all right everybody welcome back to the

Allin podcast we've got an incredible

crew today don't forget to go to our

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freeberg surprise drop with his hero Ray

Dalia live on all platforms today how

did that come about for a b little

surprise drop just great was talking

with Ray about his new book which he

just published on how countries go broke

obviously which country going broke now

America I think he talks a lot about the

historical context of what's G on with

the death Cycles in different

countries and basically at the end of

the book he has a pretty uh I think

important

recommendation to try and get the US to

roughly 3% of GDP as our net deficit net

of all expense including interest

expense so that's the recommendation to

the administration I think it's pretty

timely with the change in administration

anyway great topics to talk through and

really important book awesome well done

and we are super delighted to have in

The Red Throne Travis Ken he is the

co-founder and CEO of cloud kitchens he

also uh worked in the cab business for a

little bit co-founder and former CEO of

uber and uh yeah we had a great

interview at the all- in Summit last

year and he's back up from his media

Hiatus he's been in the lab working on

cloud kitchens how you doing brother I'm

doing really well I I got a say just

like at the summit Jason I'm I'm it's an

honor to be in the presence of such a

prominent Uber investor absolutely

absolutely I mean finally somebody has

recognized my contribution the greatness

of JAL Absolutely I'll mention it three

or four times we'll be all good I'll

give you the props you don't have to do

it for yourself anymore thank you

appreciate it

appreciate let your winners

ride Rainman David

and instead we open source it to the

fans and they've just gone crazy with it

love

Queen give everybody a little overview

of cloud kitchens and the business and

how it's going because people are

obviously

addicted to ordering food at home and uh

it's it's quite a trend yeah I mean the

the high level for it the way to think

about is it's it's about the future of

food what does the future of food look

like you go well in a hundred years

we'll start way out there in a 100 years

you're going to have very high quality

food very low

cost that's incredibly convenient and

they going to be machines that make it

they going to be machines that get it to

you and it's going to be exactly to your

dietary preferences your food

preferences Etc and it just comes to you

and it's so inexpensive that it

approaches or has surpassed the cost of

going to the grocery store that's more

of a like a today analog

so you go 100 Years of course that's the

thing nobody's going to be making food

what about 20 what about 10 and so the

company is real estate software and

Robotics that's all about the future

food and if you can get the quality

there and you can get that cost down to

start approaching the cost of going to

the grocery store you do to the kitchen

what Uber did to the

car and that's the thing

a grind it's like lot you know bits and

atams in the Uber

world this is

like five times more atoms per bit this

is like heavyduty industrial stuff

probably more along the lines of like

you know where Elon goes and some of his

companies like they're super interesting

Tech but you got to grind out those

atoms do you see people actually cooking

in the future or does it become a

centralized service and is it optimized

to People's Health and what do you think

the implications to the food supply are

if your vision holds how do you think

about all those things look uh people

will cook in the future as a hobby I

sort I make a joke at the office I'm

like I like horses I love horses but I

don't ride a horse to work right and

it's going to be a little bit like that

whereas you can cook it's It's A Soulful

thing to do it's just very

human but you know it's late you know

Mom gets home late from the office needs

to get the kids you know a nutritious

meal she doesn't have to cook it now and

she doesn't she won't have to cook it

and she won't have to go to McDonald's

either it will be high quality and

convenient and low cost all at the same

time and yes dietary preference

everything because it'll be hyper

personalized like the way the internet

is in

content plus plus plus in terms of your

specific preferences for what you

want You' got these computers rocking oh

these robots rocking I think in Philly

somewhere uh in the lab where they're

making bows yeah I mean we're out of the

lab at this point we have our machine so

we have a machine called a bowl Builder

that basically makes different Cuisine

types with bows so like think of like

like sweet greens like what they yeah

we're not working with these Brands

specifically but I'll I'll just sort of

it's a good analogy like think of

Chipotle or Cava or sweet green or you

get the idea

we created test brands that were like

those

things and built the machine at the same

time as we were building an actual

restaurant and we built that restaurant

to prove that the machine

works then we have our customers now

touring checking out we're rolling out

with five customers in April that are

using the machine and the way it will

the way it's going to go down is they

will come into and we of course we have

the real estate so we have kitchens you

know tens of thousands of kitchens

around the world they will come into one

of our kitchens in a facility it's a

delivery only

restaurant they'll prep the food in the

morning and then they will leave and the

Machine will if you will order online

door dashu breats Etc they'll order

online the way they do build your own

Bowl exactly as you want

and the bull gets all the ingredients

dispensed hot or cold sauce Etc gets

sled the bowl goes into a bag the

utensils go into the bag the bag is

sealed and then it comes out on a

conveyor

belt and machine gets the bag it goes to

the front of the facility gets put into

a locker that Locker then is sitting

there door Dash re driver comes waves

their waves their phone with an app in

front of a camera and it pops open the

locker that has the food that you're

supposed to get that's so cool so like

if you're if you're a restaurant tour

you're the grind of the on man Meal

which is the restaurant world goes away

you you basically prep and that's

asynchronous from when people order food

the machine does the final assembly or

what's known as plating essentially like

do you think there's a service in the

future where my

physiology I can share that with you

with Cloud

kitchens and you guys just can always be

optimizing my food based on what I know

is good or bad for me so first what we

do is we serve the restaurants so what

happen so chamat you'll be sharing your

dietary preferences with uberit or door

Dash or sweet green or

somebody we like our like our customer

promise at our company we serve those

who serve others or put in another way

is infrastructure for better food so we

are the either the AWS or the Nvidia or

whatever you want to call it but for

food if that makes sense we're behind

the scenes we're the infrastructure and

so you'll give your preferences right it

should be a brand like then sweet greens

or whomever Chipotle that says hey guys

share with me like a yes an encrypted

hash of your dietary restrictions needs

whatever your lipid panel and I and I'll

customize this thing and then you enable

that on the back it's pretty close jth

right you can do authenticate your Apple

Health just authenticate Apple Health

when these bowls come off the line and

see how I talk it's like an assembly

line bows come off the line on the label

on the bow is how manys of every

ingredient is in it plus a picture of

what it was before we put the lid in

that can be sent to the person while the

Bull's on its way via a courier right

what do you think Travis about this

whole Maha movement and just the food

supply itself so then what how does that

change do restaurants Embrace more Farm

to Table stuff I think look I think what

like what we've SE with Supply chains in

a bunch of different Industries it's

just going to get super wired up so

right now we're at the point of

manufacturing but what happens so you go

okay we're doing assembly then you go

okay what about prep then you go further

upstream and you're like what about

supply chain like Cisco US Foods and

then you go further up and you're like

well how does how does the mechanization

occur on farms and in agriculture and

then how does that all get wired up to

serve the customer and sort of what

they're looking for so like you really

can know exactly what kind of wheat was

put into that

food whether it was organic for real or

not like what was the actual field it

came from things like this you could

imagine like really getting tied about

supply chain as it relates to dietary

stuff and as it relates to like Maha

like hell to the yes I mean I ordered a

couple different I went to the I went to

RFK Junior's website and they have like

the he has merch he has Maha merch I

have I have the green Maha merch hat I

should have worn it today I'm all about

it get the

