AI Hype, Market Slump, Skepticism
Key Points
- The panel unanimously rejected the notion that AI companies are responsible for the recent downturn in the U.S. economy, viewing AI as a “cherry on top” rather than a macro‑economic driver.
- Recent market volatility was discussed, with participants attributing the swings more to traditional factors (e.g., Fed policy, exotic financial positions) than to hype surrounding AI investments.
- The conversation highlighted the cyclical nature of AI hype and Wall Street’s rapid swings, stressing that sustainable value hinges on clear differentiators and strong moats—not just hype.
- The acquisition of Character AI was examined as a case study, prompting a reminder that firms must understand their unique value proposition and guard against competitors replicating their offerings.
- AI governance and responsible deployment were identified as crucial themes, with experts emphasizing the need for structured research, ethical oversight, and long‑term thinking amid the current excitement.
Full Transcript
# AI Hype, Market Slump, Skepticism **Source:** [https://www.youtube.com/watch?v=1tTXTDzaPyY](https://www.youtube.com/watch?v=1tTXTDzaPyY) **Duration:** 00:32:04 ## Summary - The panel unanimously rejected the notion that AI companies are responsible for the recent downturn in the U.S. economy, viewing AI as a “cherry on top” rather than a macro‑economic driver. - Recent market volatility was discussed, with participants attributing the swings more to traditional factors (e.g., Fed policy, exotic financial positions) than to hype surrounding AI investments. - The conversation highlighted the cyclical nature of AI hype and Wall Street’s rapid swings, stressing that sustainable value hinges on clear differentiators and strong moats—not just hype. - The acquisition of Character AI was examined as a case study, prompting a reminder that firms must understand their unique value proposition and guard against competitors replicating their offerings. - AI governance and responsible deployment were identified as crucial themes, with experts emphasizing the need for structured research, ethical oversight, and long‑term thinking amid the current excitement. ## Sections - [00:00:00](https://www.youtube.com/watch?v=1tTXTDzaPyY&t=0s) **AI Hype, Mergers, and Economy** - A panel of technologists debates Wall Street hype, the strategic merit of acquiring Character AI, and whether AI companies could threaten the U.S. economy, concluding they likely won’t. ## Full Transcript
Wall Street Spooks pretty easily and
Hypes pretty easily and they're also on
a cycle that research certainly is not
structured outputs probably the most
sexy release of this summer you're kind
of breaking this fcking Bronco that uh
just came out of the blue does the
acquisition of character AI make any
sense at all you have to know what's
your value ad and how much of that is a
differentiator with high Mo so others
can't just come in and do what you do
all that and more on today's episode of
mixture of
experts I'm Tim Hong and I'm joined
today as I am every Friday by a genius
panel of technologists Engineers and
more to help make sense of another
hectic week in AI land on the panel
today we've got three guests Marina
danki as a senior research scientist
Kush vne IBM fellow working on issues
surrounding AI governance and chit vary
senior partner Consulting on AI for US
Canada and Latin America
[Music]
all right so uh let's just get into it
first story of the week uh is a big one
um but I want to start with kind of a
round the horn question let's just start
with a quick yes or no and it's a very
simple question to kind of kick off to
discussion which is are AI companies
going to bring down the American economy
uh Kush yes or no what do you think uh
no uh no and Marina no okay we have
uniform skepticism at that position and
I think that's actually what I wanted to
get into so uh if you've been keeping
your eyes on the financial news this
week markets were massively down um
across the board
internationally and uh there was a lot
of speculation as to why this was the
case um people were proposing you know
the unwinding of exotic Financial
positions uh concerns about the FED not
cutting rates but one thing that a
number of people kind of argued was
should we blame AI like the hyper AI for
this um and part of this claim was based
around the idea that the companies
really leading the downturn um and
arguably a big drag on you know indexes
like the S&P 500 were tech companies
that have made big bets on AI in the
last 12 to 24 months and so want to get
the kind of panel's opinion and Kush
maybe we'll toss it over to you first is
you know do we buy this as a theory like
why should we or shouldn't we believe
that AI is kind of a contributor to this
downturn um and and is kind of a a
popping or at least kind of increasing
skepticism around AI um having these big
macro effects I'm curious why you said
no in the the first question there yeah
I mean uh there's clearly I mean hype
Cycles with everything uh but I think
the economy has a lot more to offer I
mean it's a it's a very broad-based sort
of thing AI is kind of the the cherry on
top or the the icing on the cake so um
uh I mean yes it affects uh perception
uh and less uh
I mean of the view that it uh is really
about the fundamentals at this point I
think that'll change over time but but
