KCAA: Inside Analysis with Eric Kavanagh (Sun, 22 Dec, 2024)

KCAA: Inside Analysis with Eric Kavanagh on Sun, 22 Dec, 2024

Episode Transcript

Speaker 1 (00:00):
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Speaker 4 (00:51):
The information economy has a ride. The world is teeming
with innovation as new business models reinvent every industry industry.
Inside an Analysis is your source of information and insight
about how to make the most of this exciting new era.
Learn more at inside analysis dot com, insideanalysis dot com
and now Here's your host, Eric Kavanaugh.

Speaker 5 (01:18):
All right, ladies and gentlemen, Hello and welcome back once
again to the only coast to coast radio show all
about the information economy. It's Inside Analysis, yours truly, Eric
Kavanaugh is here with the legendary entrepreneur, the man about
i'd say, the man of my Townity's the man about
the world. He's all over the place Shanghai, Malaysia doing
amazing things in the world of modern technology. We have

(01:40):
Alvin fu his latest Gig zero two launch is about
three years old now, and he knows a lot about bitcoin,
knows a lot about enterprise technology and really global market trends.
And Alvin, I'll just throw it over to you first.
We are living in fascinating times right now. And when
I look at these foundational models that are coming out,

(02:02):
what I find so fascinating is that they are, in
many senses a reflection of human knowledge. It's like a
dynamic encyclopedia Britannic. I think gets things wrong. Sometimes they're
still hallucinating a bit, and we're all working on that.
We're working on the ground truth. But you just suddenly
have at your fingertips outrageous amounts of information which can

(02:24):
be shared and articulated in perfectly syntactically correct sentences. That's
a really big deal. What are your thoughts, having been
in the industry for a couple decades now or more,
what are your thoughts about this inflection point we're facing
right now?

Speaker 6 (02:39):
Well, Eric, thanks, thanks again for inviting me to your show. Well,
we are living in an exciting time. You know something
that never I mean, I mean we grew up. I
think we probably grew up almost at the same time,
the same era where we saw, you know, everything that
was analog, right, you mentioned encyclopedia, You know things that

(03:04):
that I think kids of today don't even know what
what in the world encyclopedia because now get access to
the internet. And now of course you know AIHL g BT.
You know, the way is rapidly moving towards an era
where nobody can comprehend and the speech will only.

Speaker 2 (03:20):
Get faster and faster.

Speaker 6 (03:22):
And AI I think it's going to be the tool
or the single single because you know, to me is
the biggest denominator that's going to disrupt every single business
in every category, every industry, and possibly our.

Speaker 2 (03:40):
Lives as well.

Speaker 6 (03:41):
So we are we are definitely living in exciting times,
and I think it's just going to get much faster
and we just have to accept and I always coined
this this this term. You know, we live in an
era where we need to learn very quickly. We also
need to unlearn what we are learned very quickly so

(04:01):
that we can relearn new things, because I think that
is the only way to stay relevant or staying ahead
of the game.

Speaker 5 (04:09):
That's a brilliant Yeah, that's a really good point. We
have to unlearn to relearn. It kind of reminds me
of one of the challenges with these models. So Watson Acts,
i'm told, was designed originally such that you could pull
out a data set and then retrain the model. And
I'm sure they did that because they realized we might
load something in and then realize, oh, we shouldn't have

(04:31):
done that take it back out. Well, most of these
other models are not built like that, and once you
train a model on something, it's very hard to untrain it.
And the example I give is like raising kids. If
you curse in front of your two year old, don't
be surprised if you curses back, because that's now in
her model, right, So it's hard to you can try,
like with your rag model, you can try now, don't

(04:53):
curse the only daddy can say that word. You know,
good luck with that one, right, What do you think
about that?

Speaker 2 (04:58):
All? Right? I mean it's easier said than done. I
mean learning, I think is given.

Speaker 6 (05:04):
Everyone learns something, right, to unlearn what they've learned, that's.

Speaker 2 (05:09):
Difficult, right.

Speaker 6 (05:10):
And if you look at you know, these days, when
you talk about you know, maybe maybe twenty years ago,
when you tell people that you have got like twenty
three years of work experience, that means a lot, right,
That's that's valuable, right, But twenty years to thirty years
of work experience in today's world, that's a lot.

Speaker 2 (05:30):
That's that's not that's not going to be helpful.

Speaker 6 (05:33):
And because you're going to come with legacy, right, lots
of baggages and and it's difficult to learn something new
and let's not even talk about learning something new. To
unlearn what you've learned for the last twenty three years difficult.
So that's why a lot of companies, when you look
at how they're moving forward, you know, what they've been
organized to achieve past success, right, is probably going to

(05:58):
make you know, probably going to be very difficult for
them to to move forward because of the legacy, right,
because it's very hard for a company that has been
around for for fifty years, one hundred years, right to unlearn,
to uncheck themselves right and reorganize them again in a
way that is totally not what they're used to.

Speaker 2 (06:20):
So unlearning is actually a lot lot harder.

Speaker 6 (06:23):
Relearning is, probably, but you need to unlearn first before
you can relearn. So again, in that, in that, in that,
in that context, while it all seems easy, it's.

Speaker 2 (06:33):
Not not so simple after all.

Speaker 6 (06:35):
But so this is something I practice all the time,
and and I'm not so young, but but I've learned
how to, you know, sometimes dump out.

Speaker 2 (06:43):
All the all the all the all the legacy and.

Speaker 6 (06:46):
Trying to keep an open mind. Now, keeping an open
mind is is also easier said and done. Now, well
what you want to keep an open mind, but you're
still structure within a framework. Right. So so that's why
I'm and I always talk about when people always say
let's think out of the box, right, But the trove
is that it's very difficult because if you're getting the

(07:08):
same people who have been in the same industry for
twenty thirty years and asking him to think out of
the box is almost next to impossible. The only way
to think out of the box right is to get
someone not from the same industry right to look in
and figure out how to think out of that industry.

