The information economy as a rod. The world is teeming with innovation as new
business models we invent every indistry industry. Inside Analysis is your source of information
and insight about how to make the most of this exciting new era. Learn
more and Inside Analysis dot Comside Analysis dot com. And now here's your host,
Eric Kavanaugh. All right, folks, welcome to the future. Indeed,
it is Monday at three Eastern time. That's when we recorded this show.
Of course some of you are hearing it later. Obviously, time for
Inside Analysis, the only coast to coast radio show that's all about the information
economy or truly Eric Kavanaugh here with our good buddy Eve Mulkers, and we
also have a special guest with us today, the chief product officer and senior
vice president, Rohini Kasturi is here with us from Solar Winds. So the
show is all about winds of change, how an industry leader is evolving.
And I'll just say a couple quick words we'll bring our guests in. Things
are changing dramatically in the enterprise software space, my goodness, in the past
twenty years. It is just remarkable how much change has taken place, whether
that's the cloud whether it's artificial intelligence, whether it's big data for example,
or open source. Open source in and of itself has really fundamentally changed how
enterprise software is designed, deployed, managed. And we have these stacks.
Now, we have GitHub, now we have Copilot, we have all these
things going on. What does that all mean. It means the world is
very, very, very complex. And so things like security and governance,
which have always been difficult, which have always been hard to do, have
gotten significantly harder. So what's the response to that. Well, obviously you
have to get better at what you're doing. And so these security vendors,
the governance vendors, the observability vendors will talk about that. Today all of
them have up their game. It's really amazing to watch. And we did
some business earlier this year with the company called Mesmo. And what those folks
do is they sit on top of your observability information pipelines and the orchestrate where
that goes. And the reason being, you start using some of these tools
and technologies and you gather all this data and at first it doesn't seem like
a problem because yeah, it's cheap to store data. Yeah, until you
get like a year in and two years in when all of a sudden,
it's not that inexpensive to store all that data, and now you're paying out
the you know what for all these different storage solutions. So point being,
it is a very very complex world these days. It's getting more complex by
the day. And then just the pricing structures. Think about what the cloud
has done to user access, to user interface, but also to simplicity and
to complexity under the hood. It's actually more complex under the hood in some
senses, but the usability is so much easier. And then the costs,
well, what happened. You went from big ticket monolith licensing costs too smaller
monthly as a service, subscription service. A lot of the open source vendors
will basically sell their services and helping you install this stuff and maintain that stuff
because it's open source. So if it's open source, that's free, right,
Not really anyway, that's a very long winded introduction and I'll throw it
over to Eve Mulgars for his opening comments. Here. Eve, it's a
very complex world these days, and the companies that are in the space trying
to secure is trying to help us govern and stay vigilant. They're having to
work over time, what's the answer, what do you think's going on?
And how we can we make the most of it? Eve, you already
said it's observability, understanding how it all connects together and see what goes where.
Having that transparency is these days very important and as I mean, all
the time in the data space. Just today had an earlier discussion with somebody
else and we were discussing about the data stake where you have the various components
that fit together to provide that insights out of your data, and it's again
how they tied together, integration and who does what's and I think having a
system in place that helps you on manage who has a responsibility of which part
from where that's of uttermost importance. Back in the days, it was pretty
simple. You went to the the main frame and you knew how to talk
to right and people could help you. So you only had a system.
But now it's so layered that Yeah, like you say, it has become
so complex in these days too. As an IT person, technical person responsible
of the infrastructure of security, to keep track of that, yeah, well,
I mean you used to have all sorts of different departments and you still
do. I think some of that is going to change here as a CDO,
for example, chief data officer is going to be also responsible for security
in his or her domain. It's not just going to be one person.
And I'll bring in Rohini in one second here, but my business partner,
doctor Robin Bloory's British, born in England. He's got a British accent and
he always has these witty things that he would say. And one of his
comments cracked me up one day when he said everyone has a security leader and
his name is scape goat, meaning like when it goes out like you're fired,
bring the next person in here so that they change anything. It's not
really that person's fault. It's just hard to stay on top of these things.
But with all of that said, Rohini, History from Solar Winds.
