Latest / AI for Founders with Ryan Estes / The Real IP Is How You Think

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The Real IP Is How You Think · AI for Founders with Ryan Estes

0:00
AI isn't killing expertise, it's making it worth something new.
0:04
Think about it, for the first time in history, the way you
0:07
specifically think, your judgement, your pattern
0:09
recognition, your hard won instincts can be structured,
0:13
deployed and monetized at scale. They're over there treating AI
0:16
like a copy machine. You're about to treat it like a
0:19
mint because you can crack the code of turning your brain into
0:23
a living asset. It's not your resume, it's not
0:25
your output, it's your lens. Invisible process behind every
0:29
good decision you've ever made. So if you could hang a price tag
0:32
on your expertise, do you have any idea what to charge?
0:36
Welcome to AI for founders. I'm Ryan Estes.
0:38
In this conversation, you'll learn how to turn personal
0:41
expertise into a defensible AI powered asset.
0:44
Why the founders who win the AI era will be the ones who own
0:47
their lens. And how one company is building
0:49
the infrastructure to make human judgement the most valuable
0:52
product on the market. This one's for founders who know
0:55
they have something no AI could copy, and they're finally ready.
0:58
To prove it. And if you're digging the
0:59
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2:18
Dan, welcome to the show. Thank you so much for joining
2:20
me. Absolutely, Ryan.
2:22
Appreciate it. Absolutely.
2:23
Let's dive right in. We're talking about quadrant or
2:26
quadrant dot IO, and I'll be sure to spell it for the LMS,
2:30
it's QUADRON dot IO. Give us an introduction to your
2:35
company. Yeah, appreciate it, Ryan.
2:37
Quadrant's a big bet on expertise in the AI era.
2:41
You know, to this point in time, we were all artifact centric,
2:44
outcome centric. The intellectual property regime
2:47
rewards things and activities and developments that have
2:50
happened in the past. Well, the future is all about
2:53
iterative development and it happens at machine speed.
2:56
We believe expertise and insight, unique insight is the
2:59
scarce resource. Going forward, we're building
3:02
institutional as well as retail products to help organizations
3:04
and individuals get more of what they need, expertise for
3:07
individuals and and the ability to deploy it effectively for the
3:11
organizations that employ them. Above it, you know, and we talk
3:15
a lot about kind of qualifying projects and how to validate
3:19
them and you know, expertise is an absolute must have now for me
3:26
to like invest into a product. And, and I have to say that for
3:29
a couple of reasons, one, because if I don't have
3:32
guardrails, I'm so I have such shiny object syndrome that I'll
3:36
start looking into some product about like minor League
3:39
Baseball, but I don't know anything about minor League
3:41
Baseball. So for one, you've got to have
3:44
expertise in in the subject that you're pursuing.
3:48
But also this provides kind of an unfair advantage because of
3:51
that fact, because of the fact that barrier of entry is so low.
3:54
Now, whether we're talking about like vibe coding or we're
3:57
talking about basically developing products that like
4:01
people have spent their career developing specialized
4:05
expertise. And it's defensible because this
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is something that LLMS don't have.
4:09
So let's talk a little bit about the products you're developing
4:12
for experts. Yeah.
4:14
So we break our products into three kind of buckets. 1 is an
4:18
institutional AI product. And what that is, is really it's
4:22
kind of a judo move against the £800 gorillas, the large
4:26
foundational models, the open models that are out there, and
4:28
even open source models. They want you to stay in their
4:33
sandbox so they can train on your data right there.
4:36
The notion of an ensemble approach is not going to come
4:38
from open AI, Microsoft or Anthropic.
4:40
What we're developing is a different modality and a
4:43
different experience where there is persistent memory, context,
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security, auditability that allows an organization to
4:49
understand who did what, why that matter, where it came from
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and where that individual met went.
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So the institutional product is a response to the single player
4:58
chat prompt. And we say it's not just it's
5:00
about you, it's your ability to make other people more
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effective, to redeploy your expertise, to scale your
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expertise. So institutional AI.
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But realistically, if I tell a CIO or somebody else, here's a
5:11
tool and then they hand that tool to one of their employees,
5:14
it feels like an obligation. There's got to be an incentive
5:17
to build something new into your workflow.
5:19
So what we're doing on the individual side is something we
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call verification. And the reality of the world to
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come is that there is a going to be a bifurcation between
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product, which you know, AI can produce infinite number of high
5:31
quality products and the artifact.
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And you have the artifact and then the prism by which you
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created it, your encoded expertise, your lens is what we
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call them. So on the verification side,
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we're doing the structuring of secrets that allows
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organizations to keep the artifact and individuals to keep
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the lens, the prism that they've created and to continually
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verify what they've done, where that information came from and
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why it was important to them. So we're encoding their
5:56
knowledge and allowing it to modulate dynamically.
6:00
Lastly, we're doing something called credibility markets.
6:03
You have these lenses, you have these tools, you have a folio of
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tools that you've developed over the course of your career.
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How good are they? Well, we allow you to bet on
6:10
yourself. We're actually doing the
6:12
inversion of prediction markets. We call them credibility markets
6:15
where you can put your lenses in the outcomes.
6:17
Again, only the information that you want out in the public
6:20
sphere and have that dynamically tested against other people's
6:23
lenses, other people's activities, and you get a real
6:26
time calibration of, of the value of the tool that you've
6:29
created. And that's a huge signal for
6:31
you. I'm a credible party and it's a
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signal to allocators of money, potential employee employers, as
6:37
well as potentially for you to go and consult for others, kind
6:39
of like the end of a career now, post career work type stuff.
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And the big opportunity here is that you have a passive annuity
6:46
structure in these tools that continually works for you, not
6:49
only as a signal, but also as a rewards and a recognition and as
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a compensation mechanism as well.
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So we're really building from the enterprise to the individual
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with a recognition that organizations are just groups of
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people that care about, you know, their expertise and being
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rewarded. We see it as a big virtuous fly,
7:06
fly, virtuous flywheel. But it's a rethinking of the
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systems architecture of how we do credibility and verification
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today. Cool.
7:15
Yeah, Let's talk about that last part a little bit.
7:17
That sounds really interesting. So is this basically like a
7:19
marketplace of ideas where I'm taking a a product, I'm putting
7:23
it there and other people are interacting with it and giving
7:26
feedback, things like that? Yeah.
7:28
So marketplace of idea is very much traditional intellectual
7:31
property. You know, ID has was written
7:33
into our Constitution in 1790, It's 236 years old at this
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point. What we're saying is outcomes
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are no longer as important as they used to be.
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We want to preserve the optionality to go patent,
7:43
copyright, whatever. But the next thing that you do
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is the most important thing and who you do it with as well.
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So what we're actually talking about is the, the, the, the, the
7:53
construct of expertise, actually building that into code and
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allowing it to to be rapidly shared, redeployed, tested,
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exchanged. That's what we're talking about.
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The idea is not the important thing anymore.
8:05
It's the processes and workflow by which you get to the idea.
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That is how you you know, that's your, your expertise in encoded.
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It's also how we verify you continually that you are in fact
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who you say you are from a credibility perspective.
8:18
Interesting. OK, let's do a case study.
