February 6, 2025 • 9 mins
In the first of a new series of weekly monologues, Ed Zitron breaks down what exactly happened with DeepSeek, and how it threatens to pop the AI bubble.
---
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
All Zone Media. Hi, and welcome to the very first
Better Offline Monologue. This is going to be a short
weekly episode where I take a quick look at something
going on in the tech industry doesn't quite warrant a
full episode. One might say, they're like quick bites of
content quibis if you will, and this is a business
model that's proven successful time and time again. This week,
(00:22):
I'm going to give you a distilled rundown of a
recent situation at Rock both the economy and the AI world.
For those of you that either need a refresh or
rejected the notion of a TUPAC podcast. At the end
of January, something happened that radically overturned not just the
(00:43):
AI industry status quo, but also called into question the
dominance of the American tech industry. Our story starts on
January twentieth, when a little known Chinese company called deep
Seek released It's our one AI model, terrifying the Western
tech behemoths that applowed over two hundred billion dollars combined
into data centers in industrial grade graphics processing units GPUs
(01:04):
for others to power generative AI models like those behind chat,
GPT and anthropics. Claud like open aizo one model, deep
seeks are one model is a reasoning model, which is
a way to say that it works through problems step
by step, showing the users the steps it took to
reach its conclusion. Generally, when you make a request of
a generative model, it generates an answer probabilistically, meaning it's
(01:26):
guessing at each next bit based on the request you've made.
In the case of open aizo one model, and indeed
deep seeks are one model, the model thinks. They use
that term loosely. These models do not know anything. They're
not thinking. They have no consciousness, but I think through
each step by generating it piece by piece and reviewing
it piece by piece with separate parts of the model.
(01:47):
In theory, this ability to reason means it's well suited
for tasks where there's a definitive right and wrong answer,
like logic and maths. It's also what it makes it
different from the standard CHAT GPT or GPT four US,
which is considerably faster, as it doesn't undertake this step
by step thinking and thus is better suited for more
open ended questions such as what would it be like
(02:09):
if Garfield had a gun? To be clear, this doesn't
mean the answers are any good now. Just a few
weeks earlier, Deep Sea could release another model, albeit a
far less fanfare, likely due to it being launched there
after Christmas, of course, but nevertheless, it was called V
three and it was still pretty impressive. V three competes
with the same model that powers chat GPTs I just mentioned,
(02:30):
which at the time of recording this is called GPT
four zero, and that's a more general purpose kind of product.
It can write code and solve maths problems, but it's
better suited for tasks that are rooted in language, writing
that term paper, summarizing a document, whatever it is you
do with this. And it's also important to know that
this is the most commonly used style of model. You're
(02:50):
not really getting reasoning in everything, at least not yet,
and I don't know how prevalent it'll ever be now.
Deep seeks Tech didn't just match open ai and capabilities.
It was also purportedly cheaper to train and to operate,
whereas open AI's GPT four model reportedly costs one hundred
million dollars to train. Some experts estimate the deep Seek's
reasoning model, called R one cost a lot less than that,
(03:14):
and their V three model actually costs less than six
million dollars to train. This figure is open to some debate,
but the big thing is about these models is they're
dramatically cheaper. They can be run on your computer, though
much slower, or they can be run another cloud infrastructure.
And in the case of the V three model, the
one that competes with chat GPT, it was actually about
(03:35):
fifty times cheaper, and the Reasoning model are one about
thirty which is crazy. Now, these are the prices that
are run on the servers where deep Seak runs, but
we're very quickly going to see as other people host
them exactly how much cheaper they are. And they're more
efficient too, which is crazy. They's so much more efficient.
And it's also important to note that they train these
(03:56):
models using older generation N video chips because they had
sanctions on them from China. They got some of the
newer ones too through weird resellers, but nevertheless this made
it much harder for them to get GPUs in general,
and thus they were able to kind of squeeze more
power out than they had to come up with really
interesting kind of assembly language level stuff where they did
(04:16):
extra things with the GPUs, the well, the fat and
happy tech executives never thought of, and Sam Altman and
his ILK from open ai never really thought of, because well,
why would they have to be why would they have
to think of that they had the unlimited money cheap
from the hyperscalers, like in the case of open Ai
funded by Microsoft, in the case of Anthropic funded by
Amazon and Google. And this is where the narrative has
(04:38):
begun to kind of fall apart, because all of this
has made it much harder to justify these companies building
new data centers and buying new in video GPUs. This
entire AI boom has been based off of the assumption
that the only way to build powerful models was to
get the biggest, most hugest chips from in video each year,
and that there was just no way to make these
models cheaper. Now as an aside, lost five billion dollars
(05:01):
in twenty twenty four and all of their products are unprofitable,
even their two hundred dollars a month open ai Chat
GPT pro subscription. I hate these terms, by the way,
They're all different. Nevertheless, everyone assumed that there was never
going to be a more efficient model and I personally
made the mistake of saying, well, if it was going
to be more efficient, surely they would want it to
(05:22):
be or they could do that, right, right, Maybe they
just have to do this stuff even though it's stupid.
