TrulySignificant.com presents Shane H. Tepper. He is a creative director, content strategist, and early leader in the emerging field of Large Language Model Optimization (LLMO). He helps brands improve visibility, accuracy, and narrative control across AI-native platforms like ChatGPT, Claude, and Perplexity.
With more than 15 years of experience spanning film, advertising, and B2B technology, Tepper operates at the intersection of storytelling and artificial intelligence. He builds content systems designed to be cited by the very models shaping how people search, compare, and make decisions in today’s AI-driven world.
His recent work includes authoring a foundational white paper on LLMO, leading AI discoverability audits, and designing structured content frameworks optimized for machine ingestion and real-world performance. He advises organizations on LLMO strategy, AI-native content development.
Visit www.retina.media.com or email Shane directly with questions Shanehtepper@gmail.com
Become a supporter of this podcast: https://www.spreaker.com/podcast/success-made-to-last-legends--4302039/support.
With more than 15 years of experience spanning film, advertising, and B2B technology, Tepper operates at the intersection of storytelling and artificial intelligence. He builds content systems designed to be cited by the very models shaping how people search, compare, and make decisions in today’s AI-driven world.
His recent work includes authoring a foundational white paper on LLMO, leading AI discoverability audits, and designing structured content frameworks optimized for machine ingestion and real-world performance. He advises organizations on LLMO strategy, AI-native content development.
Visit www.retina.media.com or email Shane directly with questions Shanehtepper@gmail.com
Become a supporter of this podcast: https://www.spreaker.com/podcast/success-made-to-last-legends--4302039/support.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:09):
And welcome back to Truly Significant dot com Presents. I'm
Rick Tolkeiny and our very special guest today is Shane Tepper.
He's the creative director, content strategist, and early leader in
the emerging field of large language model optimization. We're gonna
refer to it in the show as DOUBLELMO. He helps
(00:31):
brands improve visibility, accuracy and a narrative control across AI
native platforms like Chat, GPT, our favorite Claude and Perplexity. Shane,
it's great to have you on the.
Speaker 2 (00:44):
Show, Rick, thanks for having me.
Speaker 1 (00:48):
Let's ask this first question good When did you realize
you were playing in a completely different game than everyone else?
Speaker 3 (00:56):
That's a fantastic question, and I have a very specific
answer for it. I this was early twenty twenty three.
I was in San Francisco, California. I had just been
laid off my entire team, my entire creative team had
been laid off from a company. I was working as
a copywriter for an ed tech company, an online learning company,
(01:20):
and I was just browsing the web trying to figure
out my next move, and I came across chatchibt's first release,
their initial model.
Speaker 2 (01:29):
The release of their initial model, and I said, let's
see what this thing can do.
Speaker 3 (01:34):
So I took a creative brief, you know, the type
that a product marketing team would give to a creative
team to execute on. And I uploaded this brief to
CHATCHYBT and I asked it to generate some copy based
on this brief, and within seconds it had churned out
pretty solid Dare I say, eloquent copy? You know, copy
(01:58):
that would have taken me, a copywriter with ten years
of experience, you know, forty five minutes an hour to write.
And I said, damn, it's time for me to figure
something else out, because I don't think this is a
viable career path any longer.
Speaker 1 (02:14):
I love hearing that. That's like you've discovered this point
and you pivoted and you made a decision right there
and then that that was the new frontier for you.
I mentioned up front this large language model optimization short
(02:36):
for DOLM. I want you to give us, give my
audience the layman's explanation of what that means.
Speaker 3 (02:46):
Certainly so, I think most people at this point are
familiar with the term SEO search engine optimization that refers
to things that brands can do to show up to
rank highly on on Google results. Essentially, right, you want
you want to be in that top couple of results.
When someone you know does a keyword search relevant to
(03:08):
what your company, brand, et cetera, does, you want to
show up there first. What we're seeing now is a
massive shift from traditional search Google, bing, et cetera, over
to l ll ms, large language models like Chat, Gypt, Perplexity, Gemini,
Claude for brand discovery and comparison. So instead of you know,
(03:30):
there there are practices, there are best practices there, there
are certain strategies and techniques that that businesses can implement
in order to to show up for certain queries on
on these l ll ms. So rather than show up
for a keyword search on se O, you're you're creating
content and deploying content in a way that makes your
(03:51):
brand show up when someone enters a relevant query on Chat, GYBT, Perplexity, Perplexity,
et cetera.
Speaker 2 (03:58):
Makes sense.
Speaker 1 (03:59):
Is there a point were they where there's a divergence
between SEO and the LMO and if that's if it is,
tell us.
Speaker 2 (04:09):
About that absolutely. So.
Speaker 3 (04:11):
SEO is primarily focused on what you do, what a
brand does on its own website on its own web property.
Things from on page stuff you know, content specific the
specific words you're using. There's also stuff that you do
on the back end, behind the scenes, meta tagging and
hierarchical structuring of the content. LMOs, on the other hand,
(04:36):
tend to place much higher trust and credibility in third
party sources, so essentially what other people are saying about
your brand rather than what you're saying about yourself. The
main thing about lms is trust, credibility, authority, And you know,
you could say whatever you want about your your own brand, right,
(04:58):
but it's much more meaningful and tactful to get outside
validation of your brand, your offerings, your products, your services.
So lllms tend to prioritize that to a much greater extent.
What we're seeing is that ninety percent plus of the
(05:18):
content that is surfaced on lllms come from the twentieth
page of Google or deeper than that. So they're fundamentally different,
using different mechanisms methodologies to extract and surface content to
a user for a particular query.
Speaker 1 (05:36):
You're not saying that the strategy of getting on page
one is dead, are you.
Speaker 2 (05:44):
I'm not saying it's dead. I think I view it
more as a as a complementary sort of strategy. Right.
Speaker 3 (05:50):
SEO is still important, people are. Google is still by
far the largest search engine, but lms are rapidly catching up,
especially for a high intent type of user. We've seen
that the type of user who uses an ll M
like Google or Claw or like like chat Chiptia claud
(06:11):
is worth four point four times as much as as
a as a user on a traditional search engine like Google.
So this is a very valuable, very high intent type
of user. Someone who's ready to buy, someone who's ready
to figure out a solution to whatever problem they they have,
whatever business problem, and then and then make make a
purchase decision.
