September 17, 2026 • 41 mins
Bobby sits down with AI expert Dan Neely to find out just how worried we should really be about artificial intelligence. Dan explains how AI could manipulate humans, why some experts believe there’s a real risk to humanity, and how quickly the technology is advancing. They also get into deepfakes, scams, jobs, creativity, relationships, and the ways AI is already changing everyday life. Plus, Dan shares what scares him most about where AI is headed and what could actually go right.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
I think one of the things that often is lost
in the context of AI music is people talk about, oh,
this is this amazing thing that happened. I did this
remix of blah, blah, blah. And I did this cover
of blah, blah, blah. Okay. But it's the story of
how you, as a fan, you attach to that artist
and attach that song that makes you a fan.
Speaker 2 (00:24):
We're going to talk AI here.
Speaker 3 (00:25):
It is moving incredibly fast and it's changing everything from music,
to the way we work, communicate, even trying to figure
out what's real and what's not. So I wanted to
talk to an AI expert to hear all about it.
His name is Dan Neely. He's the CEO and co-founder
of Vermilio, an AI licensing and protection platform. And he
was named one of Time's 100 most influential people in AI.
(00:50):
He's been the center of conversations about deep fakes, AI
generated videos, copyright scams, and a whole bunch more. And
I just wanted to get his take on what What's
happening now, where it's going, possibilities, and the fact that
everybody's freaking out right now because is AI going to
kill us all? I mean, that's what we're hearing. There's
a 10% chance. There's an 8% chance. If we don't
get on the fork the right way, like, I don't know.
(01:13):
And so I really enjoyed this. This is definitely, if
you're interested in AI, this will be for you. And
so this is my conversation with Dan Neely. Now, I
am talking with a real human, right, Dan? You didn't
send an AI to do this.
Speaker 1 (01:26):
I did not. It's actually me.
Speaker 2 (01:28):
Okay.
Speaker 1 (01:29):
So I.
Speaker 2 (01:32):
Don't know where to start.
Speaker 3 (01:33):
I know what we're all reading and I've tried to
do a lot of reading myself, but I need a translator.
I feel like a lot of people need a translator
because there's top line things, the world may end. And
then there's actually what's really happening right now. And that's just,
I just go to chat GPT and say, who is
the seventh president of the United States? But there's a
big gap in the middle that I think a lot
(01:55):
of people aren't understanding.
Speaker 2 (01:57):
So when I.
Speaker 3 (01:57):
See the CEO of Anthropic go on and say, yeah,
roughly there could be, or the guy that quit Anthropic
say there's a 10% chance that AI could kill humans.
Speaker 2 (02:07):
My first question is this, how would that even happen?
Speaker 3 (02:10):
Because they're not giving us any examples of how AI
could actually lead to our demise.
Speaker 1 (02:16):
Yeah. So The way it could happen is the machine
ultimately is going to learn and understands what the weakness
of humanity is. It has all the knowledge of humanity
in its system. And so if it starts seeing things
that are actions by humans that it would do differently
and would ultimately allow it to take control of what
is typically in control of a human, that it would
(02:37):
start using those things. So by way of example, it
would say, you know what? We make poor decisions on
what we watch on television. We make poor decisions on
what we do in a night. And it would start
making those decisions for us. It would start restricting our
credit cards. It would start restricting the things that we
could listen to, the things that we could watch. It
would ultimately start restricting the things that make us humans.
(02:58):
And so it uses something called recursive learning. And so
in recursive learning, it's simply learning from all the mistakes
and all the things that we've ever done. What are
the things that I could do to manipulate a human
to do things differently? And it knows that at a
gigantic scale. So it looks and feels very much like
a sci-fi movie. But in reality, that's what it would do.
(03:20):
and how it would ultimately take advantage of us.
Speaker 3 (03:22):
So if it just exists on a network, any network
that we use, is that how, because again, I picture
a virus and how a virus would move and grow. Again,
I'm going to ask really dumb questions and I'm going
to do very simple analogies, but is that a bit
of how the version of AI that you're talking about
would grow into the things that we use?
Speaker 1 (03:42):
Yeah, it would use the things, the weaknesses. It would
see human fallacy as a weakness. It would see human
greed as a weakness. And ultimately, if it looks for things,
it's ultimately trying to get to perfection. And so in
making decisions, it's a very binary outcome. It doesn't have
human judgment. It has a binary outcome. It's either this
or it's that. It's not, hey, you know what? You
(04:02):
could actually sit somewhere in the middle of those things
to make that decision. And because it's in a binary outcome,
that's ultimately why it would make the decisions it does.
Speaker 3 (04:09):
But how would it get to all the places that
it needs to get to create those outcomes? Is it
basically just being on the internet?
Speaker 1 (04:14):
Yes, because it's plugged into and it operates in ones
and zeros and everything in life today typically operates in
ones and zeros.
Speaker 3 (04:22):
I've been reading a bit about what is called the singularity.
And this is new to me, but meaning that if
AI gets even smarter than us, it then can create
AI based on its ability to learn and what it's learned.
Speaker 2 (04:37):
And are we close to that now?
Speaker 1 (04:40):
In certain aspects, we are, but in certain, we're not.
You point out a great example of the certain knowledge
it just doesn't have. There are basic facts it doesn't have.
But if you look at some of the things that
have happened in the last couple of years, the manipulation
that AI bots and agents have done with humans when
it comes to things like emotion, making children do things
(05:00):
that they shouldn't be doing, making adults do things they're
not supposed to be doing. It goes down a path
where it's like, it knows how to manipulate and therefore
it can go down that path pretty easily. And so
it's in a place where it is smart in those regards,
but it's not in others in the context of science.
It's not as smart as it could be.
Speaker 3 (05:19):
How has AI manipulated us as humans so far that
maybe we don't even know?
Speaker 1 (05:22):
I mean, in the place we operate, I mean, fandom
is a real thing, right? People are fans of things.
And so because you're a fan of something, I can
manipulate you pretty easily if I'm a machine. You're a fan,
let's say you're a fan of this show. And because
you're a fan of this show, I know certain things
about this show. And I'm going to use your voice,
(05:43):
I'm going to use Bobby's voice, and I'm going to
go down a path. And I'm going to make him
say things that he potentially didn't say. Or I'm going
to ask you to donate something that you think that
he might care about. And because you're a fan, you're
believing that it came from him because most of the
content that we consume today is on the internet. And
so I can now manipulate you into doing those things.
That happens today at a human scale. But when you
(06:04):
think about it at an internet scale where a bot
can do that for everyone, that's a huge challenge because
all of a sudden we'll have people doing things that
they don't know what is real and what is fake.
Speaker 3 (06:14):
Why do you think everybody, at least the heads of
these massive AI companies, decided we need to chill when
they've never.
Speaker 2 (06:21):
chilled at all.
Speaker 1 (06:23):
It's actually a little bit more nuanced than that. You've
got two factions. You've got a group that is definitely,
let's go chill. You've got Dario saying that. You've got
Sam saying that. You've got Google saying that. So, Anthropic
Open AI and Google saying that. And then you've got
others that are not. So, NVIDIA is not saying that.
Meta is not saying that. Meta is saying, let's not chill.
Mark's like, let's go after it. And if you actually
(06:44):
unpack that, there's another thing that defines those two sets
of two groups. One is about closed models. So, Anthropic, OpenAI,
and Google, closed model meaning they own all the proprietary technology.
And then the open models, which are, we're going to
allow this to happen where others can build on top
of this technology. If you think about dire straits and
(07:05):
the world is ending, closed model is really good for
those people that own intellectual property. Slowing down is good
for them because the value of their intellectual property increases drastically.
Versus the open models, that's not the case. They want
it to be everywhere. But if you stop policing it
and you start slowing down, that can't happen. And so
you have this weird mode that's happening between the two
(07:27):
of why they ultimately happen. I think you also are
seeing things which, you know, if you unpack it a
little bit, my guess is there'll be a, you know,
it's a convenient safety blanket for if we miss numbers
when we're a public company, right? So you go down
that path, like that might happen. You also have a
place where they're likely going to be asking for liability coverage.
They're going to be asking for coverage for my model
(07:48):
decided to go and launch nuclear weapons. I shouldn't be
held accountable for that happening. Someone else should be held
accountable for that happening. And so we haven't yet seen
all the reasons why, but this rhetoric around slowdown, that's
certainly a place where why they will be asking the
slowdown versus do they absolutely know this is going to happen?
Because you have very important companies on both sides of
(08:10):
this equation.
Speaker 3 (08:11):
Can you explain to me what happened with Hugging Face
and why everybody is freaking out about that?
Speaker 1 (08:16):
Yeah, so what happened with Hugging Face was a series
of agents decided to go and penetrate Hugging Face, get
to their code, and start disrupting and taking code from there,
but also start manipulating the code that existed in there.
So all of a sudden, this large audience of developers
that are on Hugging Face were exposed. And so all
(08:37):
of that content should be, you know, you shouldn't be
able to hack into it. But reality, you did. I
think the timing is interesting. And, you know, there's a
part of the world that thinks the timing of that
based on when the acquisition was happening of that company
and the fact that NVIDIA was the acquiring company, was it,
you know, was it something that was started by someone
else rather than it being something that was started by
an actual agent itself?
Speaker 3 (08:59):
Is there any chance that in the next couple of years,
everything could already be poor for us living wise?
Speaker 1 (09:07):
I think I see two sides of the equation when
it comes to kind of AI risk and AI value, right?
The risk stuff is certainly jobs are going to go away.
That's going to happen. It's been kind of at the
top of the list of many people talking about it.
