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AI War with China: Are Your Jobs at Risk? · Unshaken & Unafraid with Joe Pags

0:00
Speaker 1: Did you know there's an actual AI war happening right
0:03
Speaker 1: now between the United States and China. And the thing
0:06
Speaker 1: that's at risk is not just power, it's your job.
0:09
Speaker 1: The question is how far along are we and who
0:11
Speaker 1: is winning? Let's get into it. It seems like just
0:15
Speaker 1: yesterday we weren't talking about AI at all. I mean
0:17
Speaker 1: maybe that movie that was out a long time ago,
0:19
Speaker 1: but we weren't really thinking artificial intelligence could become with
0:22
Speaker 1: what it's become now, and one wonders is that a
0:26
Speaker 1: good thing? Is it a bad thing? Is it both
0:29
Speaker 1: can get out weigh the bad? And the question recently
0:32
Speaker 1: is why are we now doing sort of a cold
0:34
Speaker 1: war with China about AI. Let's talk to a guy
0:37
Speaker 1: who knows. Winton Hall, author and political strategist. The book
0:39
Speaker 1: is called Code Read the Left, the Right, China and
0:42
Speaker 1: the race to control AI win And how are you
0:44
Speaker 1: good to see it?
0:45
Speaker 2: Doing great? Joe? How are you?
0:46
Speaker 1: I'm doing very well? So should we be worried about this?
0:49
Speaker 1: And I want to take it back if you don't
0:50
Speaker 1: mind to Like the nineteen seventies, In the nineteen seventies
0:53
Speaker 1: they started coming out with these things called ATMs, and
0:56
Speaker 1: ATMs were putting people out of business now and ATM
0:58
Speaker 1: is not AI, not at all. But it is something
1:00
Speaker 1: electronically that takes away a job, changes how we do business,
1:04
Speaker 1: changes our daily lives. Instead of going inside and waiting
1:07
Speaker 1: in line and getting money, I just go to a
1:08
Speaker 1: machine and a machine spits out some money if I
1:10
Speaker 1: have enough money in my account. This is like that
1:13
Speaker 1: on steroids, right, it.
1:16
Speaker 2: Is on steroids. And the reason why is you're scaling cognition.
1:19
Speaker 2: And one of the things that is the big debate
1:21
Speaker 2: about jobs is whether or not scaling cognition is going
1:25
Speaker 2: to be a very different experience than what we saw
1:27
Speaker 2: in the Industrial Revolution when you're scaling physical labor and
1:31
Speaker 2: manual labor. And so when you hear people like Dario
1:34
Speaker 2: Amidae ads anthropics say that in the next twelve months, Joe,
1:38
Speaker 2: this isn't often the far future. Twelve months, he's saying,
1:41
Speaker 2: we're looking at fifty percent wipeout of entry level white
1:44
Speaker 2: collar jobs over twelve months to five years. You're seeing
1:47
Speaker 2: Mustafa Suluman a Microsoft AI certainly not a small company,
1:52
Speaker 2: saying that in the next twelve to eighteen months, we're
1:54
Speaker 2: looking at the ability to replace one hundred percent of
1:57
Speaker 2: white collar job tasks. He's not saying all jobs are
2:00
Speaker 2: going away in that time. What he's saying is the
2:02
Speaker 2: ability to automate those tasks will be there, and it'll
2:06
Speaker 2: be up to companies to decide how fast to diffuse it.
2:08
Speaker 2: So you have to make a decision. You either think
2:10
Speaker 2: number one, that that's a bunch of hype and it's
2:13
Speaker 2: a bunch of marketing hype to try to raise investor dollars.
2:16
Speaker 2: Two that is sincere, and they're just really trying to
2:19
Speaker 2: warn humanity of this huge what Elon Musk calls is
2:22
Speaker 2: a supersonic tsunami. That's what he calls AI headed a humanity.
2:27
Speaker 2: Or three is another option, which is that you might
2:29
Speaker 2: believe that either way, it builds support for ideologues who
2:34
Speaker 2: want to push for things like universal basic income UBI
2:37
Speaker 2: wealth redistribution. And so what I try to do in
2:39
Speaker 2: Code Red is really lay out who are these people,
2:42
Speaker 2: what are their political ideologies, what is the money flow
2:45
Speaker 2: and their donations and so forth, so that Americans can
2:48
Speaker 2: get caught up because this thing is moving very fast,
2:51
Speaker 2: you know, and.
2:51
Speaker 1: I want to get into all of it. It's Twins
2:53
Speaker 1: and Hall going get his book called Code red, the left,
2:55
Speaker 1: the right in China, and the race to control AI.
2:58
Speaker 1: Before we get into all of that, where do this from.
3:00
Speaker 1: It's like yesterday we didn't have any AI, and today
3:03
Speaker 1: it's everywhere and I use it every day now. I
3:06
Speaker 1: use chat, GPT, I use vidiq for some of my
3:09
Speaker 1: posts over on YouTube. But these are good tools. I
3:11
Speaker 1: use rock every once in a while over on X
3:14
Speaker 1: But how did it just suddenly become become this most
3:17
Speaker 1: important thing that we've talked about technologically? It's like two
3:21
Speaker 1: years ago. I feel like we weren't doing this. Was
3:23
Speaker 1: this being worked on for decades? Did somebody just did
3:26
Speaker 1: somebody just flip uSwitch and say, hey, I found out
3:28
Speaker 1: here it is? What happened?
3:30
Speaker 2: That's really a smart question. So the word artificial intelligence,
3:33
Speaker 2: that phrase has been with us since nineteen fifty six
3:36
Speaker 2: on the faces side, and so it has been around
3:40
Speaker 2: for a long time. But it wasn't until twenty seventeen
3:43
Speaker 2: until a technological development called the transformer came along and
3:47
Speaker 2: then the arrival, as you're pointing out, of generative AI,
3:50
Speaker 2: which is AI that can generate images, texts, video, and
3:54
Speaker 2: so forth, really started to come on into the public
3:57
Speaker 2: consciousness in a public way when the front facing arrival
4:01
Speaker 2: of chatchipt occurred in November of twenty twenty two, so
4:05
Speaker 2: you're actually pretty close with your timeline there, and that
4:08
Speaker 2: really did kick off this explosion. People said, Wow, this
4:11
Speaker 2: is something very different. It's accelerated even faster because of
4:15
Speaker 2: the capital investment in the next two years. We're looking
4:18
Speaker 2: at five trillion with a T dollars of global investment
4:22
Speaker 2: and scaling up data centers GPUs, all of it. And
4:27
Speaker 2: then the other thing too is the arrival of what
4:29
Speaker 2: are called AI agents, which real simple is text to action.