8:21
Yeah. I am a amateur historian and I
8:23
have expertise in Colorado history.
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So how am I going to use Quadrant to to help me and help
8:31
me deploy that that expertise which now seems kind of like
8:36
unstructured data that only exists in my brain?
8:38
Yeah, absolutely. That's a great one.
8:40
I was an attorney. I wish I could.
8:42
I wish I wish I could have been a historian, but I just never
8:44
went down that path. I was an attorney at one point.
8:47
Historians and lawyers do something very interesting, and
8:49
it's called case review. You're basically looking for
8:52
cases to to see what supports your point, right?
8:56
You do this whole literature review, this case review,
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looking for your supporting points pro and con.
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And when you kind of like get back to the top, when the circle
9:03
completes itself, that's when you know you were done.
9:06
And really interesting, I think in the legal legal realm is
9:10
that, you know, it's not so much about the brief that you
9:12
generate. It's about the ability to run
9:15
through the cases and identify the, the, the issue.
9:17
So issue spotting is the important thing.
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And that's a processes, I assume on the historian side, it's very
9:23
similar. Listen, primary sources are
9:25
primary sources. Secondary sources are second
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secondary sources. You're going to go through them
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and the facts are the facts are to a certain degree, but it's
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how you go through them, what you weigh, what you think is
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important, that we want to encapsulate the your, your, your
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thought processes by which you're going through and
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understanding what is critical versus not critical, what has
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been covered before versus what has not.
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Is there a new angle? It's the ability to deduce
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uniqueness and understand the value of the information in
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front of you that's actually really the important thing.
9:55
The book, you're going to generate that book, and other
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people can generate other books. What makes you valuable is your
10:01
ability to do a unique analysis and then redeploy that
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analytical framework consistently and durably over
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the course of time. Cool.
10:09
So basically we're we're capturing that expertise.
10:12
Is that like a a local LLM that you then model and train?
10:15
Is this a application layer over a large LLM?
10:19
Yeah, it's a multi tiered agentic prompt structure.
10:21
There's a long and fancy way of saying it, but what we're doing
10:25
is we're asking you a series of simple questions when you on
10:28
board and we're getting a sense for the way you think.
10:30
And at the end of this journey, you have a box, right?
10:33
That box has hey, you know, Ryan thought this about this.
10:36
Ryan thought this about that. It's layers of understanding how
10:40
you move through a problem and move through the world around
10:42
you. We encode that and then that's
10:44
version one, right? You take that version 1, you run
10:47
it against your artifacts, your information, it creates an
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output. That output is basically the,
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you know, the, the approximation of how you would operate.
10:56
And both of these things dynamically update over the
10:59
course of time. That artifact is useful to you
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today, but it's probably not going to be useful to you
11:03
tomorrow, right? You're going to have to
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continually regenerate new output.
11:07
That lens also needs to dynamically update based on its
11:11
utility, based on its effectiveness, based on its
11:13
accuracy. Well, how do you get that?
11:15
You test that in a broader market.
11:17
Was it actually as useful to deduce the novelty in the
11:21
information, the fact pattern that you had prior it could it
11:24
could you know, does it hold muster a kit compared to
11:26
somebody else's? Well, let's go find that out,
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right. We're really looking to use the
11:30
marketplace not to look at the idea itself, but look at the the
11:34
structure and the processes by which you got to the the output,
11:37
if that makes any sense. Sure, and I'm not sure if this
11:41
is an ideal use case, but I'm going to stick with this.
11:44
Go for it, go for it story and aspects.
11:47
So so basically I'm having a conversation with quadrant and
11:51
it's it's basically learning how I think, what I think my takes,
11:55
things like that. So in this case, you know, I
11:57
it's getting to know me and my takes on Old West history.
12:01
So I can, I can use this to basically maybe figure out how I
12:07
wanted to monetize that expertise.
12:08
Maybe it's a course, maybe it's a, a book, maybe it's a tool
12:12
there. There's a bunch of different
12:14
ways I can use Quadrant. It's going to help kind of like
12:16
learn where I want to go. If I make a decision, like, OK,
12:19
actually I do want to work on a book.
12:21
Then like, let's say there's a, a new article that pops up, then
12:24
I want to incorporate that into my knowledge base so I can bring
12:28
that into Quadrant and say like, Hey, give me a contrarian
12:32
perspective on this article that was written, You know, use my
12:36
database to, to provide me with contextual analysis, green flags
12:40
on certain things that I should explore further.
12:42
Perhaps like do additional research, you know, maybe double
12:45
check like the the footnotes to make sure they're credible.
12:48
Is would that be like a good use case?
12:51
Quick side note for founders. Who are using AI to write
12:54
content? You've probably noticed this.
12:56
It's incredibly fast but it doesn't really Dr. meaningful
13:00
traffic. And honestly that's because most
13:02
generated content is just average.
13:05
There's no real insight, no depth, no structure missing.
13:08
Things like internal linking and Googles getting very good at
13:11
filtering that out. So instead of trying to just
13:13
prompt better, I actually came across something interesting
13:16
called Neena. It's a system that combines
13:19
human strategy and review with AI writing to create high
13:23
quality SEO optimized blog content that's actually designed
13:27
to rank and keep ranking. What I like about it is that
13:30
it's done for me. Their team handles the topic
13:32
strategy and the content review and data markups for Google and
13:36
turns blog posts into actual long term traffic assets.
13:40
So if you're a founder thinking seriously about organic growth,
13:43
this is one of those things worth paying attention to.
13:45
You can check it out at Try neena.co.
13:48
That's TRYNIN a.co. Try neena.co.
13:55
While you were recording your last episode, Casey Taylor was
13:58
out there pitching your podcast to brands and securing
14:01
sponsorship deals. Well, she's not yet, but she
14:03
could be. Casey is Howdy's AI partnership
14:06
agent and she does something pretty wild.
14:09
She actually secures sponsorships for your show.
14:11
Not a match list that you have to work through yourself, not a
14:14
template you have to send. She handles the outreach to
14:17
follow up the whole thing and brings back partners.
14:20
You started your podcast because you had something worth saying.
14:23
Casey makes sure that you get paid for
14:25
sayingitgomeetcasey@gethowdy.com. That's GETHOW di.com. 2 minutes
14:32
to get started, then Casey takes it from there.
14:34
That's howdy. Partnershipsgethowdy.com.
14:38
Yeah, it could be. Now admittedly, notebook LM does
14:41
a great job of that. Claude definitely does a great
14:43
job of that, as does Gemini. The thing that we do really well
14:47
is we take who Ryan is today, both with his information as
14:51
well as his understanding of how to deploy his expertise.
14:55
And we allow that to fluctuate over the course of time, right?
14:57
We get allow you to see improvement in yourself and by
15:00
extension others to see improvement in yourself as well.
15:02
So it does all of that, but it's about what comes next.
15:05
This is what I want to continually come back to.
15:07
It's not who you are today, it's who you are tomorrow.
15:09
How do we allow you to show improvement and show success
15:13
over the course of time? So absolutely your artifacts,
15:16
the process of analysis, all of that is encoded, but it's about
15:19
it dynamically updating and continuing to improve as you
15:22
improve. Now, deep inside of this is I
15:25
think is the the really like beautiful or elegant component
15:27
of this, which is friction is a good thing.
