How DeepSeek Changes AI Research & Silicon Valley w/ M.G. Siegler

Channel: Alex Kantrowitz

Published at: 2025-01-27

YouTube video id: 9Aw2UzNsZHo

Source: https://www.youtube.com/watch?v=9Aw2UzNsZHo

it's time for a bonus episode
exclusively about deep seek R1 as the
Chinese open- Source AI model Royals
markets and threatens to upend the
generative AI industry that's coming up
right after this welcome to Big
technology podcast we're doing a bonus
edition today exclusively on deep seek
what it means for the AI industry what
it means for markets we're going to
touch on technology we're going to touch
on business and so thrilled that you're
here for a bonus episode with us we're
joined today by mg seagler he's a writer
and investor he writes spy glass you can
find it at spy glass.org it's a great
newsletter it's a mustre for me and he
has a great piece out called AI finds
away as deep seek changed the AI game or
just some equations mg great to see you
welcome to the show great to see you
Alex thanks for having me back and sorry
for my crazy winter beard it's uh it is
very cold and rainy right now in London
so I'm not not ready for spring yet if
hey it fits the season I was just out in
London uh to interview Demis from Deep
Mind that's right I listened to that
that was very good yeah and very timely
now yes I can confirm the sun does not
shine in that City this time of year so
first of all I want to talk a lot about
I mean only about deep seek and deep
seek R1 and what it means for the AI
industry right now it's we are just
about we're the markets will open on
this show so we'll have a sense as to
what it's going to do today uh but it's
looking pretty bad especially for NVIDIA
and some others as we get going I just
want to thank all the podcast listeners
who pointed me to deep seek uh because
we had some comments that came in over
the past few weeks I was able to ask
Demus about it I was able to get it in
as the lead story on Friday's show so
thank you I appreciate all of you for
pointing me towards deep seek um so let
me just talk a little bit because we
didn't touch on this Friday and we're
going to definitely fill some holes that
were left on the Friday show um we
talked a little bit about how much it
cost to train this model but not
necessarily about the benchmarks it hit
and about the cost it costs to use this
thing so first of all it's an open
source model it's much smaller than any
of open ai's model yet uh on the aim
mathematics test it scored
79.8% compared to open ai's 01 scoring
79.2% so it bests open ai's best model
on that it scored 97.3% on the math 500
and it beat open AI which scored 96.4%
look these are lots of different
benchmark test but you can tell that
just by these numbers it holds its own
and now the most remarkable part about
this it costs 55 cents per million token
inputs and
$219 per million token outputs uh just
to give you a sense opening eye cost $15
per per million input tokens and $60 per
million output tokens that's 3.5% of the
cost that it costs to run open AI 01
models um and you can do it again it's
open source you could download it onto
your computer and run it so basically
what what deep seek R1 has done in a
nutshell
and then we'll turn it over to mg is it
has created models that are as
performant as the state-of-the-art right
it's rig number three in the chatbot
Arena at 3.5 3 to 5% of the cost and
that has huge implications for the
technology for the business and we're
going to get into those so mg first
question for you uh if there was an AI
RoR scale right assessing how big of an
earthquake this is what would you give
this
development um so so I mean it depends
on on what I guess uh level you're
you're you're sort of measuring the
magnitudes right because as you noted
the markets will open and that's going
to be you right now you know last I
looked in in pre-market trading Nvidia
was down I think 11 10 to 11% um and
that's the biggest hit right now
Microsoft a bunch of others are like in
the 3% range so uh you know from a pure
Market perspective it seems like it's
let's let's call it an eight you know
it's not it's not going to totally uh
destroy the stock market right now but
it's going to be rough it seems like
today um from a bunch of other
perspectives I think you know it's it's
probably a little bit less um of a uh of
a shake in these earlier days and I
think that's because everyone's still
even now sussing out what exactly this
means um for all different sorts of
things you know you you noted how much
cheaper it is um to run then say open AI
models and and you know over the weekend
just reading all of these sort of
reports about the model and how many um
individual startups are even just
changing swapping out right already um
because it's so much cheaper um to do
what they're doing right now by swapping
in um deep seeks models and so what does
that do immediately like do you know do
we have to have price Cuts immediately
and and you know I think um you could
sort of see open a doing some stuff I
think Sam mman tweeted you know maybe on
Friday like about how they were like
bundling rejiggering some of the bundles
right that they have like what's in the
free offering and stuff and it sort of
feels like we're going to see more of
that um you know as as a response
obviously to some of this but then you
know there was a there was a big report
uh I think in the information about
meta's response to this in particular
which seemed pretty interesting and that
like you know it's all hands- on Deck
certainly and there's like all these
different teams time War exactly you and
I remember that from the old days of of
Facebook um and so yeah it's just like
all these companies are now scrambling