Welcome to Inside Analysis. And you folks are in the middle of a big
change. You're trying to do a sort of branding and image change, but
also a pricing change. Tell us what's going on at Solar Winds and what
you're trying to accomplish. Edic and Eve, thank you so much for having
me on the show. You know you've articulated all the business challenges already very
well. Right, there's a significant transformation happening in the world, which is
cloud transformation. Data is growing and data is exploding on the clouds, Governance,
compliance are all critical requirements for companies. Security is a big challenge.
So what we realize at solar Wins is now we have been a leader in
the monitoring space for the last twenty five years, right, leader in network
monitoring, infrastructure monitoring. But the course of years we have acquired a lot
of companies and application monitoring, database monitoring, many other companies, many other
spaces. So what we're what we're doing over the last two years really is
you're embarking on this Solar Winds platform strategy, which is evolving from monitoring to
observability, right where observability becomes the founders for future autonomous operations. Right.
So we're trying to bring in all capabilities like network, infrastructure, app database,
all of them into a unified platform so that we can be more relevant
in the new world and really you make all the difference that customers are looking
for. That's the change and transformation happening. Yeah, And so you know
from an organizational perspective. From the solar winds perspective, you're looking at this
world is changing dramatically. You're looking at the risk profile shooting up, bite
Frankie. But you also are watching as machine learning and AI come along and
really mature to be able to hack through this stuff. And you know,
when you talk about monitoring and observability, usually what you're trying to do is
understand the steady state, like what is normal, what should it look like?
And then you watch out for anomalies. You watch out for changes in
behavior. I was joking with Eve that just today, as I was preparing
for this call, ironically enough, I started getting all these emails in my
inbox of confirm your subscription, confirm your subscription. I'm like, what's going
on. Someone is not necessarily hacking me. Someone just trying to be annoying
to me by setting up a script that goes and like signs me up for
all these things. So I set up a filter find any email as the
words confirm and subscription in it, and send it to my trash folder.
So you know, I won't be intensely signing up for too many new ones
because I won't be able to find the confirmation email and that's like a looking
for it. But the point is like these things can happen now, Like
they're all all these things that people can do to cause trouble, and sometimes
it's ransomware, sometimes they're just mischievous. But you know, what are your
thoughts about being able to leverage the newer technologies like ai mL to help us
kind of hack away at all the underbrush and get to the real problems they
are going to cause us trouble. Absolutely great question, Eric, So if
you look at AII is the fundamental underpinning of observability. So what I think
by that is if you once you start collecting data from infrastructures, applications,
databases and you name it in all types of notes and devices that are out
there that are spread across clouds, you do have tons of data, whether
it's logs or metrics or traces or a lot of data, and it's hard
for a davops professional or a cloud ops or an IT professional to really look
into everything to understand what the anomally is. Right, That's where AIML really
comes into picture because you're not only collecting all the data, but you start
correlating the data. You start analyzing the data, you look at where the
anomalies are, and you start pretty much giving start translating into business problems.
Right. For example, if you have a business service that you're running on
a cloud or a distributed cloud, you wan't to really know the help of
that business service, right what is the SLA of the business service in terms
of availability or performance. So let's say we say the availability is ninety nine
point nine nine nine in terms of availability, you want to know a where
is this one time where the availability is dissing? What is the root cause
for it? Is it a network that's causing a problem. Is it an
application node, Is it a database query that's causing a problem? You want
to know where the root cause is. So that's where AI and mL come
into picture, because you're really finding the root cause. You're trying to detect
their normally highlate it to the user, trying to correlate to the problem and
so on. But again, the journey of AI for us has just begun,
right because observability is just the first part of the overall journey. Where
we see the market shifting is really too autonomous operations where technologies like general DBI
and chat GPT will become the critical building blocks where the actual operations users can
start dictating their business requirements to say what they want to achieve in the right
ozation, and the underlying platform and systems will go do the right thing and
come back and say, hey, you know, I manage the application that
you wanted me to manage and remediate. I did all that their sel so
and so these are the two things that I'm not able to do in an
automated manner. Can you go fix it? It could be a storage that
is failing for the last three times or three days that replaced and so on.