That was never the case, and deep Seek proved in crucially,
deep Seak released its models under an open source license,
meaning any company can reuse and repurpose its tech without
having to pay anyone anything, any license fees or anything,
or ask anyone for permission. Open Ai, by contrast, keeps
(05:43):
its technology under lock and key. Despite their name, open
ai is a deeply secretive organization open in name only.
In summary, deep Seek has created a viable alternative to
open AI's tech and indeed anthropics that's equally capable, vastly cheaper,
an open source and proven that you don't need the
most expensive and powerful chips to do so. And they
kind of came out of nowhere. Well, deep Seek isn't
(06:06):
exactly a tiny little startup. They're also not a Silicon
Valley giant with billions of dollars of venture capital, or
someone who's backed by one of the many different companies
with a three trillion dollar market cap. They started off
as a side project from a Chinese hedge fund. No,
I'm not kidding now, still an eight billion dollars under
management hedge fund. They're not small at all. It's so strange.
(06:29):
It's a kind of cynical version of David versus Goliath,
where David is a hedge fund baby and Goliath is
several different hyperscalers taped together with a bad idea. But anyway,
put yourself in the shoes of open Ai CEO and
co founder Sam Mortmon. You've crafted this public perception of
yourself as a visionary that isn't just bringing generative AI
(06:51):
to the massives, but you're on the path that will
bring about artificial general intelligence, which is to say, an
AI that's as capable as a human being. You've crafted
this myth not just about yourself, but about your company
and what you'll do, and this has allowed you to,
in essence, to fire the laws of physics when it
comes to business. You can burn money at a rate
unlike any tech company in history, with no hope of
(07:11):
making a profit, or at least not in the short
to medium term, and no real expectation that you'll do so,
as investors will still line up to give you more money.
With your company valued and even more ludicrous numbers seemingly
every other month, you can say these outlandish things like
you need seven trillion dollars to build the infrastructure and
chip manufacturing capacity to bring your plans to life, and
you don't get laughed out of the room if I
(07:33):
said this shit, they'd asked me if I had a concussion.
You can say stuff like I want to build five
hundred billion dollars worth of data centers, and instead of
people rolling their eyes, the world's largest tech companies and
investors will say, damn man, that's sick, and then it
turns out that you were wrong. You'd always assume that
AI must be expensive, that the models used to power
(07:54):
your apps like chat, GPT and Dally their image generator,
they always cost more to build, they'd always cost more
to run, they'd always require more powerful hardware, or maybe
you just never thought about it too hard because you
never have to worry about money and to grow to
build more capable aiye moodels, you assume that you would
always need more money, and so much more money than
(08:15):
anyone's ever had, And then here comes this Chinese company
didn't just replicate the functionality of your model. And on
top of that, by the way, one is open ayes
one moat. It was the one thing that people liked.
It was their most sophisticated AI model. But this company
came along and did it on a shoestring budget, both
for actually training it even if the estimates are off
(08:37):
by like factors of ten. But these things are more
efficient too. And this company didn't even have access to
the most capable GPUs. They didn't have the server architecture
provided by Microsoft or Amazon or Google. And wow, and
what did they do next with this thing they built
that's competitive with you only real moat? They gave it away.
Oh goodness me, Sammy, things aren't looking good at all.
