Groq and Cohere CFOs: Can Artificial Intelligence Be Profitable?

Channel: Alex Kantrowitz

Published at: 2025-11-28

YouTube video id: -0t_NoQt6FM

Source: https://www.youtube.com/watch?v=-0t_NoQt6FM

What an amazing crowd we have here with
us. We're going to put this on YouTube.
So, I need you to make some noise. Let
people know you're here. Let's go.
My name is Alex Caneritz. I'm the host
of Big Technology Podcast and I'm
thrilled to be here for a discussion
about whether there's ROI in AI with
France Chad Chadwick and Simon Edwards,
the CFO of Gro and Coher. Welcome.
>> Thank you.
>> Thank you,
>> fellas. Let's start with an easy one.
MIT came out with a study that said 95%
of businesses trying to implement AI are
not getting an ROI. Then a couple weeks
later, Wharton, a similarly prestigious
business school, came out with a study
that said 74% of businesses are getting
an ROI from AI. Both of those can't be
right. So, who is closer to the truth,
Simon?
>> Well, look, I think I would say show of
hands. Who here thinks your job cannot
be disrupted by AI in the next 10 years?
So I think I think everyone believes in
the future of AI. I think everyone
understands the use cases that that
could become a reality. I think there's
a lot of experimentation right now to
find how those use cases can be applied
in a in a practical way that scales for
these enterprises. So I think it's not a
case of if but when. Um, and I just
think there's a lot of experimentation
uh between now and that kind of end
state.
>> Okay, but I'm going to go back to you
now. Which one do you think is closer to
the truth, the MIT study or the Wharton
study? 95% not getting an ROI, 74%
getting an ROI.
>> I think 74% are getting an ROI. I live
in the Bay Area and I think I can't I
rarely see a company in the Bay Area
right now who is not applying AI to
drive productivity for their engineers
or drive productivity for their customer
support reps. So I think a lot of these
use cases are today demonstrating true
value to you.
>> I think what you got to do is you've got
to look at the date stamp on both of
those studies. The date stamp of the
earlier study was where a lot of
companies were looking at proof of
concepts and they were trying something
out and they may not have had the
internal infrastructure to really take
something from the proof of concept and
build into something that would prove
out the ROI. The second study, the
Walton study, what you're seeing now is
where companies have gone beyond proof
of concept and they're actually putting
things into production where they've
built a process controls and more
investment into the use of what can be
built within AI. So obviously I'm going
to say this, but very much on the Warton
study, I think the timeline between the
first and the second proves out the
journey that the companies have been
taking.
>> Okay. Okay, but I want to ask you a
followup on this Wharton study. I think
it's very optimistic for AI. 74% of
businesses say that they're getting an
ROI from AI, but then you look at the uh
different ways that they broke down the
data. One thing stuck out to me. VPs and
above are much more likely to say that
they're getting an ROI from AI than
managers and below.
That to me just suggests that people in
the sea suite are b drinking the
Kool-Aid and they they are like of
course we're getting an ROI from AI and
then the people actually using this
stuff and implementing the projects are
telling the truth. So what do you think
about that Franis? I I
you've got in my mind I think you've got
to actually look at the results and go
back to the companies and sort of ask
them you know we've you've built in
production what are you seeing as the
results and we are what we're seeing at
cohhere with the companies we work with
we we build AI tools and models for
enterprises what we're seeing is they
keep coming back to the table with more
and more case studies in indeed a
customer a large customer a global
customer we have today reached out and
said, "We've built all of these things.
We built four or five different case
studies in the last two weeks. They're
proving the ROI, and they're going to
continue building and building on top of
that." So, I think it's it's a
continuum. It's a journey. There may be
differences in how people view the
results, but um the journey is just one
that has to be continued because
otherwise companies are going to lose
their competitive edge to their
competition. And I think I would build
on that by asking how do you measure
ROI? If every single one of your
employees you just pay for a chat GPT
pro license, your costs went up, but
where do you get leverage in your
business? Is it you need less people as
you scale? Is it you create more of a
competitive advantage and therefore able
to drive revenue growth at an
accelerated rate? Is it that you can
reduce your gross margins because you're
more efficient with the way that your
solution is deployed? Right? thinking
about how you measure ROI is something
that I think we're still in an early
stage on. Um, but that's something we
spend a lot of time thinking about with
our customers.
>> Yeah, the Wharton study was interesting
because yeah, I should caveat 74% of
companies that measure whether they get
an ROI said they were getting an ROI.
But that was only about 75% of companies
that actually measure. Let's just take a
quick poll. I'm curious in the audience
if your company uses ROI. Doesn't have
to be exact, but how many of you think
you're getting an ROI today by show of
hands?
>> I think that scientifically was like 15%
or so. Is that the reason you guys
>> Sorry, ask the other side of the
question. How many people don't think
they're getting an ROI?
>> Okay, let's let's do that. How many of
you think you're not getting an ROI from
AI today?
>> I mean, maybe that's why you're here.
So, we just have a shy audience. Is that
what you're suggesting, France?
>> Just saying. Yeah.
