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com. And now here's your host, Eric Kavanaugh. People. All right,
folks, hello, and welcome back once again to the only coast to
coast show all about the information economy. It's called Inside Analysis. Who is
truly Eric Kavanaugh here with a very special guest, one of the most renowned
cloud experts in the world, quite frankly, and a guy who I just
found out moments ago coined the acronym EAI Enterprise Application Integration. I remember those
Eddie days back in the nineties and the early aughts when we were really in
the earlier days of trying to integrate enterprise applications like ERP, Enterprise resource planning,
data warehouses, click, extreme analysis, all sorts of different systems,
which of course used different data models, different types of data. The whole
data warehousing world ben because we tried to get sense of what was really going
on in all these different systems. And now that is bigger than ever.
But Jennai is coming up and driving everything further in different directions, and we're
all just trying to hang out for dear life to a certain extent. But
we're going to talk today about the evolution of cloud computing. And David has
a new book out in Insider's Guide to Cloud Computing at stron and Mechaman.
You look that up and check it out, and I'll just give you my
two cents here on what we'll talk about today. So what is the cloud
today? Why are people going to the cloud? Why are people leaving the
cloud? You had a joke before earlier today David about the cloud eviction.
Notice, all right, and what's going on there? Sometimes it's too expensive,
sometimes it's not what people thought. Sometimes they get locked in. Remember
the concept of vendor lock in, but it's also the concept of cloud lock
in. Although Google I heard just recently decided to kill all their egress fees,
which was an interesting move. And just so people understand, when you
put data in the cloud, obviously you payp for storage. A lot of
people understand that now with their consumer lives of Apple and Microsoft selling you storage
to keep all your stuff, all your videos, it's all your files,
etc. But when you load it, of course you pay for storage.
But then the big cloud vendors in the cloud computing world were charging you a
lot of money to take the data out, so it was a penalty.
I used to always use the joke Hotel California, you can check out any
time you like, but you can never leave, at least without paying a
whole lot of money. Then it was just painful, so they wanted to
keep you there. And there are certainly some good arguments to be made for
living within a cloud environment like Amazon Web Services has tons and tons of services.
Microsoft of course is nipping at their heels. Google Cloud platform still in
third place, I think a distant third at the moment, but they were
certainly making a push, and there are others hero Coup, there's a bunch
of others out there, rack Space, Oracle as a cloud. But with
that long sort of intro, I throw it over to David Linthicum. David,
tell us a bit about your new book and Insider's guide to Why are
people going to the cloud today? Why are people leaving the cloud? Yeah?
Thanks, Eric. I mean, ultimately it's a book that gives away
the secrets that I learned in the last fifteen years of cloud computing and actually
been inside the cloud world since the late nineties and got into it with the
whole application delivery network thing, which is a predecessor to SASS, and got
into SAS and got into integration as a service, and then got into the
whole infrastructures of servicing and growth of cloud. And so I think there are
a lot of misperceptions that people, quite frankly don't understand about cloud, and
so I wrote a book which provides the realities of it in terms of what's
cost justifiable and moving to cloud, what's not justifiable and moving to cloud,
The fact that lock in is going to be around each and every time,
you're never going to get away from it, and the ability to leverage the
technology to kind of take everything to the next level. And what's occurring now
is that people are looking for the justification of cloud computing as a business asset.
So, in other words, it was all that in a bag of
chips fifteen years ago. Everybody's moving into cloud was the destination of everything.
We're going to shut everything, shut all the data centers down. And I
think a lot of people believe that, and that's turning out not to be
true, and so we're looking at cloud as a optional destination. We understand
that everything's not going to be in cloud. We're moving to a ubiquitous computing
environment where the STEP's going to run everywhere, including cloud. The cloud's just
going to be an option, and really it should be an option that's justified
in terms of its cost effectiveness and our ability to leverage it to take business
to the next level. And I think that's a spotlight that they haven't been
under yet, and I think it's a good thing that they are. Yeah,
no, that's a good point, And I'm thinking to myself, there
is so much innovation in the cloud. But as I've often said, the
rumors of on prems demise are greatly exaggerated, if for no other reason than
amortization. That's one stickiness. Data does have gravity. Data doesn't like to
be moved, at least large amounts of data really don't like to be moved.
There are all kinds of problems that happen. Records get truncated, records
get lost, that's not put in the right place. All kinds of different
things happen when you move stuff around. And let's face it, in the
older days of data centers, even in the early two thousands, being able
to realistically map what is where throughout that entire environment, even from a data
perspective, it's pretty darn challenging. I mean, some companies came along,
like Elation and some others in the data cataloging space that were our help being
kind of get to the bottom of that and figure out where stuff is.
And there's new technology for being able to scan environments and see look for metadata
fields for example, to understand where your data is. But in most data
centers, data is all over the place in lots of different formats. That's
a very difficult challenge to try to sweep up and put up in the cloud.
Right. Yeah, And now we have a new priority that the enterprises
have. And this whole GENAI stuff, the genai needs data to train,
and it needs existing data. Business data that's in mainframe systems, data that's
scattered all over the place, including different cloud providers. Most enterprises leverage a
multi cloud platform. They're leveraging more than one or two different cloud providers.
