1
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It all started with a murder.

2
00:00:01,280 --> 00:00:03,600
The murder of this AI whistleblower.

3
00:00:03,600 --> 00:00:06,600
Right after he supposedly is preparing a
drive

4
00:00:06,600 --> 00:00:09,359
for the court for discovery.

5
00:00:09,359 --> 00:00:11,560
So isn't that the perfect time to kill
somebody?

6
00:00:11,560 --> 00:00:13,679
None of his possessions are taken.

7
00:00:13,679 --> 00:00:15,960
The only thing that's missing is this drive.

8
00:00:15,960 --> 00:00:17,640
The bullet missed the brain.

9
00:00:17,640 --> 00:00:19,400
How often do you miss your own brain

10
00:00:19,400 --> 00:00:20,240
when you're trying to commit?

11
00:00:20,240 --> 00:00:21,719
You gotta move that to your partner.

12
00:00:21,719 --> 00:00:24,480
In China, that training drive can take
DeepSeek

13
00:00:24,480 --> 00:00:26,440
from last place, the last place horse,

14
00:00:26,440 --> 00:00:29,120
and leapfrog the field to make it a first
place horse.

15
00:00:29,160 --> 00:00:31,200
What do you do if you're the intelligence
agencies

16
00:00:31,200 --> 00:00:33,759
and you have a deplorables database?

17
00:00:33,759 --> 00:00:35,359
It's like Snowden all over again.

18
00:00:35,359 --> 00:00:39,719
Let Trump do the waste and let Elon do the
corruption part.

19
00:00:39,719 --> 00:00:41,679
He's gonna come up with so much stuff with
Grok.

20
00:00:41,679 --> 00:00:42,799
It's not even gonna be funny.

21
00:00:42,799 --> 00:00:45,159
It's gonna be the days of milk and honey here

22
00:00:45,159 --> 00:00:47,320
really quickly with USAID.

23
00:00:47,320 --> 00:00:48,480
It's just starting.

24
00:00:53,159 --> 00:00:54,520
George Webb, welcome to the show.

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00:00:54,520 --> 00:00:56,439
Thank you for joining me.

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00:00:56,439 --> 00:00:57,600
Marty Bent.

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00:00:57,640 --> 00:00:59,280
Marty Bent, everybody said,

28
00:00:59,280 --> 00:01:01,399
you should try to get on Marty Bent.

29
00:01:01,399 --> 00:01:03,719
I worked years.

30
00:01:03,719 --> 00:01:04,719
No, I didn't.

31
00:01:04,719 --> 00:01:05,960
But then you called me.

32
00:01:05,960 --> 00:01:06,840
So this is great.

33
00:01:06,840 --> 00:01:08,240
Marty Bent, here you are.

34
00:01:09,040 --> 00:01:14,000
Well, I'm very excited about this particular
subject

35
00:01:14,000 --> 00:01:18,719
because obviously DeepSeek was the big
headline

36
00:01:18,719 --> 00:01:21,680
about a week ago with this new open source
model

37
00:01:21,680 --> 00:01:26,359
that purportedly cost $6 million to train.

38
00:01:26,400 --> 00:01:28,840
And then I stumbled upon a presentation

39
00:01:28,840 --> 00:01:31,120
that you gave on January 3rd

40
00:01:31,120 --> 00:01:34,480
when much of the world was unaware about
DeepSeek,

41
00:01:34,480 --> 00:01:36,319
basically talking about the connection

42
00:01:36,319 --> 00:01:41,319
between that company and the open AI
whistleblower.

43
00:01:41,560 --> 00:01:43,239
And after watching that, I said,

44
00:01:43,239 --> 00:01:45,879
I have to talk to George to figure out

45
00:01:45,879 --> 00:01:50,120
how you got on this trail and were aware of
DeepSeek

46
00:01:50,120 --> 00:01:53,640
before much of the rest of the world.

47
00:01:54,640 --> 00:01:57,480
Yeah, I gave that similar presentation

48
00:01:57,480 --> 00:02:02,000
on December the 19th, also in Silicon Valley.

49
00:02:02,000 --> 00:02:04,640
And like right near all the venture partners,

50
00:02:04,640 --> 00:02:06,439
right where Sam Altman had got his start

51
00:02:06,439 --> 00:02:08,080
and got his first check from Elon Musk

52
00:02:08,080 --> 00:02:10,560
in Menlo Park at the Rosewood Hotel there.

53
00:02:10,560 --> 00:02:13,960
So I was right at AI headquarters sort of.

54
00:02:13,960 --> 00:02:18,680
And the, oh, it wants to do notifications.

55
00:02:18,680 --> 00:02:19,560
I better allow those.

56
00:02:19,560 --> 00:02:20,400
Okay.

57
00:02:20,840 --> 00:02:23,240
But it all started with a murder

58
00:02:23,240 --> 00:02:26,840
and the murder of this AI whistleblower.

59
00:02:26,840 --> 00:02:30,840
And he was the guy responsible working with
Ilya Sutskever,

60
00:02:30,840 --> 00:02:34,840
who Sutskever is the CTO of OpenAI,

61
00:02:34,840 --> 00:02:36,080
working with John Schulman,

62
00:02:36,080 --> 00:02:39,640
who does post-training for four years,

63
00:02:39,640 --> 00:02:44,640
also working with ScaleAI that does DOD work
for AI training.

64
00:02:44,719 --> 00:02:47,719
And he's with five other guys that are
world-class experts

65
00:02:47,719 --> 00:02:49,359
or four other guys.

66
00:02:49,359 --> 00:02:52,759
His last week he's alive that are world-class
experts,

67
00:02:52,759 --> 00:02:56,119
young up and coming world-class experts on
post-training.

68
00:02:56,119 --> 00:03:00,639
And then all of a sudden DeepSeek a week
later makes,

69
00:03:00,639 --> 00:03:02,359
this is just when it was version three,

70
00:03:02,359 --> 00:03:03,799
R1 hadn't even come out yet.

71
00:03:03,799 --> 00:03:07,919
I'd said, oh my gosh, the only way you do
that is to,

72
00:03:07,919 --> 00:03:10,359
and especially in the natural language
processing,

73
00:03:10,359 --> 00:03:12,319
I mean, I'm not saying that you're not doing
it

74
00:03:12,359 --> 00:03:15,159
and especially in the natural language
processing scores

75
00:03:15,159 --> 00:03:16,439
that it was getting,

76
00:03:16,439 --> 00:03:19,079
the only way you do that is put in
conversations

77
00:03:19,079 --> 00:03:21,159
like we're having between you and me, Marty,

78
00:03:21,159 --> 00:03:23,680
which is Americans talking to Americans.

79
00:03:23,680 --> 00:03:26,000
No matter how many Chinese people you hire,

80
00:03:26,000 --> 00:03:27,759
you can hire billions.

81
00:03:27,759 --> 00:03:29,680
You're never gonna get the natural language

82
00:03:29,680 --> 00:03:31,519
between me and you.

83
00:03:31,519 --> 00:03:34,599
And somebody had to feed that information in.

84
00:03:34,599 --> 00:03:39,519
Now I had previously worked with Eric Schmidt
at Sun.

85
00:03:39,519 --> 00:03:41,319
I was one of 200 guys that worked for him.

86
00:03:41,319 --> 00:03:44,239
And so there's a lot of stuff Eric was doing

87
00:03:44,239 --> 00:03:47,280
with DeepMind that kind of bled over.

88
00:03:47,280 --> 00:03:49,039
He's very close friends with Nancy Pelosi.

89
00:03:49,039 --> 00:03:50,799
And so that's kind of where it led me to say,

90
00:03:50,799 --> 00:03:54,560
hey, somebody, and remember this kid is
murdered

91
00:03:54,560 --> 00:03:56,799
in his San Francisco apartment.

92
00:03:56,799 --> 00:04:00,120
He's working for OpenAI right after he
supposedly

93
00:04:00,120 --> 00:04:05,120
is preparing a drive for the court for
discovery.

94
00:04:05,240 --> 00:04:07,599
So isn't that the perfect time to kill
somebody?

95
00:04:07,599 --> 00:04:09,879
Because that training drive can take DeepSeek

96
00:04:09,879 --> 00:04:12,079
from last place, the last place horse,

97
00:04:12,079 --> 00:04:14,639
and leapfrog the field to make it a first
place horse.

98
00:04:14,639 --> 00:04:18,000
So that's what my presentation was about on
the 19th

99
00:04:18,000 --> 00:04:19,639
and then again on the January 3rd.

100
00:04:21,000 --> 00:04:25,480
And so let's dive into the OpenAI
whistleblower.

101
00:04:26,920 --> 00:04:29,079
Again, he had this drive.

102
00:04:29,079 --> 00:04:31,920
He was blowing the whistle.

103
00:04:31,920 --> 00:04:34,959
I forget, my memory is escaping me.

104
00:04:34,959 --> 00:04:38,519
What was the public sort of positioning

105
00:04:38,519 --> 00:04:40,799
of what he was gonna blow the whistle on?

106
00:04:42,159 --> 00:04:45,639
He had come out in the New York Times in
October

107
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and said that a whole bunch of copyrighted
material

108
00:04:48,639 --> 00:04:52,519
was being used, like New York Times articles,

109
00:04:52,519 --> 00:04:53,839
but then all sorts of articles.