onesie yeah that's amazing the onesie

was crazy your bowl Builder Friedberg

you tried to do this right your and we

had a bowl Builder 10 years ago or

2015 yeah

2016 Diego saw it he actually visited it

when we built it

and we designed the system around a

canister mechanism so all the food prep

was done in a similar sort of like com

AR model and then it was loaded in bulk

and then put into little canisters and

there were 30 slots in the canister

dispenser and then the canisters would

move down the device open up and and you

could assemble bowls with rice and beans

and all sort stuff the whole thing was

automated and we were in the process of

building out our first automated store

when I actually took a medical leave of

absence from it and ultimately the

company did not get it into production

but it we had great working demos and it

was a very

yeah I mean it was just definitely a

no-brainer that this love this you love

this yeah and at the time we were we

actually had I'll tell you guys this we

actually had a term sheet with

Chipotle this was nine years ago to

actually put this into Chipotle stores

and then we were in the early

conversations with sweet green at the

time as well and obviously Jonathan and

team have gone on to develop their own

system but you know basically you can

reduce so much of like qsr down to this

bull based system and automated as

Travis is doing so it's just a

no-brainer and it's it's certainly

necessary in a time when there's either

labor shortage or labor price inflation

that's causing a real issue with the

ability and yeah this is the original

automats in in New York yeah in in the

early 20th century I love this but yeah

they had a commissary behind that wall

and they made like plates of food you

put in there you put a quarter in you

turn the knob and you get your meal out

it's the classic that's the classic

artificial artificial intelligence right

this is like the Mechanical Turk thing I

mean look here's the thing here's the a

little Nuance that's super interesting

about Automation in qsr restaurants is

that they have an existing brick and

mortar that's built a certain way that

layout is meant for humans and to for

those humans to work in certain

processes an exact and very specific way

every square inch of that kitchen and

that space is is

dialed when you go and put a machine

like this in it changes the whole thing

and so just to get going you've got to

like do you've got to like if you're to

replace the front line in Chipotle you

got to you got to take out that front

line you got to demo it yeah you got to

put in a new

machine that's the challenge that they

all had and so now it's like a huge

amount of capex my storees down for two

to three months and the economics start

to not work and by the way I still have

to have humans in that brick and mortar

and so you know look we have a different

take we're in that delivery only model

so these are it's true infrastructure

for making food behind the scenes for

delivery so you don't have these issues

and of course our setup our

infrastructure these kitchens are

designed for these kinds of machines to

be in them and vice versa we've designed

the machine to be in them when we did

this early at ITA it was like food

delivery was very early we built these e

restaurants that were smaller frint we

had an 800t restaurant that was doing 3

million a year in revenue and it had a

handful of people working in it but we

were putting about 800 people an hour

during the lunch rush through that

restaurant ordering custom B this was by

one market right so one market exactly

and so by the way did you guys notice

that jcal was plugging his product there

in the background even though it has

absolutely nothing to do with what

Travis was saying oh welcome back to the

show nothing's

changed sax is here no one else even

noticed that I just heard this voice

from above it was the Zar of AI and

crypto I was like Wow send it that's all

sit back and listen the Zar is back ZX

any anecdotes you want to share about

life in DC how how exciting it's been in

the administration the first week it's

been amazing I mean it's hard to believe

it's only been a week right so you in

the white house or that building next to

it do you have an office building you

mean the treasury building I know

somebody was talking about there was a

building next to it or what something I

don't know I have an office in the uh

old executive office building otherwise

known as the Eisenhower Building and

then I have a pass where I can just walk

over to the westwing if I want to walk

over to it there's kind of a whole White

House complex

behind the Gates that with the West

Wings part of it and the Eisenhower

Building and there a couple other

buildings in that complex it's really

cool it is really neat to to show up for

work at the White House to say that's

awesome it's like being in a movie or

something or a TV show it is really cool

you know it's awesome any interesting

meetings you can talk about and I mean I

know we are here to today to talk about

deep seek but any interesting meetings

or anecdotes from just the Vibes and

walking around what's the coffee l is

there like a commissary you run into

anybody

interesting there is a commissary

actually in the White House called the

Navy mess and I think they're just

opening up for for business now that is

one of the cooler things you could do is

you can take people to to lunch at the

Navy mess oh look forward to it J Cal

just invited himself look forward to it

I look forward to taking jamath and

freeberg

there I'll wear my Maga hat all right

well let's get started you're here

because you uh we have a very specific

he's here because the world is ending

Jason the the Western world is ending

okay the Western world is ending and

David Sachs is going to save it but we

had a little bit of a freak out the last

week regarding this deep seek if you

don't know that's a Chinese AI startup

they released a new language model it's

called R1 and it's on par basically with

some of the best models in production in

the west like open AI 01 model but they

claim and listen you can trust CLA

coming out of

China you know for what it's worth uh

they claim to have done this all for $6

million on only 2,000 gpus for

comparison open AI spent reportedly 8

100 million to train GPT 4 which you're

all using now and uh Sam claims they're

going to spend a billion dollars

training GPT 5 and so that's about 7% of

the cost of GPT

4 obviously there are export

restrictions on Nvidia h100s to China so

there's a big big debate as to if they

actually have H1s or not and uh Monday

was a blood bath in the stock market

Nvidia had the worst day in the history

of the stock market in terms of total

dollar amount of market cap lost it was

down 177% which is $600 billion tsmc was

down arm was down Brom was down so I

guess everybody's asking the question

how did they do this did they do it and

then there's a bunch of debate on

whether they stole which is kind of Rich

coming from open AI which got caught

red-handed stealing everybody else's

content and now they're crying foul that

the Chinese stole or

trained did what's called distillation

of their model in order to build theirs

Sachs obviously you are the Zar of AI

I'm curious what your take on all this

is and thanks for coming well I think

one of the really cool things about this

job is just that when something like

this happens I get to kind of talk to

everyone and everyone to talk and i' I

feel like I've talked to maybe not

everyone in like all the top people in

AI but it feels like most of them and

there's definitely a lot of takes all

over the map on deep seek but I feel

like I've started to put together a

synthesis based on hearing from the top

people in the field it was a bit of a

freakout I mean it's rare that a model

release is going to be a global news

story or cause a trillion dollars of

market cap decline in in one day and so

it is interesting to think about like

why was this such a potent news story

and I think it's because there's two

things about that company that that are

different one is that obviously it's a

Chinese company rather than an American

company and so you have the whole China

versus

US competition and then the other is

it's an open- Source company or at least

it open source the the R1 model and so

you've kind of got the whole open source

versus closed Source debate and if you

take either one of those things out it

probably wouldn't have been such a a big

story but I think the synthesis of these

things got a lot of people's attention a

huge part of Tik tok's audience for

example is international some of them

like the idea that that the US may not

win the AI race that the US is kind of

getting a comeuppance here and I think

that fueled some of the early attention

on Tik Tok similarly there's a lot of

people who are rooting for open source

or they have animosity towards open

Ai and so they were kind of rooting for

this idea that oh there's this open

source model that's going to give away

what open AI has done at 12 cost so I

think all of these things provided fuel

for the story now I think the question