Speaker 1 (06:33):
How about that? Where did that number come from?
Speaker 3 (06:37):
Oh, there are a number of of companies that and
that are that are putting out numbers. That number, in particular,
I believe came from from sem Rush. It's one of
the largest se O companies U sem Rush and HubSpot.
Speaker 2 (06:53):
But there's a.
Speaker 3 (06:54):
Ton of data being published by those companies, by uh
other by companies that have GEO SaaS platforms or l
l M O SaaS platforms. That's what that means is
in Layman's terms.
Speaker 2 (07:09):
It's a it's a platform, a software platform that shows
brands what their discoverability status is on l ll ms,
shows the metrics you know, uh uh, you.
Speaker 3 (07:21):
Know how they're how they're surfacing compared to competitors. You
know what the sentiment is, What sentiments are being expressed
when people are talking about these brands.
Speaker 2 (07:30):
Are they're talking about you in a positive way, a
negative way, a neutral way.
Speaker 4 (07:33):
Uh.
Speaker 3 (07:33):
They tell you the citation sources, like what what what
sources are being cited when content is being surfaced to
a user in response to a particular query on an
l M. These these companies, uh analyze millions and millions
of queries and and the results that are surfaced, and
they provide uh you know, they offer these they provide studies,
(07:56):
They publish studies, uh and and and bits of data.
So what I've done is I've taken data from across
these sources, and I have and the companies themselves, the
ll ns themselves also publish information on on on their
on their platforms, on their models and how they function.
Speaker 2 (08:16):
So you know what I've done is I've I've synthesized
all of this data coming from all these different sources. UH,
and I've I've.
Speaker 3 (08:23):
Assembled a playbook for how to optimize on ll ns.
So UH, to get back to your original question, I
believe that four point four times referring to the value
of an average ll M user comes from a study
done by or data published by sem Rush, a company
called Semraush sem Rush.
Speaker 1 (08:44):
Thank you, Shane. I'm on the boards of lots of
companies and I see new brands every day. I'm going
to give you an example of one. I want you
to answer this question if you can. It's a new
brand called the Right One dot net and it's going
to be in the dating app space. If you were
on their team or consulting them when optimizing for LMOs,
(09:10):
what are the key levers that you will pull for
the right One? Is it prompt design, data, fine tuning, metadata,
structuring or what else is it in the Shane Tepper
brain that we or they could tap into.
Speaker 2 (09:28):
Shouldn't we discuss my fee first? Rick?
Speaker 1 (09:31):
That means that Shane is monetizing his wisdom.
Speaker 2 (09:34):
So you mentioned a number of things. Let's start with
with queries because queries, having high quality queries is really
where you need to begin.
Speaker 3 (09:46):
Because Rick, if you optimize for the wrong queries. If
you optimize to be discovered for questions that people aren't
asking chat shept or Claude or Gemini, right, then it
doesn't matter because you're not going to be more easily
to discovered because that's not what people are looking for.
Speaker 2 (10:02):
Right.
Speaker 3 (10:03):
So it all starts with coming up with some really
high quality queries. And what you need to remember with
queries for llms as distinct from keyword searches for Google,
for instance, is that llm's LLM queries are much more conversational,
(10:24):
much more representative of natural human language.
Speaker 2 (10:27):
Right.
Speaker 3 (10:28):
So in Google you might type like best dating app
twenty twenty five or something like that, right, and you
would get your results. What someone would type into chat
GPT might be more along the lines of, you know,
I'm a single, thirty eight year old man in Atlanta, Georgia.
I'm looking to find you know, someone local to my area.
(10:51):
What are some of the best recent apps to help
me find find companionship rights. It's much longer, much more specific,
much more context is provided. And this is really one
of the main reasons that people are using lllms to
for search rather than Google is because they can be
much more specific, and they're prompting. And the more specific
(11:14):
you are, the more context you provide, the more relevant,
the more relevant the results will be. And they like that,
and they can interact with this, and then they can
ask follow up questions. Right, so they can interact with
the LLM in a more natural conversational way and get
much more specific, relevant results surface directly to them rather
(11:34):
than having to sift through a bunch of you know,
mostly irrelevant blue links.
Speaker 1 (11:40):
Very good, and let's cut to commercial because that's a
perfect bridge to that fee that you will charge that
will be well worth it. How do people contact you
and tap into your big brain that you have and
start to move their brands forward?
Speaker 2 (11:57):
So they can go to my website retin r E
t i n A Retina Media dot meet. Retina Media
is my company.
Speaker 3 (12:05):
My website is Retina dot Media r e t i
n A dot n e d i A. You can
also get in touch with me directly through through email
at Shane my first name, h my middle initial tepper
all one word that's s h A n e h
t E p p e r at gmail dot com.
Happy to discuss any of your your l l M
(12:28):
or AI business transformation related questions. Thank you for that
opportunity to plug myself, Rick, I appreciate that excellent.
Speaker 2 (12:37):
Uh.
Speaker 1 (12:37):
We will be right back with Shane after this special
message from the one and only Marcus Aurelius.
Speaker 5 (12:47):
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Conversations with big hearted people is a rare piece of literature.
This book reminds me of one of my more stirring quotes,
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Speaker 1 (14:28):
We are back with the one and only Shane Temper. Shane,
you wrote a book correct, that's correct, Dwelling in a
place of Yes. Oh my gosh, I thought I got
this right here on the side of my little desk.
Here says I dwell. It says dwell in possibilities. That's
what that reminded me of when I first saw it.
(14:50):
And I love the positive note on It's The byeline
is the surprising psychology behind fear, opportunity and smarter choices.
And the word fear is being overstudied by my team
here in Austin. I want you just to take that
(15:12):
one word and tell me why it appeared in your
title or subtitle.
Speaker 3 (15:16):
Absolutely well. One of the main premises of this book
is that we become paralyzed during the decision making process
oftentimes because of fear.
Speaker 2 (15:27):
Fear is such.
Speaker 3 (15:28):
A powerful human emotion and it's no wonder why, because
you know historically, evolutionarily speaking, there it was a pretty
dangerous world out there, so our fear response kept us safe.