Speaker 5 (07:28):
Mm hmmm, So someone like gohead, go ahead, No. I
was going to say, you bring up a lot of
really really good points here, and from an organizational perspective,
I think you're going to need a lot of introspection
and then some extro spection, I suppose, and as you suggest,
get someone to come in from outside the organization and

(07:48):
just look at your plan, look at your vision.

Speaker 3 (07:51):
You know.

Speaker 5 (07:51):
I have a really good friend who is a developer.
He's been doing this stuff for twenty odd years in
the valley in San Francisco, and he has a really
interesting mantra where he says, busy is the enemy of
creative And what he means is, if you're just constantly
behind the eight ball, if you're constantly trudging away, trudging away,
you're not thinking of creative answers. And especially in the

(08:12):
coding world and the information architecture world, there are no
limits to how you can design something these days. And
I think a lot of people who don't know code
don't realize how constrictive it is in terms of how
you have to write things. You have different ways, different architectures,
calling you, of course, modular architectures, you have monolithic. You

(08:35):
now have all these distributed systems like Kubernetes. My point
is that there are countless ways to reinvent how you're
doing things, but it does require that sort of shedding
of the old mindsets. And that's what you're talking about,
right It's being able to really thoughtfully discard the structures
of your thinking to adopt a new way of living

(08:58):
and doing.

Speaker 6 (08:58):
What do you think, well, I I can, I can resonate.
I mean, the thing is that now I work with developers,
I'm not a coder.

Speaker 2 (09:09):
I'm bye bye.

Speaker 6 (09:10):
By practice, I'm I'm supposed to be practicing law. I
actually graduated with a law degree some thirty years ago.
Uh and and my first job was I actually started
off in an advertising industry, so nothing to do with
technology and nothing to do with whatever that I'm doing
right now. So it has gone through an evolution that

(09:34):
I got into internet and gradually understand networking and then
understand one point oh two point zero and subsequently I'm
just trying to understand and and you know, you know,
have a much better grap than Web three point zero.

Speaker 2 (09:47):
So the entire evolution.

Speaker 6 (09:48):
Has taken, you know, some twenty five years or more,
right to learn and learn and also relearn and and
when I work with my my coders, now, my coders
are so bake into the way of working, right. Therefore,
whatever they think about is always you know, the practicality
and and how you code. But when you when I,

(10:11):
when I work with them, I I don't think so
much of coding. I think of user experience, you know. Uh,
I think more in terms of the practicality.

Speaker 2 (10:20):
And the the the.

Speaker 6 (10:22):
Application and how that how would that then adapt and
adapt to the to the real world.

Speaker 3 (10:27):
Right.

Speaker 6 (10:28):
So so that's why when you when we look for
people within the team, we want to find a good combination,
a combination of someone who can quote very well and
also very receptive because the probably the problems a lot
of coders is that they're also receptive, and they always
stuck right stuck within the idea the square. And then

(10:49):
the thinking is that they always start then and not
willing to move beyond. So we need to find a
good combination. You know coders, we are you know CEO
who are very open minded. And then we're got you know,
product persons who understand products very well and use experience
and the customers, so that that's a good combination. If
you look at most successful set up, they are made

(11:10):
up of combination of people who are really good in
their own domain and who are very very receptive because
in today's role, right, it's all about understanding your customer.
If you understand them and being able to execute right
according to what they need, right, and that makes the
product very successful. And that's lacking in a lot of
companies because you know, some of them are just good engineers,

(11:34):
they're good programmers and that's all about it. But to
understand the customer and take it to market sometimes lacking.

Speaker 5 (11:41):
Right.

Speaker 2 (11:41):
So so again, when.

Speaker 6 (11:42):
You want to find a team, right, you need to
find a team that are able to do what I've
just said. But if you look at most organizations I'm
talking about multinationals, the companies that has been around for
a long time, the combination that actually missing and most
of the time being led by people who have been

(12:03):
in the industry right for several years. And if even
if they will find replacement for the CEO or CFO.
Usually they will also they will they will end up
finding the old gods from another company, a similar company,
to run the company because it's safe to do so, right,
Because if you find someone who is.

Speaker 2 (12:23):
New, right, that's taking a lot of risks right.

Speaker 6 (12:26):
For a new startup, it's a lot easier because there's
no legacy, there's no historical so basically you're starting from
ground zero. But when you are taking on a mountinnational,
there are a lot of boundaries that you need to work
with and then you answer about to your shareholders, your
board of directors and so forth.

Speaker 2 (12:44):
So it's very difficult. So that's not what you're seeing
right now.

Speaker 6 (12:47):
We have seen over the last twenty years their disruption
in several sector industry.

Speaker 2 (12:52):
And many big companies.

Speaker 6 (12:54):
There was one once very big are no longer around
like companies that called that companies like no Care. And
now now what is impacting is in what we're seeing
very clearly is.

Speaker 2 (13:04):
In the auto industry.

Speaker 6 (13:05):
When you look what you're seeing right now, the evs
from China are now disrupting right the global auto industry
because the auto industry, which has been so structure and
organized right to a very well structured supply chain for
the last god knows how many years, right, are now

(13:25):
facing disruption caused by companies new EV company that build.

Speaker 2 (13:30):
Cars entirely on their own.

Speaker 6 (13:32):
They design and manufacture a car entirely by themselves in
the shortest time. If you look at most companies a
design car, it takes seven years, right, at least seven
to ten years to come up with one new model. Now,
the company in China, a company called bid As, I'm
sure you must have heard, one of the largest EV companies.

Speaker 2 (13:55):
They can come.

Speaker 6 (13:56):
Up with a new model almost every month, right now,
that's scary, right. So so the Chinese, the Chinese manufacturing
right has I mean what whatever I always say is
right Now, anything that that that that it has a
linkage to electronics, right, whether that's a smartphone or the

(14:21):
fan that you're using at home, the air conditioning system, whatever,
that is being powered by electric.

Speaker 2 (14:29):
The Chinese are really good at it.

Speaker 3 (14:30):
Right.