Right, So that's kind of how we are seeing this world of observability
evolving to autonomous operations, right right, So what you're saying, just so
are already understands, is that these large language models like chat, GPT,
like BARD, there's some others Dolly Data Bricks has rolled out their own Dolly
two point zero already. These are very powerful technologies that can essentially predict words
as what they do. But really what you're seeing is a fusion of that
technology with chatbot technology, and chatbots were actually doing pretty well in the last
couple of years in part because of the technologies behind them, the ability to
build a sort of decision tree for the chat bot to deal with, so
you identify, does this person want an address? Okay, boom, solve
that problem. Does this person want more information as a complaint, what is
this thing that's coming in and quickly sorted out? And with those decision trees
which are built using building blocks that are rudimentary and there I guess sort of
imperative in nature to use the programming language, those are good, but really
the more declarative architectures that we're seeing now can be very compelling because already with
declarative programming, and a lot of these new technologies use declarative, you're just
telling the system the end goal that you want and then it goes and figures
out how to do that, which I'm not gonna lie kind of blows my
mind, but it does get that done. And so now when you connect,
like when you have an LL large language model in between you and your
systems, that gets very interesting because you know, I've mentioned to Eve,
the first question in my mind after I saw these large language models work,
was, well, if they can write in English, French and German,
but they can write in Java and htan on coball too and guess what they
sure can, right, So they can write code for you. Now you
do have to tidy it up. But we're just at the beginning of this
process. So these lms as a sort of intermediary, as a really supercharged
chat bot for the operator, are going to be very powerful and solve lots
and lots of problems that we've really never been able to solve before, at
least not at that speed. Right, is that about? Right? Rheeny
and personetic You actually articlated it well that the conversience is what is going to
create that impact the conversionce of Jenny d VII, chat GPD. This whole
conversationally I or you know, chat bots and so on that you mentioned,
along with the business problems that you have is going to be very interesting because
imagine if you're you know, imagine yours. There is a self service doctor
portal, right, and then you can go to the website and says and
the doctor asks you, hey, Eric, what is what symptoms do you
have? And you say, I have an ear pain, this and that,
etc. And then it results in a series of questions like hey,
upload your image upload your video whatnot. Or it could be sending a swab
why an instack art to you where you're doing a test remotely like an administered
test, uploading the results and the end of it. The physician is qualifying
all these data and sending a prescription through an instack art an automated manner to
your home and you have a prescription in thirty minutes. Right. Imagine that
is the world of shift, where you're bringing really converging your conversations, your
question and answer translating into you know, the research behind in the medical terms
and so on. Right. I see that kind of convergence help helping in
various markets. Yeah, he subservability and operations markets, it helps security markets.
There are many markets there. You will see these applications coming out right,
so that it can simplify the lives of customers and end users significantly.
Yeah, sure, I'll bring even to comment on this, You're still going
to have to monitor things even though you've got this automatic observability, automatic monitoring.
I don't see people being removed from the equation anytime soon, because you
do have to kind of stand on top of things. But we are certainly
getting closer. But It's almost like it's just in time to a certain extent,
right because the bad guys have all this stuff too. The bad guys
have generative AI. The bad guys have this denial of service attack approach that
they have been mastering for years and years. So it's just this constant battle
between the bad actors and the good actors. I almost think though, that
we should be doing better at being able to identify where these people are coming
from what they're doing. They're always using proxies to kind of bounce stuff around.
But I don't know if are we are we just treading water here or
do you think we're actually getting ahead of the curve now. Well, I'm
pretty hopeful that the better part of it will will win from the from the
worst part. Instead's my ideal view on it. And I think we're having
that ethical discussion about about the whole AI. So if we can feed it
with the with the right things, we can get on top of people using
that for for the worst things. That's that's uh, that's my vision for
the future. And let's let's let's hope we're getting there. And like you
say, we can't feed the advantage with generative AI as it learns from new
data that gets added to it. You train it on a base system.
That's that's how it responds to whatever you feed it. And once it gets
new insights, it becomes smarter. And that's I think where most of the
people are for the time being pretty afraid of that, that we can't predict
how it will evolve in such a way and it becomes too smart for us.
That's that's that's the feeling what we're getting. But yeah, I think
we can do so much better. And if we can keep bias in a
certain way out of that and direct it in the right direction, we're very
well off. Yeah, now that's very interesting. Bias is an issue.
The whole proprietary nature of these things is an issue. I mean, as
of the moment, chat GPT is a black box, right, I asked
it which database do you use? And it said I won't tell you.
I'm like, okay, Well, from an explainability perspective, that does not
pass muster. All right, you're not gonna You're gonna to be careful.