not right now well I think there's been
I mean part of this I think is also
following on the tales of I think we've
been talking about it for the last few
episodes is kind of these reports coming
out of Banks and other uh you know
Financial firms kind of raising some
skepticism around kind of like the
excitement around AI so you know there's
the Goldman Sachs one that we talked
about a few weeks back and also the sequ
report that some people might have seen
um it is true though that the tech
companies have made genuinely a really
big bet on the market for AI um and I
guess I'm kind of curious you know maybe
show bit I'll throw it to you is you
know are you seeing you know clients
kind of following those Jitters are they
reading these reports and saying well
you know maybe AI is not providing you
know what we thought it would you know
should we be a little bit more cautious
about how we make these Investments so I
don't think the clients have to and I'm
talking about the 1400 companies 500
they don't have to read these reports to
realize that certain U areas AI has been
over prised in certain areas they have
they are under utilized right so there's
absolutely no confusion about the fact
that AI is going to have is having a
seismic impact on the businesses going
forward so no CEO can can say that the
next 5 years are not going to be uh
massively impacted by what AI can do
it's a question of how do you applying
AI surgically in the processes and how
do you think about a strategy of data
that then leads to an AI strategy that
then delivers value for you right so the
conversation has changed more and all
right after experimenting for 2 years we
have a good sense of where Ai and geni
are working well we now need to make
sure that we have a good mechanism to
figure out the high value unlocks in the
business appreciate that it's a it's a
combination of AI Automation and
generative AI it's not all gen handling
the entire process into end and we need
to make sure that our data real estate
and the people and and the processes are
aligned to unlock that value right so I
think there's a significant appreciation
of the value it can bring but also the
fact that it's a journey and you need to
do you need to do make steps along the
way to make sure that you're getting
that value unlocked that's very clear to
all my for 100 500 clients yeah I think
that's maybe one thing that our
listeners would benefit a lot from your
expertise on show bit is I think you
kind of put out the idea that there's
like underhyped areas of AI right um and
I'm kind of curious if there's when you
say that if you've got kind of
particular areas in mind where you're
like this is where businesses aren't
looking right like you know there's a
lot of hype in the space but like this
this seems to be where some of the
Hidden are I'm curious if you can speak
to that a little bit yes I think it's a
it's a mundane tasks it's the stuff that
uh how do you how do you make sure that
every employee across organization can
experiment in their day-to-day workflows
with AI with generative AI in a very
secure and governed way so within IBM
Consulting for example we have 160,000
Consultants who wake up in the morning
and doing all kinds of varied tasks
there are a small subset of people who
are AI gurus right they are they feel
that if it's been 20 minutes since llama
3.1 landed and we have not had it
running locally you are an embarrassment
to society right there's a small portion
of those but 85% of the other uh part of
Consulting they're doing things like I'm
going to do code creation I'm a tester
for the last 11 years I've been doing
marketing campaigns I'm going to do
Finance workflow so I'm going to get a
invoice I'm going to marry it against
the contract the the purchase order and
I'm going to approve it or disapprove it
right so those kind of mundane workflows
have a human in the loop and you need to
figure out how Excel got embedded in
those workflows you're now at the point
where you're having AI generative AI get
embedded everybody figured out how to
use Excel to improve their day-to-day
workflows right we're at that same point
today so you need to get to a point
where every IBM consultant we call that
Consulting assistant as an example it
could be a co-pilot from Asher could be
Amazon Q's Google of the world but you
need to democratize people actually
messing with their day-to-day figure out
that oh this email that I write ,00
times a month can be automated and
that's the value unlock get get CL get
your end customers to start your end
employees to start experimenting in a
governed way so that kush doesn't have a
heart attack just make sure doing this
in a way that we don't get ourselves
into trouble um yeah I think that's
actually in some ways like it has ended
up being what you're describing show bit
the kind of like 800 pound gorilla of
the AI world you know I love this kind
of joke that like you start open AI
because you really want to create like
AGI but like just slowly but surely the
gravity ational well of being like B2B
SAS uh and offering that as a service is
like really where the gigantic amount of
money uh is um Marine I did have a
question for you based on kind of what
sha just talked about here I know you
know sha you kind of made the difference
between like people saying okay if you
can't Implement Lama 3 on day one and