is okay what should we make of this I

mean I think there are things that are

true about the story and then things

that are not true or should be

debunked I think that let's call it true

thing here is

that if you had said to people a few

weeks ago that the second company to

release a reasoning model along the

lines of 01 would be a Chinese company

I think people would have been surprised

by that so I think there was a surprise

and just to kind of back up for people

you know there's there's two major kinds

of AI models now there's kind of the

base llm model like Chach D40 or the

Deep seek equivalent was V3 which they

launched a month ago and that's

basically like a smart PhD you ask a

question gives you an answer then

there's the new reasoning models which

are based on reinforcement learning sort

of a separate process as opposed to

pre-training and 01 was the first Model

released along those lines and you can

think of a reasoning model as like a

smart PhD who doesn't give you a snap

answer but actually goes off and does

the work you can give it a much more

complicated question and it'll break

that complicated problem into a subset

of smaller problems and then it'll go

step by step to solve the problem and

that's that's called Chain of Thought

right and so the new generation of

agents that are coming are based on this

type of idea of of Chain of Thought that

that an AI model can sequentially

perform tasks figure out much more

complicated problems so open AI was the

first to release this type of reasoning

model Google has a similar model they're

working on called Gemini 2.0 flash

thinking they've released kind of an

early prototype of this called Deep

research

1.5 anthropic has something but I don't

think they've released it yet so other

companies have similar models to 01

either in the works or in some sort of

private beta but deep seek was really

the next one after open AI to release

you know the full public version of it

and moreover they open sourced it and so

this created a pretty big splash and I

think it was legitimately surprising to

people that the next big company to put

out a reasoning model like this would be

a a Chinese company and moreover that

they would open source it give it away

for free and I think the API access is

something like 12th the the cost so all

of these really did drive the the news

cycle and I think for good reason

because I think that if you had asked

most people in the industry a few weeks

ago how far behind is China on AI models

they would say six to 12 months and now

I think they might say something more

like three to six months right because

01 was released about four months ago

and R1 is comparable to that so I think

it's definitely moved up people's time

frames for how close China is on on AI

now let's take the um we should take the

claim that they only did this for $6

million on this one I'm with Palmer

lucky and Brad gersner and others and I

think this has been pretty much

corroborated by everyone I've talked to

that that that number should be

debunked so first of all it's very hard

to

validate a claim about how much money

went into the training of this model

it's not something that we can imper

Ally discover but even if you accept it

at face value that $6 million was for

the final training run so when the media

is hyping up these stories saying that

this Chinese company did it for six

million and and these dumb American

companies did it for a billion it's not

an Apples to Apples comparison right I

mean if you were to make the Apples to

Apples comparison you would need to

compare the final training run cost by

Deep seek to that of open AI or

anthropic and what the founder of

anthropics said and what I think Brad

has said being an investor in open Ai

and having talked to them is that the

final training run cost was more in the

tens of millions of dollars about nine

or 10 months ago and so you know it's

not six million versus a billion okay

it's the billion doll number might

include all the hardware they've bought

the years of putting into it a holistic

number as opposed to the training number

yeah it's running it it's not fair to

compare let's call it a nuts number a

fully loaded number by American AI

companies to the final training run by

the Chinese company but real quick sax

you've got you've got an open source

model and they've the the white paper

they put out there is very specific

about what they did to make it and uh

sort of the results they got out of it I

don't think they give the training data

but you could start to stress test what

they've already put out there and see if

you can do it cheap

essentially like I said I think it is

hard to validate the number I think that

if let's just assume that we give them

credit for the 6 million number my point

is less that they couldn't have done it

but just that we need to be comparing

likes to likes yeah so if for example

you're going to look at the fully loaded

cost of what it took deep seek to get to

this point then you would need to look

at what has been the R&D cost to date of

all the models and all the experiments

and all the training runs they've done

right and the compute cluster that they

surely have so Dylan Patel who's leading

semiconductor analyst has estimated that

deep seek has about 50,000 Hoppers and

specifically he said they have about

10,000 h100s they have 10,000 H 800s and

30,000

h20s now the cost s sorry is they deep

seek or it's deep seek plus the hedge

fund deep seek plus the hedge fund but

it's the same founder right and by the

way that doesn't mean they did anything

illegal right because the h100s were

banned under export controls in 2022

than they did the h800 in 2023 but this

founder was very farsighted he was very

ahead of the curve and he was through

his hedge fund he was using AI to

basically do algorithmic trading so he

bought these chips a while ago in any

event you add up the the cost of a

compute cluster with 50,000 plus Hoppers

and it's going to be over a billion

dollars so this idea a that you've got

this Scrappy company that did it for

only 6 million just not true they have a

substantial compute

cluster that they use to to train their

models and frankly that doesn't count

any chips that they might have beyond

the 50,000 you know that they might have

obtained in violation of export

restrictions that obviously they're not

going to admit to and we just don't know

we don't really know the full extent of

of what they

have so I just think it's like worth

pointing that out that I think that part

of the story got overhyped it's hard to

know what's fact and what's fiction

everybody who's on the outside guessing

has their own incentive right like so if

you're a semi-conductor analyst that

effectively is massively bullish Nvidia

you want it to be true that it wasn't

possible to train on $6 million

obviously if you're the person that

makes an alternative that's that disrupt

you want it to be true that it was

trained on $6 million all of that I

think is all speculation the thing that

struck me

was how different their approach was and

TK just mentioned this but if you dig

into not just the original white paper

of deep seek but they've also published

some subsequent papers that have refined

some of the

details I do think that this is a case

and Sak you can tell me if you disagree

but this is a case where necessity was

the mother of invention so I'll give you

two examples where I just read these

things and I was like man these guys are

like really clever the first is as you

said let's let's put in a pin on whether

they distilled 01 which we can talk

about in a second but at the end of the

day these guys were like well how am I

going to do this reinforcement learning

thing they invented a totally different

algorithm there was the the Orthodoxy

right this thing called po that

everybody used and they were like no

we're going to use something else called

I think it's called grpo or something it

uses a lot less computer memory and it's

highly

performant so maybe they were con strain

sacks practically speaking by some

amount of compute that caused them to

find this which you may not have found

if you had just a total surplus of

compute availability and then the second

thing that was crazy is everybody is

used to building models and compiling

through Cuda which is nvidia's

proprietary language which I've said for

a couple times is their biggest moat but

it's also

the biggest threat factor for lockin and

these guys worked totally around Cuda

and they did something called PTX which

goes right to the bare metal and it's

controllable and it's effectively like

writing assembly now the only reason I'm

bringing these up is we meaning the West

with all the money that we've had didn't

come up with these

ideas and I think part of why we didn't

come up is not that we're not smart

enough to do it but we weren't forced to

because the constraints didn't exist and

so I just wonder

how we make sure we learn this principle

meaning when the AI company wakes up and

rolls out of bed and some VC gives them

$200

million maybe that's not the right

answer for a series A or a seed and

