this was the first quarter where we actually heard CEO Zuckerberg talk about newer products and a strategy of price coming out.

2:50However, you know, a lot of my clients are asking, when are we going to start seeing the results? And I would argue we're starting to see them already. But, you know, more is more. And I think we're in the early stages of this new product, call it renaissance at Meta, where we should start seeing just more coming out here in the next couple of months and quarters. You say see them already. Why do you say that? Yeah, I mean, look, it's actually pretty fascinating to think about engagement on Instagram growing double-digit globally. Instagram's been around forever. Reels has been around for a number of years, and we're still growing engagement time spent by double digits.

3:25On Facebook, I think the number was, it was up 9 % globally time spent on video. And so the bottom line is, we're seeing people use the product more. We're hearing now internally at Meta, you know, more newer products coming out. So we know Instagram's now at 2 billion DAUs. Threads is at, I think, 500 million MAUs. You have newer apps that are launching here with Forum and Seller. So we should start seeing a bunch of newer products coming out here. But the bottom line is, I think ranking and recommendation improvements because of incorporations of their LLM and AI are driving this greater engagement.

4:06And ultimately - It doesn't feel like that's showing up in the revenue growth per se because I mean, the deceleration was, and you talked about Europe a little bit. So maybe that's what you're pointing to here. I sort of walked away not having a clear picture of why ad growth is decelerating when all these ad tools are getting better, as you said. Yeah, I mean, look, I think a few things The BSI and scale of Meta and to be growing 28 % is a pretty impressive feat. I think the comment of potentially growing at 25 plus percent, that's a high in their guidance for 3Q, that is the next sort of why wasn't it more?

4:45And, you know, the company talked about three key issues that might have impacted their 3Q guidance. One of them being comfort comps with impressions, fine. Another one being the ability to really target users in Europe as some of the new DMA rules come into effect. That's something that's new that we need to deal with. But the bottom line is we're at a size and scale that, you know, we're seeing many of Meta's peers within the online advertising space not grow anywhere close to the 20-plus percent range. And Meta's growing 28 and 25 called in 3Q. I hear you. We want to see more. So you talked about new products, and there was a lot of talk last night about the enterprise strategy for meta and you know uh mark did his thing where he said uh stay tuned there's more coming we can only say so much on the call but uh they were leaning very heavily into enterprise kind of felt like they didn't say anything about a coding tool but it kind of felt to me like that's sort of where they're going with it I mean enterprise has been a a long road for meta that they've tried to tackle I'm sort of curious, you know, do you see them breaking out enterprise sales, call it two, three years from now?

5:55You know, if there's if there's a coding tool, I mean, is that where you see this story going? Because it hasn't ever caught on for them. Yeah, no, it's a totally different sale, a different purchase, a different buyer, different everything. I thought the commentary from management last night that if they're building internal coding tools for themselves, why not try to offer that to others out there. I think an unlock that we're going to have more insights will be when the API is fully released and many others can sort of access their newer models here. So that's something that on the come. One of the things that, frankly, coming out of the quarter, just to hear owned and operated, core products take precedence over enterprise.

6:36That said, it always takes a while for these core products to get to scale, right? It's taken, unbelievably, only three years, I think, for threads to get to 500 million. MAUs. But my point being is, it seems to me that given the demand for compute capacity, and potentially meta having supplies starting in 27, as their newer products launch, this could help to alleviate some of their free cash flow problems. So yeah, I bet in two to three years time, you could see them roll out a new line item of revenue. But the core is still the most important part. Was Threads a success to you? What do you think?

7:12I think it is. I think it proved a lot of things. I think it proved that Meta can actually do more than just their own, call it Instagram, Facebook, WhatsApp apps. So they built threads organically. They did it with a very small team and it got to scale very quickly. Monetization is just beginning this year. So you could argue we're just in the early days of seeing that return on investment from a revenue perspective. But the key thing here is the ability for Meta to really drive traffic across its entire, or call it Metaverse or all of his apps. I think that's the key thing. So as we see Seller or Form or newer apps launch, that has now learned how to scale these products with threads and others.