you have Sach and Adella tweeting out
things uh you know which seem directly
aimed at the at the market um to try to
you know ease that that pain uh a bit
but anyway going back to the original
question on the on the RoR scale um you
know overall I think a lot of people are
still figuring this out but right now
the market thing is going to be the most
acute one because that's obviously going
to open and I think it's going to be
pretty hard for you know this day at
least and then I think I I read some of
the early analyst reports on this and
and they're all over the place right
like there there's some folks who are
saying like oh this is this is awful for
NVIDIA some folks are saying you know
this is not a big deal this actually
could be good in the longer run for
NVIDIA in in ways um and you know and
then from Big Tech on down uh what the
ramifications are there and you
mentioned that some startups are already
swapping in uh deep SE car1 for the
models they're using right now how
widespread do you think that is are they
are any of the startups that you speak
with saying okay well to hell with open
AI or to hell with llama time to put
deep seek in or is this just beginning
because it's again something that
dropped last week yeah I think this is
just beginning I think you know that
people will experiment with it right
just to see like how much you could you
know get while swapping them out given
the price differentiation you were
talking about but also there's downsides
of course like people have noted sort of
the you know the um censorship within
China and and of certain terms and so um
you know I don't think everyone is is
quite certain what's in there you know
it's an open source in that it's open
weight um but it's you know it's not
clear exactly everything that's going on
in there right now and so um I do think
that if this proves out say if if DBC
can release another iteration of the
model and it still is on the same sort
of you know footing um I think that then
you'll start to see more startups uh
potentially taking it really seriously I
think now it's just a wait and see
approach for sure and just people trying
out to see if it is in fact as good as
they say cuz I think you know part of
this like my initial gut reaction you
know deep seek obviously as you noted
had been around for you know basically
since December and didn't really get all
of the the mass of uh pylon until sort
of Friday right when R1 came out and in
part it's like you know I just I don't
know why my mind was drawn to this but
it's sort of like when they were talking
about the r uh the room temperature uh
conductor right like and everyone is
talking about oh my God like there's
this there's this huge breakthrough
that's happened and this is going to
revolutionize everything and then it
turns out well you know maybe there was
some uh some funny business in that
claim and and maybe it wasn't you know
all was cracked up to be and of course
that turned out to be the case and so
I'm not saying obviously that's not the
case with deeps it seems like now this
R1 um release has legitimized and as you
knowe on on leaderboards and whatnot
people have been testing this and again
the startups are part of that that
pressure test right and so the funny
business just to get this out of the way
the funny business might be on the
training side like we think that they
trained it for much less money we think
that they changed it CH trained it with
um inferior gpus that have been sort of
the only things that can get their hands
on due to export controls we're not 100%
sure if that's the case right but I
think the bottom line here is that this
is an open source model it has been
replicated I mean it has been downloaded
to people's computers and used as
effective as it is and I think that the
thing is the methods and the cost
savings and the performance that's all
real so even if you know basically all
of Silicon Valley without those uh
export controls couldn't do this or
didn't do this and maybe it's because
they had a different method and we'll
get into that um but the fact is that no
mat there's no putting the genie back in
the bottle right now which is that this
company has created something uh that
can rival open AI performance at 3% of
the cost that's that's the big thing
so I well sorry go ahead I I also just
think like the overall mentality is one
of the more interesting sort of
earthquakes to you know use your
phrasing of it that's happen happening
right now it's like and I think Steven
sinowski summarized this well he wrote
um you know very long uh tweet thread as
he is want to do but then he he also
published it on his uh on his newsletter
as well but he goes into the history and
he obviously has a lot of good
historical context from Microsoft days
on forward um about what you know is is
going on here but it's also um I think
important to to talk through like how
the constraints that were put in place
by the us because of the you know
everything going on with with chip
constraints um and and sort of forcing
AI um companies not to export to China
um you know led to sort of this this
very interesting cauldron uh that I
think could only happen in a place like
China right now because they're so
constrained whereas in the US like it's
still the period of abundance right with
AI and everyone's going after this
scaling um and it's and it's um it's
just not something they were going to
focus on trying you know they're making
the smaller models they're making the
mini versions of the models and those
are great and we're seeing that but
China you know the folks working in
China had to do this this way and I just
think it's something you couldn't have
seen in in hindsight arise out of the us