Data datas in there in the basic, in the silos, and in the
data centers. And to your point, it's a mess that we haven't solved
yet. We've actually made things more complex over the last several years, adding
additional data and additional databases and purpose built databases and object based databases and cloud
based databases, things like that. And now we have this GENAI thing,
and this thing eats data for breakfast, and we have to get relevant and
correct data into these monsters in order to build the lms are going to be
of use to us. And so suddenly everybody's scurrying around saying, we have
no idea how we're going to find the data that we need to train these
systems, how it's going to be cataloged, how it's going to be managed,
how it's going to be secured, governed, and how we're going to
deal with compliance, and so there's kind of a double priority out there right
now. Number one, moving to generative AI. Everybody wants to do that,
but understanding that generative AI is not going to be effective without data to
data to support it, and they really have no way of dealing with their
very complex and poorly deployed and poorly documented data systems that are in their environments.
Right. Yeah, And I want to dive into the GENAI stuff,
probably later in the show, but let's because it's just huge and I have
some theories about it, and I think it's going to be a very interesting
ride, but a very good ride quite frankly. But as I think about
the cloud and for any listeners out there who maybe run a data center or
are responsible for data and applications and functionality, when I look at what is
happening in the cloud, by and large, there's a lot of net new
stuff being developed, so net new workflows, cloud to cloud, kind of
integrations, service providers B to B two C B two C, all that
kind of stuff connectors, right, and it's getting so much better. I
mean, just today I set up Stripe to work with Zoom and it took
me like a second to enable the app. I'm like, wow, that's
pretty cool. Like my friend Eve Molcher's was joking, Oh yeah, Like
twenty years ago, I remember trying to connect two things in the banking system
and it took me days days to figure that out. So things have changed
a lot. But when I think about going up into the cloud with existing
on prem applications, that's some pretty challenging stuff. And if you don't go
all in when you do that, you're making matters worse for yourself because now
you've got a whole another layer of complexity. I was talking to Mark Smith
years ago who was talking about work day and how a bunch of companies looked
to go into work day but they didn't get everything in there, so they
had a fair amount of stuff PRAM with a whole bunch of stuff in work
day and then you know, two years later, guess what, you have
two different environments with tons of data. That's very hard to reconcile and that's
just a nightmare. So what's your advice for companies looking to go into the
cloud but want to do so responsibly and efficiently. Yeah, it's called planning,
and we used to be pretty good at it. Back in the day,
and I think everybody has a tendency to kind of move in random ways
right now. One of the things I saw during the pandemic was a complete
mad dash to cloud. So even though we were doing lift and shift and
there was some refactoring going on and building that new applications using native technology which
were fairly efficient in the cloud, suddenly we picked up a bunch of applications
in data and we pushed it onto a public cloud provider. It compiled,
the compile was clean, and we went ahead and ran it, and suddenly
we figured out in twenty twenty two, and this is when a number of
studies came out they were paying way more than we thought that we would be
paying for the cloud. And that's because these unmodified, very inefficient applications that
were running in the data center, we put them on cloud, and suddenly
they were inefficient in the cloud as well. But only in the cloud.
You have to pay for the resources that you're leveraging. Heavy use of memory,
every use of IO, heavy use of the network, all those sorts
of things you're going to get a bill for. So suddenly everybody realized that
we made movements into the cloud we have half the stuff that runs in the
data center, half the stuff that runs in clouds, some of the edge
computing stuff, mobile computing platforms started to emerge as well. And now they're
normalizing the systems and really kind of doing the things they needed to do after
the fact. And so what's happening now is people are reactively planning and so
that in other ways, they realized that we're paying about two point five times
what we thought we were going to pay, and I wrote that. I
wrote about that in my book and the Cloud. They can't afford it.
They're looking to optimize the budgets, their ability to bring the most value back
to the business. They have this GENAI mandate that's following them as well,
and so they're running around quickly trying to normalize and pushing the systems onto the
platforms they're going to be the most cost optimized for them, and in many
instances that's going to be back on premise. So and since they're heading the
reset button, I can't imagine having those conversations with my boss. And remember
I removed that a app and that data sets to cloud and US is ten
million dollars to make it happen. Well, oopsie, Daisy, we're going
to have to put it back because it was way too expensive because we're unwilling
to invest in the money to optimize it on the cloud platform. So there's
no free lunch here. I think that's what it comes down to. And
so we can certainly try to delude ourselves and the fact that moving to a
new technology is going to solve all our problems. It's just going to create
new problems. It is going to have opportunities, but we have to put
the thinking ahead of making these opportunities viable for us. Yeah. I mean,
if you're cloud first, if you're a new company, you can go
cloud first, that's great news, right, But if you're an existing company
with data centers all over the place, that's where you have to do that.
I like the way you answer the question. It's called planning. If
you guys heard of it, where you'd like sit down and map stuff out
and you know, I wonder. I mean, attention spans are short of
these days with TikTok. You now I hear my child on TikTok. It's
affecting our attention span, our ability to focus on things. Boards are nervous
about stuff. And now that we see the economy, even though it's not
a recession, there's pressure everywhere. I mean, there's been a blood path
in the tech space, and anytime that happens, you know that someone has
to sit down and figure out, Okay, what were these people doing,
what are you doing? Who's going to do this stuff now? And when
you got this completely scattered world where twenty percent is in the cloud, eighty
percent is still on prem that's I mean for an IT architect that it's going
to leave you to pull your hair out, right. It is it is,
and that's why we have to get a little smarter in how we're going
to leverage technology layers, you know, abstraction, automation, the ability to
do things smarter rather than harder. And I think that the complexity right now
is killing us. We just have too many services that we're managing, too
much heterogeneity. Certainly, the additional cloud is compounding that, as well as
multi cloud environments and moving to edge computing and mobile computing things like that.
