Who’s Winning The AI Race? + Software’s Future — With Sridhar Ramaswamy

Channel: Alex Kantrowitz

Published at: 2026-02-12

YouTube video id: P_KI6HC8fpQ

Source: https://www.youtube.com/watch?v=P_KI6HC8fpQ

Where does the AI race go from here? And
is [music] all this AI agent hype real?
Let's talk about it with the CEO of
Snowflake right after this. Welcome to
Big Technology Podcast, a show for
cool-headed and nuanced conversation of
the tech [music] world and beyond. We
have a great show for you today. We're
going to talk about the state of the AI
race looking at the Open AI versus
Google [music] axis with someone really
knows what's going on in the
competition. We'll also take a look at
the state of AI agents and [music] what
AI programs can do when they organize
their data. Well, we the perfect guest
to do it with us here today. Sridhar
Ramaswamy is here. He is the CEO of
Snowflake [music] third time on the
show. Welcome back, Sridhar.
Alex, always great to talk to you. Thank
you for having me. So, it's been a
couple years since we've spoken. For
those who don't know you, you spent 15
years at Google.
Your last job there was the SVP of ads
and commerce. You founded Niva, an
ads-free search engine. In 2019, you
sold it to Snowflake in 2023. You became
the CEO of Snowflake in 2024. Snowflake,
for the uninitiated, $59 billion public
company. It is a data cloud company
which stores, analyzes, and helps you
share data. And you really have a front
seat to the AI race. So, let's begin
with the AI race.
Just give us your perspective on the
state of the AI race now. It seemed like
for a while there was Open AI and the
rest. Now it seems like there's two axes
that are forming, the
I'll call it the uncomfortable marriage
of Open AI and Nvidia and then the the
Google side of things where they have
the model, the TPUs, and they seem to be
giving the incumbent a run for their
money. What's your perspective?
First of all,
the AI race changes
every month.
We should all feel great about making
predictions because one of them will
come true and it'll the world will
change enough that we have to make new
predictions.
I think
the
gap
between the truly great model makers
of the present era.
Like Open AI, the Anthropic, and Gemini
very much in that mix.
And everyone else
is quite staggering.
And it's also a world in which no
incumbent should feel comfortable about
their position because things are
changing so much. And a great new model
can sometimes end up producing a lead
that's like a year long, which is an
eternity
in in today's world.
And um And so I would say from that
perspective, it's early. There's a lot
of change.
What is also
quite profound about this moment is the
things that we can get done with the
models that have already been launched.
Where it's merely an issue of stuff like
mechanics for can you get inference
capacity.
It's a lot easier to solve. I think
that's the part that sometimes people
overlook
about what is remarkable about this
moment. These models, they can do
amazing things. We'll get into some of
the things that we Snowflake are doing.
I think it is their ability to create
value. Their ability to help among the
most prized of professions today,
software engineering. I think that's the
thing that will drive so much impact.
Lots more to come, but I would say it's
very, very early in the AI race. I I
agree with you. And I want to drill down
on this a little bit because you are
somebody who has the mentality that sort
of is needed to analyze what's going on.
You're not only somebody who spent more
than a decade at Google, including time
at as in the highest ranks of the
company. You competed with Google. Mhm.
And so it's like when we think about
what's going on with the AI race now.
Google is this it's a beast and it has
this distribution advantage and in fact
we recently published some data on big
technology that showed that OpenAI had
opened up a very big lead. It's still
growing quickly. It's grown 50% web
visits January 2025 to January 2026 but
the lead is shrinking and
Google has for instance grown its web
visits by not 50% like OpenAI but 647%
in the same time period.
>> When you say web visits you mean for
things like Gemini? Correct.
Not just Google itself yet the chatbot
visits for Gemini.
And
some of the the aura around OpenAI was
predicated on it having this lead and
not letting it go. In fact Sam Altman I
think he was in India and he was like
you could try to build a model like ours
but it won't work. And now with things
like Deep Seek communicate too we've
seen people able to catch up on that
front. So it's being pushed by Google on
one hand the open source model builders
on the other.
How do we figure out how I can
continue to lead this this race if it
can or is it just one in the pack?
I mean I think the fact that it has
become
OpenAI has become the Google of choice
when it comes to chat for most of us is
actually a durable advantage.
And uh
I I I I use it quite often for all kinds
of things including solving problems in
the real world. My coffee machine not
working um or I can't open my gate
anymore. Like it the amount of use that
you can get is pretty remarkable. I
think that lead is real.
On the other hand something pretty
simple like
not simple it's hard faster image
generation
or more accurate image generation which
is what Google pioneered with nano
banana is actually having a profound
impact on things like their usage and
OpenAI was late to the game.
Just for that one feature. You would
think, "Come on, it's a small feature.
How much can it matter?" It matters.
People like being able to create things.
It just tells you that yes, competition
is actually very fierce. And uh
big companies
generally have a lot of birthing issues
when it comes to new things. It's just
It's a matter of how they work. First of
all, they don't often have a clear
perspective of what amazing means on in
a new area, and uh what they struggle
with, even if they can understand
amazing is fading out a path to that
amazing.
One can argue that uh
XAI, for example, has actually produced
what is widely acknowledged to be a
world-class model that is out there.
But that act of sheer creation is not
something that anyone should take for
granted.
It doesn't matter how many how much
resources you have. It's not that easy
to figure out all the little things that
you have to get right in order to get to
a point like that. You see other
companies with tons of money struggling
to be at the same caliber as Open AI and
Anthropic.