Speaker 6 (14:32):
Years before EVE came about, the Chinese had a hard
time catching up because it was it was based on
a very old technology called engine, the combustion engine. Today, right,
it is being built empowered by batteries, So that's electric again.
So the electrification of auto, of the auto industry, right

(14:55):
provides a new entire paradigm shift for the manufacturing the
auto manufacturing industry in China. And suddenly, right they have
gone from nowhere and became the largest EV manufacturers in
the world. Right, So they've disrupted many many industry right
and and the auto industry is the next one to
be hit now.

Speaker 2 (15:15):
So so when you look.

Speaker 6 (15:16):
At when you look at a lot of these companies,
I mean I had the opportunity to speak to some
of them, some of the CEO that I know personally,
uh and and most of them started the company almost nothing,
literally almost nothing, with a couple of people.

Speaker 2 (15:31):
And all they had was a dream that they want
to build a car.

Speaker 6 (15:35):
Right And and when you look at them, right, they've
got a lot there have almost no legacy, and all
they all they know, right is that it's huge market.

Speaker 2 (15:44):
Out there is how how do I come up with
a car?

Speaker 6 (15:46):
And the shortest sign and the competition in China is
that they are over one hundred and sixty five EV
manufacturers only it's crazy and and most of them are
not making money. Most of them are not making money.
They're all funded now of course when when when the
funding stops, like the music stops, right, So so at

(16:07):
some point they will there's going to be I mean,
as of now, there are they are already already some
have already applied for bankruptcy early, right, And I'm sure
along the way you see more and more ev companies
in China are going bus and I think that's where
the consolidation will come in. And of course there's the
more solid company, the companies at bid. You know, they

(16:29):
will continue to be, you know, a key player in
the global market.

Speaker 2 (16:34):
And and and that's a competition.

Speaker 6 (16:36):
Competition is in China on any sector super competitive, super
super competitive.

Speaker 5 (16:43):
Well, I mean China basically went hyper capitalist, you could argue, right,
I mean you went from this an older system, a
more traditional system of governance and societal developments and dynamics
to this hyper capitalist environment right now. And that's why
the economy is.

Speaker 2 (16:58):
Doing so well.

Speaker 5 (16:59):
I mean, capital breeds excellence. Competition breeds excellence. And when
you do it, when you don't have competition, that's when
things slow down and you do not get a lot
of innovation. I mean you see this in organizations too,
Like the more competition a certain company has the harder
they have to fight to get their market share, right,
real quick, we got one minute in the segway, go ahead.

Speaker 6 (17:19):
Right, I mean it is it is, it is what
it is. I mean the you know, innovation in China
is given. It's not like innovation has to be something
that we that that people are being taught what innovations
is given and and and the only way to fight
competition in China is innovation, no other way. And that's

(17:40):
why when you look at all the all the auto manufacturers,
in all the cars that's been designed, right, every single
one of them will come up with you know, very
gimmicky features and so forth, right, just to.

Speaker 2 (17:52):
Out compete each other.

Speaker 6 (17:53):
And and and then you multiply that with one hundred
and sixty five manufacturers.

Speaker 2 (17:58):
It's painful.

Speaker 6 (18:00):
And that so so so I mean short, right, that
has caused a whole disruption to the auto industry, pushing
out the Japanese brands. Right, Some of the Japanese brands
are exiting the market already, some of the some of
the American many manufacturers like GM are closing closing down
factories now they're all getting hit and German brands like

(18:21):
the Mercedes and BMW are also getting hit as well,
and it's painful because China is a market that car
manufacturers cannot afford to lose.

Speaker 2 (18:31):
It's like, it is the largest auto.

Speaker 6 (18:33):
Market in the world, right and it's a market that
it cannot afford because there's no way that you can
replace that lost revenue. And this is exactly what's happening
in China right now in the auto sector.

Speaker 5 (18:46):
That's crazy. Well, folks, don't touch it. Dow Or will
be right back with the legendary Alvin Fou. You're listening
to Inside Analysis.

Speaker 4 (18:59):
Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.

Speaker 5 (19:06):
All right, folks, back here on Inside Analysis talking to
Alvin fu your host here, Eric Kavanaugh. We're talking all
about innovation and how China has one hundred and sixty
five manufacturing companies for cars. I mean, that's just crazy.
Here in America they talk about the Big Three and
even they're having trouble and they've been subsidized and given
all sorts of money. And you know, I drove a

(19:28):
maz to me out of myself two thousand and one,
a six speed. It's a beautiful car. I don't think
there'll ever be another car as beautiful as my car.
To be honest with that, I'm selfish. But Alvin, you
are a lawyer by trade. You studied law and I
just gave a keynote this morning where I basically said,
in the not too distant future, and this mayread be
happening somewhere around the world, you, as a plaintiff, are

(19:49):
you going to have the option to go with an
AI judge and jury or a real judge and jury.
And if you do the AI judge and jury, You're
just gonna load your lawsuits. You're gonna load all your evidence.
You're going to talk to a camera for an hour
or two and answer questions. The defendant's going to do
the same thing. And once all that information is in there,
the engine's going to open, read through it and say

(20:10):
we find in favor of the plaintiff. Here's what you
have to do. I think that's going to happen. And
think about this, because you can, even with the ability
to model these days, you could even you talk about
a jury of your peers, you could scrape data from Facebook,
from Instagram, from other social platforms, and a particular geography
and then create jurors based upon the personalities in that geography.

(20:33):
Then you could even have a random jury selection. You
can do the whole thing through AI. And you look
at the legal world today, well, what is law really
boiled down to? It boils down to the practice, the rules,
and what you can do at certain points in time.
Emotion to dismiss, for example, is something you can do well.
Right now, You've got to pay people a lot of
money to do all that stuff, and they have to

(20:54):
know how to do it. Then you have to get
them to do it. I've had only one experience with
a lawyer in my life, but he was very lazy.
I mean, we we worked out, everything worked out fine,
but he was very lazy and he just didn't really care.
And that's when I learned, Oh man, when you hire
a lawyer, make sure you get one who's going to
actually work for you and care about your case, because
even that's difficult. But what do you think about this

(21:15):
prediction that AI is going to revolutionize juris jurisprudence.