You know now that we've and it's persisted into the
modern era. And while fear is still helpful, fear can
(15:53):
also hold us back from opportunities that would improve our
improve our lives, opportunities that would allow us to to
thrive in a more complete way. So, you know, that's
that's kind of what I learned about fear and how
it functions, uh, you know, from from a from a
biological self preservation perspective, and also how it can inhibit
(16:16):
inhibit us from really becoming all that we could be.
Speaker 1 (16:21):
That's right, inhibiting next philosophical question. We've done a podcast
series with the one and only Tim Love, who was
the vice chair of Omnicom, one of the world's greatest marketers.
It was called Discovering Truth. Tim would want me to
ask you this morning about where is truth in the
(16:42):
midst of these large language model optimizations.
Speaker 3 (16:47):
Absolutely, you know, you always have to be a discerning
consumer of information. You know, there's so much disinformation out there.
What we're seeing is at a number of these ll ms,
or every ll M produces what are called hallucinations.
Speaker 2 (17:06):
I'm not sure if this is a term you're familiar.
Speaker 3 (17:08):
With, but it's like it's like a factor statistic that
is that is made up and you and what happens
is these these these models put out hallucinations, hallucinid hallucinid
hallucinated information. It gets re ingested by other models during training,
and then it just becomes perpetuated, uh, throughout these models.
(17:32):
It's a big problem. Obviously, there is all there's you know,
in the pre ll M era, there was already a
large a large.
Speaker 2 (17:40):
Amount of disinformation, bad information.
Speaker 3 (17:42):
Out there, and it's really our responsibility as individuals, as
as discerning consumers of information to kind of suss out
what is right and what is wrong. And for me,
that involves, uh, consuming information from a bunch of different sources.
Is from it involves corroborating statistics across different sources, and
(18:06):
it really you know, we have a tendency to, you know,
we're familiar with confirmation bias. We have a tendency to
to readily accept any sort of information that that that.
Speaker 2 (18:18):
Validates what we already know or think that we know.
Speaker 3 (18:22):
And it is absolutely imperative I think for us to
you know, maybe take.
Speaker 2 (18:28):
A step back and say, you know, why do I
believe this? Do I have a valid reason for believing this?
Speaker 3 (18:35):
And just you know, just try and and and consume
information from a variety of sources so that you can
so that you can, you know, have.
Speaker 2 (18:45):
Good reason to believe what you believe, right you.
Speaker 1 (18:50):
That was a great Walter Croncott answer. And having grown
up on Walter Croncott and Paul Harvey two, you know,
lighthouses of truth, as we thought in North Texas, I'm
wondering if there will ever be another voice or voices
(19:12):
that we can go to singularly, or will we always
have to go get multiple sources and corroborate.
Speaker 2 (19:20):
That is such a great question I think.
Speaker 3 (19:24):
I think we are at a point in our history
where we are so polarized, we are so mistrustful that
I don't know, I can't It's hard to imagine a
single person.
Speaker 2 (19:38):
Who is viewed as as the singular source of truth,
as the ultimate reliable disseminator of information that is accepted
across you know, political, socio economic, you know, the spread
of people across across you know, politics and socioeconomics. I
(20:03):
don't see it. I don't see that being a thing.
Speaker 3 (20:06):
This would have to be someone who people are so
entrenched in how they and how they think about things.
You know, so many people are so unwilling to consider
other perspectives or the possibility.
Speaker 2 (20:17):
That they might be wrong about a certain thing. It's
hard to imagine like a Walter Cronkite type person emerging
in our society this day and age.
Speaker 1 (20:27):
What do you think, Rick, it's said, I'm sad to
agree with you. Yeah, it's it's a sad situation. I
think people are pulled and tugged certain ways, and so
I always try to, like a good old journalist, I
try to get at least four or five sources just
(20:49):
to read it, you know, pray for discernment, because I
think that's.
Speaker 2 (20:52):
What I think, that's what we all need.
Speaker 1 (20:55):
Hey, you are a character on top of being a
great professional, and this, this character of yours is coming
out with your opinion. When did you start considering yourself
to be a futurist.
Speaker 3 (21:09):
I don't know if I would if I would consider
myself not to be a futurist. I've always been someone
who likes to stay on top of trends, and you know,
I you know I've been a fan of this. This
might sound a bit contradictory, but I consider myself to
be a bit of.
Speaker 2 (21:25):
An amateur historian. So I like looking at the past.
Speaker 3 (21:29):
I like seeing you know, patterns and why you know,
across across periods of time, across cultures and societies, and
finding common threads or common common circumstances that might explain
why things things unfolded in a similar or a different
(21:50):
way in different societies in different different periods of time.
And I think I think that also applies to how
I view technology and human development in our present era.
It's very interesting to me to see how technology has
evolved and how people's relationships with technology has evolved, and
kind of like, I guess, extrapolate or try and understand
(22:14):
what implications you know that might have, you know, those
observations might have in the future.
Speaker 2 (22:23):
It's just I don't know. It's a fun little mental
exercise for me.
Speaker 3 (22:27):
I like to stay I'm the kind of guy that
if I don't know something, if someone asked.
Speaker 2 (22:32):
Me a question and I don't know what, I'm immediately
heading to Wikipedia or chat EPT. I was about to
say Google because it used to be Google, but now
it's chat cept your cloud and trying to find the answer.
I don't. It makes me uncomfortable to not know things.
Speaker 3 (22:45):
There is a lot that I don't know, and I
readily admit, you know, when I don't know something. But
but yeah, I just like to I want.
Speaker 2 (22:53):
To be prepared. I want to be prepared for whatever's
whatever is coming.
Speaker 3 (22:56):
So I think I think that's why, you know, I
have this tendency to to just to want to collect
as much information as possible and want to understand, you know,
possible outcomes, two different two different scenarios.
Speaker 2 (23:08):
So am I you know? Am I a futurist? I
don't know. Does that make me a futurist?
Speaker 1 (23:14):
It makes you a chaos theorist in watching patterns.
Speaker 2 (23:20):
That sounds cool, and.
Speaker 1 (23:21):
I think a chaos theorists isn't part of a futurist. Hey,
I love the name of your company, Retina, and I
want to know you, whether you know it or not.