Google now
has had a set of pretty deep advantages
in this area. They kept DeepMind quite
separate.
And DeepMind was all has always been at
the cutting edge of AI, and it's become
a real weapon for them in terms of
getting to the front.
And once they get there, all of the
other advantages that they have of
distribution, the bottomless uh
you know, well of money that they can
borrow from, investments in things like
TPUs, which kind of looked crazy back
then that we would invest in it. All of
those become accelerants, but I think
what one should take away is that uh
like that breakthrough, which is so hard
to achieve, especially for big companies
with specialties, Google has managed to
achieve. This just means that OpenAI and
Anthropic need to understand that any
kind of lead that they get is not going
to be a long-lived one, and they really
have to work hard and compete. Honestly,
I think that's a good thing for all of
us. Just to give you some points of
comparison,
GPT-4, by all accounts, was ready in
August 2022.
Long time ago.
And
it took Anthropic, I would say, roughly
2 years,
summer of 2024,
to have a model that was of comparable
quality to GPT-4, like 2 whole years,
which is an eternity.
And then, soon after, Anthropic launched
a coding model that was widely
acknowledged to be the state of the art,
and they have stayed there. It took
OpenAI and Google, again, a year plus to
catch up to that. It tells you that
leads are shrinking,
and there's going to be more and more
competition. And of course, there's a
pressure from things like the
open-source models, which just turned
this into a whole other ballgame in
terms of what is possible with that.
On the Google front,
given the time that you spent there, are
you are you surprised at what's happened
there? It seems like
they just kind of woke up and
started shipping with a sense of urgency
that I hadn't seen from them for a
while.
Google's always had
and the founders definitely
they
they're always well calibrated for
crises.
I remember back in 2005,
when
wallwaslive.com,
the precursor to Bing, first came out
with what appeared to be a really good
search engine,
we got into what's called a cordial.
It's like meet everyday, all hands on
deck, drop everything else. We got to be
faster, better than them. Wait, what was
it called? It was called live.com.
>> But the
It was just a It was called a cordial.
It's basically get the teams together,
show up in front of Larry, tell them
what you're doing today. And then they
went to code red with this uh
open AI thing at a certain point.
>> Yeah, yeah. But the point is um and
every year that I have been at Google, I
can think of one or more crises that
required us to operate very differently.
And what looks like a placid company
from outside is very motivated, very
driven. They've also struggled with uh
structural boundaries. Um for example,
the thing that we did uh for a social
network, which was called I forget.
Remember Emerald Sea, Google Plus? Um
that was sort of a disaster because
you know, it's it's it's first of all,
it's hard to be It's hard for a new
player to break through, especially with
uh something like the network effect of
a social network. It's just really,
really hard to do.
Um and so they struggled with new things
that uh they do, but they've also
demonstrated an ability to adapt. Uh
Google Cloud by, you know, Google Cloud
is a pretty big success. Obviously, a
lot of credit goes to Thomas for making
that making that happen. It is an
adaptable company. It is a malleable
company. So, it's I'm not surprised and
uh you know, I'm not that close to
Google anymore, but folks speak about
how one of the really cool things about
DeepMind is having uh Sergey in the mini
kitchen, just hanging out, talking to
people. And so that that sense of time,
that sense of what is a pivotal moment,
that's what great leaders bring. And
Google's always had that in spades.
I remember uh when Google Plus launched,
um
I actually was supposed to go to meet a
friend at Facebook that weekend.
And they were going to supposed to have
their their barbecue, their company
barbecue, and they canceled it. And I
was like, "What happened?" And he's
like, "Don't you realize we're at war?"
That's correct. And it seems like that's
really what's happened with both Google
and OpenAI, two code reds.
>> That's what greatness takes. Either you
realize this crucible moment and go all
out.
So, the question is where to focus,
right?
>> [clears throat]
>> There was uh there were some reports
recently that um Nvidia CEO Jensen Huang
has been saying privately that he
doesn't love OpenAI's business approach.
And you could read that as maybe as
that's the finances. Uh I really read
that as as uh criticism of focus. And I
could be speculating here, but OpenAI is
doing the consumer chatbot, they're
doing video generation models, they're
doing the device, and they're doing
enterprise now. And enterprise is
actually going to be a big push for them
this year. And in fact, you're part of
it.
>> with them.
>> Yep. Just announced a $200 million
partnership with OpenAI.
Um
and I think for our purposes, it would
be great to hear your perspective on
why enterprise is a worthwhile bet for
them, and where they stand compared to
Anthropic, which has been focused on
enterprise from the beginning. One issue
we should all keep in mind is that um
when you're seizing lots of ground
when times are early
if you're successful, people will call
you a genius.
On the other hand, they don't go well,
and a threat shows up in the main thing
that you do
people say lack of focus.
For the longest time, Google was
criticized for being a one-trick pony in
search.
And after a while, it was criticized for
having too many efforts that lacked
focus. And now we are back to putting
Google as a hero because they succeeded
in Gemini. So, we should all remember
that judgments are post fact and
dependent on the outcomes produced
rather than the actual strategy. There's
a little bit of that. Having said that,
OpenAI has a lot to offer
uh enterprises.
And um
we are excited to partner with them
because many customers are joint
customers of Snowflake and of OpenAI.
We've created an agentic platform called
Snowflake Intelligence that's been quite
transformative. Over 2,000 customers,
fastest growing product, over 2,000
customers are using it
pretty much uh you know, 3 months after
we released the product to GA.