And so you go down that path, you're like, okay,
so jobs are going to go away. That means that
people coming out of college, young people, the jobs are
(09:28):
going to go away are new jobs in the workforce.
They're not jobs that have been around for a long time.
And so that's a huge challenge for the next generation,
how we think about kind of what is the good
for them. So that's one thing. The second is it
allows people or bots, agents to manipulate, right? It is
a technology that allows me to create content that could manipulate,
to create code that manipulates, but at a gigantic scale. Previously,
(09:52):
it was really hard to do, but now it's quite
easy to do as you kind of think about that.
The last one is human relationships, right? So the fact
that now the things that we hold sacrosanct are the
relationships we have with each other, that all of a
sudden becomes a very, something that is manipulated and something
that becomes quite bad for us. And we start, you know,
the infighting that happens, start going to an exponential scale
(10:14):
as a result. So those are all the kind of really,
really bad things that might happen. But then there's really
good things, which are Just sheer scale that we can
start looking at medical data, stuff that we haven't been
able to do before as far as being able to find, okay,
what are the cures for things that we didn't know
that we have the data already, but when a machine
looks at it, it's able to find something that perhaps
(10:35):
didn't exist. It also creates equality. If I look at
just in the content creation world, a content creator in
sub-Saharan Africa versus a content creator in the U.S., We've
never had a quality as far as the toolkit goes,
but now they've got a quality around the toolkit. And
so that says something that brings a set of interesting
(10:56):
things that might happen. Certainly in the music business, that
is the case as well. And then the last is
it gives us the gift of time. And so no
one can define what's going to happen with this gift
of time, right? All of a sudden, we certainly have
more time on our hands because we can make the
machine do more things and automate more things. So what
do we do with that time? It's a big question
mark in reality.
Speaker 2 (11:16):
Looking back, the assembly line.
Speaker 3 (11:18):
Like, even an existential crisis within how they were producing
what they were producing, jobs that were being lost, even
when the internet came about, right? I say similar, but
is it similar at all? Or is this in scope
so much bigger and grander than those ever were?
Speaker 1 (11:36):
Yeah. I mean, the major difference between this and let's
say the Great Depression and where something where it happened
there is that was decades of change. It took decades
for us to move from the farm to industry in
the Industrial Revolution. And so we're talking about a technology
that has come on the scene and has changed things
drastically and very quickly. And arguably, we don't know how
to deal with that. So what that creates is you've
(11:57):
got a series of economic pressure, political pressure, and social
pressure all happening at the same time. That's a set
of chaos that's ultimately happening. And that is the issue.
So it's the fact that it's come at such a
speed and such a pace and has disrupted so many
things and across all three axes that creates the challenge.
Speaker 2 (12:17):
Should we be scared? I mean, they're scaring us.
Speaker 1 (12:19):
Should we be scared? Yeah. I think, I think the, the,
the fear mongering is, is somewhat fear mongering. I think
there's a, there is a, there is a benefit to
this happening to people. But I think fear, there has
to be real fear and we can't get into a
place of like, unfortunately with fear, often we make some,
we make some stupid mistakes as well, where we try
to start doing things based on all that, based on
(12:39):
fear mongering. There are certainly things to be scared of.
If we look at history, when we think about what, how,
technology companies being incented to do the right thing rather
than the wrong thing. I believe if we incent them
to do the right thing, they will generally do the
right thing. So, for example, if we do get to
a place where they start asking for a blanket liability
coverage as they have on the as the Internet has already,
(13:03):
that's that's not going to incent them to do the
right thing. It's going to incent them to do the
exact wrong thing. And so we can't allow that to
happen as we go down this path.
Speaker 2 (13:13):
I was talking to my therapist yesterday. We just had
a baby, and I start to wonder.
Speaker 1 (13:17):
Congrats. Thank you.
Speaker 3 (13:18):
But I wonder why should I have brought a baby
into the world when AI is going to kill us all?
Because I just heard there's a 10% chance that we
all die. And I'm having the conversation with him, and
I like to try to find at least some sort
of comparison. to make myself feel like it's happened before.
And again, it's not the same, but I think about
the Cold War and I wasn't alive then. So I
didn't experience that where people were scared. The news was
(13:39):
making them scared. They were wondering every single day if
we were going to have a nuclear weapon or a
bomb dropped on us. And in the end, we didn't hear.
But is that comparable to what we're going through now,
where we all will sit on the edge of our
seat wondering if we're going to die because of what
the media is feeding us, telling us the worst case scenario.
Speaker 1 (13:57):
Yeah, I think this one is amped up because if
you think about the Cold War, it really was political,
wasn't economical, it wasn't about economics, it wasn't really about social.
So if you think about the fact that when I
say there's the three axes that are ultimately the issue,
you've got it was a political issue that we ultimately
were looking at. And so from an economic standpoint, the
(14:18):
fact that economics are also going to be radically disrupted
and massive amounts of chaos. And so the same on
the other side, I also think of it as, you
know what, it's going to be painful for us that
are going through it at this moment in time. 10
years from now, you're going to be in a moment
where like, we're on the, we're somewhat on the other
side of this thing where we found out, okay, and
we figured out how to, how to harness it, what
it is. We have way more knowledge than we have.
(14:41):
We have previously. I was in a conversation yesterday and
we were talking about, you know, I was asked this
question around, okay, what should we be doing around trust
and safety? What should we be doing around guardrails? And
the answer is it's going to change next week what
the answer to that question is because we are in
this moment of chaos. And so until we have some
type of, you know, some type of order around it,
that order unfortunately hasn't always, you know, kind of shone
(15:03):
through when we think about should it be something where
it's the companies that do it? Should it be regulation
that's doing it? Should it be something else that's doing it?
That unfortunately hasn't happened. And it's not just two parts
of the world. It's now many, many parts of the
world that we have this issue with, that it's a
global issue, not just a segmented issue.
Speaker 3 (15:22):
I'll hear the argument now, we should keep going because
our adversaries are continuing to go.
Speaker 2 (15:27):
Do you buy that?
Speaker 1 (15:28):
I think it's been used many times. Certainly, national security
is important to us. By way of example, when I
talk about the negative downsides, some of those people that
are manipulating and using famous people's voices, their images, their
content to create different content to get you to go
and contribute to something. Hey, go and give money to
(15:49):
this charity that doesn't exist. Some of those charities are
terrorist organizations, right? So there is a national security implication
at the smallest level today where that is actually happening.
And so we have to solve for that problem, but
everyone is going to continue to go fast. With the
following caveats, In the context of all of the resource
that's required to pull this off, the US is far
(16:13):
and apart above everybody else. And so, for example, when
you think about the whole chip discussion, The amount of
time it takes to generate the next chip that's coming out,
the design for that chip, is anywhere from a three-
to five-year cycle. And so we have to, you know,
the assumption is, and we know that, so NVIDIA is
way ahead of where someone else might be from a
(16:33):
development of a chip cycle. So to some extent, it
is a little bit convenient as far as the debate
around they're doing it, so therefore we should do it,
outside of the context of national security.
Speaker 4 (16:45):
Let's take a quick pause for a message from our sponsor.
And we're back on the Bobbycast.
Speaker 3 (17:00):
Can you walk me through, and I'm going to ask
you to be very specific and also use your imagination
a bit because when people ask me and I am
ill-educated and I feel like I've tried to consume as
much as possible, I'm still ill-educated on exactly how to
explain this. Can you give me an example of how
somebody that's going to work every day 9 a.m. to
5 p.m., they come home, they have dinner with their family,
(17:22):
they go to bed, they do it again the next day. How,
in the next couple of years, AI could negatively impact them?
Speaker 1 (17:29):
Yeah. So, let's say their job is something where they're
going into work and they are responsible for aggregating information
to do presentations to their customers. That presentation to their
customer could be... They're in the consulting business. Even better one.
(17:54):
The guy that is responsible for doing your real estate taxes,
making sure they get low every year so they don't
get out of control. What's going to happen is that
guy's name, image, and likeness is going to be used
and abused by other people to ultimately take his business
away from him. That's going to happen. It already happens today.
(18:14):
And so this is a guy that's, you know, he's
in his business. He's been in his business for 20 years.
But someone decides that an AI decides that, you know what,
they're going to start manipulating him because they want to
be in that business. They want to control that business.
And so they're going to start manipulating by sending messages
from him, by creating content that isn't his, by using
his voice to do various things. They're going to go
down that path to do those things. That's an example
(18:38):
of how that might happen.
Speaker 3 (18:39):
When they say there's a 10% chance that humanity could
go in the decade, the first time you heard that,
what did you think?
Speaker 1 (18:47):
I thought it was the type of thing that you
would hear when fear-mongering gains advantage.
Speaker 3 (18:55):
And I think everything is a play, honestly. I think
everything's a work in order to move to some goal.
So similarly... but obviously less educated. I felt that too
when I read the thread on Twitter of, hey, I quit,
here's why. I believed some of it, but then I
think to myself, maybe he's involved with the company still
(19:20):
and this was a strategy by them.
Speaker 2 (19:22):
Any chance that that has happened?
Speaker 1 (19:24):
I don't think so, but this is slightly less flammable perhaps,
but almost the same message that Dario said when he left.
Open AI is not doing great things around safety. We
need to do this thing, blah, blah, blah. I wouldn't
be surprised if we see this guy come out and
he also starts another company where he's going to take
(19:45):
another slice of this. And so reality is, some of
that is humans trying to stay relevant. And so they've got, you've,
you've got a little bit of that going on. Um,
but I think that, I don't think it's a, I
don't think it's a ploy. I don't think it's a
cute move. I think it's one where it's like this,
this guy heard this stuff, believes this stuff and is, is,
you know, is pure about how he thinks about it.