maybe the right answer is 2 million so

that they do these deep seek like

Innovations constraint makes for great

art what do you think uh freedberg when

you're looking at this well I think it

also enables a new class of investment

opportunity

given the low cost and the speed it

really highlights that maybe the

opportunity to create value doesn't

really sit at that level in the value

chain but further Upstream apology made

a comment on Twitter today that was

pretty funny or I think ref this about

the ra he's like turns out the rapper

may be the the mo the the mo which is

true at the end of the day if model

performance continues to improve get

cheaper and it's so competitive that it

commoditized much faster than anyone

even

thought then the value is going to be

created somewhere else in the value

chain maybe it's not the rapper maybe

it's the user and maybe by the way

here's an important point maybe it's

further in the economy you know when

electricity production took off in the

United States it's not like the

companies are making a lot of money that

are making all the electricity it's the

rest of the economy that Acres a lot of

the value well you're about to see a big

test of this because if open AI raises

40 billion at 340 billion that just hit

the wire the underwriting IC at 340

billion exactly what you just said

freeberg it is the rapper meaning chat

GPT is the next killer app it's getting

to a billion plus ma hundreds of

millions of da it's competing for

Consumer usage that's that's the model

that's the model is like consumer usage

which puts them on a on a collision

course with meta it's the only company

that could really impact that because

the only company right now that has

billions of eyeballs of daus per day and

who and by the way Zuck said this in his

earnings release he's like there's only

going to be one company that brings AI

to a billion plus people and it will be

us some version of that quote in his

earnings released yesterday and

Microsoft showed weakness in the in

their cloud and then Microsoft's down

six% today and you know I think it's a a

window for open AI to say we're going to

go up against meta this is it we're

going to be the players what you

everyone's kind of ignoring Google at

what do you guys think is happening

right now between open Ai and Microsoft

cuz if it's true that this distillation

thing actually happened well there's

only one place where you could have

distilled the o1 model it's on aure so

what the hell is going on over there

well and there R1 is supported on

explain distillation real quick yeah so

when you have a big large parameter

model the way that you get to a smaller

more usable model along the lines of

what saaks mentioned is through this

process called distillation where the

big model feeds the little model so the

little model is asking questions of the

big model and you take the answers and

you refine and by the way you can see

this Nick I sent you a clip you guys can

see this I mean it's there's clearly

distillation happening Nick can you show

the clip of the of the deep seek run

where it shows the China answer and then

deletes it what was Winston's job in

1984 right and it sort of starts to go

through this whole

summary and then the person says are

there any actual states that currently

do

that hold on here it goes it says North

Korea wait goes China and then wait

watch this boom so the reason why this

is happening is like you're seeing this

Chain of Thought you're seeing the

several layers and then it's catching it

after the fact so we know that this is

distilled from some other

model and my only point there it's a

little tongue and cheek is right now

when you go and use open AI you're using

it sitting in an Azure instance

somewhere right so this is Microsoft's

Cloud infrastructure that runs it so it

begs the

question it's not that it's 's fault

open ai's fault that this distillation

happened and I'm not trying to assign

blame but typically if this were to

happen you'd look to your cloud provider

and say how are you letting this happen

and I don't think anybody's had a good

answer for that well and the cloud

provider is hosting R1 now so they're

literally

undercutting chat GPT and open AI at the

same time just to clean that up they're

they're hosting their own copy of it

right because r1's been open when you

say they who are you who are you

referring to saxs Microsoft yeah

Microsoft is hosting their version of R1

which means they are actively subverting

their partner open Ai and pushing people

to a cheaper model well whatever I mean

look Amazon's going to host their own

version of R1 grock has a version of

R1 just red out it's open source now

have who has R1 on his laptop you know

yeah exactly yeah but if it was if it

was stolen as um and the IP was stolen

as Sam is claiming that would be like

You' think he'd be able to call up S and

say hey can you not put the stolen IP on

your server and promote it to everybody

at a lower cost it just shows Microsoft

has no loyalty to open AI yeah and they

have but you think they would have

loyalty they have loyalty what it would

take to distill

01 like brute

force it wouldn't be like oh geez I

can't believe it was distilled it would

be like such a massive number of calls

against an API or against something

something that you it wouldn't be

unnoticed and oh they did actually came

out and said they blocked some

suspicious activity recently yeah no but

they're always doing that they're always

that's that's constant you're always

doing that that's like you know the old

school you know go go ahead s let me let

me address the distillation point so I I

mentioned this a few days ago on on Fox

news that I thought it was likely or

possible that desolation had occurred

and there was some evidence for this and

it became like a news story and I I

didn't even realize that saying that

would be news because it's kind of an

Open Secret Silicon Valley everyone I

talked to they're doing some level of

distillation because you need to test

your model against theirs anyways yeah

and every single person I've talked to

basically has agreed that there was some

distillation here from open

AI now that doesn't mean it was the only

thing going on here I mean to be sure

the Deep seek team is very smart and

there were some Innovations but also

there was some distillation and really

this wasn't even a fresh news story I

think from the point of view of Silicon

Valley because a month ago we had a

press cycle in Silicon Valley when deep

seeks V3 model came out that deep seek

V3 was self-identifying as chat GPT when

you would ask it who are you like what

model are you five out of eight

times V3 would tell you that it was

Chachi

T4 and there's lots of videos and

examples of this online that have been

posted right the point is that we knew a

month ago that V3 had been framed on a

substantial amount of chat GPT output

obviously because V3 was

self-identifying as chat GPT and there's

two ways that that could have happened

so the let's call it innocent

explanation is that deep seek had

crawled the web and found lots of

published output from chat GPT and then

trained on that and that wouldn't be a

violation of open AI terms of service or

their IP or the other explanation would

be that they used the API from open Ai

and basically you know went to town yeah

went to town and there's no way I think

based on what we know to prove that one

way or another but I know what most

people think happened and at the end of

the day open AI can probably figure it

out and they they've indicated that they

think there was

some improper distillation here in the

financial times it says opening eye says

it has found evidence that Chinese

artificial intelligence startup DC used

the US company's proprietary models to

train its own open source compan right

that's what I'm referring to so they say

they've been very clear about this by

the way you have to be sympathetic I

think to open AI in this because if

you're building a startup you're trying

to raise money we've all gone through

this cycle guys where it's like there's

momentum we celebrate internally the

momentum that's what gets you the energy

to push your team even further and

harder and then all of a sudden it turns

out that some portion of that like it

Travis said it well like you're there's

probably a chart inside of open ai's

offices where you're showing how many

times these apis are getting hit right

you know how many times these end points

are getting hit it all looks positive

and then you realize that some portion

of it was actually bad and trying to

undercut your

value it's a hard pill to swallow and

then you have to course correct very

quickly you have to lock down this is

one area where security exactly we have

not talked about this like you have to

lock these models down now you have to

lock the endpoints down look in the

Biden Administration if this had

happened the first conversation would

have been we need need to kyc the people

that use these models and it's like what

are you talking about we don't kyc the

cloud if you're trying to use like an

ec2 endpoint or an S3 bucket you don't

have to all of a sudden prove who you