If we were tracking the number of times you've said
see in this conversation, it's a lot.
Speaker 2 (23:40):
You're very as an see like I see because.
Speaker 1 (23:44):
You were a very visual person and you talk about
seeing things, seeing things, proving things. Why did you name
it Retina other than the all the reasons I just mentioned.
Speaker 2 (23:54):
So this company, I actually named it Retina about ten
years ago.
Speaker 3 (23:58):
I was working in video perduceduction at the time, and
video of course a visual medium. I called it Retina,
and then, you know, as I sort of transitioned into
brand visibility and brand discoverability, I kept the name because
it was still relevant. It's sort of like a timeless
name for you know, I've worked in like branding and
marketing for a while, and you know that has to
(24:20):
do with making.
Speaker 2 (24:21):
Your brand scene. So yes, said c again, making your
brand visible.
Speaker 3 (24:26):
So Retina seemed appropriate, and that's why I chose Retina
at the time, and that's why I continue to use
that name.
Speaker 1 (24:33):
I think it's brilliant, but it makes me want to know,
what are you doing with Retina media inside large language
model optimization? So you're protected, you're number one, you're not misrepresented.
Speaker 2 (24:46):
Da da da da da exactly. Yes.
Speaker 3 (24:49):
So So what we do is we we give our
brands an idea of where they stand currently in terms
of their visibility and discoverability, how they compare in terms
of discoverability and visibility to their to their competitors. You know,
what people are saying about them, how you know, in
terms of the sentiments they're expressing when they're talking about
(25:10):
your brand, and where where these where these citations, sources,
where these citations are coming from, the sources of these citations,
where are these conversations happening about your brand, or where
is this information being being pulled in from? And then
based on all those all that information, oh and before that,
coming up with really really really good queries that represent
(25:32):
what your prospects are actually you know, typing into an
ll M to to find out about your brand or
to find out find out uh, you know, if they
have a problem, they'll type that in and then we
want your brand to be surfaced as a solution to
that problem. And then the the hard part, or the
trickiest part is Okay, great, I see what I'm I see.
(25:53):
I'm ranked eighth among my competitors. That's not very good.
Speaker 2 (25:56):
I have a a six percent discoverability rate. That's not
very good. How do I improve that?
Speaker 3 (26:02):
And that involves a multifaceted strategy of looking at where
people are having discussions about your brand, you know, and
where they're being and what sources are being brought into
the lms and then and then strategically deploying, creating and
deploying content to those places so that when in you know,
(26:24):
over time, and this is this is an ongoing strategy.
And this is another interesting thing, Rick. The citation sources
change very rapidly, so something more than fifty percent month
over month.
Speaker 2 (26:35):
So something that is being cited, you know, in July,
might be might not.
Speaker 3 (26:41):
Be completely might be completely ignored in August. So it's
a constant sort of cat and mouse game of understanding
where these conversations are happening and how can you insert
content strategically into those places so that your brand is
surfaced when someone types in a elevant query and an eleven.
Speaker 1 (27:04):
Man. That's impressive. And it leads to the final question.
Considering that I think you're a futurist straight up, Okay,
what do you think will be the most disruptive development
in large language model optimization technology in the next six
weeks or six months?
Speaker 3 (27:25):
I think it's I think it's that last piece that
I was just talking about.
Speaker 2 (27:29):
Is the hardest is the hardest part?
Speaker 3 (27:31):
Uh, you know, it's we can we can come up
with really good queries, we can we can you know,
surface metrics and insights about how your brand is performing
relative to to your competitors. But understanding this sort of
volatile world that uh that is where that is how
(27:53):
content is being surfaced exactly, you know that the sources
and how to how exactly to effectively you know, get
your brand into that area where where content is being
extracted and surfaced on chat, GBT, perplexity, cloud, et cetera.
That is going to be the key thing. And companies
(28:16):
in this space are are.
Speaker 2 (28:17):
Starting to to get in on this.
Speaker 3 (28:20):
Uh they're making you know, within their platforms, they're making
strategic recommendations like write a blog post on this topic
or a white paper on this topic, or you know,
you know, former strategic partnership with whatever company.
Speaker 2 (28:33):
And talk about this.
Speaker 3 (28:35):
But they're very kind of like disjointed recommendations for how
to create and deplay content. And I think the companies
that kind of figure out how to orchestrate you know.
Speaker 2 (28:47):
More campaign level strategies, right like multi multifaceted, multi pronged
approaches uh to to to increase visibility.
Speaker 3 (29:01):
That all work in concert to achieve a visibility objective
that can be tied back to the data that's gonna
be the key there is truly getting an understanding of
what moves the needle in terms of improving discoverability uh
and and and visibility on these platforms uh and and
really providing.
Speaker 2 (29:22):
These sort of actionable insights to to the client.
Speaker 1 (29:26):
Excellent that last oratory sent it like Marshall dllon in
the wild wild West of large language model optimization.
Speaker 3 (29:37):
You know, we're all just we're all just trying to
figure it out. You know, it's such an early it's
so early in this in this field, in this industry,
and new information, new insights drop almost on a daily basis,
certainly on a weekly basis, and and we're all just
trying to wrap our wrap our heads around it. And
I'm trying to, you know, be one.
Speaker 2 (29:57):
Of the early ones to get my head wrapped around it.
Speaker 1 (30:01):
Well, that leads to we got to have you back.
We'll have you back as often as you want to
be on, but I'd like to talk to you at
least three times a year so you can give us
you'd be the give us a state of the industry rapport.
You and your support team were the first to contact us,
and our success made to last network here in Austin
(30:24):
got You've given us some great information. I'm going to
have to listen to this again just to capture everything
that you just said. And I was listening intently to Shane.
Thank you so much. Give out information on your company
one more time, and where do we buy the book
Dwelling in a Place of Yes, The Surprising Psychology behind Fear,
Opportunity and Smarter Choices.
Speaker 3 (30:45):
Certainly, my company is called Retina Media R E T
I N A Media. That website is Retina dot Media
uh and you can also contact me via email Shanehtepper
at gmail dot com, a.
Speaker 2 (31:00):
S h A N E H T E P P
E R at gmail dot com. And Dwelling in a
Place of Yes is available for download on Amazon Kindle
no physical no physical copy available yet.