Speaker 6 (21:21):
Well, I think AI it's not just going to replace,
you know, the current you know, legal system, the potentially
is going to also revolutionary the healthcare industry and so
many other industry right now, now, going back to what
you have just said, I do believe, I do believe

(21:42):
in a very very near future you're going to have
a situation where you know, it's going to be an
AI judge, it's going to be an AI lawyer. I
mean it, it's doable because you can, you know, machine
train them, you can and and they can continue learning,
right and you and and the thing is that now
they can do they can work twenty four hours a day, right,

(22:04):
human human cannot work any four hours a day. And
they will know everything. The judges and the lawyers will
know you know some of this stuff well, but not everything.
But your AI just will know everything every single sat
statutory law, all the all the proceedings and so forth.

Speaker 2 (22:24):
Right, So so.

Speaker 6 (22:25):
He's going to be a much more knowledgeable, knowledgeable lawyer now.
But but but having said that, right, there's one part
of the legalism, the law that requires you know, some
some some deep you know, some critical thinking, especially into
the facts of the case. Now on the law you
can change, but the facts of a case right, sometimes

(22:45):
can be very subjective. I think this is where uh,
I'm not saying human will play better at all, but
suddenly human can think better well right now, as far
as far as the next you know, five ten years,
right until really it comes to day where you know,
a g I is is you know, becomes a reality

(23:07):
where where you can't even out compete or are you
an out talk and in AI, I think that's where
a g I comes in, right.

Speaker 2 (23:15):
But I think in a short period of time you're
going to get.

Speaker 6 (23:19):
AI lawyers, a I a I judges, but they are
they're just going to play They're going to play a
very element elementary role of a judge. Right, So basically
very simple cases, right, very straightforward cases.

Speaker 2 (23:35):
Right.

Speaker 6 (23:35):
I think for for cases that require that s more,
there's there's more that requires you know, critical thinking and
for for or even on a few cases right that
you probably need, right, a human judge.

Speaker 2 (23:49):
Right. I don't think this would this would they would
be replaced.

Speaker 6 (23:53):
In in the far in in the future, but at
least I think the magistrate court, you know, the very
low low, low level judges or likely be replaced by AI,
including very low level lawyers as well.

Speaker 2 (24:09):
Yeah, I think similarly, yeah.

Speaker 6 (24:12):
Similarly, similarly to the healthcare and medical medicare, where the
GPS are going to be replaced right, right, what's going
to be left by the specially because what people want
to talk to, they want to talk to specialists, they
want to talk to experts, right, But AI can do
that that that that's very straightforward jobs.

Speaker 2 (24:33):
So I think that's where that's.

Speaker 6 (24:34):
Where AI is going to play a huge role in
replacing very general roles, even for lawyers and doctors.

Speaker 2 (24:43):
Yeah.

Speaker 5 (24:43):
Well, and I'll tell you what. One other topic I'd
love to get your opinion on is transparency. So open
AI originally was open and now it's a black box.
So I joke they should change their name to close
the AI because they're not open anymore. But there is
this all mode that just came out which is true
open and they even give you access to the training data.
And from my mind, I believe transparency will be crucial

(25:08):
for auditability and for fairness with these models, because if
they're not transparent, if they are a true black box,
then there's no real way to audit them and the
truth be told, the complexity of these things is so
tremendous that even if it's completely open source, it's still
not like the average person could come along and understand
what the heck it's doing. These are very very very

(25:30):
complex systems, and they're all different. Now, I try to
explain that there is no limit to the number of
ways you can set up a deep learning module, how
you define the parameters, how many parameters you have, how
many inference layers you have. You could even make them dynamics.
So there's really there's no limit to how complex they
can be. Not the complexity necessarily equals better value or

(25:51):
better efficiency, because it doesn't. That's why I think these
small language models are going to become fairly prevalent. But
what do you think about transparency in code and architecture?
I know that goes against the grain of traditional capitalists
thinking with intellectual property, but let's face it, these models
have kind of they've absorbed all this intellectual property and
then now they have it trained in their systems. So

(26:14):
it's like, is I P even a thing anymore? What
do you think about about legislation to mandate that AI
models be transparent in their code and in their training data.

Speaker 6 (26:26):
I think I think the the regulation around this area
is coming uh and and I think we need regulation
around it because if not right, then how how do
you define transparent?

Speaker 3 (26:40):
Right?

Speaker 6 (26:40):
So what what what is transparency? And how much is
being transparent right?

Speaker 5 (26:46):
Right?

Speaker 2 (26:46):
And and the.

Speaker 6 (26:48):
Models that you just talk about what what what what
can be defined as a good model, And that's that's
the same problem that was saying that if you want
to build an AI model, then you've got to make
sure that whatever that you input into the model is
making sure there's a.

Speaker 2 (27:08):
Lot of truth to it, right, because it's all about
truth right, right.

Speaker 6 (27:13):
And and if if you only if you only teach
or input models and data that you want it to
model that way, then it becomes biased.

Speaker 2 (27:24):
Right.

Speaker 6 (27:25):
Now, imagine if if the lawyers are being the AI
lawyers or AI doctors are they are being skewed towards
the pharmaceutical industry, just like how the pharmacies the farmer
the farmer companies are lobbing the government. Can you imagine
right that the AI doctors or AI lawyers are being
skilled biased, right, and that that will.

Speaker 2 (27:48):
Not work for all of us? Right? So so what
you what you teach the model? Right?

Speaker 6 (27:55):
I think that needs to be that needs to be
defined right now, there's no there's no regulation and also
because it's is we're moving so fast, right and and
as I'm sure you know, regulation always play catch up role. Right,
Just like how it's playing a catch up role into
digital asset, it's also playing a catch up role to AI,
and everyone's trying to learn and as much as we
we we are trying to understand it's better, you know,

(28:16):
the the transparency, privacy, cybersecurity and so forth. There's so
many areas and even copyrights when we look when we
look at generative AI, every single image that's being generated
on generative AI.

Speaker 2 (28:30):
Is a copy of something.

