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Do Good Work is not a label but a way of living.
It is the constant and diligent effort to achieve a new level of excellence in one’s own life.
It is the hidden inner beauty behind the struggle to achieve excellence.
It is not perfect but imperfect.
It is the effort, discipline and focus that often goes unnoticed.
The goal of this podcast is to highlight that drive.
The guests I have on this show emulate this drive in their own special way. You’ll be able to apply new ideas into your own life by learning from them.
We will also have 1on1 episodes with me where we’ll dive into my own experiences with entrepreneurship and leadership.
Every episode is designed to provide you with ideas that you can apply and grow in excellence in all areas of your life, business and career.
Do Good Work,
Raul
INTRO
Today on the podcast, I'm
joined by Patrick Rooney.
He has a background in cognitive science
going back to about 20 plus years where
he worked on neural networks, precursors
to large language models as a student
around 2005, and has been doing applied
AI work in business for over a decade
now, and now runs Leona's strategy.
The most interesting part
of this conversation was.
Patrick draws a hard line between
two completely different things
that we collapse into one word.
He separates scripted autonomy,
which is what an AI agent does when
it runs and does work for you while
you get coffee from personhood.
Autonomy, which implies rights and will.
And interiority, most founders, including
I in the past making this mistake, use
personhood language for a scripted tool.
And Patrick makes the case that
this isn't just an imprecise
or a mistake in vernacular.
It's actually a leadership problem that
shapes how your whole team relates to
technology, and it's very fascinating.
It's an actually really interesting point.
Patrick came into this work from
a philosophical and theological
background layered on top of a
technical training and actually
using the world in ai, in real world
client services and delivering value.
This is a combination you don't
really run into often in the
conversation about AI adoption,
he's genuinely not interested
in either the utopian take
for AI or the fear narrative.
And he wants to be precise about
what these systems actually do and
what it means for humans using them.
We get into why LLMs are plausibility
engines and not truth seekers.
What founders should not outsource to the
model and know that does not mean your ip.
There's actually something more they
shouldn't outsource to the model.
And why?
Doubling down an inhuman person
connection is actually the correct
strategic response to AI at scale.
Right?
Let's get into it.
PODCAST
Raul: So one of the things also is we take
whatever the output is as gospel or truth.
Can you help me kind of help founders
and help our teams as we work with LLMs?
Like how do we navigate that and
how are LLMs actually structured
as not truth seeking, but also
just to continue the conversation.
Help us unpack that a little bit.
Patrick Rooney: Yeah, I think this
is an incredibly important point for
thinking about the difference between.
Intelligence per human
intelligence and LLMs generally.
So let me just mention one
point on intelligence broadly.
LLMs, they're focused on plausibility,
right on continuation of conversation
in a, in a way that makes sense.
And often we're really fortunate that
the byproduct of that is the truth.
It's the right answer for us practically,
it's the right answer factually.
As humans, though, we also have
the ability to focus on the
truth just for its own sake.
Not because it's practical, not because
it's gonna get me food or it's going to
improve my status, but because I really
want to understand, I want to know.
My kids are a great example of this.
Like they'll just go off and look at
books on geography, books on dogs.
They're not gonna get them
better grades in class.
They're not going to impress
their friends necessarily.
It's, they really want to
know about these subjects.
have an interest in what's out
there, what's true and what's false.
That's distinctively human and I
think it's a distinctive aspect of any
intelligent being, any intelligent agent
that it can, not that it always does,
but it can focus on the truth for its
own sake and find that satisfying LLMs
Raul: own reward, right?
Patrick Rooney: Exactly, exactly.
Lms.
Instead, they're focused on
executing the tasks we give them.
Right.
So it's like, okay.
How do I get to the most
reliable, plausible answer based
on the conversation thus far?
And usually
Raul: of the time that works.
That's good.
Yeah.
Patrick Rooney: on the training data,
based on the engineering most of the
time, like 99 point something percent,
it's gonna give you a good answer, but
Raul: Yeah.
But.
Patrick Rooney: thing as
caring about the truth itself.
Raul: I mean, I haven't had my
agent say, Hey, I'll be right back
taking a picnic just to enjoy.