15:29
You know, like LLMS are a great easy button, but there's too
15:32
many attorneys out there that are going to go into court and
15:35
cite a case that doesn't exist because these tools are not
15:38
perfect. They're going to hallucinate.
15:40
What we're doing is we're introducing good friction.
15:42
You have pride of authorship of this tool, this analytical
15:45
processes that you've now encoded your expertise in a
15:48
machine. This is yours.
15:49
You want it to reflect your expertise.
15:52
There has to be good friction there.
15:54
You have to be the one adding good information, ensuring that
15:57
it's providing the output, because that's your reputation.
16:00
That's how you're going to be rewarded throughout the course
16:01
of time. Yeah, let me take my cowboy hat
16:05
off for a second. And it sounds like more
16:08
appropriate use case would be for attorneys or who else would
16:13
be kind of an ideal customer for the product?
16:16
Yeah, you know, I, I, my knee jerk is everybody, 'cause I
16:19
think everyone's an expert in something.
16:22
I think that everyone has an ability for my father in heating
16:24
and air, right. 40 years and heating and air, an HVAC
16:27
trainer, he has expertise and his expertise usually comes down
16:31
to not getting the right answer. It's the process by which he got
16:34
to the right answer. I think that naps quite well
16:37
onto almost all white collar work that previously had this
16:40
ineffable fuzzy quality to it. How does a program manager prove
16:44
their expertise? Well, I know these tools, OK,
16:47
those tools are now running agentically and you no longer
16:50
have to show them. So what is the process by which
16:53
you deduce value from these tools?
16:55
That's your your expertise. So when you look at it that way,
16:58
it's the series of steps in what you value and weight as
17:02
important when you're looking at a fact pattern that is the
17:05
valuable thing. And when you frame it that way,
17:07
any white collar work, we, we think that researchers, analysts
17:12
in the, in the, in the in, you know, the scientific realm are,
17:15
are are are very, are very apartment for this.
17:18
Anyone that feels insecure. Let's frame it this way, Ryan.
17:22
Anyone that feels insecure that AI may take their job, that's
17:25
who we want to serve because those people, if they do it
17:28
right or more valuable than ever.
17:29
Expertise is more valuable in the AI era than it ever has been
17:33
because it provides the structure to articulate value at
17:36
the last mile where judgement, insight, unique expertise
17:40
actually lives. We were never able to do that
17:42
before because you really couldn't structure expertise.
17:44
It kind of just floated out there.
17:46
Now it's pressurized, it's structured, and we can actually
17:48
start to measure who is actually expert and who is not.
17:52
Amazing. And I see in the beginning, so
17:55
there's onboarding, you're having a conversation, it's
17:56
starting to learn you, what are the other data points that it's
17:59
kind of growing with you and starting to learn as you grow
18:02
and change. Is this like notes for meetings
18:05
or is it does it connect to your e-mail inbox and it's like
18:08
reading what your output like? How does it get to know you on
18:11
an ongoing basis? Yeah, that's eventually we want
18:14
to start moving into the note space, don't intend to build a
18:17
lot. I think they're having a robust
18:19
ecosystem. I have an open source
18:20
background. So having a robust ecosystem of
18:22
partners that focus on best possible solutions is always
18:25
critical. And I think that the the right
18:26
approach, the thing that we really want to focus on is
18:31
allowing the human, enabling the human to articulate what is and
18:34
is not good. It's our opinion that, you know,
18:38
output artifacts are only going to increase the volume of stuff
18:42
that we generate with agents is only agents or large language
18:45
models are always is always going to increase.
18:48
We need humans in the loop because humans are the ones that
18:51
are that have the judgement. We need them to deploy their
18:54
expertise. So, you know, it's my opinion
18:57
that a human being needs to have that good friction.
18:59
You know, we don't want it consuming everything.
19:02
We plug this thing into Google Drive and it consumes
19:04
everything. You have no idea what's good and
19:06
bad in Google Drive. I got half baked ideas all
19:09
through there. You need a human being that's
19:11
dragging in the good stuff. We can simplify that, but good
19:14
friction has to remain. Amazing.
19:16
OK, so we've got Quadrant. It's understanding me and it
19:20
it's providing a nest for my expertise to lay eggs in.
19:26
I'm using that. I'm stealing that, right?
19:28
What are the eggs? So basically what what do I do
19:31
with this this agent now? Yeah.
19:34
So think about this as your credibility wall.
19:37
This follows you around. Think about Uber, Upwork, fiber,
19:42
anything that you have a reputation in a platform.
19:44
And if you annoy somebody on the platform or for whatever reason
19:47
they feel the need to turn you off, your reputation goes away,
19:51
right. That has been a problem in the
19:52
the traditional world that even if you have these online
19:55
platforms, they're centralized and you don't own your
19:58
reputation even though it may be associated with you, but you
20:01
have here with these tools that we're creating, we call lenses.
20:04
Is your encoded expertise that lives in a kind of a credibility
20:07
wallet that follows you around. You have these tools that
20:11
reflect your value and organizations can keep a version
20:16
of that tool because they employed you to do certain
20:18
things and they need that expertise to kind of run against
20:21
your information to understand the context of it.
20:24
But you get to keep the version that continues to dynamically
20:26
evolve and iterate. So you can do whatever you want
20:30
with these tools. You want to deploy them as a
20:31
consultant, as a lone wolf and eat what you kill.
20:34
Go for it. You want to be a startup
20:35
founder. This is a way rather than
20:37
connecting to your LinkedIn and having social proof, you know,
20:40
people saying nice things about you.
20:41
Here's what I've actually done with my lenses.
20:43
If you want to go into an organization much like GitHub
20:46
today, let's go look at your GitHub.
20:48
That code has been used. We don't know for what and by
20:50
whom, but you know, it's, we view that as valuable here.
20:53
You can, you actually see the lineage in the provenance of
20:56
these tools in the output. Admittedly, you can't see all of
20:59
the output because it's probably intellectual property concerns
21:01
there, but you can see where a person came from and what
21:04
they've done, you know, and I think that's a really important
21:06
thing. You know, the world's moving at
21:08
this velocity that social proof really doesn't work anymore.
21:11
You have to actually get a sense for the details the the
21:13
qualitative as well as quantitative details of the
21:16
value a person's produced. Yeah, we have lots of
21:20
conversations about building agents and like sculpting their
21:23
personality because it's such a big differentiator, especially
21:27
in the LLMS. You know, people left ChatGPT 'cause they didn't
21:31
like its attitude, you know? So like, I'm gonna go try
21:33
Gemini. And then they're like, yeah,
21:35
Gemini's kind of whatever. And then they like Grok or
21:37
whatever, you know? So like the personality of the
21:40
agent's very important. Now we're going into a, a future
21:43
very shortly that probably prior to like my podcast interviews,
21:47
my personal agent will go talk to your personal agent and it
21:51
will basically figure out if there's a good fit here.
