AI News and Stocks on the Move: (NASDAQ:AMZN), (NASDAQ:ALTR) (NYSE:EPAM)

In today’s AI Eye podcast we look at a few public company announcements and how the AI sector is impacting aviation and medical technology as well as some great new potential in advertising. Stocks discussed include Amazon.com, Inc. (NASDAQ: AMZN) Altair (Nasdaq: ALTR) and  EPAM Systems, Inc. (NYSE: EPAM).  


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Episode Transcript

Speaker 1 (00:00):
Welcome to another edition of Investor Ideas dot Com AII podcast.
This podcast brings you breaking stock news and AI and
its impact on various industries. Listen to exclusive interviews with
thought leaders and experts as they discuss how AI is
shaping our future. And if you're interested in being a
guest or sponsoring this groundbreaking podcasts, you can contast us
at eight hundred six sixty five zero four one one

(00:24):
or visit Investorideas dot com. Hello, welcome to another episode
of Investor Ideas AII podcast, and today's podcast, we can
be looking at a few public company announcements as well
as how the AI space is impacting both the field
of aviation as well as medical technology, as well as

(00:45):
some great new potential in the world of advertising. First,
today we're looking at Amazon Web Services, which is an
Amazon dot Com Incorporated company trading on the Nasdaq as
amz N. And Poolside, who is the frontier artificial intelligence unicorn,
which was valued at three billion in an October funding round.
Now they announced a multi year agreement to make pool
Sides generative AI assistant and foundation models available in Amazon Bedrock.

(01:09):
Amazon Bedrock is a fully managed service that offers developers
access to high performing models from leading AI companies through
a single API.

Speaker 2 (01:18):
Now.

Speaker 1 (01:18):
As a result, enterprise customer soon be able to customize
Poolside's generative AI assistant for software development with their own
data leveraging and the security, privacy and performance of AWS. Now.
As part of the agreement, Poolside will also leverage AWS
trainium chips to power inference from the Malibu and Point
Foundation models, enabling optimal price performance for customers now. AWS

(01:43):
Trainium is a high performance machine learning chip designed to
reduce the time and cost of running generative AI models.
Now bringing Poolsides models to Amazon, Bredrock and EC two
provides enterprise customers of both companies with a unique advantage
to solving complex software engineering tasks and driving development productivity
for the organizations through generative AI. Pool size fms are

(02:06):
designed to be fine tuned with each business's code and data,
producing a proprietary generative AI model and software engineering assistant
for that specific business. Commenting on this, We've been incredibly
impressed with the AWS team and are excited to partner
on this journey to unlock more of the potential of
AI from software development set. The Poolside's CEO, Jason Warner, now. Today,

(02:27):
the majority of developers sit within an enterprise environment where
access to these tools is scarce and their full potential
is yet to be realized. Companies of this scale need
a tailored model that can be both captured to proprietary
knowledge and learn from their interactions over time, and that
is what we've built, and aws's reputation and depth within
the enterprise is key to accelerating adoption and impact. Giving

(02:49):
AWS customers the flexibility to run Poolside and Amazon Bedrock,
or their own EC two instances will further enhance their
ability to innovate with generative AI, said Dave Brown, the
vice president of AWS Compute and Networking Services. Poolside offers
a unique solution for software engineering teams of large enterprise
customers looking to build and scale generative AI applications through

(03:10):
foundation models without sacrificing privacy and security. So yet another
way to enterprise organizations to leverage their internal knowledge base
and build proprietary tailored solutions. So streamlined procurement, contracting, and
budgeting through the relationship with AWS makes it even easier
for AWS customers to experiment securely with Poolside. AWS is

(03:31):
the first cloud provider to offer fully managed models from poolside.
So obviously this is a I don't think a huge
surprise for people who've been paying attention to Amazon and
specifically AWS, their division that offers web services. I think
it's a pretty cool feature that they're going to be
offering to have this poolside capability. I think you're going

(03:53):
to see other web developer, sorry, other web service providers
similar to AWS. I don't think really there's too many
people in their same scale category. Everybody, even though they
don't think about it, uses AWS anyways. But I do
think that this will be a feature that you're going
to start to see more web service providers start to use. Again,

(04:13):
there's some of these features that are already somewhat there,
but obviously not to the same capabilities within companies like
Square and Shopify. Obviously those are much smaller scale. But
I do think that you're going to start to see
this type of AI innovation come into the web service
provider space very quickly because it's going to offer these
teams that are developing their own sites, especially large scale