'cause there's like, there's truth.
I mean, truth for its own
sake, it's its own reward.
Appreciating art or something beautiful or
a landscape is its, it's its own reward.
Or preaching something good about like
a, a good aspect of something like when
you appreciate, I mean, it's gonna sound
super contemplative, but like you look
at either like I have in the backdrop
of my, one of my screens here is Big
Sur and just like the coast, like you,
you look at that and you appreciate it.
Because it's just like, it's beautiful.
It's really cool.
And like that's its own reward.
Or even for the Artemis flight crew.
Like I, one of my other screensavers
or desktop photos is Earthrise or the
earth from the moon's perspective.
So it's kind of, it's kind of fascinating
and yeah, my agents are not taking
vacations and leisurely wasting time.
Patrick Rooney: Yeah.
The advantage of the agents
is that they're so focused
on the practical, right?
They, they do exactly
what you tell 'em to do.
They, you know, they don't get tired.
Maybe they run out of
context, you know, but.
That's it.
That's their advantage.
They're just practical.
Our advantage as humans is that we
care about things for themselves.
We have this thing called leisure, and
that just means caring about th things
that are good or beautiful or true.
Raul: Mm.
I think it's a powerful thing to also
keep in mind as leaders to still flex
that muscle and understand based on
everything that comes outta you in the
array and what you're looking at and the
different docs or decisions to be made.
Still like what you mentioned, owning
the output, owning what do we take, what
do we discard, what do we act on, how
do we implement it and why we do it.
I think that's important.
Patrick Rooney: Yeah, and,
and that's a deeply humanizing
aspect of your business, right?
If you do bring it back to.
Here's something we do that's grounded
in truth, that's grounded in beauty.
If you're in of creative or if it's
grounded in goodness, if you're really
mission driven, like those are your unique
contribution as an owner, as a business,
Raul: It's one of the key things as a
leader, that's what you're gonna focus on.
Where are you leading people to
the team and your clients, and
what are the values that you share?
And those are the probably the most
important pieces of your culture.
Patrick Rooney: definitely.
Raul: Patrick, before we dive into like
intelligence, perceived intelligence,
out of my personal curiosity, what
drove you really to nudge more and
not just accept LLMs at surface value?
Is it a personal curiosity?
Is it more that you want to
understand why these things work?
Or is there a deeper, like
a deeper concern with how
we're using it as society?
What really drove you to understand
deeper the reasons behind intelligence,
both human and artificial?
Patrick Rooney: Yeah, great question.
It's, it's been a long standing
interest of mine, so I can go back
to, say my college days when I was
studying intelligence as a subject,
cognitive science was my major.
And that really attacks intelligence
from different angles, computer science,
neuroscience, philosophy, et cetera.
So even back then, like 20 years ago.
Yeah, there was this notion,
Hey, we might be able to create
something that's simulating or,
or rivaling human intelligence.
Obviously it, it took a long time for that
to emerge, but already a long time ago.
I, I found that fascinating and wanted
to understand is that perspective, right?
Like, I mean that Right.
I think for all of us, it's a little bit
unnerving at times to think Yeah, they're,
they're thinking machines or thinking
software like right in front of me.
It, you know, it threatens our
sense of uniqueness, our sense of
potentially ownership or responsibility.
So there are a lot of dimensions that I
think, engage all of us, and I've just
been aware of them perhaps a little
bit longer than the average person
because of my background both in work
doing AI the last several years and
going all the way back to college.
Raul: Yeah, IEX in the background.
Yeah.
Was there a moment where you
realized was it just like gradual
over time or was there one moment in
time where your stance is a little
bit, I wouldn't say controversial.
It could be controversial depending on
who you talked to in Silicon Valley,
but that LLMs aren't intelligent.
Was it something that you just
realized from it or through like
a conviction that you had over
time, or was it one moment in time?
Patrick Rooney: That it's, it's been
a longstanding conviction, right?
So as I just kind of suggested.
It's a, it's a, an issue.
I kind of had to start thinking through
years ago because I worked a little bit
on stuff as a student in classwork, so
I'm not, not going to overstate it, but
worked on what were precursors of LM.
