Cloud Wars Live with Bob Evans

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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:

Outcomes Drive Palantir’s Growth: Wahlquist connects Palantir’s growth to the tangible returns customers see after adopting its technology. Rather than treating AI as a standalone investment or another piece of enterprise software, customers increasingly expand their Palantir relationships because successful initial projects create opportunities for broader deployment. He points to strong net dollar retention as evidence that existing customers are spending more after experiencing ROI. The underlying philosophy is straightforward: when an investment generates meaningful business value, executives are willing to repeat and expand it.

Production LLMs Create New Problems: Getting an LLM working is only the beginning. Wahlquist discusses the stochastic and probabilistic nature of models, changing provider guardrails, security requirements, model deprecations, and unpredictable behavior across edge cases. Technically switching from one model to another might appear simple, but ensuring that a replacement works reliably across production workflows is significantly harder. This creates a fundamental enterprise question: how do businesses build durable operations on technology whose behavior and availability can change?

Protect the Enterprise Decision Loop: One of the conversation’s most important ideas is that a business can be understood as a collection of decisions. Companies continually observe data, apply logic, take actions, measure outcomes, and adjust future decisions. Wahlquist calls that feedback loop a source of business “alpha” — the proprietary knowledge that helps one organization outperform another. As AI agents become participants in enterprise decision-making, ownership of that loop becomes increasingly important. Companies should understand who controls the data, logic, actions, outcomes, and learning generated through those processes.

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

AI’s ROI Gap Persists: Enterprises are enthusiastic about AI and are spending heavily on tokens, but Lord says meaningful returns remain uneven. Software engineering has emerged as the standout, with AI agents producing what he describes as two- to threefold productivity improvements and increasingly executing lengthy assignments autonomously. The challenge is translating that success into disciplines such as finance, manufacturing, retail, oil and gas, and insurance. In these areas, agents can generate presentations, emails, and briefing documents, but they aren't yet consistently transforming complex, long-running workflows.

Evaluations Define What 'Good' Means: Lord repeatedly returns to evaluations as the foundation for enterprise AI. Because models are nondeterministic, companies can't assume an agent will reliably produce the desired result simply because it performed well once. Instead, businesses need to codify what successful performance actually looks like across their specific workflows. Lord compares an evaluation to a product requirements document: it creates a measurable baseline against which an agent can continuously improve. Once organizations can evaluate performance, they can improve their agents and harnesses, decide which models are appropriate for particular jobs.

Long-Horizon Work Is the Frontier: Generating an email or presentation is fundamentally different from completing a 20-hour investment-banking assignment. Lord describes knowledge work as a complex trajectory involving data rooms, Outlook, Slack, Excel, Bloomberg, FactSet, colleagues, and potentially hundreds of individual actions. Humans continuously manage context while navigating those systems, but today's AI agents still struggle to execute these long-horizon workflows reliably outside software engineering. That distinction helps explain why impressive AI demonstrations haven't always translated into enterprise transformation.

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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