Enterprise customers are fussy about
using products only um in uh in in GA.
And uh it's among our fastest growing
products ever launched.
And uh it's
it's focused on data in Snowflake. Back
to your point about focus, we wanted to
make sure that we created a product that
could enhance the value of things that
people had already done with Snowflake.
We didn't want to go and pitch our
enterprise customers and say, "Hey,
we're doing something dramatically new,
you know, work on it with us." We said,
"You can get value from your data a
whole lot faster." Not only that, we
also said, "We live what we preach." And
so I often show them things like our
sales agent, which puts the every piece
of information that my sales team has
about every customer at my fingertips.
What meetings does customer have
yesterday? What are the outstanding use
cases? All of that is available to me,
but it's also programmable. I can I can
get the information the way I want,
share it the way I want. And uh but
there's a lot more in this world of
agents and enterprise. How do you help
people take action? How do you help
people be better grounded about the
consequences of their action? How do you
help them analyze situations? These are
the things that we are excited to be
collaborating with OpenAI on. Yes, one
part of it is us using their models, but
I think the much more interesting thing
is going to be what are areas that are
very amenable to AI creating value? And
how do we make sure that we make it easy
for enterprises to realize that value?
To make this super concrete, I was
visiting a big manufacturer yesterday.
They make my eyes kind of popped out
when they said, you know, listen, we
have 5 million SKUs.
5 million SKUs that they sell.
And part of the issue is they have
trouble pricing this.
Because it's it's a big dynamic
marketplace. We don't know what
competitors are pricing it at. We don't
know what kind of like you have to take
into account the margin that we have on
the product, the NPS for the product.
Can you create an agentic system that
can help us do pricing better? We have
all our data on Snowflake.
And that's an That is a situation in
which the power of agentic technology,
the ability to look at a complex
situation, break it down, follow best
practices for how work should be done is
going to be a big multiplier for how
they get their work done. There's
potentially hundreds of millions of
dollars of additional revenue that this
company can make if they can do a better
job just with this one single project.
That gives you an example of the kind of
things that people are looking to do
together with with with with OpenAI and
Anthropic and the data platform like
Snowflake.
>> So how does the product work? It would
be a agent basically that goes and takes
a look at the pricing and then with the
GPT model, I mean, explain exactly what
Well, this is a great question and it it
go it goes to a topic that I'm pretty
passionate about. I call it what does
work look like in the future.
And today our work is pretty much we go
look at our email, we go look at our
to-do list and then decide what are the
things that we should be that we should
be doing or you know, if you're like me,
you have meetings on calendar where um
work shows up.
Uh the future that we envision very much
is um you describe what you want systems
to do. Hey, these are the kinds of
things that I should be looking at every
day. For example, I look at our revenue
alerts every day. I go and look at the
dashboard. If there's a if there is a a
big up or a big down, I send out
questions and so on. Very automatable.
And so, you have an agentic system that
is connected both to the past
information that's typically sitting in
Snowflake or what was performance like.
Um it is also it has access to things
like prediction models that say if
something changes, what does the future
look like? Also things like ambient
information, your emails, your
documents, other or even things like the
stock market. Um ambient information
about the world. And uh your work very
much becomes these are the five topics
that you should be paying attention to
and here is a brief for these five
topics and potentially even have
recommendations. So, you give the agent
a task. You give it basically like you
would an employee. You give it these
this instruction. Uh if you are let's
say the manufacturer, right? You say,
"Hey, I want you to take a look at the
pricing
>> I want you to look at the spread between
how I price, how the market is pricing,
identify the top 10 opportunities I
should be paying attention to in my
department today, generate a report for
me." My job is, "Okay, I'm going to go
through this, go through the
recommendation, and figure out what do I
change? And if I want to make a change,
what approvals do I need to get within
the company?" So, it does the legwork
for you. You come in and your decision
is basically your your task is basically
to make the decisions. And you might
decide
>> to spend a week looking at all the
different pricing.
>> our support team. We have changed our
support team from 50 people writing
software, 300 people using this software
to help debug support cases, to much
more of a builder user model where there
are set of tools available within our
coding agent Cortex code. And whenever a
support case comes, they use these tools
to analyze what is happening, and then
they tell the customer what to do. And
sometimes they decide, you know, these
tools are not enough. I need to build a
new tool, and they add that tool itself
to the suite of tools that everyone else
can use. So, this is work
self-correcting, getting itself better
over time. And the goal is just things
get done a whole lot faster. Already,
we're seeing 10x, not 10%, 10x
reductions in the amount of time that it
takes to debug complex cases that come
in.
And so, let's just go to this question
of is this working? Because there's been
a lot of discussion of agentic AI.
Every time we talk about it, there's
always like a segment of the audience
that says, you know, this is still a lot
of hype, push back harder,
conceptual largely still, and you know,
this is something that, you know, might
in demos look really good, but when you
actually put it into practice,
it struggles. What's What is your read
on that?
You got to walk the walk.
We were in Davos together. Yes.
>> And uh, you know, 2 weeks ago, and I
probably met
20-odd CEOs, CIOs, lots of partners. And
uh, my sort of SOP, standard operating
procedure for each of these meetings
would be I would ask our sales agent for
information about the customer.
What's the state of our relationship
with, take your pick. And uh, it
generates a report. I would turn it on
and show my phone to them.
Um, and they would go, holy cow. Mhm.
>> [clears throat]
>> But uniformly,
not one of these CEOs
has the same tools that I do.
