Palantir's forward deployed architect Chad Wahlquist on ontology, the death of dashboards, and the Evolve tool cutting token costs 60%

it. Thank you so much for taking time to come with us. Thanks for coming. Great to be with you.

Have a great rest of your time.

Thanks.

And up next, next

we have Chad Walquist. First, I'm going to tell you about Crowd Strike. Your business is AI. Their business is securing it. Crowd Strike secures AI and stops breaches. Welcome to the show. How are you doing, Chad?

Great overcoat. That's a new one. That's an Eleaniano special. It is.

Oh, yeah. He is the master.

Giving us a run for our money.

Yeah, it's fantastic. Uh, anyway, kick us off with an introduction on yourself, how you fit into Palunteer, a little bit of backstory. I'm sure we have a ton of questions to run through.

Uh, first, how often do you guys do these things? Because it feels like this feels like an annual It feels like an annual event.

Yeah,

but we're getting the call every month. Karp carp karp talks about you know manipulating time working you know a quarter at palunteer is like a day or a year at an exact so that it kind of makes sense.

Yeah I'm like actually 23.

Yeah time the time warp is real. So we do these quarterly. So I'm I'm a ford deployed architect technically. Yeah.

I do what is needed and so doing the needful is kind of the palunteer way is like there's no job below me. And so no matter if I'm out on the edge with customers, I'm talking to executives, explaining the ontology, doing YouTube videos.

That's all what I'm doing. So really the goal is how do we help people decomp problems differently and apply the technology new ways.

Can AI do decomp?

Yes.

Okay. Unpack that because that feels like the secret sauce. That feels like the special thing about Palunteer is actually being able to bring someone in who understands an organization. I think a lot of people see AI tools. A lot of people see AI tools. No, a lot of people see AI tools and they and they think uh okay very defined workflow input output but now instead of just math that Python can deal with you can deal with some text and that's great but demp to me has always felt less like let's go into your HR system and understand the basic job description and like oh someone uploaded this resume versus oh Steve actually does this completely outside of that system and marketing has two two platforms for this thing and engineering has three systems for CAD files and the all the clues that have built up over decades sometimes hundreds of years for some of these organizations like you that's what was so special about the forward deployed engineer program the Palunteer model y

I'm surprised to hear you say I AI can do it at all it feels like the final boss

well this is where the the really the palunteer thesis is humans and AI working together

and so the way we thing about this is modeling our business process and we heard some other people talking about this of modeling my business process and the ontology um because the LMS don't necessarily have an a world view or world model of your business and your operations the ontology provides that okay

and so when we talk about demp this is really about actually now I make more data computable as well so we think about LM on the agents and I'm interacting also we use LM to make more data computable and then model that in the ontology of how things are really working and so what we're actually doing a lot of times now is is building out that world view and then running multiple agents over this actually um being combative towards each other right and so actually working against each other and having critiques and so after you do that you can also then give the human human in the loop feedback about this and iterate on this and so what we find is that's really a scaling mechanism it's like a new power tool right I think you guys were just talking about this the kind of the perspective around jobs and all this stuff it's like when you gave carpenters power tools there weren't less carpenters there were more I could do more with it right it's an empowering thing

yeah So, uh, how often like I I I I'm interested in the, uh, like the pie in the sky, Palunteer pitch, understand your entire business, run your entire business on Palunteer. And then some of the nitty-gritty where sometimes like the lowhanging fruit is like, wait, there's a like there's someone's job to just like take a form and type it into a sheet. Like, we have we've had image recognition for a long time. let's actually go and implement that and get that into a database, get that into the ontology, get that into Palunteer so then we can start building on top of it. And it feels like there might be a tension there. Obviously, both processes are speeding up, but h how do you how do you sort of like keep the project centered around the big goal while still chopping wood on all the things that actually need to happen?

Yeah. And I think this comes back to the forward deployed piece and like what do we deliver outcomes and and we work backwards from that rather than hey I have this data I'm going to build a data warehouse and then I'll build reports because all my data is in one place that's the that's the field of dreams and no one shows up

right and so really when we demp things and work backwards from that you know the simple things like the form filling out there's a lot of that now the one approach that we see a lot is you know enterprise software is going to force you into their box.

Sure.

Right. You go fit you go fit into this box.

Yeah. Well, then you know, okay, did I take away the special sauce which was my company because people were doing these all these kind of amalgamations. Hey, 40 ways to do a PO.

Yep.