>> More hands went up for the positive ROI.
Okay. All right. We'll take it. We'll
take that's that's good data for us.
>> Yeah.
>> Um on the Gro side,
>> yeah,
>> there was this moment, was it earlier
this year? I can't believe it's been
this year, uh that Deep Seek came out
and seemed to drive the cost of
reasoning models down uh by something
like a 40th of what Open AI was uh was
offering. And Grock's mission, of
course, is to make the compute side of
this much cheaper. And we hear all the
time about how much the cost of GPUs are
setting back these companies. Um
why why do you think it's still a
question now in terms of whether compute
is going to be cheap enough to run the
proof of concepts or even the
inproduction applications that people in
the audience are building and how does
that get solved?
>> Yeah. So for those of you who don't know
uh Grock is a is a we design and
manufacture uh silicone and chips
designed specifically to run AI
inference. So once those models have
been trained those models run on AI on
Gro's infrastructure and we do it not
only at a a rate and a performance that
is uh market leading at a cost that's
also very differentiated. And so I think
we're excited about these new open
source models, Alex, because um they
give optionality to a lot of our
customers. I think the most important
thing though is I think we're still
seeing the industry is supply
constrained. And so it's not a case of
Nvidia or Grock or pick your chip
manufacturer, but how do all of us
deploy as much capacity as needed in
order to serve all of these use cases?
Because coming back to the the earlier
point, there's a lot of experimentation.
Everyone sees the future which is AI at
scale.
How do we serve all of those workloads
and how do we serve them all equally?
>> But is is compute the bottleneck or is
it power? Uh Satya Nadella was recently
on this very interesting podcast with
Brad Gersonner. Sam Alman also joined.
The thing that made the headlines was
Sam Alman uh was asked about how he's
going to fund 1.4 4 trillion in
investment with 13 billion in revenue
and he sort of had a fit. Uh but the
other thing Satya said later was he has
chips that are not plugged in and he
wants to plug them in but power is the
issue. So is it computer power that's
the bottleneck?
>> Yeah. I think I saw a statistic that
says by 2028 in the United States a
quarter of all of the energy produced
will be used to run AI workloads. Right?
Which is which is an amazing statistic.
It's a lot of energy.
>> Sorry.
>> A lot of energy.
>> A lot of energy. I think where we
actually view this as a a competitive
differentiation for Grock. Um our chips
run um in a very price performant way.
So in other words, we can serve tokens
faster and at a lower cost than our
competitors. And we think that's a
really relevant use case given kind of
the supply constraints you're seeing in
power.
>> Okay. Francois to you. Um, a lot of
people here probably came wondering how
they can get an ROI from AI. Cohhere is
all about putting artificial
intelligence into play in business use
cases. What should they do?
>> Well, what they should do is work with
folks like Coher to build a strategy.
>> You can't just say work with Coher.
Let's give a real answer. It's you
you've got to build a strategy as to
where you want to deploy it across your
company and and and to Simon's point,
think about how you actually are going
to measure the ROI because it's all
about setting the stage up front and
then bringing in the partners to help
achieve those results. And so if if you
just throw money at the AI question, you
might not get the right results. you
might not know if you've got the right
results, you might not even know what
your ROI ROI actually is. So, what we're
seeing with a bunch of our large
enterprise customers and and and even
the government, right, both civil and
defense, they're starting to build a
framework and a strategy around what
what they want to do, how they want to
do it, when they want to do it,
importantly, how much money they going
to invest in this, and then how do they
measure it at the back end. Mhm.
>> It's we're beyond to my initial point,
we're beyond just sort of saying, "Oh,
let's build something and see what it
does." I think there's still some
experimentation that companies are
willing to do, but they are spending
much more time planning up front. Back
to the Wharton study, they actually
broke down uh the level of the size of
companies and the ROI that they were
getting. And maybe not surprisingly, the
biggest companies 2 billion plus a year
in revenue were getting an ROI from AI
at 57%. So much lower than the 74%
measured, the ones that were getting the
most ROI, smaller companies. And so
Franco, I wonder what you think about
that in terms of does it tell us a
lesson that the companies that are
smaller and therefore they can adapt a
little bit more easily, are they the
ones in position to get the most bang
for their buck when it comes to building
AI today? And is there a lesson that all
of us can take in terms of that
adaptability aspect? Yeah,
we all know and we all talk about the
speed of AI and the AI adoption and what
you're doing to build it into your
enterprises. I think there's there is a
true sort of nimleness that smaller
companies are able to adopt to to sort
of put in place their plan and then
implement it across multiple different
functions within a company to be able to
get that greater ROI. the more that you
can integrate agents and multi- aents
across various functions within your
companies, the greater ROI you're going
to get. And I think that proves out at a
smaller company. The larger companies,
they've got more
uh infrastructure that's been built for
over a period of time. And so peeling
that away to be able to then build your
AI tools and your AI models internally
just takes a little longer. And I think
there's a real interesting nuance there,
right? In that smaller companies are