7:52And that integration, I think, is really key. So your view is that, you know, I think if you would have asked people maybe three years ago on Meta's ability to launch net new products that were not necessarily copycat features of other companies. I mean, the answer I think probably at that point would have been, well, what is the new thing that Meta has launched that has really gotten traction? Your view is that they have launched these new products that have gotten traction, and that might be an indication of them being able to launch something like a coding tool that could also get traction.

8:32Is that what I'm reading from you? Yeah, no, I think that's right. And I would also say when you have 3.6 billion daily active people coming to one of your sites, you have tremendous scale and license really to be able to get to scale for other newer products down the road. And there are so many individual businesses just within Facebook alone. Let's look at marketplaces that have an opportunity. Last thing I would say, Akash, is the business agent side, i.e. the ability for agents to now and converse directly with their consumers through meta is something that's taken off. And that just launched about two months ago in early June.

9:12So we now have a million SMBs on it. Okay, so let's talk about profitability then, which was sort of the heart at what investors are reacting to. So free cash flow, I think less than a tenth of what it was this quarter last year. I mean, to Ron Josie, what would be too low? I mean, you know, they might start burning cash. What are the signals that, hey, we've hit a threshold, but we're going a bit too far? Yeah, I mean, look, in our model, we think they'll burn cash actually this year, next year, and into 2028. And a lot of that is predicated on their vision and their CapEx spend for everything coming down the pike.

9:55So as we get to this, so I understand what the stock is doing today, understand the concerns about revenue and AI and when do we see the ROI. But this was the first quarter and we actually heard management talk about these newer, call it, products and to see a vision for the next one to three, three to five years. So your question about free cash flow and profitability, look, at the end of the day, we need to see that growth come through. And the market clearly is having some digestion issues with the 25 % growth for 3Q. That's call it shorter term, as long as we can see consistent, continued growth.

10:28And we just talked about potentially enterprise being a revenue stream, but more importantly, the core owned and operated business, whether it be business agents, whether it be their editing tool or image tool or whatever it is, driving greater growth, that's going to drive the ROI. So their balance sheet is incredibly healthy. They've got the cash. They can withstand a year or two or more of negative free cash. But we really need to start seeing the results here. And this is where we started the conversation saying, the results, our engagement continues to be impressive with Instagram growing, you know, as fast as they are from a time spent perspective, even on core Facebook as well.

11:03So really, that's our vision. That's how we look about, look at the company. These compute offers that Mark talked about on the call, you know, we're getting a lot of offers at a premium of what we've invested in the compute. Do you think they take those offers? You know, I think it's good. I think they do. I would say. You think they do? Okay, because he was a little wishy-washy. He said, you know, we have them. He wasn't really excited about them. He was like, they're there. We'd rather not. He didn't say this, but. Yeah, I mean, look, make no mistake. I think at the end of the day, they built up their capacity for their owned and operated business, full stop.

11:43And we're waiting for that owned and operating business to really ramp up. And, you know, we've been talking about this for some time, but we've heard Google's ex-CEO years and years ago talk about ubiquity first revenue later url right and so we're waiting for these new products to launch as they launch um you know i don't know if they necessarily need all the capacity that met has grown to grown into and so what i think was the the eye-opening event that happened was a the demand they're seeing for potentially their compute b we've seen partnerships that google signed with others and and the the dollars that are associated with it and we just started talking a little bit about free cash flow if this compute capacity on a short-term basis can help alleviate some of the free cash flow challenges and potentially go into an enterprise and and maybe sell compute or if their frontier model can be that right of the art that's interesting but make no mistake the 3.6 billion daily active people the core products are still really the core of what we're looking for at meta here and i think that's still their uh their north star as well.

12:46Okay. So last question for you. I mean, our co-executive editor, Martin Pierce, wrote last night that, you know, Mark might consider taking a page out of Seth Nadella's playbook and maybe putting a lid on costs a little bit here. I mean, he's a little bit more ambitious with his strategy, it seems. It sounds like you wouldn't agree with that. Sounds like you're okay with the investment and the pace of CapEx right now? I'll tell you, as long as we can see that product strategy play out, and this was the first quarter in a while where we've seen CEO Zuckerberg talk about these newer, multiple newer products that are on the come, whether it be newer apps, whether it be meta one with subscriptions, whether it be business agents, or ranking of recommendation improvements, and the list goes on, news models, of course, and I think we're months away from more products coming out of MSL.