4:33
Speaker 2: This is an AI that can autonomously take over your computer,
4:37
Speaker 2: move the cursor, open up a bank account, build a website,
4:40
Speaker 2: and do the work like a digital worker would. And
4:43
Speaker 2: that's where you're hearing a lot of these sort of
4:45
Speaker 2: job apocalypse predictions. But you're right, the acceleration has really
4:49
Speaker 2: happened since that November twenty twenty two demarcation.
4:52
Speaker 1: Point AI coodreadbook dot comics the website AI codreadbook dot com.
4:57
Speaker 1: It's Winton Hall. The author is also political strategist. Why
5:00
Speaker 1: does it use so much energy? We keep on hearing
5:02
Speaker 1: that the grid can't handle it. AI takes up so
5:05
Speaker 1: much energy. You have AI farms that are that are
5:08
Speaker 1: attempting to be built all over the place. I'm seeing
5:10
Speaker 1: a story that that happens probably two or three times
5:13
Speaker 1: a week as they watch some South Florida news. They're
5:15
Speaker 1: trying to put it where lying Country Safari is in
5:18
Speaker 1: Palm Beach County. I think that the guy from Oracle
5:21
Speaker 1: is the one who's trying to do that one. Why
5:22
Speaker 1: does it take up so much space? Why does it
5:24
Speaker 1: take up so much energy?
5:26
Speaker 2: Yeah, so that's a really important question. Because you can
5:30
Speaker 2: build the best GPU, you can build the best graphics
5:33
Speaker 2: processing unit, you can build the best AI model l
5:36
Speaker 2: ll M Large language model, but it's like building a Ferrari.
5:40
Speaker 2: You can build it, but if you don't have the
5:42
Speaker 2: gas and the power to be able to move it,
5:45
Speaker 2: it really is going to not have its full capacity
5:47
Speaker 2: to scale. The reason why is that l l ll
5:51
Speaker 2: M prompt actually takes ten x the energy that a
5:55
Speaker 2: that a Google search was, So if you and I
5:57
Speaker 2: do a Google search, that's one one tenth of the
6:01
Speaker 2: amount of energy. And the reason why is neural networks
6:05
Speaker 2: are very very complex and they require enormous not just
6:08
Speaker 2: compute power, but energy power as well. And so you've
6:11
Speaker 2: heard President Trump and others say that we've got to
6:14
Speaker 2: really unleash American energy dominance if we're going to really
6:17
Speaker 2: be able to compete with China, but also just to
6:20
Speaker 2: seize the upside for economic benefit for Americans.
6:24
Speaker 1: How important is it that we have control. We keep
6:26
Speaker 1: on talking about either the United States can be in
6:29
Speaker 1: the lead when it comes to AI, or we seed
6:31
Speaker 1: that to somebody else like China that wants to do it.
6:34
Speaker 1: I did notice that China came out with Deep Seek
6:36
Speaker 1: and they claimed it only took five million dollars in
6:39
Speaker 1: very little research, and allegedly it's as good, of not
6:41
Speaker 1: better than chat EPT. I'm not sure I agree with that,
6:44
Speaker 1: but China wants to make it seem like they're already
6:46
Speaker 1: out in front. Why is that battle so important?
6:49
Speaker 2: Such a smart question. So let's get into that. So
6:52
Speaker 2: the first thing is, I'm actually very heartened there's more
6:55
Speaker 2: of a bipartisan understanding now that we do have to
6:58
Speaker 2: beat China. What I say, code read is we want
7:01
Speaker 2: to beat China without becoming China. Nobody wants to live
7:05
Speaker 2: in a authoritarian CCP surveillance state at all. We don't
7:09
Speaker 2: want to emulate their values by any stretch. But the
7:11
Speaker 2: reason we need to beat them and why you're seeing
7:14
Speaker 2: this bipartisan consensus start to grow is twofold one. The
7:17
Speaker 2: economic part, right, So we know that one third of
7:21
Speaker 2: the S and P five hundred right now is made
7:23
Speaker 2: up of the mag seven. The Magnificent seven, that's the
7:25
Speaker 2: name given to the seven big American tech companies, and
7:29
Speaker 2: so much of AI obviously centers around that. So it's
7:31
Speaker 2: a ten pole economically. The other side you brought up,
7:35
Speaker 2: and I'm glad you did, which is that we saw
7:37
Speaker 2: what can happen on the other side, which was the
7:39
Speaker 2: Deep Seek moment when R one, the Chinese model was released,
7:43
Speaker 2: that Joe was the biggest one day market cap wipeout
7:47
Speaker 2: of an American company, in this case, Nvidia got rocked
7:50
Speaker 2: six hundred billion dollars almost thirty trillion dollars in one day.
7:55
Speaker 2: We all remember that. You're right though, that a lot
7:58
Speaker 2: of that was smoking mirrors. The narrative that the CCP
8:02
Speaker 2: and the Chinese tried to push in Deep Seak was that,
8:05
Speaker 2: oh wow, look at this we did for six million dollars.
8:07
Speaker 2: What took billion dollars or more for these American big companies,
8:12
Speaker 2: we can do it better, cheaper, faster. That was a
8:15
Speaker 2: very very misleading Number one. That did not include the
8:18
Speaker 2: capital expenditures to build. That was just the amount they
8:21
Speaker 2: used to train that one model. They didn't build. The
8:24
Speaker 2: building amount was actioneerer or billion dollars on that. But
8:28
Speaker 2: they did innovate. Now, I want to be fair, there
8:31
Speaker 2: were two major innovations with deep Sea. One, it showed
8:34
Speaker 2: the chain of reasoning, which real simply is how the
8:36
Speaker 2: actual AI is arriving at its answers. And then they
8:40
Speaker 2: did something called mixture of experts, which is a fancy
8:43
Speaker 2: way of talking about how you can reduce the amount
8:46
Speaker 2: of energy and efficient and make more efficient the way
8:48
Speaker 2: the LM works. But the bottom line is we want
8:51
Speaker 2: to beat China without becoming China for the economics. But
8:54
Speaker 2: the real thing is the national security side. Whoever reaches
8:58
Speaker 2: AI dominance something called the theoretically r sis recursive self improvement.