110
00:04:53,839 --> 00:04:55,879
And that would explain a lot.

111
00:04:56,879 --> 00:04:58,680
But I said that there was something more

112
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and I interviewed the mother

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and I went with them to the crime scene.

114
00:05:02,079 --> 00:05:04,680
First of all, it's a murder, not a suicide.

115
00:05:04,680 --> 00:05:09,199
Elon Musk tweeted four times after we showed
the video.

116
00:05:09,199 --> 00:05:11,959
And the second piece of it was

117
00:05:11,959 --> 00:05:14,319
it wasn't just copyrighted information.

118
00:05:14,319 --> 00:05:15,720
Like I had alluded to before,

119
00:05:15,720 --> 00:05:18,439
it's more the NSA type Snowden information

120
00:05:18,439 --> 00:05:20,360
where you and I are calling each other on the
phone

121
00:05:20,360 --> 00:05:21,920
with natural language.

122
00:05:21,920 --> 00:05:24,879
And then also you and I are texting each
other.

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00:05:24,879 --> 00:05:29,840
The call data records and then the SS7 text
type messages.

124
00:05:29,840 --> 00:05:31,639
And there's hundreds of millions of those

125
00:05:31,639 --> 00:05:34,120
they collect every day at NSA.

126
00:05:34,160 --> 00:05:36,160
It's that type of volume of information

127
00:05:36,160 --> 00:05:37,319
that seemed to go there.

128
00:05:37,319 --> 00:05:39,959
It seemed to go through Scale AI.

129
00:05:39,959 --> 00:05:42,399
They seem to put DeepSeek in front of Scale
AI

130
00:05:42,399 --> 00:05:44,800
when they're partnering overseas.

131
00:05:44,800 --> 00:05:46,720
So for all those reasons, I said,

132
00:05:46,720 --> 00:05:48,680
it's not just copyright information.

133
00:05:48,680 --> 00:05:51,639
It's this NSA Snowden type information

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00:05:51,639 --> 00:05:53,439
that's being used to train.

135
00:05:53,439 --> 00:05:56,280
Because just doing New York Times articles

136
00:05:56,280 --> 00:06:01,280
or whatever, there's a training set called
the Common Crawl.

137
00:06:01,759 --> 00:06:04,279
And it's every website, et cetera.

138
00:06:05,159 --> 00:06:07,639
And there's transcripts in there and so
forth.

139
00:06:07,639 --> 00:06:12,639
But it doesn't give you the depth of
sequential reasoning

140
00:06:13,359 --> 00:06:15,719
and how people go through sequential
reasoning

141
00:06:15,719 --> 00:06:20,719
like how a doctor does the reasoning,
diagnostic reasoning.

142
00:06:20,759 --> 00:06:22,839
It had that capability.

143
00:06:22,839 --> 00:06:25,439
It started scoring better and better on the
USMLE

144
00:06:25,439 --> 00:06:28,199
which is the Medical Licensing Examination.

145
00:06:28,199 --> 00:06:30,639
It jumped way up on USMLE.

146
00:06:30,639 --> 00:06:32,199
And when I saw the medical reasoning,

147
00:06:32,199 --> 00:06:35,240
I said, oh, that's different.

148
00:06:35,240 --> 00:06:38,479
That's like a doctor talking to another
doctor,

149
00:06:38,479 --> 00:06:40,199
going through the sequential steps,

150
00:06:40,199 --> 00:06:43,079
going through the flow chart of the
diagnostic reasoning.

151
00:06:43,079 --> 00:06:45,000
That is new.

152
00:06:45,000 --> 00:06:47,479
And that's where I started zooming in.

153
00:06:47,479 --> 00:06:48,680
And then the mother told me

154
00:06:48,680 --> 00:06:50,759
that there was a government information
source

155
00:06:50,759 --> 00:06:53,079
that she couldn't reveal.

156
00:06:53,079 --> 00:06:55,479
And that's a government information,

157
00:06:55,479 --> 00:06:57,959
a classified information source that could
not be revealed.

158
00:06:57,959 --> 00:06:59,159
And so that's when I started saying,

159
00:06:59,160 --> 00:07:01,720
oh, we're on to something hot here.

160
00:07:03,720 --> 00:07:05,720
And so it seems apparent,

161
00:07:05,720 --> 00:07:09,040
you're not gonna, when the diagnostic like
conversation,

162
00:07:09,040 --> 00:07:10,400
like being able to pass that test,

163
00:07:10,400 --> 00:07:12,280
you're not gonna find that information in the
New York Times,

164
00:07:12,280 --> 00:07:13,560
probably not on YouTube either.

165
00:07:13,560 --> 00:07:17,200
That is very specific to the domain of
surgery

166
00:07:17,200 --> 00:07:18,040
or whatever it may be.

167
00:07:18,040 --> 00:07:20,000
Doctors have a certain language.

168
00:07:20,000 --> 00:07:22,320
And so it would be odd for a training model

169
00:07:22,320 --> 00:07:25,879
to basically be able to articulate what two
doctors would

170
00:07:25,879 --> 00:07:27,720
talking on a diagnostic call.

171
00:07:27,720 --> 00:07:32,160
So I guess is the theory that is it open AI

172
00:07:32,160 --> 00:07:35,360
or was it DeepSeq was feeding these large
language models

173
00:07:35,360 --> 00:07:38,840
from data centers that the MSA had access to
in Utah

174
00:07:38,840 --> 00:07:40,480
or something similar to that?

175
00:07:42,880 --> 00:07:45,000
I've known Eric for a long time.

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00:07:45,000 --> 00:07:46,560
I've known him for 30 years,

177
00:07:46,560 --> 00:07:48,880
and I've known what he's done in AI for 30
years.

178
00:07:48,880 --> 00:07:52,920
So I'm kind of going on personal information
here

179
00:07:52,920 --> 00:07:57,040
a little bit, but Eric started the Defense
Information Board.

180
00:07:57,880 --> 00:08:02,880
This was a kind of Clinton and then a Biden
initiative

181
00:08:04,560 --> 00:08:07,440
to basically take government technology

182
00:08:07,440 --> 00:08:08,880
and move it to Silicon Valley.

183
00:08:10,000 --> 00:08:13,320
It was the Defense Innovation Unit, the DIUX,

184
00:08:13,320 --> 00:08:17,080
and it was called Experimental and start DIUX
Experimental.

185
00:08:17,080 --> 00:08:18,720
It was right by Google.

186
00:08:18,720 --> 00:08:23,720
It was right by Moffitt Airfield

187
00:08:24,080 --> 00:08:25,840
where Silicon Valley was born.

188
00:08:25,879 --> 00:08:28,679
So there was a lot of connections with
DeepMind,

189
00:08:28,679 --> 00:08:29,519
that is a service.

190
00:08:29,519 --> 00:08:32,240
So there's a lot of metadata that's behind
this judgment.

191
00:08:33,799 --> 00:08:35,120
But to answer your question,

192
00:08:35,120 --> 00:08:40,120
I think that information was initially used
to train open AI

193
00:08:40,959 --> 00:08:43,759
because it's gonna be sensitive.

194
00:08:43,759 --> 00:08:46,439
It's gonna be also used to train scale AI

195
00:08:46,439 --> 00:08:48,159
for the really sensitive stuff

196
00:08:48,159 --> 00:08:50,639
for like the classified and top secret stuff.

197
00:08:50,639 --> 00:08:53,079
But the proprietary stuff, you're in my
conversations,

198
00:08:53,120 --> 00:08:56,480
private conversations for open AI for sure.

199
00:08:56,480 --> 00:09:00,120
And then you can create what's called a
distillation model,

200
00:09:00,120 --> 00:09:05,120
which is much slimmer, 100th to 1,000th of
the size

201
00:09:05,560 --> 00:09:08,480
if you're trying to do a specialized AI.

202
00:09:08,480 --> 00:09:09,960
So if you just want something

203
00:09:09,960 --> 00:09:13,639
that analyzes deplorables conversations and
then says,

204
00:09:13,639 --> 00:09:17,040
who's gonna be a terrorist out of this group,

205
00:09:17,040 --> 00:09:18,840
you can train a distilled AI.

206
00:09:18,840 --> 00:09:21,040
And this is what I think DeepSeq got.

207
00:09:21,039 --> 00:09:23,120
And then this is what I think the
intelligence agencies

208
00:09:23,120 --> 00:09:27,039
were trying to move offshore before Trump
took office.

209
00:09:27,039 --> 00:09:29,120
And this is why this all happened so quickly

210
00:09:29,120 --> 00:09:30,759
and there was such a market disturbance

211
00:09:30,759 --> 00:09:32,240
with Nvidia and so forth.

212
00:09:34,360 --> 00:09:35,199
Yeah.

213
00:09:35,199 --> 00:09:38,000
And this is where I'm trying to figure out
the connection

214
00:09:38,000 --> 00:09:40,240
between obviously open AI,

215
00:09:42,079 --> 00:09:44,919
Sutra Balaji worked for them and DeepSeq

216
00:09:44,919 --> 00:09:49,919
is the theory that he was taking the training
information

217
00:09:50,919 --> 00:09:54,079
from open AI and giving it to DeepSeq

218
00:09:54,079 --> 00:09:55,559
so that they could train their model?