There's reasons why we're moving in those directions and they do provide innovative differentiators for
companies to do so and I think that's awesome. You want to leverage technology
to build IP that's really going to create the value within your system, but
you have to do so in a way that's going to be scalable and it's
going to be optimized to bring the most value back to the business. And
that seems to be a bummer, does you know? I always call myself
the designated buzzkill in some of these meetings. But we're going to have to
go through this at some point in time to make sure we can justify the
utilization of the technology, cloud and everything else. And so it becomes an
orchestration of many different platforms that we have to leverage that are going to get
us to the end state that we're looking for, and that requires a tremendous
amount of pre thinking, pre planning, proactive and proactive looking at data.
You just mentioned the thing earlier, data normalization. The ability to get that
under control huge issue, and we've got to start picking away at this now
because it's not going to get any better. We're just making things more complex
and hoping some technology will come along to fix it, and that's never going
to happen. You know. I think somebody self that there are some cool
technologies I've come across over the years, and I wonder if you've seen anything
that is good. I mean, in the development world, use all kind
of interesting things tracers to figure out to do your troubleshooting to understand what the
heck's going on out there. So there are technologies that's a company Extra Hop
I ran across gosh about ten years ago or so, that have a very
interesting approach where and they're primarily for security, but there are other use cases
as well where what they will do is they will siphon off your web i'm
sorry, your network traffic and use it to create a digital twin of your
existing environment so you can see all the databases where the applications are. You
can kind of see data as it's flowing between them. That's an interesting one.
But where I'm going with this question is is there any set of technologies
that can really help understand map out and then render some cost efficiency models around
what it really takes to move something from on prem to the cloud. How
much money can we realistically expect to save. I mean, how do you
get there? Is it just such a manual effort that you have to kind
of build out spreadsheets to do it or how does that actually happen? Yeah,
that's not going to work very well. We're doing Excel spreadsheets for everything.
Some of the Finnops technology that's underway, and a lot of it's going
to be driven by AI into itself, is promising because we're able to assess
not only in efficiencies we have in our current configurations in the cloud, but
also on the on premise systems as well and using the same technologies for BO.
So anything that operates cross platform is going to be a huge help because
we're not building additional silos on the particular platforms to solve a particular problem.
Instance, We're solving a holistic problem that's going to go completely across the enterprise.
And some of the phinnops tools and I'm seeing developed out there are starting
to move in that direction, and that's going to be hugely beneficial because we're
able to see usage patterns, we're able to do accountability, we're able to
deal with security, and also we're able to understand what legacy and technical debt
exists that we need to prioritize to fix, including data normalization. Yeah,
I'm actually giving a talk at the Data Universe conference coming in April to New
York City. A panel discussion that the word technical is in parentheses and the
title is forever in your technical debt, right, because it's just everywhere.
And I joked I heard a great definition of technical debt one day when one
guy said the definition of a legacy system is any system in production, right,
Yes, which I thought was pretty funny. But there are some more
nuanced definitions that say, okay, after a certain number of years, or
it's not well supported, there's not good documentation, you know. But to
a certain extent, these lllms can help with a lot of that stuff.
I mean, you look at the whole copilot concept and the ability now in
some cases at least, to be able to feed code into an LM and
say what does this do? Right? I mean, that's pretty powerful to
help deconstruct and understand what's out there. But I mean, you know,
the code base of some companies is like millions of lines, so you can't
pump that into chat, GPT, at least not yet, right. But
some of these tools I think are going to help us hack away and get
closer to the goal that we're looking for. What do you think it's got
to because we can't do this manually. So if we're going to have these
this complete complex environment excuse me, yeah, holda go ahead. We're going
to have this complete complex environment, we're going to have to understand how to
normalize it, and we're going to need tools and talent to help us.
And I think that's going to come from the AI stuff because it can take
and make sense of a multitude of patterns in data and applications and API usages
and different architectures and able to make sense of this stuff. And also the
thing it's able to do is build a knowledge of these systems, what to
do right and what not to do, to fix these issues and guide you
in a way to get better, incrementally better over time. Get your data
normalized, get your platforms normalized, get your APIs normalized, get your code
normalized. It's going to allow us to be better people by leveraging this technology.
I don't think we're there yet. I think people are putting too much
emphasis on it, but we will be soon. Yeah, that's interesting stuff.
I think that it is going to help hack away. But it's almost
like you have to stop the world and get off for a minute to be
able to rationalize all that stuff and reconcile it and understand where you're even going.