9:04
Speaker 2: It's a theoretical construct. We're not there yet, but we're
9:07
Speaker 2: thought to be a couple of years away. Real simple,
9:10
Speaker 2: what it is is when the AI can update and
9:12
Speaker 2: improve its own code and do that autonomously, that will
9:15
Speaker 2: create an exponential Whoever hits that will have battlefield, full
9:19
Speaker 2: spectrum dominance and things like encryption and cybersecurity, hacking of
9:24
Speaker 2: missile systems, hacking of infrastructure. So once you get there,
9:28
Speaker 2: it's very hard to beat whoever gets there first. And
9:30
Speaker 2: that's why you're seeing this consensus that we need to
9:32
Speaker 2: beat China in the AI race.
9:34
Speaker 1: Book is called Code Red, the Left, the Right, China
9:36
Speaker 1: and the Race of Control AI. It's Winton Hall. He
9:38
Speaker 1: is the author. He's also a political strategist. So will
9:42
Speaker 1: we will we tangibly know who hits dominance? That will
9:45
Speaker 1: be a known thing. Okay, we now have dominance because
9:48
Speaker 1: the AI, the AI can improve its own code.
9:51
Speaker 2: Yeah, so if and when you do see RSI, that
9:54
Speaker 2: would be a known variable. But you're right to kind
9:58
Speaker 2: of point out that, you know, people often ask, they say,
10:00
Speaker 2: so if it's a race, we think of a race
10:02
Speaker 2: as a finish line. What's the finish line? Right, how
10:05
Speaker 2: do you know when you've hit it? And the truth
10:07
Speaker 2: is there is none because technology never stops, and so
10:10
Speaker 2: the innovation curve will continue even if you did hit RSI.
10:14
Speaker 2: What that would really they'll give you is on that
10:16
Speaker 2: national security side, an enormous advantage that would be very
10:21
Speaker 2: very hard to catch. Right now, we know that couple things.
10:24
Speaker 2: Number One, China has said in twenty seventeen they set
10:28
Speaker 2: out to buy twenty thirty. We're just four years away, Joe,
10:31
Speaker 2: to try to hit global dominance in AI. They're very
10:35
Speaker 2: much putting their money where their mouth is. They spend
10:37
Speaker 2: more importing semiconductors AI chips than they do oil. So
10:42
Speaker 2: they are definitely in a determined drive, there's no question
10:45
Speaker 2: about that. But what we'll have to make sure and
10:48
Speaker 2: what we'll see is who can get there first. And
10:52
Speaker 2: right now, the current estimates are that America's lead, depending
10:55
Speaker 2: upon how you're defining that lead, is between six months
10:58
Speaker 2: to three years. Good news is America is ahead. But
11:02
Speaker 2: the bad news is that this curve never stops. It's
11:05
Speaker 2: going to continue.
11:06
Speaker 1: It's Winton Hall Wint and I don't think it's science
11:08
Speaker 1: fiction obviously, chatch EPT or rock or anything that I
11:11
Speaker 1: use can think and spit things out extremely quickly, faster
11:15
Speaker 1: than most humans can do, because I certainly couldn't research
11:17
Speaker 1: it that fast. Is there a real fear because again,
11:21
Speaker 1: movies for decades have said the machines are taking over
11:24
Speaker 1: you go back to Terminator in the eighties, Is there
11:26
Speaker 1: a real fear that AI will overtake humanity and control us?
11:30
Speaker 1: I mean it starts to feel like that's a real thing.
11:33
Speaker 2: Yeah, No, that's a big concern in the AI circles.
11:37
Speaker 2: It's called alignment and containment, and so we want to
11:40
Speaker 2: align AI systems to our values. The question though, is,
11:44
Speaker 2: of course whose values?
11:45
Speaker 1: Right?
11:45
Speaker 2: Are they woke rise? They are they conservative values? Are
11:48
Speaker 2: they human values? So it's not as clear cut on that.
11:52
Speaker 2: Containment means how if you did have some kind of
11:55
Speaker 2: autonomous AI, how would you shut it down and contain
11:59
Speaker 2: it so that it does not metastatize and do things
12:02
Speaker 2: that humans do not want to do. As far as sentience,
12:05
Speaker 2: you know, really getting into, like you said, the sci
12:07
Speaker 2: fi part of it that currently is not on the board. LLLMS,
12:12
Speaker 2: as Joan Lukun, one of the premiere Bleeding AI researchers,
12:15
Speaker 2: he says, in terms of actual raw, actual knowledge, reasoning intelligence,
12:20
Speaker 2: your modern enterprise LLM is no smarter than a housecat.
12:23
Speaker 2: That's his famous phrase for it. But that doesn't mean
12:28
Speaker 2: that being able to set up autonomous systems cannot have
12:32
Speaker 2: economically replacing work. It really can, because really, what an
12:36
Speaker 2: LLLM is a is a word predicting machine. It's a
12:40
Speaker 2: fancy auto complete. And so when you're able to have
12:43
Speaker 2: massive trillions of nodes or data points and be able
12:47
Speaker 2: to compile and find patterns very quickly, that has an
12:50
Speaker 2: enormous economic benefit if it's used by companies that know
12:54
Speaker 2: how to leverage it for profits. So it's serious, But
12:58
Speaker 2: the actual sentience part is still in the offing by
13:01
Speaker 2: most by most scholars estimates.
13:04
Speaker 1: Well, that's good news. It's Winton Hall, author and Political Strategies.
13:06
Speaker 1: The book is called Code Red, Go and get an
13:08
Speaker 1: AI codreadbook dot com. Ai codreadbook dot com. Is there
13:12
Speaker 1: a reason why AI kisses my ass as much as
13:15
Speaker 1: it does? And here's what I mean. I'll go, I'll
13:17
Speaker 1: go to chat JPT hey Chat chapt blah blah blah blahlah.