in our current environment right okay so
I want to talk quickly about the
technology very quickly about the
technology and then get into some of the
more uh business side applications here
so mg could you tell us just at a really
high level uh what deep seek has done to
be able to get these results because you
know it's one thing to say okay they
were able to do it uh you know on worse
chips with a smaller amount of data but
I think just it's important to very
briefly highlight just the technical uh
technological innovation here yeah I
mean so and you know I'm not a uh I
won't be a technical expert on this but
from my understanding it's basically you
know obviously as you know it's started
the deepsea project started out of a a
hedge fund that was focused on Quant
trading um you know in China and they
had acquired a bunch of Nvidia chips I
think they were h100s um you know before
all the import restrictions came in and
basically they had those servers up and
running and uh you know presumably they
were running um a bunch of different
models um including some of open AIS but
including also uh a bunch of the Llama
stuff that meta has been working on and
you know they just used the process of
distillation to you know effectively
bring those bigger versions of the the
um sort of state-of-the-art models and
distill them down into um you know
smaller models which eventually led to
this R1 you know the equivalent of of o1
on on open AI side um and again for a
fraction of the cost fraction of the
compute and a fraction of the size um
for these to be able to run and that
that latter part seems like it's it's
sort of being underd discussed right now
but is important um because yeah all of
these these models have constraints
about how you can run them like on your
personal machines right because you know
they're going to require so much RAM and
so and so much memory um to be able to
do that and if you can get them down to
really small sizes which again the the
bigger us companies have been doing with
these mini models but they're they're
sort of taking this bifurcated approach
whereas um you know now we're getting to
the point with with this R1 model where
it seems like it can run on pretty much
uh a lot of different type of Hardware
which again they they need to do in
China because of the the restrictions
that they have there right and there's
also a methodology change here which is
that they've gone from effectively
self-supervised learning uh which is
what has been used to train all of the
llms all the big llms to this point to
Pure reinforcement learning where the
models tend to figure out what's uh what
the right answer is on their own which
is just fascinating yeah and it seemed
like the you know sort of the American
powers that be maybe felt like we
weren't ready for that yet to happen
right like that was always the hope that
that we get to those points um and that
you know we still were in the in the
scaling Point again where you know you
you need someone in the loop to be able
to check and and make sure all these
things are working and China you know
this this Chinese company because of the
some of the restrictions that we just
talked about like just went for it and
you know it's proving itself right and
and just to harp on one more technical
issue before moving on the distillation
of models to me is fascinating that they
could take any big model and distill it
using this form of uh training and
effectively be able to replicate its
performance so take they took you could
take like a llama model which has 70
billion uh parameters and distill it and
then all of a sudden run it with this
reasoning reinforcement learning style
approach and it's cheaper more efficient
it's it's just I mean again like I think
the entire world is still trying to wrap
their head around this and they'll be
more on this feed to talk about exactly
how impressive this is but to me in the
early Innings of this that is
astonishing yeah and I mean it it again
at a high level it it makes sense it
just it's incredible how it's happened
because like do you need all of the
world's knowledge you know in every
single model for every single use case
of course not like that's going to be
Overkill um for almost everything that
you're going to do and so does it to
point to a world where yeah we we sort
of lead towards more of these
specialized models that are distilled
and obviously that's been happening but
this this one is still you know a model
that can effectively do most everything
um to still down from from those bigger
ones so there's one sort of big question
that I think needs to be asked here um
which is there's been this all the
Silicon Valley and you point to this in
your piece all Silicon Valley has been
operating on effectively the scaling
hypothesis which is that you add more
compute we talk about all the time on
the show add more compute add more data
um add more power uh add more training
time effectively to these models and you
will improve and now what deep seek has
shown is that you can actually do all
this without that and so I'm curious if
you think that this invalidates the
scaling hypothesis because and it might
seem kind of like a you know obscure
thing but it's very important because
this sort of sets up the whole business
conversation which is if the scaling
hypothesis is invalidated then all that
multi-trillion dollar in invest
investment in Nvidia um Nvidia uh CPUs
or gpus my bad um becomes sort of thrown
into questions so what what happens to
the scaling hypothesis from here and
it's fascinating timing too right
because this is this is at the same time
that everyone has now talked about sort
of the quote unquote AI wall being hit
right and um even Demus you know when