revolutionize all your business you know
processes in the first day you're a
waste of society um I'm kind of curious
so there's one standout company here
which is NVIDIA uh which is Hardware um
and that is a company that has been hit
in the stock market rather hard um and I
think was one of the examples that
people said see this is why AI is hyped
um do you do you buy that I mean is
NVIDIA indeed the most valuable company
uh in the whole world uh and you know
how should we think about sort of
Hardware in this picture like will
Hardware continue to be sort of the most
valuable kind of piece of this AI Pi at
least as far as like the stock market is
concerned I mean it's a dependency so
just talking of pure engineering terms
you are pretty uh much tied to it
because it's very much a dependency I
will say as far far as Nvidia going up
in value crashing in value um Wall
Street Spooks pretty easily and Hypes
pretty easily and they're also on a
cycle that research certainly is not
they want to know all right q1 what do
you got Q2 what do you got Q3 what do
you got it's not the rate at which
research actually happens so when you
have preliminary results Wall Street
will get over excited and then the
results next time are not as good and
then they get over depressed and we
actually have the same thing in research
where I'm like I can't guarantee you
that the research breakthroughs are
going to happen in three months on the
dot this is not how this you got to
deliver your Q2 breakthrough Marina
right I can't I can't promise my Q2
breakthrough so um I would also say that
this is also to some extent uh a
mismatch between the schedule of Wall
Street in the schedule of research in a
in a new area in an area that we don't
yet understand very well and that's I
think a lot of what we're actually
seeing here yeah that's fascinating is
almost you're saying like we should not
be looking to the stock market to judge
the value of the AI space in part
because like the market doesn't know how
to Value it at the moment is kind of
what you're saying is like I don't think
it's very clear yet I I don't know if K
but you disagree but I actually don't
think we actually know very well yet how
to Value AI properly I'm I'm with Marina
on this uh and I I don't think that the
common uh stock investor understands the
impact especially in Enterprise space
and what can do for people so we've been
we've been just dunking on AI stocks
saying that hey you are leading to the
downfall of economy we look at the
positive that it has done right it's
also contributing insanely towards the
overall economy right so you should give
AI enough credit to lift the entire
stock market up as well not just the
training week and say hey U the the
market is down X points because of the
large Nvidia swing which like just look
at the world we live in right in the
last few months Nvidia has swung a
trillion dollars in market cap a
trillion this just pause and realize how
much of an impact that's having on on
people right it is people reacting to oh
my God I don't want to miss
out but also not knowing at what point
are you investing in the fundamentals or
are you pulling out of the stock too
early right like even massive companies
like Arc invest ended up missing the
boat on
Nvidia lost a billion dollars of
opportunity there right so you need to
understand the fundamentals and stay
long in the market versus going and
reacting to these quarterly ups and
downs
I don't know I'm you're not arguing this
but I think it's almost like you could
make out the argument that like you know
what the biggest meme stock in the whole
world is it's Nvidia you know it's it's
not it's not GameStop it's not anything
like that I was just gonna agree with
Marina I mean uh the fact is that uh and
what chth was saying as well I mean this
is a it's a long game uh we don't really
know how to Value things uh yet uh it's
not like some commodity where you can
like grab it and hold on to it and see
what it's doing so um uh I think we'll
we'll get better um just like we've had
trouble valuing data as well um uh
valuing the models and what we can do
with them is going going to be part of
this as
[Music]
well so I'm going to move us on to our
second segment uh of the day um so open
AI this week announced the new feature
uh they call structured outputs um and
uh this is huge uh although it might not
seem like it on the surface for people
who are like not in the day-to-day work
of of AI um effectively what they're
offering is for the very first time uh
model developers uh are allowed to
basically work with their system um to
constrain their outputs to match
specific schemas that are defined by
Engineers um and this is a little bit
nerdy but I think it's actually worth
kind of walking through the technical
points here because I think it's one of
the areas where if you dive a little bit
into the technical kind of understand
what's going on you may recognize why
out of a summer of lots and lots of
announcements of AI this may actually
end up being the biggest announcement of
the summer in some ways um so I'm going
to try to explain this and then I think
Marina you'll keep me honest you should
be like that's completely wrong Tim
you've completely misunderstood what
they're trying to do the way as I
understand it is that language models
are of course Very powerful they can do
all sorts of remarkable things but the