Speaker 3 (31:15):
I'm working on that, but you can download it to
your your kindle and read it there.
Speaker 1 (31:21):
Fantastic. Thanks again, Shane. Hold on one second after we
sign off here, and I just want to thank you
folks for joining us today. I know that you found
this show informative and make sure and contact Shane with
any follow up questions. As we always say, we wish
you success, but on your way your own unique way
to significance.
Speaker 2 (31:41):
I really
And welcome back to Truly Significant dot com Presents. I'm
Rick Tolkeiny and our very special guest today is Shane Tepper.
He's the creative director, content strategist, and early leader in
the emerging field of large language model optimization. We're gonna
refer to it in the show as DOUBLELMO. He helps
(00:31):
brands improve visibility, accuracy and a narrative control across AI
native platforms like Chat, GPT, our favorite Claude and Perplexity. Shane,
it's great to have you on the.
Speaker 2 (00:44):
Show, Rick, thanks for having me.
Speaker 1 (00:48):
Let's ask this first question good When did you realize
you were playing in a completely different game than everyone else?
Speaker 3 (00:56):
That's a fantastic question, and I have a very specific
answer for it. I this was early twenty twenty three.
I was in San Francisco, California. I had just been
laid off my entire team, my entire creative team had
been laid off from a company. I was working as
a copywriter for an ed tech company, an online learning company,
(01:20):
and I was just browsing the web trying to figure
out my next move, and I came across chatchibt's first release,
their initial model.
Speaker 2 (01:29):
The release of their initial model, and I said, let's
see what this thing can do.
Speaker 3 (01:34):
So I took a creative brief, you know, the type
that a product marketing team would give to a creative
team to execute on. And I uploaded this brief to
CHATCHYBT and I asked it to generate some copy based
on this brief, and within seconds it had churned out
pretty solid Dare I say, eloquent copy? You know, copy
(01:58):
that would have taken me, a copywriter with ten years
of experience, you know, forty five minutes an hour to write.
And I said, damn, it's time for me to figure
something else out, because I don't think this is a
viable career path any longer.
Speaker 1 (02:14):
I love hearing that. That's like you've discovered this point
and you pivoted and you made a decision right there
and then that that was the new frontier for you.
I mentioned up front this large language model optimization short
(02:36):
for DOLM. I want you to give us, give my
audience the layman's explanation of what that means.
Speaker 3 (02:46):
Certainly so, I think most people at this point are
familiar with the term SEO search engine optimization that refers
to things that brands can do to show up to
rank highly on on Google results. Essentially, right, you want
you want to be in that top couple of results.
When someone you know does a keyword search relevant to
(03:08):
what your company, brand, et cetera, does, you want to
show up there first. What we're seeing now is a
massive shift from traditional search Google, bing, et cetera, over
to l ll ms, large language models like Chat, Gypt, Perplexity, Gemini,
Claude for brand discovery and comparison. So instead of you know,
(03:30):
there there are practices, there are best practices there, there
are certain strategies and techniques that that businesses can implement
in order to to show up for certain queries on
on these l ll ms. So rather than show up
for a keyword search on se O, you're you're creating
content and deploying content in a way that makes your
(03:51):
brand show up when someone enters a relevant query on Chat, GYBT, Perplexity, Perplexity,
et cetera.
Speaker 2 (03:58):
Makes sense.
Speaker 1 (03:59):
Is there a point were they where there's a divergence
between SEO and the LMO and if that's if it is,
tell us.
Speaker 2 (04:09):
About that absolutely. So.
Speaker 3 (04:11):
SEO is primarily focused on what you do, what a
brand does on its own website on its own web property.
Things from on page stuff you know, content specific the
specific words you're using. There's also stuff that you do
on the back end, behind the scenes, meta tagging and
hierarchical structuring of the content. LMOs, on the other hand,
(04:36):
tend to place much higher trust and credibility in third
party sources, so essentially what other people are saying about
your brand rather than what you're saying about yourself. The
main thing about lms is trust, credibility, authority, And you know,
you could say whatever you want about your your own brand, right,
(04:58):
but it's much more meaningful and tactful to get outside
validation of your brand, your offerings, your products, your services.
So lllms tend to prioritize that to a much greater extent.
What we're seeing is that ninety percent plus of the
(05:18):
content that is surfaced on lllms come from the twentieth
page of Google or deeper than that. So they're fundamentally different,
using different mechanisms methodologies to extract and surface content to
a user for a particular query.
Speaker 1 (05:36):
You're not saying that the strategy of getting on page
one is dead, are you.
Speaker 2 (05:44):
I'm not saying it's dead. I think I view it
more as a as a complementary sort of strategy. Right.
Speaker 3 (05:50):
SEO is still important, people are. Google is still by
far the largest search engine, but lms are rapidly catching up,
especially for a high intent type of user. We've seen
that the type of user who uses an ll M
like Google or Claw or like like chat Chiptia claud
(06:11):
is worth four point four times as much as as
a as a user on a traditional search engine like Google.
So this is a very valuable, very high intent type
of user. Someone who's ready to buy, someone who's ready
to figure out a solution to whatever problem they they have,
whatever business problem, and then and then make make a
purchase decision.
Speaker 1 (06:33):
How about that? Where did that number come from?
Speaker 3 (06:37):
Oh, there are a number of of companies that and
that are that are putting out numbers. That number, in particular,
I believe came from from sem Rush. It's one of
the largest se O companies U sem Rush and HubSpot.
Speaker 2 (06:53):
But there's a.
Speaker 3 (06:54):
Ton of data being published by those companies, by uh
other by companies that have GEO SaaS platforms or l
l M O SaaS platforms. That's what that means is
in Layman's terms.
Speaker 2 (07:09):
It's a it's a platform, a software platform that shows
brands what their discoverability status is on l ll ms,
shows the metrics you know, uh uh, you.
Speaker 3 (07:21):
Know how they're how they're surfacing compared to competitors. You
know what the sentiment is, What sentiments are being expressed
when people are talking about these brands.
Speaker 2 (07:30):
Are they're talking about you in a positive way, a
negative way, a neutral way.
Speaker 4 (07:33):
Uh.