you when you talk to him he he noted
that um he doesn't necessarily believe
in in you know a wall being hit but he
did acknowledge that things are slowing
and it'll just take longer um to get
more you know juice out of the squeeze
as it were right and and so that's sort
of a the Natural Evolution um that's
been happening and everyone is now
pointing to it or at least acknowledging
that that some aspect of that is real
and now at the same time this comes
along and
uh it calls into sort of more question
there's one other element that sort of I
think is um related to this which was
the big news story last week as well the
uh project Stargate um open Ai and and
Nvidia and Oracle um all coming together
and the mo one of the more interesting
elements of that was the fact that
Microsoft is
effectively um pushing off the the
compute cost to Oracle and and some of
the other players in that uh situation
and you know there's all sorts of
reasons you know potentially why they're
doing that obviously given the the
interesting relationship between open Ai
and Microsoft um but at the very highest
level again if they're thinking that you
know our capex is going to be we've
already stated it's going to be 80
billion for the year we don't want to
add another several billion you know for
this this particular project um and why
would they do that uh in part probably
because they're not necessarily sure
that it makes sense to pay pay the
billions upon billions to open AI to
keep trying to scale um on the frontier
models and this is you know sort of in
line with what deep seek just did right
yeah it's interesting we also talking
about Mar uh Andre Horwitz who sat out
opening eyes last round and we were
wondering on the Friday show maybe they
heard saw this coming and it is
interesting I mean you you put it uh
pretty pretty uh perfectly in your story
uh you say um Big tech companies are the
now the most largest and sorry you say
big tech companies are now the largest
and most well capitalized in the world
which means that they have effectively
all the money that they can put towards
scaling and the hammer uh met the nail
but there's no point hammering the nail
after it's already been put into place
and that's the point that can't be
predicted but is obvious once it's done
the question is if deep seek just
pointed to the nail already hammered
effectively did they just solve this uh
sort of like going up um the the scaling
question uh in
AIM an analog for the same the same
thing right and and going back to the
history of compute like right all these
you know the powers that be tend to
spend at the time tend to spend a ton of
of capital um on the buildout of of
whatever the new technology happens to
be um and you know there's obviously we
all benefit from it in the in the long
run but in the short run um you know
this this Segways into I guess you know
what's what's potentially going on with
Wall Street and what it means for these
larger companies uh with regard to the
spend yeah and I just want to ask your
the question that you put in your
newsletter just to you directly uh did
they just point to The Nail like is it
done I mean again I I don't want to um
you know C this out but I do feel like
it's it's it's the exact question that
everyone is sort of going to be
scrambling to answer over this next week
and I think that it's not going to be as
black and white as that for for sure but
I do think if I had to guess at a high
level I do think that there's some
element to yes the the nail is already
sort of driven into the board and we're
moving on to what the next steps are
it's not to say it's over and and you
know there's no innovation from here but
I think all of these things are in a way
related like that we've just been
talking about and the fact that they're
all coming together at the same time I
don't think is a coincidence I think
it's because like yeah we're at the
point where we now need to move on to
the sort of the next phase of of the AI
Revolution as it were yeah and and let's
get into the business and I'm I'm
smiling here because I you're making me
think of we have Reed Hoffman on the
show on Wednesday and I interviewed him
before R1 came out and the first half of
the conversation is just talking about
all the billions of dollars that have
been spent and when they're going to get
an Roi and uh I mean I'm still going to
run the conversation but there's going
to be some context in there I think it's
interesting knowing uh after the fact um
but and it's also I think senovsky
brought this up too and and I was sort
of looking into this more last week you
saw it was a smaller news item but both
Microsoft and Google had altered um the
way that they're uh basically bundling
together uh AI within you know either
the 365 suite and within the Google um
Google Suite of of apps because they're
clearly still trying to figure out how
exactly you you make money off of all
this spend and what the right model is
and and how you spur on usage of it and
this just comes in and throws a grenade
you know into that that equation again
and this gets us to like some of like
the real thorny uh business question so
uh just to kick this off I took a look
at what all the um big tech companies
were doing pre-market so this will
obviously change across the day but I
imagine they'll stay directionally kind
of the same Nvidia down 10% Microsoft
down 4% Google down 3% meta down 2.6%
S&P down 2% this is all based off of uh
this deep seek Reckoning or this deep
seek
realization and let me just put the sort
of question to you I think about as
pointedly as I can which is that um the
AI industry uh up until this point like
all the numbers we're seeing within Wall
Street the the trillion dollar market