problem is that they kind of output in
sort of non determinative ways they like
produce outputs that are kind of
difficult to kind of constrain and
standardize and this has been a really
tough problem because you know you have
to take Ai and then you have to connect
it to all the other all these other
traditional systems that are expecting
structured data right like there's a
computer just being like Oh well I'm
expecting a table that has like the
following elements within it and it's
been very hard to kind of like integrate
language models with that um and is what
open AI is saying here that you can
finally for the first time do that
reliably uh correct me if I'm wrong I'm
just kind of thinking through this the
thing I'm actually going to push back on
is this whole finally for the first time
thing this is not for the first time the
fact that we before were like all right
structured outputs semi-structured
outputs are where it's at we used to say
what you do with unstructured data this
is work that I've done for years is you
try to turn it into something more
structured so it's features and you can
feed it into a you know classifier feed
it into ML and and go from there then
everybody said oh Foundation models all
right now we can doesn't matter we no
more structure is needed no more data is
needed nothing is needed we're just
going to go and have unstructured data
is going to everything you go and you
work with that for a while and you go no
guess not all right we're going to go
ahead and walk it back a little we're
going to walk it back a little let's go
back to the fact that especially if
you're trying to mix and match a
heterogeneous system you do need
structure output because these things
don't know how to talk to each other so
I'm going to pretty strongly push back
on the for the first time and go back to
no now that we're trying to be practical
about it we've gone back to the fact
that you need to impose a bit of
structure I would also say that this is
with like the success of uh code models
where we see that there already is a lot
more structure imposed on what kind of
things can go in and can go out there's
some lessons being learned there again
going oh maybe we don't do just
generally unstructured text and we're
going to go back to having a bit of a
mix um Kush would you agree with that
particular we're kind of back yeah yeah
no I mean I think that's exactly right I
mean one way to look at it is I mean
you're kind of breaking this bucking
bronco that uh just came out of the blue
in the last couple of years and bringing
it back to uh where it should be right I
mean the the control and the governance
I mean all of that is part of making
these things practical right and um I
think another way to look at it is uh
and one good thing about these language
models is uh that they're very creative
they're coming up with all sorts of uh
different things but it's really a
tradeoff safety versus creativity and um
the control the the constraint is bring
us back to to that safety aspect and um
uh if you're inspiring a poet I mean go
ride that Bronco it's all good um but uh
I mean for all of the Enterprise use
cases that uh uh that we care about that
are going to make the the productivity
differences and all that sort of stuff
then uh that extra control is is where
it's at yeah for sure so show bit am I
um am I just being an open AI shill
here just really hyping this feature
where I guess Marina is just telling us
like this has all been said and done
before you know they're just selling
something that everybody has known how
to do for a long time so so Tim hot take
on this this is the first time open AI
is now appreciating and admitting that
the whole workflow end to end won't be
done by an llm they have admitted by
releasing this that at step number three
somebody's going to call an llm and
expect it to behave in a structured
manner so it can be a part of a team
that does an end to and flow other
aspects will be automation RPA there'll
be some regular AI they'll be just PL
old API calls but now llm they have
admitted to this by releasing this that
it's now down to a subtask level versus
being the llm that's going to do the
entire process end to end right so I
think it's it's a really hard take on
what they're doing for for practical
deployments for me in the field we uh we
are we are the launch partners with open
Ai and whatnot right we do a ton of open
AI with clients in our workflows last
week um on Monday actually we were
working with a large Healthcare client
where we're reading greams of different
documents and stuff and we extracting
things from those documents right so
talking about my HealthCare coverage I
need to know what's in network what's
out of network what's family coverage
what's single coverage and so and so
forth so using an llm to go extract
things out from it every time we run
this against our rubric of uh checking
the accuracy there quite often response
back with a blurb instead of giving me
the in network and out of network so the
way we used to solve this historically
we would ask questions in a manner and
then we provided some coaching saying
just respond with the actual dollar
amount the problem there used to be it
responds back with saying
14.9 and in three out of 10 cases it'll
forget to put million in front of it
right there's like practical issues with
you having with leveraging these large
