Palantir's Al Targeting System Running the Iran War

So, what the heck does Palanteer

actually do?

>> Left click, right click, left click.

Magically, it becomes a detection.

>> This demo by the chief AI officer for

the Department of War is perhaps the

closest look we've gotten at Maven Smart

System.

The AI powered software at the front

lines of global conflicts today. The big

bottleneck in modern warfare is the time

it takes to target your adversary. And

in the first 12 hours of the Iran War,

the US struck nearly 900 targets and

over 13,000 targets in 38 days. This is

a massive increase over the way things

used to be. And central to that is Maven

Smart System. I want to walk you through

how this thing actually works and then

show you how much of these capabilities

we can actually build using commercial

and off-the-shelf tools minus of course

the missiles at the end because the

applications for situational awareness

go way beyond the battlefield. And

because TED is over, this is exactly why

I've been heads down building God's Eye

View into something bigger than a

YouTube series. And if you've been

asking me when you can actually use this

stuff, you're going to want to stick

around till the end. So, let's get into

it.

All right, so there's this military

concept called the UDA loop. Observe,

orient, decide, and act. This is

honestly what you do when you wake up in

the morning and decide to go get your

coffee or eat a burrito in the evening.

The key point though is your decision

cycle needs to be faster than your

adversary because if you do that, they

can basically never catch up. They can't

orient. They can't plan. They're always

in react mode. And the whole idea behind

Maven is to compress that entire loop.

And we're going to walk through every

single step so it's crystal clear. Okay.

First is the backstory. This was really

interesting to get into because to

understand why Maven exists, you need to

understand what came before it.

Adversarial targets were tracked in

Excel spreadsheets. PowerPoint was used

for mapping network connections between

all of them. Google Earth was there for

zooming in and out. As one officer put

it, "We've killed more people on

Microsoft Office than you would ever

imagine." I mean, just think about it.

The modern commander with all the crazy

sensor systems at his disposal is going

into battle with Microsoft Office and

Google Earth. Maven was created to fix

all of that. Basically, Google Earth for

war, but with AI telling you what's

actually on the screen. All right, so

here's what they built. What you're

looking at are feeds from satellites,

drones, signals, intelligence, prior map

data, all layered together on a globe.

You can see which aerial assets are in

the area, as well as different target

designations. Now, we're seeing a drone

view. You can see the vector outlines of

the buildings that are highlighted. With

a couple clicks, you load up your, let's

say, road network data, so you can see

how exactly to make it to that point.

And then, when you zoom in, you see all

these dots. These are basically

detections made using computer vision.

And you'll notice there's a number

associated when you hover over them and

nominate things to the board. That

number is its stable identifier. The

idea being that you can tell this is

indeed the same card that let's say

you're seeing in a satellite view versus

a drone view versus CCTV. Then you can

take these detections and nominate these

to a literal cananban board like a task

tracker where you can drag cards from

todo to done. These vertical columns

each have their own different process

and they essentially represent what

different teams would have been doing.

Once you've taken these detections, you

can take advantage of other workflows to

figure out what is the right asset to

task to execute that target. You can

choose different criteria. You can

optimize different metrics like time to

target, how much fuel, munitions,

distance, etc. And from all of that, it

gives you a recommendation that a human

actually approves a plan which it goes

about and executes or as the chief AI

officer put it in order to get to our

desired instinct, actually closing a

kill chain. So basically the Department

of War loves this because Maven is

taking something that used to take a

room full of analysts, right, to go

generate that course of action for them.

Now that entire complicated pipeline can

all be collapsed down into one software

that all these teams use to make the

stuff happen. The AI starts recommending

which asset should prosecute that

target, which drone, which missile

system, which aircraft based on

proximity, payload, rules of

engagements, and then you approve that

plan. So you go from like detection to

strike decision in one system. And just

for a second, imagine that this has been

happening thus far in Microsoft Office

and Google Earth. Now, let's map this to

the UDA loop. The sensor feeds that are

coming in, right? Like the satellites,

aerial, drone, all that stuff. That's

the observation. The system is fusing

and classifying what it sees. That's

orientation. The canban board and the

course of action generation, that's

decision. And then finally, actioning

the target. That's the acting bit. We're

going to come back to this demo

throughout the video cuz each section

breaks down one step of this loop. Now,

here's the part that's super complicated

and controversial. There's pretty good

consensus based on reporting that the AI

that's doing this like natural language

intelligence queries inside Maven,

that's Claude, built by Anthropic.