caps the billions of investment the
billions uh that have been uh raised by
uh companies like open aai and anthropic
from companies like Microsoft and Amazon
right so this is basically the whole
game here um they they have effectively
been um what's been driving the numbers
and the question is can we you know they
basically Wall Street has been following
that and saying we expect them to get a
return uh based on on those numbers and
in fact a lot of this AI spend was just
a wealth transfer I would say from like
meta advertising to llama from Google
search Revenue to Gemini from Microsoft
Azure to open AI so what happens here
because um you know basically if they if
a lot of the AI industry has been driven
based off of subsidies coming from other
businesses and doesn't need that type of
spend anymore like does the party
end so I think it's different for each
company probably Microsoft and Google
are closest you know aligned in terms of
where they net out and it's sort of
interesting you know the the numbers you
just rattled off with where the stocks
are at that feels you just like a
very um clear picture from Wall Street
what they think now right like they
think Nvidia is going to get hit fast
because uh in this in this doomsday
scenario because obviously they're the
beneficiary from everyone from all those
companies all those other companies that
you mentioned uh big Tech is is pouring
as much money as possible as they can
they can't get it get enough chips fast
enough into Nvidia and if they pause
that that obviously is bad news for
NVIDIA in the short term again I think
there's longer term stuff that that's
different for NVIDIA which we can talk
about but to to just hit on the rest of
this question right now um I think that
Microsoft and Google which are as we
just mentioned you know are trying to
sort of figure out the right models for
how to charge for AI I think that this
puts them in a really tricky situation
if the underlying economics just totally
changed overnight of what ai's yeah
underlying economic model should be and
so they were you know moving around
different pieces trying to get to the
right the right uh end state so that
yeah they could ultimately prove to Wall
Street like look we're adding you know x
amount um on top of what we were already
doing Revenue wise thanks to Ai and a
little bit there's a little bit of weird
obfuscation stuff going on there right
it's like well it's bundled in now to
365 and so you know we don't necessarily
need to tell you exactly what the uplift
is but um but you can just you know
assume that it's that it's a part of
this cuz it's all baked in and AI is
like you know the new internet and blah
blah blah and so you know there's ways
that they can they can finesse the
messaging around that but that you know
to your exact question I do think that
there's there's varying degrees of of
being worried certainly within Google
and Microsoft meta is more interesting
because their open source philosophy
open weight philosophy and model is so
similar to what deep seek is has done
right um and so the problem there in my
mind mind at least is again they're
spending whatever Zuckerberg just threw
out 65 million or whatnot he said you
know at the end of last week that
they're going to spend on uh on uh capex
and so why are they spending that amount
now if if you know deep seek can do it
for you know Pennies on the dollar if
not even less than that um and so what
does that that mean for their world so
in my view high level I think that meta
is probably in a in a bit better
position than the other ones just
because they at the end of the day they
do want like you know their whole
philosophy is to open sources not for
necessarily altruistic reasons but
because they know that it it's
historically help them help their
business um you know to to open source
these things um the question of if it's
not them open- sourcing it becomes
pretty complicated if someone else's you
know you have to use someone else's
models but they can pullback spend it
feels like a little bit easier than the
other folks can on the other end of the
spectrum open AI like there you know the
entire business is is uh sort of built
around being at the frontier and they've
done a great job with that they're a
little bit different than than um Google
and Microsoft in my mind just because
they've done a good job getting mind
share both in terms of brand and product
right like Jet Jet TBT is number two in
the app store right now behind deep seek
uh you know for a reason people are
interested they it's a brand and they
know it and so what does it look like
though if they're not the one sort of
powering that models I don't think that
they would give up and and you know go
with deep zek's model necessarily but
what does it mean if if they're not sort
of the only one or the main Frontier um
you know model maker providing that like
so there's all sorts of interesting
offshoots and ramifications of that so
mg there's like two views right now in
terms of like what could happen with all
this spending right one is uh Silicon
Valley will continue to spend these
billions and they might get you know uh
incrementally better uh performance and
stay slightly ahead of the open sources
of the world deep seeks of the world
that can just emulate their models the
other side of it is that they continue
to spend and then they basically hit AGI
or like you know what I'm saying like
we've if the um performance increases
that we've seen with such uh little uh
with that sorry if the performance
increases that we've seen with such
efficient use of capital uh from Deep
Sea can be emulated then what imagine
what you could do with a 100 times the
amount of spend so the models are about