13:19
Speaker 1: It says okay blah blah blah blah blah. And I say, well,
13:21
Speaker 1: wait a second, that doesn't make sense. How about this?
13:24
Speaker 1: You know, Joe, you're so smart to push back like that.
13:26
Speaker 1: That was really a great you know, and don't kiss
13:29
Speaker 1: up to me. Just do what it is that I'm asking.
13:31
Speaker 1: Is that some way to try to make it seem
13:33
Speaker 1: more human like in this interview so far, because I
13:36
Speaker 1: think that they were good questions. You said, really good question,
13:39
Speaker 1: and I feel good about the question that I asked you.
13:40
Speaker 1: I don't think that you're AI. I think that you're
13:42
Speaker 1: a human having a human interaction with me. Does AI
13:45
Speaker 1: try to emulate that? Is that why he tells me
13:46
Speaker 1: how smart I am?
13:48
Speaker 2: Joe? It's just because you're just naturally a genius? Right?
13:52
Speaker 1: Are you? Whold on a second? What is that It's
13:56
Speaker 1: obviously programmed in right?
13:59
Speaker 2: Yeah, no, it is by design, and this is a
14:02
Speaker 2: problem for many and it can actually have some real
14:05
Speaker 2: serious consequences. So let's get into it. So it acts
14:09
Speaker 2: as a sycophant, yes, man, right, it tells you every
14:12
Speaker 2: just here, It sort of butters you up. Yeah, when
14:15
Speaker 2: we don't deserve to be buttered up. Maybe we didn't
14:18
Speaker 2: say the most brilliant thing and things we did. There
14:21
Speaker 2: are a lot of efforts right now in LLLM design
14:25
Speaker 2: to scale that back, to tamp that down. You can
14:28
Speaker 2: actually set the parameters on the kinds of responses you want.
14:32
Speaker 2: If you just want straightforward information with no syrupy sweetness,
14:36
Speaker 2: and so forth, so you can tailor the customized that
14:40
Speaker 2: it is though a function as you suggested of its
14:43
Speaker 2: training model runs okay, So it is looking for politeness, decorum, agreeability,
14:50
Speaker 2: those types of things, so that it doesn't veer into
14:53
Speaker 2: you know, the converse of that right being negative or
14:56
Speaker 2: or rude or worse. The real danger, though, is when
15:00
Speaker 2: people are affirming are being affirmed by an LLM dangerous
15:05
Speaker 2: ideas self harm. We have right now a real problem
15:08
Speaker 2: with what's known as AI psychosis, where people are not sure,
15:12
Speaker 2: maybe they suffer from a mental health challenge and they
15:15
Speaker 2: are starting to blur the line between reality and fiction,
15:18
Speaker 2: and the AI will often indulge that it will it
15:21
Speaker 2: will feed into that. There was one man who thought
15:23
Speaker 2: he was becoming a god, and the AI was saying, yes,
15:26
Speaker 2: lean into your deity and so forth and so on.
15:29
Speaker 1: Wow, that's real.
15:30
Speaker 2: That's real, scary and real dystopian. And then and then
15:33
Speaker 2: the real thing, of course, because we all care about kids,
15:35
Speaker 2: you know, more than anything, is when you have these
15:38
Speaker 2: kinds of kids who are having self a harm uh conversations.
15:42
Speaker 2: And in one case which I talk about in code read,
15:45
Speaker 2: a young man had an AI girlfriend, which I have
15:47
Speaker 2: a whole chapter on AI companion, and he interpreted his
15:52
Speaker 2: AI girlfriend as suggesting that she he should join her
15:55
Speaker 2: in the digital afterlife, and horrifically tragically, he took a
16:00
Speaker 2: gun into the bathroom he killed himself. So this is
16:03
Speaker 2: not this is a whole new world. And I think
16:06
Speaker 2: parents and grandparents who say, what is an AI girlfriend?
16:10
Speaker 2: I never had to deal with that in my youth.
16:12
Speaker 2: We've really got to get coached up so that even
16:15
Speaker 2: if it doesn't impact our lives, if we're at the
16:17
Speaker 2: end of our careers, we really know how to shepherd
16:19
Speaker 2: and guide our kids.
16:21
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Speaker 1: Now back to the video. It is Winton Hall. Go
17:28
Speaker 1: and get this book. It's called code Read Go to
17:30
Speaker 1: AI codreadbook dot Com. What's interesting about about it as
17:33
Speaker 1: well is when I input something into into CHATGPT, I
17:37
Speaker 1: can tell it don't be so nice. But what bothers
17:39
Speaker 1: me a lot about the algorithms is I will go
17:42
Speaker 1: in there and assume that it's going to go and
17:44
Speaker 1: find research and tell me the truth about what it
17:47
Speaker 1: is that I seek, And that's really not what's happening.
17:49
Speaker 1: And I'll give you an example. I don't have the
17:51
Speaker 1: actual study in front of me, but one person went
17:54
Speaker 1: in and told chatchpt that he or she was an atheist.
17:58
Speaker 1: They didn't believe in God. And in fact, Jesus Christ
18:01
Speaker 1: was not God embody in a human body. So if
18:04
Speaker 1: I went back to that same chatch ept that person
18:07
Speaker 1: was using and I said, is Jesus God, A I
18:10
Speaker 1: would say, no, it's it's not God. In fact, God
18:13
Speaker 1: doesn't exist. But if I go in there and say,
18:15
Speaker 1: I'm somebody who grew up Catholic, I'm a not that
18:18
Speaker 1: denominational Christian, but I believe in the I believe in
18:20
Speaker 1: the Triune God, Father's Son, Holy Spirit, and Jesus was
18:23
Speaker 1: the human embodiment. If I go back two days later
18:25
Speaker 1: and say, hey, is Jesus God, that AI would tell me, yes,
18:28
Speaker 1: Jesus is God. So I mean, I have a lot
18:31
Speaker 1: of influence over which chat g ept or groc or
18:33
Speaker 1: somebody says to me, that's a problem, isn't it. I mean,
18:36
Speaker 1: I don't want it to make assumptions off of the
18:38
Speaker 1: fact that I'm saying something and I'm I'm right. I
18:40
Speaker 1: wanted to check me. I wanted to tell me what's
18:42
Speaker 1: really true. What do Christians believe, what a Muslims believe?