219
00:09:55,559 --> 00:09:56,879
Somebody was.

220
00:09:56,879 --> 00:09:59,240
The first four guys I wanna talk to

221
00:09:59,240 --> 00:10:02,439
are the four Chinese friends he's had since
high school.

222
00:10:02,439 --> 00:10:05,159
I don't think Suchir was involved in that at
all.

223
00:10:05,159 --> 00:10:10,159
I think he's very ethical and he probably got
very uneasy

224
00:10:11,399 --> 00:10:14,039
when the law students started coming in

225
00:10:14,039 --> 00:10:16,279
because, hey, I'm the guy on the hook here.

226
00:10:16,279 --> 00:10:18,319
I'm the guy who pushed all that,

227
00:10:18,360 --> 00:10:21,640
all those terabytes and petabytes in,

228
00:10:21,640 --> 00:10:23,400
common crawls about a petabyte.

229
00:10:23,400 --> 00:10:25,720
I'm the guy who pushed all those petabytes in
there.

230
00:10:25,720 --> 00:10:28,200
And now I'm the one who going to jail.

231
00:10:28,200 --> 00:10:31,400
So that's when he came forward in October

232
00:10:31,400 --> 00:10:34,200
and did the expose in the New York Times.

233
00:10:34,200 --> 00:10:36,840
Well, that caused a whole bunch of things to
happen,

234
00:10:36,840 --> 00:10:41,840
which is now everybody knows that open AI
might have had

235
00:10:41,879 --> 00:10:44,680
and now this stuff is gonna be discovered.

236
00:10:44,680 --> 00:10:47,440
So now what do you do if you're the
intelligence agencies

237
00:10:47,440 --> 00:10:51,360
and you have a deplorables database, an NSA
database

238
00:10:51,360 --> 00:10:54,360
and you're using AI and the NSA information,
what do you do?

239
00:10:54,360 --> 00:10:55,840
It's like Snowden all over again.

240
00:10:55,840 --> 00:10:58,200
It's like Clapper and all over again.

241
00:10:58,200 --> 00:11:02,280
You gotta move that to your partner in China.

242
00:11:02,280 --> 00:11:06,360
And that's why I think the DeepSeq just went
through

243
00:11:06,360 --> 00:11:09,680
the roof in terms of natural language
processing.

244
00:11:09,680 --> 00:11:11,560
A lot of people are saying other things,

245
00:11:11,560 --> 00:11:14,200
but that's the theory of the case.

246
00:11:14,920 --> 00:11:16,080
Hmm.

247
00:11:16,080 --> 00:11:19,200
And I mean, you said you visited the crime
scene,

248
00:11:19,200 --> 00:11:22,680
Mr. Cheers, mother, what was that like?

249
00:11:23,680 --> 00:11:28,680
Well, there's blood, vertical drops from
blood like a nose,

250
00:11:29,840 --> 00:11:32,560
like he was hit from behind or something like
that,

251
00:11:32,560 --> 00:11:34,120
but vertical, few sprinkled,

252
00:11:34,120 --> 00:11:37,600
like when you have a nosebleed after you
shave or whatever.

253
00:11:37,600 --> 00:11:40,360
And then three pools of blood out of the bat,

254
00:11:40,360 --> 00:11:43,000
that was into the sink and his hair into the
sink,

255
00:11:43,039 --> 00:11:44,600
like he was hit from behind.

256
00:11:44,600 --> 00:11:46,679
And then three pools of blood leading out

257
00:11:46,679 --> 00:11:48,200
as he's crawling out of the bathroom.

258
00:11:48,200 --> 00:11:50,879
And then he dies in the sort of the door jam

259
00:11:50,879 --> 00:11:55,200
of the bathroom about 10 feet away.

260
00:11:55,200 --> 00:11:57,279
And then there's another little spot of blood

261
00:11:57,279 --> 00:11:59,679
over where the backpacks are.

262
00:11:59,679 --> 00:12:01,799
Remember he had just come back from a
four-day hiking trip

263
00:12:01,799 --> 00:12:03,840
with these four Chinese guys.

264
00:12:03,840 --> 00:12:05,639
And there's one spot of blood.

265
00:12:05,639 --> 00:12:07,480
Now he would have had to shot,

266
00:12:07,480 --> 00:12:09,480
after he supposedly shot himself,

267
00:12:10,480 --> 00:12:12,559
he was bleeding by the sink supposedly,

268
00:12:13,119 --> 00:12:15,079
and then sat down, we had a forensic expert
say

269
00:12:15,079 --> 00:12:16,319
then he sat down in the bathroom,

270
00:12:16,319 --> 00:12:18,759
then he shot himself through the forehead.

271
00:12:19,799 --> 00:12:21,479
The bullet missed the brain.

272
00:12:21,479 --> 00:12:23,239
How often do you miss your own brain

273
00:12:23,239 --> 00:12:25,439
when you're trying to commit suicide?

274
00:12:25,439 --> 00:12:28,359
Lodged between the C1 and the C2 process

275
00:12:28,359 --> 00:12:30,879
in the dens of the C2, I believe,

276
00:12:30,879 --> 00:12:33,039
and the process of the C1.

277
00:12:34,199 --> 00:12:35,799
And he didn't die right away.

278
00:12:35,799 --> 00:12:37,079
So who does that?

279
00:12:38,519 --> 00:12:41,079
So there's a whole bunch of things

280
00:12:41,079 --> 00:12:42,439
that don't add up in the suicide.

281
00:12:43,280 --> 00:12:44,120
And then nothing is taken.

282
00:12:44,120 --> 00:12:46,720
His computer isn't taken, his phone isn't
taken,

283
00:12:46,720 --> 00:12:49,600
none of his possessions are taken.

284
00:12:49,600 --> 00:12:50,760
The only thing that's missing

285
00:12:50,760 --> 00:12:53,160
that the parent tells me about is this drive.

286
00:12:54,440 --> 00:12:57,560
Seems like somebody killed him for the drive.

287
00:12:57,560 --> 00:13:01,040
And so that's kind of where the case is right
now.

288
00:13:03,120 --> 00:13:07,440
And what's the latest on the Chinese
roommates?

289
00:13:08,560 --> 00:13:10,480
Well, I know all their names.

290
00:13:10,480 --> 00:13:11,880
I want them to come forward

291
00:13:11,879 --> 00:13:15,679
because I want to say that they aren't a part
of this.

292
00:13:15,679 --> 00:13:18,360
They are, and their lifelong friends

293
00:13:18,360 --> 00:13:23,360
are not roommates of his, but one works for
Scale AI.

294
00:13:25,960 --> 00:13:30,960
Ben Chen, Brian Chen, excuse me, works for
Scale AI.

295
00:13:32,799 --> 00:13:35,559
That's the important player in this DOD
initiative

296
00:13:35,559 --> 00:13:39,080
to put AI into, like the NSA.

297
00:13:39,080 --> 00:13:43,040
Another one, Chris Chang works for Alphabet
Company,

298
00:13:43,040 --> 00:13:45,560
Foreign Air Schmidt Company, Alphabet,

299
00:13:45,560 --> 00:13:48,879
which is Waymo, the cyber cab company.

300
00:13:48,879 --> 00:13:52,120
And then another one, the two other guys,

301
00:13:52,120 --> 00:13:56,360
Tony Zhu, ZHU runs the Berkeley lab,

302
00:13:56,360 --> 00:14:01,360
the Berkeley AI lab, BAIR,

303
00:14:01,480 --> 00:14:04,560
which is infested with Chinese spies.

304
00:14:04,560 --> 00:14:06,480
It's the most international university

305
00:14:06,480 --> 00:14:08,960
and the most Chinese university in America.

306
00:14:08,960 --> 00:14:10,000
And this is where I'm saying,

307
00:14:10,000 --> 00:14:12,879
these guys don't necessarily have to be
involved at all.

308
00:14:12,879 --> 00:14:15,159
If they're using a shared drive at the BAIR,

309
00:14:17,159 --> 00:14:21,039
somebody could be lifting their stuff off of
the Berkeley lab.

310
00:14:21,039 --> 00:14:22,920
And the other guy, William Gann,

311
00:14:22,920 --> 00:14:26,519
is another post-training expert, right?

312
00:14:26,519 --> 00:14:29,360
So this is not like just four buddies

313
00:14:29,360 --> 00:14:32,639
that play basketball together in high school.

314
00:14:32,639 --> 00:14:34,759
And so what I'm saying is I want those four
guys

315
00:14:34,759 --> 00:14:37,399
to come together so they can establish,

316
00:14:37,399 --> 00:14:40,840
because they worked together for four days on
Catalina,

317
00:14:40,840 --> 00:14:42,879
so they can help establish all the things

318
00:14:42,879 --> 00:14:45,240
who cheered it and said before his death.

319
00:14:45,240 --> 00:14:48,840
That helps people crowdsource the information

320
00:14:48,840 --> 00:14:50,879
of what his thoughts are,

321
00:14:50,879 --> 00:14:52,679
so that we can figure out the murder.

322
00:14:53,799 --> 00:14:54,639
Yeah.

323
00:14:54,639 --> 00:14:55,720
They have not come forward yet,

324
00:14:55,720 --> 00:14:57,879
and I want them to.