So like neural networks, right?
The, that's some of the
architecture underlying it.
And even back in, let's say 2005
ish, there were these claims that
you can make a neural network.
Decide to do something, it does X or Y
or it discriminates like it, it chooses
between this stimulus and that stimulus.
So a lot of the language used
decades ago was already very
anthropomorphic, very human centered.
And like, you know, back then you
could see there's a huge difference
between what those systems were
doing and what we do as humans.
So it was easier to push back.
But over the years I've had
to kind of refine my thinking.
As that gap seems to
get smaller and smaller.
Raul: Hmm.
One of the things, so the interesting
part that alluded us for, for recording
this pod is that like your, your
thesis and statement, and I'm, I'm
not disagreeing, but I just wanna
kind of work with this with you.
If what we're using
isn't truly intelligent,
Patrick Rooney: Mm-hmm.
Raul: it's trained based on a
similar methodology that humans
are trained, for example, like,
Patrick Rooney: Yep.
Raul: Through culture,
education, upbringing.
Obviously there's a difference between
the 40 years though, like whatever,
how old people are listening to this.
Patrick Rooney: Yep.
Raul: 60, 20, 30 years of human
training versus LLM training.
But there's also the reality
that I'm also training and
they're not doing it perfectly.
Obviously, some of the outputs but
I'm also training like a fleet of
agents and I have agents doing certain
things for me on a habitual basis.
And some of the things are pretty
impressive and it's kind of.
Mirrors what intelligence would look like.
Patrick Rooney: Yep.
Raul: I think there's even some
blurs of like, what is human versus
what is what an LLM produced.
What
Patrick Rooney: Yep.
Raul: of the, i, I don't know if
to use your words, but explanation
for intelligent looking outputs
and implications of intelligence.
Like how do we derive that
and have a hierarchical order?
And then we'll bring it to practical
terms of like, why this question
and this framework actually matters.
Patrick Rooney: Yeah.
Right.
So what you're alluding to,
I'd say what are different?
You could say variations
on the Turing test, right?
That things, the Turing test.
Probably a lot of folks.
Listening or familiar with it, right?
It was, it goes back to early 20th,
mid 20th century Alan Turing, we've
got LL at that time just computers,
in theory, not yet in practice,
potentially replicating human behaviors.
Now you're, what you're describing
is we're actually seeing that.
Right?
So if it looks like what
we're doing, why isn't it?
In fact intelligent.
Like if it, if it walks like a duck,
duck and quacks like a duck, why
isn't it a truly intelligent duck?
And what you started talking about
there was like, training I think
is a great angle to break in on.
So if you look at the training of, of
an LLM, it's going to be distinctive,
say from the training, the education,
the formation of a human being in
that it is really heavily centered on.
Text inputs, those are transformed into
numerical intermediaries, embeddings, and
then ultimately you get, you'd get text
outputs from the system as it's tested
and eventually after it's been trained.
So the slice of the world that
it's ingesting and working with is
actually, it's two things about it.
Number one, it's really limited, right?
So like, imagine that you had nothing
in your experience except books,
like you didn't interact with.
Actual humans, right?
It didn't involve being a baby
and having all the, you know, the
physical, visual, tactile stimuli,
but it, it's just focused on language.
But your world would be totally
different and impoverished, right?
Raul: on, based on the inputs.
It, it
Patrick Rooney: Yes.
Raul: limited there.
But, but I think the argument is that
some of the outputs in the, the real world
value, or at least the economic value
that is creating, is somewhat comparable
to like entry level workers or even for
junior assistants or even in some cases,
Patrick Rooney: yeah.
Raul: wouldn't say pure PhD, but some,
like, I've, like I've heard some stories
of auto research going overnight.
It what would take PhD seven
years, it would do in a week
or in 36 hours, 48 hours.
So I think it's understanding this is
important, but also the outputs are pretty
I wouldn't say jaw dropping, but sometimes
it's like, it's pretty impressive.
What's
Patrick Rooney: Right.
They're, they're massively impressive.
Like, I, I don't want to understate that.
Right.
In addition to what you just mentioned,
we have mathematical proofs for
unsolved problems that LMS are
either completing independently or
at least assisting researchers with.