Speaker 6 (28:31):
Else, right, right, and usually uh, that's something else being optimized.

Speaker 2 (28:37):
But you know true AI, right.

Speaker 6 (28:39):
So what what levels of of of of optimization right
then you need to do before it becomes a new
a new image or a new copyrighted material.

Speaker 2 (28:50):
So right now nobody, nobody has a definition.

Speaker 6 (28:54):
So so basically what we are all doing is copying
each other, right and it could be an image of
a cartoon on the Disney or that could be a
copyright of Marvel and whatever that may be. So so
we all are taking these images, right and then you know,
repurpose them.

Speaker 2 (29:14):
But what about copyright?

Speaker 6 (29:16):
So so I think regulations will catch up, and we
need one because if we don't have one, right, then
which standard and which center are we following right? And
what is right to you may not be right to
to me, or what's right to one one country may
not be right to another. So I think I think
they need to put this in place so at least

(29:37):
there's a more defined role and as to how you
train your model and what's what's transparency.

Speaker 5 (29:44):
Right, Yeah, I think that then all the right it's
just up for grabs. I mean, if there's no if
there are no rules, you know, or if there and
here's the other side of the equation, you know. I've
got a very colorful history. And for a couple of
years I worked for a rush and I often do
my Russian inmitation. They worked for Alexandri Khananik and we
kid the post Rasha and he was Boris Yeltsin's financier.

(30:08):
And he tells me stories that basically way back in
the day, in what the nineteen eighties, nineteen nineties, basically
he said, from his perspective, and he became a multimillionaire
by the age of twenty four or twenty five because
he got into the construction business and he did commodities
trading at the very beginning of the internet. So if
you can see that lumber is one rouble of pound

(30:29):
over here in ten over there well by Sell, and
he did that before and he had a first mover
advantage basically. But one thing he told me I thought
that was very interesting was that the system back then
in Russia was set up so that you could not
not steal. That's how he described it. But you weren't
supposed to steal from you weren't spposed to steal more
than your boss, right, Like the senior director can steal
a lot, the manager can steal a little bit the

(30:51):
new guy, but it not steal very much. And this
is this is what he tells me, right. And then
what I picked up on which I thought was very interesting,
was that the laws were so complex. And this goes
I'll tell you what. This goes back to laut Su,
because I love Chinese philosophy. Laut Su said three thousand
years ago, when the laws are complex, the bandits will abound.

(31:12):
And his point was that bad guys love very complicated
law because they can navigate it. If they can understand it,
they can take advantage of it. And I think that
getting back to this AI overthrow that I'm talking about,
when you see this permeate the legal industry, healthcare industry,
financial services, et cetera. It's good for the bad guys,

(31:32):
but it's hopefully good for the good guys. And I
think we can start to unwind some of that stuff
because from a prosecutorial perspective, if everyone's breaking the law
all the time, then you can be very selective on
who you prosecute, and that is a bastardization of the
law and the whole point of having laws. Right, So,
what do you think about that in general? Because I

(31:53):
think that we have to get back to basics. And
you know, I'll give you one last fun quote when
I describe this to my business partner, doctor Robyn Bloer,
who is a very very intelligent guy, a British man.
I explained it two years ago. He goes, do you
know how many rules Napoleon had? I was like, no,
He said seven, And he was talking about going back

(32:14):
to basis, going back to first principles. And I think
we kind of need that because it's like things have
gotten so complex now from many different perspectives that we
have to distill in order to be able to govern
effectively in the future. We're going to have to distill
down to core values and rethink all these processes for
how we do things.

Speaker 7 (32:34):
But what do you think, right, Well, if you if
you look at most most profession, right, whether that's going
to be the medical profession, the financial industry, or even laws,
it's actually quite complex.

Speaker 6 (32:49):
And I think they are complex for reasons. They are
complex because then everyone has a job, right, everyone gets
to keep your job right. And if you look at
the financial sector, it's so so complex to the point
that that the regular guy on the street right will
not understand that they need a wealth advisor, that they

(33:12):
need that they need someone to advise them what to
do right.

Speaker 2 (33:15):
And that's why there's a lot of roles and jobs around. Right.
So when you look at when you look at you're
talking about laws.

Speaker 6 (33:22):
Now, I think in especially technology, uh, you need a framework,
you need regulation, But at the same time, you need
a very balanced framework because if the regulation comes in
and it is complex and how to understand, how to apply,
or or it becomes so strict that that you then

(33:43):
then there's no innovation, right because now people have to
start to think, right now, I can't do this, I
can't do that, and then I need this license I
need that license or the cost of even operating it
becomes very high. Then then instead of you pushing innovation,
you not having no innovation. So I think they need
to be a very balanced framework. And I think that's

(34:05):
that's difficult. Right, So where do you again, when you
go back to transparency, right, so how do I Where
do I draw that line? I think that's that's something
a lot of regulators are having a lot of difficulty,
and that applies the digital assets. Same thing when I
look at a lot of new regulations that's been designed,
especially especially tech related.

Speaker 2 (34:25):
Right, the problem is is where do you draw that line?

Speaker 3 (34:28):
Yeah?

Speaker 5 (34:30):
Yeah, let's pick this up after the break. That's a
very very good question. Where do you draw the line?
And how malleable should that line be? When do you
redraw the line? We'll pick this up right after the break.

Speaker 6 (34:39):
Don't touch.

Speaker 5 (34:40):
I tell you're listening to the Inside Analysis.

Speaker 4 (34:48):
Welcome back to Inside Analysis. Here's your host, Eric Tabanac.

Speaker 5 (34:56):
All right, folks, back here on Inside Analysis with the
legendary Alpha and Food dialing in from the other side
of the planet. So thanks for staying up late here
on my time, and Alvin, I want to ask you
a question because we're asking these deep, thoughtful questions about
where do you draw the line with regulations, with innovation,
with transparency, what are the rules by which we must live?