(08:59):
And this is where Sam Moultman's at. This is where
open ai and the companies that are backed to it,
and their competitors, this is where they're all at. The
decisive lead they once enjoyed has like a puddle on
a hot day, evaporated. And you'd see that happen a
lot here in beautiful Las Vegas, Nevada. Now, don't get
me wrong, open ai still burns money. But now when
Sam Moretman dusts off his begging bowl. Investors will ask,
(09:22):
perhaps for the first time, one very simple question, why
All Zone Media. Hi, and welcome to the very first
Better Offline Monologue. This is going to be a short
weekly episode where I take a quick look at something
going on in the tech industry doesn't quite warrant a
full episode. One might say, they're like quick bites of
content quibis if you will, and this is a business
model that's proven successful time and time again. This week,
(00:22):
I'm going to give you a distilled rundown of a
recent situation at Rock both the economy and the AI world.
For those of you that either need a refresh or
rejected the notion of a TUPAC podcast. At the end
of January, something happened that radically overturned not just the
(00:43):
AI industry status quo, but also called into question the
dominance of the American tech industry. Our story starts on
January twentieth, when a little known Chinese company called deep
Seek released It's our one AI model, terrifying the Western
tech behemoths that applowed over two hundred billion dollars combined
into data centers in industrial grade graphics processing units GPUs
(01:04):
for others to power generative AI models like those behind chat,
GPT and anthropics. Claud like open aizo one model, deep
seeks are one model is a reasoning model, which is
a way to say that it works through problems step
by step, showing the users the steps it took to
reach its conclusion. Generally, when you make a request of
a generative model, it generates an answer probabilistically, meaning it's
(01:26):
guessing at each next bit based on the request you've made.
In the case of open aizo one model, and indeed
deep seeks are one model, the model thinks. They use
that term loosely. These models do not know anything. They're
not thinking. They have no consciousness, but I think through
each step by generating it piece by piece and reviewing
it piece by piece with separate parts of the model.
(01:47):
In theory, this ability to reason means it's well suited
for tasks where there's a definitive right and wrong answer,
like logic and maths. It's also what it makes it
different from the standard CHAT GPT or GPT four US,
which is considerably faster, as it doesn't undertake this step
by step thinking and thus is better suited for more
open ended questions such as what would it be like
(02:09):
if Garfield had a gun? To be clear, this doesn't
mean the answers are any good now. Just a few
weeks earlier, Deep Sea could release another model, albeit a
far less fanfare, likely due to it being launched there
after Christmas, of course, but nevertheless, it was called V
three and it was still pretty impressive. V three competes
with the same model that powers chat GPTs I just mentioned,
(02:30):
which at the time of recording this is called GPT
four zero, and that's a more general purpose kind of product.
It can write code and solve maths problems, but it's
better suited for tasks that are rooted in language, writing
that term paper, summarizing a document, whatever it is you
do with this. And it's also important to know that
this is the most commonly used style of model. You're
(02:50):
not really getting reasoning in everything, at least not yet,
and I don't know how prevalent it'll ever be now.
Deep seeks Tech didn't just match open ai and capabilities.
It was also purportedly cheaper to train and to operate,
whereas open AI's GPT four model reportedly costs one hundred
million dollars to train. Some experts estimate the deep Seek's
reasoning model, called R one cost a lot less than that,
(03:14):
and their V three model actually costs less than six
million dollars to train. This figure is open to some debate,
but the big thing is about these models is they're
dramatically cheaper. They can be run on your computer, though
much slower, or they can be run another cloud infrastructure.
And in the case of the V three model, the
one that competes with chat GPT, it was actually about
(03:35):
fifty times cheaper, and the Reasoning model are one about
thirty which is crazy. Now, these are the prices that
are run on the servers where deep Seak runs, but
we're very quickly going to see as other people host
them exactly how much cheaper they are. And they're more
efficient too, which is crazy. They's so much more efficient.
And it's also important to note that they train these
(03:56):
models using older generation N video chips because they had
sanctions on them from China. They got some of the
newer ones too through weird resellers, but nevertheless this made
it much harder for them to get GPUs in general,
and thus they were able to kind of squeeze more
power out than they had to come up with really
interesting kind of assembly language level stuff where they did
(04:16):
extra things with the GPUs, the well, the fat and
happy tech executives never thought of, and Sam Altman and
his ILK from open ai never really thought of, because well,
why would they have to be why would they have
to think of that they had the unlimited money cheap
from the hyperscalers, like in the case of open Ai
funded by Microsoft, in the case of Anthropic funded by
Amazon and Google. And this is where the narrative has
(04:38):
begun to kind of fall apart, because all of this
has made it much harder to justify these companies building
new data centers and buying new in video GPUs. This
entire AI boom has been based off of the assumption
that the only way to build powerful models was to
get the biggest, most hugest chips from in video each year,
and that there was just no way to make these
models cheaper. Now as an aside, lost five billion dollars
(05:01):
in twenty twenty four and all of their products are unprofitable,
even their two hundred dollars a month open ai Chat
GPT pro subscription. I hate these terms, by the way,
They're all different. Nevertheless, everyone assumed that there was never
going to be a more efficient model and I personally
made the mistake of saying, well, if it was going
to be more efficient, surely they would want it to
(05:22):
be or they could do that, right, right, Maybe they
just have to do this stuff even though it's stupid.