13:38As long as we see that, and we can actually see what the value could be going forward, then I think they should be investing as, you know, and we hope to get that, hope is the wrong word, but we would then model the returns and hold them to that. But frankly, if we are in a world where, you know, scale begets scale and the three key resources that really are very hard to find between compute, energy, and intelligence, if Meta can put that together, then frankly, I think Meta should be investing to that. And they've got the balance sheet for now to do it. Great. Well, Ron, I want to thank you for coming on.

14:15That is Ron Josie, Managing Director, Senior Internet Analyst at Citi here on TITV. Thanks for having me, Gus. Microsoft shares jumped after its results last night. Azure growth accelerated to 43%. The company actually told us for once how big Azure is, crossing$100 billion in revenue. For more on that, I want to bring on Rishi Jaluri, Managing Director of Software Equity Research at RBC Capital Markets. Rishi, welcome back to the show. It's great to have you here. Thanks for having me. Great to be back. Okay. What was the headline from Microsoft for you? Yeah. To me, the headline is a really strong quarter.

14:53We continue to see acceleration in Azure. We continue to see the monetized AI at the application layer, especially with Copilot. and they're somehow maintaining responsibility with CapEx, they're obviously increasing it, but they're still going to remain free cash flow positive this coming year. So basically, all the ingredients they needed to put together to have a strong quarter. And, I mean, unlike the story at Meadow, where it sounds like they're going to be burning cash for a little while here, Amy Hood is saying, we're not going to burn cash at all this year. Yeah, that was surprising to me, right?

15:28You know, ahead of the earnings, I had raised my CapEx forecast. And now the kind of fully burdened apples to apples, CapEx is roughly in line or maybe slightly above where I'd expected. But they're not going to burn cash in a single quarter, right? At least, you know, given where Amy has talked about it. Obviously, that can change, but I think that's really surprising. And, you know, I've gotten questions around any needs on capital, and I don't foresee that, right? I don't foresee that they're going to have to do debt or equity or converts or SPVs or anything like that. So$100 billion for Azure, was that higher or lower than you thought it was going to be?

16:02Oh, that was higher. Higher, okay. I thought they were going to be doing, you know, a 40, 41 % type Azure number. They did 43 % cost of currency Azure. Well, I meant the size, the$100 billion for Azure. I mean, this is the first, I think it was the first time we got that number, right? Yeah, they've given us a lot of disclosures. So yeah, the rough math was roughly in line with where my numbers are, or just because they've given us the bits and pieces over the years to get there. Got it. Okay. So look, I want to unpack this with you a little bit because now that we have a picture of the size of Azure and we have the growth rate, you know, we have AWS tonight.

16:39We had Google last week. I was wondering, have you done any modeling on your end sort of to see where these businesses could converge at all in size? And I mean, you have GCP that's growing 80 plus percent. and I think, you know, at$25 billion in the quarter, you know, I don't know. Have you done any of this modeling? Yeah, yeah. No, no. I would caution maybe two things, right? That makes it a little bit harder to go apples to apples. One is the lack of comparability in the numbers. And so we know, you know, for example, Google includes the Google Workspace or what we might know as the G Suite in that number.

17:21So, you know, obviously Microsoft's not including Office in their Azure numbers. So that's one thing that makes it a little bit less apples to apples. The other thing is just how all these companies are accounting for AI training revenue. As you recall, Microsoft has not included at least open AI training revenue in that Azure number. You know, maybe some of the Anthropic stuff does show up in there. You know, I believe Amazon does include Anthropic in there. Google, I'm a little bit unclear. So there's a little bit of kind of a lack of comparability, but I think directly you're thinking about it right.