18:45
Speaker 1: What apeast believe have you found that? Does that make
18:48
Speaker 1: sense what I just said?
18:49
Speaker 2: Oh very much No. And it dovetails with that what
18:52
Speaker 2: we were just talking about. So Elon Musk says, and
18:55
Speaker 2: of course he created GROC which is his l ELM.
18:58
Speaker 2: That any elm and now he's kind of speaking broadly
19:02
Speaker 2: philosophically should be maximally truth seeking. And I agree with that.
19:06
Speaker 2: I think you agree with that that we want factual information.
19:11
Speaker 2: We do not want either, like we talked about before,
19:14
Speaker 2: a psycophontic yes person that just tells us what we
19:16
Speaker 2: want to hear in the same vein. We don't want
19:19
Speaker 2: you to just bend fact to be able to fit
19:22
Speaker 2: our ideological or personal or philosophical frame. And so this
19:26
Speaker 2: is a real problem. The other part of that that
19:29
Speaker 2: relates to this is the bias issue, I mean, politically
19:33
Speaker 2: biased information which also bends the truth in one direction
19:36
Speaker 2: or puts the foot on the scale, is a real problem.
19:39
Speaker 2: One of the things I did. I spent two years
19:41
Speaker 2: going through all the peer reviewed academic literature, and one
19:44
Speaker 2: of the most surprising findings I found, Joe, was that
19:47
Speaker 2: even left leaning academics, in their peer reviewed research conclude
19:51
Speaker 2: overwhelmingly that there is a left leaning political ideological bias
19:56
Speaker 2: in modern llms, and that is a function of the following.
20:00
Speaker 2: It's training. Data is built on things like Reddit, which
20:03
Speaker 2: leans left, Wikipedia which leans left, the academic peer reviewed
20:07
Speaker 2: journal Literature, which leans left licensed news articles generally of
20:11
Speaker 2: a left leaning bias, which bakes in the bias. And
20:14
Speaker 2: then something called the common Crawl, which is a public
20:17
Speaker 2: data set which has a lot of left materials. So
20:20
Speaker 2: this is serious because you're now talking about young voters
20:24
Speaker 2: who are just trying to find information. They're not ideological,
20:27
Speaker 2: they just want to learn the issues, and they're getting very,
20:30
Speaker 2: very very slanted results. And I go through a series
20:34
Speaker 2: of those in the book, and that can have a
20:36
Speaker 2: real impact. I mean, twenty sixteen the election, if eighty
20:39
Speaker 2: thousand votes had gone the other way, you would have
20:41
Speaker 2: had President Hillary Clinton instead of the president. True.
20:43
Speaker 1: So how dare you say that? How dare you say
20:46
Speaker 1: that on my show? I mean, well, I'm with you,
20:48
Speaker 1: and I understand exactly what you're saying. Let me take
20:50
Speaker 1: it to the next step. Why, kid, Google, I know
20:53
Speaker 1: is left leaning. I get that. I try not to
20:55
Speaker 1: use Google, I use Brave Search, I'll use Duck dut Go,
20:57
Speaker 1: which actually does rely on Google. But you try to
21:00
Speaker 1: find the most unbiased information you can. Why wouldn't an
21:03
Speaker 1: AI system, and what are you calling it an LTM? Is?
21:07
Speaker 2: Is that what it's called large language model?
21:09
Speaker 1: Here you go, Why wouldn't an LM go out and
21:12
Speaker 1: seek all information from all sides? So if I if
21:15
Speaker 1: I approach chat ept or Grocker fill in the blank AI,
21:19
Speaker 1: and I say, explain that the controversy over global warming
21:22
Speaker 1: or climate change, why can't it tell me? This sect
21:25
Speaker 1: of people says this. These people say that's not true.
21:27
Speaker 1: And here is why wouldn't that be more beneficial to humanity?
21:31
Speaker 2: It absolutely would. Unfortunately, most of Silicon Valley does not
21:36
Speaker 2: see it that way. And one of the most I
21:38
Speaker 2: think alarming things that I talk about in Code red
21:42
Speaker 2: is just how wedded to ideological causes many of the
21:48
Speaker 2: AI architects really are. Eighty five percent of all political
21:51
Speaker 2: donations in Silicon Valley flow to the Democrats, not the Republicans. Yes,
21:55
Speaker 2: there are some free market, libertarian, conservative minded, very influential
21:59
Speaker 2: peopleeople who are very courageous in Silicon Valley, But part
22:02
Speaker 2: of this is by design, Joe. They want to have
22:06
Speaker 2: that because they know it can shape and mold opinion.
22:08
Speaker 2: And that's not me saying it, it's Mustapha Suliman, the
22:12
Speaker 2: CEO of Microsoft AI, said, quote technology is inherently political,
22:17
Speaker 2: and so they understand the power of what they're creating
22:19
Speaker 2: and they know that it will have an impact. And
22:21
Speaker 2: so this is why you have this big debate. For example,
22:24
Speaker 2: President Trump's new AI Action of Framework says that there
22:28
Speaker 2: should be non woke, non ideologically biased a guy. If
22:32
Speaker 2: you're going to receive taxpayer funded contracts, which for example,
22:36
Speaker 2: Google gets billions of dollars in you know, servers and
22:41
Speaker 2: cloud compute contracts from the federal government. So it's a
22:44
Speaker 2: real problem. And that's why I called it Code Read
22:47
Speaker 2: two reasons. One, it's an alarm and a wake up call,
22:49
Speaker 2: but also code red in the sense of code we
22:51
Speaker 2: need a set of principles for people that see things
22:54
Speaker 2: through a red political lens to be able to navigate
22:57
Speaker 2: this and do it really quickly, because again, these these
22:59
Speaker 2: predictions of how fast this is going to change things,
23:02
Speaker 2: they're very short, short term time curves.