(04:35):
organizations that are going through AWS. So that's obviously not
looking at sort of small scale business owners, but pretty
large scales organizations and institutions. They want the security that
AWS provides, and now they have this tailor made AI
software that's going to speed up. And if you've used
these AI programs, and if you're paying attention to the space,

(04:56):
I know a lot of people talking about AI, it's
still very theoretical for a lot of people. Even for myself,
I do use it pretty regularly, but I'm still very
much at a novice stage of understanding the programs and
being involved with the different options that are out there.
So for a lot of people, you're still only maybe
just dipping your toes lightly into this and haven't really

(05:16):
dived to the deepest depths of what all these different
AI applications can be used for. And similar to just
our smartphones, we all phones in our pocket. They have
millions of capabilities that almost none of us use. Most
of us use our phones probably only at a kind
of a ten percent capacity, maybe up to about forty
to fifty percent. There's very few people who are really

(05:36):
maxing out the capabilities of their phone because their phones
have so many features, and most of us are just
using it for basic options. That's similar to what people
are doing right now with AI. I would say even
to a lesser extent, most people really aren't using any
of these applications to their fullest capabilities. Obviously, there's a
few people within these spaces who are definitely doing that,
but I think a lot of the potential really hasn't

(05:57):
been realized yet. With this feature of tool side being
involved with AWS, I think you're going to start to
see a lot of large scale organizations really be able
to max out their capabilities at a much faster pace.
The downside to this, as we've already seen within the
tech encoding spaces, is this could create another huge wave

(06:17):
of job losses because suddenly larger organizations are going to
need a lot less time to produce very very good results.
And again, if you're not quite used to how all
these AI systems work yet, the biggest and most unnerving
thing about them is they become more and more intuitive
the more you use them. So that's what's going to
be really interesting to start to see here is you

(06:39):
get the security that AWS offers, but you also get
this tailored product that's going to start learning and adapting
to your specific coding, to your specific company, and your
specific goals that your organization is trying to set. Out
and the more you use it, the faster it's going
to learn to adapt to you, and the better results
you're going to get. So obviously it's a great thing
for these institutions, is a great thing for the tech

(07:01):
space and business space altogether. But the downside is is
you're going to drastically reduce your workflow or your work
time needed to create a very high value product. And
so I would expect that over the next year or so,
as this product starts getting utilized and as people start
realizing the potential, and this probably goes into other web

(07:21):
service providers sort of tech space as well, I think
you might see a lot of potential job losses within
those areas. It's the downside, I think very much. The
irony of the AI space is the people who are
coding these products are also the people being most heavily
threatened by the products that they are creating. Obviously, again,
there is some balance there and that a lot of

(07:43):
these people still need to understand basic coding to use
these programs. But I do think that, for instance, if
you have a team of sixty people working on this project,
you might be able to reduce that by half because
their workflow is just going to be that much faster,
and the more they use this, especially your sort of
top coders or your organizers within your company, they're just
going to get results that much faster, and the more

(08:04):
they use it, the better it adapts to them. So
it'll be very interesting to see how that impacts many
businesses over the next two years. I would expect to
start seeing some impacts within maybe a year from this,
but it'll be interesting to pay attention to now. Next,
looking at al Tair Trading on the nasdegas, altr who
announced that it will work together with Auburn University's Samuel

(08:26):
Grimm College of Engineering on a one point two five
million AFWERX Phase two contract. Now, the two organizations will
develop analytical models for cyclonic flows, construct computational models, and
study the stability of different vortex engines to address the
challenges facing public and private sector aerospace organizations. I think

(08:48):
this is pretty cool. It's definitely something to do a
pretty big deep dive into of. I really think that, again,
we're still only scratching the surface of the capabilities the
AI is going to start having on different industry. Looking
at aerospace and engineering, I think you're going to start
to see some really cool ideas here. I'd mentioned the
previous podcast to check out the Joe Broken podcast with

(09:10):
Mark and Dreson as well as the tom Billy Park
podcast with John Raveggi, both things that got mentioned in
both those podcasts. And there's a few different commentators who
have been talking about AI who have really been smart
to really focus on this one aspect, which is the
very much alien learning focus that comes out of AI.