language models and then we like okay
fine just give me the entire thing and
then like to Marina's point I'll just
use a small regx somewhere to extract
what I need from it and then I'll plug
it back in that was a horrible way of
doing things in production yeah that's
awful having a commitment now saying
that this is the JS I'm going to get and
if you can't fill that number if you
don't don't know what the single
coverage is for outof Network it'll be
null it'll be blank then I can do
something in a structured manner raise
some some alerts and have a workflow
accordingly I think it's brilliant
they're allowing you to do this this
combined with the price drop that we got
50% decrease in inputs 33% in the
outputs makes it very very easy for us
to plug it in the 40 Mini price is just
Rock Bottom it's slow it's very
inexpensive to deploy mini even the
fine-tune versions of mini now they're
allowing you to go fine tun these models
very very easily have a structured oper
around it so they've understood the fact
that instead of doing a generic top down
I'll take care of the entire thing all
the way down to a subtask level it has
to be fine-tuned for that task it has to
be super inexpensive and has to be a
good contract on what the input and the
output structure coming out right in
other words like a good a good tool to
be used in the
Enterprise um so the super interesting
takes on this it definitely went in a
direction that I wasn't expecting but I
think is like very helpful in kind of
thinking through why open AI did this I
think the final aspect of this I want to
touch on is it was very funny I mean as
someone who you know is a software
engineer kind of turned into a lawyer
you know I like read this very long blog
post about structured outputs and then
at the very end it's like oh by the way
it's not eligible for zero data
retention which I think was a very
interesting part of the announcement was
basically like normally the promises
that open AI will not train on any data
that you send in through the API on the
Enterprise basis but in this one case if
you send in a schema they're going to
they're going to train on that um and I
guess for our listeners I think it'd be
useful for them to hear a little bit
some intuitions for why it is that open
AI sees this data as so uniquely
valuable right that they're going to say
we've got this General policy of zero
data retention but for this tiny little
segment we're going to cut out a hole
and if you send us our schemas we
definitely want to train on that um
Chris I see you nodding but I don't know
if you want to speak to speak to why
they would do something like this yeah I
mean uh I was reading the announcement
as well and uh I think the there's
they're taking two different technical
approaches to make this work right one
is just training on more and more of
these schemas the second is constrain
decoding using this uh context free
grammar to really make sure that um uh
what comes out is uh is really I mean
matching the the schema and stuff so on
the first of the two I mean it's really
hard to to get this sort of variety of
uh what kind of schemas are going to be
out there this is not something you can
just download from the web and uh I mean
in some of our work we also I mean look
at very unique Enterprise sort of um uh
policy documents or or other stuff like
that and it's just not easy like um I
was uh talking with one of my group
members yesterday we were trying to
figure out what are like different
policies for um or guidelines for
different professions and I was looking
like can I get the New York State barber
license uh guidelines like what does a
barber need to do to do their job and
like there's tons of stuff like that
that um is like really not out there so
I mean just the the uniqueness of it is
is the key I think I think that's that's
absolutely right and I think that will
be coming sort of the increasing battle
it seems like right as like all of the
easy to get data is now accessible now
the kind of question is like who's got
these kind of access to like very hard
to get data and this kind of these
schemas are they're they're valuable
tokens right they're they're unique
tokens um in a lot of ways so this has
been a big struggle for us with our
clients in Enterprise settings we go
through Enterprise security govern
when we take a new product and we have
to make sure that it's being used in a
particular way everybody signs off on it
and so on so forth right so we we're
struggling with this with our
Enterprises when when you Outsource your
API calls to a third party then every
time the API calls change or they do
something differently or now in this
case there's the retention issue with
with the schemas right you need to go
back through the whole process and I
don't think Enterprises have a good
mechanism to understand capture and then
act on each one of these incremental
updates that happen so it scares me a
little bit that enterprises will end up
approving a product in a particular
state but it so rapidly evolves with
features and stuff that you won't be
able to go back in time and say I have
to this small incremental thing has to
be done differently the data scientists
will start getting super excited about
these function calls and and about these
structured outputs and start using it
and then that's where Kush and team are
going to come in and say guys time out
