Cloud Wars Live with Bob Evans

00:03 — According to recent reports, Microsoft is set to reveal its Maia 300 AI accelerator as early as September this year. For context, Microsoft launched Maia 200 back in January with the direct aim of reducing the cost of AI inference.

00:27 — The Maia 300 is expected to support both AI training and inference, but the key story here is the scale of ambition behind it. Now, Microsoft is once again partnering with TSMC, Taiwan Semiconductor Manufacturing Company, who delivered the first iteration of Maia chips. However, this time, the scale is much larger. increasing from tens of thousands of Maia 200 accelerator units.

00:54 — It's increasing from tens of thousands of Maia 200 accelerator units to potentially hundreds of thousands, or even over a million Maia 300 chips. For me, this initiative is really about Azure. By investing in these chips, Microsoft isn't necessarily trying to compete with NVIDIA. Rather, it aims to ensure that Azure doesn't rely entirely on NVIDIA infrastructure.

01:14 — This move will give Microsoft greater control over the cost, supply, and performance of the AI infrastructure running on Azure. And beyond that, Microsoft CEO Satya Nadella has stated that building the company's own silicon to work alongside NVIDIA and AMD chips is a priority.

01:35 — The successful launch of Maia 200 demonstrated that Microsoft's custom silicon strategy can work, albeit on a relatively small scale. The hope now is that Maia 300 will enable Microsoft to scale this strategy into a genuine competitive advantage for Azure.

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Bob Evans speaks with Chad Wahlquist, an Architect at Palantir, about what is driving the company’s extraordinary growth and, more importantly, what customers are getting from its technology. Wahlquist argues that Palantir’s momentum comes from helping enterprises solve difficult operational problems rather than simply deploying AI or chasing model benchmarks. Their conversation explores AI sovereignty, business outcomes, Palantir’s Ontology, LLM complexity, customer operating leverage, and the importance of retaining control over enterprise decision-making.

Outcomes Over AI Hype

The Big Themes:

The Big Quote: “LLMs don’t just magically fix everything. They also create new problems that you have to go solve.”

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00:03 — We've had a lot of big numbers come out for Q2 reports. Google Cloud is at 82 percent. AWS growth jumped up to 37 percent. But I have to take a little more time here to dig into what I'm calling the Palantir phenomenon. Here's a company that, over the last three quarters, has seen its growth rate go from 71 percent to 85 percent to 93 percent.

01:12 — What are customers asking Palantir right now to do for them? That may seem like a goofy, simple question. But if you grow at 93%, you have to be doing something special. What is it that customers are asking for, and that Palantir is delivering, that drove this 93% growth? Chad goes into a lot of detail about that and offers some unique insights there.

02:34 — I think that that's still valid, but the bigger issue now around sovereignty is who controls your data, and not only the data, but how that data is being used to drive AI output. Palantir is making the case that the large language model companies are incorporating the data and everything that goes with it into their own models.

03:27 — Finally, we had a fun conversation considering this company that has grown 93%. It is predicting that the U.S. commercial business, for the next year, will grow 134 percent, but Palantir doesn't have prices for its software.

04:00 — They go in. They talk about what the customer wants to do. They say, "Okay, given that, if we drive the value you're looking for, you pay us this." And then, as that seems to be working quite well, the customer often comes back and says, "Hey, that was terrific. Let's do this, this, and this in addition."

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00:03 — In a big coup for Salesforce, the Pentagon has approved the use of its Missionforce national security platform to launch AI agents on the most sensitive unclassified missions, and that's because it's been granted Impact Level Five, or IL5 , authorization.

00:21 — The first major user will be Army Human Resources Command, which will use the technology to help with soldier and family support and everyday admin tasks. Later, there will be other use cases, including recruiting, personnel management, and logistics. The big thing here is that Salesforce AI can now take actions and automate workflows.

00:47 — Handling routine tasks while human specialists can stay in the background and remain responsible for sensitive decisions. For the Pentagon, this is quite a jump, moving from AI that, beforehand, solely advised to AI that can actually execute tasks autonomously in military operations. That's no small thing, and I think the fact that Salesforce has won this contract is a massive reflection.

01:14 — On the level of security and governance that the company has in place around its agentic infrastructure, and that will certainly be an attractive proposition for customers. Government contracts always help to boost the credentials of a company, but here, in the age of AI, to have a government department okay the use of agents in an autonomous capacity is a very big win indeed.

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In this special episode of Cloud Wars, Bob Evans speaks with Garrett Lord, co-founder and CEO of Handshake, about one of the biggest challenges facing enterprises today: turning the extraordinary promise of AI into measurable business outcomes. Lord explains why AI agents have delivered dramatic productivity gains in software engineering but have yet to achieve comparable results across many other knowledge-work domains.

Making AI Agents Work

The Big Quote: “The point that most enterprises are at right now is they want to bring agents into production beyond software engineering.”

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In today’s Cloud Wars AI Minute, I break down the rise of agentic reasoning loops and why their impact on AI token consumption and costs could be significant.

Highlights

00:12 — Today's topic is going to be agentic reasoning loops. Everyone's moving to this concept where we can have a planning, act, observe, and reflect type of process, which allows us to be able to get much deeper analysis and much more resilient implementations across AI, across the world.

01:03 — Now, the other thing is with this, we're seeing that about 33% of enterprise software will run on this kind of approach by 2028. Also, the other part of this is that because these different loops are taking place, we're actually seeing that tokens are going to get more and more consumption happening off the back end.