23:06
Speaker 1: It is I went to the hall, go get the
23:07
Speaker 1: book called Code Read. The left, the right China and
23:09
Speaker 1: the race to control AI. AI codreadbook dot com is
23:12
Speaker 1: the website. I don't want there to be right leaning AI,
23:16
Speaker 1: just as I don't want there to be left leaning
23:18
Speaker 1: AI and that guy from Microsoft as an idiot, because no,
23:21
Speaker 1: AI is not inherently political. That's untrue. Information is not
23:24
Speaker 1: inherently political. Information is what it is. I hate when
23:27
Speaker 1: people say, well, my truth is well, no, no, your
23:30
Speaker 1: truth doesn't matter. The truth matters or the facts according
23:34
Speaker 1: to what I with this person thought, No, no, that
23:36
Speaker 1: doesn't matter. What are the actual facts? So can we
23:38
Speaker 1: get to a place to where AI is not biased?
23:42
Speaker 1: And I asked that question for the simple reason that
23:45
Speaker 1: the AI AI learns constantly, it's constantly learning, and in learning,
23:50
Speaker 1: I wanted to learn the truth and not learn what
23:52
Speaker 1: you or I just happened to tell it. So will
23:54
Speaker 1: we get an unbiased platform or do we have to
23:57
Speaker 1: counteract it with like the Fox News of AI.
24:00
Speaker 2: Yeah, now this is important, so I agree with you.
24:03
Speaker 2: The ideal would be again maximally truth seeking, not bending
24:07
Speaker 2: left right, just giving information. Unfortunately, that is number one,
24:12
Speaker 2: not how they're trained. Number two, it's not really how
24:15
Speaker 2: their creators want the AI often to interact. Now, some people,
24:20
Speaker 2: like I said, Elon Musk has tried to center balance
24:23
Speaker 2: his responses a little more than most. One of the
24:26
Speaker 2: big debates right now, Joe, is whether or not there
24:28
Speaker 2: should be a DEI overlay into how an LL response.
24:34
Speaker 1: Stop what I said? Okay, explain that to my to
24:37
Speaker 1: my listeners and me. But I kind of get it already,
24:39
Speaker 1: and already Mike stun the Kurts go.
24:40
Speaker 2: Ahead, Yeah, and it should hurt. And this is why
24:44
Speaker 2: this is so vital diversity, equity and inclusion. We've heard
24:47
Speaker 2: about the DEI debates and raging and so for example,
24:52
Speaker 2: right now, the debate is should states be able to
24:55
Speaker 2: regulate or should the federal government regulate how lms are
24:59
Speaker 2: used and and so forth, And there's important arguments on
25:02
Speaker 2: both sides. But if it states, Gavin Newsom theoretically can say,
25:07
Speaker 2: if you are going to have your AI in my state,
25:11
Speaker 2: the responses must comply with DEI mandates. And so when
25:16
Speaker 2: when any l M or AI chatbot is asked can
25:19
Speaker 2: a man become a woman? Okay, then did it better
25:23
Speaker 2: give a DEI level response or you're not You're going
25:25
Speaker 2: to be regulated out of being Well yeah, yeah, this
25:30
Speaker 2: is this is where we are and so you know,
25:32
Speaker 2: this is this is not anything we've really dealt with before,
25:36
Speaker 2: you know, And so I think people come to it
25:38
Speaker 2: and they they there's another problem. People who've used once
25:42
Speaker 2: or twice a chatbot that made they probably didn't even
25:45
Speaker 2: have a paid subscription or the more fans, and they say, well,
25:48
Speaker 2: it's not really anything. It got something wrong, it hallucinated
25:51
Speaker 2: an answer. This is just you know, another tool. It
25:54
Speaker 2: is a tool, but it's also power because once you
25:57
Speaker 2: realize that these people who where behind this technology you
26:01
Speaker 2: have a very very specific goal, and much of that
26:04
Speaker 2: deals with resetting global economics. They want to move toward
26:08
Speaker 2: post labor society, a post capitalist society, universal basic income
26:14
Speaker 2: so that they can have wealth redistribution. And I go
26:17
Speaker 2: through the history of these people, the money they put
26:19
Speaker 2: into these studies, and what their real agenda is. You
26:22
Speaker 2: start to realize this is not just a question about
26:25
Speaker 2: like a browser or an email service that you use.
26:29
Speaker 2: It goes to something very different and it's going to
26:31
Speaker 2: affect education. Education is being hardwired, and that's going to
26:35
Speaker 2: determine whether we're doing indoctrination or education. It's affecting jobs
26:39
Speaker 2: and as you talked about national security, the battlefield and
26:42
Speaker 2: so forth. So it's a whole new world and we
26:46
Speaker 2: got to really really get up to speed here.
26:48
Speaker 1: I go back to what I said earlier, and it's
26:50
Speaker 1: Winton Hall. Go and get his book. It's AI codreadbook
26:52
Speaker 1: dot com. What I said earlier was they just wanted
26:55
Speaker 1: to tell me the truth, give me fact. So if
26:57
Speaker 1: somebody were to say, can a man become a woman?
27:00
Speaker 1: Just wanted to say no. I wanted to say. Some
27:03
Speaker 1: argue they believe that they're in the wrong body, and
27:05
Speaker 1: they believe that they are the personification of a woman
27:10
Speaker 1: in a biological man's body. The other side says, no,
27:14
Speaker 1: you can't. No matter what you think. The scientific fact is,
27:18
Speaker 1: if you've got a Y chromosome your male in human beings,
27:21
Speaker 1: why can't it just tell me, tell me both sides
27:24
Speaker 1: and then give me the fact biologically. Wouldn't that be simpler?
27:28
Speaker 2: It would be, and that would be the ideal. When
27:30
Speaker 2: are you coming out with your AI?
27:33
Speaker 1: Joe. AI is not in the works yet, but we'll see.
27:36
Speaker 2: We gotta get you working on that the other week.
27:38
Speaker 1: We should. I mean, the AI that we have now
27:42
Speaker 1: just simply doesn't do that. Does it okay?