But folks, don't touch that. Delb right back. You are listening
to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric
Tabanaugh. All right, folks back here on Inside Analysis talking to the cloud
duru himself, David Lintigham. He's got a new book out and Insider's Guide
to Cloud Computing. We're talking about why go to the cloud? Why leave
the cloud? That's kind of a fun conversation. I think that, you
know, one of the funnier lines I've heard over the years was from someone
who said that the most unpredictable line out of on your budget is the AWS
bill because you don't know where it's going to be. Now you mentioned finnops
and financial operations data. Basically that is really taking off, and I think
for all sorts of great reasons, because it can kind of track and Observeability
is a related technology and a sort of overlaying technology, if you will,
being able to watch what comes in and let's face it, machine learning is
very good at tackling vast amounts of data of otherwise really boring data that human
beings don't want to look at, but noticing changes in patterns, noticing patterns
themselves, noticing ups downs, something is different. Maybe take a look at
this now. So it's the confluence of all those things that's creating this finops
space, which I think is going to help us get a much better handle
for how much cloud costs, what value are beginning from all that stuff.
And this is what CTOs and CIOs need right in CFOs, they need some
understanding of what it's really costing and then what it's doing for the business,
right Yeah, And I think sometimes it's the finops system is going to tell
you about all the inefficient is that that particular CIO or CTO built over the
years, and so it's going to provide bad report cards, and I think
that's going to come out in many in many instances. In other words,
if I do take a finnop system and it's going to be trained with best
practices from ten thousand experts and massive amounts of data, and it's able to
look at my particular systems and look at the configuration of the systems, look
at the optimization, look at the ability to solve the business problems, all
those sorts of things. I suspect that a lot of them will provide them
with the DNUS in terms of what was built and the technical debt that was
built, and really lots of mistakes that have to be corrected. But I
think it's okay to get a good, honest assessment as to where we are
now in terms of our as a state, no matter how bad. Many
of those instances are bad mistakes that are made and start progressing to more optimized
it. You know, leveraging AI is the ability to direct it and it's
kind of funny because it's also the destination of the systems that we're building.
Yeah, this is what does get me very excited is thinking about the protect
you mentioned checking configurations, right, so you can set up these algorithms to
give them access to your environment. They check the data, they can go
through and see what all the settings are, and if you have a baseline
to understand what that means, then you can give really good advice. I
mean Boomy we mentioned them, I think before the call, or maybe at
the beginning. Boomy has a data lake of like two hundred million integration patterns
that they've trained a large language large language model on That gets pretty interesting because
you can see, Okay, I'm connected to my on premise, connected to
this system and that system, this system over here, am I doing it
correctly? And it can look and see well, actually, if you do
it this way and that way, you can do something better. That's pretty
interesting stuff. And I think that's where a lot of these LL and AI
in general is going to come in very handy, is in making recommendations to
the user based upon its training data and what it's determined so far. Right,
absolutely, and the AIOps has grown up around that, the ability to
leverage in a system to assist you in operating your environment in a much more
effective and efficient way, which is very much like what Boomy's doing, it
sounds like, And so it is going to be a layer of intelligence we're
able to put between us as the human beings that are bad at making decisions
in a real time way, and then having sets of automation that are driven
by AI, they're going to be able to make better decisions. And I
think we're going to have to throw the keys over to those sorts of mechanisms
to allow it to continually improve itself. Can I remember it's not thinking about
office politics, it's not thinking about the budget. It's just thinking about how
to do something better and really kind of using the intelligence of ten thousand experts.
And I think we're ripe and ready for some technology to come along to
really assist us with that, and that seems to be a net positive thing.
Yeah. I think we were talking on the break about the media and
how a lot of media outlets are having real issues now, and I think
that there is those are straws in the wind. And you look at how
cloud computing changed all that, right, because we used to have newspapers that
was tangible. Someone had to print. It costs a lot of money for
the ink in the paper and you got to get it out there. And
then everyone digital. And I remember it was like in the late nineties that
I would read a story in the paper and be like, wasn't that like
last week? And I realized, no, it was last night. I
read it online. That's why it seemed old to me, because it's in
the paper. And that sort of long tail is falling off pretty healthily now
if you will, of print publications, like you don't see too many magazines
anymore. You don't see too many printed publications, newspapers, et cetera.
So they're all getting it from the web. But then there are downsides too
because everything moves so fast and you don't really check things, and they're ever
wants to get out there. And it gets back to planning and responsible behavior.
And I guess that all maps to the cloud too. Right, You
have to think through this stuff and don't just jump right in, but watch
and see where it's going. But just to finish my point on the media
and how the cloud is changing their world, it's huge, I mean information
consumption. In my opinion is that no media organizational structure can live the old
fashioned way in this new world because these large language models, and I think
what's going to happen here it's going to be very interesting, is that up
in the cloud you're going to have the ability to navigate through systems of record.
Think like for the federal government, the Federal Register. You connect an
MLLM to the Federal Register, and all of a sudden you can have very
interesting conversations about what is actually happening in your government in layman's terms, instead
of trying to read legal lease or contracts or even think about the laws that
we passed, fourteen hundred page laws just feeding into an LM and started asking
you questions about what this stuff is that's going to I mean, that is
a hugely game changing dynamic. Where I was talking about this a couple of
weeks ago on the show. Even like in processes for lawyers, there's a
law firm Morrison Forster that's eighteen hundred Pensylvania Avenue charge very large amounts of money
for their services because they know a lot of stuff. They're very smart.
But now these large language models trained on a corpus of legal documentation and legislation,
et cetera, can answer all those questions for you, even simple process
questions. So you know a lot of these AI white collar situations are going
to be impactful for people going to take away jobs or ultamately change jobs.