So that's, you know, cutting
edge of intelligence.
Right.
So what, what's going on
that's different though?
I think that there's, the one
thing I mentioned, kind of
the, the text or often code.
Heavy inputs very different from the
kind of experience that, that we have.
And importantly that that doesn't
marry up with, it doesn't connect
directly to things in the world.
So I think that's kind of the
fundamental underlying point there.
It's language, but it's
language dissociated from
the world that we experience.
What it, but it does a fantastic
job, right, of taking that language,
transforming it into other language
or code as the case may be.
It's reliant though.
This goes, so this is the second point.
It's reliant on basically millions, tens
of millions, maybe billions of people's
input via the training data, right?
The, the internet essentially for the
foundation models it's reliant on.
Trainers who are shaping it.
It could be reinforcement learning via
human feedback the engineers that are
building the system, and ultimately
everyday users like you and me who are
going to the system and directing it to do
certain things and correcting it if it's
not acting in the way that we want it to.
So we've got a whole network of people.
And their experience that's
feeding into these systems.
And somebody who's an AI researcher
at the forefront who has made a
very similar observation is signing.
She, I think I, I'm roughly
in the ballpark of his name
with his pronunciation there.
And you know, he's working
with Jan Koon, formerly from
Meta on some novel AI research.
He's made like these very points
in his discussions of whether
AI is truly intelligent or not.
So what I am offering is
not a unique perspective.
It's not really unorthodox either.
There, there are plenty of people out
there who are steeped in the field
that would say, yeah, there are,
there are true distinctions between
intelligence as we've understood and
defined it and what an AI is doing.
And that's not to in any way downplay the
incredible achievements of the systems.
Raul: Just to see if for what it is.
Because also the other things too
that are implications of intelligence
to what you also shared with me was
that is there a will or autonomy?
Is there personhood?
Is there rights?
Patrick Rooney: Right.
Raul: an AI have the right to
control a human vice versa?
Eventually.
And I think this also has
other implications for
legal, ethical, and economic.
Right now, obviously we're leveraging
it as a, as a, as a thought
partner, as a tool to be able to
grow and even some business models.
AI is the business model
and that's actually.
Of the key things that I'm seeing
and working with clients with to help
them, you know, evolve into the future.
But I think having this discussion to
understand fully and also understand the
limits and implications so that we know
what to do and also what not to do to
bring it more practical and down to earth.
Like based on what you're saying,
what are some of the things that
founders should not be doing with ai?
We, we may or may not like
this is probably a litany list
Patrick Rooney: Yes.
Raul: based on this thesis and this
framework of AI looks intelligent,
it's not really intelligent.
It's a tool we crafted.
However, it per it, it works
and it has significant outputs
that generate economic value.
It helps, I mean, some of my clients
went in some of the biggest deals with
some of the work that we did co with ai.
So it's, a real thing
and a real matter to it.
Not to get too caught up in that, but
Patrick Rooney: Yeah.
Raul: Push away the technology
as if it's just another
Photoshop or just another iPhone.
Like it's not, it's
more powerful than that.
But what are some of the things
that we should be cognizant of not
doing as founders leveraging this,
Patrick Rooney: yeah.
So there, there are a few things
you started mentioning there
that I think if AI were really
intelligent, it would fall out.
That it's, it has these characteristics,
so things like, yeah, it's
got will or autonomy, right?
It, it's, it's its own person.
So there are rights and
responsibilities associated with that.
That is we, there's a certain
way we should be treating LLMs
like human beings and yeah.
And then potentially.
Raul: classes, by the way, by,
there are some classes of thought.
'cause I started breaking down.
Like I think there are
different metaphysics of ai.
Some people do believe that it
has rights and do believe that
Patrick Rooney: Mm-hmm.
Raul: a personhood and
is treated a certain way.
Patrick Rooney: Yeah.
Raul: so there, there are multiple
thoughts around this perspective
and this, this could be a con,
like I, I alluded in the beginning.
This podcast itself could be like a
controversial statement said in Silicon
Valley to some people, depending
Patrick Rooney: Yeah,
Raul: are.