Speaker 3 (07:33):
They tell you the citation sources, like what what what
sources are being cited when content is being surfaced to
a user in response to a particular query on an
l M. These these companies, uh analyze millions and millions
of queries and and the results that are surfaced, and
they provide uh you know, they offer these they provide studies,
(07:56):
They publish studies, uh and and and bits of data.
So what I've done is I've taken data from across
these sources, and I have and the companies themselves, the
ll ns themselves also publish information on on on their
on their platforms, on their models and how they function.
Speaker 2 (08:16):
So you know what I've done is I've I've synthesized
all of this data coming from all these different sources. UH,
and I've I've.
Speaker 3 (08:23):
Assembled a playbook for how to optimize on ll ns.
So UH, to get back to your original question, I
believe that four point four times referring to the value
of an average ll M user comes from a study
done by or data published by sem Rush, a company
called Semraush sem Rush.
Speaker 1 (08:44):
Thank you, Shane. I'm on the boards of lots of
companies and I see new brands every day. I'm going
to give you an example of one. I want you
to answer this question if you can. It's a new
brand called the Right One dot net and it's going
to be in the dating app space. If you were
on their team or consulting them when optimizing for LMOs,
(09:10):
what are the key levers that you will pull for
the right One? Is it prompt design, data, fine tuning, metadata,
structuring or what else is it in the Shane Tepper
brain that we or they could tap into.
Speaker 2 (09:28):
Shouldn't we discuss my fee first? Rick?
Speaker 1 (09:31):
That means that Shane is monetizing his wisdom.
Speaker 2 (09:34):
So you mentioned a number of things. Let's start with
with queries because queries, having high quality queries is really
where you need to begin.
Speaker 3 (09:46):
Because Rick, if you optimize for the wrong queries. If
you optimize to be discovered for questions that people aren't
asking chat shept or Claude or Gemini, right, then it
doesn't matter because you're not going to be more easily
to discovered because that's not what people are looking for.
Speaker 2 (10:02):
Right.
Speaker 3 (10:03):
So it all starts with coming up with some really
high quality queries. And what you need to remember with
queries for llms as distinct from keyword searches for Google,
for instance, is that llm's LLM queries are much more conversational,
(10:24):
much more representative of natural human language.
Speaker 2 (10:27):
Right.
Speaker 3 (10:28):
So in Google you might type like best dating app
twenty twenty five or something like that, right, and you
would get your results. What someone would type into chat
GPT might be more along the lines of, you know,
I'm a single, thirty eight year old man in Atlanta, Georgia.
I'm looking to find you know, someone local to my area.
(10:51):
What are some of the best recent apps to help
me find find companionship rights. It's much longer, much more specific,
much more context is provided. And this is really one
of the main reasons that people are using lllms to
for search rather than Google is because they can be
much more specific, and they're prompting. And the more specific
(11:14):
you are, the more context you provide, the more relevant,
the more relevant the results will be. And they like that,
and they can interact with this, and then they can
ask follow up questions. Right, so they can interact with
the LLM in a more natural conversational way and get
much more specific, relevant results surface directly to them rather
(11:34):
than having to sift through a bunch of you know,
mostly irrelevant blue links.
Speaker 1 (11:40):
Very good, and let's cut to commercial because that's a
perfect bridge to that fee that you will charge that
will be well worth it. How do people contact you
and tap into your big brain that you have and
start to move their brands forward?
Speaker 2 (11:57):
So they can go to my website retin r E
t i n A Retina Media dot meet. Retina Media
is my company.
Speaker 3 (12:05):
My website is Retina dot Media r e t i
n A dot n e d i A. You can
also get in touch with me directly through through email
at Shane my first name, h my middle initial tepper
all one word that's s h A n e h
t E p p e r at gmail dot com.
Happy to discuss any of your your l l M
(12:28):
or AI business transformation related questions. Thank you for that
opportunity to plug myself, Rick, I appreciate that excellent.
Speaker 2 (12:37):
Uh.
Speaker 1 (12:37):
We will be right back with Shane after this special
message from the one and only Marcus Aurelius.
Speaker 5 (12:47):
This is Marcus Aurelius reappearing to proclaim that Truly Significant
Conversations with big hearted people is a rare piece of literature.
This book reminds me of one of my more stirring quotes,
waste no more time arguing what a good man should.
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Be be one.
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career and questioning what lies beyond success, this book is
for you. Dive into forty soul stirring stories from luminaries
like doctor Jane Goodall, Ed Asner and Emily Chang, stories
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That outlasts you.
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Authored by Rick TOLKINI, Truly Significant will challenge your view
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And ignite a life of impact.
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Order now at TinyURL dot com backslash Truly Significant and
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Read she gave outrageously extended grace, unceasingly and lived to
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Visit truly significant dot com and celebrate the most truly
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Speaker 2 (14:21):
How bold of you to.
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Make your next chapter matter and be truly significant.
Speaker 1 (14:28):
We are back with the one and only Shane Temper. Shane,
you wrote a book correct, that's correct, Dwelling in a
place of Yes. Oh my gosh, I thought I got
this right here on the side of my little desk.
Here says I dwell. It says dwell in possibilities. That's
what that reminded me of when I first saw it.
(14:50):
And I love the positive note on It's The byeline
is the surprising psychology behind fear, opportunity and smarter choices.
And the word fear is being overstudied by my team
here in Austin. I want you just to take that
(15:12):
one word and tell me why it appeared in your
title or subtitle.
Speaker 3 (15:16):
Absolutely well. One of the main premises of this book
is that we become paralyzed during the decision making process
oftentimes because of fear.
Speaker 2 (15:27):
Fear is such.
Speaker 3 (15:28):
A powerful human emotion and it's no wonder why, because
you know historically, evolutionarily speaking, there it was a pretty
dangerous world out there, so our fear response kept us safe.
You know now that we've and it's persisted into the
modern era. And while fear is still helpful, fear can
(15:53):
also hold us back from opportunities that would improve our
improve our lives, opportunities that would allow us to to
thrive in a more complete way. So, you know, that's
that's kind of what I learned about fear and how
it functions, uh, you know, from from a from a
biological self preservation perspective, and also how it can inhibit
(16:16):
inhibit us from really becoming all that we could be.