I think we are going to see some legislation perhaps at some point, but
before we see that, we're going to see some lawsuits. I think some
companies are going to start suing open Ai and some of these others, you
know. But I guess they'll just take a leaf out of the Uber playbook,
right. I remember when this is a bit of a tangent, But
we can pick this up in the second segment coming up in a minute.
But I remember when Uber was really spreading like wildfire across the United States.
I can't think to myself, they have to be running a foul of local
ordinances and regulations on taxi cabs because in many cities you have to get a
medallion to drive a taxi cab, and people will pay a lot of money
for those medallions. I know a guy who paid like fifty thousand dollars in
cash and a brown bag in a grocery store parking lot to get his taxi
medallion in New Orleans about ten years ago. So when Uber came around,
he was I'm sure not too happy about that. But what they did is
they just stacked up a whole bunch of money for legal expenses and for lobbying,
and basically it is ram out of the stuff through everywhere they could go.
And in Paris they fought back and I think they've had some real trouble
in a few other places too. But you know, probably what's going to
happen is there are a lot of lawsuits. They're gonna be hacking through stuff,
and they're gonna be a lot of confused legislators trying to figure out what
actually is going on as we try to pass some legislation. It's gonna be
tough, it's gonna be weird, but it's not going to be stopped.
I can tell you that what folks don't touch up, that would be right
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not all of our whereas we'll qualify it terms. Welcome back to Inside Analysis.
Here's your host Eric Tabinaugh, So to tell you, these large language
models are absolutely taking us to the future by giving us a reflection of the
past. Because these large language models like chat, GPT, like Barred,
like Dolly, these are all different large language models. They are all models
built upon a corpus of text, most of it from the web. I
think, what is its chat GPTs everything before some data in twenty twenty one
or something like that. But Barred from Google already has connectors to the real
time web. So those who you used chat chat TPT are probably seen.
It'll answer questions saying, oh, I'm sorry as a large language model,
I don't have access to real time data, etcetera, etcetera. The rest
assured that's going to change because they are going to work on this, because
the challenge with these things is that they do hallucinate. They do make stuff
up by fusing together bits of information. Now, they're also incredibly powerful though,
at being able to generate text that is very organized, that is very
syntactically correct, for example, so they understand meaning, they understand syntax,
they understand what things are supposed to look like. It's great at writing poetry,
but it's also going to be very powerful in these chatbot scenarios. But
the quality is an issue. The providence is an issue. Maybe, Rohani,
I'll throw it over to you. These are big issues. I don't
think Congress is going to get its act together to legislate soon enough, so
the industry is going to have to solve it for itself. And to me,
providence is huge. We need to know where you got this. And
the other side of the equation is the well security, governance and things of
this nature. But you know, providence. I mean, I wonder how
effective they could do that if they hadn't thought that through in the architecture.
This thing going back to re architect to fits some of that stuff. It's
going to be hard, but I'm guessing that they probably can get visibility into
how it's piecing things together. And then and you score certain things, you
can say, hey, this is false. It's it's just like like with
Yellow Pages online or Google, like is this your business, claim your business,
etc. You're be able to do those things. But I almost think
everything is moving too fast, right, How are we going to solve this?
What do you think, Roany? I think the fundamental problem will become
the data quality score. I end up having a big use case where companies
will start asking, hey, what is the source of data? Where did
you get this data from? Right, And they'll essentially be a solution to
say, hey, this the score is high because it's taking from sources ABC,
and if you take it from another source that you know, the score
could be weakerrect it becomes the foundational underpinning. And then, like you said,
all the problems around data security, data governance, data compliance, all
of this have to be solved by some of these large vendors that are rapidly
pushing jen a DBI or chat gptr, you know, other tools that are
going to be available, right, so I think you have the owners on
them to demonstrate that. And then the other shift that's also possible is if
you take companies like Nvidia, right, they're innovating a lot on AI and
developing new technologies and solutions. Right. What I can also envision is a
company is going back to this comfort zone off, Hey, let me do
a lot of this on my own private cloud, where it is everything is
in my own secure environment, especially large enterprises, financial companies where they want
all the tools but start running in their own data center or in their own
private cloud. Right, if that happens, then there is going to be
a new wave on the cloud journey as well. That might be a pose