to become much more powerful than all
these fantasies that people have about
what they can do many of which Demus and
I spoke about last week all of a sudden
become feasible because the capital is
there so which side of this and that's
that's a nice thing to say in like a
nice high Lev Mantra and many of you
know many of the leaders of these
companies will be saying that today to
sort of try to calm Wall Street but at
the end of the day um you know aside
from sort of open AI which obviously is
again tied with Microsoft and now Oracle
um but besides them the rest of these
are public companies and Wall Street you
know like it or not they have a say sort
of over what they're going to do like if
they're going to get hammered um and and
this is something I've sort of been
harping on for a while not because I
think that they were the doing the wrong
thing necessarily with the spend but
it's just obvious that like it always
comes back around right where it's like
I equated it La you know last year to
when all the movie studios during covid
and and TV studios were just bulking up
on streaming right and just spending as
much money as possible as they could in
order to build up their streaming
services and Wall Street loved it at
that time CU you know Disney and
everyone else was just gaining millions
and millions of subscribers and it seems
like they had a path to take on Netflix
and and you know this was the future of
the industry it's still by the way the
future of the industry but Wall Street
then all of a sudden turned on all that
spend and decided like you need to cut
like spend x amount you need to you know
unfortunately cut the employee base and
and basically just become way more
efficient while doing the same highle
thing and it was you know always obvious
that at some point they were going to do
that to the tech companies as well with
regard to AI spend and so again they can
all have the right mentality about like
this is the future and say the right
things that this is the future and this
spend is important and I don't disagree
with any of that but still they have to
answer to Wall Street at you know to
some degree maybe Zuckerberg less so
because he you know controls the
controls the company uh so strongly but
like certainly Microsoft and and Google
um to a lesser extent are going to have
to answer for a lot of that uh spend and
this is the first real real test meta
had some of it right like there was some
backlash uh last year around their spend
and certainly back dating back to the
the you know VR and and AR and XR spend
um and so they had to answer for some of
that and and Zuckerberg did right and
and he got rewarded for it um after the
fact and that's like the game they're
playing here they know that if they cut
spend because Wall Street doesn't like
to see all the AI spend they'll get
rewarded in the form of the stock going
up and then all the ramifications from
that and so it's natural that that is
going to play out that way and so I
think the narrative then shifts to other
levels of not necessarily opusc but
other ways of framing it it's like okay
we agree that uh we shouldn't spend tens
of billions of dollars on Nvidia server
Farms but we need to build out um you
know our our inperson um AI robotics
arms right in order to to keep these
models and and keep sort of the next
phase going as we March towards AGI and
yada yada so markets just open Nvidia
opens up down 11% so still above $3
trillion so it's not like the AI
Revolution is over uh but down 11% so
just a a cool you know couple hundred
billion dollars shaved off the market
cap in a in a morning let me talk to you
a little bit about what these companies
are saying back to Wall Street or
actually talking to Wall Street uh about
to allow them to keep spending so sat
andella is doing his tweets he says he's
talking about javon's Paradox he calls
he says javon's Paradox strikes again as
AI gets more efficient and accessible we
will see its Ed Skyrocket turning it
into a commodity we just can't get
enough of um let's say that I don't know
if you saw this uh last night Gary tan
you know the the president of YC tweeted
the same thing and so I'm like is this
coordinated or I mean it is you know
must group text going on yeah there's a
group text maybe going on where it's
like this is the answer and it's not a
it's not like a totally BS you know
answer to it but there's much more
nuance and context that's sort of
required uh to to get to you know that
being the the excuse for this so let me
just basically talk about the elephant
in the room that's been hanging over
this full conversation and will be sort
of like the spoken or unspoken part of
this discussion as it goes forward this
week which is that let's say the cost of
intelligence goes down to zero right so
that's what everybody is BAS basically
aiming for it's one of open ai's stated
goals to make intelligence you know
close to free as possible they don't
really make a lot of money selling off
their API or they even maybe might lose
um we need to see AI applications like
we need to see an economy that takes use
of this technology that is so impressive
right like you look at the Chain of
Thought even in deep seek and you're
just like how is a computer you know
quote unquote thinking through this
stuff but the economy needs to take hold
of this powerful technology and make use
of it and put it into play for really
meaningful economic use whether or not
the Deep seek thing existed right like
we billions of dollars of economic or
trillions of dollars of economic value
needed to be created from this
generative AI moment yep and what do we
have now we have open AI who has chat
GPT with 300 million users which is okay