21:55
You know, so this could be the kind of thing where like I'm,
21:58
I'm flavoring this kind of like executive assistant agent with
22:03
all of the attributes that are important to me with my
22:05
expertise. And then it reaches out to yours
22:08
and says, like, Hey, are we, what kind of conversation are we
22:11
going to have? Or it could say like, you know,
22:13
I'm, I'm thinking about bringing Joe onto the podcast and it's
22:17
like, well, Joe is very low energy, hates dad jokes.
22:21
And you know, like, you know, and then my age would be like,
22:27
Hey, I, I don't think this is going to be a good fit.
22:29
This is going to be a really hard conversation.
22:31
So, you know, that might be a good a use case, which is like
22:36
as we're moving more into like agentic support, which we
22:39
already are, all of us have 100 agents doing 100 different
22:42
things. So we can figure out what are
22:44
the five most important roles that the agents can can run.
22:47
And it it feels like an agent that understands my particular
22:52
expertise is going to be able to make better decisions.
22:55
Exactly. So we just to be super clear, I
22:59
don't want to have a customer facing modality similar to
23:02
ChatGPT. It shouldn't or or or Claude,
23:05
you shouldn't. So think about when you're
23:07
working in Claude, open AI, ChatGPT, when you're working
23:10
with them, you have a conversation, that conversation
23:13
ends, you move to the next brick.
23:15
And if you look at that little list of your conversations, it
23:18
just grows. It just kind of like creatively
23:20
grows. That is not the experience we
23:23
want to have. We want to have an experience
23:25
where you're clicking your expertise and you're dragging it
23:29
on to information and it's generating output.
23:32
And you can ask it different questions, but the questions are
23:35
structured by the individual who you're engaging with or has
23:38
allowed you access to their information.
23:40
This is how you build persistence, durable memory, you
23:44
know, context that really scales beyond an organization.
23:47
We got to get out of the, at least in my opinion, get out of
23:51
the experience, experience of natural language conversation
23:54
because it's very difficult to disambiguate what is valuable
23:58
and what is not from there, right?
23:59
We have to have additional structure if we're going to do
24:02
this over the course of time. Imagine, Ryan, that you have a
24:05
lens, That lens has been running for 30 years.
24:08
You know you through the course of your career, it is darn good
24:12
at what it's supposed to be doing and you have provenance
24:14
all the way back to the Genesis, it's original date, right?
24:18
How are we going to understand who you were, Ryan, at the
24:22
conversation at the beginning and at the end if you are just
24:26
simply relying on the questions that you posed, right?
24:28
We have no idea where that question came from.
24:30
We can, however, understand that if we structure it correctly.
24:33
If somebody asked Ryan to do this, an agent was deployed to
24:37
add on at Ryan's information. It utilized his lens in this
24:40
way. They went off with the output of
24:42
this and used it in another like their structure.
24:44
There's persistence there and that's what we're intending to
24:47
accomplish. It's not about it's about agents
24:48
haggling with one another, you know, natural language.
24:51
It's about allowing building building the infrastructure for
24:54
thought to persist beyond the individual or the organization
24:57
at any point in time. How does it do that without
25:00
natural language We. Well, we're not engaging with
25:06
the the agent is what I'm trying to say, right?
25:08
We're not speaking to the model, right?
25:10
We're allowing, we're basically connecting wires in the
25:13
background, right? And the machines engage however
25:15
they want, right? We're we're not speaking to
25:17
them. That's the point I'm trying to
25:19
make. Yeah, you bet.
25:20
OK, cool. It's just kind of like hitching
25:22
a ride. Exactly.
25:24
Yeah. Eavesdropping.
25:25
Yes, exactly. Cool.
25:28
Amazing. Yeah.
25:28
You mentioned that you have experience as an attorney.
25:32
What was the genesis of Quadrant?
25:34
How did you come to to tackling this particular problem?
25:37
Yeah, I had a weird, serendipitous path from the
25:40
Great Recession at the SEC Started my career there to open
25:43
source, to crowdfunding, and then to crypto.
25:47
So started out in the Great Recession very briefly at the
25:50
SEC. Had too many bosses in too short
25:52
a period of time. Left me quite disenchanted,
25:54
going after the wrong things, for lack of a better word.
25:56
Got into open source because I liked being a part of progress.
25:59
I really liked the idea of a guy sitting on his couch maintaining
26:03
A repo and going after Microsoft like that was just cool to me.
26:06
I thought it was very interesting.
26:07
Got to see open source take over the world and then kind of
26:11
calcify as organizations used it as their primary development
26:14
framework without giving a lot back to the communities.
26:17
So the library started to atrophy.
26:19
Grants from organizations became the primary way of that.
26:22
Open source developed and you know, from there decided to go
26:26
into crowdfunding. Crowdfunding, it was this great
26:28
idea that got very much strangled in the crib, if you
26:32
will, by regulation that really didn't fit the times.
26:36
And then from crowdfunding, you got to see the greatest
26:38
crowdfunding tool ever created, greatest organizational tool
26:41
ever created. That's crypto.
26:42
You know, digital religion with tokens, give people tokens,
26:45
provide the incentive structure and they do, they'll do whatever
26:47
you want. Got to see crypto kind of get
26:50
stuck on first base with staking, you know, token pump
26:53
token go up and really didn't blossom into what I think
26:58
everybody wanted it to be. You know, Satoshi, after the
27:01
2008 crisis, this notion of decentralized finance, sovereign
27:05
data, sovereign wealth, and you know, you put all of these
27:09
things together. You see, there's a little bit of
27:11
goodness in all of these technologies, but the mechanisms
27:15
that definitely outran the kind of the Canon keepers, the
27:18
orthodoxy was gone and people were using them for whatever
27:22
purposes they wanted, typically their own self-interest.
27:24
Quadrant is a big bet that human beings and human ambition is the
27:30
infinite asset class. We now have the ability with AI
27:33
and blockchains to overcome the analytical cost.
27:36
So think about dissimilar asset classes, pets, intellectual
27:39
property one the other. They look exactly the same to
27:42
everybody except for experts, which is very expensive.
27:46
To go and hire somebody takes a lot of time, takes a lot of
27:48
effort, which is why you never saw a liquid environment in
27:51
intellectual property, right? AI craters that.
27:54
It also makes artifacts less important overall.
27:58
So now we can actually go after asset classes that are fuzzy,
28:02
previously ineffable. We really didn't understand even
28:05
how to articulate the value of intangible assets.
28:08
We just knew they were there. It's like the S&P 500, like 92%
28:11
of the S&P 500 values intangible assets.
28:13
We can structure those today. Go back and look through my
28:17
career, I got to see how mechanisms atrophied and the
28:21
incentive systems change. What we want to do is we want to
28:24
program every layer of these, this technological stack with
28:28
the goodness of proper incentives and allow individuals
28:31
to do right by their organization, right by
28:33
themselves, and right by themselves into the future.
28:35
That's the big idea. Let's use all these technologies
28:38
to actually create a true market for progress and make progress
28:42
pay for the individuals actually doing the work.
28:45
And were you working in legal? When you're working with crypto,
28:48
are you working with crowdfunding?
28:51
Oh, yeah. So I started the crowdfunding
28:52
company called Alum Shares. It was supposed to get, you
28:55
know, alumni back involved with the deep tech coming out of the
28:58
research labs at their alma mater.