I mean, this is an incredibly disruptive time, right it is, And
I think it's okay to start thinking about, you know, how to leverage
this technology effectively. We also have to look at the ethics of it.
But I probably would trust an AI lawyer than a single lawyer because they're going
to have the knowledge of every case, every case law where a person can't
think about everything. And so if I'm just researching something and getting advice,
I'm going to get it directly from directly from the AI engine. And I
think that's that's where things are going. So a lot of these information oriented
jobs where you're just providing advice and recommendations, you're going to find that the
aipath is going to be a much more fruitful way to do those things.
I mean, that is such a mammoth transformation. It really, it's it's
gobsmacking to think about it. Like terms of service. So We've got a
little side project we're working on and I was talking to one of my partners
and we were talking about well, like legal documentation like disclaimers and things of
that nature for software applications, et cetera. I said, just go to
chat GPT and tell it to write up a generic one. Boom, You're
going to get it because it looks just like all the others. I mean,
who actually reads these things anyway, right? I mean how many One
thing that does annoy me is with software as a service, they'll say,
okay, click here to say that you have read and understand the terms of
service. I'm like, don't make me a liar like click here to get
passes. I'll agree to it, but don't make me say I've read an
understandable read it maybe a liar. That's when they do that. Yeah,
it's it's going to change some of that. So in terms of service,
we can tell the engine to read it for us and tell us what the
exposures are, what it actually means. The ability to take legal documents.
I sign them all the time. I'm not a lawyer. Some stuff to
have them reviewed by a lawyer, but it would be so nice if I
had some sort of a higher intelligence it's able to consume that and spit out
the way I should consider it. And the thing is just kind of bringing
everything back to technology decisions. The same thing with that, you know,
the architectural decisions, the ability to look at the configuration of technology in some
sort of an optimized way. Right now, we have so many biases and
so many political decisions that are made that come into how people pick technology.
That's how we're making the mistakes and building up the technical debt. So the
assist us was that as well, and I think it's completely acceptable for people
to leverage that technology to do things that we just don't have time to research.
Yeah, and I think that's going to be a big new way of
doing business, is to leverage these llms all day long, ask it stuff.
I mean, what I've learned is that it's very good at describing basic
technology patterns and usages, like understanding how do routers work? Or understanding how
does Internet protocol work? What is HTTP all about? Things where there's enough
published information that there are enough vectors that are similar enough that it can go
out and find all that stuff, right, Because basically what they've done is
they've vectorized text. And a vector, of course, is just it's a
point and then a direction right, and then you It has lots and lots
and lots of those like gajillions to use one of my mother's favorite words,
and it can do very good pattern matching across those environments. And that's all
it's really doing, right, It's just pattern matching based upon your prompt to
give you something that it thinks you want, right, That's all it does.
And so the ability to have an amalgamation of information that saves me from
having to go collect it and find it and come up with my own opinions
on it are come up my own disseminations of the information. So I'm going
to be I'm going to be a bias by the information that I'm able to
find. And so this puts a layer between all the information out there and
the ability to aggregate it into some sort of a form where it's easier consumed
by me, and I'm going to have less biases. I'm able to get
to the essence of what the information means. And you know, I use
AI for my research all the time and do that for just that purpose.
In other words, I can certainly google something and go through all the documents
out there and come up with a you know, basic understanding of what something
means. Or I can you know, go to an AI search engine,
well whatever, chat to EP versions that I'm using or barred and pull up
and have it disseminate what all that information means, and not have it make
a decision for me, but instruct me as to what's important, what are
the attributes, what are the different patterns out there and the common patterns it's
able to find. And as a researcher, that's absolutely an imperative to me.
Yeah, and you know, to bring this back to a real focus
on cloud. Let's think about Google. Like you said, I could google
something. I Google is in the crosshairs of AI engines now, granted Gemini,
that's Google's version, and that is the one I prefer. I use
Bard all the time every day. And to your point to think about how
Google's business model on search terms has been just so powerful, but is now
holding it back in a lot of ways. Right, you go and Google
something, and you've got to scroll down through all the sponsored links before you
get to any real content. And someone actually told me, who's pretty well
informed, that even the organic search results you're get in Google aren't entirely organic
anymore. I thought that was kind of an interesting thought that he just threw
out. There is a very very smart, very well connected guy, so
I don't think he made that up. But now that whole model is byzantine
compared to what these AI engines do, because to your point, you can
ask it very specific questions and they'll give you the synthesis. It won't give
you a bunch of links where you have to do the homework. Again.
I mean, Google is now of course giving you short definitions of things and
then pointing you to where you can find the rest, but that's not as
good as what you get from BARD or chat GPT when you ask it these
questions. So I think this is that is a huge transformation for how we're
interacting with the web of information. And yes, hallucinations are a problem.
Everyone's working on that with these RAG models, retrieval augmented generation. I think
that's where all the action is going to be in the future, and I
think that's going to be the future interface for senior executives working with their enterprise
data. It's going to be some AI, large language model that has access
to all these different information systems and can help you figure out what's happening in
your business right now and what you can do about it right You're right.