325
00:14:57,879 --> 00:14:59,559
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327
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329
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330
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332
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335
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336
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338
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339
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342
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343
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347
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349
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350
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375
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I mean, this is, if information comes too
late

376
00:16:57,799 --> 00:17:02,799
and the theories do find some validity in
reality,

377
00:17:03,439 --> 00:17:04,639
like this is a pretty big deal.

378
00:17:04,639 --> 00:17:07,960
I mean, because AI race is the hottest topic

379
00:17:07,960 --> 00:17:09,079
in the world right now.

380
00:17:09,079 --> 00:17:12,599
And I mean, DeepSeek just launching and open
sourcing,

381
00:17:12,599 --> 00:17:16,599
their models alone sent US equity markets
down 20%

382
00:17:17,639 --> 00:17:19,519
in one day, like the next Monday.

383
00:17:19,519 --> 00:17:22,720
And I think it's become pretty clear,

384
00:17:22,720 --> 00:17:24,359
at least from a narrative perspective,

385
00:17:24,359 --> 00:17:27,440
that artificial intelligence and its future

386
00:17:28,120 --> 00:17:30,840
in our world is becoming a national security
issue

387
00:17:30,840 --> 00:17:34,279
that the Trump administration seems very keen

388
00:17:34,279 --> 00:17:36,680
on ensuring that the US leads.

389
00:17:36,680 --> 00:17:41,680
And that's, nobody seems to be drawing this
connection

390
00:17:42,680 --> 00:17:45,160
between DeepSeek and OpenAI that you have.

391
00:17:45,160 --> 00:17:49,480
And since DeepSeek launched, there are one
model,

392
00:17:49,480 --> 00:17:51,240
I haven't heard anybody talking about this
publicly

393
00:17:51,240 --> 00:17:52,080
except for you.

394
00:17:52,080 --> 00:17:53,720
And I think that's important.

395
00:17:53,720 --> 00:17:57,480
And talk to people who train AI models

396
00:17:57,480 --> 00:18:01,079
and do post-training and they say volume is
the key.

397
00:18:02,319 --> 00:18:04,680
A lot of the human annotation is helpful.

398
00:18:05,640 --> 00:18:09,200
Like if you train AI and say, here's 100 good
examples,

399
00:18:09,200 --> 00:18:10,440
here's 100 bad examples,

400
00:18:10,440 --> 00:18:14,279
now here's 500 million phone calls

401
00:18:14,279 --> 00:18:16,440
I want you to go through and score.

402
00:18:16,440 --> 00:18:18,880
And then I can create an adversarial network

403
00:18:18,880 --> 00:18:21,920
that I then grade that set of tests.

404
00:18:21,920 --> 00:18:25,680
And then I can have humans go back in and
say,

405
00:18:25,680 --> 00:18:27,240
well, there's 100 good and 100 bad

406
00:18:27,240 --> 00:18:28,600
and here's where you graded it wrong

407
00:18:28,600 --> 00:18:31,480
and then run it again in an iterative way

408
00:18:31,480 --> 00:18:34,680
and it gets better and better and better as
it's scoring.

409
00:18:34,680 --> 00:18:36,360
And this is what Scale AI does.

410
00:18:36,360 --> 00:18:38,840
It's not just brute force and ignorance.

411
00:18:38,840 --> 00:18:42,279
It's iterative training, iterative
post-training

412
00:18:42,279 --> 00:18:44,320
so that the model gets better and better.

413
00:18:44,320 --> 00:18:45,720
That's the key.

414
00:18:45,720 --> 00:18:48,240
And the people who wrote the paper on that,

415
00:18:48,279 --> 00:18:52,279
that iterative post-training are John
Schulman,

416
00:18:52,279 --> 00:18:56,079
Ilya Suskeever, William Gann, Tony Zhu,

417
00:18:59,240 --> 00:19:01,200
and Suchir Bhaji.

418
00:19:01,200 --> 00:19:04,799
These are the experts in iterative
post-training.

419
00:19:06,599 --> 00:19:08,000
So why is that important?

420
00:19:08,000 --> 00:19:11,880
Well, like you said, it's for all the
marbles.

421
00:19:11,880 --> 00:19:15,759
If we can't figure out how DeepSeq did this,

422
00:19:15,799 --> 00:19:17,440
it's like Sputnik,

423
00:19:17,440 --> 00:19:19,960
it's like not being able to figure out the
atomic bomb

424
00:19:19,960 --> 00:19:21,799
and the Germans getting the atomic bomb

425
00:19:21,799 --> 00:19:23,640
and us not in World War II

426
00:19:23,640 --> 00:19:27,519
or the Russians getting it in the Cold War
and us not.

427
00:19:28,759 --> 00:19:31,240
It's that polemic of an event

428
00:19:32,079 --> 00:19:34,160
and to catch up so quickly,

429
00:19:35,519 --> 00:19:37,319
usually is espionage.

430
00:19:37,319 --> 00:19:41,000
If you remember the atomic bomb case was,

431
00:19:41,000 --> 00:19:45,279
there was supposedly the Rosenbergs with
seven type pages

432
00:19:45,279 --> 00:19:46,879
and explain the explosive lens.

433
00:19:46,879 --> 00:19:50,639
No, it was an agent Sonia and Klaus Fuchs

434
00:19:50,639 --> 00:19:53,599
doing big files every day for months.

435
00:19:53,599 --> 00:19:56,519
That's what did it through the Cambridge
Five.

436
00:19:56,519 --> 00:19:59,799
And a similar thing happened with the
Russians,

437
00:20:02,039 --> 00:20:05,559
getting all the secrets they got for bio
weapons

438
00:20:05,559 --> 00:20:07,559
and all these other things, infiltration.

439
00:20:08,559 --> 00:20:12,039
And what people have ruled out in this whole
discussion

440
00:20:12,039 --> 00:20:13,160
is infiltration.

441
00:20:13,920 --> 00:20:17,360
But we know that we've had many, many Senate
meetings.

442
00:20:17,360 --> 00:20:19,040
Rubio was big on this for a while

443
00:20:19,040 --> 00:20:22,160
that all these American universities were
being infiltrated

444
00:20:22,160 --> 00:20:23,720
but everybody has said, no, no, no,

445
00:20:23,720 --> 00:20:26,160
no infiltration all of a sudden.

446
00:20:26,160 --> 00:20:27,519
It's the Chinese work harder.

447
00:20:27,519 --> 00:20:29,400
We've had a whole bunch of apologists come
out

448
00:20:29,400 --> 00:20:31,720
and say, the Chinese have just worked harder.

449
00:20:32,600 --> 00:20:33,519
It shows you what you can do

450
00:20:33,519 --> 00:20:36,120
with a little bit of grit and spit.

451
00:20:36,120 --> 00:20:37,720
And then you have the other folks who say,

452
00:20:37,720 --> 00:20:42,720
no, there's a secret cache of 50,000 hopper
chips

453
00:20:44,080 --> 00:20:49,080
H800s and they actually cut that out of their
reporting.

454
00:20:49,480 --> 00:20:53,000
And there's a new report out

455
00:20:53,000 --> 00:20:58,000
by a company called Semi Analytics that says
this.

456
00:20:58,200 --> 00:21:03,200
And then they got a firm, a hedge fund firm
named High Flyer

457
00:21:04,980 --> 00:21:07,240
to buy these chips secretly

458
00:21:07,240 --> 00:21:11,880
before the trade restrictions came in on
that.

459
00:21:11,880 --> 00:21:16,200
And when you work at the US State Department

460
00:21:16,200 --> 00:21:19,480
and you're working with things that are doing

461
00:21:19,480 --> 00:21:21,480
a complex math, supercomputing math,

462
00:21:21,480 --> 00:21:25,780
thousands of processors, servers, you tend to
notice.

463
00:21:25,780 --> 00:21:28,280
If it's not being done in a joint venture,

464
00:21:28,280 --> 00:21:30,200
you tend to notice when they're doing that.

465
00:21:30,200 --> 00:21:31,960
And so that's really what the,

466
00:21:31,960 --> 00:21:33,840
there's only two choices right now.

467
00:21:33,840 --> 00:21:36,480
One is they're just plucky little Chinese.

468
00:21:36,480 --> 00:21:37,760
They're really doing a great job.

469
00:21:37,760 --> 00:21:40,760
And then the other one is there's this secret

470
00:21:40,759 --> 00:21:44,839
50,000 hopper server, right?

471
00:21:44,839 --> 00:21:49,839
The key learning in the last three months

472
00:21:52,640 --> 00:21:55,359
is that post-training, especially iterative
post-training

473
00:21:55,359 --> 00:21:57,640
is the thing that brings the expertise.

474
00:21:57,640 --> 00:22:01,960
It's not regurgitating New York Times
articles

475
00:22:01,960 --> 00:22:04,119
that are talking about ice skating days

476
00:22:04,119 --> 00:22:07,440
at Rockefeller Center, right?

477
00:22:07,440 --> 00:22:12,039
It's people that are putting in the expert
level

478
00:22:12,039 --> 00:22:14,600
and especially the sequential reasoning

479
00:22:14,600 --> 00:22:19,120
that's bringing this reasoning capability to
DeepSeek.