a question mark. So I think we are going to discover a lot in
the next three to five years THEREK. Yeah, that's interesting, Eve of
bring you back in on this one. You know. I believe on prem
is going to have a very long tail. I don't think these on prem
data centers are going away anytime soon. And I've already seen kind of some
straws in the wind. There's a company I saw a Tara Data at their
event in twenty nineteen where what these folks do is they can come in and
make your on prem servers look like S three. It's kind of like Minio,
So Minio it does something it's like S three for everyone else basically,
right, they created a storage mechanism for allowing you to store lots and lots
of data. So I think these on prem data centers are going to have
a kind of a long tail. If you can scan your environment, know
what functionality you have, know what you can go of you it should be
possible to kind of reinvent these data centers as private clouds, but I don't
know. You know more about stuff that stuff than I do. What do
you think? Yeah, I definitely think that that will be a way to
go on one of the options where we will go. You see the discussion
already going out with the cloud. We thought, yes, it's still scalable,
it is very flexible, but the costs day they are sky high because
you did the architecture not as you should do it in the cloud. I
mean, just turn it on whenever you need to compute whatever you need to
memory. So we were not used to put up the architecture in such a
way, and now by bringing that hybrid environment and in place, that's a
different way of on how you can put up your architecture. And with some
privacy concerns, I think there is a certain move back towards the on prem
solutions as such, Still, the trade off between KPAX and opex is still
a very difficult thing for a lot of companies to anticipate the costs of what
they will have in the future. And you will see that there will be
definitely a hybrid approach into setting that up. Wherever you need a flexibility the
scalability, you will move that that load to the cloud and a small part
will still will be on prem and they will take the solutions, like you
said, the Aws and street Buckets on prem. As such, it was
natively designed for the cloud in such a way, but you will see that
it will be adapted to on prem solutions as well. Yeah, the cloud
native movements I think is going to impact on prem data centers. I'm not
sure exactly how that's going to shake out, but you know, I'd be
curious to hear Rohany's thoughts on this. I mean, you folks are now
moving from a traditional licensing model to more of a subscription service. Is that
going to be just in the cloud or is that also going to be for
on prem stuff and how do you how do you pull that off and how
do you not cannibalize existing revenue streams. So the good part about the Sullivan
strategy, Eric, is we want to meet customers where they are on their
cloud journey. Right. What we really mean by that is we announced the
two flavors of the Solovan's platform last year. One is called the Hybrid Cloud
Observability Offering, which is essentially offering a customer can install in a private cloud
or a public cloud of their choice, but it's managed by them, right,
meaning self hosted, self managed by the customer. That's one model where
we provide a subscription model. Right, they deploy the software, manage on
their own and it's their data centers for data privacy, security, all reasons.
The second model we also introduced is the SaaS platform. Right. You
know, the SOLOS platform is available as a service on natively, on AWS
and Azure. So we are asking customers say, we are going to give
you the underlying cloud as a choice. Right, you decide whether you want
the instance or a tenant, your tenant to be running on a Natables or
Azure environment across the globe. So that's the flexibility that we gave. But
of course the SaaS is a subscription service, you know, whereas on Treme
you can buy a subscription software that you can install. You will get updates
pretty much every quarter and periodic updates, you will be eligible for refreshents on
and so forth. We did all that from a product site, but then
to simplify that, we also changed or simplified or pricing, packaging and licensing
as well, because we do have many many products in the market. Right
in the past, which had different types of licensing between fend all of them
into a node based licensing. So we pretty much as the customers like,
hey, how many notes do you have? The note could be an application
or a database or a network or anything, and say hey, what do
you like to manage and how many notes do you have? And it's prized
accordingly so that it becomes easy for a customer to understand what's their total cost
of ownership? Yeah, as soon do I get DOY and so on?
Sure, you know, I'm sitting here, I'm always thinking of metaphors and
different ways to describe things. And to me, the network still is the
lifeblood of systems and applications, right, whether it's a big company are small,
there's always a network. And it's almost like if you can monitor the
network very closely, it's like getting your blood work done right when the doctors
take your blood and they run tests on your blood while they're figuring out what's
happening in your body by virtue of what they see in your blood, right,
and you have to learn things over time. Of course, with AI
and m L and the healthcare industry, Wow, is that going to change?
The testing market and hope will have to give much much less blood.