17:54Like if we kind of plot these together on their current trajectory, you have a point where they all tend to hit a similar sort of size in the next several years on that current growth trajectory they're on. And so you do expect that Google will be as big as Azure and maybe even AWS one day? Yeah, to be clear, that's assuming the current growth trajectory, right? And my colleague covers Google and probably has his own viewpoints on that. But, you know, look, I think at the very least getting the math for Azure to be the same size as AWS, it's not hard to get there, right? And, you know, it's always been the viewpoint of AWS as a leader, Azure as the laggard, or maybe the number two.

18:38And now I think at this point, you know, most of the viewpoints are Microsoft and Amazon are similarly strong positions when it comes to infrastructure, both for traditional cloud compute and for AI infrastructure. Right. So there were questions last night as well about the supply-demand dynamics of compute. They're obviously investing heavily in their data center build. Were you satisfied with Amy Hood's commentary on how they're thinking about overcapacity and the risks of that? I am, right? Like Microsoft, in contrast to the others, did make the mistake to try to dial back on CapEx a while ago.

19:17And they've admitted that that was a mistake that they made, that they left deals on the table or they had to work with a lot of third-party vendors. And so they're not going to repeat that mistake. Now, the one escape valve that Microsoft has, I think puts them in a unique position, is they have a lot of third-party deals out there, right? With Oracle, with CoreWeave, with Nebius, and I cover Oracle. And what could happen in a situation where Microsoft somehow overbuilds capacity is they have the ability to dial that back, what they're doing with the third parties, and bringing that in-house.

19:52They have talked publicly. Their goal is to bring all the third-party capacity back in-house. And so they have a little bit of an escape valve, especially even this fungible architecture, that they can spend a lot. And at some point, if they maybe overbill, they're not going to be sitting in an unused capacity. Right, right. The other thing that came up last night was satya talked about towing the line between open source and closed source and he's written about this as well this is kind of an interesting like it's just kind of a thorny issue for them right because they they've benefited so much from the closed source boom and yet now he's coming out swinging basically saying the world should be open you know you should have control over um over your own models and stuff like that so what did you make of his added comments last night on that.

20:45Yeah, look, it's very consistent with what I would have expected of Sethi. If you remember a year and a half ago when we had the big DeepSeq moment, Sethi was actually introducing to all of us, right, Jeevan's paradox, and he was talking about the concept of distillation and why that's so important. And they were one of the first platforms to actually support hosted DeepSeq, you know, on Azure. So there's data sovereignty and then gets to a lot of privacy concerns. I'm not surprised to hear that. If I think about, you know, what is the value proposition that Microsoft can offer a lot of customers, especially at the application layer, it's really getting the best of multimodality that I don't need to use GPT 5.6 Sol or Fable 5 for everything, that some can be done with older generation models, some can be done with small language, medium language models, and some can be done with, yes, open source.

21:30And so I think this is very consistent with what kind of the talk track I would expect out of Setia. To be fair, they still have, obviously this very strong economic relationship with open AI, and that's what's powering a lot of their first-party model development. But I think it is very much in Microsoft's interest and candidly in the customer interest to have that multimodality over time. Well, and it sort of gets to, it feels like from a customer interest point of view, I feel like Seth and Adela wants to say open source is the future. But from an investor perspective, you know, closed source has to remain part of the story because, as you said, open AI contributes so much to their business.

22:09Yeah. And look, I think it's not an either or scenario. My mental model is ultimately, we're going to hit an 80-20 world where about 80 % of AI workloads can be done with older models, non-frontier models, or open source. And 20 % probably require whatever the frontier model at that time will be. Now, if we think about this in like dollars per AI workload, the TAM is still in that kind of fat 20 % rather than in the long tail. And I think that's just going to be the ground reality. It's not an either or, it's going to be a mix of everything above. And that's where you heard Microsoft when they announced and launched critique and counsel, they were leaning into that multimodality and how do you kind of compare the different models in live, in real time and kind of use the best answer, use the best tool for the job.

22:55And I think that's exactly the direction we're going to be headed in enterprises. Right. A couple more questions for you. So Copilot was, I mean, it saw some some considerable growth in the quarter. Another thing that we've been talking about on the show is growth is one thing, whether or not customers are seeing the value from it and it's working for them is another question. What are you hearing from customers on that level? Yeah, look, I'm glad you asked that. And the answer to that is customers are finally starting to give more positive feedback to Office Copilot, right? A year ago, the feedback was definitely a lot more mixed to be charitable.