Patrick Rooney: so that's a
great point you make there.
All the things that we are discussing
right now, these are like summarized
versions of what, what are actually
very profound debates and, and
discussions about personhood about.
What is the nature of
thinking and intelligence?
What is the nature of an artifact
versus a natural thing, right?
And, and some of these go back
centuries, if not millennia.
So, so we're not new to these topics,
and you could, we could spend hours.
I what I just want to, I would want to
point out what I would want people to
take away is if you hear a perspective
like that, that this is a person,
this, this LLM or this instance,
this chat bot is a person, is that.
That comes with a bunch of assumptions
that a, a whole load of baggage behind it.
So it's not something that
should be said glibly or naively.
And I think in fact, there, there are
good reasons to argue the opposite,
but I, I wouldn't expect in like that
20, 30 minutes to be able to convince
somebody that there's no reason to
at all to believe that there's some
personality or personhood behind these.
Raul: That's fair.
the things that we shouldn't.
So one of the things, maybe we shouldn't
say that it's, this is my, I mean, we
are using that different right now.
This is my AI coworker or my
Patrick Rooney: Yes.
Raul: or we
Patrick Rooney: Right.
Raul: like who is we?
So what are other, other
things to be cognizant aware,
Patrick Rooney: Yeah.
Raul: for, in practical terms, like in
the field building agents with ai, with
Patrick Rooney: So, so, yeah.
Raul: some sort of autonomy,
but they're scripted autonomy,
Patrick Rooney: Yes.
Yeah.
Right.
That term autonomy, right.
Has different layers and
different meanings to it.
So if you say it works autonomously,
you mean like, Hey, I can walk away and
it'll do something while I get coffee.
That's fine.
But it autonomy has this kind of other
connotation that it, it's an independent
person with rights and that's what I
would, I would've people shy away from
those really anthropomorphic terms.
Yeah, like the really anthropomorphic
terms, you know, if you use it.
Ca you know, casually, Hey, it's
an AI assistant, not a big deal,
but deeply personifying your ai.
We've seen instances where that actually
has, that has led to mental health issues.
And that's a,
Raul: hm.
Patrick Rooney: something
of a tangent, right.
But that's, that's it's
made national news.
People who have become fixated on
that founders, I would think are less
likely to to develop that particular,
Raul: as leadership,
Patrick Rooney: yeah.
Raul: with
Patrick Rooney: Alright.
Raul: too.
Like they, they,
Patrick Rooney: Yeah.
But to, to model it, I think for,
for their teams in particular
is it's just healthy behavior.
But a really positive thing, I
would say is to continue to take
ownership of what's going into the
ai, how it's working and the outputs.
Right.
So not so much the Yeah.
Downplaying what the AI AI can do,
but instead emphasizing your role
as a human and as an owner of.
This tool and this process, like,
hey, I need to ensure that the inputs
I'm putting in here for my clients,
that they're, they're clean, that
they're accurate that I've handled
them responsibly, that the tool handles
them responsibly, data securities,
forced that whatever comes out also
has gone through appropriate scrutiny.
Like it, it's a huge time saver,
but that doesn't give us you know,
the re any reason or any, it doesn't
clear us with the responsibility to
take ownership of what's going on.
Raul: Yeah, the term that comes to mind,
this is easier 'cause it outsourced
thinking sometimes you're doing, I mean,
on a good day I'm personally able to do
maybe six to 13 things in a day like.
Patrick Rooney: Mm-hmm.
Raul: Big things, like things that
matter with my agents and like a
good day is like 70, maybe 80 things.
Like actual things.
Not just decisions made.
Not just ideas
Patrick Rooney: Yeah.
Raul: So with that volume, obviously,
I mean, what's that derivative?
Six to seven, maybe like
a 10 to five X volume.
There are some things like
the time saving, sometimes the
output you do accept as fact, but
you'll have to think critically.
And this, I'm literally using more brain
power at the end of the day now just to.
Cognitively work my way through
and identify this or that,
or weigh the information.
And I think that's one of the key
fallacies that you just mentioned, is
not just taking ownership of what goes
in data security, privacy, et cetera.