Of course, that was the big Thronto's promise, right, and I think
she just went to jail, so she learned the hard way. You got
to be careful about how you promote things and what your transparency is. But
I bring this up because I'm curious to know from your perspective, from the
solar winds perspective, you can learn so much about what's happening in a particular
environment by just watching the network traffic. I mean, there's other things that
you do, but the network really is a primary source of information to be
able to help you identify problems. Right that Pusson intended bus and said that
unique advandas that we have is we brought in capabilities from fifty odd products that
we've had historically, like into the performance monitoring, network traffic analyzer, infrastructure
analyzer, then you name it, application database, you know, you name
it, all sorts of things across private and public. We brought all the
data that gives us a very unique advantage to say, hey, what's your
health score. If you want to ask what's the health score of a nice
devinment, we can give you a score but then start pinpointing to where the
problem is. If you say the score is ninety nine, we can tell
you where that one is missing and what are the specific devices or nodes or
creations that are kind of leading to the problem. Right going your first question
as well, Yeah, and chat GPT is in these large language models,
they are very knowledgeable about systems issues, about studyings, configurations, all these
things. All this information is out there. Again, you have to let
it verify it, be careful what you trust when you get out of these
things. But I think that a combination again of these traditional tools like you
have and a large language model can be very powerful in terms of giving very
detailed descriptions to the user what is happening because you can plug into these systems,
check all the different systems, all the monitor and it's coming across,
and be able to ascertain, hey, this is the problem. That's the
problem. Then you just have to go about remediating the problem, and you
can even give you the instructions of how to do that. So it's very
very useful because it educates people while it's solving the problems. Right broheeny absolutely
absolutely no, you are becoming like a not a chief architect for ABS,
Like we were discussing a few minutes, agreages the data quality. Right,
So because we are actually getting real time data from all these various types of
devices, we call it the full stack observability pretty much take from cloud services,
you know, infrastec of services, application, databasis, you name it.
We're taking logs, metrics, traces in a lot of real time information.
We aggregate into a single source of truth or a single data platform because
that data platform, because we know that data is right and that's real.
And then learning language models on top of the data to train that data is
going to be critical so that we can say when we detect an anomally and
when we can have a conversation with a customer to say, hey, here
is the problem with this health score. We can really pinpoint to the specifics
amount of data or specific set of data, and we know that data is
the quality is right because that's time. It's not synthetic. I mean,
synthetics can help predict from a prediction use case, but from understanding the actual
SLA of your business service today, it's data and the data quality is like
highly accurate. Right, So that's where we have a unique advantage to kind
of paint that story to customers. This is get I mean, I sit
here and I kind of have the wheels turn in my head as I'm piecing
stuff together. And you know they're talking now about oh, having an AI
run a business, for example, can you train an ai'm out with to
actually run your business? And I mean the short answer is, well,
yeah you can. I don't know that you should yet, maybe at some
point sort of, but I think it's always going to be sort of a
copilot. But I will say the thing is, man with these machines,
they're able to calculate so many queries, do so much thinking, if you
will, or really it's processing so fast, much faster than a human.
And they can watch for things. So like, if you are a senior
executive company, you got ten thousand employees working for you, I mean,
you got to watch out for some privacy issues obviously, but just knowing the
level of interaction between different groups can give you signal. Knowing how much these
two people work together can give you signal. And we're in the beginning of
a whole new era. So this all that stuff also applies to it.
Governance, it security, observability, all those things. Keeping the trains run
on time is extremely important. That's what these folks do all day and now
with these kinds of technologies, I mean, you're the ideal scenarios that you
get a list of ten things to focus on today. That's that's how it
changes your days. You walk in and you've got code read on you know,
some network issue, you got code orange on some daily base issue,
and that's how you spend your day. So it's going to tell you what
to do pretty much all day long. Is that about right? I'll throw
it over to you to comment on efforts persons. So actually, if you
look at the vision that we have painted for slurvance, it's evolving from monitoring
to observability to autonomous operations. So what we are seeing is the world of
the co pilot, in the world of ID and DEV and secops operations,
wherein the users can start defining their business requirements. They can be guided on
business requirements, but most importantly given feedback on business requirements to say, hey,
you know the available ability you ask me for the availability of this business
service or the cost of this business service to be at one hundred dollars a
month. I try to spin up so many notes. It's the cost is
going above about the range right, or it can go about the range right.