23:30But now I think since Sethia stepped into the product or back in November, and the subsequent releases, feedback has been a lot more improving. So it's not only that you have this accelerating paid ads where they added 10 million paid co-pilot users last quarter, it's the fact that people are actually starting to get real value out of it. And I think it's going to show up in the users metrics. It's going to show up in the engagement metrics. They shared a metric last quarter that engagement with Office co-pilot was on par with Outlook. I think we're going to hear more and more of those sort of data points from Microsoft just to illustrate that people are not just using this It's not just self-aware, but people are actually getting real value out of it.

24:07Okay. And what about Xbox? I mean, this is a business in transition right now. Do you think they should sell off that business at all? I can't really don't. Actually, I get the challenges there. Is that because you play Xbox? I think video games have gotten too complicated for someone of my age these days. They used to be a lot simpler when I was a kid. But, you know, the reason I think is because they have such a great combination of gaming assets here, right? It's not just a physical Xbox, right? If you remember, they obviously had the Activision deal. They have a lot of first-party properties.

24:43And to me, I think the ultimate vision and ultimately where gaming can go is in that Game Pass product, right? That this effectively becomes, for lack of a better analogy, the Netflix of gaming. It's going to take a while to get there. where there's kind of a hardware and meet the customer where they are and mobile strategy that takes a little bit of time to get there and drive that adoption. You also have to get latency down. But that, to me, is really the future of what turns around the fortunes of the gaming business. Right. And Rishi, I just want to go back very quickly before I let you go.

25:13I want to go back to what we were talking about with the sizes of the cloud businesses possibly converging. Because you mentioned that based on your model, So it sounds like you do see Azure getting to be the size of AWS at some point. Is there a timeline that you've put on that at all based on the growth rates? Look, I think if you extrapolate the current growth rates, it's probably within the next few years that you could get to that. Again, understanding it's not purely apples to apples. And then the growth rate comes up to eight, right? But like, yeah, you could kind of draw that line and get to a convergence point within the next several years.

25:47Okay. And when that happens, what's the implication of that? I mean, beyond just size, in terms of competitive positioning, do you see anything changing with respect to how they position the product or what happens after that? Yeah. I think both companies are in relatively different swimlines. Obviously, the AI boom has made a little bit of a difference on that and who the partners are and everything. but ultimately like, you know, Microsoft Azure has been the like, you know, large enterprise global 2000, um, you know, is, is where people are. And Amazon is where a lot of net new companies are building and, you know, they tend to grow and expand massively from there.

26:27And especially like a lot of tech forward companies use them. I think we're going to see these kind of somewhat distinct swim lanes. And so, you know, I'm not, I'm not trying to tell a story of like, Hey, Azure is going to leapfrog, uh, Amazon. You know, I still think Amazon for the foreseeable future will be larger, but the, the kind of path I see is it's going to be instead of a number one, number two, it's going to be more of a 1A, 1B type of competitive dynamic between the two. A 1A, 1B in the sense that they're still catering to sort of different classes of customers. Is that what you mean?

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26:58That's right. But in terms of like market leadership position, they're in kind of the same swim lane where there's maybe five years ago, it was very distinct who was number one, who was number two. Got it. Okay. Okay. Well, Rishi, I want to thank you for coming on. That is Rishi Jaluria, Manager and Director of Software Equity Research at RBC Capital Markets here on TITV. The former CEO of cloud infrastructure company HashiCorp, Dave McJanet, has a new gig where he once helped companies with their cloud transition. He is now trying to help them transition to using AI agents with his new company, Dome Systems.

27:32I want to bring on Dave to share more about what he's working on. Dave, welcome to the show. It's great to have you here. Thanks for having me. So you rode the on-prem to cloud transition for many years at HashiCorp. I wonder what your reflections, what you learned from that transition that you are now applying to this new transition with people hopefully using agents and AI more. Yeah, I think there's a lot of consistency to how we've approached not just HashiCorp, but also to be honest, some of the companies we did prior to that, which have followed this notion of platform transitions. And when there are platform transitions, mainframe to client server, client server to cloud, that tends to dislocate existing software's category spend because that new platform architecture introduces new ways to solve historical problems.