(35:17):
Because and if there are too many rules, there are
no rules. I guess that's the argument I was making
in the last segment. If there are too many rules
and they're not comprehensible, then there may as well be
no rules. And also, if you don't understand a rule,
there's no way that you can consciously adhere to it
because you don't understand it. And I think some of
these laws they've come up with, you're like, what on
earth is this saying? But I will say that these

(35:39):
beautiful language models are so good at summarization. You can
take highly complex, draconian language, throw it into a model
and say what does this mean. It's going to do
a pretty good job to tell you. But in terms
of things like copyright and ownership and really understanding providence,
I think that's a very fascinating use case for blockchain

(36:02):
because it is immutable. Of course, bitcoin sits on top
of blockchains, and there's all these complex ways of mining
for the coins and trading the coins and consensus and
then we can get all into that stuff. But the
thing I find interesting is using blockchain as a system
of record to know who owned what and when. So
you think about deeds, you think about property, for example,

(36:24):
and who owned which property? Right now, it's a fairly
convoluted process that has lots of different players. The title
company for example, has to come in, the bank, the owner,
the county recorder of deeds. All these people do stuff,
and they're bad guys who come in and manipulate that
system too. But when I think about the future in
like copyright for example, or whose idea was this, who

(36:47):
took that photo? I think we can use blockchain as
a foundational layer to record who did what and when,
and that's going to be very valuable in the future.

Speaker 2 (36:58):
But what do you think, Well, that's that's the power blockchain.

Speaker 6 (37:02):
I mean, that's that's that's what we are all trying
espressly the web tree, right, trying to promote decentralization, you know,
the blockchain. You talk about the benefits of it, right,
I think that there's a lot of I think every
sector uh is going to be impacted, right or it's
going to benefit right from the adoption of blockchain, right,

(37:23):
you're going to see not just not just you know
we're talking about copyright, right, it could be supply chain
where the supplies are coming from.

Speaker 2 (37:31):
Right, it could be music.

Speaker 6 (37:33):
Right, uh, copyright music, copyright movies, or even togonization Like
togalization is actually a very big sector because a lot
of things today that we we we have to be
all physical, right, the physical asset. Imagine being able to
take all the physical asset in the world, right, all
the real asset could that that could be properties, you know, companies, equities,

(37:56):
goal and so forth, and you tognize them and and
having tokenized them knowingly that you can keep a record
of all these assets. At the same time you're able
to trun for the asset seems right anyway, cross border
right right. So plus, I mean it's a it's it's
mind blowing and and and that's that's the reason why

(38:17):
I have I've gone into blockchain some some eight years ago.

Speaker 2 (38:20):
When I when I when I took a.

Speaker 6 (38:21):
Look at the power of what you could do and
the potential that we can impact business in the future.

Speaker 2 (38:27):
I just I just think that there is massive.

Speaker 5 (38:30):
Yeah, I think you're right, and you so like in
the risk management world, in the financial services world, clearing
houses things of this nature. To your point, if you
have a sophisticated blockchain system underneath a lot of that
stuff becomes very easy. You could transfer the assets, move
them all around, and have a record and immutable record.

(38:50):
I remember Madge, I think the guy's name was, who
for a short period of time was the see I
think the CFL maybe for Twitter, and he's the guy
who testified before Congress, and that from what he had learned,
any number of developers at Twitter had root level access
and you could go in and change something. You could
change data like a in a thread for example, and

(39:12):
then there's no log file, or you could actually delete
the log file. And I've heard of that in some
other instances too, and it's like, well, that's not immutable.
It's the point is like, that's mutable. Like if you
can go in and delete the log file and that
you know, you change something, that's no bueno. Right, That's
not good, Which is why I think we need some
level of blockchain to underpin these systems. Right, to have

(39:35):
the true system of record that keeps check of everything
that happened, so you can undwind anything. You can run
an AUDITAI, you could do impact analysis all that kind
of stuff. That again is another power of blockchain. What
do you think I mean.

Speaker 6 (39:48):
Government should love this, right, imagine a wall way, imagine
all the all the invoicing, right, all the revenue big
capture on the blockchain.

Speaker 2 (40:00):
Right.

Speaker 6 (40:01):
So if you if you capture all the accounting on
the blockchain, you don't need to do audit anymore because
it will self audit on his own.

Speaker 2 (40:09):
That's right, right, that's right and right.

Speaker 6 (40:12):
And not only did he sell audit on his own
automatically the.

Speaker 2 (40:17):
Taxes, yeah right, this is the thing.

Speaker 5 (40:20):
Right, So it's like you look at tax preparation. I
remember I was, I got QuickBooks, and I'm sitting there
and banging through all this quickbook stuff and I'm trying
to do my taxes for corporate taxes, and I realized,
you know, this should not be that difficult. And you
see it now in QuickBooks where they you start typing
in a company name and go, oh is it this company.

Speaker 2 (40:40):
With this address?

Speaker 5 (40:41):
Like, oh, yeah, actually it is. Well, a restaurant obviously
sells food. An accounting firm does accounting. You can tell
by the registration of the companies what they do, and
that should be also very good for auditing and for
automating the process of doing your taxes, like this is
a hard cost for my big business. Take it off
the bottom, take off the top line. Like, so, I

(41:03):
think we are getting close to that. But to your point,
there are a lot of people try to stop that
from happening because their jobs are going to go away.

Speaker 2 (41:10):
What do you think, Well, well, definitely, definitely.

Speaker 6 (41:15):
This is this is a point that I always said, right,
technology is a man to make our lives easier, right,
And and if you don't, if you don't reskill yourself,
or if you don't relearn something new, right, then you're
going to have no job period, right right, right right,
And and there's no no way that you can stop technology,

(41:37):
no way, right. Blocking will come AI will come up
to your job, uh and and they will come a
day where everything is going to be, you know, very
crackable because now not only that it's crackable, it's also.

Speaker 2 (41:50):
Immutable, right, so every record is being kept.