I wanna talk to you about that in a second
for local, but also knowing that the
output, you also need to think, and when
people say outsource your own thinking, I
think this is where the real outsourcing
might happen, where the outputs or
the formulas or the strategies or
whatever it produces, you could take as.
As truth.
And I think you just have to counter
argue like, okay, did you look at this?
Did you do that?
This research?
Is there any other implication that I
would be right based on my assumption,
based on my experience and have a
Patrick Rooney: Exactly.
Raul: And usually depending on your
setup I thankfully don't have to argue as
much anymore 'cause I'm, it's, it's much
more embedded the way that I'm working.
It's, it's, I mean, for better or for
worse, it's training like 2000 documents
of mine, like, for like actual writing
and thinking and stuff like that.
But, But in some cases, if I'm just
using I'm not gonna name the app's
names, but if I'm just using like an
l an LLM model, frontier model on,
on one of my my apps on the phone.
I do have to argue, hey, is
that the right assumption?
Hey, is that the right thing?
Can you audit your work and your
research and do some strong man
and strong man arguments, argue
against yourselves, present to
me, and then validate like there's
Patrick Rooney: Right.
Raul: to think about before you
just accept the output is true.
So I think that's one key
Patrick Rooney: Yeah.
Raul: And one of the, one of the the
biggest rebuttals I have, and I hear, and
I think it's legit, but I also, I mean
at this point it's, may not be as legit.
Is like, well, if I just train AI
on all my ip, I lose everything.
There's a level of truth there.
But how would you argue that, and
how would you think about that based
on your actual use case and what
you're seeing even now that you're
working on your, on your firm?
I.
Patrick Rooney: Well, if, yeah, if
you train it, first of all, I think
that there's a, there's a, a kind
of technological answer to that,
which maybe is too simple, which if
you, if you own the model, if it's
locally, you know, run right, you,
you can keep your IP private, right?
You're, you're not just giving it away.
Raul: if you downloaded everything,
you got the Mac studio, you put Kimmy
or GLM, like you're hosting your own
LLM, so like you don't really host
it to the cloud, so you're good.
Patrick Rooney: Yeah, so it and
there, you know, on top of that,
right, there are protections you
can have with cloud-based models.
I'm not telling anybody to go out and put
all your IP into any of those, but it's
not simply going to show up, you know,
like across the ocean because you, you've.
Uploaded a couple documents.
The but bigger, I mean, the, the other
thing I would emphasize though is the,
the thought that went into crafting those
materials and the ownership, like the
actual thought that goes into deciding
that yes, this output is the right output,
like affirming it, that's still yours.
So you're the one deciding, hey.
I, I want to upload this set
of documents, these sets of
files to train the, the system.
It's not deciding that autonomously.
Now, of course, you can get systems
that are more and more capable, right?
That of grabbing your files and, and
selecting, picking, and choosing.
But you're still, at some point
you're making that decision.
You are also ultimately going to
be the one with the, the yes no.
Say regarding whether it's good
enough for your business, whether
it's good enough for your clients and
what I've, you know what I'm saying?
On both the philosophical level and
then on the very practical level is
don't give up that responsibility.
I know sometimes it
can be tempting, right?
Because hey, it's, it's easier.
Yeah.
You can still.
Raul: at the end of the day, legit
at the end of the day where I
have to make decision because I,
I mean, it's a thought partner.
Like I've, I've had like a, I have
like nine agents now working with,
like, I run the business on ai
Patrick Rooney: Yeah.
Raul: at the end of the day when
I'm making decisions, sometimes
it says the recommendations.
And I'm like, okay, just come
with your recommendation.
I'm like burnt out on thinking and
this is not, 'cause, I mean, I think
for a living, like legitimately
and the people say, look, AI's
gonna come for your job, I think.
Yes and no.
It will for like simple things, but for
your, what you just mentioned, what gets
pushed to reality based on your unique
experience, based on your empirical
like life experience, not just what's
on text, what's good for the client?
The judgment call, the trust
call, the relationship call.
Those are things that aren't gonna
be just swiped away from you.
Patrick Rooney: Yeah, and, and people.
I think there are a
couple dimensions of that.
People still value performance,
like even a podcast.