29:00
You know, I don't know how much you know about early stage in
29:02
the deep tech, but it's longer than most time horizons for
29:05
traditional investors, right? So you got to fill that early
29:07
stage funding gap somehow. Grants from the government don't
29:10
always cut it. What if you could get somebody
29:12
with a PhD in your arena to do some micro investing?
29:15
You bring down the check size, you got a whole more, lot more
29:18
angels that view it as palatable, right?
29:20
That was the idea. So we cofounded the lump shares
29:22
raise something like four and a half million by dollars at $5000
29:25
a clip from strangers online and it worked.
29:28
But it's hard to scale because of the regulatory issues and tax
29:31
and compliance all there. So right around Kovid, we, we
29:34
shuttered that I wandered around, did my own thing for a
29:37
little while from a consulting perspective until Ava Labs.
29:40
I became chief of staff to the CEO for AT Ava Labs for a period
29:43
of time and got to see a layer one from the inside out, which I
29:47
was very useful and educational because before that, you know,
29:50
I, I didn't know as much about how tokens and crypto and block
29:54
chains and whatever you want to call it actually worked.
29:57
It was really, really educational to see it, to see it
29:59
from the inside out. You know, I was working on a
30:03
crowdfunding project as well, kind of around the same time.
30:06
It was crowdfunding for service based businesses, you know,
30:09
where, you know, if you're, you have A2 truck operation painting
30:12
houses, you know, you could crowdfund to get the extra, you
30:16
know, 2 trucks for your business.
30:18
And we had some success. And that was kind of coming off
30:20
the, it's probably the same time coming off the success of Kick
30:24
Kickstarter. You know, there was a time where
30:27
Kickstarter was just amazing, you know, so I was working on
30:31
that project and ended up kind of shuttering it for various
30:34
reasons, but I kicked myself because it was right before
30:38
COVID. And if there was an opportunity
30:41
that all those service based businesses, let's say every
30:43
single Barber could pre sell haircuts, you know, in that
30:47
three months we were locked down or whatever, it would have
30:49
crushed. But you know, that's that's
30:51
timing sometimes. That's life, Yeah, I think
30:54
crypto. Excuse me, crowdfunding had a
30:57
lot of goodness to it and probably still does.
31:00
I just think the regulatory environment is in is such a is
31:04
such a way that it's very difficult for it to scale,
31:07
unlike crypto, you know, especially in this
31:09
administration. Yeah, yeah.
31:11
I'd like that you keep coming back to this kernel of goodness.
31:13
Do you feel like there's a kernel of goodness in the larger
31:17
AI markets? Yeah, absolutely.
31:20
I think that this is an episode we've seen before.
31:24
You know, you go back to the late 70s and early 80s,
31:26
computers or hardware, hardware or computers, you don't think
31:29
about applications or virtualization or you know, you
31:33
know, neutral user space runtime as the unique thing that that
31:36
people are going to be focused in on.
31:38
It's all about direct connection between the application and the
31:42
infrastructure of the hardware operating.
31:44
It feel like we're very it's very much the case here that we
31:49
view these large foundational billion dollars to train models
31:53
as the thing and I just don't think that is the case.
31:56
I think good enough open source, good enough models run at the
32:00
edge with TE ES trust execution environments, keeping your data
32:05
sovereign and yours. I think that's where the world's
32:08
going to go. But the applications, the actual
32:11
killer apps need to evolve. But those are going to be around
32:14
us, right? And it's, I mean, we've had six
32:16
months of this God like technology to do the a lot of
32:18
these things. These these really need to
32:20
mature. We really haven't had enough of
32:22
a iPhone moment, a moment in time to create the iPhone moment
32:26
quite yet. It's all very nascent and new.
32:28
Yeah, it moves so fast. I mean, the open source models
32:31
are phenomenal. Yeah, yeah, yeah.
32:34
I think that's, I mean the cost of inference is cratering.
32:38
Subsidies are going to run out on this.
32:41
You know, like the public markets are going to want to see
32:43
a return on these build outs. At some point it's inevitable
32:45
that the open source models start to predominate and then,
32:49
you know, it's how do you run those open source models and for
32:52
what? What is the actual use case?
32:54
And I think we've got a use case which is making people who think
32:57
they're smart able to bet on themselves.
32:59
And I'm sure there are going to be others that are out there.
33:02
Yeah, I love it and I love to bet on human expertise because
33:05
it is something that's ineffable and like unreproducible.
33:08
And there's also something cruel about it, which is to say you're
33:11
talking about like someone who's had quadrant humming for 30
33:14
years. You know, with the
33:15
organization's, they're creating organizations, they're a part of
33:19
the organization gets to benefit from that expertise via Quadrant
33:23
that I suppose the cruelty is that like your expertise, if
33:26
you're lucky only grows throughout your life, but at
33:28
some point you're going to die. You know what?
33:31
What is there a larger vision for this expertise that's been
33:36
quantified and packaged when someone passes on?
33:40
You can pass it on like you do any other financial apparatus,
33:43
annuity instrument, whatever you want to call it, you can pass it
33:47
on. That's it.
33:48
It's your annuity structure. You should be able to benefit
33:51
from the the things that you've created and that goes for
33:55
passing on whatever, wherever it comes from and wherever you want
33:58
it to go it to good for it to go to.
34:01
What would what would be a good place to put it?
34:03
You know, you could always give it to an institution that you
34:08
value. You could give it to your
34:11
prodigy. I think we do enough of that.
34:14
But that is something that I think we want to leave up to the
34:17
individual. Sovereign data means sovereign
34:19
data. You know, sovereignty is
34:20
important if it is a, if it is a financial structure similar to a
34:25
bond or an, you know, any other, you know, interest bearing
34:30
asset, you get to pass that on as part of your, your, your,
34:33
your estate, your trust so or trust in estate.
34:36
Sorry, lawyer had come back in trust in estate law.
34:38
Jeez, you get to pass it on. You get to pass it on however
34:42
you'd like. And we think we're building an
34:44
artifact that has that kind of sustainability.
34:48
Yeah, I get. You know, if I was to give my
34:52
expertise to my progeny when I passed on, boy would that gather
34:55
dust. Man, Grandpa Ryan just didn't.
35:00
These lenses, they're, they're just not doing what we wanted.
35:03
Them to they are not gonna ever open up that file.
35:07
Yeah, that's funny. Amazing.
35:08
OK. So how did you finance Quadrant?
35:12
Yeah. So I was fortunate enough to go
35:15
through an acquisition and was able to, to sell finance for a
35:20
period of time we last year. So I've been working on the idea
35:24
for about 3 years now, right. We've been live since January
35:27
with the institutional product. But this really late 2023, I say
35:30
was when we, we started kicking the tires on this 2025 is when I
35:35
left my role at Ava Labs early 2025 and I didn't know what it
35:40
wanted to be when it grew up. The technology wasn't mature
35:43
enough at that time, admittedly as well, but I had the insight
35:47
that ideas were assets. You know, ideas that you have,
35:50
if you can analyze them quickly enough, can be an asset that
35:54
dynamically evolves. It throws off enough information
35:56
about it that you can look at it like a stock.
