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 wants 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 called 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 O an open source company or at

least an 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 come up and 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 120th a 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 the 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 Bas llm model like Chachi

P40 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 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 flinking

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 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 things really did drive the the

new 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 3 to 6 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 Val

validate a claim about how much money

went into the training of this model

it's not something that we can

empirically 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

anthropic 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 6

million versus a billion okay it's a

billion dollar number might include all

the hardware they've bought the the

years of putting into it a holistic

number as opposed to the training number

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

compare let's call it a Soup To 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 Six

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 of that 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 then they did the H 800s in 20123

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 C cost of a

compute cluster with 50,000 plus Hoppers

and it's going to be over a billion

dollars so this idea that you've got

this Scrappy company that did it for

only six million just not true they have

a substantial compute

cluster that they Ed 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 semiconductor 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

disruptive 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 Saks

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 kud

which is nidia'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 raer he like out the rapper may be

the the moat the the moat 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 with 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