It will be personalized how they want to consume information. You know. It'll
even sing them a song about sales results, you know, And I think
that's perfectly fine. I think the ability of personalization and the ability to find
information in much more effective ways, and the ability to get to the essence
of what that information means is something that we don't do well today, and
a lot of bad decisions are made based on misinformation coming in from something and
miss understandings and not having all of the information in front of them making technology
decisions for instance. Keep coming back to that, So the ability to have
a personalized view of how we're making decisions and then put it in the context
of how I understand it, and also put it in how I learn in
other words, either a video or audio or text or whatever. It's just
going to be much more effective. People are going to have a much more
effective day. Maybe it won't not work as much as we do, and
the ability to get to decisions that are going to be much more balanced and
much more directed at the outcome of the business. Well in the ability to
track things too, and to audit things right. That's the other really cool
side of machine learning in general is that machines don't sleep, machines don't lie.
Machines are working all the time. But if you set it up properly,
you can have an audit trail for who is using the recommendation. So
I mentioned I think most of the benefit that AI will bring will come through
recommendations suggestions that come from the system. This could be in healthcare, it
could be in banking, it could be in retail, it could be anywhere.
But you'll make a kind of like use the analogy the other day of
in baseball, when the pitcher shakes off the catcher, I always go,
oh, no, here we go. We get a comfort ball because listen
to the catcher. The catcher knows man. The catcher is probably the best
person. It's like your mother on the field there to listen to your catcher.
But we'll be able to track how often Bob or Susan or Fred or
whatever went with the recommendation, how often they did not, and if they
do better without going with the recommendation. Okay, well we need to learn
from them. If they don't do very well by not going with the recommendation,
we have a conversation with them. Why are you not going with the
recommendations? Like you're not making sales, you're not listening to the recommendations.
The point is that's another thing you can track. It's another form of instrumentation
in your enterprise and environments. I know the break's coming up quickly here,
so I'll just flesh us out for this one. But that's a very,
very big deal because hitherto you've just kind of relied on people telling you what
they've been doing all day and shaking their word forward and then looking at the
results. But in the future, you're going to have many more metrics to
understand. You know, how much time did they spend working on these projects,
how often did they listen to the recommendations of this engine, how long
did they engage with this engine? All that stuff that can be tracked,
and that's a big deal. But don't touch up to folks, and be
right back. You're listening to Inside Analysis. Expect now welcome back to Inside
Analysis. Here's your host, Eric Tabanac. All right, folks, back
here on Inside Analysis talking about the evolution of cloud computing. And I've got
David Linthikom. He's got a new book out there and Insider's Guide to Cloud
Computing. Let's let's get back to some advice. You've been in this business
for so long, so you've seen and you understand that that AI is very
powerful, and to our point in these conversations we're having here, it doesn't.
I mean, it has a little bit of bias just from what it's
picked up by absorbing all as content, but it doesn't have a whole lot
of bias. And I think that it's going to be really interesting to see
that getting humans out of the loop is a big part of the success story
going forward. You always want humans in the loop, curating these engines,
watching them, monitoring them. You can't just turn it on and walk away.
But I think it's going to be the case that we're going to learn
that the AI make better decisions in a lot of cases about what to do
because it can consider all these factors that are hard for human beings to consider,
and it can absorb so much information and process it so quickly. So
I think we're going to go through a period of time of learning to let
go of control and learning to let these AI things, kind of like we
do when you follow your phone on a map quest through a Google Maps to
take you somewhere right. What do you think? No, absolutely, I
think that the AI is going to take over a lot of the mundane information
oriented tasks that are going to be rising up to more strategic decisions, which
I think is perfectly fine. So we make most of the decisions we make
is based on our own knowledge of information that we and normally it's an impression
that we have. It's not normally accurate because we're human beings. We can't
understand everything to the degree of accuracy, and the ability to turn that over
to an AI engine, I think is going to be a productive is going
to be a productive instate. I certainly don't like making those decisions. I'd
rather have you know, somebody, certainly the mundane stuff. How I feel
at fill out a time card? Are you know, looking at you know,
doing a security audit, which is just a matter of running numbers.
And we have people who do that. And if I can turn it over
to an AI engine and they're going to be able to do that thing is
going to be able to give you much more of an accurate response. And
that's going to be a tool that's going to go to not only my productivity,
but also go to the ability to optimize the value that's coming back to
the business because they're getting the right answers cheaper price. We don't have to
hire and fire people to do that kind of stuff. Then people do what
people do. We're good innovative creatures. We're good at making decisions and looking
at the innovation of the technology and how to use it. And that's the
role we're going to play, which I think is a much better role than
we're playing today having everybody involved with the mundane stuff. So I welcome that
world. I guess I welcome my robot overlords. You know, that's what
some people say, but you know, at the end of the day,
it's going to make us better humans if we use the technology in the right
way. Yeah. I think that's a really good way to put it.
And it's kind of the point I'm trying to make about learning to ride this
wave and to use these tools. And I also think that we'll see,
thanks to the power of AI delivered mostly via the cloud. Quite frankly,
we're going to see more of a flattening again of organizational hierarchies. I think
it's going to become much more collegial, and that's what everybody wants anyway.
I mean, not too many people really enjoy telling other people what to do,
and those people are probably not the people who you'd be telling other people
what to do, because what you want are what do they call it,
servant leaders, right, that's really what you're looking for as someone who will
help help you as a leader, not just telling not to boss you around.