Well, maybe it is okay to do 40 ways, but my software can't handle it and it's fragmented, right? And so there's there's actually a middle ground because, you know, for a long time

customization was kind of a four-letter word, right? No, no one wanted to do that. And I think that's where we think about malleable software actually. How do we help you be more different, not more similar?

Interesting. And that's so that when we decomp problems thinking about not only the the kind of the uh quantitative piece but the qualitative piece and the people and process around this how do we enable those people to do the things that made them special?

Is is software getting more malleable because I I I can look at it two ways. I can look at uh one you know obviously AI agents are incredible at coding. They can run they they can make changes very very quickly that would take you a day in just a few minutes. Y

at the same time I see you know so many screenshots of people saying I implemented this feature and the GitHub is plus a million lines of code and at a certain point like the context window is growing as fast as the code generation's growing like there's a I I'm a believer in the answer to bad slop is good slop and more slop maybe but what are you actually seeing on the malleability of software because sometimes the most malleable software in the past has been oh well there was a really incredible engineer who figured out this problem and baked it down to a 2,000line repo and you can actually just put it in your own context window so it becomes more malleable and you can use it as a building block. Yep. And that feels like that's going away and I want to make sure that we've that we're ready for when it goes away and it remains malleable.

Well, I think what what's missing is the the malleable enterprise scaffolding that you and that's what we think about the ontology and foundry and the platform and then Apollo that allows us to go deploy these changes. So it gives us the right amount of structure but the right amount of freedom. So I think that's the balance we try to find is that malle malleability in the middle where we can actually scale. We can enable people to do things differently while still creating enterprise you know grade robust secure scalable software. And so it's actually a balance there about how

I can enable that engineer that you know has been doing that now they can write code much faster. they can oversee things and that enterprise scaffolding in the middle allows us to actually create the right guard rails create a safe system of work for them to go develop things in. Um and then it's also the feedback loop. So the other thing that we do with our ontology and our platforms is implicit and explicit feedback from users using it. So the udal loop that I create and really that udal loop allows our customers as they're doing workflows they're giving feedback to agents. Now can agents help them do more based on the feedback. So both explicitly saying hey that was wrong that sucked or I chose this option. Now if you do that enough agents can start to learn from that. So we actually store that in our ontology to allow it to scale. So it's really that human centric process around AI. AI is not like we shouldn't be thinking about AI from the sake of AI for AI. It's AI to enable humans to do more. Yeah,

that's the frame.

Udaloo, observe, orient, decide, act, right? Uh I have a different question, but you can you can go.

Uh if you were giving if you had 30 minutes

to uh give feedback to the AI labs, what are the kind of key areas? Let's say the frontier labs, right? Uh leading models. What uh what are the kind of key areas that you would be focused on?

Yeah, I mean I think when we think about the enterprise space, you know, we

one you're like don't compete with us. No, I actually like I I think optionality is a good thing. Like I am agnostic to where you store your data, where you store, how what model you choose, what compute you use. So like we we can allow you to use any of that because the last thing that actually drives an outcome is replatforming, moving to another

and that goes back to the onrem culture, the secure cloud culture, ITAR compliance, like this is in the DNA of the company.

Yeah. And so how do we actually enable people where they are instead of the focus on oh if you replplatform everything to Palanteer everything will be great. like well actually you've probably been replplatforming for years. Can we enable what you have to go do these new things? So when we think about like the model companies and it's you know how do we ensure that we can give the feedback loops around you know tool usage and um you know

yeah that's the kind of that's the kind of stuff I was uh wanting to get your point of view on is like are I'm sure you're getting into the nitty-gritty

with individual models where where they're spiky where there's you know where there's shortcomings etc.

Yeah. So we we actually just launched I just put a YouTube video out last week on this new tool called Evolve. We talked about it in the kind of the halftime show where customers are using actually AI to help them understand which model. So like maybe you know the the the the meme around hey make it exist first and then make it good.

Yeah.

Most of the time I see people a building with agents they're using the latest frontier model. I just got it working and then then all of a sudden the token maxing and everyone everything else and you're like oh my gosh I just blew through my whole budget. Yep. So we built a tool called evolve that will actually go analyze the logs in production about how these models are operating, what people are doing with them, the architecture over it, um, and actually be able to swap out different models from different providers or hey actually for most of this workflow you can use this model that's older and actually without thinking and and test time compute it. It's more deterministic

or even cached models

cache models and then or hey if you actually just have this piece of data in the ontology then you would eliminate all this and 50% of your cost. Yep. And so you know some of the customers McCarthy talked about this at our halftime you know they they were able to in two days eliminate 60% of their token cost by rearchitecting picking a different model and in prompt tuning. So it's the combination of all those the permutations get really hard especially when it's in this probabistic models we've have tools to do this in the deterministic world

prompt tuning it's a instead of don't make mistakes it's okay to make some mistakes if the mistake is going to cost just a little bit I'm fine because don't make mistakes that's going to cost me a fortune. Well, there there were there's there was some chatter yesterday around uh uh something a model was doing to be more efficient was uh talking in in like this bad.