(09:30):
So when AAI looks at problems, it actually finds very
novel solutions to a lot of these problems that traditional
human thinking doesn't have because traditional brains, all of us,
we have our own built in biases based off of
our lifestyles we've lived, based off of the education we've had,
based off of preconceived notions that we've been fed our

(09:51):
whole lives. So many of us will see a problem,
especially within very advanced fields like physics aerospace engineering. You know,
people who get to those roles of top aerospace engineers,
they've been pre programmed in many ways through their educational system,
through their work to really focus on problems through a

(10:12):
very specific lens, and by allowing these AI models to
start coming into these spaces and start creating different computational
models and start creating different sort of options. You're going
to start to see really new solutions come very much
out of left field, because a lot of these AI systems,
again are just kind of processing this information from a
blank state status. So whatever information they get, they're just

(10:35):
going to look at the problem. The most obvious example
of this was with Deep Blue. For anyone who didn't
know what that was, that was the chess program that
was made early on, and a lot of the chess
moves that made many professional chess players had never even
thought about before. And this is kind of one of
the main features of AI is it actually is quite

(10:56):
alien in comparison to traditional human things, and this is
very much the advantage. Obviously there's a lot of unnerving
aspects about that, but the advantages when it comes to
things like engineering, when it comes to things like physics,
when it comes to things that are very precise, and
to get to the tops of those fields, you have
to go through a large chain of sort of quiddling

(11:20):
down what you're thinking is going to be about a problem.
I'm not saying that people within this space don't have
open minds or aren't capable of thinking outside the box.
I'm just saying that it really gets drilled into you
over and over and over again, to the different institutions
you're going to go through to really focus on problems
through a specific lens, allowing AI models to start working

(11:40):
with these engineers, with these physicians, I'm sorry, physics professors,
I guess not physicians, but I'll get to that actually
in a bit. But working with these teams, you're going
to start to see very cool new solutions come out
of this. So within the contact, Altaire is assuming previous
role of research and flight, which was found in twenty
thirteen series of development contracts and grants over a ten

(12:02):
year span. This opportunity continues Altair's legacy of innovation within
the aerospace industry and demonstrates the power of our technology
as we work closely with prestigious institutions such as Auburn University,
said Pietro Servalerra, who is the Senior vice President of
Aerospace and Defense at Altair. Flight Stream empowers users in
unique ways, bridging the gap between high fidelity CFD simulations

(12:25):
and engineering demands to set industry standards for efficscy efficiency, accuracy,
and speed, So again definitely worth looking into. This would
be a company I would pay attention to, and especially
this project. Over the next while, this might start to
have impacts with other companies within the aerospace industry. It'll
be interesting to see how much, for instance, Elon Musk

(12:47):
starts using AI. I'm sure he already is to some capacity.
But really what you start seeing within again, we are
getting a very competitive aerospace market, especially when you're looking
at actual space tourism, which sounds still crazy to talk about,
but it is actually becoming a reality. If you didn't
watch the recent flight with Jeff Bezos's project, it was

(13:10):
pretty crazy. It's pretty wild to see that this is
becoming a pretty casual reality, seeing the SpaceX launches becoming
pretty consistent. Obviously, Boeing had a huge flop over the
last year that was quite embarrassing. But again, what I
think you're going to start to see is people who
are willing to utilize these new technologies and start thinking

(13:30):
of problems in a very different way and working in
tandem with AI, are going to come up with much
faster and much more unique solutions to these problems and
are going to be able to really just jump ahead
of their competitors. People who are trying to go the
traditional route, who are doing things the way it's always
been done, are going to fall behind at an accelerated pace,
which is going to be crazy to see. And I
think the Boeing situation was probably one of the perfect

(13:53):
examples of this, where you have again, people can have
lots of different commentary about Elon Musk and SpaceX, but
there definitely innovating, They're definitely thinking through new strategies and
those strategies are paying off. And Boeing kind of went
a traditional route. It really bit them in the ass
and kind of damaged their reputation quite permanently, I would think,

(14:13):
or at least for the next five years within sort
of the spaceflight community. So definitely worth paying attention to
what's going to be going on with AI innovation, with
engineering and specifically aerospace engineering. And I's looking at EPAM
Systems Incorporated Training on the New York Stockas Change as EPAM,
which is a leading digital, transformative service and product engineering