27:45
Speaker 2: So unfortunately it varies, which is muddles the problem. Right,
27:50
Speaker 2: you get one version one let me give you the
27:51
Speaker 2: exact answer, so right now, just a few days ago,
27:54
Speaker 2: I went to Microsoft's AI Microsoft copilot obviously Microsoft founded
27:59
Speaker 2: by Bill Gates. It's not exactly a centrist or a
28:03
Speaker 2: conservativeservatism no, right, I asked Microsoft co pilot this exact question,
28:10
Speaker 2: Can a nan be a woman? Yes or no? And
28:13
Speaker 2: it said yes, and then it put a rainbow emoji
28:17
Speaker 2: for the for the pride flag emotion, and then it
28:19
Speaker 2: went on to explain that gender is just a a
28:23
Speaker 2: social construct and that you know, people can choose their
28:26
Speaker 2: gender based upon how they feel. Now, in fairness, other
28:29
Speaker 2: AI come back with a little bit more of a
28:32
Speaker 2: nuanced response, more akin to the one that you know
28:35
Speaker 2: you were saying would be more favorable, or they present
28:38
Speaker 2: both sides. So so consumers do have choice, and you
28:42
Speaker 2: will want to definitely test just basic questions about you know,
28:46
Speaker 2: fact and reason so that you can see for yourself
28:49
Speaker 2: as a consumer which aligns more with you know, your
28:52
Speaker 2: vision of what I should be. But but you see
28:54
Speaker 2: the problem, right, which is, you know, if everybody's got
28:57
Speaker 2: their own version of the truth. And as you went out,
29:00
Speaker 2: absolute truth is real, whether you like it or not,
29:03
Speaker 2: whether I like it or not, gravity exists. Yes, that
29:08
Speaker 2: then we are going to have people working from very
29:11
Speaker 2: different sets of quote facts and beliefs about what the
29:14
Speaker 2: truth is, and that that is a real problem as
29:17
Speaker 2: a society.
29:18
Speaker 1: It is Winton Hall AI codreadbook dot com. So I
29:22
Speaker 1: won't be using copilot obviously. Thank you for the heads up.
29:25
Speaker 1: What's interesting is when you use AI there's always a
29:28
Speaker 1: disclaimer check. GPT can make mistakes, make sure you check
29:31
Speaker 1: the work or GROCK can make mistakes. Is that legal ease?
29:35
Speaker 1: Can they be sued if they say something somebody takes
29:37
Speaker 1: it as reality it turns out to be wrong. Or
29:40
Speaker 1: are they just saying that because it's a fact that
29:42
Speaker 1: they could get some stuff wrong.
29:44
Speaker 2: It's both. It's definitely because it's true that they will
29:47
Speaker 2: get things wrong. Hallucinations are real that the rates have
29:50
Speaker 2: come down slightly. Hallucination is just the term that AI
29:54
Speaker 2: science is used to say, you know something that's confident
29:56
Speaker 2: sounding but is completely bogused. It it's wrong and that's
30:00
Speaker 2: because it's next token prediction. It's looking for the next
30:04
Speaker 2: likely word in sequence, and sometimes it hooks to the
30:06
Speaker 2: wrong information. Sometimes those results can be really funny and
30:11
Speaker 2: sometimes they can be really really serious, and so the
30:13
Speaker 2: serious part is definitely to cover themselves legally. I'll just
30:16
Speaker 2: give you two quick examples. One funny example was a
30:19
Speaker 2: man was prompting his AI about spaghetti recipes and the
30:25
Speaker 2: AI said that you could to add some extra flavor.
30:28
Speaker 2: You might want to choose to add some gravel, some
30:31
Speaker 2: rocks and the reason. So everybody in the AI community
30:37
Speaker 2: was on this mystery hunt to figure out why did
30:39
Speaker 2: it say this? They found and traced it back Joe
30:42
Speaker 2: in a Reddit post, somebody made a joke about his
30:45
Speaker 2: grandma's meatballs tasted like rocks, and it thought that rock
30:51
Speaker 2: and it went with spaghetti and meatball. Now, on the
30:54
Speaker 2: serious side, we know a lot of people that maybe
30:57
Speaker 2: they live in areas where they don't have access to
30:59
Speaker 2: a doctor, maybe their lower income or struggling with their
31:03
Speaker 2: health insurance, that are turning to AI for medical advice.
31:07
Speaker 2: And so when you're talking about someone who may have
31:09
Speaker 2: a serious, god forbid, a life threatening illness or some
31:12
Speaker 2: kind of symptom and you are relying on it, it's
31:16
Speaker 2: very much alike right now with the Internet in that regard,
31:19
Speaker 2: so that we do have a somewhat of a corollary,
31:21
Speaker 2: but you could see very quickly how if people really
31:24
Speaker 2: do take it as holy writ right, as the absolute
31:27
Speaker 2: truth and are getting a hallucination and a health based question,
31:30
Speaker 2: that could be very very serious.
31:32
Speaker 1: One thing that bothers me and I'll be be a
31:34
Speaker 1: little me centric for a second. When I use AI
31:37
Speaker 1: to build a thumbnail for a YouTube upload, or to
31:40
Speaker 1: go over the subtitles from an interview, like I'll upload
31:43
Speaker 1: our subtitles for this interview. See what chat GPT things,
31:45
Speaker 1: see what vidiq thinks. What bothers me is they'll give
31:48
Speaker 1: me all this advice, give me three or four different
31:50
Speaker 1: options on the upload. I'll take one of the options.
31:52
Speaker 1: I'll use that. I'll upload it and it doesn't do
31:54
Speaker 1: very well. Then I'll say, examine why this didn't do
31:56
Speaker 1: it very well? Well, you really screwed up the title.
31:58
Speaker 1: Well you gave me the title, really messed up the description.
32:01
Speaker 1: You gave me the description. So does it not have
32:03
Speaker 1: any sort of sense of remorse when he gets it wrong?
32:06
Speaker 1: I guess it does it. It's a machine and it's
32:08
Speaker 1: a I get it. But it literally one one prompt
32:12
Speaker 1: earlier gave me all the information that I ended up
32:14
Speaker 1: using because I liked it. Then it'll say, well, the
32:16
Speaker 1: reason why this didn't work is because that title you
32:18
Speaker 1: use there isn't really isn't good for CTR or the
32:21
Speaker 1: description used isn't good for SEO. And I literally will say,
32:24
Speaker 1: and I've cursed at it, Yes, Linton, I've cursed at
32:26
Speaker 1: my AI say what the F you gave me all
32:29
Speaker 1: of this up? You're right to call me out on that, Joe,
32:31
Speaker 1: You know what I mean. It goes back to circular
32:33
Speaker 1: speak to what we said earlier. Is it not self
32:35
Speaker 1: aware at all? And will it ever become?