Speaker 6 (41:54):
So imagine a world where if everything is being tracked
on the blockchain, there's no money laundering, right exactly where
all the moneys are being found? Where where are the
transaction taking place where what time and and and everything
is trackable and that that day will come, and that
day will come. Now, of course there's going to be
a lot of resistance, right, just like you know how

(42:15):
people are resisting e V, how people once resisted the internet,
and and I mean it's it's it's common, right and
now uh, you know you've got people who's also resisting AI. Now, yeah,
it's going to come after your job, regard like it
or not, right, you know, they will come at a
time where many people are going to lose their job,

(42:38):
and and many jobs have already been replaced, especially in
the financial sector. Imagine all the analysis that that was
done by an analyst.

Speaker 2 (42:48):
AI can do that that's right, right, right.

Speaker 6 (42:52):
And and anything that that can can be written, you know,
the books done by copywriter journalism. Yeah, I can do
a much faster job. I remember I was just telling
my friends. You know, in good old days, I need
like at least you know, a day or two to
prepare a PowerPoint for presentation.

Speaker 2 (43:12):
Right these days, I can do like twenty in a day. Yeah.

Speaker 6 (43:16):
So if you if you know how to harness you know, technology,
it can really benefit you and increase your productive productivity
by by leaps and bound really. But at the same time,
if you if you if you sit and do nothing,
it will work against you.

Speaker 5 (43:32):
Yeah.

Speaker 2 (43:34):
It similarly like any other technology.

Speaker 6 (43:36):
I mean, And to me, I I don't have a
specifically love from any technology because to.

Speaker 2 (43:40):
Me, it's a tool.

Speaker 6 (43:41):
It's a tool to get humanity to the next level.
And you know, and not only that, it gets the
next level. So so the next level would mean how
how that technology and enhance our line and so that
we can go on and do things that we love
and leaving those mundane jobs right to the AI to
do it right, like how it aim takes over the

(44:03):
job of a judge, right of a low entry lawyer.
I mean, I think a lot of easy job AI
will do a much better job.

Speaker 5 (44:11):
Yeah, I think you're right about that. And it does
the tedious stuff. To your point, you don't No one
wants to do tedious work. No one wants to pour
over ten thousand documents to find the seventeen that are
missing a particular field. And that's something that AI and
automation can do in seconds, just scan through, Oh these
are the seventeen. You know, it's going to be very
good for pointing out what else needs to be done.

(44:35):
It'll find things. Now Again, these models, depending upon the models,
they are not perfect and they probably never will be perfect,
nor will human beings. Human beings aren't going to be perfect.
But I will say that at least in the old
days of database technology ACID transactions, as we talk about,
they were designed to deliver what is in that information
system or that system of record. So AI is not

(44:57):
going to take over everything, but it is going to
be an augmentation an analysis tool, and an automation tool
and a quality assurance tool. I mean, that's that's kind
of what some of these other folks are focused on now.
On the far end of an output from an LM
is some kind of a QA process. But folks don't

(45:18):
touch out to podcast ponus segment is coming up next
on Inside Analysis. All right, folks are back here on
Inside Analysis. You're truly Eric Kavanaugh with the legendary Alvin Fou.
We're talking all things AI and crypto and blockchain and
what's coming, what's happening. And I just shared with him
my thoughts on the death of journalism and my speech

(45:39):
at the Javit Center earlier this year where I ask people,
do you know where inflation came from? No one had
any idea. I showed them a chart where you see
the price of gas on average in America went from
under two dollars to over five dollars inside like a
year and four or five months, which is a shocking
spike in price. Well, of course that affects everything. Is

(45:59):
everything has to be shipped around, food products, whatever, is
a factor. But more than anything, it's monetary policy. And
when you just go and print money, which is what
you do with THEAC currency, this is print money. Every
time you print a dollar, the value of every other
dollar goes down a little teeny tiny bit. And if
you print freaking five to ten trillion dollars in a
short period of time, that's what's going to cause inflation.

(46:22):
And Alvin, what really pissed me off, to be honest,
was that the storyline here in America and the American
media back when this was happening was, oh, inflation is global.
They kept saying that inflation is global, implying that it's
not our fault. I'm like, well, you're right, it is global.
That's a true statement, but it doesn't nullify my statement,
which is that the printing of money caused the inflation.

(46:43):
So we did cause inflation, and that hit everybody in
the world. I was in Belgium in I guess it
was what it was like two and a half years
ago maybe, and the cab driver was telling us that
the elderly people in Belgium, many of them don't even
drive any they don't go anywhere. They're stuck in their
homes because they're unfixed income and they can't afford the

(47:04):
petrol to even go visit their child two hours away
by car. So this affected everybody. It's not a story
that the media talks about, which I find bizarre. I
think it's political, to be honest. But what do you
think about this, and what do you think about how
how does that map to blockchains meteoric rise, Because of
course blockchain is not fiat currency. It does have a

(47:27):
specific set amount of coins to be mined and as such,
and I believe it was designed as a hedge for
fiat currency. That's my theory. And if you look back
and see when the paper was written and when all
that stuff happened, well guess what it was like back
in two thousand and seven eight timeframe. So it was
either remarkably precient or at least coincidentally timed with this

(47:51):
collapse of our or near collapse of our financial system.
But what do you think about all that?

Speaker 6 (47:57):
I think the man, the person who wrote this right
certainly was impacted by the event, right uh and and
and because of this event that has impacted him, that
he lost trust in this in this centralized system. That
that pushed him right to rewrite the whole concept of bitcoin,

(48:21):
which is it lives, it lives.

Speaker 2 (48:23):
In the decentralies.

Speaker 6 (48:24):
Well right, Uh, it's defictionary with twenty one million bitcoin
that's going to be emitted, you know up until it's
still about maybe a hundred years from now, But every
four years it's going to half, right, So it's going
to be less and lesser and less, a big cooin
along the way. So that's why the concept of bitcoin

(48:44):
has always been a hedge over year, right, right, Because one,
on the one hand, you get you get the fats
who are constantly printing more money I think right now
is about a trillion dollars every hundred days, right, uh,
and and and you and you have bitcoin that's deflationary,

(49:05):
and there's going to be less and lesson bitcoin. And
that's why you have companies at MicroStrategy accumulating lots of bitcoin.