I see.
>> That's the difference between actually
getting the work done, making AI serve
meaningful needs, um and yes, the hype
that you're describing. All of the
people that are um in the camp that
you're describing have never had useful
products built for them that deliver
meaningful value. I speak as somebody
that lives this. The amount of feedback
that my poor team gets about how
difficult the mobile experience is, how
to make it better. We just launched like
Face ID authentication. That's a big
deal because I don't have to log in um
all the time. It's taking care of all of
those kinds of nuances, making
enterprise data come alive, available
for you, and then helping you with
decisioning. That's the magic, and
that's why you're hearing people say
it's hype, but it's companies like
Snowflake that are actually living what
we are preaching. And I give the same
feedback to my exact team.
Which is, "Hey, all of you need to be
demanding tools that are as good as the
one that we have for the sales agent,
and our team should be providing them to
you, and you should be using them
day-to-day in how you can work better."
I agree
that there is work to be done, but the
sheer potential of something like this
is uh magical. I'll give you one more
small example of something that is
cooking this very week. I'm working with
our uh ops team, or operations team that
helps manage Snowflake, the software
running in the cloud, about how to get
on a more agentic bandwagons. Like, you
know, super crudely infrastructure
engineers. They're all like, "What is
this? You know, we know better." But
we're walking through this journey of,
"No, no, let's create tools that our
coding agent can use." And you will
genuinely find that it's a lot easier.
And so someone created a tool that'll
help detect things like, "Oh, are there
problems with um warehouses resuming?"
Warehouses are a basic unit of work that
gets stuff done for our customers. And
when our customer says, "Start this," we
want it to start quickly. Um in like 10
seconds, I had generated a histogram of
resume times, put a nice graph, and I
sent it to the team. This
with one prompt. All English. Um, on top
of a tool that somebody had built to
look at resume times in warehouses, and
the team is like, "Holy cow."
That's the magic of agent tech
platforms, but yes, you have to do the
legwork to put them into place with the
guardrails, things like that. But
there's real magic here. Couple of
things. So, first of all, what you're
saying is kind of reminding me of
something that Arthur from Mistral, the
CEO of Mistral, said here couple couple
weeks ago, which is basically that the
technology has these capabilities, but
it's not just like it's not like in that
AGI mode, tell it what to do and it can
it can do it.
It it it in in many ways, getting
enterprise AI to work is a managed
service,
which means that it could take some time
for what you're talking about to be
visible within the entire economy as
opposed to those who have already put
the time to figure it out.
>> Well,
that's also where magic can happen.
Right. And uh
you know, I told you that uh we uh
released a new product called uh Cortex
Code, which is our data coding agent.
Uh, we launched it to GA yesterday.
And uh it dramatically lowers the amount
of time that it takes to get stuff done
on Snowflake. Mhm. We all get carried
away with how does AI make it easier for
a business user like me to get access to
my data? That's great. But on the other
hand, everything from how do you set up
a database to how do you move data from
a production like on like a transaction
database over to Snowflake for analysis,
how do you build a machine learning
model, how do you build an agent that
you can then give to the business user?
Cortex Code is meant to address all of
that again in natural language, and part
of what we have built there, or what we
call a series of skills
that help automate this work. And this
is a theme that's going to come up again
and again, which is how do you use AI
to make launching AI products go faster,
right? That's the feedback loop that one
needs to be on. It's a little bit I'll
It's a little bit of a red pill moment.
Where you're like, wait. You mean I can
release new software products pretty
much every day?
Because releasing a new piece of
functionality is as simple as writing a
recipe in English, which all of us are
very capable of doing. I think using AI
to make AI go a lot faster is something
that we are excited about. And this
product is among the best in terms of
how do you get it from Snowflake?
It's It's interesting that you talk
about how easy it is to build software
now. That has been both a benefit for
software companies and something that
people are worried about because where
is where is the
Look, you know, where is the moat if
it's so easy to build? This is from
This is from Ben Thompson's pretty
interesting his his perspective. He
says, AI coding doesn't kill software.
Customers pay for products, not code.
They're paying for support, compliance,
integration, security patches, someone
else owning the never-ending maintenance
commitment. That stuff doesn't just go
away because writing the initial app got
cheaper. There's a butt here though. He
says, but
>> [snorts]
>> if every software company can write
infinite code cheaply, the competitive
dynamics change. The SaaS playbook of
finding a niche and growing your slice
worked when building was expensive. Now
everyone can build into adjacencies
overnight. Shifts from growing this pie
to It shifts everything from growing the
pie to fighting for share.
It's something that
you know, it seems like you're enabling
and you're living. Yeah.
I think there is going to be a
concentration towards platform players.
Um but I would also be cautious about
general
pronouncements for the simple reason
that we are all actors in this space. We
all get to change the outcome. I feel
very good about Snowflake as a data
platform,
but I honestly do not want to be in a
situation where access to Snowflake is
always mediated through someone else.
That's always a very dangerous place to
be, especially in a moment like
This is the reason that we develop not
only Snowflake intelligence, which is
the best way for a business user to get
in for to get access to their business
information that is trustable through
the devices that they want, like their
phones, rather than trudge through
dashboards, but we are also investing
massively in how do you make creating
data products? How do you make creating
applications a whole lot easier?