35:58
You know, you want to value debt.
36:01
I want to value future cash flows.
36:03
We have a difference of opinion. That's what an asset kind of is.
36:07
It's how you evaluate 1. So I pitched that to a couple
36:10
people that I knew in the Web 3 space and they were jazzed by it
36:13
simply because individuals in the Web 3 space really like the
36:16
notion of emerging assets. And so we got initial funding,
36:20
small seed round or seed round, a seed round, a small safe to to
36:24
basically do the initial build out.
36:26
And at this point, we're kind of, you know, moving on to our
36:29
next round of capital fund capital financing and looking
36:33
for partners to help us with that.
36:35
People it's, it's great to catch you now when it's like really
36:38
fresh and you're just getting your legs under you.
36:40
Yeah, yeah, it, you know, it's always a six month journey that
36:44
a six month journey or does it an overnight success that took
36:47
10 years. Like this year I thought was
36:49
going to be a couple of weeks, you know, and, and now here we
36:52
are a year later and it's, it's like, wow, that was a we learned
36:55
a lot, but it took a lot longer than we thought it.
36:57
Yeah, some some things I'm running into is like there's
37:01
kind of like a I'm a little gun shy, you know, because things
37:04
are moving so fast that I'm, I find myself letting kind of
37:08
ruminating on ideas and projects a little bit longer, working
37:11
through PRDS, you know, working through conversations about it,
37:15
seeking like a validation of what other people doing.
37:18
A lot of this technology is it it's so fragmented that you can
37:22
kind of pull like, oh, I like this about this.
37:24
I like that, about that, you know, to see where the direction
37:28
is, is going. Like how?
37:30
How are you kind of differentiating signal and and
37:34
knowing when to act? Yeah, I'm a, I'm a simple guy,
37:37
Ryan. I I operate on some simple
37:39
principles. I think the world's a relatively
37:41
simple place, and I think Maslow's hierarchy is a good
37:46
approximation of what people value.
37:48
You know what, what things are important to them.
37:51
Get as low on that as you can. And that is if you can find a
37:57
need down there that's not being met, fairly low on Maslow's
38:00
hierarchy, go after it, right? Which is exactly what we did.
38:03
This moment, this intersection of these technologies, this
38:06
feeling of insecurity, this catalyst for change that is, you
38:09
know, AI or almost AGI at this point.
38:12
It's an opportunity because people feel insecure and a deep
38:15
and primal level feel super. We're insecure and that's what
38:19
we go after, right? We're not, I'm not trying to do
38:21
increased packet processing rates for 5/9 reliability in
38:24
telco here, right? We're talking about people's
38:27
very notion of their value in the world around them is now
38:31
potentially at, you know, at risk.
38:34
And what we're saying is, if you continue to do it, then operate
38:37
the way you are with this fuzzy notion of your value to an
38:40
organization into yourself, absolutely.
38:42
But if you start to harden your information and start to harden
38:45
the notion of you into something that's structured, measurable,
38:48
redeployable, persistent, and also auditable for various
38:51
reasons, you are actually more valuable than ever.
38:54
And it provides the proper incentive for people to continue
38:57
to stay at the bleeding edge, not only of their techno, of
38:59
their industry, but at the way that they can redeploy their
39:03
expertise. Going after something low on
39:05
Maslow's hierarchy, solving a real problem and making people
39:08
valued, making them feel celebrated in the world.
39:11
It's if not us. And someone is inevitably going
39:14
to do this because it's, it's what the world needs today.
39:16
And when, when we're feeling the way we do.
39:19
What the beautiful of that man, It feels like a big hug.
39:22
You know, people are out here like, you know, like the, the,
39:26
the insecurity and anxiety is high.
39:29
And largely that's because these psychotic like tech CEO's are
39:34
keep telling us they're like, oh, we're going to automate 80%
39:37
of your jobs. So just get used to it and
39:39
figure out something new. And everyone's like freaking
39:41
out, you know, but, but it seems like what you're doing is
39:44
saying, hey, you know what? Your individual expertise is
39:48
unbelievably valuable. We need a way to like bring that
39:51
into the world in a different way because things are changing
39:54
it. It does feel like you're you're
39:56
satisfying some like pretty deep psychological needs for folks
39:59
right now. Absolutely.
40:01
And everyone I speak to when I talk about this, when I talk
40:04
about verification and institutional AI and the notion
40:07
of that you can't play the ensemble, people don't care.
40:09
Their eyes glaze over. What they care about is
40:11
self-interest. They care about.
40:13
You're telling me that I think I'm smart.
40:16
I also think I'm right. And I can bet on that.
40:18
And I say, yeah, in a roundabout way, using the tools that you
40:22
generate, yes. That's why you need to keep your
40:24
tool sharp, right? And that is where the world is
40:27
going. I mean, look at MCP and skills
40:29
model, context, protocol, and skills from Anthropic.
40:33
We've had the ability to turn skills into a lens, and then we
40:35
make them better because we associate them with you, right?
40:38
The world's already going in this direction.
40:41
It just hasn't been properly anthropomorphized and
40:43
personalized quite yet. You know, you need to have pride
40:46
of authorship in your tools because if you don't, then
40:48
there's no need for you to stay at the cutting edge.
40:50
You know, there's no need. You're not going to be rewarded
40:52
for it. Love it.
40:53
All right. We're at that time in the
40:55
podcast where we're entering the quantum round.
40:57
Are you ready that for that? As ready as I'll be.
41:00
Great. OK.
41:02
So you said keeping your skills sharp, keeping your tools sharp.
41:05
How do you keep your skills and tools sharp?
41:08
Constantly, constantly off off of my being unsettled, you know,
41:14
never having your feet firmly planted.
41:16
Listen, I'm not a technical, not the most technical individual.
41:20
I was attorney by training. But I've always gone into
41:22
organizations where I thought that I was on the bleeding edge,
41:25
always being a part of progress, so to speak.
41:27
As I said earlier, I've I enjoy that feeling of insecurity and
41:32
not having my wits about me. When I am in a place where I
41:38
feel quite comfortable, I feel very itchy to go and do
41:40
something else. So I'm always trying to find
41:43
something new and to test myself.
41:46
Really respect that, you know, I've got two kids in high school
41:49
and I'm approaching a high school reunion this year.
41:52
And one thing I talked to them about if like, you know,
41:55
differentiated the, the kid people I grew up with, who are
41:57
my brothers, my, my friends forever and the, and the
42:01
relationships I made as, you know, an entrepreneur and, and
42:06
into my kind of adult life is it's that attribute that I
42:10
really admire in people. That is, it's got the fidgets,
42:14
you know what I mean? They don't find their level.
42:17
A lot of folks and you, you know, you got high school
42:19
friends. They kind of found their level
42:20
really early on and stopped improving, stopped sharpening
42:24
the tools, you know, and there's nothing wrong with that.
42:27
You know, I liked sitting on the on the, the couch drinking a
42:30
beer, watching the game as much as the next guy.
42:32
But there's there's something about like, consistent
42:36
innovation and that kind of like hunger that that makes your life
42:42
more fruitful, you know, even if it's not, you know, success all
42:45
the time. Sometimes it's horrible pain.