Oh. Oh. Kan caveman caveman prompting. Um

the caveman prompt method actually works. What how how often are you working with a company that is having call it like a mini chat GBT moment within their enterprise and then they're just like let's not tell anyone about this because I imagine like there's all these there's clearly places where

what does that mean their product is taking off like Chad GBT or

well so they've found a way to apply AI in a way that is highly highly effective and gives them an edge

oh interesting

uh but

yeah like like the theoretical like technology transform. So, so X people are very loud, right? Figure uh they they're like, I just had this

done using everything.

Yeah, I just had a product work for 30 hours on this thing. They'll talk about it. But if you're a Fortune 500 and you figure out how to do something, it's not like you want to like put put your hand up and say like, I figured something out, right? Like secrets are valuable and

these advancements and kind of breakthroughs are not going to be uniform. the airline industry will never be the same and then your direct competitor copies you and you're like,

"Yeah."

And so, and so part of part of why, you know, right now the meme is token maxing. Um, and that's an obvious going to be an obvious era area of debate. People are happy to go talk about it, say, you know, CEOs might say, "Hey, let's stop doing this." Um, but there has to be all these other kind of pockets of interesting moments where we won't hear about them until they become kind of like standard operating procedures

or you see it in the the the uh earnings and the economics piece, right? Yeah. Yeah. So, I Yes. Unfortunately, X is not the real world,

you know, and there there's a lot of grift and noise and, you know, podcasting, PMing and, you know, that kind of stuff that goes on,

but I I I think in the real world, yes, there is the halves and have nots. I mean, we were just talking about AIG, like when you can start to actually do the underwriting and, you know, have quotes back in hours or days instead of months on these highly complex enterprise, you know, kind of insurance agreements.

If you don't have that, how are you ever going to compete? Yeah.

And so when we think about this of the N of one, right, you know, that those are the companies that we're going after and we see where there are those moments that are not public. It's the competitive advantage. interesting category because you can imagine AIG, you know, is um, you know, working with a potential customer or renewing a policy and that customer is going and talking to all of AIG's competitors. Yep.

And uh, if AIG is able to turn around,

you know, a quote or a policy in 24 hours and then it takes another player,

you know, two weeks because it's, you know, complicated. So many so many teams will just say like hey we you know you know especially once you have two bids you can basically say like okay this that third fourth fifth we'll kind of wait on those because we have a good option here

well it builds trust the other piece here so when you think when you see people operating that with that level of efficiency what else can you do so I see this whether I'm doing you know SAP migrations the least sexy thing you could talk about but hey if I can cut your SAP migration

let's give it up for

yeah it's like the the least you know exciting thing on on paper but Actually, if if you're spending hundreds of millions of Yeah, you guys get it. But hundreds of millions of dollars on a migration and we can cut it in half.

Yeah,

that's a massive deal.

Uh, back on the udaloop, observe, orient, decide, act on the observation side, what is the supply and demand imbalance for dashboards? Like, and what I mean by that is is when you're working with a company, is there is there more demand for dashboards? more people asking, hey, we need a dashboard for this, we need a dashboard for that. And you have to back people off and say, I don't know if the dashboard's right for this. Like, you might just want to do an ad hoc analysis or actually go and see. Uh versus you're seeing so much opportunity that you're like, okay, we want to push dashboards out everywhere. Like what? Walk me through dashboarding right now because I've always been like sort of like, oh, the too many dashboards. You build them and then no one looks at them.

Yeah, I want to kill all dashboards.

Okay, that's my perspective. dash I mean KPIs and dashboards should be a byproduct of operational applications where I'm making decisions. So we talk about the udaloop I have to actually act for things to hit the bottom line be valuable

in the actual application

in the application. So as I need those things and it's going to inform a better decision. Yeah,

that's where I want those metrics. That should be a byproduct. Not if I go out with the goal of building a dashboard, it's going to be the field of dreams again. No one shows up.