Anthropic actually was one of the first

to deploy large language models in

classified military settings. In fact,

the Washington Post reported that Claude

is central to the US campaign in Iran.

But Anthropic obviously drew a line

which Daria got a lot of flack from the

administration for. basically said, "You

can use claude as long as it's not used

for fully autonomous weapon systems or

mass domestic surveillance." The Trump

administration replied by declaring

anthropic a supply chain risk. Their

argument was, "Hey, you're a contractor.

You provide us the tool. You have no say

over how it's actually used." And when

you combine this with other reporting

that seems to suggest that it's actually

clawed 3.5 sonnet that's actually being

used in the system, it really makes you

think like, are these models actually

good enough to deploy in the wild? So,

the AI company that first brought their

large language model to a classified

setting, that's a key part of this Maven

smart system is now at the center of

this battle with the government. And

based on recent reporting, it seems like

a deal can still be struck. But the

tension is very real. And we'll come

back to this cuz it matters.

All right, so Maven is obviously only as

good as what it can see, the underlying

data that is made available to it. So,

let me show you what that actually is.

Obviously, you've got crazy spy

satellites in orbit, keyhole stuff, the

new things that SpaceX is building.

You've got synthetic aperture radar,

which we've extensively covered in the

past. Both military and commercial

satellite systems like ISI and Capella

Space that fire literal radar pulses

through cloud through darkness that

return 25 cm resolution imagery of the

ground. We've covered this previously.

Very dual use tech, the same stuff to

figure out if a landslide's going to

happen or if a bridge is about to break,

is also critical to these kill chains.

Now, here's the stuff that most people

don't know about. wide area motion

imagery or whammy. They started off with

a system called Gorgon Stair. This is

mounted on those classic MQ9 Reaper

drones and basically it uses 368 cameras

stitched together into a 1.8 gigapixel

composite image so that you can

persistently surveil an entire small

city from 25,000 ft. Think of this like

Google Earth Live. If you've actually

seen the movie Enemy of the State

featuring Will Smith, this program was

actually inspired by that movie. That's

freaking wild to me. So the idea is once

you've got this persistent image of

absolutely everything, when something

bad happens, let's say an IED goes off

in Afghanistan, you can then rewind to

see where that car originated from.

There's an even more advanced version of

this called Argus is that can cover 36

square miles with enough resolution to

track individual pedestrians. This way,

as long as you have an airplane or a

drone, you know, orbiting over a city,

you can figure out exactly what's

happening on the ground in the past, in

the present, and of course the future as

well. So, if we go from wide area to

narrow areas of monitoring, that's what

you might usually associate with drone

footage, essentially FMV or full motion

video. You've got these Reaper drones

that are capable of taking these live

feeds directly into Maven for real-time

object detection and classification. And

what's interesting is that these drones

can also now operate in GPS denied

environments, right? Like which Iran

obviously is and other conflict zones

across the region. They're constantly

getting jammed and spoofing GPS, right?

You can't rely on GPS in those

scenarios. If you remember, we've talked

about visual positioning systems in the

past before where you can take a camera

image, match it to a prior 3D model to

figure out exactly where that photo is

taken. Drones use this technology, too.

And Vantor's got something called the

Raptor system that matches that onboard

camera feed against their precision 3D

terrain model. So, Vantor's used a 30cm

satellite imagery to build this coarse

3D model of the world. And that becomes

the key for it to figure out exactly

where every drone photo was taken.

Suddenly, the drone doesn't even need

GPS. It knows where it is just based on

the terrain. Now, as you probably saw

with God's Eye View, one or two layers

by themselves are only so interesting.