are you just use a credit card and go

that's the whole point of why

proliferation can happen so quickly but

if we take the wrong takeaways from this

period there's going to be a bunch of

people that'll clamor to like lock these

folks down and make Innovation go much

slower I don't I think that that would

be bad here's the other side and totally

agree Chim but here's the other side you

go through the white paper you see what

it is they did what they innovated on

yeah the science behind it the

thoroughness and you're like these guys

are badass it doesn't it does not feel

or sound like somebody who took

something just when you get through it

it could be that it could be that open

AI wrote the white paper for them just

putting it out there but it's real

Innovative I agree with that real

Innovation strong Tech you're like this

is legit I agree with that but in that

paper they're very hazy about where the

data is coming from and they're they're

fairly transparent about everything else

they did but they're not really clear

about the data and specifically they say

that to get from V3 which is the base

model to R1 which the reasoning model

they had about 800,000 samples of

reasoning they were quite unclear about

where those reasoning samples came from

from by the way it is remarkable that

you can get from a base model to an R1

with just 800,000 samples but this is a

problem like we meaning like the Western

AI Community we've been trudging around

on this path where we've been very we

had a very Orthodox approach the only

way you can do reinforcement learning is

through po okay but is that true and it

turns out that if you're like a really

smart team that has no other choice you

move away and you invent your way out of

it and so we have to get that example

too I think it's technically brilliant

some of the things they've done but they

also use constraint as a very much a

feature not a bug and the the Western AI

economy has been the opposite so far I

think the best part of this is the fact

that Sam mman was supposed to be doing

open source he made it a Clos Source

company he stole everybody's data every

got caught red-handed he's being sued by

the New York Times for all that and now

the Chinese have come and open sourced

all the stuff he stole and he's got a

real competitor on the original Mission

of what opening ey was supposed to do so

I have zero sympathy for him or the team

over there I'm glad that this is all

going open source it should have been

open source and it's better for Humanity

and the fact that the Chinese did it to

Sam wman has come up and for him

stealing everybody else's content that's

my okay there you have it but I don't

have strong opinions on it it's

hilarious does nobody see the irony in

this he was supposed to be doing open

source because Jal I will say the models

are closed you're right yeah there was

there's the lawsuit with Scarlet

Johansson for stealing her voice even

when she said no there's a real question

and and people have asked New York Times

and then there's now the question about

YouTube data being used to train the

video models so there's a lot of being

on their on their heels a little bit so

I definitely I definitely see your point

stealing I think yeah I think the the

really all the pressure right now I

think is on meta because I think meta

has to show up with the next iteration

of llama that beats and exceeds

Gemini that exceeds R1 and I think that

that is going to be crucial for us to

have a counterweight to whatever China's

going to put out after this and but I

mean jamat it's open source like does it

not so this so this is my point Embrace

and extend Embrace and extend meta has

to embrace and extend everything that

these guys have shown meaning like meta

is buying tens of thousands of Nvidia

gpus great but what did this show this

shows that actually Cuda high level

languages in general I think we've all

known that they suck okay and so we've

all been going through it thinking that

it's like the right thing to do deep

seek throws it out the window they use

something called PTX what does meta do

is critical now to understand they need

to embrace this stuff and this is where

I think again apologies to the Invidia

bles but it's going to create a more

heterogeneous environment and the reason

is because there's too much money and

risk on the line to go through a single

point of failure a chip a highle

framework to get to that chip that's

nuts so I think like that kind of like

Emperor has no close moment is upon us

well let me ask you another question

let's assume that we start the world of

AI today so there's no Legacy of the

last three years and you wake up today

and there's this open- Source model

that's 670 billion parameters you can

run it on your desktop computer it's

completely available everything's

completely transparent and I ask you the

question forget about all the big

companies that are involved in

everyone's strategy historically what's

the model today to build value here

where do you

build equity value as a business if

you're going to start a company if

you're going to invest as a as an

investor where do you go the first is

you have to build a shim and I think the

reason why a shim is really critical is

that there's so much entropy at the

model level what this should show you is

you can't pick any model and the problem

is that the people that manipulate these

models the machine learning engine and

whatnot they become too oriented to

understanding how to get output of high

quality using one thing meaning it

shouldn't have been the case that we

have Engineers that can only use sonin

right that's the anthropic model right

it shouldn't be the case that people can

only use open AI or people can only use

llama right now that is kind of what we

have you don't have the flexibility to

hot swap as models change so if you're

starting a company today the first

technical problem I would want to solve

for is that because tomorrow if it's R2

or alibaba's model or llama I would want

to be able to rip it out and put it back

in and have everything work and right

now we can't do that the answer to your

question fre is the answer your question

hold on the answer to your question is

the application layer because this is

all going to become storage it's like

YouTube being built on top of storage or

Uber being built on top of GPS all these

Innovations are being commoditized and

this one is happening faster than all

the rest do you want to be in the

storage business or you want to be in

the YouTube business do you want to be

in the Uber business or do you want to

be in the GPS chip business I mean

they're both decent businesses but Gavin

Baker came on this podcast and said this

the fastest deprecating asset in the

world was a large language model he's

been proven right they're not worth

anything they're all going to be open

sourced they're all going to be

commoditized and that's for the best of

humanity and now we're going to be on

the application Level the hardware level

with robots and I think that's where the

opportunity is Travis what do you do

what company do you start today if you

start a company today given where the

world is at given the open- source

models like what do you do oh I'm

getting so excited um look I I think the

first the first degree out is it's what

you got is there a rapper company okay

so of course maybe those companies

already exist and then is there a tools

company right so in a funny way even

though Facebook could be the rapper they

have a tools

business that these you know that the

that deep seek is basically challenging

going full open source and like putting

something out there that's really good

and what has to happen is fa meta has to

decide like we are going to embrace and

extend this we're going to make sure

that all the developers come to us that

all the cool applications get built here

so I think it's like there's a tools

business and then there's the rapper

business um and then you know look when

AI here's the one thing on the Nvidia

thing that I would counter with a little

bit of what's been said here is like

when AI gets cheap you know what's going

to happen guys there's going to be a lot

more I right I don't think I think the

price elasticity on this one is actually

positive so as the price goes down

that's right the revenue usage

everything's going to go up we through

the RO this is a history of tech forever

since like Bill Gates said I don't know

what to do with more than 64 kilobytes

of memory like you know the question is

did we cheap oil cheap oil in the United

States drove the Industrial Revolution

right and like when we started

discovering oil suddenly we were able to

build factories and make stuff that we

never imagin possible and so then you're

like okay AI is like you know it's going

to get cheap it's going to be oil but

it's also going to be specialized for

different tasks like you're going to

start getting into nuances of like what

is the invest the investor AI look like

what does the autonomous car AI look

like what does the uh Google search I'm

trying to figure

some like yeah so you could go vertical