So you can do a lot of business requirements around availability, cost,
performance, security, a lot of them. It's really becoming a partner and
an operations partner or a copilot partner for enterprises and customers, right, So
really guide them, really work with them hand in hand as a partner to
solving their business challenges. Yeah, and you know why that's so cool And
we'll pick this up in the next segment here. Why that's so interesting is
because you're going to be able to adjust the dials on your compute and say,
oh, I want to dial up security today because I'm seeing all these
hackers try to hit our wall. It's just like a start t right,
like put all put all power towards the engines, put all power towards the
photon beams, etc. You're going to have that capacity. That is cool
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Welcome back to Inside Analysis. Here's your host, Eric Kabanov. All
right, folks, back here on Inside Analysis talking to Eve Mulchers of seven
W Data and Rohini Costui from Solar Winds, and we're talking about the winds
of change, how things are really changing in the software world. All so
many companies are moving to subscription models, software as a service as sort of
an accepted standard now. And what I like to tackle in this last segment,
it's really the conversion or the convergence I should say, of different functionalities
and different roles, because we've had different groups for years and years now and
there are reasons for that. But I think some of that stuff that's going
to start to change a because organizations are not going to be able to keep
as many employees. Frank, I think you're going to see shrinking of headcounts
in large organizations, in part because of CHAT, GPT and things of this
nature. In part because of just headwinds financial pressure, etc. And so
you're going to see some convergence here. And let's think about the governance world,
the security world, the compliance world, the systems management world. All
four of those groups really benefit from observability, benefit from knowing how is the
network performing, how are our databases performing, how fast are things operating?
Or the classic line when you're talking to someone on customer service on the phone,
all I'm sorry, so our systems are running slow today. All right,
well, why are your systems running slow? Is it because you have
crappy systems and you have an upgraded It's because you've been hacked? Is it
because you know what's going on out there? Well, that's what all these
these IT technicians have done for years and years and years now SRIS they typically
call them Site reliability engineers. That the people are trying to figure out what's
going on. They're managing the ticketing systems and just figuring out how to sort
things all day long. It's some of these people do. And I think
that you're going to see real convert there, which is good because we shouldn't
have a completely separate governance group from a completely separate security group from a completely
separate uptime group, Like, all these people are using the same data at
some point in time, So shouldn't they be collaborating more and shouldn't one be
feeding the other as opposed to several people all trying to consume the same information.
So this all gets down to workflow, It gets down to organizational hierarchies
and all sorts of different things about how stuff gets done. But the fact
is that a lot of these AI engines are now able to reach across different
systems and basically execute what used to take three four people in various stations to
get done. So that is going to change your organ chart, it's going
to change your titles, and it's going to change what people do how people
do them. All this stuff is coming. It's coming fast and furious,
and we're hearing it. I'd be curious to hear from your perspective, it's
solar wins. What are you doing to manage the sort of change side of
the equation what they call change management even for the human side of things,
How are you how are you managing that at So we're willings, So,
first of all, two things right. First of all, we are a
company in transformation, rapidly evolving. Our product portfolio from on TREMP to cloud
right. So during the last two years, we went through changes in terms
of evolving the team skill set in the product organization, developing a lot of
cloud development capabilities, cloud ops capabilities, as sorry capabilities, you name it.
You know you talked about several roles. A lot of that have changed
and converged as well. We went through a lot of that shift. We
are going through a lot of changes on the marketing side, go to market
side, customer success support side, across the functions. We are changing and
continuously evolving. But I think the key here is how will it change in
the next five years? Again in the right Ladybi and chat GPT and I
think the convergence can start from depending on the company's goals. Because if the
company's goals are, hey, I want to use new tools to achieve more
productivity, more scale, and more agility, what would happen is their EBITA
margins can still be low. They can take all the productivity gains from these
new tooling and they would hire more people to do more. If there are
companies that are more EBITA focused, they can achieve more EBITA and say hey,
I'm going to optimize, cosse, reduce the and consolidate roles, and
that will start happening from the C suite itself, right, There will be
consolidation of some SPEECE suite roles. For example, you know we're talking about
what do we call the prompt engineer as opposed to a typical engineer, right,
or the prompt nurse or a prompt doctor as opposed to a typical doctor.
So a lot of that is going to lead to like, for example,
today we have an architect role and a designer role, a product or
product management, and an engineer who's writing code. I see conversions happening in
some of those rules where a designer who can think through what needs to be
solved can produce beautiful code and secure your code to really solve business challenges.
Similarly, there will be convergence on customer success and support. I do see
disruption happening across the board in all types of roles, Eric, but I
see it as just like virtualization and cloud have led to changes in the companies.