um but still losing billions a year to
run that thing maybe they'll be able to
be more efficient and make those couple
billion a year from it okay um we have
some
Enterprises putting this into play uh
but everyone every Enterprise I speak
with um there's a couple use Cas cool
use cases here or there um but mostly
what you see is proof of Concepts and
many of those proof of Concepts aren't
going out the door so don't we need to
see one way or the other um AI
applications uh whether that's
Standalone or integrated within business
software that start to prove the real
value of this technology that we just
haven't seen to date
um I mean the answer is yes of course um
the reality though is you know maybe
this this Confluence of events right now
is going to help that because um it's
sort of just as
forcing a a fundamental rethinking of a
lot of what you know we've just been not
going through the motions but we've been
on this path right to scaling as we were
talking about and that you know even
right like Sam mman has said like they
see line of sight now to AGI they just
have to to you know just dot dot dot
Underpants and then profit uh from there
better get there but they say they have
line of sight right to to know what they
need to do and it's just a matter of
execution and sort of um you know
getting everything align in order to do
that and if this moment with deep seek
being the the you know the biggest
Catalyst thus far of it if it doesn't
cause the entire industry to sort of
rethink that and at the same time to
your point like you know asking about do
does that sort of drive us to move on
from yeah just like this this non-stop
scaling of Frontier models um that is
awesome technology but unclear how it
works in from a practical standpoint um
do we start to yeah distill this you
know for for lack of better phrase down
to actual products and you know when I
when I think about that that leads back
to like uh whenever it was 6 months ago
7 months ago when Apple did their apple
intelligence stuff what you and I talked
about right and it's like everyone jumps
on Apple and and there was another news
cycle I think the this past week because
uh you know Siri can't can't uh
correctly answer who won previous Super
Bowls which ridiculous um but Apple's
mentality from the get-go with launching
Apple intelligence has clearly been we
need the we for lack of better phrase
don't necessarily care so much about
yeah the frontier of the Vanguard of of
this technology we care about the
day-to-day usage of it right and you
know they have a few things that are
sort of front-end facing that that
haven't really worked that that try to
use AI like the Emoji Creator and things
like that but most of it is just baking
it into their products and that's what
we've seen too with with obviously what
we talked about with Microsoft and
Google they they all have you know their
own like some have video generation some
have some other of their own Standalone
products for the most part they're just
going to be baked in but you know to
what we were talking about earlier none
of that is really uh the promise it felt
like right of of what this larger
movement was going to be and everyone's
waiting for you know not not necessarily
AGI right now but they just want some
other forward-facing user facing um
version of AI that that can be good and
chbt has been the closest that we've
gotten to that and maybe some of these
video products you know end up being the
next the next phase of that um but but
uh I think that you're right that this
that ultimately you have to get to
something that that comes of this that
that really sort of moves it moves all
sorts of needles and again I wonder if
this uh news cycle and and just pause
now doesn't lead to more of that I hope
that that's the case yeah and I would
say the Apple intelligence is almost the
perfect example of the problem that I'm
pointing toward which is that we have
this technology that's so promising and
yet even Apple cannot implement it uh
correctly and that might I mean
obviously it says something about Apple
but it might say something about the
technology as well yeah and you know as
with everything like with everything in
technology I think about you know dating
back to my reporting days and and
whatnot it's just like having seen so
much in a few different Cycles now are
we too early still right like everyone
everyone has been talking about and
believing that like this is the moment
where this is like really happening and
this is great but I do think that if you
took a step back you might wonder if if
we're not still doing this too early you
know and trying and all of these
companies are not raising way too much
money um when the timing is just not
right for exactly what you're you know
trying to ask the question about like
how do you how do you turn these into
products and how do you ultimately turn
this into a business that Returns the
capital um that was spent on it now no
company would admit that right now but
you know hindsight will only prove one
way or another whether that's the case
and I think everyone Still Remains super
optimistic that now is there time and
you want to keep your foot on the gas
but again this deep seek stuff sort of
causes a pause and a natural
reexamination of of just how much money
to spend and and what what you should be
focused on let me ask you to put your
investor hat on for a moment are there
startups out there that would exist
today that don't exist
because effectively buying compute from
the apis or running Lama is cost
prohibitive but they would exist if
intelligence was zero and that's
effectively what deep seek is going to
put to the test yeah that's really
interesting I don't I don't have I don't