There's I think some,
Raul: Matter.
Patrick Rooney: excitement and engagement.
And these are real people.
They're doing something live.
Yes, there can be flaws and foibles,
but we're seeing them kind of
perform for us and produce, you
know, hopefully interesting output.
Likewise there.
You just on that, that ownership side.
Right.
I think people really value that.
I can go to a human, I can go to Raul and,
and hold, you know, if I need to hold him
responsible for, you know, this business
engagement we had, he's the one who's
going to follow up, you know, and ensure
that things head down the right path.
This LLM doesn't actually have.
A, an inherent investment
in what I'm doing.
Like it, you know, at the end of
the day, it doesn't make money, it
doesn't feel, it doesn't feel shame.
It you know, it can simulate
all these things, but it, it is
not a locus of responsibility.
So practically, I want that
person on the other side.
Even if, you know, he uses
AI for 90% of his work.
I want that person who's responsible
for any high stakes engagement.
Raul: Yeah, to take the
outcome, take the ownership.
is, this is really good to understand.
Again, it's not intelligence.
One of the things also like maybe
feel free to kinda argue me this,
Patrick Rooney: Yeah.
Raul: We were at a dinner table
and we were chatting about this and
one of the other guys is that was
there, business owner, great guy.
Actually my connected two.
But, he, he does a lot of database work
and then puts on LMS on top of that.
So he works, he works at the data layer
and then the, the model layer and then
the application layer, for businesses.
And he was saying, 'cause he,
he like same perspective, like
this stuff is not intelligent.
I know how it works.
However we perceive it as
culture that it is intelligent
it's passed the terrain test.
And then when we all believe
that it's reached a GI.
Whatever that means.
Patrick Rooney: Yep.
Raul: agi, we did define terms, but
if we perceive that and we behave
that way, then regardless if it did or
didn't, it did because we're behaving
if it did as culture, even though
the truth underlying may be not.
So what's your take on that?
I mean, based on everything that we just
Patrick Rooney: Well, I, you know,
I'm gonna maybe, I put some words
in his mouth, but he's basically
saying if it, if it tricked me into,
Raul: the argument No, I'm, I'm
Patrick Rooney: yeah.
Raul: argument that,
Patrick Rooney: Yeah.
Raul: culture, if we, we
believe because he's saying
Patrick Rooney: Okay.
Raul: told him, well,
Patrick Rooney: Yeah.
Raul: that it's there.
We're gonna behave as if it's
Patrick Rooney: Okay.
Raul: So therefore it's there.
Patrick Rooney: Yeah, no, well that
actually, that's kind of the, the
original premise of the Turing test,
that if it can deceive you, it's real.
Right?
That if it can deceive
you, that it's intelligent.
It really is.
And what I think.
We want to go back to this foundational
distinction in just a history of thought,
which is appearance and reality, right?
So if I can, if wax sculpture, for
example, can trick me into thinking
Michael Jackson is, you know, right
behind my shoulder, is Michael
Jackson really behind my shoulder?
And is he, is he there until I, I reach
out and feel it and realize, oh no,
this is not, this is not a human being.
This is a sculpture.
The answer is clearly no.
You know it.
The fact that it was deeply convincing
that it was maybe based on a detailed
study of like every feature of his
face, his posture, his clothing.
Raul: to that future though, we're
Patrick Rooney: Yeah.
Raul: to that future world consensus
of the, of society and culture.
Even though right now in US, the sentiment
is really low for using these tools.
Hopefully, we'll, we'll flip that.
But we're going to that future where,
where there's gonna be a culture
alignment saying like, yes, this is it.
This wax statue is real,
even though it's not real.
How do we, how are you
gonna lead as a founder?
What are the things you're
gonna work with your clients on?
How are you going to reframe?
Like, what's, what are the real
implications you're gonna take?
'cause I think that's the future we're
gonna head in because no one's gonna
be able to judge it in my opinion,
like if they reached it or not.
'cause they're, everyone's saying,
we reached a GI, it's here.
Patrick Rooney: Right.
Raul: That, that was like leaked
two weeks ago, three weeks ago.
whether it's there or
not, one, does it matter
Patrick Rooney: Mm-hmm.