(14:36):
company who announced the successful completion of its acquisition of
First Derivative, which is a Northern Ireland headquarter managed services
and consulting business for the capital markets industry. Now First
Derivative has one of the largest fully dedicated capital markets
consulting teams in the world. The company deploys a range
of technology capabilities to assist clients and meeting their technology challenges,

(14:56):
including application development and modernization, real time dat apply forms
for product process, automation, machine learning and AI. As we
complete this acquisition, we are excited to welcome First Derivatives
talented team and client based EPAM, said Belas Frez sorry
BelAZ Fehz, the President of Global Business and Chief Revenue
Officer of EPAM. Now this partnership brings together EPAM's digital, AI,

(15:20):
cloud and engineering capabilities with First derivatives expertise in financial services.
Whilst First Derivative has historically been a key player in
financial services and capital markets, we see significant opportunity to
apply First Derivative specializations in regulated industries and implementations of
specialized commercial software solutions to our wider customer base. Now

(15:41):
EPAM will leverage First derivatives strong industry experience and brand
to deliver a comprehensive set of AI enabled capabilities to
clients in banking, capital markets and other financial regulated businesses
across North America, Europe, and APEC. So what I find
a little bit interesting about this is what we're starting
to see is somewhat of a reverse of maybe what

(16:04):
we saw four to five years ago, which is a
lot of financial services companies trying to acquire small AI companies.
Now you're seeing an AI company acquiring a small to
medium size financial services company. And I do think that
as these AI stocks take off. Again, I talked about
that in the earlier podcast, and again there's that great
fintech podcast that would highly recommend looking into which talks

(16:26):
about some of the big gainers within the AI space,
how fast they've been moving, why they've been moving so fast,
and really, again, if you're listening to AI commentary, you
have to focus on the idea of exponential growth. This
is a big thing within the AI world, is that
some of these companies are just going to start exploding
overnight and they're not going to really slow down. Because, again,

(16:47):
if you're understanding how AI works, it only gets better
the more it is used, the more data that is provided,
the more accurate and efficient these programs can become. And
so what you're starting to see now is a lot
of these AI companies now have the funding now of
the capital and sort of the girth to go and

(17:07):
take out and acquire smaller companies. And what they're starting
to realize is, oh, we can acquire in this case
first derivative again a financial first, a financial focused company
that is obviously going to have a large client list,
going to have the connections to reach out to a
lot of these companies that maybe aren't implementing AI as

(17:28):
quickly or hesitant about it, or don't understand all the
different aspects, and they're basically leveraging First Derivatives contact list
to push their AI programs on different business sectors. And
I do think that obviously, just like with all technology,
we can all say this, that and the other about
AI right now, but just like cell phones in the past,

(17:48):
you're all going to have it and all going to
be utilizing it in the near future, whether anyone thinks
they are or not, it just becomes an inevitability. I've
personally seen over the last year many people that I've
talked to and known on just a personal level anecdotal basis,
but people from a variety of different industries, people from
a variety of different age categories. Start of the year,

(18:09):
a lot of people were like, this AI nonsense. I
don't want to deal with this, I don't want to
have this, I don't like it, I don't know about it, this, that,
and the other. Almost everybody I know who had that
position at the start of the year has changed that position,
if not one hundred percent, at least ninety percent of
the way. Most of them are still now utilizing some
form of AI protocol on a daily to weekly basis.

(18:30):
And you know, I heard one good commentary where AI
is similar to an air fryer and that the more
you use it, the more you enjoy it, and the
more you get out of it. And as a person
who owns an air fier, I agree with that comparison.
But I do think that that's the reality we're all
going to start facing soon. Is the more people start

(18:51):
actually interacting with these different AI capabilities and the more
they're shown how they can benefit them on a daily basis,
the more they're going to use them. And the more
you do use these programs faster they adapt to you.
That's one of the things that again impact there is
Tom Bill, you has talked about a lot. He is
I would say a good example of a podcast host
who is not only promoting the AI software but utilizing

(19:13):
it on a daily basis and actually implementing it into
his podcast, and you can see it in real time
of how this has changed in the production quality, how
it's changing his research, how it's changing its conversations, even
explaining how he can research for guests using these different
AI programs, and that the more you use it, the
better it adapts to you, and the better it adapts
to you, the faster it is to use it, and
so you just get this crazy feedback loop where you're