28:26For example, how you think about security in the mainframe era is totally different in the cloud era, but actually conceptually the budgets are the same. So I think that's the consistency of pattern is the opportunity to create category-defined companies comes out of platform transitions. And certainly on-prem to cloud is a big transition. As your previous guest was just describing, it continues to go unabated. It's still going on. It's amazing. It's amazing. Yeah. And actually, I was reflecting, I actually worked at Microsoft a long, long time ago. And to see the scale of what Microsoft is today, it's almost hard to fathom that Azure is a$100 billion business.

29:04I mean, Microsoft in its entirety wasn't a$30 billion business 20 years ago. Yeah. So, but it comes from that sort of platform transition construct. So, you know, what we were focused on at HashiCorp was enabling the Global 2000 to sort of navigate that transition of how do I adopt this new operating model for how people run cloud infrastructure and the requisite problems around infrastructure security network and then it to get solved. Yeah. And when we saw the AI transition, you can see it is a platform transition in the same sense. It looks slightly different than the on-prem to cloud. But to us, you know, AI is really just fundamentally a different application architecture.

29:44So in the old world, I used to have a piece of code calling a back-end system. In the new world, I had a piece of code calling an LLM that is calling a back-end system. And that is incredibly powerful in as much as it's new kinds of applications, but it requires a different platform that is going to be created, different place to run compute workloads, different control planes for how those applications are controlled, how you think about security differently. So that pattern is the same. Right. Let me ask you this a slightly different way. I mean, I wonder as you went through that transition and managed that transition, I wonder what surprised you as you went through it for for many decades.

30:25And what surprises do you think we are going to see with this agentic AI transition that you are trying to get ahead of? What don't people know yet? I think there are a couple of things. I think the first one is there will be heterogeneity of models, there'll be heterogeneity of platforms. And that is actually more profound in this world than it was in the cloud world. And the previous guest just made the point, you're gonna have frontier models for 20 % of the workloads and long tail for the next. I'm just indicative of it. It's not going to be a three hyperscaler world. It's just not. So actually, the heterogeneity is actually more intense across both the kinds of platforms that people will deploy onto.

31:07So it's not just Amazon, Microsoft, Google anymore. Now it's ServiceNow, Salesforce, Workday. They will all have authentic platforms that they offer because this new way of building applications is super, super compelling, and they want you to be able to build those on their platforms. So you'll be heterogeneity of platforms, and then you get heterogeneity of models. And I think what you're seeing now is sort of the early innings of, oh, there's just a few winners. No, this is a profound, different way to build applications. And so there's going to be much more heterogeneity. That's point number one.

31:34Point number two is, I quote Paul Moritz, who was this former CEO of VMware, who would say these transitions are both more profound than you anticipate and take longer than you anticipate. And I think that is a truism that was true of the cloud translation and is true of the AI one. So while we're all looking at it, you know, in an excited way today, it's very, very, very early. And it's going to take many, many years, a decade plus for this to play out just the same way the cloud is. Right. So going back to what you were saying about heterogeneity of models specifically, I mean, do you see open source models being more than 50 % of the models that are used in, call it, I don't know, two years, three years?

32:18Yeah, it's hard to predict. And I think where Spectator is, just like anybody else, I'm kind of in awe at the velocity at which these things are going. I think what we heard over the last nine months or so is really like, there's actually three categories, right? There's the large frontier models. Then there are small language models for special purposes. And those are actually very prevalent inside enterprises. We want to train a specific model to do a certain thing. And then there are general purpose open source models. And I think that ubiquity of choice is going to endure for a long period of time.

32:52I don't know. Are any of those three classes, are any one of those three particularly good for agents? I think the answer is you see things through the emergence of model routing as a need, just indicative of the fact, actually, the agents are going to use all of them. So if I'm building a loan processing application and I'm an insurance company, I may train a model specifically for that purpose, and I'll run that locally on my own infrastructure or state. And that'll be a dedicated small language model. And my agents will then use that to automate the business process. If I have a general purpose chat bot, I might expose that to a frontier model because it's a general purpose use case.