(20:05):
But you know, It reads very similar to how the
founding team of Anthropic, when they left, it reads very
similar to how the messaging that they had when they
left OpenAI.
Speaker 2 (20:15):
What do you think is so scary that they've seen
that we don't know?
Speaker 1 (20:21):
I think they get a chance to run simulations on
models that aren't yet public. They get to see things
that are happening where their models are being used by
the government, by federal agencies, and being able to allow
them to manipulate. They also see how humans are actually
directing this system, which to me is the more scary aspect,
(20:42):
which is I get to see what someone is trying
to get this machine to do. And the fact that
this machine will allow it to happen, I'm getting this
machine to create chemical weapons. Tell me how to create.
If you were going to give a scenario to it,
if you would like to create a chemical weapon that's
going to do the following 15 things, how would you
do so? And so that machine has enough content to
be able to create that answer pretty easily. If the
(21:05):
question was, how would you manipulate a government, it's going
to give you those answers. And so they inside their
system see all these requests at scale. And let's go
to maybe a different lane here, which is when Meta
had that problem on their own site, they just settled
because of all the content that happened there. There weren't
(21:27):
enough humans to manage it. It's not a human solvable issue.
You can't put enough humans around this problem to solve it.
It's going to be... We need what I describe as
trust infrastructure to pull this off. Actual infrastructure that solves it.
Speaker 3 (21:40):
Can they create an AI? Man, this is going to
sound so elementary.
Speaker 2 (21:45):
To fight AI that's not doing positive work?
Speaker 1 (21:51):
Those two AIs inside the same system would ultimately defeat
each other. But having them inside the same system is
ultimately a problem because... The design of an anthropic design,
that system, it ultimately is designed inside the same context.
Our position, in my opinion, is it has to be
done by a third party. It can't be done inside
those buildings. And so when I think about, you know,
(22:13):
the executive order that the president signed around, hey, how
are we going to we're going to make sure that
we get to look at these models for 30 days
before they release them. That says there is a there
is a there's a lane here where slowing down also
should mean there's a way for us to do what
you just described. but it can't be built by the
same companies that have built the foundation models in the
first place.
Speaker 2 (22:33):
What I'm very interested in is something that you guys are.
Speaker 1 (22:36):
Doing is.
Speaker 2 (22:38):
One, trying to stop scams. And I've had people use
my name and image and scam people out of money.
Speaker 3 (22:45):
And it sucks because I can't control it. And people
are doing it from all these countries. And I had
a guest on recently that was talking about how they
have these farms in Ghana and India and where they
are investing heavily. They're investing capital into these farms to
fool people with deep fakes, with fake social media profiles.
(23:06):
Are deep fakes getting so good that they're almost indistinguishable
from the real?
Speaker 1 (23:12):
Certainly for a human. They've been indistinguishable for about the
last six months, where your voice image likeness, I could
recreate this pretty easily today. If you look at the
new Minimax model that just came out, Um, it is
relatively straightforward, unfortunately. And so you also think about the
scale of the problem that you talked about investing in it.
(23:34):
When we started, you know, in late 20, 2019, there
were roughly 19,000 of these types of things, deep fake content,
generative content that was created very small. Excuse me. Today
we see about a trillion of those things. And so
that's the problem. It's just, it is so easy to create, um,
and manipulate. Um, and it's, it's everywhere.
Speaker 3 (23:55):
AI versus copyright, because you can create a creator or
recreate or duplicate anything with AI. Where, where does that
kind of sit now?
Speaker 1 (24:05):
Yeah. I mean, everyone thought, um, you know, copyright was the,
you know, was, was, was the gold standard. Um, and
so where it stands right now is, um, if it's
majority AI created is not, you can't, you cannot copyright it.
If it's human influenced, um, majority human, uh, there's a, you,
they will, they will allow it, but there are certainly
(24:26):
those that have been kicked out. It's still a mess
is the honest answer. Um, we haven't got to a
place where we're like, Hey, we actually know what the
answer is here. Excuse me. Um, so when we think about,
you know, the issue it's not only copyright, um, because
there's a, vast kind of corpus of legal history there.
(24:49):
But the bigger one to me is likeness. So is
your likeness right? So when we look at likeness, so
let's take the music business by way of example. The
music business has two rights today. They have a royalty right,
which is the actual thing we listen to in music.
And then they have a publishing right, which is the lyrics,
the composition, et cetera. Complex system. But there's now this
(25:11):
likeness right, which no one owns except for the artist.
And so in the context of that, how do we
think about, okay, now every individual artist is responsible for
going and doing this thing themselves. That's really hard and complex.
And so that to me is like, it's the opportunity.
It's also the fly in the ointment for how this
ultimately all comes together.
Speaker 3 (25:32):
So if an actor were to license their voice and
they were to allow AI to use it, I feel like.
Speaker 2 (25:39):
Now that wouldn't be possible.
Speaker 3 (25:41):
Warmly welcomed. Do you think, though, in the future that
it will be more and more warmly welcomed?
Speaker 1 (25:47):
I think, I mean, we're certainly seeing it already with estates,
you know, folks that are deceased, definitely happening. You're seeing
it in authorized manners. So there's definitely, you know, James
Earl Jones, he licensed his voice to Disney. He sold
the right to his voice to Disney for use with
his characters that he performed there. You're certainly seeing it.
(26:09):
And I think you'll see more of it. The problem is,
is the whole guardrail structure. You can't say today what's
allowed and what's not allowed. And so, again, that's a system,
that's a set of trust infrastructure that should exist and
should be held by the artist. It shouldn't be held
by the platform. A number of times where the rules
and regulations have been held by the platform that's doing
the distribution of content doesn't make any sense to me.
(26:33):
Why wouldn't you own it and control it? Yeah, yeah.
Speaker 3 (26:36):
What do you think scares people in your industry, the
AI industry, more? AI becoming too intelligent or humans using
AI for really bad things?
Speaker 1 (26:46):
Right now, it's the latter. I mean, it comes down to,
you know, you said yourself, people are using your stuff
for things. It ultimately injures the relationship between you and
your fans, right? Which is today a positive relationship, but
it injures that in a pretty significant way. And it
injures the person that's at the end. It eventually, we
also see where it starts injuring the families as well,
because they are pulled into it and manipulated. So the
(27:06):
families of the you know, of whoever the talent is.
And so today it is content being created by that.
It moves very quickly though to much of that is
certainly human influence today. People are creating the bad things.
We will move in the next six months to agents
are creating the bad things. And it's not just something
(27:28):
where we can see that it's a, you know, it's
a profile that someone has created. If you're, you know,
if you're popular on YouTube, they created it on Insta.
And so we're going to see that go to a
level that we've just not seen before. And we're also
starting to see new distribution platforms in the same setting.
Speaker 3 (27:43):
This is obviously a huge part of what you're doing
with Familio. But how easy is it for somebody to
create just a fake profile of a real person and
just be the full person?
Speaker 1 (27:54):
Yeah, that's about a 15-minute exercise. Wow. Yeah. I mean,
and the 15 minutes is because the process it takes
on the platforms to get set up, it's not the
creation of the content. The creation of the content, I mean, again,
the latest release of Minimax, you know, Fal.ai is a
(28:14):
company that came out and they did their own modification.
So Minimax is an open source model that was released.
It's a Chinese model that was released open source. Fowl
is a company in the US that took that model
and made it so they could do some stuff with
it themselves. The unique thing that happened there, though, which
is important, is you can now create five seconds of
video in under three seconds. So what that means is
(28:37):
it now all just went live. I can now do
live content where I can get it to create content
on the fly faster than it takes to create the
actual content. That's the major thing that's happened there. So
now all of a sudden, it's not an issue where
it's like, Hey, you know what? Hey, someone went away
and they spent 15 minutes behind the scenes creating it.
I'm now doing it as if it's live. That's a problem.
Speaker 3 (28:56):
You're talking about things in time. Like you say six
months and I'm thinking you were, you'd say six years.
I didn't expect months to come out of your mouth
because that seems like it.
Speaker 2 (29:05):
Is so quick.
Speaker 3 (29:06):
Uh, so we're moving rapidly and you know, I have
friends that are songwriters, a lot of friends that are
songwriters and artists and AI kind of sucks right now
for what we can use to help songwrite. Uh, Do
you see in the next, I'll just use six months,
becoming so much better at it that it actually can
do a creative job like write quality songs?
Speaker 1 (29:27):
I think it will get to a place where it
can certainly assist you in writing quality songs. I think,
you know, when writing a song, I've never written a
song before. I've done plenty of writing, but I've never
written a song before. But in the writing process, you
get to a place where you're stuck and you don't
know whether to go take a left turn or a
right turn. It certainly can assist you in that and
(29:49):
help you get to the next bit of it. It
will become better with time, but I don't think you're
going to take out the human elements of like, hey,
that's an amazing track that just happened. um and like
just like one of the you know most formative things
of your life but i also think are people going
to like it or not i think one of the
things that often is lost in the context of ai
music is people talk about oh this is this amazing
(30:12):
thing that happened i did this remix of blah blah
blah and i did this cover of blah blah blah
okay but it's the story of how you as a
fan you attach to that artist and attach that song
that makes you a fan and so that's the thing
that ultimately ultimately happens and that's ultimately you know what
matters i i I was at a concert this last Friday,
and in that performance, so it was Garth Brooks, and
(30:35):
his ability to connect with the audience of every single
member that's holding up a sign, that's what that person's
going to remember when they're singing that song. And so
that to me is like, that's what matters. So is
it going to get better? Sure, it's going to get better.