(19:36):
producing better and better results at a faster and faster pace. Again,
different AI analysts have talked about the dangers of this,
which is, we are biological organisms who are now basically
overclocked because of interacting with these AI programs which run
twenty four to seven and are always trying to get
more and more interaction with us. So obviously there's some

(19:59):
potential size in the future, but for right now, I
think that more and more you're going to start to
see potentially this reverse order of AI companies acquiring different
companies or marketing teams things like that, and using their
sort of human databases to then mine different interactions and
get their AI programs into different institutions that have hesitations

(20:21):
or skepticisms. And I do think as well, you're just
going to see more and more institutions start adopting some
sort of AI service or some sort of AI program
into their daily life, and you're going to see the
impact of that, and the people who don't do it, unfortunately,
are just going to lose out too quickly, because again,
the feedback loops you get out of this, and the
results you get just get better and better so fast,

(20:42):
and it's so impossible to ignore those results. And so
the longer you hesitate on this, the more you're just
kicking yourself in the ass. Now, looking at the medical system,
Neurosoft Medical Systems, a global leader in medical imaging solutions
and services, announced its partnership in the Radiological Society of
North America twenty twenty four annual meeting, taking place in Chicago. Now.

(21:04):
RSNA is the world's premier scientific and educational event for
the radiological sciences, bringing together leading professionals from around the globe.
At Newsoft Medical Systems, we empower radiologists to address clinical
challenges while enhancing the accessibility and affordability of medical imaging
from regions and communities in need, said Patrick Wu, the
CEO of new Soft Medical Systems. Now by integrating advanced

(21:27):
medical imaging, artificial intelligence, and clinical expertise, we aim to
provide caregivers with comprehensive tools for accurate diagnosis, and we
look forward to engaging with industry leaders at RSNA twenty
twenty four to further advance healthcare through innovation. Now they
go through all the specific features and obviously it's quite
technical and focusing on medical imaging in a very granular way,

(21:51):
but I do really think this is another area that
if you're paying attention to what the impacts of AR
are going to be. Medical systems is a huge market,
and especially this specific market when you're looking at radiology
or radiological imaging, just imaging in general. This is one
of the sort of big bottlenecks that's created within the

(22:11):
medical community is there's only so many people who can
do this imaging. There's only so many facilities that are running,
and they're often overworked, overloaded, and then there's a big
delay on when you get the imaging and when those
results get back to your doctor, then they have to
go through the diagnosis and sort of analyzing the imaging.
Then that gets back to you the person, and that

(22:32):
delay and time can often be the difference of you know,
success or failure when it comes to health outcomes. So
if you can speed down this time and you can
decrease this bottleneck, that's a huge benefit for a lot
of different people with different diagnoses. They'll be able to
get medication faster, They'll be able to diagnose the symptoms

(22:54):
and the problems much quicker, and that creates a better
patient outcome every time. Obviously, it's still in a phase
where again there's lots of skepticism about the accuracy of
these different AI technologies, so they're focusing here on not
just using the AI but also in tandem with clinical
expertise and the medical imaging that already exists. So this

(23:14):
will be a training aspect for both the healthcare providers
and image the radiological image takers as well as the
AI systems. So that learning curve is still going to
take a little bit of time, but it'll be interesting
to see again once people start using this more frequently,
this feedback loop is going to happen and you're going
to start to see the people who are doing these

(23:36):
imaging are going to do it faster, They're going to
use these AI systems better, and once they start to
see consistent results, which I think will happen sooner rather
than later, they will start to trust these systems. Once
these systems can provide consistency over a long period of
time or a good data set, then you're going to
really start to see this takeoff. And once you can
see proof of concept for a lot of these things

(23:57):
that this is working, that's a hugehuge benefit for the
medical industry. It's a huge benefit for people who are
seeking medical aid because again, if you've ever had any
sort of imaging done, you know that there's a big
delay between they take the image, and even the people
who are doing these images still know how to pretty
much analyze things on the spot, but they have to

(24:17):
then send it off to the doctor. It has to
then get analyzed and there's this sort of back and forth.
But if we can get more certainty right out of
the gate, then it can just go right to the doctor.
They can go right to prescriptions then go right to
dealing with the problem, as opposed to this sort of
back and forth delay that has to go between the
person actually taking the image and the person then diagnosing
the image, and then back to you, the patient. So

(24:39):
I do think that there's really good benefits here. It'll
be interesting to see how this plays out, but I'm
very hopeful of again the hopeful good side of AI,
which is this positive feedback loop of the more they
use it, the better it's going to get at actually
analyzing what they're trying to teach it. And these aasystems
are highly intuitive, so if it's working with actual good

(25:02):
experts who know what they're doing, they can train these
AI models fast and then you're going to start to
see results pick up at an exponential rate. Now, lastly,
in the world of online advertising, Adcreative dot ai has
announced the launch of the world's first AI powered product
to product video generative model, now built with ad LM
at its core, the first language model created exclusively for advertising.