Basically, I think there is going to be more of a flattening like
we saw in the eighties. I remember when a lot of middle management went
away in the nineteen eighties when there was a recession, and we're probably going
to see something like that again, because it's at the coal face, as
they say, where work gets done, and way up high where its strategic
decisions get made. Those are the most important places, and I think in
the middle it's all going to flatten out a little bit, especially because the
nature of what we're doing so much of it is going to change because it's
going to be automated, it's going to be AI fueled, AI driven.
So it's going to be an interesting period of time now as we adapt to
this confluence of technologies coming together. What do you think? No, I
couldn't agree more. I think that it is going to be a revolution.
It's an industrial revolution in essence. It's going to occur in twenty twenty five
to twenty twenty six, and we're going to have to rethink and reevaluate how
we're leveraging this technology and how the human beings need to interact with it,
and where it's going to have value, where it's going to be and I
think organizations are preparing to make those decisions. Now there's whole future work that's
going on, or people are rethinking the role of human beings at work and
the ability to look at we have a declining you know, declining birth rates
in some countries and you have to have people who fill into that. And
so it's a matter about doing things in smarter ways with the technology when we
have an opportunity, since this technology is a huge weapon that's in front of
us now to do a tremendous amount of good. And I think that information
jobs people who you know, do some of the mundane stuff in some of
these organizations, they're going to be displaced, they're going to go away,
but I think they're going to rise up and do things that are more rewarding
and also compensated better. And also the ability to automate the training of them
and the ability to have them learn how to run the prompts is going to
be the next generation of this stuff, and I think people are going to
desire to do that more so than sitting in front of an Excel spreadsheet for
ten hours a day. Right, that's a good point. Let me ask
you one question about and we talked about finops, about cost, and you
think about how much money has already gone into training these models, for example,
the training Chat GPT. I've heard some stories about how many GPUs it
took and how long it took to train that stuff, but then to actually
run them, I mean, right now it's very inexpensive. Is there another
shoot that's going to drop in two, three, four years? Do you
think in terms of cost of these things as companies like Microsoft, like Google
try to pull the string and make more money from that stuff, is that
what's going to happen? What do you think? It's not going to be
cheap And I think whether you run in the cloud or on premise. Also,
the specialized processes you just mentioned in GPUs, and there's processes in development
right now or lower power and are going to be more expensive. So it's
takes a tremendous amount of processing. And so if you're going to process your
own data, you're going to have to store that data someplace. You're going
to have to process that data on a specialized process is typically a GPU.
You're going to have to pay for that infrastructure, and people, I don't
think understand how much that's going to cost. And it's going to be hugely
cost prohibitive in many instances, and so we have to figure out to put
those justifications in now. Yeah, and I guess the Microsofts of the world
are banking on the size of the audience, the size of the customer base,
because what was sucked in to Della talking about we're building things for billions
of people to use. So if you spread that out over billions of people,
yeah, you can make your money back. That's probably why Microsoft is
doing so well right now. I'm just thinking to myself, when is that
shoe going to drop? Is it going to be? Because I mean,
we see this all the time with the freemium model, right. I use
a service called Bitley, which has been very useful for me. In the
early days, you could only buy an enterprise license or the free version,
and I leaned on them have a cheap version, and I said, oh,
we just can't do it right now, and now they do. Now
you can do eight bucks a month or twenty eight bucks a month or something,
and that is it's a very established standard. But I did get addicted
to it. And now if you want to have X number, you have
to pay like thirty or forty bucks a month. Or something like that.
So it's an established model. We understand that product led growth is one way
to describe all that stuff, but really knowing where the costs are going to
bend is part of the ballgame, right and understanding it's hard to figure out
exactly how that's going to happen, right, Yeah, I think the majority
of the AI is going to happen in the cloud because it's more convenient to
do there. It's actually a more expensive model. It's more expensive to use
the resources in the cloud being on premise, and I think a lot of
organizations, just like we did on the pattern of the last ten years,
are going to start out in building their initial prototypes and systems in the cloud
and then look at the expense and look at the opportunities for pulling it back
on premise, or even looking at the micro clouds that are being developed these
days, anything with the dot AI, their GPUs as a service, things
like that. In other words, specialized microclouds that are just doing AI.
I think that's going to be an option, but everything is going to be
more expensive than people think it is right now, based on what I have,
like conversations, and then I do the back of the mac back of
the Napkin calculations. It's going to be to get to where people want to
be, they're going to be spending a lot more money in the r right
now. Yeah, that's my thinking is that there's so much investment in getting
people onto the systems, you know, getting client acquisition basically, and you're
going to get addicted to it. You're going to want to use it all
the time, and the cost they are going to go up, up,
up, up up, and you're going to have to figure out how to
deal with that. And you know, maybe some of these smaller models are
coming out too, which aren't so extravagant, small language models people are talking
about, for example, Mistral's got this mixture of experts they talk about,
So there are going to be ways to make it more sustainable. But even
a small language model is an incredibly intense initiative to build and to run.
It just takes a lot of money and there are a lot of sunk costs
baked in there, right, so it is going to be a little bit
dicey, I think. Closing thoughts, we've got about sixty seconds before the
live show closes out. Where do people get your book? I'm sure online
where should they find out more about you? Amazon? Look, look me
up on LinkedIn. I have seventy two classes out on LinkedIn Learning and also
teach at LSU, so you can find me in all those places. So
you went, did you go the LSU? No, never been on the
campus. Oh no, kidding, that's too funny because I lived in New
Orleans for a number of years, and of course LSU is a you know,
very well known. I heard that. Yeah, the people love their
LSU in Baton Rouge obviously, but cool stuff. So that new book is
remind me again. It's an Any's Got to Cloud Insider's Got to Cloud Computing
and published by Pearson's and you can find it anywhere finer books are sold.