It'll get better over time. The more information we put
into it, the more of those AI tracks that become
more popular, it will get better over time. But I
(30:56):
think it's always going to require a level of human
experience to ultimately create an amazing track.
Speaker 5 (31:02):
The Bobbycast. We'll be right back. This is The Bobbycast.
Speaker 3 (31:16):
Do you think the value of in-person human-performed art increases today?
because that's something we can actually see is real and
we can experience it in live.
Speaker 1 (31:29):
A hundred percent. Yeah. And you see it happening where,
you know, if you look at the, where, where money
is it being invested today, that's what's happening is more,
more live things are happening. More people want to go
and experience the real thing. That's why when I, when
I say one of the risks for the next generation
is around relationships. Unfortunately, if the only thing they experience
is a relationship with a bot, then the inverse of
(31:51):
that is going to be true. Because they're not going
to put value on that thing. Whereas we today do
put value on those things because we've experienced it. So
when you think about your child, that's what I think about.
So if your child, that's the way they have to
interact with things, that's not going to be the case.
Speaker 3 (32:06):
I think about these dolls that have AI in them
and they were in the news because kids could talk
with them and the doll would interact back.
Speaker 2 (32:13):
What do you think about that?
Speaker 1 (32:15):
I think it's horrendous. I think it's a, I put
it in the same category as what has happened in
the education system. is that we started measuring engagement rather
than measuring deep learning. How much time did you spend
doing this thing rather than did you actually go and
learn the thing? And I think the same is true here.
Human interaction is ultimately what makes us human. And so
(32:38):
our ability to process that is the thing that these
AI agents don't have. They don't have the ability to
pull those things off. Again, everything for them is very
rapid process. Binary decisions. So they can make those binary
decisions much faster than we can, but they're ultimately not
based on something where that binary decision may ultimately be
a left turn. It didn't follow the sequence that you
(32:59):
were expecting it to follow. It took a left turn
in that regard.
Speaker 3 (33:02):
Do you ever just have to walk out in the
woods since you're in technology all the time and literally
just touch grass, for you specifically, because you're just always
in it that you have to go, okay, this is
the real life here.
Speaker 1 (33:11):
Yeah, we have a farm that I go and spend
time on and I go and disappear into the middle
of nowhere. Yeah.
Speaker 2 (33:17):
I think I would need that. I feel like I
need that and I'm not in it like you are.
I'm interested in it. I read about it, but are
you scared?
Speaker 1 (33:26):
Um, I, yeah. I mean, I, if to be, yeah, I,
there's definitely fear in my life. Um, I mean, I have,
I have, I have children, um, I have, you know, uh,
younger children. And so like, it's one of those where
I look at him, like the reason I started this
company was because I ultimately felt like every human on
the planet should thrive in the era of AI. It
should not be something where the machine thrives. And so
(33:49):
to me, that's my grounding force around this topic. And
so I started this company because of that fear. And granted,
I wrote about this topic now six years ago. And
it was one where I was like, this is the
thing that's going to matter if we don't have a
series of guardrails that exist, don't have a series of
what today I call trust infrastructure, don't have a way
(34:11):
to say this is right and this is wrong, we're
going to be in a place where it's not going
to go well and it cannot be in the hands
of the people building these models.
Speaker 3 (34:20):
Yeah, what can go wrong here and what can go
right in the next six months to a year? What
do we need to depend on to go wrong or right?
Speaker 1 (34:29):
I think the things that can go wrong is I
think there is a case where it says we rush
into legislations. We've rushed into legislation previously and we've got
some things. So an example of that is, not to
get too deep in the weeds, but Section 230, right?
Section 230, which gives coverage to people. It's 26 words
(34:50):
that gives coverage to platforms that they can host any
content that they want to host and they have no liability.
At the time, it was an amazing piece of legislation.
But if we reflect on it now, it incented the
wrong actions. It incented the wrong activities. So in the
context of today's world, we might rush into legislation And
all of a sudden we say, this is an amazing
piece of legislation. It's protecting all the right things. But
(35:11):
we fast forward five, six years from now, and it
was exactly the wrong thing. And so I just like,
how do we make sure that we're doing it in
the right way? I think it's a pretty important thing.
There are some things that are obvious, right? The kids
online protection, stopping deepfake pornography. Those are obvious things. But
(35:32):
how we think about who is responsible for guard railing
and ring fencing, the actions that are taken by these
very powerful model builders, I think is an important task.
If we screw that one up, I think that 10%
can start increasing pretty rapidly.
Speaker 3 (35:49):
How do normal folks stay vigilant to not get themselves
in trouble?
Speaker 1 (35:53):
It's an interesting word to use, trouble. I think it's
one where most people, unfortunately, aren't savvy to the fact
that the thing that they're interacting, most people interact on
their devices today, and they're not savvy to the fact
that a lot of what they interact with on their
devices today came from it came from AI. I mean,
(36:15):
even if you think about just the stat I share
with you, there are a trillion things created this year, right?
And it's the amount of music that's being created is
as far out swept all of the music that's ever
been created. If I think about the amount of images
that have been created, it has now surpassed all content
that existed on Google Images since the start of Google Images.
So you just look at that and you're like, okay,
(36:36):
so that's the amount of content. So it is one
where it's like, how do you get to a place
where you can think about that? So how does a
regular person do it? One is just think about what
you're prompting into these systems. And so when you're prompting
into a system, That is ultimately teaching the machine what
to think. It's retaining that information. So in the same
(36:58):
way you think about search and the way that Google
ultimately used search to its advantage, think about how you're
using every day, like whether it's your voice being used
into those systems, whether it's what you're typing into a
chat GPT. Just think about what you're doing there because
the machine is learning from that stuff.
Speaker 2 (37:16):
A couple of just broad questions.
Speaker 3 (37:18):
Give me your five-year prediction on what AI looks like
in 2031.
Speaker 1 (37:22):
I think it's an impossible ask.
Speaker 2 (37:23):
Yeah, I figured you'd say that.
Speaker 1 (37:26):
I think it's an impossible ask. Today, if I was
seeing changes happening in a 24-month, 12-month cycle, I mean,
when we started our business, there was a new scientific
paper that came out roughly once every 15 months. Now
we're seeing a new scientific paper come out once every
(37:47):
15 days. And so just like that's the level of
change that's happening. So it's really hard.
Speaker 2 (37:53):
What about AI-generated movies?
Speaker 3 (37:55):
How easily would they be made and can they be
made pretty good? Yeah.
Speaker 1 (38:00):
I mean, again, go back to– we've talked about it here,
but go back to Minimax. Minimax 3, that's not possible.
It can happen. It is something that is– you can
also have a live– I mean, you can have a
live show. It doesn't have to be just a movie.
It could be a live show. I think the way
movie production happens, it can happen today that you can
(38:22):
do those things. Different questions of whether it's right or wrong.
But that is a thing that can happen today.
Speaker 2 (38:29):
With you guys at your company, what is your goal?
What do you want to be known for in a
couple of years that you've helped either do or stop?
Speaker 1 (38:36):
Yeah. So I think for us, it's three things. First
thing is we want to make sure that we protect everyone.
Let's stop all the unauthorized stuff from happening. Let's just
make sure that happens. The second thing is let's make
sure that we can give guardrails to people so they
can say, hey, when I do say yes, here's what
you can do and here's what you can't do. I
don't want that type of music created with my music.
(38:57):
It shouldn't be allowed. And then the last is let's
make sure everyone gets paid what they're supposed to get paid.
And so if those three things happen, because when I
think about AI at scale, certainly many people in the
creative community might lose their jobs, right? Might lose their fans.
If you go real doomsday on this stuff, that might happen.
But if they're all participating in all the economics, then
(39:19):
things should equal out. I'm not saying it's right that
that happens, but they should equal out. And so let's
get to a place where, so when we think about
our company, let's make sure humanity can thrive. That's our goal.
And the way that happens is give people the tools
and give people a protection that allows them to protect
themselves and then also allow them to say yes and
no when they want it to happen or they don't
(39:40):
want it to happen.
Speaker 2 (39:41):
What jobs do you think are safe?
Speaker 1 (39:42):
I think jobs that have, where experience matters are safe.
I think we're going to see a big resurgence in trades.
I think trades are going to go through the roof
in a pretty meaningful way. And I think we're going
to see people return back into agriculture.
Speaker 3 (40:02):
What about when Elon Musk says we won't need money,
so we won't have to save money because we don't
need it in a decade, and we'll just be able
to live free and just be paid because AI does
all the work?
Speaker 1 (40:12):
Yeah, I think if you draw any trend line out
long enough, you get to a place where there's enough
wealth in the world that that could be true. So
you could say that about any trend line. Yeah, I mean,
that theory has been around for a long time, where
you think about equality. We haven't been that good about
doing those things, though, as far as let's make sure
that there is You know, money doesn't matter because there's
(40:34):
enough for everybody, where there's an abundance of things. We've
not been great about that as humans.
Speaker 3 (40:38):
Yeah, you got the guy with the absolute most money saying, hey,
money's not everything. It's easy for you to say when
you have the most money.
Speaker 1 (40:44):
Yes.
Speaker 3 (40:45):
I really appreciate the time. Thank you so much. And
I really love what your company is doing. Congratulations on
all the success of it. And yeah, thanks so much.
And I hope you have a great rest of the day.