But when you start putting in different

sources of intelligence together, that's

where you get that classified grade

picture. Signals intelligence is

obviously a huge part of how these

systems work. I'm talking intercepted

communications, electronic emissions

integrated alongside the imagery. We've

talked about RF satellite constellations

in orbit like Spire that do dark vessel

detection, right? like vessels that are

turning off their transponder. In

addition to that, you have entire shadow

fleets, right, that are not just turning

off their AIS, but they're actively

masking or offiscating their presence.

But there's another source of data that

solves that problem, too. And that's

called advertising intelligence. So,

sure, you're a shadow fleet. You're out

in the wild. You've actually repainted

your ship and you think you're

scot-free. But turns out the people on

board have a cluster of devices that

have Candy Crush installed. You can use

that ad network data to geollocate where

that ship is. That's the world we live

in. You can offiscate all other signals,

but the fact that you've got some folks

on board that like playing Candy Crush

means you will be pinpointed. And then

there's a secret one that's been

reported lately, the RQ80, America's

most classified stealth reconnaissance

drone. It was publicly exposed for the

first time during this war when it

emergency landed at an air base in

Greece this past March. It's a flying

wing that's believed to operate at

60,000 ft plus for 24-hour autonomous

missions. And it's believed to have been

tracking Iran's mobile missile

launchers, the kind of targets that move

and hide. You need that persistent

overhead coverage to catch them. Now,

does this have a SAR system on board? Is

it something more like whammy? We just

don't know, but I suspect it's a

combination of modalities. Now, of

course, on top of all of this, you layer

in commercial satellite imagery from

like Vantor, Planet Labs, you've got the

maritime AI tracking data. All of it

gets fused together into one picture.

That's the observe step. Maven basically

can see everything. Now the question is

how do you make sense of it?

This is where we go back to left click,

right click, left click. This is orient,

decide and act happening in one

interface. Now watch what happens when

you do this at scale. Obviously I've

been covering Operation Epic Fury deeply

on this channel. If you've seen those

God's Eye View videos and our Hermos

coverage, you know the timeline already.

Now, Epic Fury kicked off Feb 28th, and

in the first 12 hours, the US struck

nearly 900 targets across Iran. But

perhaps the most dramatic single

operation was Car Island. On March 13th,

the US hit over 90 military targets

simultaneously. I'm talking naval mine

storage, missile bunkers, air defense

systems in what Trump called one of the

most powerful bombing raids in the

history of the Middle East. Critically,

the oil infrastructure was deliberately

left intact. So, how do you coordinate

90 plus simultaneous strikes? Suddenly

software like Maven earns its keep.

Orient, decide, act all premputed using

the latest sensor data and executed and

tasked in parallel. Now here comes the

critical trade-off, right? Like is there

a cost to speed? Is there a point at

which Maven's accuracy surpasses that of

a human analyst? And if it does, there's

still going to be a gap and at a large

enough scale that gap is going to

manifest itself in targets getting hit

that shouldn't have been hit. I think

this is where we're also going to have

to contend with how this technology is

deployed in the wild. Right? The

Department of War right now is a very

clear stance. There are no fully

autonomous weapon systems. Everything

has a human in the loop. But think about

self-driving vehicles. At some point,

self-driving gets accurate enough where

it's better than the average human

driver. But if you add up enough of

those drives, people are going to die.

And what happens when the systems make

incorrect decisions? Like why was that

incorrect decision made? Is it the model

maker like Claude or Anthropic that's at

a fault here? Is it the sensor data

perhaps that it was made on? or even

underlying maps data. For example, the

underlying map data that you have your

AI digest incorrectly classified a

school, whose fault is that? Now, look,

there are plenty more questions to get

in here. But the broader question

remains, when you're processing

thousands of targets in 24 hours, what

gets missed and who's at fault there?

And then the question gets heavier. Once

these systems get good enough to justify

not having a human in the loop, what

happens when you're processing thousands

of targets in a short period of time?

And to be clear, this isn't just

Palanteer. Like Android has lattis,

right? Very similar concept, different

platform. Take a bunch of these sensor

systems together and create a fused

operational picture of the battle space

and have AI handle the orient and decide

steps. But this common operational

picture isn't just useful on the

battlefield. This is the same kind of

platform. The same Palunteer technology

actually runs the UK's NHS CO data

system. It optimizes logistics for the

world food programs. It runs Airbus's

supply chain. Oil and gas giants use it

to create incremental revenue

opportunities. It catches money

laundering for banks. I mean this is the

same udal loop, the same fusion layer

just being applied to different

problems.