And so yes, it should be you you're going to have to build some of those things. The other side of this also is when you think about a data warehouse like literally I won't go too deep into this technical riff but like you know Kimble and dimensional modeling was built in 96 for scaling databases and you're still modeling in the same way in 2026

for your dashboard your Tableau whatever those things are and like that's not actually how the world works in rows and columns you need complex things to model how the world really works and that's what we think about the ontology which means I can reuse it for an operational application KPIs agents all on one single ontology which it makes it the compound effect where as I add things in I'm now compounding with each individual decision I'm design working with gets better and better and better for the next use cases I connect across my business.

Yeah. Is there an analogy there to just the deployment of AI tools currently? I'm I'm just reflecting on the the NoSQL boom and I don't know how strong this was. This is probably just like an online take but this idea of like why would you ever want a relational database? Why would you ever want a schema? Don't never do a migration ever again. Uh and the future looked like a win-win almost like I I think Postgress installations probably grew and so did MongoDB and other non-reational databases. uh and people use Reddus for things and they use all sorts of different tools and we built and we stood on the shoulders of giants and we got more giants and then you know that means full employment for you obviously but uh but I'm wondering like as like are you seeing uh glimmers of of the AI tools eating into different pieces of the technical stacks or is it all like yes and across the enterprises? Um I think it's yes and and in in a couple different things there is when you think about the real world it is not just rows and columns you can't describe everything with measures and attributes

and so it's actually multimodal and so like we think about this in our ontology where you can have one semantic object that actually has a CAD file and an image a CB model and tabular stuff in one semantic thing of a plant.

Yeah.

Which means I'm starting to talk in the language of my business. So being able to have the multimodal representation where in other places oh I have to have MongoDB and I have to have a SQL database here and I have to have an S3 bucket here to put all of these different things to store them in ways well we can do that all in the ontology vectors everything else so that that's really the the goal around how do I model the real world how it actually works and make that transparent so you're not having to figure out which technology to put in a time series thing for sensors on a you know oil platform

don't care right and that that's where we want to have the non- differentiated heavy lifting like truly in the platform to remove the friction about getting stuff done.

How common is it for a business with more than a hund00 million of revenue to have very little understanding of how their business actually works? Like maybe they own maybe they own maybe they know like the main thing which is like you know

we make a product and try to sell it for more than it costs to deliver. Yeah. Um but uh but but is is some element of uh how how much can chaos and mystery be reduced effectively today? Because it feels like we're entering an era like

you go back um

you know 50 years and

uh

the level of like mystery in a large company would have been like is almost unconceivable today, right? because you have different time zones, different offices, you know, no email, all that stuff. And now like mystery and chaos is probably

uh reduced dramatically, but uh still there's companies that that uh maybe maybe before you start working with them, I'm curious what those look like.

Yeah, I mean we work with a lot of a lot of different varieties of companies. Um you know, I joke that a lot of times, you know, companies make money by accident. like they don't actually know what their most profitable product is and often they're trying to sell the thing that isn't isn't actually the most profitable and actually not selling the thing that actually is profitable and it comes back to how they've modeled their data is to aggregate it up to KPIs and other metrics when you actually need to model at the finest grade how your business operates to get a true cost of goods sold for example or true cost to serve like you that's very complicated it's very complex so like we really think about how do I embrace that complexity so that I can truly understand tactically at the edge how do I do more of the things that are good and less of the bad. It's that simple. And those get peanut buttered across with KPIs and metrics. And people don't actually know how their businesses are operating. I can't tell you whether it's a hundred million dollar company or a $50 billion company how many times I see this that they don't actually understand how they're making money at a fine grain.

Yeah.

Uh last question. Uh, is there a world in the future where a company gets created, let's say on Stripe Atlas, and the first account they sign up for

other than that is, let's say, a Palunteer.

Yes, that's interesting.

I would love that. And so, we do have a Palanteer for builders program. We have small companies that there's people here that are two person startups, you know, that are working in their attict in Canada. I mean, like, so it it is literally um any size company. Come come work. There's a free dev tier people can come build. You actually there's actually a Shopify integration in Palunteer. You can go hook up to your Shopify and pull in Palanteer. Yeah, there are people doing this. Now, are we always great at selling it or telling the story? Sure. No.

But but there are companies doing this and I do think there's a day where it's going to be ubiquitous because I also think, you know, there's some some guys here that have, you know, they hey, my my business is dying. I was, you know, I was down 10% negative margin on on what I was selling and through using Palanteer. I they watched our YouTube videos and they built it themselves and increase to a 9 or 10% positive margin in three months.

That's great.

And so like people can go do it. I think that's the great American story is like how do we enable that and I think we'll get there. Um it might take a little time.

I love it. Well, thank you so much for taking the time. Great to see you. We will talk soon. Uh our next guest is joining in just 15 minutes. We're going