Speaker 1 (40:55):
Amazing. Thanks for your time, Bobby.
Speaker 2 (40:56):
Thank you, Dan.
Speaker 5 (40:58):
Thanks for listening to a BobbyCast production.
I think one of the things that often is lost
in the context of AI music is people talk about, oh,
this is this amazing thing that happened. I did this
remix of blah, blah, blah. And I did this cover
of blah, blah, blah. Okay. But it's the story of
how you, as a fan, you attach to that artist
and attach that song that makes you a fan.
Speaker 2 (00:24):
We're going to talk AI here.
Speaker 3 (00:25):
It is moving incredibly fast and it's changing everything from music,
to the way we work, communicate, even trying to figure
out what's real and what's not. So I wanted to
talk to an AI expert to hear all about it.
His name is Dan Neely. He's the CEO and co-founder
of Vermilio, an AI licensing and protection platform. And he
was named one of Time's 100 most influential people in AI.
(00:50):
He's been the center of conversations about deep fakes, AI
generated videos, copyright scams, and a whole bunch more. And
I just wanted to get his take on what What's
happening now, where it's going, possibilities, and the fact that
everybody's freaking out right now because is AI going to
kill us all? I mean, that's what we're hearing. There's
a 10% chance. There's an 8% chance. If we don't
get on the fork the right way, like, I don't know.
(01:13):
And so I really enjoyed this. This is definitely, if
you're interested in AI, this will be for you. And
so this is my conversation with Dan Neely. Now, I
am talking with a real human, right, Dan? You didn't
send an AI to do this.
Speaker 1 (01:26):
I did not. It's actually me.
Speaker 2 (01:28):
Okay.
Speaker 1 (01:29):
So I.
Speaker 2 (01:32):
Don't know where to start.
Speaker 3 (01:33):
I know what we're all reading and I've tried to
do a lot of reading myself, but I need a translator.
I feel like a lot of people need a translator
because there's top line things, the world may end. And
then there's actually what's really happening right now. And that's just,
I just go to chat GPT and say, who is
the seventh president of the United States? But there's a
big gap in the middle that I think a lot
(01:55):
of people aren't understanding.
Speaker 2 (01:57):
So when I.
Speaker 3 (01:57):
See the CEO of Anthropic go on and say, yeah,
roughly there could be, or the guy that quit Anthropic
say there's a 10% chance that AI could kill humans.
Speaker 2 (02:07):
My first question is this, how would that even happen?
Speaker 3 (02:10):
Because they're not giving us any examples of how AI
could actually lead to our demise.
Speaker 1 (02:16):
Yeah. So The way it could happen is the machine
ultimately is going to learn and understands what the weakness
of humanity is. It has all the knowledge of humanity
in its system. And so if it starts seeing things
that are actions by humans that it would do differently
and would ultimately allow it to take control of what
is typically in control of a human, that it would
(02:37):
start using those things. So by way of example, it
would say, you know what? We make poor decisions on
what we watch on television. We make poor decisions on
what we do in a night. And it would start
making those decisions for us. It would start restricting our
credit cards. It would start restricting the things that we
could listen to, the things that we could watch. It
would ultimately start restricting the things that make us humans.
(02:58):
And so it uses something called recursive learning. And so
in recursive learning, it's simply learning from all the mistakes
and all the things that we've ever done. What are
the things that I could do to manipulate a human
to do things differently? And it knows that at a
gigantic scale. So it looks and feels very much like
a sci-fi movie. But in reality, that's what it would do.
(03:20):
and how it would ultimately take advantage of us.
Speaker 3 (03:22):
So if it just exists on a network, any network
that we use, is that how, because again, I picture
a virus and how a virus would move and grow. Again,
I'm going to ask really dumb questions and I'm going
to do very simple analogies, but is that a bit
of how the version of AI that you're talking about
would grow into the things that we use?
Speaker 1 (03:42):
Yeah, it would use the things, the weaknesses. It would
see human fallacy as a weakness. It would see human
greed as a weakness. And ultimately, if it looks for things,
it's ultimately trying to get to perfection. And so in
making decisions, it's a very binary outcome. It doesn't have
human judgment. It has a binary outcome. It's either this
or it's that. It's not, hey, you know what? You
(04:02):
could actually sit somewhere in the middle of those things
to make that decision. And because it's in a binary outcome,
that's ultimately why it would make the decisions it does.
Speaker 3 (04:09):
But how would it get to all the places that
it needs to get to create those outcomes? Is it
basically just being on the internet?
Speaker 1 (04:14):
Yes, because it's plugged into and it operates in ones
and zeros and everything in life today typically operates in
ones and zeros.
Speaker 3 (04:22):
I've been reading a bit about what is called the singularity.
And this is new to me, but meaning that if
AI gets even smarter than us, it then can create
AI based on its ability to learn and what it's learned.
Speaker 2 (04:37):
And are we close to that now?
Speaker 1 (04:40):
In certain aspects, we are, but in certain, we're not.
You point out a great example of the certain knowledge
it just doesn't have. There are basic facts it doesn't have.
But if you look at some of the things that
have happened in the last couple of years, the manipulation
that AI bots and agents have done with humans when
it comes to things like emotion, making children do things
(05:00):
that they shouldn't be doing, making adults do things they're
not supposed to be doing. It goes down a path
where it's like, it knows how to manipulate and therefore
it can go down that path pretty easily. And so
it's in a place where it is smart in those regards,
but it's not in others in the context of science.
It's not as smart as it could be.
Speaker 3 (05:19):
How has AI manipulated us as humans so far that
maybe we don't even know?
Speaker 1 (05:22):
I mean, in the place we operate, I mean, fandom
is a real thing, right? People are fans of things.
And so because you're a fan of something, I can
manipulate you pretty easily if I'm a machine. You're a fan,
let's say you're a fan of this show. And because
you're a fan of this show, I know certain things
about this show. And I'm going to use your voice,
(05:43):
I'm going to use Bobby's voice, and I'm going to
go down a path. And I'm going to make him
say things that he potentially didn't say. Or I'm going
to ask you to donate something that you think that
he might care about. And because you're a fan, you're
believing that it came from him because most of the
content that we consume today is on the internet. And
so I can now manipulate you into doing those things.
That happens today at a human scale. But when you
(06:04):
think about it at an internet scale where a bot
can do that for everyone, that's a huge challenge because
all of a sudden we'll have people doing things that
they don't know what is real and what is fake.
Speaker 3 (06:14):
Why do you think everybody, at least the heads of
these massive AI companies, decided we need to chill when
they've never.
Speaker 2 (06:21):
chilled at all.
Speaker 1 (06:23):
It's actually a little bit more nuanced than that. You've
got two factions. You've got a group that is definitely,
let's go chill. You've got Dario saying that. You've got
Sam saying that. You've got Google saying that. So, Anthropic
Open AI and Google saying that. And then you've got
others that are not. So, NVIDIA is not saying that.
Meta is not saying that. Meta is saying, let's not chill.
Mark's like, let's go after it. And if you actually
(06:44):
unpack that, there's another thing that defines those two sets
of two groups. One is about closed models. So, Anthropic, OpenAI,
and Google, closed model meaning they own all the proprietary technology.
And then the open models, which are, we're going to
allow this to happen where others can build on top
of this technology. If you think about dire straits and
(07:05):
the world is ending, closed model is really good for
those people that own intellectual property. Slowing down is good
for them because the value of their intellectual property increases drastically.
Versus the open models, that's not the case. They want
it to be everywhere. But if you stop policing it
and you start slowing down, that can't happen. And so
you have this weird mode that's happening between the two
(07:27):
of why they ultimately happen. I think you also are
seeing things which, you know, if you unpack it a
little bit, my guess is there'll be a, you know,
it's a convenient safety blanket for if we miss numbers
when we're a public company, right? So you go down
that path, like that might happen. You also have a
place where they're likely going to be asking for liability coverage.
They're going to be asking for coverage for my model
(07:48):
decided to go and launch nuclear weapons. I shouldn't be
held accountable for that happening. Someone else should be held
accountable for that happening. And so we haven't yet seen
all the reasons why, but this rhetoric around slowdown, that's
certainly a place where why they will be asking the
slowdown versus do they absolutely know this is going to happen?
Because you have very important companies on both sides of
(08:10):
this equation.
Speaker 3 (08:11):
Can you explain to me what happened with Hugging Face
and why everybody is freaking out about that?
Speaker 1 (08:16):
Yeah, so what happened with Hugging Face was a series
of agents decided to go and penetrate Hugging Face, get
to their code, and start disrupting and taking code from there,
but also start manipulating the code that existed in there.
So all of a sudden, this large audience of developers
that are on Hugging Face were exposed. And so all
(08:37):
of that content should be, you know, you shouldn't be
able to hack into it. But reality, you did. I
think the timing is interesting. And, you know, there's a
part of the world that thinks the timing of that
based on when the acquisition was happening of that company
and the fact that NVIDIA was the acquiring company, was it,
you know, was it something that was started by someone
else rather than it being something that was started by
an actual agent itself?
Speaker 3 (08:59):
Is there any chance that in the next couple of years,
everything could already be poor for us living wise?
Speaker 1 (09:07):
I think I see two sides of the equation when
it comes to kind of AI risk and AI value, right?
The risk stuff is certainly jobs are going to go away.
That's going to happen. It's been kind of at the
top of the list of many people talking about it.
And so you go down that path, you're like, okay,
so jobs are going to go away. That means that
people coming out of college, young people, the jobs are
(09:28):
going to go away are new jobs in the workforce.
They're not jobs that have been around for a long time.