and Silo siloed air quotes understand

what I'm saying abolutely yeah so

there's a a thing called Jin's Paradox

which kind of speaks to this concept SAA

actually tweeted about it which is the

it's an economic concept where as the

the cost of a particular use goes down

the aggregate demand for all consumption

of that thing goes up so the basic idea

is that as the price of AI gets cheaper

and cheaper we're going to want to use

more and more of it so you might

actually get more spending on it in the

aggregate that's right because more and

more applications will become cost feas

economically feasible exactly that is I

think a powerful argument for why

companies are going to want to continue

to innovate on Frontier models you guys

are taking a very strong point of view

that open source is definitely going to

win that the leading model companies are

all going to get commoditized and

therefore there'll be no return on

Capital and basically continue to

innovate on on the frontier I'm I'm not

sure that's

true you for one thing the the R1 model

is is basic comp to 01

which which open AI released four months

ago and was training on internally call

it N9 or 10 months ago so open AI is on

03 now its Frontier is ahead of where R1

is anthropic and Google I think have

things in the work and even meta that

may be ahead of where R1 is so I think

R1 or deep seeks done a good job being a

fast follower here is it's not clear

that this is the frontier and those

Frontier Model companies now having seen

what might have happened with

distillation have a pretty strong

incentive to make sure that doesn't

happen again and they're going to be

taking countermeasures I mean there's a

question of like how much you can do to

stop it but I think it's a little

premature to conclude that there's no

reward for being at the frontier anybody

uh have any other questions for Sachs

before we drop him off to go back to

serving the American people before we

drop him off one final point on on the

whole open source verus closed Source

look I I'm not going to take sides in

that but I I think that it's a mistake

to just view what happened here as oh

it's this like Plucky upstart that's

like doing the community huge service

out of the goodness of its heart you

know it's basically open sourcing all oh

they stole it they stole it it's a

Chinese come on you still have this huge

geopolitical aspect to it right and deep

seek is a Chinese company they trying to

catch up and so if you're if you're

behind and you're trying to catch up

then open source is a strategy that

actually really makes sense for you and

you know they're trying to basically

undercut the leading American companies

and I I don't think they did it with $6

million I mean they have massive

resources behind them so I think some of

the the pro deep seek Vibes I think are

they're a little bit naive you know in

Silicon Valley it's like that's only the

uh people who worked for Sam previously

and quit who feel that way

I think there's a lot of like support

for deep

sea Valley because again people think

that they're doing this huge service for

the community and I think it's a little

bit more self-interested that than that

it could be both right I mean there

there is a theory that they're trying to

undercut and neuter the lead and at the

same time there's a b bunch of people

who believe in open source and nobody

should control this and certainly not

Sam wman should be the person who

controls it so two things could be true

at the same time David thank you so much

for coming on we appreciate it and uh

thank you for all coming on your podcast

David we appr David I know that this is

and now we're going to talk about a

bunch of other crazy stuff gentleman

David yes thank you all right thanks to

David Sachs for coming in and uh you

know I guess let's open up the aperture

here and talk a little bit about

relations with China we're obviously in

a bit of a cold war with them we have

tariffs we have

Taiwan and then we have uh the sort of

trade war going on here with uh exports

of h100s where do we want to start

gentlemen and you know Travis you've got

some uh deep you're one of probably five

American entrepreneurs who ran an at

scale business with Uber and the DD

relationship in China so you have a

unique position of understanding

business in this along with maybe Tim

Cook and Elon are the only other two

people who've really had an at scale

business there maybe Disney they have

Disneyland there

yeah what's your take on the

relationship and what's going here GE

China how's China going to operate

differently than the US Travis from your

experience your point of view tell us a

little bit about the culture and

business ethics in China particularly as

it relates to AI okay so okay so look we

I had this thing this is I'm going back

almost 10 years here Uber day we're

running Uber

China

and I mean I cannot there's no way I

could express the frenetic intensity of

copying the that they would do on

everything that we would roll out in

China and it was so

epically intense that I basically had a

a

massive amount of respect for their

ability to copy what we did I I just

couldn't believe it we would do real

hard work make it we dial it and it

would be epic and it would be awesome

we'd roll it out and then like two weeks

later boom

they've got it a week later boom they've

got it and of course I use that to drive

our team and there's so many great

stories I mean we had we had like

400 Chinese

Nationals in Silicon Valley at our

offices in San Francisco we had a whole

floor for the China growth team and it

was primarily Chinese Nationals we had

Billboards on the 101 in Silicon Valley

in Chinese

Uber Billboards to join our team in

Chinese to to serve the Homeland right

um it was like an allout War it was

really epic it was epic and by the way

when you went to that floor in our

office you were in China like they red

China style like the desks were

literally smaller like the density of

the space was it was China

okay so but what happens is when you get

really really good at coping and that

time gets Tighter and Tighter and

Tighter and Tighter and Tighter you

eventually run out of things to

copy and then it flips to creativity to

creativity and Innovation now at the

beginning you you know it's sort of all

over the place like the kind of

innovation when it was new was like what

you know you're like really but as they

exercise that muscle it gets better and

better and better so if you want to know

about the future of food like online

food delivery you don't go to New York

City you go to

Shanghai right what's an example like of

like something really Innovative their

doesn't doesn't mwan do drone delivery

and stuff like here's an example if you

went to offices like let's say shanhai

um Beijing any of the major cities hongo

Etc the office buildings have hundreds

of lockers around their

perimeter so that everything that you

get whether be food or anything else but

especially food it's just the the

couriers drop them off in these Lockers

in the at the office buildings and then

there are a whole other set of people

that are sort of like inter office

Runners Runners that then bring it to

your

office as an example like and when you

see it you're like and it's epically

efficient and it's you know they're

taking advantage of their economics on

labor and things like this it wouldn't

exactly work that way here but a lot of

the Innovation you will see coming out

on Uber Eats or door

Dash like the stuff that's coming out

now is stuff that existed three years

ago four years ago in China maybe longer

so like eventually you cross that

threshold of coping and you you're

innovating and then you're leading and I

think we see that in a in in a whole

bunch of different places yeah here's a

look at these smart lockers that you can

see you're just available for sale when

when you go online but yeah these things

are crazy and you've experimented with

those as well didn't you have like a

commissary Concept in DTLA well look we

okay so we got a couple things so we

have in every one of our facilities and

we've got you know hundreds of

them we'll have lockers there so the The

Courier then waves their phone in front

of a camera the right Locker pops open

they get the food from there and they go

The Courier pickup is asynchronous from

production of food you never you don't

have lines anymore there's no more lines

which then speeds up delivery shortens

the amount of time shortens is reduces

how much money you spend on

couriers and we've got a whole other

thing this doesn't work in it probably

wouldn't work in China because well for

a lot of reasons but let me explain what

it is it's called picnic where if you

are in an office building you order food

you go to a website you order whatever

it is from a 100 different restaurants

those restaurants happen to be in my

facilities

and there'll be one Courier that goes to

one of our facilities and picks up 50

orders at a time brings it to an office

puts it there's a shelf on every floor

you get notified when your food arrives

and it arrives the same T time every day

and you just go to the Shelf get it on