Absolutely, it's going to be the case
that there's a lot of functionality that
sits in complex applications. We're
actively working with all of those
folks, whether it's a ServiceNow or a
Salesforce or SAP, with whom we have a
big partnership in creating this agentic
future together. Agentic future is very
much going to be what I said, past,
present, future, and actions. And so, we
think we stand a very, very good chance
of being the platform where this work
happens. But as I said, it's a footrace
and it's all about creating value really
fast for your customers. Um, and I would
shy shy away from X is going to win or Y
is going to win. Um, the the companies
that are going to win are the ones that
have great capabilities, but also take
the time to figure out how to create
value for their customers. There's an
We're speaking on uh Wednesday, February
4th. This is going to go a week later.
Uh, but there was an interesting thing
that just happened uh this week that I
think we should talk about, which is you
made such an interesting point where
What when I when I asked you about this,
you said, "Listen, we do not want to be
an input into somebody else's software."
And uh uh this week Anthropic released
uh or or within the most recent days
Anthropic released a legal plug-in and
the market got wind of this and then all
of a sudden Thompson Reuters had I think
it had its worst day on the market in
history. Stocks like Legal Zoom just you
know dropped like a rock and I think and
I was trying to think through like why
this could be because it was just one
legal plug-in from Anthropic and the
perspective might be that
with generative AI there is a risk that
some software shifts from being the
place you do the things. You know,
LexisNexis, you do the research there to
an input into a platform and if that's
the case I think what the market is
thinking is that you lose that control
that you had. You become you become a
feature in a platform as opposed to the
platform itself. That's the risk. It's a
very real risk.
I think people that were confident about
their position in the world because they
were essentially walled gardens for data
and functionality
and are slow at providing modern ways of
dealing with information are going to
struggle in this world. This is the
reason that I stress us living by what
we speak in terms of AI and agentic
platforms and this future of work
concept precisely because unless you
live it, you don't actually feel it. And
unless you live it and feel it, you're
not going to help your customers get
there. I think
niche
niche SaaS software providers that
basically benefited from lock-in. Think
about it. If you use a piece of SaaS
software, logged into it on your
browser, God help you if you want your
data back. Just like not going to
happen.
Yeah. What this current moment is
pointing out is that that's a very
dangerous place to be and a lot of these
players risk becoming dumb back ends to
the models, which is why Snowflake is so
leaning forward on agentic AI and living
by what we speak because that's the
place where value is going to get
created.
The market doesn't really seem to know
what it's doing when it comes to
software. It doesn't really seem to know
how to value software in this moment.
Uh, this is from Liz Thomas.
Uh,
she says software's forward 12-month
price-to-equity ratio has compressed
from 33.1 to 23.2,
multiple contraction of 30%, which is
wild because software gets these big
valuations because of what it is. Here's
another stat. The SaaS index from Talia
Goldberg. SaaS index is down 32%
year-over-year despite most companies
meeting or beating plans while the
markets are up 15%. Is
what do you think the market's reaction
is here? Is it just We had Bret Taylor
on, he said it was just kind of the
uncertainty of who wins. Is that your
perspective or why do you think
um, despite like like, you know, Talia's
saying here, the the fact that these
companies are beating their earnings
expectations, they're still getting
hammered and the multiples are
contracting?
There are a few things that we should
take into consideration here. As you
know, companies are valued not on what
they're doing today, but on what they're
going to do in the future.
And uh, I would actually distinguish
data platforms like uh, Snowflake from
pure
software providers operating on a
subscription model. Not that it's a bad
model, but the way they have operated is
AI became another skew for these folks.
And uh,
customers have had to sign up for AI
products regardless of whether they
created value or not. That's sort of
become the favored way of becoming AI
native.
I think what the current moment points
to is a real risk that that is not a
winning AI strategy.
Meaning that work is not going to get
done by interacting with a chatbot on a
particular SaaS app that you used.
Which is why
our our vision of agents operating on a
data platform that has much of the
analytic insights about the past as a
lot of our customers do, but with the
ability to bring in integrations via
MCP, via other APIs for how do you talk
to other systems? I think that's the
compelling vision. I think companies are
going to win if they have both a
convincing vision for how work gets done
in the future, but are able to back it
up with and here is how we help you, the
customer, get it done fast. The model
makers approach it from the from this
view of the model is everything and
nothing else matters. We approach it
from the viewpoint of it's the entirety
of the experience. It's the model,
that's why we partner with all of these
folks. It's the most critical data
that's valuable to your company, but
it's also integrations with the
operational systems that really help get
work done. I think that's the compelling
vision for how work gets done. What the
markets are in some ways pricing is the
fact that AI as a bolt-on to SaaS
software does not feel like a winning
strategy.
Um, you know, I feel much better about
the path that we are pitching. Also, our
products are consumption based, meaning
that if something doesn't get used as
much, there's not a penalty um to like
to just building them and uh and and
using them as much as you want.
>> But can I ask, I mean, you know, as
we've had this conversation, the idea
that people would come to like a
Snowflake agent, right? Cuz all of data
is there. So, they can go through all
these use cases that we talked about.
And that's compelling. But, why doesn't
that just end up getting subsumed into
some like you know, master agent that
has not just not just this Snowflake
data, but everything else? It gets.
That's very much the fear that we need
to operate with. That's very much the
opportunity of the moment. Mhm. Okay.
[clears throat]
Um the big model makers want to create a
world in which all of the data for all
of the enterprises is easily available
to them. Through like a chat GPT?
Through
Yes.
And you know, everything else the world
is just a dumb data pipe that feeds into
that big brain.