there has to be a good discipline around
how you govern incremental updates that
are happening to these so you don't get
yourself into trouble so I think that's
a very unaddressed issue with at least
my Enterprise
[Music]
clients so I'm going to move us on to
our final uh story of the day um it was
announced last week that Nome shazir who
was the CEO of character AI was going to
rejoin Google along with a core team
from his company um and also that Google
was going to acquire a license to all
character IP um this is widely seen
though it's disputed as an acquisition
ultimately of character um which had
raised something like $150 million and
was basically building sort of
personalized companion AIS um and so I
really want to go into this story
because it's very interesting and part
of a trend of uh Acquisitions in the
space if you will um that I think are
very interesting and I think get us to
thinking a little bit about how this
Market's going to evolve and what we
really anticipate from AI startups the
next 12 to 24 months Kush I wanted to
turn to you first is you know why is a
company like Google interested in a
company like character AI at all you
know like it feels like Google's got all
the resources in the world to do all the
AI um why are they acquiring companies
at all at Great cost like it feels like
couldn't they just build kind of a
character product on their own um and
we' love to get your thoughts on what do
you think is motivating this in the
first place yeah I think that's the
similar question like why does IBM
research exist versus um why don't we
just tell me a little more about that
yeah yeah I mean why don't we just keep
acquiring a lot of startups I think uh
there's always going to be a balance
between kind of organic growth and and
uh kind of uh the acquisition sort of
thing um uh there's always a spark of
some idea it you can't assume that uh
you're going to have all of them and uh
uh I mean in these cases there is
something unique there's something where
there's a market that they've touched on
and something that I think only a
startup can can maybe tap into because
um they have a different pulse of the
scene so I think it it makes sense to
for a company like Google to to have a a
mix of ways that they they grow yeah for
sure I to push you a little bit further
on that do you think it's cuz like is
there some kind of compliment like
what's the angle that I think you think
Google's trying to chase after here
because I I mean it's a search company
right like ultimately um this feels like
very consumer uh in some ways of what
they're trying to do yeah I mean maybe
they don't think they're they are a
search company going forward I don't
know um maybe they're uh edging on to
there's I mean more things or or other
things but uh I think just uh once you
get I mean something interesting
something exciting uh that just draws
customers to you draws consumers to you
and then uh uh you can keep them and get
them into other stuff so yeah as part of
a pivot for sure um so maybe we could
take the other angle at the story I
think which is you can see it from the
perspective of the acquirer why would
Google do something like this but I
think it's also worth investigating it
from the perspective of the startup um
you know Marina there was a bunch of
commentary online where people were
saying look you've seen Adept go through
a similar transaction there's another
company called inflection that went
through a similar transaction these are
companies that have raised an enormous
amount of money um and by all accounts
would be very successful right like
maybe some of the most successful
startups in the AI space um but as yet
the founders are choosing to to sell um
effectively right they're they're
choosing to go and join the big tech
companies um do you have a theory for
that like why would you I mean if I'm
sitting there I'm n shazir you know I've
raised $150 million that's certainly
more money than Ever Raised right um
what is what is motivating these kind of
Founders to say okay well actually want
to kind of throw in with the big
companies rather than trying to make it
on my own and does it suggest you know
problems in the startup Market do you
think I mean even 150 million can be
burned through pretty quickly if you're
doing a whole bunch of your own training
what is
15 everything else there might be a a
case here of again if there's an
understanding that you want to have a
sort of a pre-baked user base or a
pre-baked you know set of being able to
use a whole bunch of um of resources
which a company like uh Google company
like meta they're going to be uh really
quite good with that um again
potentially other people to collaborate
with I really will second what kush said
which is you've had one or two or three
good ideas it doesn't mean that you're
going to have 40 and they really are a
ton of extremely interesting smart
people who are working in these
companies so it may be that there's a
desire to to also do that and as well
and and have that partnership be a lot
more close in order to be able to to see
that that yeah I mean zooming out to the
macro level I mean cha do you think that
um like what does this prage I guess for
kind of like startups in the AI space in
general like are you seeing more AI
startups over time because I think
there's almost one way of reading this
which is well even if these companies