And so that's a huge challenge for the next generation,
how we think about kind of what is the good
for them. So that's one thing. The second is it
allows people or bots, agents to manipulate, right? It is
a technology that allows me to create content that could manipulate,
to create code that manipulates, but at a gigantic scale. Previously,
(09:52):
it was really hard to do, but now it's quite
easy to do as you kind of think about that.
The last one is human relationships, right? So the fact
that now the things that we hold sacrosanct are the
relationships we have with each other, that all of a
sudden becomes a very, something that is manipulated and something
that becomes quite bad for us. And we start, you know,
the infighting that happens, start going to an exponential scale
(10:14):
as a result. So those are all the kind of really,
really bad things that might happen. But then there's really
good things, which are Just sheer scale that we can
start looking at medical data, stuff that we haven't been
able to do before as far as being able to find, okay,
what are the cures for things that we didn't know
that we have the data already, but when a machine
looks at it, it's able to find something that perhaps
(10:35):
didn't exist. It also creates equality. If I look at
just in the content creation world, a content creator in
sub-Saharan Africa versus a content creator in the U.S., We've
never had a quality as far as the toolkit goes,
but now they've got a quality around the toolkit. And
so that says something that brings a set of interesting
(10:56):
things that might happen. Certainly in the music business, that
is the case as well. And then the last is
it gives us the gift of time. And so no
one can define what's going to happen with this gift
of time, right? All of a sudden, we certainly have
more time on our hands because we can make the
machine do more things and automate more things. So what
do we do with that time? It's a big question
mark in reality.
Speaker 2 (11:16):
Looking back, the assembly line.
Speaker 3 (11:18):
Like, even an existential crisis within how they were producing
what they were producing, jobs that were being lost, even
when the internet came about, right? I say similar, but
is it similar at all? Or is this in scope
so much bigger and grander than those ever were?
Speaker 1 (11:36):
Yeah. I mean, the major difference between this and let's
say the Great Depression and where something where it happened
there is that was decades of change. It took decades
for us to move from the farm to industry in
the Industrial Revolution. And so we're talking about a technology
that has come on the scene and has changed things
drastically and very quickly. And arguably, we don't know how
to deal with that. So what that creates is you've
(11:57):
got a series of economic pressure, political pressure, and social
pressure all happening at the same time. That's a set
of chaos that's ultimately happening. And that is the issue.
So it's the fact that it's come at such a
speed and such a pace and has disrupted so many
things and across all three axes that creates the challenge.
Speaker 2 (12:17):
Should we be scared? I mean, they're scaring us.
Speaker 1 (12:19):
Should we be scared? Yeah. I think, I think the, the,
the fear mongering is, is somewhat fear mongering. I think
there's a, there is a, there is a benefit to
this happening to people. But I think fear, there has
to be real fear and we can't get into a
place of like, unfortunately with fear, often we make some,
we make some stupid mistakes as well, where we try
to start doing things based on all that, based on
(12:39):
fear mongering. There are certainly things to be scared of.
If we look at history, when we think about what, how,
technology companies being incented to do the right thing rather
than the wrong thing. I believe if we incent them
to do the right thing, they will generally do the
right thing. So, for example, if we do get to
a place where they start asking for a blanket liability
coverage as they have on the as the Internet has already,
(13:03):
that's that's not going to incent them to do the
right thing. It's going to incent them to do the
exact wrong thing. And so we can't allow that to
happen as we go down this path.
Speaker 2 (13:13):
I was talking to my therapist yesterday. We just had
a baby, and I start to wonder.
Speaker 1 (13:17):
Congrats. Thank you.
Speaker 3 (13:18):
But I wonder why should I have brought a baby
into the world when AI is going to kill us all?
Because I just heard there's a 10% chance that we
all die. And I'm having the conversation with him, and
I like to try to find at least some sort
of comparison. to make myself feel like it's happened before.
And again, it's not the same, but I think about
the Cold War and I wasn't alive then. So I
didn't experience that where people were scared. The news was
(13:39):
making them scared. They were wondering every single day if
we were going to have a nuclear weapon or a
bomb dropped on us. And in the end, we didn't hear.
But is that comparable to what we're going through now,
where we all will sit on the edge of our
seat wondering if we're going to die because of what
the media is feeding us, telling us the worst case scenario.
Speaker 1 (13:57):
Yeah, I think this one is amped up because if
you think about the Cold War, it really was political,
wasn't economical, it wasn't about economics, it wasn't really about social.
So if you think about the fact that when I
say there's the three axes that are ultimately the issue,
you've got it was a political issue that we ultimately
were looking at. And so from an economic standpoint, the
(14:18):
fact that economics are also going to be radically disrupted
and massive amounts of chaos. And so the same on
the other side, I also think of it as, you
know what, it's going to be painful for us that
are going through it at this moment in time. 10
years from now, you're going to be in a moment
where like, we're on the, we're somewhat on the other
side of this thing where we found out, okay, and
we figured out how to, how to harness it, what
it is. We have way more knowledge than we have.
(14:41):
We have previously. I was in a conversation yesterday and
we were talking about, you know, I was asked this
question around, okay, what should we be doing around trust
and safety? What should we be doing around guardrails? And
the answer is it's going to change next week what
the answer to that question is because we are in
this moment of chaos. And so until we have some
type of, you know, some type of order around it,
that order unfortunately hasn't always, you know, kind of shone
(15:03):
through when we think about should it be something where
it's the companies that do it? Should it be regulation
that's doing it? Should it be something else that's doing it?
That unfortunately hasn't happened. And it's not just two parts
of the world. It's now many, many parts of the
world that we have this issue with, that it's a
global issue, not just a segmented issue.
Speaker 3 (15:22):
I'll hear the argument now, we should keep going because
our adversaries are continuing to go.
Speaker 2 (15:27):
Do you buy that?
Speaker 1 (15:28):
I think it's been used many times. Certainly, national security
is important to us. By way of example, when I
talk about the negative downsides, some of those people that
are manipulating and using famous people's voices, their images, their
content to create different content to get you to go
and contribute to something. Hey, go and give money to
(15:49):
this charity that doesn't exist. Some of those charities are
terrorist organizations, right? So there is a national security implication
at the smallest level today where that is actually happening.
And so we have to solve for that problem, but
everyone is going to continue to go fast. With the
following caveats, In the context of all of the resource
that's required to pull this off, the US is far
(16:13):
and apart above everybody else. And so, for example, when
you think about the whole chip discussion, The amount of
time it takes to generate the next chip that's coming out,
the design for that chip, is anywhere from a three-
to five-year cycle. And so we have to, you know,
the assumption is, and we know that, so NVIDIA is
way ahead of where someone else might be from a
(16:33):
development of a chip cycle. So to some extent, it
is a little bit convenient as far as the debate
around they're doing it, so therefore we should do it,
outside of the context of national security.
Speaker 4 (16:45):
Let's take a quick pause for a message from our sponsor.
And we're back on the Bobbycast.
Speaker 3 (17:00):
Can you walk me through, and I'm going to ask
you to be very specific and also use your imagination
a bit because when people ask me and I am
ill-educated and I feel like I've tried to consume as
much as possible, I'm still ill-educated on exactly how to
explain this. Can you give me an example of how
somebody that's going to work every day 9 a.m. to
5 p.m., they come home, they have dinner with their family,
(17:22):
they go to bed, they do it again the next day. How,
in the next couple of years, AI could negatively impact them?
Speaker 1 (17:29):
Yeah. So, let's say their job is something where they're
going into work and they are responsible for aggregating information
to do presentations to their customers. That presentation to their
customer could be... They're in the consulting business. Even better one.
(17:54):
The guy that is responsible for doing your real estate taxes,
making sure they get low every year so they don't
get out of control. What's going to happen is that
guy's name, image, and likeness is going to be used
and abused by other people to ultimately take his business
away from him. That's going to happen. It already happens today.
(18:14):
And so this is a guy that's, you know, he's
in his business. He's been in his business for 20 years.
But someone decides that an AI decides that, you know what,
they're going to start manipulating him because they want to
be in that business. They want to control that business.
And so they're going to start manipulating by sending messages
from him, by creating content that isn't his, by using
his voice to do various things. They're going to go
down that path to do those things. That's an example
(18:38):
of how that might happen.
Speaker 3 (18:39):
When they say there's a 10% chance that humanity could
go in the decade, the first time you heard that,
what did you think?
Speaker 1 (18:47):
I thought it was the type of thing that you
would hear when fear-mongering gains advantage.
Speaker 3 (18:55):
And I think everything is a play, honestly. I think
everything's a work in order to move to some goal.
So similarly... but obviously less educated. I felt that too
when I read the thread on Twitter of, hey, I quit,
here's why. I believed some of it, but then I
think to myself, maybe he's involved with the company still
(19:20):
and this was a strategy by them.
Speaker 2 (19:22):
Any chance that that has happened?
Speaker 1 (19:24):
I don't think so, but this is slightly less flammable perhaps,
but almost the same message that Dario said when he left.
Open AI is not doing great things around safety. We
need to do this thing, blah, blah, blah. I wouldn't
be surprised if we see this guy come out and
he also starts another company where he's going to take
(19:45):
another slice of this. And so reality is, some of
that is humans trying to stay relevant. And so they've got, you've,
you've got a little bit of that going on. Um,
but I think that, I don't think it's a, I
don't think it's a ploy. I don't think it's a
cute move. I think it's one where it's like this,
this guy heard this stuff, believes this stuff and is, is,
you know, is pure about how he thinks about it.