your floor and dip it right back into

your meeting saving people time at the

office giving them selection on food

especially in food deserts but even

going like there's a sweet green right

down there in my current in my office

right now I could save 20 minutes by

just using our own service versus doing

that and you get at the same price

because the Courier economics The

Courier is bring is delivering 50 orders

at a time so Courier costs go basically

to zero what do we think of the um of

the export controls here should we chth

be maybe Banning more h100s or other

chip sets going there or is that futile

I don't know the answer to that and I

think that's I think saxs

and president Trump will make a good

decision but here here's the Curious

Case of the export controls Nick I sent

you a couple of tweets if you want to

just bring this up so the first thing

that people are claiming is that deep

seek is getting access to a bunch of

Nvidia gpus using Singapore as a back

door so essentially you create a

Singaporean shell company you place an

order with Nvidia Nvidia fulfills that

into Singapore and then the chips go

someplace uhoh and so there's a bunch of

examples where people are saying that

you're talking about up to a quarter of

all Nvidia

Revenue goes into

Singapore and the speculation right now

is that 100% of those then go into China

which is an enormous claim because

that's a huge amount of of nvidia's

Revenue now the interesting thing is if

you actually try to understand well

maybe that's not true and maybe it's

sitting inside of Singapore this is

where that kind of unravels so just to

be clear like Singapore is about 250 or

260 square miles like it's like a small

small place also the Tik Tok

headquarters

and I tried to find out how many data

centers are in Singapore and it's about

a 100 and so you would think that okay

well what does that mean 100 could mean

anything but then you look at the energy

and they publish that and all of those

100 data centers consume about 876 megaw

so these are are small data centers

right and the entire industry is like a

one and a half2 billion doll Revenue

business so I do think that saaks and

the administration are going to have to

dig into this and figure out what their

opinion should be but there is

clearly a ton of of these chips going

into

Singapore I don't think anybody knows

where they end up and the question is

what does America think about that and

why did we Implement these export

controls in the first place and if

there's a simple back door how do you

want to react if the US finds a path I

mean let's talk about like what happened

with sanctions in Russia and other prior

kind of sanctioning efforts around the

world but as you kind of close the the

floodgates and close access the

buyer or the receiver of those goods or

that Capital are going to look elsewhere

they're going to look to create a market

somewhere else and so if we do cut off

access to Nvidia ch we do cut off access

to US exports are we not kind of

recognizing that the second order effect

of that is that China will take IP that

they've stolen copies that they've made

to Travis's point and develop and build

out their own Fabs and they'll find ways

to copy the asml technology and you know

at the end of the day there's a lot to

put together and I know it's deeply

technically complex but if ever there

were a group of people in the history of

human civilization to pull it off it's

probably the modern Chinese to be able B

to say let's go build our own it's worse

than that our own infrastructure this is

a great point but it's worse than that

the models today are capable of

Designing chips for you that don't rely

on the most complicated technologies

that asml creates I mean look one of the

luckiest things that happened to Gro was

we designed our chip at 14 nanometer

which is effectively in the spectrum of

Technology like VHS and beta so we chose

a simple simple technology stack to

build to

the latest cuttingedge chips at like 2

nanometer that use these complicated

asml machines it's not clear that the

yield is actually that good so why would

you spend all that money and if China is

forced to engineer its way around it

yeah freeberg the answer to your

question is they'll use these models to

design chips that can be manufactured in

simple ways and they'll make simple

stuff so this I'm just not sure it

solves the problem is my point well it

doesn't and this is why I think like it

doesn't solve the real problem which is

how do we incentivize people in America

to really out engineer and out innovate

competition or AI I sure's in an era of

extraordinary abundance and that

abundance ultimately reduces the the

drive for conflict and things are better

off or the other version as well is that

China could just bear the cost as a

central authority of building an

incredibly great model right and they

will spend all the money and then they

will tell the Chinese companies you can

distill from this model for free because

we have a golden vote and a seat on your

board anyways which is effectively de

facto what happens if you get big enough

in China so there's that possibility as

well where one Central Authority Bears

the capex of creating something that

then everybody else can can draft off of

and let's talk a little bit about open

AI uh they're in Washington asking for

money now is that the uh is that the

concept now is that the our government

should back the rumor today was they're

raising 40 billion at a 340 billion doll

preone with MSA potentially being the

lead I would love Travis's read on this

because Travis has taken large money

from from Masa in the past and has been

through this but how does he think about

make this decision obviously we all know

and I mentioned you guys the meeting I

had with him last summer where he

basically kicked me out of the room

because my company's not generative AI

like someone said you should go meet

with Masa so I'm like sure I'll sit down

with him and start talking and he just

like looked at me and he's like uh this

is not generative AI I only do

generative AI I think you're company

will be very successful you will be very

successful goodbye and he was walked out

and that was like the end of everything

so great yeah that's all he's doing now

so this is the big BET right so okay so

I need to I need to bust a myth I did

not take money from Masa so he begged me

to take money for years and we did not

take it because he is

a he's uh what's the word I'm looking

for he's a he's a promiscuous investor

so once he once he invests in you you

should probably count on him in using

your information and investing in all of

your competitors at least that's

historically what he's done so I didn't

go there but then he just kept investing

in all my competitors and they kept

subsidizing these markets and then I'm

like maybe I should have just saturated

soaked up the money that was there so

the one of the things you should think

about like when you look at like oh is

open AI taking a lot of money from aasa

type situation is it's a little bit of

like a double-edged sword is if you

don't take that money it goes somewhere

else but if you do take that money just

know that whatever intelligence they get

when they go through the process of

giving you the money and maybe hanging

around the board or who knows what is

going to be used to do other things and

that is the nature of the Masa machine

so you're damned if you do damned if

you're don't but you got to pick and if

the money's going and it's flowing and

and access to Capital is a strategic

competitive weapon or Advantage you must

you must play ball now we were able to

we we we did stuff with the Saudis

before even Vision fund existed they

stroked a three and half billion dollar

check when that was like the biggest

thing that ever

happened so we were okay with not having

the M of money but that M of money then

went to all of our

competitors door Dash and so in this

open AI context Travis I mean like just

knowing what you know about AI is this

going to be a competitive Advantage for

Sam to raise 40 billion where does it go

when he's up against

we don't know what in China Microsoft

alphabet and meta well look I think this

goes to some of the things that shath is

saying which is like if if constraint is

the mother of invention or or whatever

that that euphemism is the the the

aphorism is if if that's the case you

get into a real weird spot when you get

over capitalized over capitalized in the

in the Uber model like the war was

subsidizing rides for market share

essentially being the rapper for

transportation and using the parlons we

were using earlier to in the in in this

discussion so it was necessary you're

screwed if you don't the the question is

do you get to this place of over

capitalized too big you know too

bureaucratic too loose too weak too soft

and with when you have an open source

model that's very smart and it's a

thousand flowers blooming lots of

innovation happening

everywhere could be an overwhelming

force uh now I think there's going to be

different sectors treat it different

ways where like going full stack in