Raul: Two, if it does matter,
what do we do about it?
And three, based on what we do
about it, how do we practically
implement that in business?
Patrick Rooney: Yeah, I mean,
it's a fantastic question and I'll
throw out a, a couple thoughts.
I think it, it, it's doubling
down on human connection on
in-person human connection, right?
And I'm not saying this to undermine,
you any online businesses like I've,
I've worked remotely for several years.
I'll continue to engage in business
online, but I think maintaining that
human contact is a, a strong reminder to
us of what we actually can do in person
when it's like, oh, I can see that.
Yeah, your, your coffee cup just spilled.
Let me help you clean that up.
Right?
It's tiny things that make a lot
of difference when we have those
tangible elements in our relationship.
So I would say.
To the extent you're able to do that,
even if it's up not in work, but
personal relationships cultivate that.
I think that's, that will then having
that as a primary foundation will then
kind of spill over into the honor line
relationships and I think ground them.
So my, my view is having those kind
of tangible in-person relationships
helps make the online site stronger.
It's a, it's a, it's having
the right foundation.
The, yeah, the other analogy, I
just, it's kinda like video games
versus, you know, playing sports and
video games versus in person, right.
You can, you can pick up some interesting
coordination skills, et cetera,
playing whatever tennis like used
to be we tennis like long time ago.
And
Raul: yeah.
Yeah.
That was fun.
Patrick Rooney: Yeah.
Raul: Then
Patrick Rooney: And it's,
Raul: VR glasses.
Patrick Rooney: you can get, you can
get better at it, but you, you're
still gonna f find out there's a gap.
There's a huge skill gap when
you go out and you touch.
Tennis racket in the real world.
So do, do those, like get grounded
in those, those activities?
I think that, again, they, they'll spill
back over and help you with all the,
what I'll call derivative activities.
That is, they're, they're
built on that foundation.
So the online, the
simulated stuff with LLMs.
Those are all good.
They're great tools for building
business connections for for building
projects, but don't ever let that become
Raul: value.
Patrick Rooney: from the
Raul: Yeah.
Patrick Rooney: concrete.
Raul: Just having that recognition.
I think the, the practical takeaway to
a GI here, not regardless, relationships
matter, but also even in my own, like
going deep, like real deep, trusting
your own human based intuition
and judgment based on experience.
Not only just what the model outputs,
even if the model is super compelling.
The other approach is the other, the
other key thing to keep in mind is
also you, you have the ability to know
what's right and to know what to do.
So don't plaque on your own reasoning.
Patrick Rooney: Yep.
Raul: something to keep in import,
like how, I forget how much
we invested into, trillions of
dollars into training these models.
Now I'm pretty sure like cumulatively
yet your brain getting, getting cost a
trillion and, it's kind of fascinating
when you think about it that way, that,
Patrick Rooney: Yes.
Raul: we take we take our own
brain thinking for granted.
So you can also reason your way through.
Patrick Rooney: Yeah.
Raul: and then also vernacular matters.
The way that you talk to it, the way
that you personified or not personified
and the way that you train your team
is important because your team may
not be having these discussions.
You may not be thinking about this day
in and day, and it's easy to go with the
flow and to get sucked into whatever.
Whatever's being pushed
in terms of ideology.
So it's just good to have your own
stance and your own perspective.
Even if you disagree, that's great,
but at least you thought about that
and you thought about your own thinking
which I think very few people do.
'cause thinking about your own
thinking is called writing.
It's definitely right about how you think.
Patrick Rooney: Yes.
Raul: I think is a good loop on
where can people find you if you're
writing or posting more about this,
and also sharing your insights.
Where's the best place
for people to go to?
Patrick Rooney: Yeah, right now
LinkedIn is a great place to find me.
I'm happy to share
contact details with you.
I've also got a, a business
website, leonis strategy that's,
that's a little more limited.
So I'd say that the, the LinkedIn
side of things is gonna be perfect
for anybody who wants to connect.
Raul: Sounds good.
We'll put those links in the show notes.
Patrick, thank you again for being on.
Patrick Rooney: Thanks so
much for having me, Raul.
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