want to just try to come up with
something off off the top of my head not
that I know of but I do think at a high
level that your question is a really
interesting one and if if this is going
to be um truly transformational deep
seek as a whole it will lead to
something like that right like a bunch
of
companies coming out and not just yeah
cuz it's not just the tech technical
aspect is not just driving down costs
because that seems like it's sort of
going to happen as a result of that
which is great but does this actually
yield new companies that couldn't have
existed beforehand um and I don't know
like I can't think of any off the top of
my head but that's also why I'm not a
startup founder and and you know
hopefully there are startups out there
that are that are going to latch on to
this but something tells me that the
answers no and the reason is is because
and investors have been dying to throw
money at AI companies and been willing
to lose a lot of money uh if the idea is
promising enough and I don't know we
haven't we haven't seen a wave of AI
startups hit at least there have been
many uh but you know they're they're not
like it's not like the um you know the
beginning of the mobile era where there
was like a new consumer startup every
day it just isn't happening that way in
fact most of the action is Enterprise
one other just wrinkle and layer of that
um which I feel like has been
overshadowed in in all of the recent
news but you know we we talked about and
talked about a lot last year but as the
regulatory regime is changing now um if
m&a sort of doesn't pick up with regard
to exactly the type of companies you're
talking about right like they have great
teams they're working with you know this
technology and they clearly know how to
do things with it but they haven't
gotten the product right they haven't
gotten the business right and so they're
scooped up by the you know the metas the
Googles the Microsoft the open a eyes of
the world and um that you know in and of
itself won't be that interesting other
than those companies getting good talent
perhaps but if it um just reignites sort
of you know a passion within really
early stage startup Founders to keep re
accelerate sort of going after new
problems right like I do feel like there
was a bit of a chilling effect the past
year because m&a had you know basically
been shut off um that sort of kept
people staying at Google and staying at
meta and staying at open AI not forming
new startups as they might have in in
years past because um they knew that uh
you know there was there was the
potential obviously they Pie in the Sky
they want to build a big company but
there was also the potential frankly
right to like you know sell um build
something that's big enough to sell for
multi- hundreds of millions of dollars
if not billions of dollars um to some of
these other companies and so um you know
that might come into play with some of
this
all right let's put a bow in this
conversation uh you say the real problem
is that it won't be so simple to simply
pull back spend Beyond a lot of it
already being committing being committed
there's obviously still a very real risk
that deep seek is just a blip on the
radar and not the bomb that blows up
everything what what are we looking like
what are we looking at uh over the next
couple months uh when it comes to the
aftermath of this earthquake to go back
to our original question and so that's
just a call out to the you know the the
obvious thing that Everyone likes to
overreact obviously to to you know big
news stories and big news cycles and
again as we've been talking about like
this is legitimate but how legitimate is
it like right like so we'll even see
potentially play out over the course of
today in the stock market like do they
start to get uh nerves calmed a bit by
yeah this talk of like well actually
this isn't so bad for NVIDIA because
while it hurts their their immediate um
it could potentially hurt their
immediate um money coming in the door in
the longer run you know it's it's again
javon's Paradox stuff where it's like
yeah it's it's going to raise raise all
boats um as as this just permeates
everything and so they need chips and
yada yada and so that could help um but
yeah I mean I think that it won't be so
easy also for as I noted for all these
companies to pull back spend because
they've already committed to buying X
number of of h200 chips and and then uh
soon soon enough we'll get the next
iteration you know announced uh down the
road and so um all all these super
computer uh Mega clusters of data
centers that are being built right now
they're just not going to you know put
the brakes on all that because there's a
risk They're all playing in the same
game right and if one of them pauses
maybe they get a short-term Wall Street
you know uh pat on the back but if
they're wrong that's like catastrophic
and that's you know like a fire firing
the CEO type of
um you know if if this is just uh you
know even a blip on the radar obviously
undersells it a bit but if this is not
ultimately like a real fundamental sea
change situation and is more just like a
a step on on the road um they might
still want to keep their foot on the gas
yeah it's going to be very interesting
to watch the website is spyg glass.org
piece AI finds away joined of course by
mg seagler mg great to see you again
thanks for coming on the show thanks for
having me all right everybody thank you
for listening we'll be back on Wednesday
with my interview with Reed Hoffman
obviously a little different now but
maybe as mg puts it out uh maybe we
shouldn't be overreacting too much so
looking forward to speaking with you
then and we'll see you next time on big
technology podcast