Speaker 1 (16:21):
That's right, inhibiting next philosophical question. We've done a podcast
series with the one and only Tim Love, who was
the vice chair of Omnicom, one of the world's greatest marketers.
It was called Discovering Truth. Tim would want me to
ask you this morning about where is truth in the
(16:42):
midst of these large language model optimizations.
Speaker 3 (16:47):
Absolutely, you know, you always have to be a discerning
consumer of information. You know, there's so much disinformation out there.
What we're seeing is at a number of these ll ms,
or every ll M produces what are called hallucinations.
Speaker 2 (17:06):
I'm not sure if this is a term you're familiar.
Speaker 3 (17:08):
With, but it's like it's like a factor statistic that
is that is made up and you and what happens
is these these these models put out hallucinations, hallucinid hallucinid
hallucinated information. It gets re ingested by other models during training,
and then it just becomes perpetuated, uh, throughout these models.
(17:32):
It's a big problem. Obviously, there is all there's you know,
in the pre ll M era, there was already a
large a large.
Speaker 2 (17:40):
Amount of disinformation, bad information.
Speaker 3 (17:42):
Out there, and it's really our responsibility as individuals, as
as discerning consumers of information to kind of suss out
what is right and what is wrong. And for me,
that involves, uh, consuming information from a bunch of different sources.
Is from it involves corroborating statistics across different sources, and
(18:06):
it really you know, we have a tendency to, you know,
we're familiar with confirmation bias. We have a tendency to
to readily accept any sort of information that that that.
Speaker 2 (18:18):
Validates what we already know or think that we know.
Speaker 3 (18:22):
And it is absolutely imperative I think for us to
you know, maybe take.
Speaker 2 (18:28):
A step back and say, you know, why do I
believe this? Do I have a valid reason for believing this?
Speaker 3 (18:35):
And just you know, just try and and and consume
information from a variety of sources so that you can
so that you can, you know, have.
Speaker 2 (18:45):
Good reason to believe what you believe, right you.
Speaker 1 (18:50):
That was a great Walter Croncott answer. And having grown
up on Walter Croncott and Paul Harvey two, you know,
lighthouses of truth, as we thought in North Texas, I'm
wondering if there will ever be another voice or voices
(19:12):
that we can go to singularly, or will we always
have to go get multiple sources and corroborate.
Speaker 2 (19:20):
That is such a great question I think.
Speaker 3 (19:24):
I think we are at a point in our history
where we are so polarized, we are so mistrustful that
I don't know, I can't It's hard to imagine a
single person.
Speaker 2 (19:38):
Who is viewed as as the singular source of truth,
as the ultimate reliable disseminator of information that is accepted
across you know, political, socio economic, you know, the spread
of people across across you know, politics and socioeconomics. I
(20:03):
don't see it. I don't see that being a thing.
Speaker 3 (20:06):
This would have to be someone who people are so
entrenched in how they and how they think about things.
You know, so many people are so unwilling to consider
other perspectives or the possibility.
Speaker 2 (20:17):
That they might be wrong about a certain thing. It's
hard to imagine like a Walter Cronkite type person emerging
in our society this day and age.
Speaker 1 (20:27):
What do you think, Rick, it's said, I'm sad to
agree with you. Yeah, it's it's a sad situation. I
think people are pulled and tugged certain ways, and so
I always try to, like a good old journalist, I
try to get at least four or five sources just
(20:49):
to read it, you know, pray for discernment, because I
think that's.
Speaker 2 (20:52):
What I think, that's what we all need.
Speaker 1 (20:55):
Hey, you are a character on top of being a
great professional, and this, this character of yours is coming
out with your opinion. When did you start considering yourself
to be a futurist.
Speaker 3 (21:09):
I don't know if I would if I would consider
myself not to be a futurist. I've always been someone
who likes to stay on top of trends, and you know,
I you know I've been a fan of this. This
might sound a bit contradictory, but I consider myself to
be a bit of.
Speaker 2 (21:25):
An amateur historian. So I like looking at the past.
Speaker 3 (21:29):
I like seeing you know, patterns and why you know,
across across periods of time, across cultures and societies, and
finding common threads or common common circumstances that might explain
why things things unfolded in a similar or a different
(21:50):
way in different societies in different different periods of time.
And I think I think that also applies to how
I view technology and human development in our present era.
It's very interesting to me to see how technology has
evolved and how people's relationships with technology has evolved, and
kind of like, I guess, extrapolate or try and understand
(22:14):
what implications you know that might have, you know, those
observations might have in the future.
Speaker 2 (22:23):
It's just I don't know. It's a fun little mental
exercise for me.
Speaker 3 (22:27):
I like to stay I'm the kind of guy that
if I don't know something, if someone asked.
Speaker 2 (22:32):
Me a question and I don't know what, I'm immediately
heading to Wikipedia or chat EPT. I was about to
say Google because it used to be Google, but now
it's chat cept your cloud and trying to find the answer.
I don't. It makes me uncomfortable to not know things.
Speaker 3 (22:45):
There is a lot that I don't know, and I
readily admit, you know, when I don't know something. But
but yeah, I just like to I want.
Speaker 2 (22:53):
To be prepared. I want to be prepared for whatever's
whatever is coming.
Speaker 3 (22:56):
So I think I think that's why, you know, I
have this tendency to to just to want to collect
as much information as possible and want to understand, you know,
possible outcomes, two different two different scenarios.
Speaker 2 (23:08):
So am I you know? Am I a futurist? I
don't know. Does that make me a futurist?
Speaker 1 (23:14):
It makes you a chaos theorist in watching patterns.
Speaker 2 (23:20):
That sounds cool, and.
Speaker 1 (23:21):
I think a chaos theorists isn't part of a futurist. Hey,
I love the name of your company, Retina, and I
want to know you, whether you know it or not.
If we were tracking the number of times you've said
see in this conversation, it's a lot.
Speaker 2 (23:40):
You're very as an see like I see because.
Speaker 1 (23:44):
You were a very visual person and you talk about
seeing things, seeing things, proving things. Why did you name
it Retina other than the all the reasons I just mentioned.
Speaker 2 (23:54):
So this company, I actually named it Retina about ten
years ago.