That was never the case, and deep Seek proved in crucially,
deep Seak released its models under an open source license,
meaning any company can reuse and repurpose its tech without
having to pay anyone anything, any license fees or anything,
or ask anyone for permission. Open Ai, by contrast, keeps
(05:43):
its technology under lock and key. Despite their name, open
ai is a deeply secretive organization open in name only.
In summary, deep Seek has created a viable alternative to
open AI's tech and indeed anthropics that's equally capable, vastly cheaper,
an open source and proven that you don't need the
most expensive and powerful chips to do so. And they
kind of came out of nowhere. Well, deep Seek isn't
(06:06):
exactly a tiny little startup. They're also not a Silicon
Valley giant with billions of dollars of venture capital, or
someone who's backed by one of the many different companies
with a three trillion dollar market cap. They started off
as a side project from a Chinese hedge fund. No,
I'm not kidding now, still an eight billion dollars under
management hedge fund. They're not small at all. It's so strange.
(06:29):
It's a kind of cynical version of David versus Goliath,
where David is a hedge fund baby and Goliath is
several different hyperscalers taped together with a bad idea. But anyway,
put yourself in the shoes of open Ai CEO and
co founder Sam Mortmon. You've crafted this public perception of
yourself as a visionary that isn't just bringing generative AI
(06:51):
to the massives, but you're on the path that will
bring about artificial general intelligence, which is to say, an
AI that's as capable as a human being. You've crafted
this myth not just about yourself, but about your company
and what you'll do, and this has allowed you to,
in essence, to fire the laws of physics when it
comes to business. You can burn money at a rate
unlike any tech company in history, with no hope of
(07:11):
making a profit, or at least not in the short
to medium term, and no real expectation that you'll do so,
as investors will still line up to give you more money.
With your company valued and even more ludicrous numbers seemingly
every other month, you can say these outlandish things like
you need seven trillion dollars to build the infrastructure and
chip manufacturing capacity to bring your plans to life, and
you don't get laughed out of the room if I
(07:33):
said this shit, they'd asked me if I had a concussion.
You can say stuff like I want to build five
hundred billion dollars worth of data centers, and instead of
people rolling their eyes, the world's largest tech companies and
investors will say, damn man, that's sick, and then it
turns out that you were wrong. You'd always assume that
AI must be expensive, that the models used to power
(07:54):
your apps like chat, GPT and Dally their image generator,
they always cost more to build, they'd always cost more
to run, they'd always require more powerful hardware, or maybe
you just never thought about it too hard because you
never have to worry about money and to grow to
build more capable aiye moodels, you assume that you would
always need more money, and so much more money than
(08:15):
anyone's ever had, And then here comes this Chinese company
didn't just replicate the functionality of your model. And on
top of that, by the way, one is open ayes
one moat. It was the one thing that people liked.
It was their most sophisticated AI model. But this company
came along and did it on a shoestring budget, both
for actually training it even if the estimates are off
(08:37):
by like factors of ten. But these things are more
efficient too. And this company didn't even have access to
the most capable GPUs. They didn't have the server architecture
provided by Microsoft or Amazon or Google. And wow, and
what did they do next with this thing they built
that's competitive with you only real moat? They gave it away.
Oh goodness me, Sammy, things aren't looking good at all.
(08:59):
And this is where Sam Moultman's at. This is where
open ai and the companies that are backed to it,
and their competitors, this is where they're all at. The
decisive lead they once enjoyed has like a puddle on
a hot day, evaporated. And you'd see that happen a
lot here in beautiful Las Vegas, Nevada. Now, don't get
me wrong, open ai still burns money. But now when
Sam Moretman dusts off his begging bowl. Investors will ask,
(09:22):
perhaps for the first time, one very simple question, why
Host
Ed Zitron
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