Yeah right. I think if the companies can embrace this change and learn,
they can use it to their advantage to growing and you know, even
growing their profits and hiring even more people. Yeah, that's kind of how
I see it. But you still see how it's going to play out.
Yeah, and you know, yeah, that's a good point. Eve.
I'll bring you back into this because Eve is an excellent prompt engineer. He's
learned a lot about leveraging this technology, and it is a different sort of
thing. It's different than a Google query. You have to give specific instructions
and be precise. The more precise you can be, the more closer you're
going to get to the answer that you're looking for. But to me,
I think the real winner is going to be the companies who figure out how
to leverage this technology at some scale in some significant capacity like customer service for
example, that's going to be a real big one customer service, but also
just operations and simple things like think about the annoying side of booking flights to
go somewhere. Even to this day, even with all these engines, it
takes me like thirty forty freaking minutes going back and forth checking the different times,
Okay, is this gonna work? Is that? How much is this?
How much is that? And these are powerful engines, and it still
takes like forty freaking minutes when you could have your smart little bot and say,
hey, bot, I want tickets to Austin, Texas. I want
it to be other three hundred dollars within this date range go and just hit
your button and itally ding ding ding ning. That's like just going back and
forth by itself doing all this stuff for you and then guess what it comes
back and it gives you. You just get the alert thing. Here's your
thing. Do you want to buy it now? Yes? Or no?
Boom, buy that sucker. I mean that seems complex. It is complex,
but we're not far from that. I think the companies that figure out
how to enable all these new technologies are just going to crush it out there.
But Eve, what do you think? Yeah, what you're referring to
is as the workflow orchestration. What we know already for twenty thirty years these
systems, but they are set up in a certain sequence on how the design
is thought that the workflow would work. And I think with the large language
models and how they are trained, they're becoming hyper personalized. So there are
adapting to how you behave and how you would like to book your flight and
what you want to get and instruct you on how to do that. And
that's where I think is the power in doing that. Just before Rohney said
about hiring even more people, I just recently saw an interview about the big
resets and exponential organizations where they said, back in the days, she needed
ten thousand people to run a company and to scale and to have that all
these resources. And then it came down and now it AI technologies. You
will be capable of running your company with just two people and the rest will
be will be done by AI system by technology. So you can do the
ideation, but you can do it at such a speed and at such a
scale that we will have a different type of organization these days. And that's
where I think it's definitely these guys had amazing thoughts in the interview. Definitely
something you need to watch. But that's that's my feeling where organizations will go
to and with AI, things will become even more well, cheaper, even
easier available, and that's going to be the big transformation what we will be
seeing. So as a as a human being, we can get the augmentation,
but we need to change and we need to adapt in such a way.
And I don't know if we're already ready up to that new way of
doing things. Yeah, it is going to require change of workflow and change
of what people do every day. We're Heini, maybe off throw it back
over to you. I'm still maintaining that the major way in which AI affects
our day to day lives is going to manifest itself in the form of suggestions
I suggest you do this that the other thing, whether it's send out emails,
call this person, just basic stuff because it's not that complicated what you're
doing in your job. But if you give the AI access to your workflow
and your systems, it can track all these things and see who you're talking
to. I mean, Google's already doing this in a lot of different ways
in your Gmail. Like when you type an email. If you're in the
Gmail clients and you type an email to a friend, you're type in their
name, It'll like say, do you want to add this person? Do
you want to add that person? Because it knows from recent interactions that those
people are on the thread. I mean, that's simple, but it's still
pretty compelling and it saves us time and effort. Right Rohani, Productivity is
one part of it right, Well, I can even imagine the world of
it's an ADVII diseases getting make it funny here we're in, you know,
because we are going to be directed to behavior seton way and asked to you
know, guide it every seton way saying hey, you can do this,
you can stand up, you can do this, do that, And that
can also result in so much influence on us, and that can result in
a new disease called the generative AI disease or something else that lable ten years
from now. Right. But again, productivity gains are going to be significant,
like eave set you know, there can be startups of five to ten
people that can be producing enormal seats um and uh you know, uh,
simplicity, security, all of this will become corner stones, right and yeah,
so we're that's right, We're going to read all these things for sure,
and you're going to be able to, you know, set your boundaries
for what what constitutes security, what constitutes privacy. It's kind of a folks
podcast polit segment is coming up next. You're listenings Inside and Out Southern California's
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