480
00:22:20,440 --> 00:22:21,799
Yeah.

481
00:22:21,799 --> 00:22:25,759
Now, going back to the apologist saying,

482
00:22:25,759 --> 00:22:29,320
it's just Chinese putting in more effort,
more time.

483
00:22:29,320 --> 00:22:34,039
Their STEM, focus on STEM education

484
00:22:34,039 --> 00:22:35,519
is what's giving them the sleep.

485
00:22:35,559 --> 00:22:38,240
It just completely contradicts everything we
know

486
00:22:38,240 --> 00:22:40,799
from Chinese just stealing Western IP

487
00:22:40,799 --> 00:22:42,960
and copying it and iterating on it.

488
00:22:42,960 --> 00:22:45,200
And then, I mean, just last week,

489
00:22:46,160 --> 00:22:49,559
you had somebody from the Federal Reserve get
arrested

490
00:22:49,559 --> 00:22:53,839
for getting caught sharing secrets with the
PBOC.

491
00:22:53,839 --> 00:22:57,599
In terms of Fed policy, obviously Eric
Salwell,

492
00:22:57,599 --> 00:23:02,000
California rep was dating a Chinese spy.

493
00:23:02,000 --> 00:23:06,279
You had the event with, what's his name?

494
00:23:07,920 --> 00:23:11,079
Senator McConnell, Mitch McConnell,

495
00:23:11,079 --> 00:23:14,319
was his sister-in-law, was murdered on his
ranch.

496
00:23:14,319 --> 00:23:16,119
His wife was Chinese too, yeah.

497
00:23:16,119 --> 00:23:20,759
So Mark Zuckerberg is buying GPUs and
creating bio hubs.

498
00:23:20,759 --> 00:23:22,920
His wife, the Chow, I'm here in Irvine

499
00:23:22,920 --> 00:23:26,799
at the Chow Cancer Center for mRNA vaccines.

500
00:23:26,799 --> 00:23:29,680
So there's AI generated.

501
00:23:29,680 --> 00:23:32,360
So there's a lot on the table here.

502
00:23:32,360 --> 00:23:35,080
But if you go back to my original reporting,

503
00:23:35,080 --> 00:23:39,000
in March, 2020, we looked at a AI lab

504
00:23:39,000 --> 00:23:44,000
that was being established in Wuhan in
December of 2019.

505
00:23:45,480 --> 00:23:47,160
Right, in December.

506
00:23:47,160 --> 00:23:52,160
And the program was called, from DARPA, was
called Domain.

507
00:23:52,400 --> 00:23:55,160
And the idea is that you figure out the lock

508
00:23:55,160 --> 00:23:58,480
and key mechanism between the ACE2 receptor
and the spike.

509
00:23:58,480 --> 00:23:59,440
Right?

510
00:23:59,440 --> 00:24:03,559
And using AI informs,

511
00:24:03,559 --> 00:24:05,320
when you're trying to do serial passaging

512
00:24:05,320 --> 00:24:06,680
and you're trying to get from a bat,

513
00:24:06,680 --> 00:24:11,680
because you wanna say, okay, the bat gave it
to the humans.

514
00:24:11,720 --> 00:24:12,559
Right?

515
00:24:12,559 --> 00:24:16,799
You have to then go into the lab and put
needles in bats.

516
00:24:16,799 --> 00:24:17,640
Right?

517
00:24:17,640 --> 00:24:20,519
And then come up with something close to a
bat,

518
00:24:20,519 --> 00:24:21,600
like a ferret, right?

519
00:24:21,600 --> 00:24:22,960
That has a similar ACE2.

520
00:24:22,960 --> 00:24:25,160
And you have to build a genetic bridge

521
00:24:25,160 --> 00:24:28,519
all the way to humans in order for that story
to wash.

522
00:24:28,519 --> 00:24:29,360
Right?

523
00:24:30,359 --> 00:24:33,319
That's a lot of, they had 20,000 different
spikes

524
00:24:33,319 --> 00:24:34,879
that they tried.

525
00:24:34,879 --> 00:24:36,039
But if you have AI

526
00:24:36,039 --> 00:24:38,439
and you have a little bit better idea
genetically,

527
00:24:38,439 --> 00:24:40,879
what's the next rung of the ladder
genetically

528
00:24:40,879 --> 00:24:42,719
to getting to humans,

529
00:24:42,719 --> 00:24:46,959
the AI can inform your iterative serial
passaging.

530
00:24:46,959 --> 00:24:48,119
And now we have the receipts.

531
00:24:48,119 --> 00:24:51,679
Elon Musk came out yesterday with the
receipts.

532
00:24:51,679 --> 00:24:55,159
We gave $40 million to Ben Hu at the lab.

533
00:24:55,159 --> 00:24:57,639
And this is where we said AI was being used

534
00:24:57,640 --> 00:24:59,080
in a joint venture,

535
00:24:59,960 --> 00:25:02,120
in a joint venture between DARPA

536
00:25:02,120 --> 00:25:05,040
and the Chinese Academy of Sciences.

537
00:25:05,040 --> 00:25:05,880
Right?

538
00:25:05,880 --> 00:25:08,160
And that's where they got the 50,000 hoppers.

539
00:25:09,280 --> 00:25:11,440
High Flyer didn't wake up one day.

540
00:25:11,440 --> 00:25:14,680
I can't remember Wen Feng, the CEO.

541
00:25:14,680 --> 00:25:15,520
He didn't wake up one day.

542
00:25:15,520 --> 00:25:18,920
And you know, I've been trading a lot of
stocks lately

543
00:25:18,920 --> 00:25:22,759
and I just think I need to pick up 50,000
hopper GPUs.

544
00:25:22,759 --> 00:25:25,240
You know, 8800 is a nice number.

545
00:25:25,240 --> 00:25:26,960
I think I'm gonna go get 50,000 of those.

546
00:25:26,960 --> 00:25:29,920
No, it was very much in the strategy

547
00:25:29,920 --> 00:25:31,600
of the Chinese Academy of Sciences.

548
00:25:31,600 --> 00:25:36,600
It very much was in the joint venture with
USAID Predict.

549
00:25:38,120 --> 00:25:40,120
And I told this to Donnie O'Sullivan

550
00:25:40,120 --> 00:25:43,200
in a nationally televised interview of CNN

551
00:25:44,319 --> 00:25:48,519
that it was USAID Predict and it was a joint
venture.

552
00:25:48,519 --> 00:25:50,480
And this is gonna be the same place

553
00:25:50,480 --> 00:25:51,680
why they bought the chips,

554
00:25:51,680 --> 00:25:53,039
why they bought the GPU chips,

555
00:25:53,039 --> 00:25:55,480
because this lock and key mechanism,

556
00:25:55,480 --> 00:25:57,400
when you're trying to figure out the spike
match

557
00:25:57,400 --> 00:26:01,200
to the H2 receptor is exactly the type of
heavy compute

558
00:26:02,200 --> 00:26:03,400
that you need to do.

559
00:26:03,400 --> 00:26:07,039
And I know the project, I know Eric worked on
the project,

560
00:26:07,039 --> 00:26:09,039
Dennis Isabis worked on the project.

561
00:26:09,039 --> 00:26:11,160
It was called Alpha Fold.

562
00:26:11,160 --> 00:26:15,039
And Dennis just won the Nobel Prize for Alpha
Fold.

563
00:26:18,279 --> 00:26:21,480
So I know Nancy Pelosi was doing this.

564
00:26:21,480 --> 00:26:25,759
Nancy Pelosi uses Eric as her personal
investment advisor.

565
00:26:27,039 --> 00:26:30,079
These chips were procured at the AI lab in
Wuhan.

566
00:26:30,079 --> 00:26:32,200
Now they might've moved it.

567
00:26:32,200 --> 00:26:34,640
They might've moved the chips to another lab,

568
00:26:34,640 --> 00:26:36,980
but the Chinese just don't go out and buy
chips

569
00:26:36,980 --> 00:26:38,120
for no reason.

570
00:26:38,120 --> 00:26:40,620
They go out and buy chips very strategically.

571
00:26:41,559 --> 00:26:43,640
And they didn't even know at that time

572
00:26:43,640 --> 00:26:45,279
GPU chips were so important.

573
00:26:45,279 --> 00:26:48,279
The only way that knowledge could have come

574
00:26:48,279 --> 00:26:53,279
was Eric Schmidt at DeepMind and Google,
right?

575
00:26:53,680 --> 00:26:56,160
And the only way it could have been approved
for export

576
00:26:56,160 --> 00:26:59,359
was through help in Congress through Nancy
Pelosi.

577
00:26:59,359 --> 00:27:04,359
That's the only way she's moving off into AI
for medical

578
00:27:04,440 --> 00:27:07,920
in a big way, expect big things from Nancy
Pelosi

579
00:27:07,920 --> 00:27:08,960
and Eric Schmidt.

580
00:27:08,960 --> 00:27:09,799
This trip was brought to you

581
00:27:09,799 --> 00:27:11,559
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582
00:27:11,559 --> 00:27:14,559
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583
00:27:14,559 --> 00:27:16,599
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584
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585
00:27:18,919 --> 00:27:19,759
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586
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587
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588
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589
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and truly optimizing your position.