So when you pull all these capabilities

apart, the same pieces that the defense

sector is using can be mapped to

civilian capabilities that exist right

now. So observe you've got the sensing

layer, you can buy satellite imagery.

SAR imagery is also commercial. You've

got capernicus and if you want to go low

resolution, satellite and plane tracking

data is effectively free. The more

expensive thing is AIS ship tracking

data, but that too is accessible. In

countries like Japan, you have a

plethora of CCTV camera feeds. You can

fly your own drones. You can buy robots.

You've got social media adtech data. All

of this is stuff that you can either

access or purchase. Then you have the

Orient step. This is classification and

fusion. This is exactly what computer

vision and large language models already

do. Let's say you take your own personal

security camera and give it to Meta

Segment anything model. It can figure

out, hey, here are the objects in the

scene. Then you give it to Claude or

Gemini to tell you what those things are

doing and it can infer the context of

everything else that's going on. It can

check to see, hey, you were supposed to

get an Amazon delivery and that is

indeed the person that's now showing up.

Basically, you can build your own AI

layer that makes sense of all the data

that's at your disposal. And this

technology is available to everyone. I

mean, the more I use my own like Ring

camera, I am so frustrated by how

limited this thing is and the fact that

it has to go to some centralized server

to do such basic things for me annoys

me, especially when I know the AI tools

at my disposal are capable of so much

more. And then finally, you have decide

course of action generation, right? So,

for the military, that means

recommending a weapon and an asset. For

us, it's things like what should you

actually be paying attention to? What

changed? What's anomalous? and have the

system surface that automatically. Not a

barrage of detections like unknown

person detected at this time stamp that

doesn't tell me anything and then act in

Maven. That's kinetics or deploying

human assets. For us, it's decisions.

It's knowing what's happening and being

able to respond to it in near real time.

Now, I've already been building this and

here's the road map. The first

application is God's eye view. You've

seen this tool evolving and I've been

building it to fuse open source

intelligence into a common operational

picture. what's happening in the world

visualized the same way these military

systems do it but with publicly

available data. You've seen it in my

coverage of Epic Fury and Straight of

Hermuz. And clearly y'all love it

because it's the most unbiased

perspective we can have without the

usual color of traditional media. And

let me tell you, that's getting a lot

more capabilities because now I've got a

team. The second application is

situational awareness for your own life.

Your own data, your own cameras, your

own feed, your own context fused into

the same kind of unified view, not

surveillance. I'm talking about

awareness of what's happening in your

sanctum sanctorum. You've got the map of

the world and everything that's publicly

available and then your own island that

you control. Want to share it with

others? Go for it. You want to keep it

private, you can do that, too. Private

by default and optionally sharable with

others. I mean, the cool part is you can

buy all of these fancy sensors. The AI

is commercial, too. The only piece that

doesn't exist is the fusion layer, the

thing that pulls all of this together

into one picture. And that's what I'm

building. Observe, orient, decide, act.

That's how all these platforms work. And

honestly, that's how great

decision-making works in general. You're

probably just doing it slower without

all of this technology. So, that's where

I'm going next. God's Eye View is the

window to the world that we all deserve

to see. I'm going to keep monitoring the

global situation as it evolves, getting

sharper, faster, and soon something that

you can actually use yourself. And then

there's the second surface, Argus. The

same framework turned inwards to your

world, your data, your context, so you

can create automations around the

physical world that you can't even

imagine today. So, this is what I hope

you'll get clarity about what's

happening in the world, but then also

your world. And by the way, if you can't

wait for me to release this, so many of

you reached out and I've responded to

you giving you the ingredients so you

can at least go make the viewer

yourself. Just point your clanker of

choice at my Substack post and they'll

do a really good job. If you take my

YouTube video transcripts in addition to

that, it'll take you rest of the way

there. But if you are willing to be

patient, I've got some really exciting

updates coming for you very soon. Blavo

signing off and I'll see youall on the

next one. Cheers.