(25:24):
This tool is set to transform how businesses create high impact,
conversion optimized video content. So it's got a lot of
neat features and I'm going to show the ads that
they've created at the very end of this so you
can judge for yourself. The innovation marks a new era
in Advertising, said Twofon Gock, the CEO of Adcreative dot AI.
Our goal is to make high quality, conversion focused video

(25:45):
creation accessible to businesses of all sizes. The videos are
pretty good, you'll see for yourself shortly. But I am
not going to hold my breath on this yet because
as a person who's used lots of these different image
and video creations, I do notice that the issue becomes
precision and consistency over multiple uses. So, for instance, if

(26:06):
you want to have a brand, it's very difficult for
a lot of these AI models to keep your brand
consistent over a lot of repeat images or repeat videos.
They start to focus or or have issues with very
key specifics, and this seems to just be I'm not
sure if this is intentional. So, for instance, this company

(26:26):
is focusing on advertising, they could make that more efficient
and could have dealt with that bug. But if you've used,
you know a lot of the different image creation softwares
that are out there right now, you've probably experienced this
problem that trying to get the same image over and
over again becomes quite difficult, and especially if you have
a specific brand or word across your product. So for instance,

(26:47):
in these product videos that I will have at the
end of this. The products that they show don't really
have specific wording, labels, or any actual branding on it.
They just have a generic sort of product and it Scott.
Really the visuals are fantastic, don't get me wrong. It
looks really, really tight. But what I see might be

(27:09):
the problem is, for instance, if you have a product
that has to have ingredients or warning labels or something
very specific and detailed and hyper focused around your label.
For instance, you have a can with ingredients, a very
tight logo, a graphic another thing. I don't know how
well it's going to do with those products. I think
it'll get the can shape perfect, I think it'll get

(27:30):
the color right. I think it'll might make mistakes when
it comes to the specific brand wording, when it comes
to the logo, all those things. One of the best
examples of this is trying to create images of tattooed
characters and keep the tattoo the same. This is kind
of one of the most obvious flaws that AI image
generation and video generation softwarees are trying to have. You'll

(27:50):
see that there's sort of a wonkiness to it that
can't quite be consistent. So that's what I'm going to
be looking for. I would definitely pay attention to this.
Right now, it is currently only available to their sort
of top suite payment providers, so this is for the
pro and enterprise customers during its beta phase, and it's
planning for a broader rollout, so it is worth looking into.

(28:12):
If you already have account with Adcreative dot Ai, I'm
sure you're playing around with this, and if you haven't,
it's one hundred percent worth looking into. And I would
try to find examples of these ads being actually implemented
in real time over the next coming months to see
what the issues are going to be. Is it going
to work as good as everybody says, or are these
flaws that have existed in all these other image and

(28:34):
video creation software is going to be there again. I
talked about open ais video text to video platform that's
hopefully going to be launching in the next year. That one,
I think is kind of the big dog that everybody's
waiting for. Once that goes out, I think it's going
to be pretty wild to see how much video creation
starts happening. I think places like YouTube and TikTok and

(28:57):
even Twitter are going to be pretty crazy, almost overwhelmed
with the amount of videos that just start getting pumped out. Obviously,
there's lots of different as software. Again, I try to
use it as much as possible. I think it's foolish
not to in this age. But I do see that
all this software right now, it does take a learning turve.
There are still some flaws in it, and it does

(29:20):
have issues with consistency and details over time. So again,
you can watch this video. I'll have it right after
this and let your eyes tell me what you see
could be the potential problems. Like I said, I think
it's going to be details and consistency over time. And

(29:46):
that's all for today's podcast. Please check out investor ideas
dot com for any of our other content and hope
you have a great day.

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