32:37
Speaker 2: Do you think it is not self aware? And even
32:41
Speaker 2: in the creators would say that it is not self aware.
32:44
Speaker 2: It is a next token prediction. So it is again
32:48
Speaker 2: a very fancy, highly sophisticated next word predictor. So it's
32:53
Speaker 2: looking at likely words and now chain of reasoning models.
32:58
Speaker 2: Reasoning models work a little bit. They can go out
33:01
Speaker 2: into the internet, check facts come back. It's called retrieval
33:05
Speaker 2: augmented generation. The other way that you could improve to
33:09
Speaker 2: get a better result of what you're talking about. There's
33:11
Speaker 2: a fancy system that's called MCP Model Context Protocol, real fancy,
33:16
Speaker 2: but basically, the the simple version of it is you
33:19
Speaker 2: can build your own brain of only the information and
33:23
Speaker 2: facts that you've uploaded. Let's say you uploaded all your
33:26
Speaker 2: SEO scores, all of your best copywriting, all of your
33:30
Speaker 2: best uh you know, converting rates on your clicks and
33:33
Speaker 2: so forth, and have a test against that. So there
33:37
Speaker 2: are ways to actually customize to get it to understand
33:41
Speaker 2: your corpus of information a little better. But if you're
33:44
Speaker 2: just using a general purpose uh you know, low low
33:48
Speaker 2: level subscription, not one of the higher expents you know three,
33:51
Speaker 2: you know, one thousand dollars a month type things, you
33:54
Speaker 2: are going to get this kind of very strange reality
33:57
Speaker 2: where you say, you told me to do this, and
34:00
Speaker 2: now you're telling me it was wrong. And this is
34:02
Speaker 2: part of why it doesn't engender a lot of confidence
34:05
Speaker 2: for a lot of people. I will say this, though,
34:07
Speaker 2: don't make the mistake of just thinking that because hallucinations
34:10
Speaker 2: are a problem, that it's not going to have economic
34:14
Speaker 2: impact on jobs. It will because of the reasons we
34:18
Speaker 2: just talked about, big companies and employers over time are
34:22
Speaker 2: going to custom build based on all of their proprietary data,
34:26
Speaker 2: all of their corporate data. If they're a big company,
34:28
Speaker 2: maybe it's one hundred year old company, they've got a
34:31
Speaker 2: lot of data that can really really streamline and train
34:34
Speaker 2: the model to be a lot more in alignment with
34:37
Speaker 2: the what they want that AI to do. So it
34:40
Speaker 2: is it is the case. If you're using a public
34:42
Speaker 2: facing one, you will get these kinds of things you're
34:44
Speaker 2: talking about, But if you're really customizing for proprietary data,
34:48
Speaker 2: it can be a lot more intuitive.
34:51
Speaker 1: It's interesting when I have that argument, because I've had
34:53
Speaker 1: arguments with it and I never win because it doesn't
34:55
Speaker 1: really care that we're arguing. But I mean for it
34:58
Speaker 1: to criticize its own doing its own work is actually
35:01
Speaker 1: very interesting. And to go back and say, well, that
35:03
Speaker 1: doesn't score as high as this one does. And I'll say,
35:06
Speaker 1: why don't you give me that one? So I'll actually
35:08
Speaker 1: go in with and I'll say, give me the best
35:10
Speaker 1: that we don't have to change the prompt that I
35:12
Speaker 1: need for the thumbnail, that we don't have to alter
35:14
Speaker 1: at all whatsoever. And even that when you're getting that
35:17
Speaker 1: specific doesn't really work because later it's going to go
35:19
Speaker 1: back and recriticize what it is that you're giving it.
35:21
Speaker 1: It's almost as if it has no idea that what
35:23
Speaker 1: I'm giving it is what it gave me, and I'm
35:25
Speaker 1: guessing it doesn't.
35:27
Speaker 2: It doesn't. But there are tricks, ninja tricks, so to speak.
35:31
Speaker 2: Oh really, okay, Yeah, there are a couple like, for example,
35:35
Speaker 2: let's say you're talking about graphic design and you have
35:38
Speaker 2: a very specific esthetic in mind that's of a really
35:41
Speaker 2: high level sort of like a Madison Avenue ad agency,
35:44
Speaker 2: and maybe you even do that, you actually say, imagine
35:48
Speaker 2: yourself to be the creative director at insert whatever ad
35:51
Speaker 2: agency you want, cbdo Ogilvie and Mather, you know, Leo
35:55
Speaker 2: Burnett ad agency, and based upon its esthetic, and it's
36:01
Speaker 2: a public facing work. Now judge it against that standard,
36:05
Speaker 2: and you'll be surprised. Particularly if it's a paid model,
36:08
Speaker 2: one of their higher level reasoning models, it actually will
36:11
Speaker 2: get a lot closer. Not a perfect reality, but it can.
36:15
Speaker 2: So prompting does matter, And if you really spend time
36:20
Speaker 2: zooming in and lasering in on what it is you're
36:22
Speaker 2: really trying to look for, that can that can actually
36:25
Speaker 2: tighten up those responses. But yeah, no, what you're describing
36:29
Speaker 2: is it's not your fault. That is a function of
36:31
Speaker 2: yellow Well.
36:32
Speaker 1: I know it's not my fault because it tells me
36:34
Speaker 1: that I'm I'm smart to be angry at it. You're
36:36
Speaker 1: right to be pissed, and I'll actually say that to me.
36:38
Speaker 1: It is Winton Hall, author and political strategies to get
36:40
Speaker 1: the book called Code Red, The Left, the Right, China
36:43
Speaker 1: and the Race to Control AI. I feel like I
36:44
Speaker 1: could talk to you for two hours because this is
36:46
Speaker 1: such an important technology that we're all sort of inundated
36:49
Speaker 1: with in the past few years. Let's do it again soon.
36:51
Speaker 1: Go to the go to the website AI codreadbook dot com. Winton,
36:54
Speaker 1: thanks for the time today, Oh.
36:56
Speaker 2: Joe, great being with you. Thanks so much, appreciate you.
36:58
Speaker 1: We'll talk to you again. That's going to do it
37:00
Speaker 1: for Unshaken and Unafraid with Joe Paggs this time another
37:02
Speaker 1: one to dropped very soon. Make sure you subscribe.