likely growing quicker and AI enables
them to create leverage as they're
growing versus larger companies already
have that infrastructure that Francois
mentioned. And so actually in a lot of
use cases as those AI solutions are
effective they result in the need to
take out cost in order to truly realize
the the ROI as opposed to just creating
leverage from growth. So Simon, to
follow up on that, when it comes to the
ROI question, is it the fact that
there's this conventional wisdom that AI
costs so much to deploy that it's making
the ROI picture difficult or is it
processes? Is it is it actually the cost
of deploying AI not as bad as the fact
that new processes need to be developed?
>> I think it's the latter. I think you
know how many people in the audience
right have been involved in these large
ERP implementations or SAS rollouts that
consume a ton of resources across the
organization take a long time and then
the the go live is you know 9 months in
the future with these AI solutions you
can get value immediately but you get
value immediately and then have to go
completely reconfigure your processes as
an afterthought as opposed to design it
in
>> Google recently uh held a earnings
conference call and Sudar Pachchai
shouted out a number of companies that
were using Gemini and generating many
millions of tokens and then someone did
the math and they said that's an
incremental maybe like couple million
dollars and uh this year we've seen so
much money go into this field I mean if
you think about just big tech's
investment alone it was something like
300 billion in infrastructure buildout
uh at the start of the year now it's in
the 400 billion range if my numbers are
are correct. Are we in a bubble? How
does this make sense, Simon?
>> This was the easy question you told us
was coming, right?
>> Um, look, I think as we stand here today
and look back, no one regrets um the
amount of money that was invested in
railroads or the amount of money that
was invested in fiber networks as part
of the internet roll out. And so, I
think 10 years from now, no one will
look back and say, I wish we spent less
money on AI infrastructure. But all
those were bubbles.
>> I'm sorry.
>> Those were bubbles. The fiber was built
in the internet bubble.
>> Well, I think it's important to to
define what bubble means in that
context. And I think I think there's two
dimensions to it. There's one which is
just the infrastructure buildout. And
then I think there's the second which is
are valuations of private and public
companies too high. Are we overleveraged
in the economy? You know those I think
hindsight will be 2020 in the future. I
would say as it relates specifically to
the valuation question, I believe that
companies that are emerging right now,
we will see the next batch of trillion
dollar companies. And so I think a lot
of venture investors and a lot of growth
investors are are picking their bets.
Um, but I don't think we'll ever regret
the amount of infrastructure that we're
building today.
>> Franuis, what do you think about the
bubble question? Yeah, I think there are
potentially certain areas. When when we
speak about AI, it's a two letters that
cover a large sway of different
different things. There may well be a
bubble in certain areas and we got to be
careful on some of that, but I don't
think it applies to the whole industry.
And even if there's a sort of a
correction that comes, it may impact
some companies, it's not going to impact
every single company. But but wait a
second because AI does go through this
process where there's moments of
development over exuberance and then a
drawback of investment which is called
AI winter. So I don't think necessarily
that there will just be a continued
buildout on the infrastructure that's
built right now. We could end up very
easily in an AI winter if if the
marketing and the investment isn't going
to make sense in a couple years.
>> I'll come back to your first question.
If companies can prove out that they
have an ROI on their investment,
there'll be many companies that will
continue to grow and flourish as
providers to those companies
>> and the the buildout of infrastructure
today is underwritten by the growth
projections that these companies are
seeing.
>> And you believe in those growth
projections? Well, I haven't seen their
detailed growth projections, but I do
believe that as just as we've all talked
about AI will disrupt all of our jobs. I
don't believe we've reached saturation
of AI workloads within large
enterprises. And I think there's a lot
of head headroom to grow. Whether or not
the companies that are investing that
capital today will be the ones who are
successful in the long term, there's a
lot of execution between now and then.
>> You know, I I always find it funny to be
the one asking these like, come on, like
where's where's the ROI? uh questions
because at the end of the day the
computer we're talking to the computers
like sometimes it's easily to forget
easy to forget that but there is so much
talk and and marketing coming from the
industry that you wonder you know how is
this going to make sense but then again
I was sitting with Dario Amod the CEO of
anthropic a couple months ago and he
said that the company did uh 1 billion a
couple years ago and it's scheduled to
do something close to 10 billion in run
rate by the end of the year. So this
10xing continues to happen. All right,
last question for both of you. Um, are
you believers in AGI?
Who wants to start? Frantois.
>> Well, I I'll start with the cohhere
slogan, right? Our slogan is ROI over
AGI. As long as our business customers
are getting an ROI, that is what we're
focused on.
>> I'm excited about the future. That's
what I will say. I I'm I'm not
necessarily the technologist in my
company, but I'm excited about the ideas
that we're we're a part of.
>> Okay, let's hear for our panelists.
Thank you everyone. You've been an
amazing audience. Thank you. Thank you.