That's the vision that they would like
to see come true. And the vision that I
would like to see come true is, "Hey, we
host the most important data for every
company and the most important
predictive models for every company. And
I can create agents that can deliver
substantial value." But, by the way, we
also follow like others do an
interoperability strategy because if a
customer comes and says, "I want to
build a data product on Snowflake."
Fine, it can have an AI interface, but I
really want it to be accessible
somewhere else. I don't get to say no to
that. The only people that win are the
ones that effectively deliver what
customers want. Right? Is this going to
be the the big battle
field in technology over the next couple
years?
I mean, we we even had an example, I
think it was Amazon who like protested
in a big way from having I think
Perplexity scrape its pages. And it
seems like this is going to happen on
consumer and this is going to happen
because this is this a conversation that
OpenAI has with you? "Hey, Sridhar, we'd
love to have your you know, all your
data available in chat GPT enterprise."
You stick to customer choice. What do
customers want? If they want to access
data through a Snowflake intelligence
agent, the OpenAI doesn't uh team
doesn't say no. If on the other hand,
our customers want to expose
you know, data like important enterprise
data that they have as an MCP endpoint
into ChatGPT, we don't get to say no.
So, then how much agency does a software
company actually have like one in your
position? Because if it is up to
customers,
it's all about creating products and
value. It's not about anyone No one has
an insurmountable ChatGPT like OpenAI
doesn't get to say, "The only way you
get 5.2 is to come to ChatGPT."
Right.
>> I don't get to say, "The only way you
get to access data on Snowflake is to
come to Snowflake intelligence." It's a
little bit off It's pretty much may the
best player win. And so, it's very much
about creating value. And the burden
that you have is is large because it if
people are going to go to like a
specialized bot as opposed to a
centralized bot, that specialized bot
has to be
you know, orders of magnitude more
useful because it's requiring a
different behavior. Or maybe I'm wrong.
>> Maybe maybe maybe maybe not. Um I It's
This is the part It's very very early.
And remember, we're still living in a
world I don't know how many tabs you
have open, Alex. Mine is 200. Okay,
that's the state of my world.
>> That's pretty good. And uh
I have enough that I can't read the tab
names. I'll put it that way.
>> Command shift A if you use Chrome is
your is your magic answer to all
problems, but still um and so, I think
it's early. Yeah.
Uh when when your stock price gets kind
of caught up in like the the market has
says category, you know, this category
must do this, and your stock price gets
caught up. How do you manage that as a
CEO? Because it must be in some ways
frustrating to see that like the market
acts on categories versus individual
companies.
>> It's my job
to make us stand up. It's my job to make
sure that our prospects are clear. It's
my job to make sure that our company
accelerates to seize the moment that is
today.
And come have these come have these
conversations. Um yes, the markets are
reacting to the best information that we
have.
If we get clubbed with other SAS
software providers, that tells you that
I have more work to do. That's fine.
Yeah.
Okay, I want to talk to you about the
about Shadow AI and how people are
individuals are starting to
build their own AI programs. We've seen
that a lot over the past couple weeks.
Um so let's do that when we come back
right after this.
And we're back here on Big Technology
Podcast with Sridhar Ramaswamy, CEO of
Snowflake. Sridhar, great to have you on
the show. Thank you for coming back.
Always good to chat. What did you think
when this
Open Claw called bot molt bot moment
happened when people started running all
their own agents on their on their
computers and doing crazy things?
Well, I hope they were not running them
on their own computers. But still
>> somewhere and got their API keys
exposed.
>> Uh exactly, exactly. You know, I think
all rules of security don't vanish
because of because because of AI.
It's remarkable.
I'm fortunate in that I have two young
sons who are both in software.
And
you know, I get to see the world through
their eyes.
And as it turns out, one of them had one
day
between
when he came to San Francisco from he
moved from New York and when he started
his job on Tuesday.
And in that one day when I was at work
and he was home, he had managed
to get like
you know, an Ubuntu instance on AWS
completely separate from everything else
including his laptop. Thank God. And
he had
set up Open Claw on it as his personal
AI assistant. And uh it comes with
things like uh Telegram integrations.
You can talk to it. He started using it
as his to-do list. And he has set up a
little chatbot for giving me a summary
of cool AI happenings on X because I
told him like X can be a lot. I don't
like to spend that much time on it. I
still want to get what's important. So,
I get like uh a briefing every day of
cool things happening in uh AI done
entirely by the chatbot. Tell him not to
plagiarize that cuz I could be in
trouble if he does.
>> Okay.
>> I think it took all of a few hours for
him to do that.
>> this newsletter.
>> And uh but funnily enough, he was uh
or to build the entire self-contained
working thing that can literally react
to any question that he has. If he If he
says, "Hey, I have this hobby and I need
you to help me get better at this
hobby." It'll start sending him messages
every day about what should he do to
like learn a new skill. It's the
general-purpose nature of this is truly,
truly mind-blowing.
Took him a few hours to set up. Yeah.
That's the wildness of the moment. But
funnily enough, he's 26 and he was like,
"Yeah, yeah, yeah, I want no part of
this Multibook thing. I think it's a
bunch of hype. I think it's actually
people posing as uh you know, as agents
that are posting this." He wanted no
part of that.
And so, it's It's fun. I think it's a
remarkable moment in terms of you know,
you know, in terms of what is happening
out there. Um but I do think that you're
seeing what happens as um
these agents are you know, agent
frameworks become easier and easier to
use and set up and people will figure
out a set of security guard guardrails
for how to use that and uh and and
things like that. This is I think uh
it's it's a pretty remarkable moment.
