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Speaker 1: Palenteer outward results, boosting its revenue guidance for the year

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Speaker 1: to arrange of two point seventy four billion to two

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Speaker 1: point seven five billion, ahead of estimates, with AI demand

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Speaker 1: also helping Palenteer boost its profit outlook. There's been growth

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Speaker 1: in business with government and in commercial customers. AIP Palenteers

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Speaker 1: Artificial intelligence platform and other products has quote transformed the

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Speaker 1: business in a level more than a year. CTO Sean

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Speaker 1: Sanka joins us to discuss. Sean on the call this

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Speaker 1: transformation that doctor cart was talking about. I've been to

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Speaker 1: a few AIP cons, but as CTO, you're working on it.

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Speaker 1: I think just explain the basics of that. What's changed

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Speaker 1: within Palenteer from a technology perspective and in the domain

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Speaker 1: of artificial intelligence.

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Speaker 2: Well, what you really see in the market is this

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Speaker 2: massive bottleneck between prototype being and production, and that happens

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Speaker 2: to be where AIP is most differentiated. And that differentiation

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Speaker 2: is built on a decade of deep technical investments, investments

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Speaker 2: like the ontology, the OSDK, the security and business primitives

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Speaker 2: that we built throughout the platforms like functions, actions, automations

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Speaker 2: in this pipeline that we have that's really focused on

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Speaker 2: addressing that. And I think what's changed.

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Speaker 3: As you've been to these AIP.

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Speaker 2: Cons is that the market now understands how severe that

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Speaker 2: bubblenek is.

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Speaker 3: I think your last guest was just talking about that.

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Speaker 2: Where is so easy to build a charismatic AI prototype.

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Speaker 2: That's about the amount of effort of building a PowerPoint slide,

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Speaker 2: But it's also that amount of utility. And unlike traditional

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Speaker 2: deterministic software, this kind of powerful stochastic genie that is LMS,

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Speaker 2: it requires a lot more work to get to production,

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Speaker 2: maybe ten to one hundred times as much work, and

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Speaker 2: that requires the tool chain that we've assembled with AIP.

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Speaker 1: I've had a few conversations with your CI his cop

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Speaker 1: about his frustration with the PowerPoint deck versus the reality

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Speaker 1: of shipping products.

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Speaker 3: We'll put that to one side for now.

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Speaker 1: Show. There was a heavy emphasis on working with the

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Speaker 1: military in the written materials and on the call. Could

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Speaker 1: you just get the basics of what you'll work with

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Speaker 1: the military looks like present day.

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Speaker 2: Look In the commercial world, you call it a value

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Speaker 2: chain from the hand of your supplier to the hand

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Speaker 2: of your customer. In the military, it's about a kill

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Speaker 2: chain from censor to shooter. But really at the most

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Speaker 2: abstract level, it's the same thing. We're trying to enable

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Speaker 2: our war fighters to have information, dominance and decision advantage.

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Speaker 2: How can I see you know, to quote Sun Sue,

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Speaker 2: if you know your enemy and you know yourself, you're

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Speaker 2: going to win, and so you know, how can I

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Speaker 2: see everything there is to know about the threat? How

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Speaker 2: can I understand everything I have to combat and deter

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Speaker 2: any aggression from those threats? And when you look at

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Speaker 2: the geopolitical landscape right now, it could not be more dangerous.

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Speaker 2: Everything that's going on in Eastern Europe, the massive tensions

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Speaker 2: that are that exist in the Middle East, and the

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Speaker 2: ongoing issues that we have and deturing aggression in the Pacific.

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Speaker 1: Beyond the importance of data, where does plant to sit

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Speaker 1: in the defense ecosystem or even the defense supply chain.

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Speaker 1: Are you sort of very closely aligned with hardware makers,

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Speaker 1: the aerospace community or it is the MOLTI more focused

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Speaker 1: on you just going direct to different arms of the

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Speaker 1: defense base with government.

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Speaker 2: Yeah, we're touching all of it. One of the things

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Speaker 2: I'm most excited about. I've been calling the first Breakfast

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Speaker 2: as an antidote to the nineteen ninety three last Supper

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Speaker 2: that led to the consolidation of our defense industrial base,

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Speaker 2: where it went from fifty one primes down to five,

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Speaker 2: because we forget that at the dawn of World War Two,

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Speaker 2: we didn't have a defense industrial base.

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Speaker 3: We had an American industrial base.

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Speaker 2: Chrysler made missiles, General mills, this Serial company made inertial

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Speaker 2: guidance systems. And so we have this moment right now

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Speaker 2: in defense tech where one hundred billion dollars or more

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Speaker 2: of capital has been deployed, a boluss of founders have

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Speaker 2: shown up. There's a lot of creativity, a lot of

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Speaker 2: energy as a company that's kind of been pathfinding over

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Speaker 2: twenty years. Not only have we developed our software that

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Speaker 2: gives these warfighters unique advantage like the Maven contract that

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Speaker 2: CDO just recently awarded for nearly half a billion dollars,

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Speaker 2: but really this software infrastructure that's required to deliver modern

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Speaker 2: American software to the battlefield in air gapped environments at speed,

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Speaker 2: at pace, well.

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Speaker 3: Ahead of the threats of twenty twenty seven.

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Speaker 2: So we've been working very closely with both traditional primes,

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Speaker 2: integrating our software to their hardware, helping them actually with production.

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Speaker 2: If you think about the fifty percent of our business

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Speaker 2: that's commercial oriented. How do we build jet engines and

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Speaker 2: satellites faster, better, cheaper?