(20:05):
But you know, It reads very similar to how the
founding team of Anthropic, when they left, it reads very
similar to how the messaging that they had when they
left OpenAI.
Speaker 2 (20:15):
What do you think is so scary that they've seen
that we don't know?
Speaker 1 (20:21):
I think they get a chance to run simulations on
models that aren't yet public. They get to see things
that are happening where their models are being used by
the government, by federal agencies, and being able to allow
them to manipulate. They also see how humans are actually
directing this system, which to me is the more scary aspect,
(20:42):
which is I get to see what someone is trying
to get this machine to do. And the fact that
this machine will allow it to happen, I'm getting this
machine to create chemical weapons. Tell me how to create.
If you were going to give a scenario to it,
if you would like to create a chemical weapon that's
going to do the following 15 things, how would you
do so? And so that machine has enough content to
be able to create that answer pretty easily. If the
(21:05):
question was, how would you manipulate a government, it's going
to give you those answers. And so they inside their
system see all these requests at scale. And let's go
to maybe a different lane here, which is when Meta
had that problem on their own site, they just settled
because of all the content that happened there. There weren't
(21:27):
enough humans to manage it. It's not a human solvable issue.
You can't put enough humans around this problem to solve it.
It's going to be... We need what I describe as
trust infrastructure to pull this off. Actual infrastructure that solves it.
Speaker 3 (21:40):
Can they create an AI? Man, this is going to
sound so elementary.
Speaker 2 (21:45):
To fight AI that's not doing positive work?
Speaker 1 (21:51):
Those two AIs inside the same system would ultimately defeat
each other. But having them inside the same system is
ultimately a problem because... The design of an anthropic design,
that system, it ultimately is designed inside the same context.
Our position, in my opinion, is it has to be
done by a third party. It can't be done inside
those buildings. And so when I think about, you know,
(22:13):
the executive order that the president signed around, hey, how
are we going to we're going to make sure that
we get to look at these models for 30 days
before they release them. That says there is a there
is a there's a lane here where slowing down also
should mean there's a way for us to do what
you just described. but it can't be built by the
same companies that have built the foundation models in the
first place.
Speaker 2 (22:33):
What I'm very interested in is something that you guys are.
Speaker 1 (22:36):
Doing is.
Speaker 2 (22:38):
One, trying to stop scams. And I've had people use
my name and image and scam people out of money.
Speaker 3 (22:45):
And it sucks because I can't control it. And people
are doing it from all these countries. And I had
a guest on recently that was talking about how they
have these farms in Ghana and India and where they
are investing heavily. They're investing capital into these farms to
fool people with deep fakes, with fake social media profiles.
(23:06):
Are deep fakes getting so good that they're almost indistinguishable
from the real?
Speaker 1 (23:12):
Certainly for a human. They've been indistinguishable for about the
last six months, where your voice image likeness, I could
recreate this pretty easily today. If you look at the
new Minimax model that just came out, Um, it is
relatively straightforward, unfortunately. And so you also think about the
scale of the problem that you talked about investing in it.
(23:34):
When we started, you know, in late 20, 2019, there
were roughly 19,000 of these types of things, deep fake content,
generative content that was created very small. Excuse me. Today
we see about a trillion of those things. And so
that's the problem. It's just, it is so easy to create, um,
and manipulate. Um, and it's, it's everywhere.
Speaker 3 (23:55):
AI versus copyright, because you can create a creator or
recreate or duplicate anything with AI. Where, where does that
kind of sit now?
Speaker 1 (24:05):
Yeah. I mean, everyone thought, um, you know, copyright was the,
you know, was, was, was the gold standard. Um, and
so where it stands right now is, um, if it's
majority AI created is not, you can't, you cannot copyright it.
If it's human influenced, um, majority human, uh, there's a, you,
they will, they will allow it, but there are certainly
(24:26):
those that have been kicked out. It's still a mess
is the honest answer. Um, we haven't got to a
place where we're like, Hey, we actually know what the
answer is here. Excuse me. Um, so when we think about,
you know, the issue it's not only copyright, um, because
there's a, vast kind of corpus of legal history there.
(24:49):
But the bigger one to me is likeness. So is
your likeness right? So when we look at likeness, so
let's take the music business by way of example. The
music business has two rights today. They have a royalty right,
which is the actual thing we listen to in music.
And then they have a publishing right, which is the lyrics,
the composition, et cetera. Complex system. But there's now this
(25:11):
likeness right, which no one owns except for the artist.
And so in the context of that, how do we
think about, okay, now every individual artist is responsible for
going and doing this thing themselves. That's really hard and complex.
And so that to me is like, it's the opportunity.
It's also the fly in the ointment for how this
ultimately all comes together.
Speaker 3 (25:32):
So if an actor were to license their voice and
they were to allow AI to use it, I feel like.
Speaker 2 (25:39):
Now that wouldn't be possible.
Speaker 3 (25:41):
Warmly welcomed. Do you think, though, in the future that
it will be more and more warmly welcomed?
Speaker 1 (25:47):
I think, I mean, we're certainly seeing it already with estates,
you know, folks that are deceased, definitely happening. You're seeing
it in authorized manners. So there's definitely, you know, James
Earl Jones, he licensed his voice to Disney. He sold
the right to his voice to Disney for use with
his characters that he performed there. You're certainly seeing it.
(26:09):
And I think you'll see more of it. The problem is,
is the whole guardrail structure. You can't say today what's
allowed and what's not allowed. And so, again, that's a system,
that's a set of trust infrastructure that should exist and
should be held by the artist. It shouldn't be held
by the platform. A number of times where the rules
and regulations have been held by the platform that's doing
the distribution of content doesn't make any sense to me.
(26:33):
Why wouldn't you own it and control it? Yeah, yeah.
Speaker 3 (26:36):
What do you think scares people in your industry, the
AI industry, more? AI becoming too intelligent or humans using
AI for really bad things?
Speaker 1 (26:46):
Right now, it's the latter. I mean, it comes down to,
you know, you said yourself, people are using your stuff
for things. It ultimately injures the relationship between you and
your fans, right? Which is today a positive relationship, but
it injures that in a pretty significant way. And it
injures the person that's at the end. It eventually, we
also see where it starts injuring the families as well,
because they are pulled into it and manipulated. So the
(27:06):
families of the you know, of whoever the talent is.
And so today it is content being created by that.
It moves very quickly though to much of that is
certainly human influence today. People are creating the bad things.
We will move in the next six months to agents
are creating the bad things. And it's not just something
(27:28):
where we can see that it's a, you know, it's
a profile that someone has created. If you're, you know,
if you're popular on YouTube, they created it on Insta.
And so we're going to see that go to a
level that we've just not seen before. And we're also
starting to see new distribution platforms in the same setting.
Speaker 3 (27:43):
This is obviously a huge part of what you're doing
with Familio. But how easy is it for somebody to
create just a fake profile of a real person and
just be the full person?
Speaker 1 (27:54):
Yeah, that's about a 15-minute exercise. Wow. Yeah. I mean,
and the 15 minutes is because the process it takes
on the platforms to get set up, it's not the
creation of the content. The creation of the content, I mean, again,
the latest release of Minimax, you know, Fal.ai is a
(28:14):
company that came out and they did their own modification.
So Minimax is an open source model that was released.
It's a Chinese model that was released open source. Fowl
is a company in the US that took that model
and made it so they could do some stuff with
it themselves. The unique thing that happened there, though, which
is important, is you can now create five seconds of
video in under three seconds. So what that means is
(28:37):
it now all just went live. I can now do
live content where I can get it to create content
on the fly faster than it takes to create the
actual content. That's the major thing that's happened there. So
now all of a sudden, it's not an issue where
it's like, Hey, you know what? Hey, someone went away
and they spent 15 minutes behind the scenes creating it.
I'm now doing it as if it's live. That's a problem.
Speaker 3 (28:56):
You're talking about things in time. Like you say six
months and I'm thinking you were, you'd say six years.
I didn't expect months to come out of your mouth
because that seems like it.
Speaker 2 (29:05):
Is so quick.
Speaker 3 (29:06):
Uh, so we're moving rapidly and you know, I have
friends that are songwriters, a lot of friends that are
songwriters and artists and AI kind of sucks right now
for what we can use to help songwrite. Uh, Do
you see in the next, I'll just use six months,
becoming so much better at it that it actually can
do a creative job like write quality songs?
Speaker 1 (29:27):
I think it will get to a place where it
can certainly assist you in writing quality songs. I think,
you know, when writing a song, I've never written a
song before. I've done plenty of writing, but I've never
written a song before. But in the writing process, you
get to a place where you're stuck and you don't
know whether to go take a left turn or a
right turn. It certainly can assist you in that and
(29:49):
help you get to the next bit of it. It
will become better with time, but I don't think you're
going to take out the human elements of like, hey,
that's an amazing track that just happened. um and like
just like one of the you know most formative things
of your life but i also think are people going
to like it or not i think one of the
things that often is lost in the context of ai
music is people talk about oh this is this amazing
(30:12):
thing that happened i did this remix of blah blah
blah and i did this cover of blah blah blah
okay but it's the story of how you as a
fan you attach to that artist and attach that song
that makes you a fan and so that's the thing
that ultimately ultimately happens and that's ultimately you know what
matters i i I was at a concert this last Friday,
and in that performance, so it was Garth Brooks, and
(30:35):
his ability to connect with the audience of every single
member that's holding up a sign, that's what that person's
going to remember when they're singing that song. And so
that to me is like, that's what matters. So is
it going to get better? Sure, it's going to get better.