that have raised so much money can't
make it
independently uh you know like no one
can make it right like we're about to
see a lot of consolidation in the AI
startup space I think the the core
values the fundamentals haven't changed
you can't have a thin wrapper around an
open AI API call and expect it to keep
keep drawing more right so you you do
realize that the intellectual property
that you've built is what people are
going to pay for and the talent that you
have that you have assembled that
particular team that's what is is uh
golden now big companies will try to
walk around Acquisitions and come get
very creative to work around any of the
antitrust rules and things of that
nature as well right so in this case
they're not acquiring it they are
getting hiring some people or they're
licensing some terms and so on so forth
right so you can see that there are
there some motivation on not just
outright acquiring it but on the flip
side just like any startup environment
you'll also see big companies like whz
which Google was trying to acquire and
uh whz walked away from
$23 billion
offer and uh this I'm just laughing
because it's like that's like a
literally hilarious amount of money
right that is insane and O who's the the
co-founder of BZ he wrote a very
humbling letter to all the employees
explaining them why you're not getting
rich today right Essen explain to them
why I'm I'm not taking this offer it's a
very humbling offer but here are the
reasons why we believe that going IPO is
a big bigger value ad and so on so forth
right historically we have seen a lot of
misses and hits and misses Yahoo trying
to sell itself to Google or like Netflix
to Blockbuster all of these have been
multiple reminders that you you have to
know what's your value ad and how much
of that is a differentiator with a high
Moe so others can't just come in and do
what you're doing right so it takes a
while to understand the rhythm of where
you lie where you lie in the competitive
landscape and R forecast I think we put
undue pressure on co-founders on the on
the founders who who are just passioned
about building a product but now all of
a sudden we are we are surrounding them
with Venture capitals who have different
objectives than what you mean I need to
build a
business yeah I think they need to bring
back Silicon Valley as a as episodes in
today's world with llm that's right yeah
it's for sure yeah I saw this great
Twitter thread that was on like if we
modernized you know Silicon Valley what
would it be and just like everybody's in
AI basically um I mean it goes to a
point that Marina raised earlier in our
first segment though is like it almost
kind of feels like this is almost like
the micro version of the market being
not able to price these startups
properly like it feels like in a lot of
these cases like these big companies
like goog are like ultimately acquiring
the talent versus necessarily like the
product um I guess character you can
maybe debate because it actually had a
big install base but it feels like at
the core of it is just simply like
here's a team of people who seem to be
able to get what they want out of the AI
and like that actually ends up being
like this huge value that's almost
separate from like did you have a
blockbuster AI product release um and
yeah it kind of goes to these
interesting questions that I'm thinking
about now about like how do you how do
you actually value these companies right
because it's just like so unclear in
such a fluid
environment um any final thoughts on
this um super super interesting and I I
think again I mean to argue against
myself you know this is also during the
same week we saw a bunch of top
leadership from uh opening I leave right
and so it's not necessarily all
consolidation it's possible that you
know people are moving between big
companies and also creating like new
startups onto themselves um so any final
thoughts to round this out for today um
just one um I mean conversation I was
having with my brother-in-law last week
not related to this but uh I mean the
difference between running your own
business versus doing a job in a big
company right and the lifestyle sort of
issues there and um I think like I mean
the point you were making before Tim
like uh if you just want to make one
product versus building a business I
think maybe a lot of the folks that are
um getting into this right now um are
not in it for maybe that lifestyle or
for that uh uh business building sort of
uh sort of way of of going about it so
maybe it's just a way for them to return
back to their natural sort of State um
so so that could be driving it as well
kind more of the lifestyle issue yeah I
believe that for sure yeah it's I mean
personally crazy to do a startup so and
then got a friend who was a Founder who
is like it's literally an irrational act
to do a startup
so um well great on that note uh no
shade to anyone else who has already
been on mixture of experts as a panelist
but I have to say this is my favorite
panel the marina Kush show bit you know
power Trio is basically like we just get
the best conversations all the time so I
appreciate all three of you coming on
the show and for all you listeners
thanks for joining us this week uh if
you enjoyed what you heard you can get
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platforms everywhere and we will see you
same time next week