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Speaker 3: In addition to the new entrants who need to achieve.

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Speaker 2: Scale and time, value of money is everything for them.

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Speaker 1: Whenever we have an executive on this program, I always

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Speaker 1: go to our audience and say, you know, what is

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Speaker 1: it that you want to know from? In this case, Palenteer,

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Speaker 1: I would say most of the questions were about warp speed,

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Speaker 1: very basic ones. Why now with warp speed and the

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Speaker 1: obstacles to rolling it out making it sort of more

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Speaker 1: readily available, pervasive out their shop.

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Speaker 3: Yeah, that's great.

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Speaker 2: I'm so excited about Warpspeed is what I'm spending all

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Speaker 2: of my time on and really shaping the R and

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Speaker 2: D roadmap around. Speed is our modern American operating system

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Speaker 2: for manufacturing. And the reason why now, you know, for

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Speaker 2: the better part of twenty years we have helped traditional

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Speaker 2: manufacturers build planes, trains, automobiles, and ships.

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Speaker 3: But most of those folks are.

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Speaker 2: Stuck in a legacy mode of how they're operating, and

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Speaker 2: you're able to help here or there. But what's unique

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Speaker 2: about the reindustrialization movement that's happening right now in America

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Speaker 2: is that these these founders, they're alumni of Palenteer, of Tesla,

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Speaker 2: of SpaceX, and they understand that the traditional erp plm

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Speaker 2: PLC software doesn't really work. That most of these successful

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Speaker 2: companies have had to build their own software and that

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Speaker 2: is really an unaffordable journey. So that there's this massive

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Speaker 2: opportunity to take the power of AIP and the historical

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Speaker 2: experiences we have throughout the value chain of production to

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Speaker 2: help our customers bend their atoms better with bits.

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Speaker 1: I have you know, Ceto, I kind of have some

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Speaker 1: quick fire questions that I've never been able to answer

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Speaker 1: about Palenteer in the first sUAS and since AIP and

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Speaker 1: warp speed over the year. What's the kind of key

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Speaker 1: foundational model LLLM that you've been building on top of.

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Speaker 1: I know you partner as well as sort of think

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Speaker 1: in house about the model or foundation level, but that's

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Speaker 1: a question that comes up quite a lot. Who are

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Speaker 1: you building on top of and working with?

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Speaker 2: Well, I think all the value is really going to

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Speaker 2: create at the application layer, and what we've seen in

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Speaker 2: production is that you actually need a menagerie of models

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Speaker 2: and you you know, if you think about the most

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Speaker 2: expensive frontier model out there right now, it's a thousand

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Speaker 2: times more expensive than the cheapest open source model for

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Speaker 2: ten percent more ELO ten percent more IQ. As a

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Speaker 2: rough proxy, that's not a compelling price performance trade off.

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Speaker 2: You already see that the first versions of GPT four

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Speaker 2: have been sunset. You know, we need to think about

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Speaker 2: this problem as Okay, what is the infrastructure I need

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Speaker 2: to make I'm turning my software into something that is

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Speaker 2: now stochastic.

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Speaker 3: It's not deterministic anymore.

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Speaker 2: Computer scientists that we have trained, they are all used

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Speaker 2: to writing deterministic code.

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Speaker 3: You know.

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Speaker 2: This is this harkens more to how we think about

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Speaker 2: the transition from analog circuits to digital circuits, the sort

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Speaker 2: of error correction you need, the sort of infrastructure you

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Speaker 2: need to think about having so that you can have

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Speaker 2: the abstraction to.

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Speaker 3: Think about these things as being digital. That's where I

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Speaker 3: think the colony is going to be go ahead. Sorry,

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Speaker 3: And so we're very.

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Speaker 2: Focused on helping our customers get the right models for

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Speaker 2: the right use cases, auto evaluate that at automatically generate

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Speaker 2: iterations on the prompts that get them there. And part

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Speaker 2: of our theory is that really prompts are for developers,

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Speaker 2: chat is a dead end. We're guiding our customers through

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Speaker 2: this journey here. They get there pretty quickly to realize

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Speaker 2: that we should be thinking of llm's as a new

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Speaker 2: type of run time the way I might write a

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Speaker 2: Python function.

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Speaker 3: Well, okay, I'm going to write an LEM function too.

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Speaker 1: Sean, for the commercial half of the business, is there

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Speaker 1: a specific Hyperscale cloud platform or partner that you work

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Speaker 1: with to support its growth.

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Speaker 2: We're working with all of them. We have deep and

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Speaker 2: very valuable relationships there. So we're very happy with that.

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Speaker 1: This last year has been about a IP and growth.

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Speaker 1: What's the next twelve months like for Palenter Schaump.

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Speaker 2: Well, I think it's really about deepening the investments that

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Speaker 2: we have with AIP that are dressing this bottleneck between

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Speaker 2: prototyping and production. You know, if you look at David

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Speaker 2: Kahn at Sequoia's article on the six hundred billion dollar

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Speaker 2: whole and revenue.

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Speaker 3: I think this is where the whole is.

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Speaker 2: This is the bottleneck in the market is the most

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Speaker 2: important problem to solve, and the folks who solve at first,

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Speaker 2: and I think we're in the pole position there, have

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Speaker 2: the opportunity to take the entire market.

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Speaker 1: Sham Sangkas, CTO of Palenteer, it's great to have you

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Speaker 1: back on Bloomberg Technology.

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Speaker 3: Thank you so much,