Speaker 2 (49:12):
And not only my strategy is doing that.

Speaker 6 (49:14):
There are lots of companies UH in Corporate America and
also now companies in Japan and Singapore and even China
right acquiring uh a bitcoin and and putting that on
the balance. Now, if you believe that, that's why it's heading,
you know, And and I don't think the money supply,

(49:35):
the money printing will stop ever. So I don't think
because because unless if they can game change their current
deficit massively, right, the current interest repayment alone is more
than a trillion dollars, right, is bigger than the entire
you know, the annual defense budget of the US. That's

(49:57):
just interest repayment. So unless I'm drastic, is changed on
the deficit, you're going to end up keep keeping you know,
you're you're going to end up printing more money.

Speaker 2 (50:07):
And I think bitcoin is the answer.

Speaker 6 (50:09):
Bit Bitcoin is a solution right to the FIAT prom
in in the US. And I think that's the that's
that's probably the only way that I can see. And
I think my I think Mike Sailor has coined up.
You know, I'm not going to go to Mike Casaida.
I think a lot of people must have read about him.
I think the idea behind it being a digital capital,
a digital goal as as as as a hedging and

(50:32):
at the same time, right for potentially using bitcoin that
to repay the the United States.

Speaker 2 (50:39):
You know that that's been there has been.

Speaker 6 (50:42):
Yeah, I think right now it's like thirty six strellion dollars.
And if you at the at the trajectory that bitcoin
is going in value at some point, I think, you know,
you you could probably use bitcoin right as a payback
to your debts. And that's that's potentially. That's that's that's
put that's quite potentially if you look at the jectory
of what's the value of bitcoin is going to be

(51:04):
in twenty one years from now where you know, Michael said,
the sale already even at base right, they have got
three model, the base model, the bowl case, and also
the bad case.

Speaker 2 (51:17):
Even for the bad cases about four million dollars.

Speaker 6 (51:20):
Right, the base case is like thirteen million dollars and
the bowl case is forty nine million dollars.

Speaker 2 (51:29):
So if you multiply the.

Speaker 6 (51:33):
Bitcoin reserve, that trumpet has been has been promoting, right,
you know, trying that the miss is also trying to
push through Congress.

Speaker 2 (51:40):
Right, that's going to be like what.

Speaker 6 (51:44):
One million of bitcoin in the next five years, So
trying to acquire two thousands of bitcoins annually.

Speaker 2 (51:50):
I mean that's doable, right.

Speaker 6 (51:52):
I Mean my thinking is that if America could print money,
why not print more money and buy more bitcoin? Right?

Speaker 5 (52:00):
That's crazy, right? So wild?

Speaker 2 (52:03):
Yeah?

Speaker 6 (52:03):
And I mean if I can print money out of Tina,
I would print more money out of Tina and then
buy more bitcoin.

Speaker 2 (52:10):
Right? Why would I? Right?

Speaker 6 (52:14):
Why why am I printing money and and then giving
giving to countries you know with lost costs?

Speaker 3 (52:19):
Right?

Speaker 2 (52:19):
I'm not going to name all those countries.

Speaker 6 (52:21):
I mean, I don't want to get too political, but
the fact is that if they can print more money,
they should print more money and buy bitcoin and a
bigcoin you know, price continues to increase. Right, then what's
what's the problem with printing more money?

Speaker 3 (52:37):
Yeah?

Speaker 2 (52:38):
Right?

Speaker 5 (52:38):
Well, and you know, speaking of money printing, and I'll
just kind of close on this. I saw this story
yesterday Javier Malay down in Argentina with his chainsaw and
all this crazy stuff.

Speaker 2 (52:48):
He was saying.

Speaker 5 (52:50):
He was going to cut government spending and bring down inflation.
And apparently Argentina now has a surplus. The government has
a surplus in their budget for the first time and
one and twenty three years, I mean, wowsers. So we
are very inn l Salvador too, I think the president
of El Salvador also invested heavily in bitcoin and of

(53:12):
course was ridiculed. Well, it's like, geez, he's looking pretty
good now, rightly, Christmas.

Speaker 2 (53:18):
This is a huge bel.

Speaker 6 (53:20):
Ye, I mean El Salvadore bitcoin holding is now at
least you know, six hundred million dollars over six hundred million.

Speaker 2 (53:27):
Dollars, right and yet debt crazy.

Speaker 6 (53:29):
Yeah, that that death is about eighty million dollars to IMS,
so she could easily pay back already. So so there's
not a non issue, I think at the end of
the day, right for corporate America, for America, I think
the way to solve this long term problem bitcoin reserve
is certainly one one solution, and al Salvado has proven

(53:54):
that and putting the right leaders around the country like
Argentina has also proven that.

Speaker 2 (53:59):
So so maybe Trump, as.

Speaker 6 (54:00):
Much as a lot of people may not agree or
may not like him, but The fact is, I think
if there's one person who can fix that problem, I
think I think Trump and his and his team which
included Elon Mass and I'll just right they they are
in a position to solve this problem.

Speaker 5 (54:18):
Yeah, I love it. What a great way to end
the show. Alvin Food. Look this gentleman up online LinkedIn.
He's got a huge following, something like four hundred thousand followers,
always sharing excellent fun content, very insightful, very incisive. Loved
Have you on the show. Look him up, folks, We'll
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Speaker 14 (59:11):
NBC News Radio. I'm Lisa Carton. A holiday season government
shutdown has been averted. The House passed a stopgap measure Friday,
with the Senate following suit, passing the bipartisan funding bill
by a vote of eighty five to eleven. Senate Majority
Leader Chuck Schumer praised the development.

Speaker 6 (59:27):
There will be no government shutdown right before Christmas. This
is a good bill.

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It'll keep the government open.

Speaker 14 (59:35):
This capta chaotic week that saw President Electromp and Elong
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President Biden signed the measure on Saturday. President Electromp says
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not stop overcharging the US for use of the key
Central American waterway. Trump called the fees being charged by
Panama ridiculous and highly unfair. Lisa Carton NBC News Radio