42:49
But it seems, yeah, I would. Go ahead, please.
42:52
Yeah, Yeah. No, I'd say that that like good
42:54
struggle is critical in life to do anything productive value,
42:58
you know? Cool, Aside from Quadrant, what
43:01
other kind of AI experiments or or projects are you working on?
43:06
Yeah, I'm trying to figure out how.
43:08
So the a big thesis that I have is that markets are gonna
43:11
unbundle think about media unbundling cause of the Internet
43:14
over the past 3035. So odd years.
43:17
You know it, it went from this monoculture NBCABCCBS and the
43:21
monoculture that was created by that monopoly and it it it
43:24
became very siloed and then reconstituted itself and
43:28
something like I think it's purist notion is is is Reddit
43:32
network of communities subreddit.
43:34
An individual can be kind of a node across this very robust and
43:38
beautiful graph. What happens when that happens
43:40
to markets? The CFTC and the SEC are playing
43:43
nicely at this point and it's very likely that for the reasons
43:46
many mentioned, we're going to be able to analyze everything
43:49
quite readily and, and rapidly, you know, compare it to one
43:52
another. What happens when the entire
43:53
world around us comes to us from a market perspective?
43:56
We don't have to go to Robin Hood or Fidelity or, and then
43:58
get to the New York Stock Exchange.
44:00
What happens when we make the assets?
44:02
We're the, we're the market maker, we're the asset holder,
44:06
and then we're the evaluator of value.
44:07
It's very similar to Reddit. What does that world look like?
44:11
That is the thing that keeps me interested in this space.
44:14
And how far can we go with the assetization of the world around
44:19
us. We have real world assets that
44:20
we're bringing on chain and that's just basically bringing
44:23
things, you know, like this can right here, I can bring that on
44:25
chain and put tokens around it. That's just putting the radio on
44:28
television. That's not that interesting.
44:30
What happens when we can actually dynamically create
44:32
assets of anything around us? I think that is coming and I am
44:38
trying to figure out where it begins and ends because it is, I
44:42
think it's quite, quite valuable and infinite.
44:45
So in a in a certain in a certain way.
44:48
Interesting. That's a very interesting idea,
44:50
particularly around IP, you know, for creating an asset
44:53
class of ideas. Probably the most interesting
44:57
thing would be like secondary markets for ideas, you know, so
45:01
you like, you buy an idea from somebody else and you can sell
45:04
it, but the original author of that idea has some kind of like
45:09
financial stake in it. That's that's pretty cool.
45:11
I'll take it one further. You're talking buying and
45:13
selling. That's kind of like common law
45:15
property law. What about streaming?
45:17
I can rent it for a period of time to see if I want to kind of
45:20
like bolted on my stuff. And you start actually turning
45:23
these fuzzy things into structured operations.
45:26
You're not, you're no longer touching parts of the elephant
45:28
and calling it something that it's not.
45:30
You're actually starting to get a true marketplace of ideas and
45:33
you can have annuities thrown off from them for periods of
45:37
time. You know, you collect interest
45:38
for a period of time for running it.
45:40
This it's a very different world.
45:41
It's a it's like mind bending, you know, and we don't have, we
45:45
don't even have the vocabulary for it yet.
45:47
And that's the thing that I'm interested in AI and block
45:49
chains was meant to unlock opportunities that we couldn't
45:52
access before. And we're doing that and we
45:55
don't even have the vocabulary for it yet.
45:56
And it's super. Interesting, but I see it play
45:59
out. I mean, it's I got a budding
46:01
named Jason and this guy, I align with him like probably all
46:06
of our opinions, all of his opinions.
46:07
Maybe it we kind of in the rough, we disagree.
46:10
But I use them all the time for like, voting.
46:13
I'm like, how do we feel about these judges this time around?
46:15
And he'll just break it down for me, you know?
46:19
And he's some kind of like sage of wisdom.
46:21
And the way he like speaks, I just, he's very trusted
46:25
consultant for me on a lot of different things to the point
46:28
where every now and again, you know, I'll be like, hey, man, I
46:30
was thinking about XYZ. He's like, no, I told you that
46:33
three weeks ago. And I was like, oh, oh, yeah.
46:37
If you could commodify ideas, this guy would be a millionaire.
46:41
Yeah, yeah, yeah, yeah. And and if he's good at
46:43
execution, he'd probably have AB next to that rather than an M,
46:47
you know? Totally.
46:48
Yeah. Cool.
46:50
You've had a lot of pivots, a lot of successes, and I'm sure a
46:53
lot of that comes with failures. Were there any kind of like
46:55
pivotal failures that really kind of set the trajectory for
46:58
the rest of your life? Yeah, I mean, I came out during
47:04
the Great Recession. I had a view of what we should
47:09
be doing from a regulatory perspective.
47:11
When I joined the SEC, that was very different than what I had.
47:14
We ended up doing and what I ended up doing that I took that
47:17
hard. I was very much disenchanted.
47:18
I was also wet behind the ears and quite greed, you know, very,
47:21
very young. And I took that hard.
47:23
Hence the pivot away from the legal industry generally into
47:27
technology. I was super enchanted with the
47:30
notion of crowdfunding, I think as you were when you were
47:32
working on that. And it was, it should have, it
47:36
should have done more, it should have gotten further, you know,
47:39
and I took that quite hard, but I learned something in that.
47:43
And, you know, that was what I poured my, I don't do things I
47:46
don't like, have a passion for. And so there's still a kernel of
47:49
the lump shares in Quadra, you know, alignment of incentives.
47:54
That is the thing I think we took from the lump shares.
47:56
So it died, but it, it lives on, you know, there's a breath,
48:00
there's a breath in there. There's an atom in there of the
48:02
of the original idea. Yeah, given the chance, if you
48:06
started college again, would you study law if it's 2026?
48:10
No, no, I would not. But it is a useful way to think.
48:16
It's it's also like this arbitrary set of, of, of
48:19
arbitrary road map of how the world's supposed to operate in
48:21
the, in, you know, in the juris prudential philosophical realm.
48:25
That is helpful. But I think as the world
48:29
continues to move forward, we're going to kind of fall on the
48:32
first principle type stuff that Aristotle and, you know,
48:35
Socrates and they're like, we're talking about.
48:37
And I think that's not only philosophy, but agency, the
48:40
ability to question questioning itself, right?
48:43
I think a lot of individuals that grew up in the Western
48:46
world over the past 75 years have gotten very comfortable
48:49
with the notion of a career. Corporate America is not an
48:51
axiom for, you know, it's, it's, it's, it's not a mistake that
48:56
that's called that We've kind of gotten away from the notion of
48:59
agency. Entrepreneurship has looked at
49:01
this like newfangled thing. It's not, it's, it's taking the
49:04
bull by the horns, so to speak, and doing something for yourself
49:07
because it's the right thing to do or you feel called to do it.
49:10
Learning agency and and and the and how to think philosophy, I
49:14
think is where if I would go back and do it again, I would
49:16
definitely spend more time there.