590
00:27:27,359 --> 00:27:28,500
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591
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592
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593
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594
00:27:35,720 --> 00:27:37,439
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595
00:27:37,439 --> 00:27:40,799
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596
00:27:40,799 --> 00:27:42,480
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597
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598
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599
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600
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601
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602
00:27:52,960 --> 00:27:54,759
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603
00:27:54,759 --> 00:27:56,860
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604
00:27:56,860 --> 00:28:01,640
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605
00:28:01,640 --> 00:28:02,820
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606
00:28:02,820 --> 00:28:04,680
Make sure you're protecting it the right way.

607
00:28:04,680 --> 00:28:05,960
Make sure you have a good partner.

608
00:28:05,960 --> 00:28:06,799
That is Unchained.

609
00:28:06,799 --> 00:28:09,720
Go to unchained.com slash tftc.

610
00:28:09,720 --> 00:28:10,559
Well, with all that,

611
00:28:10,559 --> 00:28:13,400
that's what I'm trying to get in my mind is
like,

612
00:28:13,400 --> 00:28:15,759
who are the good and the bad guys here?

613
00:28:16,240 --> 00:28:19,000
Or are they all bad guys just competing with
each other?

614
00:28:19,000 --> 00:28:22,240
Because it seems like there's some level

615
00:28:22,240 --> 00:28:25,240
of espionage cooperation between US actors,

616
00:28:25,240 --> 00:28:29,119
Chinese actors, and now with Doge getting in,

617
00:28:29,119 --> 00:28:34,119
attacking USAID and other forces,

618
00:28:34,599 --> 00:28:36,680
whether it be an AI trying to delineate

619
00:28:36,680 --> 00:28:39,660
between Western AI and Eastern AI.

620
00:28:39,660 --> 00:28:42,839
It's just like, it's such a murky landscape

621
00:28:42,839 --> 00:28:45,400
that it's like, who's actually on?

622
00:28:46,040 --> 00:28:48,040
On my side as, yeah.

623
00:28:48,040 --> 00:28:50,960
Bad actor, John Brennan, bad actor.

624
00:28:50,960 --> 00:28:55,440
Dr. Sun Shong has immunity, a bio partner

625
00:28:55,440 --> 00:28:58,560
with cancer, mRNA vaccines, bad actor, right?

626
00:28:58,560 --> 00:28:59,720
He's trying to be a good actor now.

627
00:28:59,720 --> 00:29:03,400
He's trying to sidle up and say chummy things
about Trump.

628
00:29:03,400 --> 00:29:05,800
And where are all these cancers coming from?

629
00:29:05,800 --> 00:29:10,800
Gee, I promoted SV40 promoters in all the
mRNA vaccines.

630
00:29:11,040 --> 00:29:13,259
And now he's like, where did all the cancers
come from?

631
00:29:13,259 --> 00:29:15,019
Why aren't all the cancers spiking?

632
00:29:16,579 --> 00:29:20,339
You put the SV40 promoter in the mRNA,

633
00:29:20,339 --> 00:29:22,299
and then you're wondering why three years
later

634
00:29:22,299 --> 00:29:23,379
there's a spike in cancers.

635
00:29:23,379 --> 00:29:24,579
Okay, I get that.

636
00:29:24,579 --> 00:29:27,519
And you also sell cancer vaccines, okay.

637
00:29:27,519 --> 00:29:29,259
So those are the bad actors.

638
00:29:29,259 --> 00:29:31,259
We've been saying that for eight years.

639
00:29:31,259 --> 00:29:35,460
And what is really good, you mentioned this
about Doge,

640
00:29:35,460 --> 00:29:39,400
is Elon's able to get now with Treasury
access, right?

641
00:29:39,400 --> 00:29:41,940
He's able to get in and see the
disbursements.

642
00:29:42,500 --> 00:29:45,580
He's able to see the secret budget, right?

643
00:29:45,580 --> 00:29:47,820
And we said it was USAID,

644
00:29:47,820 --> 00:29:50,620
we said it was USAID Predict, Donnie
O'Sullivan

645
00:29:50,620 --> 00:29:52,660
in March of 2020.

646
00:29:52,660 --> 00:29:55,360
I just published the video again, the phone
call again.

647
00:29:56,980 --> 00:29:59,019
It's not like human investigators anymore.

648
00:29:59,019 --> 00:30:01,340
Human investigators, you can put them on
false trails

649
00:30:01,340 --> 00:30:02,180
and so forth.

650
00:30:02,180 --> 00:30:04,620
AI goes in and looks at all the disbursements

651
00:30:04,620 --> 00:30:08,019
of the US government and sees what has a
matching PO

652
00:30:08,019 --> 00:30:10,420
and what has a matching voucher, right?

653
00:30:10,420 --> 00:30:13,100
And what has a matching cost justification.

654
00:30:13,100 --> 00:30:15,220
And you can see the open POs.

655
00:30:15,220 --> 00:30:18,340
It's like when Donald Rumsfeld on September
the 10th

656
00:30:18,340 --> 00:30:21,500
said we got two million, two trillion
missing.

657
00:30:21,500 --> 00:30:25,180
And then the next day the plane hits the
auditor's office,

658
00:30:25,180 --> 00:30:28,420
right, but Dove Zakheim was the auditor,
right?

659
00:30:28,420 --> 00:30:29,259
Yep.

660
00:30:29,259 --> 00:30:33,100
It's like we know where the two billion
trillion went.

661
00:30:33,100 --> 00:30:36,779
When he puts his AI at Grok or you take any
of the AI,

662
00:30:36,779 --> 00:30:38,340
they're gonna be able to quickly go in

663
00:30:38,339 --> 00:30:40,779
and see what doesn't have a match.

664
00:30:40,779 --> 00:30:42,059
What doesn't have a matching voucher?

665
00:30:42,059 --> 00:30:43,699
What doesn't have, if it's a good,

666
00:30:43,699 --> 00:30:45,740
what doesn't have a matching bill elating?

667
00:30:45,740 --> 00:30:46,559
If it's a service,

668
00:30:46,559 --> 00:30:49,220
what doesn't have a matching statement of
work?

669
00:30:49,220 --> 00:30:50,899
You're gonna be able to see that immediately.

670
00:30:50,899 --> 00:30:53,459
And we already know it's AI Predict.

671
00:30:53,459 --> 00:30:57,059
We already know it's the $40 million to
Benhoo

672
00:30:57,059 --> 00:31:00,980
and there's gonna be other USAID Predicts
around the world

673
00:31:00,980 --> 00:31:02,259
that we've done this.

674
00:31:02,259 --> 00:31:04,379
We can just follow Nathan Wolf and MetaBiota,

675
00:31:04,379 --> 00:31:08,319
another USAID partner, Predict Partner.

676
00:31:09,319 --> 00:31:10,159
But by zooming in and he's there,

677
00:31:10,159 --> 00:31:12,720
he was there till midnight last night.

678
00:31:12,720 --> 00:31:14,759
So he's zooming in, he knows what he's doing.

679
00:31:14,759 --> 00:31:15,960
He's going for blood.

680
00:31:15,960 --> 00:31:19,159
He's a shark that smells the blood in the
water.

681
00:31:19,159 --> 00:31:20,799
And we're really getting down now

682
00:31:20,799 --> 00:31:25,319
to how a bunch of insiders wanted to use high
tech

683
00:31:25,319 --> 00:31:28,039
and use DARPA technology and farm it out

684
00:31:28,039 --> 00:31:29,480
to their partners in China

685
00:31:29,480 --> 00:31:31,359
so that they can make reputations profits

686
00:31:31,359 --> 00:31:33,799
rather than sharing that and keeping that
technology

687
00:31:33,799 --> 00:31:35,480
here in the United States.

688
00:31:35,480 --> 00:31:37,039
That's the story.

689
00:31:37,039 --> 00:31:40,399
And if you notice all of the companies in AI

690
00:31:40,399 --> 00:31:42,359
that got congressional approval and so forth

691
00:31:42,359 --> 00:31:45,519
are all in Nancy Pelosi, San Francisco.

692
00:31:45,519 --> 00:31:49,039
OpenAI and Grok were in the same building at
one point

693
00:31:49,039 --> 00:31:51,720
before they moved to their bigger
headquarters, right?

694
00:31:51,720 --> 00:31:54,319
And then of course, ScaleAI is in San
Francisco.

695
00:31:54,319 --> 00:31:57,000
And of course, MetaBiota is in San Francisco.

696
00:31:57,000 --> 00:31:59,599
They're all in Nancy Pelosi's back pocket

697
00:31:59,599 --> 00:32:02,599
for a very good reason, congressional
protection.

698
00:32:02,599 --> 00:32:05,759
And Elon is all over this and it feels great.

699
00:32:06,519 --> 00:32:08,359
Yeah, well, that's a big question.

700
00:32:08,359 --> 00:32:12,960
I mean, the surface level marketing of Doge

701
00:32:12,960 --> 00:32:17,039
is we need to cut costs to put the US economy
back

702
00:32:17,039 --> 00:32:20,319
on solid footing because of our debt
situation

703
00:32:20,319 --> 00:32:21,160
and all of that.