And you've got some several books out there, right, seventeen books. I
wrote the book on Enterprise Application Integration B to B application Integrations, so at
a cloud convergence. Even wrote a book called Killer Doss Utilities twenty years ago.
It's I think the coolest book I've ever wrote, as big as a
doorstop. Hiller Dos Utilities looking has a shark on the has a shark on
the cover, and came with the it came with a three point five inch
floppy. Wow, that's pretty impressive. I remember the free point five inch
floppies. Man, I remember my first portable portable compact computer. It was
like forty five pounds. Good break your back lifting that thing up. They
go for thousands of dollars on eBay now, so it's if you held on
to it, it'd be worth some money. Wow. While folks, you've
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three one ninety seven, and old political truism notes that where there's a will,
there are a thousand won'ts. And what a hurricane of wonts swirled out
of Washington's power centers and march to pummel Joe Biden. Corporate lobbyists in their
congressional hirelings howled at him for declaring that he would seek a tax increa on
corporations to pay for the essential, overdue job of repairing and expanding our nation's
and equated dilapidated infrastructure. Blowhard Mitch McConnell, the GOP Senate leader, blustered
that poor corporate America should not be singled out to bear this burden. But
wait, Mitch singled out the corporate giants in twenty seventeen to receive a massive
cut in their tax rate, so even with a slight increase now they'll pay
much less than regular people. Also, the giants worm loopholes in the law
to cut their taxes further. Indeed, fifty five of the biggest most profitable
corporations paid zero in US income taxes last year. As Bernie Sanders points out,
if you paid one hundred and twenty dollars for a pair of Nike shoes,
you paid more to Nike than it paid in federal income taxes over the
past three years while it made four billion dollars in profits. Mitch and his
fellow hypocrites cynically profess that they support restoring America's infrastructure, but he says,
asking our corporate political funders to pay more is not going to get support from
our side. So who do they want to pay for it? You,
working people and the poor. Senator Roy Blunt, a Missouri Republican and a
leader of Mitch's team, points to putting more user fees on drivers and adding
taxes on consumers as the way to go. This is Jim Hitler saying,
to see a list of other major corporate scott Laws who have been pocketing billions
in profits yet paying zilch for the upkeep of America. Go to the Institute
on Taxation and Economic Policy ITEP dot org. The election is March fifth.
There's a new Marshal in town. Vote Derek Marshall for twenty third Congress by
March fifth. We all know we need to change Congress. The current Congress
has nothing but fight with each other. He've forgotten why we sent them there
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town. The rent is two darn high. Let's marshal an affordable housing and
real affordable universal healthcare. Let's marshal better union jobs. The current congress member.
But here's party first. Our incumbent party politician voted no on the recently
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He said we need a secure border, but now he's rolled it back
to play politics. We need to marshal real solutions. Derek Marshall for twenty
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My name is Derek Marshall and I approved this message NBC News Radio. I'm
Chris Garagio. GOP presidential hopeful. Nikki Haley is slamming former President Trump for
his silence on last week's death of Russian opposition leader Alexi Navalni. Speaking of
the name Sees this week, she called it both concerning and a problem that
Trump has not responded to Navali's unexpected death in prison. She noted President Biden
and other world leaders lay the blame directly on Russian President Putin. Haley said
either Trump believes it's cool for Putin to have one of his opponents killed,
or Trump doesn't consider Navalne's death that important. And a Nikki Hally surrogate says
he believes former President Trump's legal battles will cause him to run out of campaign
resources. The errantcy itself has a record low amount of cash on hand because
of his leadership and his type of message. Speaking on Fox News Sunday,
New Hampshire Governor Chris Sanunu noted a recent campaign finance filing that showed Trump had
spent fifty million dollars on legal fees. In addition to legal fees, a
judge on Friday ordered Trump to pay more than three hundred and fifty million dollars
in fines in his civil fraud case. That came just three weeks after a
jury awarded e Gen Carrol eighty three million dollars in her defamation case against Trump.
Israeli Prime Minister of Benjamin net Yahoo says he pulled out of ceasefire negotiations
over the war in Gaza because of delusional demands by Hamas. Netanyahu said Israel
sent negotiators to Cairo last week, as requested by President Biden, but he
added they won't be going back until Hamas changes its demands. Hamas is calling
for a permanent ceasefire and the release of more than fifteen hundred prisoners from Israeli
jails. Netnah who also said Israeli forces will continue their offensive in the southern
border town of Rafa, where more than a million Palestinians are seeking refuge.
California is bracing for more possible flooding over the next few days. Santa Barbara
County along the central coast is once again expected to be among the hardest hit
areas. Emergency officials are issuing evacuation warnings for some areas because of possible flash
flooding and landslides, possibly through Wednesday. Heavy mountain snow was also predicted,
along with gusty winds. I'm Chris Karagio, NBC News Radio, NBC News
on CACAA Lomalinda, sponsored by Teamsters Local nineteen thirty two, Protecting the Future
of Working Families, Teamsters nineteen thirty two dot org. K CIA Radio has
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