01:38 — So one of the driving factors of this is going to be the increase that we're seeing, and just to give you perspective of what we're starting to see, it's about a 17x on a single chapter that we're starting to see happen as a result of this. So what it used to do when we just did simple RAG patterns.

02:01 — Now we're seeing about 17 times the amount of tokens being consumed, and when we start seeing that level of token consumption, we're going to see that while we're getting better answers, there's also going to be a higher cost that's associated to it.

02:28 — But it doesn't matter that the tokens are necessarily coming down in cost because of the fact that what we used to see is that an average transaction would be about three cents, and now we're seeing it move to about 15 cents across the board, and so this is going to be one of those things that we really need to be thinking about as you start to build out your solutions.

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In today's Cloud Wars Minute, I look at why Palantir's approach to AI sovereignty, sales, and customer value deserves the software industry's attention.

Highlights

00:02 — I wanted to talk a little bit more today about Palantir, and I know I've been mentioning the company, digging into what they did in Q2 and their discussions about customer developments, the huge advances some customers are making as they use Palantir.

00:18 — And the reason I want to do this is because when a company almost doubles its growth rate against some very intense competition, that deserves extra scrutiny. What's going on there? Why is this happening? So, today I wanted to take a look at the CEO of Palantir, Alex Karp, as the company posted recently its Q2 numbers.

01:31 — Just to recap, Q2 Palantir's revenues soared 93% to 1.9 billion dollars. Their U.S. commercial business grew 149 percent and is now almost bigger than their government business, and their net revenue retention was up 157 percent, which shows that once customers start to work with Palantir, they quickly accelerate the investments they're making with Palantir.

02:26 — His first point, he said, there is a revolution underway for independence and for AI sovereignty. He said this is well underway. It's taking place. Businesses are going to demand that they be in control of their data and all the metadata surrounding that, the processes, the related workflows, that give the great value to that data as it's used for AI outcomes.

03:10 — His second big point, he said, we are accomplishing this stunning industry-best growth, while he said we have a minuscule and shrinking sales headcount. So, while Palantir has been undergoing this incredible growth rate, they've been reducing the size of their sales organization.

04:21 — The third point, he said, we've always aspired to be paid as a derivative of value creation for customers. So, they don't have a price list. He's saying, we set up an agreement up front with customers that said if, in working with us at Palantir, you achieve the business outcomes you want and we're a significant contributor to that, then we will extract our fees, our payment, our revenue as a derivative of that value that's being created for the customer.

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In today's Cloud Wars Minute, I look at why Oracle is rejecting a single-model AI strategy and embracing a flexible, multi-model future.

Highlights

00:03 — Oracle has announced that it's extending its partnership with Google Cloud and will be making Google's Gemini models available across its enterprise AI portfolio. This includes Oracle Fusion Cloud Applications, NetSuite, Oracle AI Agent Studio, and Oracle Cloud Infrastructure, or OCI.

00:22 — In true Oracle style, the company will embed Gemini into business applications and AI agents, allowing customers the flexibility to use Google's models within their existing workflows. At the same time, customers will have the freedom to switch between the various models offered in Oracle's suite. Now, what this is doing is giving customers more choice when they build Fusion-native agents and agentic apps.

00:53 — Oracle and Google Cloud already have a strong relationship, but the broader story here is how Oracle is positioning itself as an AI control plane that delivers outstanding infrastructure without the need to roll out a host of foundation models itself. Now, the company is really embedding itself in this area, and I think it's working out incredibly well for it.

01:17 — The company has really pushed the idea of flexibility, interoperability, and choice. Now, Oracle, from very early on, has really avoided that single-model strategy, saying that's a strategy that's aging quickly, and instead, Oracle's building a platform that can adapt as the AI landscape continues to evolve.

01:40 — I think this really ties in with the ambitions of those enterprises that want to take advantage of rapid AI developments while still controlling their data, applications, and workflows. And that's where Oracle stands out in its ability to bring together infrastructure, applications, and multiple AI models while seamlessly enabling companies to maximize the benefits of the latest AI developments.

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In today’s Cloud Wars AI Minute, I break down the rapid rise of forward deployment engineering and why getting AI into production is becoming the next competitive advantage.

Highlights

00:10 — Today, the topic is going to be why we're seeing AI move to forward deployment engineering type of scenarios, and this industry trend has actually increased in just year-over-year growth of forward deployment engineering job postings in just the first half of 2026, increasing by 1,000%.

01:29 — You actually have to get to deployment, and the big problem there is that between 88 to 95% of all AI pilots that are being built today are actually never actually getting to production. So we have a whole lot of interest in the market and a lot of stuff that's being really cool and is being done, but we're not actually seeing it get to production. Hence the need for FDE, or forward deployment engineers.

02:04 — Two, you actually have embedded engineers to go help a client to be able to get up and get going. And just to give you guys a typical situation here, now we're starting to see a lot of the pay for these type of roles. They're getting very, very valuable in this market space.

03:04 — So this is a major, major shift in what we're seeing, and it's also why even in DCI and our training offering, I'm looking at building out how to be able to move toward an FDE-type approach engagement, specifically for partners, because you're gonna really have to think about how you move to AI being able to get about 99% code coverage in order to be able to play in this space and be able to have fixed-fee, high-quality implementations that actually drive into production implementations.

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