704
00:32:21,160 --> 00:32:23,680
But it seems like there is a layer below
this,

705
00:32:23,680 --> 00:32:28,680
which is we are truly gonna drain the swamp
and lay bare

706
00:32:28,720 --> 00:32:31,920
all the corruption that has been happening

707
00:32:31,920 --> 00:32:34,680
behind the scenes for many decades now.

708
00:32:34,720 --> 00:32:37,480
And this is what, I mean, the $40 million
receipts

709
00:32:37,480 --> 00:32:42,480
to Ben Hu came out in Wuhan Institute of
Virology.

710
00:32:44,160 --> 00:32:45,600
It's just starting.

711
00:32:45,600 --> 00:32:47,519
He's gonna come up with so much stuff with
Grok.

712
00:32:47,519 --> 00:32:48,680
It's not even gonna be funny.

713
00:32:48,680 --> 00:32:50,960
It's gonna be the days of milk and honey here

714
00:32:50,960 --> 00:32:53,039
really quickly with USAID.

715
00:32:54,279 --> 00:32:57,000
And we've been following them ever since
Haiti

716
00:32:57,000 --> 00:33:00,680
and Rajiv Shah became the 37 year old
youngest head

717
00:33:00,680 --> 00:33:03,160
of USAID for the Clinton Foundation.

718
00:33:03,160 --> 00:33:04,960
He then went on to run the Clinton
Foundation.

719
00:33:04,960 --> 00:33:07,759
Then he went on to run the Rockefeller
Foundation.

720
00:33:07,759 --> 00:33:10,080
So this has been an insider's game for a long
time.

721
00:33:10,080 --> 00:33:13,680
This has been an insider slush fund in virus
and vaccines,

722
00:33:13,680 --> 00:33:16,840
especially cancer vaccines for a long time.

723
00:33:16,840 --> 00:33:18,400
And Elon's getting to the heart of it.

724
00:33:18,400 --> 00:33:22,200
And to me, that's more important than finding
out,

725
00:33:22,200 --> 00:33:23,759
six guys working on a pothole

726
00:33:23,759 --> 00:33:25,560
and there should have been three.

727
00:33:27,120 --> 00:33:30,160
Trump's doing that already with voluntary
bailouts.

728
00:33:30,160 --> 00:33:35,160
I think the people can opt out for a year or
two salary.

729
00:33:36,440 --> 00:33:39,240
Yeah, that's gonna be the best thing

730
00:33:39,240 --> 00:33:40,720
because there's gonna be no lawsuits

731
00:33:40,720 --> 00:33:43,320
because people are gonna take voluntary
packages.

732
00:33:43,320 --> 00:33:45,120
So I would just say, let him do that.

733
00:33:45,120 --> 00:33:47,600
Let Trump do the waste part of government

734
00:33:48,600 --> 00:33:51,680
and let Elon do the corruption part

735
00:33:51,680 --> 00:33:54,240
because Elon has the tools.

736
00:33:54,240 --> 00:33:58,160
Because if somebody says, oh, there's a
billion records,

737
00:33:58,240 --> 00:34:00,680
that's nothing for Grok.

738
00:34:00,680 --> 00:34:04,240
So I'm really excited to watch Elon as he's
going here.

739
00:34:04,240 --> 00:34:05,960
He's there till midnight last night

740
00:34:05,960 --> 00:34:08,880
and Mike Johnson's watching over him.

741
00:34:08,880 --> 00:34:11,639
Like, oh God, Elon, don't find another thing,

742
00:34:11,639 --> 00:34:13,039
another deep state thing.

743
00:34:13,039 --> 00:34:15,360
You got the CIA guy watching him.

744
00:34:15,360 --> 00:34:17,360
So it's really kind of fun right now

745
00:34:17,360 --> 00:34:19,639
to be a news consumer right now.

746
00:34:20,639 --> 00:34:22,840
Yeah, I mean, and just the reaction

747
00:34:22,840 --> 00:34:27,039
from the incumbent entrenched bureaucrats,

748
00:34:27,519 --> 00:34:29,599
the one guy who was at the treasury

749
00:34:29,599 --> 00:34:32,639
who resigned abruptly because they asked to
see

750
00:34:32,639 --> 00:34:34,079
where all the payments were going.

751
00:34:34,079 --> 00:34:39,079
And then the pearl clutching that has ensued

752
00:34:39,519 --> 00:34:43,599
has become clear that Elon has set up beds

753
00:34:43,599 --> 00:34:48,599
in the USAID office and has six
crack-to-zoomers

754
00:34:48,679 --> 00:34:51,719
getting on their laptops and diving into
everything.

755
00:34:52,239 --> 00:34:57,079
What, in terms of, I don't know if you have
any hopes

756
00:34:57,079 --> 00:35:00,159
or whatever, but like, what is the best
outcome

757
00:35:00,159 --> 00:35:01,000
in your opinion?

758
00:35:01,000 --> 00:35:02,399
What are the potential ramifications

759
00:35:02,399 --> 00:35:05,319
of everything being laid bare?

760
00:35:05,319 --> 00:35:09,679
I've waited five years for this USAID
predicting to break

761
00:35:09,679 --> 00:35:11,639
with the Donnie O'Sullivan thing in March,

762
00:35:11,639 --> 00:35:14,000
almost five years ago now.

763
00:35:14,000 --> 00:35:19,000
And the last 24 hours has just been fantastic
for me.

764
00:35:19,840 --> 00:35:23,800
It's exceeded my expectations, but beyond all
measure,

765
00:35:23,800 --> 00:35:25,239
Elon's all over it.

766
00:35:25,239 --> 00:35:27,000
And I just wanna see this continue

767
00:35:27,000 --> 00:35:28,719
because getting to the dark budget,

768
00:35:28,719 --> 00:35:29,920
getting to the black budget

769
00:35:29,920 --> 00:35:32,280
is how they control the government.

770
00:35:32,280 --> 00:35:34,639
It may be a small percentage of the total
budget,

771
00:35:34,639 --> 00:35:38,559
but it's the dark hand that takes the
democracy away

772
00:35:38,559 --> 00:35:39,840
from you and me.

773
00:35:39,840 --> 00:35:42,039
So getting to that dark hand like he's doing,

774
00:35:42,039 --> 00:35:45,760
and he's already showing how incredible he's
doing it,

775
00:35:45,760 --> 00:35:47,880
that's the juice for me.

776
00:35:47,920 --> 00:35:49,599
The money and the waste and everything,

777
00:35:49,599 --> 00:35:52,000
that can happen later, but getting to the
people

778
00:35:52,000 --> 00:35:54,440
who have their hand on the throat of
democracy

779
00:35:54,440 --> 00:35:55,880
is a beautiful thing.

780
00:35:55,880 --> 00:35:59,079
Get that vice grip off the throat of America

781
00:35:59,079 --> 00:36:00,360
and let America breathe again.

782
00:36:00,360 --> 00:36:05,160
And I think the American people are so
industrious

783
00:36:05,160 --> 00:36:08,320
that just take the hand off their trachea,

784
00:36:08,320 --> 00:36:09,360
and watch them go.

785
00:36:10,280 --> 00:36:13,039
Yeah, no, it's funny, just coincidentally
over the weekend,

786
00:36:13,039 --> 00:36:16,599
I started reading a book called Gold Warriors
that dove into

787
00:36:17,559 --> 00:36:22,559
the history of Japan's pillaging of gold and
artifacts

788
00:36:22,839 --> 00:36:26,799
from much of Asia over the course of the late
19th

789
00:36:26,799 --> 00:36:28,319
and early 20th centuries.

790
00:36:28,319 --> 00:36:30,559
And I haven't gotten to the point in the
book,

791
00:36:30,559 --> 00:36:33,839
but I think what it talks about is how
post-World War II,

792
00:36:33,839 --> 00:36:38,839
how the US sort of found that large gold
stash

793
00:36:39,719 --> 00:36:43,440
that Japan had accumulated and used it as a
slush fund

794
00:36:43,440 --> 00:36:47,639
for black budgets for many decades.

795
00:36:47,639 --> 00:36:51,079
Lonsdale in the Philippines, Lonsdale ran
that program

796
00:36:51,079 --> 00:36:53,720
and then Lonsdale went later on to other
things.

797
00:36:53,720 --> 00:36:57,240
And I worked very closely with a guy, John
O'Loughlin,

798
00:36:57,240 --> 00:37:00,800
whose father was kind of went head to head,

799
00:37:00,800 --> 00:37:03,679
supposedly worked with Lonsdale for him,

800
00:37:03,679 --> 00:37:05,519
but he knew that he was the traitor.

801
00:37:05,519 --> 00:37:07,800
And I've been trying to prove to the son

802
00:37:07,800 --> 00:37:12,800
that it was your dad versus Lonsdale in all
of this.

803
00:37:13,240 --> 00:37:15,480
In the Kennedy White House, but there is,

804
00:37:15,480 --> 00:37:20,480
and it's not a slush fund for the State
Department.

805
00:37:20,980 --> 00:37:23,600
It's a slush fund for the Central
Intelligence Agency

806
00:37:23,600 --> 00:37:25,680
hiding behind the State Department.

807
00:37:25,680 --> 00:37:27,280
I just wanted to clear that up.