Yeah, Multibook 175,000
posts, 1.1 million comments as of It's
the social network for the AI bots as of
the time we're speaking. So, I don't
think it's entirely I mean, if that's
entirely human, it's a pretty successful
social network on the rise. So, it's
done that in a week.
Pretty interesting.
Uh you
made some predictions ahead of the year,
and one of them really stood out to a
couple of them sales. Maybe we could
talk about them both, but uh one of them
that I found really interesting was you
said shadow AI will drive enterprise
adoption from the bottom up. Employees
who select their own free AI tools will
will remain the primary driver of
enterprise AI adoption in 2026. Rather
than waiting for IT departments to
sanction approved products, workers are
using chat GPT, Claude, and other
consumer AI tools for their daily work,
forcing organizations to catch up.
I think that's so interesting, and it's
something that I've talked about on the
show before how it seems like there's
these two tracks.
Companies that are kind of slow to move
and adopt these tools, and individuals
that are starting to find ways to use
them in their work. Why do you think
that is? First of all. I mean, anyone
who's been inside a even moderately
sized company knows that it's filled
with approvals and lawyers, and um you
know, uh pilots. I have a simpler
answer.
>> Yes.
It's the true 10Xing of the moment.
I talked to you about
how
with something like a Cortex code, you
can get a job that you need to do on
Snowflake. Like working with the data is
tough. It's tedious. Yeah. You have to
get lots of things right. A lot of
little details.
Can use
RCLI
and just automate this stuff and get it
done in less than a tenth of the time it
would have otherwise have taken you.
Right. That is remarkable.
And uh I now write documents. This is
without officially approved enterprise
uh version of our chatbots.
I write position papers coming out of
dialogues that I have with these
chatbots. I say, "This is the situation.
These are my thoughts. These are the
options. What do you think?" We sort of
go through almost a Socratic process of
debating stuff and producing something
that looks mighty polished. Mhm. Ready
to I've done pricing studies entirely
inside chatbots. Right.
>> We have to change prices.
>> Because sometimes when I like ask them
to do the numbers, okay. I I never I'm I
have never ever run a coding agent with
accept all my recommendations. Okay. I
am as anal as they come. Okay. Um my
first rule is when I started using our
coding agent was never delete a data
never ever delete a database. Never ever
switch an account because I have access
to production systems that have
snowflake data. I'm like, "Don't switch
to it when I'm playing around with
something else." You got to put the
guardrails. You got to be smart about
how you work. Mhm. Um and you got to
check the work.
Right.
>> And so when I did the pricing studies,
like, "Hey, plonk this for me. How does
revenue and margin change?" You got to
go study the work. But it's a massive
accelerant and the benefit that you get
from something like this, unlike a
hand-written doc, is let's say you
decide to change your mind and want to
introduce another new thing, you know,
normally we just don't do that in a
document or a study because so tedious
to go make all the changes.
These chatbots, they don't get bored.
They're like, "You want to redo this
work?" Not a problem. They redo the work
for you. Um I think it's that value
creation that's driving the adoption.
And it's not like we are actually trying
to be a lot more receptive to this
because we know that we would rather
have a tool with enterprise controls
than just have everything go
underground. And uh so it's it's it's
worked pretty well. And I'm And most
companies are also doing things like
approve AI policies on top of snowflake.
For example, a lot quicker than what
they have what they would have done
before because it is that value creation
that they're all hung hungering for.
Right, but I think the thing is and I
and I mean this is your prediction so we
can go deeper into it is that
individuals is it a 10x thing at the
moment? I would say yeah, there's
definitely value to be found in these
applications. But it is interesting that
it's the individual and maybe this is
normal the individuals are finding this
technology and doing it in in a way that
you describe as shadow AI, right? Where
companies are a little bit slower to
move. So how does that change the
dynamic of companies if you have a
couple of people in there that are like
leaning all the way into the tools and
the company is like yeah, we're in we're
working through this. Uh well, part of
what every company has to do
is to figure out how to embrace these
change agents
and uh make sure that they're surfacing
what they want to do and the value that
they're getting to everyone. Mhm.
I wanted to roll out Cortex Code to the
entirety of our solution engineering
team, 2,000 people. It's a lot of
people.
And uh the way we we did that was um we
selected a subset of them, about 30 40
people
and uh give them a little bit of
training and said hey, you should go try
this out, see what this is like. We
called them our AI champions. Mhm. We
celebrated the fact that these were the
forward-leaning folks and uh we also
made them
effectively responsible for spreading
the word down to the different uh to the
different teams.
Change in any large company is not going
to come from top-down mandates. You
know, let's face it.
What I know about AI
is minuscule compared to the sum
totality of what my 9,000 people know
about AI. And you need to create an
environment in which the most
progressive of the ideas that are coming
up, the most innovative of the people,
they have a way to quickly surface the
idea up. In fact, for the next all-hands
um Um, I've been working with my comms
team, it's in a it's in a few weeks. Uh
they wanted to have, you know, our
regular all-hands standard set of
discussions with the exact staff. I said
uh I want to spend 2 minutes personally
because I have to say something as a
CEO. I want the rest of the time to be
devoted to finding these firebrands,
looking at what they do, and
highlighting this as the champions we
need to figure out how to identify and
how to learn from. And we have to
embrace the moment in terms of how do
you use our collective wisdom to drive
our organizations forward. It's very
interesting because it seems like
as these tools get better, there are
going to be companies that will have
that mentality.