certain industry sectors is going to

matter and then in other places having

like a very sort of chaotic everybody

does a little slice is going to be okay

in other places and I think we could

probably spend days or hundreds of

dozens of hours just talking about the

nuances there you know well it seems

like there's some degree of relationship

between the Stargate announcement with

Masa and Sam standing up there with

Larry and then Sacha showing up in the

conversation as well and this raise and

the idea that more Hardware more

infrastructure faster creates emote and

I guess that's the real thing you have

to believe which becomes harder to

believe in the context of what happened

in the last week I personally think that

these models are and I've said this for

a while it doesn't make sense to one

large do everything model this mixture

oferts architecture ultimately you can

kind think about taking a large model

making two copies of it and then trying

to shrink each model down to whatever

the necessary so that you run two models

in less frequently meaning that that

combination of two models uses less

power and takes less time and then you

do the same thing again and you shrink

it down to four and then 12 and

eventually you have lots of smaller

models some of which in some cases are

experts at one thing like doing

mathematics or reading or writing but

the reality is we don't know how whether

humans have kind of thought about the

world the right way that the AI May

resolve to having smaller expert models

that we don't really understand why

that's the expert on something but you

have a network of very small kind of

things that work together and that

ultimately leads to a like

commoditization not just in kind of

model cost and in development and

runtime but also in like what's needed

like do you really create much of an

advantage by having all these dat c key

this is the key point I think freeberg

is you're not going to get an advantage

by having more h100s at a certain point

and the actual Advantage is going to be

in the IP and owning content and the

really smart thing to do would be for

somebody to go buy Reddit Kora the New

York Times The Washington Post and

Disney and take all that IP and then not

allow other people to use it sue the

hell out of them every time they try I

take Washington Post off that list but

yes but I'll say New York Times comes

off the list too well whatever I mean

all those

are definitely going to be what would be

great about those is you could then like

a patent troll then tell anybody else

who's absorbed New York Times stories

historically or Disney and you could

just sue the hell out of them and then

you've got the best most proprietary one

you're describing you're describing text

so you're describing text content which

is a fraction of where this is important

so video I think you can recognize that

Google's YouTube Content Library is

probably 100 to 200 times larger than

the rest of the internet

combined to do it well actually Jas

you're such an old school copyright guy

you're such an old school Med guy by way

sorry I believe in artists and their

right to content yeah we've had a series

of conversations that I I feel very

confident to tell you that they do have

the right in in a good chunk of that

content not in a lot of the copyrighted

content that the big media companies

have given them but a lot of user

generated content they do have the right

and they are using it and they're

legally doing it and then there's the

separate kind of body of content which I

think comes for example from Tesla Tesla

has an extraordinary advantage that they

were really pressed to put cameras on

everything years ago and that gives them

this ability to build models that do

self-driving so I think that there's a

lot more data advantage that arises in

certain industry segments than others

and that's where the moat will lie and

that moat will allow you to actually

build better products that get you a

more persistent advantage in gathering

more data that's ultimately where I

think this resolves to it may not

necessarily be about who's got the

biggest data center Network yeah I mean

here the thing guys at some point the

amount of data becomes the long pole in

the tent at some point the quality of

the algorithms becomes a long pole in

the tent and more compute is not going

to change that we I don't think we're

there yet that's the one thing that

counters the cheap AI means more AI is

is there enough data and or algorithms

to make the more AI to make it work and

I do agree with the the siloing it and

getting expert and getting better in

these ways um but I think this is an

interesting sort of trade-off between

some of these these variables I got just

got offered 2500 bucks to put Angel my

book into because Harper Collins did a a

deal with uh Microsoft and so I'm

thinking 500 per year I think it's for

three years is the license and they just

did this blanket license for every book

they didn't look at your sales they

didn't look at how desirable it was it

was just like a blanket deal everybody

gets 2500 bucks per book for 3 years and

I think I'm going to just do it just to

support proper licensing so that people

can start going down uh this path but

let's get into Doge it's been a uh I

think we're in 10 days into this

Administration and um Trump formally

established Doge the department of

government efficiency in an executive

order apparently elon's been spending a

lot of time at the offices bunch of wins

doge is claiming on the interwebs to be

saving American taxpayers around a

billion dollars a day $3 for every

American every day about $1,000 a year

in savings for each US citizen and they

claim they can triple this and so for a

family five that would be about what

$155,000 a year maybe $60,000 during

Trump's uh second term we got $36

trillion in debt have fun with some

numbers there if you

like but the key announcement was a very

similar to the Twitter execution the

ability for people to resign done in a

very kind way eight months of severance

is is being offered to federal workers

they expect 5 to 10% of federal workers

to take this buyout

and it's

um I mean this could be something like a

hundred billion dollar in savings eight

months of severance um is not actually a

legal concept that you can do so these

are some sort of

buyouts and there's obviously some hand

ringing about it but I think they're off

to a good start they've also been

canceling leases as we talked about you

know pre-election

there is so much space not being used

that uh the federal government is

terminating a ton of stuff they own and

going to sell it and consolidating folks

and at the same

time all of this is happening everybody

has to return to office who wants to go

first here with uh you know the sort of

first 10 days of

Doge I see some eggplant emojis in the

group chat first 10 days how do group

chat what's that about I'm adding you

right now how are you not in the group

chat I'm adding you right now I'm

literally every time one of these hits

the group chat it's just hilarious

eggplants people are like oh my God

we're not burning

tax and and the eggplant always comes

from free bird first I'm outing him as

an egg I'm I'm a I'm a big Doge eggplant

guy oh so much

eggplant oh so freeberg tell us about

how much eggplant you love this there's

nothing that I would say is particularly

surprising in the first week a lot of

this was kind of talked about leading up

to the

inauguration VI and Elon published their

piece in the Wall Street Journal a

couple weeks ago they talked about the

mechanisms of action that they could

utilize to kind of Drive reduction in

cost one of which was come back to the

office another one of which is you know

giving people a buyout offer and by the

way the buyout offer is not new Bill

Clinton did the same thing during his

presidency yeah if you guys remember

when he tried to balance the budget get

to a surplus which he did successfully

and his intention was to actually reduce

us Death To Zero by the year 2013 and he

had a very specific economic and fiscal

plan for doing that which he put into

place incredible era I think we're

seeing them take the actions that they

said they would do they said they would

demand to employees federal employees

come back to the office and they assumed

some degree of attrition from that and

now the bi offer and we'll see how far

things go with the courts with respect

to their ability to stop a legislative

or statutorily mandated spending there's

a big question mark here on how much

Authority the executive branch has in

stopping spending and how much they're

not allowed to stop because it's

demanded by law it's demanded by

Congress and Acts or laws that have

passed and so that's going to be the big

test here over the next couple of months

a lot of lawsuits will fly but courts

will ultimately adjudicate and we'll see

how far the Doge intention can take

things and then there's a separate set

of efforts around legislative action

here there's about a $2 trillion annual

deficit right now in the United States

um federal government 2 trillion a year

and if you look at the the doo book on