It'll get better over time. The more information we put
into it, the more of those AI tracks that become
more popular, it will get better over time. But I
(30:56):
think it's always going to require a level of human
experience to ultimately create an amazing track.
Speaker 5 (31:02):
The Bobbycast. We'll be right back. This is The Bobbycast.
Speaker 3 (31:16):
Do you think the value of in-person human-performed art increases today?
because that's something we can actually see is real and
we can experience it in live.
Speaker 1 (31:29):
A hundred percent. Yeah. And you see it happening where,
you know, if you look at the, where, where money
is it being invested today, that's what's happening is more,
more live things are happening. More people want to go
and experience the real thing. That's why when I, when
I say one of the risks for the next generation
is around relationships. Unfortunately, if the only thing they experience
is a relationship with a bot, then the inverse of
(31:51):
that is going to be true. Because they're not going
to put value on that thing. Whereas we today do
put value on those things because we've experienced it. So
when you think about your child, that's what I think about.
So if your child, that's the way they have to
interact with things, that's not going to be the case.
Speaker 3 (32:06):
I think about these dolls that have AI in them
and they were in the news because kids could talk
with them and the doll would interact back.
Speaker 2 (32:13):
What do you think about that?
Speaker 1 (32:15):
I think it's horrendous. I think it's a, I put
it in the same category as what has happened in
the education system. is that we started measuring engagement rather
than measuring deep learning. How much time did you spend
doing this thing rather than did you actually go and
learn the thing? And I think the same is true here.
Human interaction is ultimately what makes us human. And so
(32:38):
our ability to process that is the thing that these
AI agents don't have. They don't have the ability to
pull those things off. Again, everything for them is very
rapid process. Binary decisions. So they can make those binary
decisions much faster than we can, but they're ultimately not
based on something where that binary decision may ultimately be
a left turn. It didn't follow the sequence that you
(32:59):
were expecting it to follow. It took a left turn
in that regard.
Speaker 3 (33:02):
Do you ever just have to walk out in the
woods since you're in technology all the time and literally
just touch grass, for you specifically, because you're just always
in it that you have to go, okay, this is
the real life here.
Speaker 1 (33:11):
Yeah, we have a farm that I go and spend
time on and I go and disappear into the middle
of nowhere. Yeah.
Speaker 2 (33:17):
I think I would need that. I feel like I
need that and I'm not in it like you are.
I'm interested in it. I read about it, but are
you scared?
Speaker 1 (33:26):
Um, I, yeah. I mean, I, if to be, yeah, I,
there's definitely fear in my life. Um, I mean, I have,
I have, I have children, um, I have, you know, uh,
younger children. And so like, it's one of those where
I look at him, like the reason I started this
company was because I ultimately felt like every human on
the planet should thrive in the era of AI. It
should not be something where the machine thrives. And so
(33:49):
to me, that's my grounding force around this topic. And
so I started this company because of that fear. And granted,
I wrote about this topic now six years ago. And
it was one where I was like, this is the
thing that's going to matter if we don't have a
series of guardrails that exist, don't have a series of
what today I call trust infrastructure, don't have a way
(34:11):
to say this is right and this is wrong, we're
going to be in a place where it's not going
to go well and it cannot be in the hands
of the people building these models.
Speaker 3 (34:20):
Yeah, what can go wrong here and what can go
right in the next six months to a year? What
do we need to depend on to go wrong or right?
Speaker 1 (34:29):
I think the things that can go wrong is I
think there is a case where it says we rush
into legislations. We've rushed into legislation previously and we've got
some things. So an example of that is, not to
get too deep in the weeds, but Section 230, right?
Section 230, which gives coverage to people. It's 26 words
(34:50):
that gives coverage to platforms that they can host any
content that they want to host and they have no liability.
At the time, it was an amazing piece of legislation.
But if we reflect on it now, it incented the
wrong actions. It incented the wrong activities. So in the
context of today's world, we might rush into legislation And
all of a sudden we say, this is an amazing
piece of legislation. It's protecting all the right things. But
(35:11):
we fast forward five, six years from now, and it
was exactly the wrong thing. And so I just like,
how do we make sure that we're doing it in
the right way? I think it's a pretty important thing.
There are some things that are obvious, right? The kids
online protection, stopping deepfake pornography. Those are obvious things. But
(35:32):
how we think about who is responsible for guard railing
and ring fencing, the actions that are taken by these
very powerful model builders, I think is an important task.
If we screw that one up, I think that 10%
can start increasing pretty rapidly.
Speaker 3 (35:49):
How do normal folks stay vigilant to not get themselves
in trouble?
Speaker 1 (35:53):
It's an interesting word to use, trouble. I think it's
one where most people, unfortunately, aren't savvy to the fact
that the thing that they're interacting, most people interact on
their devices today, and they're not savvy to the fact
that a lot of what they interact with on their
devices today came from it came from AI. I mean,
(36:15):
even if you think about just the stat I share
with you, there are a trillion things created this year, right?
And it's the amount of music that's being created is
as far out swept all of the music that's ever
been created. If I think about the amount of images
that have been created, it has now surpassed all content
that existed on Google Images since the start of Google Images.
So you just look at that and you're like, okay,
(36:36):
so that's the amount of content. So it is one
where it's like, how do you get to a place
where you can think about that? So how does a
regular person do it? One is just think about what
you're prompting into these systems. And so when you're prompting
into a system, That is ultimately teaching the machine what
to think. It's retaining that information. So in the same
(36:58):
way you think about search and the way that Google
ultimately used search to its advantage, think about how you're
using every day, like whether it's your voice being used
into those systems, whether it's what you're typing into a
chat GPT. Just think about what you're doing there because
the machine is learning from that stuff.
Speaker 2 (37:16):
A couple of just broad questions.
Speaker 3 (37:18):
Give me your five-year prediction on what AI looks like
in 2031.
Speaker 1 (37:22):
I think it's an impossible ask.
Speaker 2 (37:23):
Yeah, I figured you'd say that.
Speaker 1 (37:26):
I think it's an impossible ask. Today, if I was
seeing changes happening in a 24-month, 12-month cycle, I mean,
when we started our business, there was a new scientific
paper that came out roughly once every 15 months. Now
we're seeing a new scientific paper come out once every
(37:47):
15 days. And so just like that's the level of
change that's happening. So it's really hard.
Speaker 2 (37:53):
What about AI-generated movies?
Speaker 3 (37:55):
How easily would they be made and can they be
made pretty good? Yeah.
Speaker 1 (38:00):
I mean, again, go back to– we've talked about it here,
but go back to Minimax. Minimax 3, that's not possible.
It can happen. It is something that is– you can
also have a live– I mean, you can have a
live show. It doesn't have to be just a movie.
It could be a live show. I think the way
movie production happens, it can happen today that you can
(38:22):
do those things. Different questions of whether it's right or wrong.
But that is a thing that can happen today.
Speaker 2 (38:29):
With you guys at your company, what is your goal?
What do you want to be known for in a
couple of years that you've helped either do or stop?
Speaker 1 (38:36):
Yeah. So I think for us, it's three things. First
thing is we want to make sure that we protect everyone.
Let's stop all the unauthorized stuff from happening. Let's just
make sure that happens. The second thing is let's make
sure that we can give guardrails to people so they
can say, hey, when I do say yes, here's what
you can do and here's what you can't do. I
don't want that type of music created with my music.
(38:57):
It shouldn't be allowed. And then the last is let's
make sure everyone gets paid what they're supposed to get paid.
And so if those three things happen, because when I
think about AI at scale, certainly many people in the
creative community might lose their jobs, right? Might lose their fans.
If you go real doomsday on this stuff, that might happen.
But if they're all participating in all the economics, then
(39:19):
things should equal out. I'm not saying it's right that
that happens, but they should equal out. And so let's
get to a place where, so when we think about
our company, let's make sure humanity can thrive. That's our goal.
And the way that happens is give people the tools
and give people a protection that allows them to protect
themselves and then also allow them to say yes and
no when they want it to happen or they don't
(39:40):
want it to happen.
Speaker 2 (39:41):
What jobs do you think are safe?
Speaker 1 (39:42):
I think jobs that have, where experience matters are safe.
I think we're going to see a big resurgence in trades.
I think trades are going to go through the roof
in a pretty meaningful way. And I think we're going
to see people return back into agriculture.
Speaker 3 (40:02):
What about when Elon Musk says we won't need money,
so we won't have to save money because we don't
need it in a decade, and we'll just be able
to live free and just be paid because AI does
all the work?
Speaker 1 (40:12):
Yeah, I think if you draw any trend line out
long enough, you get to a place where there's enough
wealth in the world that that could be true. So
you could say that about any trend line. Yeah, I mean,
that theory has been around for a long time, where
you think about equality. We haven't been that good about
doing those things, though, as far as let's make sure
that there is You know, money doesn't matter because there's
(40:34):
enough for everybody, where there's an abundance of things. We've
not been great about that as humans.
Speaker 3 (40:38):
Yeah, you got the guy with the absolute most money saying, hey,
money's not everything. It's easy for you to say when
you have the most money.
Speaker 1 (40:44):
Yes.
Speaker 3 (40:45):
I really appreciate the time. Thank you so much. And
I really love what your company is doing. Congratulations on
all the success of it. And yeah, thanks so much.
And I hope you have a great rest of the day.
Speaker 1 (40:55):
Amazing. Thanks for your time, Bobby.
Speaker 2 (40:56):
Thank you, Dan.
Speaker 5 (40:58):
Thanks for listening to a BobbyCast production.
Host
Bobby Bones
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