And there'll probably be companies with
leaders who are just like, "I don't know
about,
you know, all this AI stuff." And it
could actually change the competitive
balance of industries pretty quickly if
you have
organizations with more permission
versus less.
I would I would distinguish it more as
progressive organizations.
Okay, what is that? I
What What I mean by that is we always
have to balance.
Um, I will flip out if I find out that
anyone's running open claw on a
snowflake laptop. Please don't do that.
That's not safe. We will help you get
like a free Ubuntu machine on AWS if you
want. Uh there are smart things that
people should be doing and dumb things
that they should not be doing.
A progressive head of security is an
important asset here where they let the
innovation happen Yep. without making
people do unsafe things. Uh we are
custodians of data for some of the most
valuable companies in the world, and we
take that part very, very seriously. And
so it is that balance that uh that one
needs. But back to your point um about
changing competitive dynamics, very,
very, very real.
Uh I think we can end here. You also
have this interesting prediction about
big tech big tech's grip on AI models
loosening. I'll just read a little bit
of it. For years conventional wisdom
held that only a handful of tech giants
could afford to build competitive AI
models. In 2026 that will change. New
approaches to training, like those
developed by Deep Seek, have shown that
building the biggest, most expensive
models isn't the only path to strong
performance. You know, we're a year This
is a great timing. We're a year after
Deep Seek
didn't fully change the AI industry in a
way a lot of people anticipated. And so
it's interesting to see that that is the
prediction you made, especially if I'm
because if I'm right Snowflake did try
to build some foundational models and
then decided that was not the game you
wanted to play. I think foundational
models became very expensive to build.
We now have four players that are
creating models that are like wide wide
widely acknowledged to be the state of
the art. But a new clan model came out
yesterday that is shockingly close to
the best Sonic model that there is from
Anthropic. There continues to be a lot
of innovation in the space. I think
that's very very healthy for us. And
from a selfish perspective, Snowflake as
a data platform prefers a world in which
there are many people making great
models, especially open source models,
because we also have a really good
infrastructure team. We are very good at
running them at at at scale. But um
This is a world where a lot of value is
being created and a lot of change is
happening. And I think being nimble and
ready for that future of agentic AI,
that future of work, while always having
a laser focus on what makes a difference
to your customer, Mhm. Those are the
enduring qualities through the year.
Life will keep changing.
>> You're comfortable with the Chinese open
source models?
So we test them, we use them, we try to
we we try to learn
from them.
We also partner with uh US companies
that are trying to create open source
models. There's actually a company
that's based in uh in in Brooklyn uh and
San Francisco that is um uh that that
that we work with.
>> Which one? Um this if I remember this is
Reflection AI. Okay. And uh it's a
remarkable company. I think uh there is
a lot that we are missing out in not
having a robust open AI ecosystem.
We uh sometimes get caught up in this
world of uh you know, we have the best
AI companies on the planet, but we also
should understand that much of their
work has effectively become walled off
from the rest of the world. You and I
simply do not know what techniques
OpenAI and Anthropic are adopting to
produce the great models.
You can say, how does it matter?
Google Search, for example, pretty much
died as an academic
as an academic area after Google became
big. Why? They published nothing. And
they were ahead of everyone else by a
million miles. The area just died. Mhm.
And that was okay for us geopolitically
because Google was an American company.
I think part of what you're reacting to
is this fear now of open source is not
here, but much more in in a situation
where there is no winner.
What is happening right now is that it's
the Chinese companies that are
publishing their work. And what then
happens is all the universities, all the
students and professors in our country
are looking at their work and figuring
out how to build on top of it. And so
academia is diverging from what's
happening in the research labs. That's
part of the danger of this moment. And
that's the reason why we need to have a
more robust ecosystem. If it'd been if
it had been a world in which there was
one model maker that was a winner, and
it was an American company, I think we'd
have a slightly different attitude. It's
very clear now that that's not going to
happen, hence the fear about about open
models. And then if these, you know, I
think there's been such so much
conversation about the Chinese open
models over the past couple weeks.
Um,
you know, I think Demis Hassabis said uh
at the crack of the new year that the US
or the West is four years ahead, sorry,
four months ahead of them. Recently
there's been some uh discussion that
it's kind of
you know, closer than that. Uh is what
happens in the world where like those
models become on par with the leading US
foundational models.
For most of us? Yes.
It opens up lots of opportunity. Mhm.
The as you know, the very existence of
something, knowledge about the existence
of something can spur innovation in
other areas. You don't even have to know
exactly what someone did. This is
history has shown this repeatedly. Just
knowing that something is possible makes
people work feverishly on making the
same thing happen. You can bet that
Reflection is looking at it and going,
we can do better than Right.
So from a macro perspective, I would say
that that is actually a positive.
Because Mistral's going to figure out
how to reverse engineer all of this
stuff stuff and go one step forward,
which will be good for Europe. And
Reflection will figure out how to do
this in the US. This will also force
Meta to be doing more things in the in
on in the US.
I think in a weird way, that's actually
a net positive for us as a whole. I
think the impact on the model companies,
that becomes a little bit more little
bit more murky, but
Welcome to Welcome to this world, Alex.
You know, this is change every month.
It's constant.
The website is snowflake.com.
Sweetwater, so great to see you. Thank
you for coming down. Thank you, Alex.
Always a great conversation. Definitely,
really is. We hope hope we can do this
again soon. Thank you. All right,
everybody, thank you for listening and
watching and we'll see you next time on
Big Technology Podcast.