33:36So I just think you're going to see all of them. And I think of agents as an application programming paradigm, that's going to be how new applications are built. And therefore it's going to be across all different kinds of models. It's just a pattern that will undoubtedly exist. So what about the issue of runaway agent costs? And we've all heard the stories of, I didn't even know that I was getting charged this much. I didn't even know the agent was working this much in the background, how are you trying to solve that issue with Dome systems? So when we kind of, we sort of try to fast forward to the end and say like, you know, for, in the cloud era, what happened was you had this sort of cloud 1.0 notion where people were swiping their credit cards and just building apps on Amazon.

34:16And then ultimately, ops security and finance said, stop, enough. And there was sort of this cloud 2.0 moment, which is really what allowed cloud to take off was when people figured out that the inside your company, you need to exert some platform level controls. For example, I need a consistent way to provision things. And if everybody provisions this way, you can do whatever you want, because that way I can ensure you're not provisioning something that's too large that I'm going to get billed for. I think that same parallel is now happening in the AI world, which is the early days of AI are people just do whatever they want.

34:47And you're seeing these massive bills. I mean, we see them ourselves. And I think invariably for AI to become ubiquitous in in the same way the cloud did, you need sort of a platform engineering team inside these companies who spend most of the money on IT to be able to determine what those services are that they're gonna provide to allow people to deploy applications in this agenting manner in the same way that they did in the cloud manner. I think that's where it's going. If you think about how to do that specifically in the agent world, well, what is an agent? It's a piece of code calling them all, calling some backend systems.

35:20It's actually not any more complicated than that. The magic is the model. So therefore the control points that you need to exert if you're an enterprise are, how do I broker which models those agents are going to call? How do I constrain the tools those agents are going to call? And how do I register those agents as code, like I manage any other type of code? So those end up being the three control points. So specifically around the model cost, you have to broker access to the different models. So that platform team needs to create the infrastructure that says, you have a budget, and if you go over this budget, we're going to route you to an open source model.

35:54Or if you're in the exact tier, you get to use the high-end model and nobody else does. And those have to be created at the platform level so that you can then say yes to people building on this new model. Otherwise, things get out of control in some way they do in the cloud world. And so that's really the philosophy that we've taken, which is if you step back, you need a platform team to lasso code models and tools on an integrative platform so that I can now enforce consistent rules associated with the entire tool chain, not just when an agent talks to a model, but can that agent talk to the model?

36:27Should it talk to the model? What's it trying to do? And then I'll decide if it can do the whole thing and not just brokering the one-off proxy of the connection. Great. Well, Dave, I want to thank you for coming on. That is Dave McJanet, CEO and co-founder of Dome Systems here on TITV. That does it for today's show. Reminder, we are on this stream Monday through Friday at 10 a.m. Pacific, 1 p.m. Eastern. If you can't make it then, episodes are available on theinformation.com, on our YouTube channel, or wherever you get your podcasts. Make sure to follow us on social media on X, Instagram, and TikTok.

37:00I am already excited for our next show tomorrow. Have a great rest of your Thursday. Bye-bye for now.

37:10Thank you.

From the publisher

Citi Managing Director Ron Josey talks with TITV Host Akash Pasricha about Meta's ad deceleration and long-term enterprise AI strategy. We also talk with RBC Capital Markets Managing Director Rishi Jaluria about Microsoft’s record $100B Azure run rate and Copilot adoption, and we get into controlling runaway AI agent costs with Dome Systems Co-Founder and former HashiCorp CEO Dave McJannet.


Articles discussed on this episode: 

https://www.theinformation.com/newsletters/the-briefing/meta-learn-cost-control-microsoft


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Chapters:

00:00 - Introduction

00:01:13 - Meta's Q2 Deceleration & Free Cash Flow Crunch

00:15:22 - Microsoft Azure Crosses $100B & AI Monetization

00:28:17 - Former HashiCorp CEO Dave McJannet on Dome Systems & AI Agents


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