Speaker 3 (23:58):
I was working in video perduceduction at the time, and
video of course a visual medium. I called it Retina,
and then, you know, as I sort of transitioned into
brand visibility and brand discoverability, I kept the name because
it was still relevant. It's sort of like a timeless
name for you know, I've worked in like branding and
marketing for a while, and you know that has to
(24:20):
do with making.
Speaker 2 (24:21):
Your brand scene. So yes, said c again, making your
brand visible.
Speaker 3 (24:26):
So Retina seemed appropriate, and that's why I chose Retina
at the time, and that's why I continue to use
that name.
Speaker 1 (24:33):
I think it's brilliant, but it makes me want to know,
what are you doing with Retina media inside large language
model optimization? So you're protected, you're number one, you're not misrepresented.
Speaker 2 (24:46):
Da da da da da exactly. Yes.
Speaker 3 (24:49):
So So what we do is we we give our
brands an idea of where they stand currently in terms
of their visibility and discoverability, how they compare in terms
of discoverability and visibility to their to their competitors. You know,
what people are saying about them, how you know, in
terms of the sentiments they're expressing when they're talking about
(25:10):
your brand, and where where these where these citations, sources,
where these citations are coming from, the sources of these citations,
where are these conversations happening about your brand, or where
is this information being being pulled in from? And then
based on all those all that information, oh and before that,
coming up with really really really good queries that represent
(25:32):
what your prospects are actually you know, typing into an
ll M to to find out about your brand or
to find out find out uh, you know, if they
have a problem, they'll type that in and then we
want your brand to be surfaced as a solution to
that problem. And then the the hard part, or the
trickiest part is Okay, great, I see what I'm I see.
(25:53):
I'm ranked eighth among my competitors. That's not very good.
Speaker 2 (25:56):
I have a a six percent discoverability rate. That's not
very good. How do I improve that?
Speaker 3 (26:02):
And that involves a multifaceted strategy of looking at where
people are having discussions about your brand, you know, and
where they're being and what sources are being brought into
the lms and then and then strategically deploying, creating and
deploying content to those places so that when in you know,
(26:24):
over time, and this is this is an ongoing strategy.
And this is another interesting thing, Rick. The citation sources
change very rapidly, so something more than fifty percent month
over month.
Speaker 2 (26:35):
So something that is being cited, you know, in July,
might be might not.
Speaker 3 (26:41):
Be completely might be completely ignored in August. So it's
a constant sort of cat and mouse game of understanding
where these conversations are happening and how can you insert
content strategically into those places so that your brand is
surfaced when someone types in a elevant query and an eleven.
Speaker 1 (27:04):
Man. That's impressive. And it leads to the final question.
Considering that I think you're a futurist straight up, Okay,
what do you think will be the most disruptive development
in large language model optimization technology in the next six
weeks or six months?
Speaker 3 (27:25):
I think it's I think it's that last piece that
I was just talking about.
Speaker 2 (27:29):
Is the hardest is the hardest part?
Speaker 3 (27:31):
Uh, you know, it's we can we can come up
with really good queries, we can we can you know,
surface metrics and insights about how your brand is performing
relative to to your competitors. But understanding this sort of
volatile world that uh that is where that is how
(27:53):
content is being surfaced exactly, you know that the sources
and how to how exactly to effectively you know, get
your brand into that area where where content is being
extracted and surfaced on chat, GBT, perplexity, cloud, et cetera.
That is going to be the key thing. And companies
(28:16):
in this space are are.
Speaker 2 (28:17):
Starting to to get in on this.
Speaker 3 (28:20):
Uh they're making you know, within their platforms, they're making
strategic recommendations like write a blog post on this topic
or a white paper on this topic, or you know,
you know, former strategic partnership with whatever company.
Speaker 2 (28:33):
And talk about this.
Speaker 3 (28:35):
But they're very kind of like disjointed recommendations for how
to create and deplay content. And I think the companies
that kind of figure out how to orchestrate you know.
Speaker 2 (28:47):
More campaign level strategies, right like multi multifaceted, multi pronged
approaches uh to to to increase visibility.
Speaker 3 (29:01):
That all work in concert to achieve a visibility objective
that can be tied back to the data that's gonna
be the key there is truly getting an understanding of
what moves the needle in terms of improving discoverability uh
and and and visibility on these platforms uh and and
really providing.
Speaker 2 (29:22):
These sort of actionable insights to to the client.
Speaker 1 (29:26):
Excellent that last oratory sent it like Marshall dllon in
the wild wild West of large language model optimization.
Speaker 3 (29:37):
You know, we're all just we're all just trying to
figure it out. You know, it's such an early it's
so early in this in this field, in this industry,
and new information, new insights drop almost on a daily basis,
certainly on a weekly basis, and and we're all just
trying to wrap our wrap our heads around it. And
I'm trying to, you know, be one.
Speaker 2 (29:57):
Of the early ones to get my head wrapped around it.
Speaker 1 (30:01):
Well, that leads to we got to have you back.
We'll have you back as often as you want to
be on, but I'd like to talk to you at
least three times a year so you can give us
you'd be the give us a state of the industry rapport.
You and your support team were the first to contact us,
and our success made to last network here in Austin
(30:24):
got You've given us some great information. I'm going to
have to listen to this again just to capture everything
that you just said. And I was listening intently to Shane.
Thank you so much. Give out information on your company
one more time, and where do we buy the book
Dwelling in a Place of Yes, The Surprising Psychology behind Fear,
Opportunity and Smarter Choices.
Speaker 3 (30:45):
Certainly, my company is called Retina Media R E T
I N A Media. That website is Retina dot Media
uh and you can also contact me via email Shanehtepper
at gmail dot com, a.
Speaker 2 (31:00):
S h A N E H T E P P
E R at gmail dot com. And Dwelling in a
Place of Yes is available for download on Amazon Kindle
no physical no physical copy available yet.
Speaker 3 (31:15):
I'm working on that, but you can download it to
your your kindle and read it there.
Speaker 1 (31:21):
Fantastic. Thanks again, Shane. Hold on one second after we
sign off here, and I just want to thank you
folks for joining us today. I know that you found
this show informative and make sure and contact Shane with
any follow up questions. As we always say, we wish
you success, but on your way your own unique way
to significance.
Speaker 2 (31:41):
I really
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