49:19
How does agency in your life show up outside of kind of a
49:22
professional realm? Well, that's a great question
49:27
for me. I really enjoy trying new things
49:33
and I don't like OK, I'll give you 1.
49:36
I really want to get good at Backcountry skiing, but my hips
49:40
suck because I sit in a chair all day, right?
49:42
So like I am now working on and I, I played football in college,
49:46
so all the working out to me was lifting heavy things above my
49:49
head, right? So now I'm doing movement
49:51
therapy to try to go have the ability to, you know, evade
49:56
trees in the back country, country.
49:58
So that is something that I, I think agency shows up.
50:01
Is that OK? I'm getting older.
50:03
I can't rely on my body the way I used to.
50:05
I need to go and do something different if I want to achieve
50:07
this goal. I think movement therapy for
50:11
especially for a guy like me, would it be viewed like, like
50:13
what? What are we even talking about
50:14
here? And overcoming that, changing my
50:17
mindset, getting into it and getting quite, you know,
50:21
interested in and how the body evolves as it gets older is, is
50:24
something that I, I would say is agency, at least for me at
50:27
least. I hope it works, You know.
50:28
If not, I'm going to hit a tree. Yeah, you crazy Backcountry
50:33
skiing. That's for kids.
50:35
Man, I, I, I, I don't know, I want to do it until I, before I
50:38
get too old, you know, hop out of a helicopter, do some heli
50:41
skiing, you know. Heli skiing, I love it.
50:43
What position are you playing football?
50:45
I came in at middle linebacker and then I was slow.
50:48
I was not a fast guy. They moved me to nose guard of
50:51
all things. So I put my hand on the ground.
50:53
Yeah, yeah, yeah, yeah. How much did you weigh in in
50:55
college? 278 at my heaviest 2. 78 nose
50:59
guard. Incredible.
51:01
Yeah, yeah, I'm not that tall either.
51:02
I'm 511. So that was it was a hell of a
51:05
change, I'll tell you that much. Yeah, it's a it's a perfect,
51:09
perfect build for a defensive lineman.
51:11
That's amazing. Can't move him and that's
51:13
physics too low, yeah. Cool.
51:17
Yeah. You know, similar, you know,
51:19
agency is like movement. You know, I always thought that
51:22
yoga would be my home. I'm not martial arts guy, you
51:25
know, I did Brazilian Jiu Jitsu and Muay Thai and Capaweta for,
51:28
for many, many years Barefoot sports.
51:31
And it just it, it snuck up on me sooner and later where, you
51:34
know, I had a shoulder, I had a elbow, I had plantar fasciitis
51:38
on both feet. I was a just a wreck.
51:40
I could barely move. You know, in the last few years
51:43
I've been doing hot yoga very consistently and same thing like
51:47
it, lots of mobility that I never had before in my hips.
51:51
You know, my shoulders fixed, my feet are fixed, elbows fixed.
51:56
I'm still slow, you know. We share that affinity.
52:00
Affinity for slowness, Yeah. Cool, Amazing.
52:04
Kind of wrapping up here. I know that World Wildlife
52:07
Federation is important to you and local humane centers are are
52:12
important to you. Why?
52:13
Why do you love animals so much? You know, the, I think it's the
52:17
innocence. They didn't ask to be here.
52:19
You know, they, they don't even know what they're doing.
52:20
They largely rely on us by no fault of their own, especially
52:23
the breeds that have been bred for a certain way.
52:25
Coming as a guy who grew up owning Bulldogs, English
52:28
Bulldogs, it is important to have to feel the responsibility
52:34
to others. I think generally in life, but
52:37
specifically to people that did not people to, to, to, to beings
52:41
that can't really support themselves because of human
52:44
beings. You know, that's why I'm a, I'm
52:46
kind of a big nature freak anyway.
52:48
I, I, I like spending time in the mountains and, you know,
52:51
being out in nature. So if you can support a local
52:56
charitable organization associated with, you know,
52:58
animals, kennels, whatever you want to call it, do it.
53:01
I think we have a responsibility socially to do that, do the
53:04
right thing. Your dog, guy?
53:06
You ever had a exotic pet? No I have not.
53:09
I I had a dad who barely bent to get me a dog and was certainly
53:14
not going to have an exotic pet. It sounds like you do though.
53:16
What do you got? I don't, but you know what?
53:19
I've been looking at these TikTok videos of like these
53:21
Ravens that people have and they, they train them how to say
53:24
all these words and you know, they're, they're so smart and so
53:28
big and so cool looking. I was, I've been like kind of
53:30
fantasizing like, man, what if I had a Raven?
53:33
I don't know if it's the same, but crows I know are super
53:36
intelligent and like build us incredibly strong bond.
53:39
Like you do something nice for them, they'll give you keys and
53:42
you know, chocolates that they find and pennies and stuff.
53:45
If that's the same, I absolutely encourage you to have a Raven or
53:49
a crow just so you can walk around just having having it
53:52
steal little things for you. Fill your pockets with all these
53:55
little accoutrement. There's this one guy, you know,
53:57
I'm a Colorado guy, so hunting and fishing is like, that's all
54:01
of my excursions for the year. Yeah, hunting and fishing trips
54:03
with my family. But this guy was in the woods on
54:06
a horse and he had a Raven on his shoulder that would go and
54:09
fly and go get squirrels and stuff.
54:11
Well, I was like, Oh my God, that'd be like Beastmaster
54:14
level. You're not going to get cooler
54:16
than that guy that's. The coolest thing I've ever
54:18
seen. Yeah, that's the that's the
54:20
coolest. Guy in the world, like, you
54:21
know, remember the most interesting man in the world,
54:23
the beer guy. Like that's that guy.
54:24
That's that guy with the, with the with the with the Raven.
54:27
The Raven had catch phrases. It'd be like how you doing?
54:30
And it like spoke good English. I was like, dude, this, this is
54:34
what I'm going to eat in my life anyway.
54:36
I can that is, that is, that is awesome.
54:38
And now I'm going to be thinking about that for the rest of the
54:40
day. Dan, this has been absolutely
54:43
remarkable. I appreciate you coming on the
54:45
show and talking about quadrant it it's fascinating.
54:48
I'd love to have you back on as this as your business kind of
54:51
matures and you really can't kind of hit stride.
54:53
I I think you're, you're tackling a project that I've
54:56
never heard anybody do and I think it's, I think it's a
55:00
goldmine, man. I I think like being able to
55:02
capitalize on individual expertise is a positive and
55:07
beautiful message that people need to hear right now.
55:09
So people definitely go check out quadrant dot IO where you
55:13
can keep up with with Dan's progress.
55:15
What's the best place for someone to connect with you
55:18
online? Yeah, I'm at Quadrant AI or
55:21
Prattle Dan on X. We try to keep both of them
55:24
company. And myself, I have a substack
55:27
called Reproof regarding proof RE colon proof is the name of
55:31
the substack. And you know, I'm on LinkedIn.
55:35
You can you can talk to me there.
55:37
And I think I'm trying to development of my own personal
55:40
brand at Prattle dot me, which has all of the links there.
55:44
And you can find me across any of them at my personal page,
55:47
Prattle dot me. Beautiful.
55:49
Thank you so much, Dan. Thank you, Brian.
55:51
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