808
00:37:28,160 --> 00:37:32,280
But yes, that hand has been on the throat

809
00:37:32,280 --> 00:37:34,360
of US government since World War II,

810
00:37:34,360 --> 00:37:36,380
since the creation of the CIA.

811
00:37:37,680 --> 00:37:38,519
Yeah.

812
00:37:39,519 --> 00:37:41,920
And it's funny, and all the quote unquote

813
00:37:41,920 --> 00:37:44,400
conspiracy theories have been coming true for
many years.

814
00:37:44,400 --> 00:37:49,000
And this would be the ultimate, told you so,

815
00:37:49,000 --> 00:37:51,599
the CIA and the intelligence apparatus

816
00:37:51,599 --> 00:37:53,159
is really running things behind the scenes,

817
00:37:53,159 --> 00:37:57,159
they're running amok and they're completely
unaccountable.

818
00:37:57,159 --> 00:38:00,679
And again, going back to the ramifications,

819
00:38:00,679 --> 00:38:02,559
do we have Nuremberg trials for this?

820
00:38:02,559 --> 00:38:04,119
Like what, like who?

821
00:38:04,119 --> 00:38:05,440
A lot of the people are dead.

822
00:38:05,480 --> 00:38:10,480
I mean, I see, get the JFK and JFK
assassination MLK out.

823
00:38:11,360 --> 00:38:13,119
So all those guys are dead,

824
00:38:13,119 --> 00:38:17,159
but at least then people will see what's
happened, right?

825
00:38:17,159 --> 00:38:22,159
And then, I always say, I'm not for the whole

826
00:38:22,159 --> 00:38:25,079
Nuremberg thing because then they say you're
being hateful.

827
00:38:25,079 --> 00:38:28,880
I'm for all putting them on like an island,
like Elba,

828
00:38:28,880 --> 00:38:32,880
and let them play pickleball or whatever they
want.

829
00:38:33,680 --> 00:38:36,320
As long as they're on like a little Elba
island

830
00:38:36,320 --> 00:38:38,680
and not hurting anybody, that's the number
one thing

831
00:38:38,680 --> 00:38:40,119
is defanging them.

832
00:38:42,000 --> 00:38:43,880
I don't necessarily need to see anybody hang

833
00:38:43,880 --> 00:38:46,039
is because as soon as you call for somebody
to hang,

834
00:38:46,039 --> 00:38:47,280
then they say you're being hateful.

835
00:38:47,280 --> 00:38:50,400
So I say, let's just have a pickleball
tournament on Elba.

836
00:38:52,240 --> 00:38:54,360
I mean, Trump is talking about

837
00:38:54,360 --> 00:38:57,000
spinning up more facilities on Guantanamo
Bay.

838
00:38:57,000 --> 00:39:02,000
He alluded to a short island for, yeah.

839
00:39:02,480 --> 00:39:03,400
That would be good.

840
00:39:06,880 --> 00:39:09,880
Yeah, so in terms of from today,

841
00:39:09,880 --> 00:39:12,280
obviously there's been a lot of news in the
last 24 hours,

842
00:39:12,280 --> 00:39:14,119
but what do you think people listening to
this

843
00:39:14,119 --> 00:39:15,960
should be paying attention to?

844
00:39:15,960 --> 00:39:16,800
Elon Musk.

845
00:39:16,800 --> 00:39:18,079
Days and weeks to come.

846
00:39:18,079 --> 00:39:20,139
Elon Musk USAID predict.

847
00:39:21,239 --> 00:39:23,320
It's gonna answer the question of the last
four years.

848
00:39:23,320 --> 00:39:25,159
It's gonna answer the question of the
lockdown.

849
00:39:25,159 --> 00:39:28,159
It's gonna answer all of the exfiltration to
China

850
00:39:28,159 --> 00:39:29,519
over the last 10 years.

851
00:39:29,559 --> 00:39:34,559
I think it's 20 with SARS, MERS, SARS 2
progression.

852
00:39:36,559 --> 00:39:39,360
It gets also into people where they've
outsourced things

853
00:39:39,360 --> 00:39:41,800
from Fort Detrick, the Erasmus lab.

854
00:39:41,800 --> 00:39:43,759
It gets to how they've outsourced things

855
00:39:43,759 --> 00:39:47,759
to American universities to hide gain of
function

856
00:39:47,759 --> 00:39:52,219
type things, UPMC, UTMB, UNC.

857
00:39:52,219 --> 00:39:57,219
So it's all these things, which is the
corrupt dark hand.

858
00:39:57,459 --> 00:39:59,859
And then just the whole slush fund for
overthrows,

859
00:39:59,859 --> 00:40:03,299
Libya, Syria, Sudan, Yemen, Ukraine
overthrow.

860
00:40:03,299 --> 00:40:07,059
It also answers the private deals like the
private

861
00:40:07,059 --> 00:40:10,219
taking of the Cold War, highly enriched
uranium

862
00:40:10,219 --> 00:40:12,980
and giving it to Iran and getting 15%

863
00:40:12,980 --> 00:40:15,859
as a commission for the DNC versus the people

864
00:40:15,859 --> 00:40:17,299
in the United States.

865
00:40:17,299 --> 00:40:22,259
So all of these things are come from the
corruption side,

866
00:40:22,259 --> 00:40:23,699
is what I call it.

867
00:40:23,699 --> 00:40:26,459
And then Donald Trump will do great on the
waste.

868
00:40:26,500 --> 00:40:29,139
He's a builder, he's always trying to make
sure

869
00:40:29,139 --> 00:40:30,900
that things are efficient.

870
00:40:30,900 --> 00:40:33,019
But I think Elon is the one to watch

871
00:40:33,019 --> 00:40:34,019
on the corruption side.

872
00:40:34,019 --> 00:40:35,820
He's really doing some great stuff.

873
00:40:37,260 --> 00:40:38,659
Do you think he's trustworthy in the long
run?

874
00:40:38,659 --> 00:40:41,780
There's a lot of people who are skeptical of
Elon

875
00:40:41,780 --> 00:40:45,340
and his motives and what he's trying to do
with

876
00:40:45,340 --> 00:40:47,059
outstanding it's an everything app.

877
00:40:48,340 --> 00:40:49,340
Yeah, the everything app.

878
00:40:49,340 --> 00:40:52,980
I saw Whitney's, Whitney Webb's thing on the
everything app.

879
00:40:52,980 --> 00:40:53,820
It's sort of like the last thing.

880
00:40:53,820 --> 00:40:55,539
Are you guys related?

881
00:40:55,539 --> 00:40:56,539
No, no, no, no.

882
00:40:56,539 --> 00:40:58,820
I would be proud to have her as a daughter.

883
00:40:58,820 --> 00:41:00,019
She's excellent.

884
00:41:00,019 --> 00:41:03,500
I love and I've talked to her and wonderful
writer

885
00:41:03,500 --> 00:41:06,460
and her series on Jeffrey Epstein and
corruption in America

886
00:41:06,460 --> 00:41:10,059
with going back to Robert Maxwell, great
stuff.

887
00:41:10,059 --> 00:41:13,500
They just throw a lot of Mr. Greens and Mr.
Whites

888
00:41:13,500 --> 00:41:18,500
and Mr. Browns and Mr. Blacks into the
citations.

889
00:41:18,619 --> 00:41:20,980
And that kind of gets in the way a little bit
in the book

890
00:41:20,980 --> 00:41:24,579
because that's deliberate to obfuscate the
real actors.

891
00:41:25,019 --> 00:41:27,940
But she does fantastic stuff.

892
00:41:27,940 --> 00:41:31,980
But I disagree with her in the Peter Thiel

893
00:41:31,980 --> 00:41:34,699
and Elon and the everything app.

894
00:41:34,699 --> 00:41:38,259
And this is gonna be social control

895
00:41:38,259 --> 00:41:42,699
and the whole turn off your sole source of
funding anytime

896
00:41:42,699 --> 00:41:46,699
like WeChat, Tencent has this WeChat app in
China.

897
00:41:46,699 --> 00:41:50,579
And this is gonna be what Elon wants to do
here.

898
00:41:50,579 --> 00:41:53,579
It's sort of like the last helicopter out of
Saigon.

899
00:41:54,219 --> 00:41:55,420
And they're gonna kill everybody

900
00:41:55,420 --> 00:41:57,659
when they get in the CIA annex.

901
00:41:58,579 --> 00:42:01,139
Do you get on the helicopter or do you say,

902
00:42:01,139 --> 00:42:03,500
no, I don't trust the pilot.

903
00:42:03,500 --> 00:42:07,059
It's kind of like, we don't have a choice,
really.

904
00:42:07,059 --> 00:42:08,259
We just don't have a choice.

905
00:42:08,259 --> 00:42:11,139
It's either Adam Schiff or Elon Musk at this
point.

906
00:42:11,139 --> 00:42:14,139
And the helicopter, Adam's not picking us up.

907
00:42:14,139 --> 00:42:16,259
He's picking up the elites, right?

908
00:42:16,259 --> 00:42:18,739
So that's the way I see it.

909
00:42:18,899 --> 00:42:23,899
I like Whitney's healthy skepticism.

910
00:42:24,219 --> 00:42:27,099
I like that healthy skepticism because Elon
will see that

911
00:42:27,099 --> 00:42:29,819
and then it'll keep him honest.

912
00:42:29,819 --> 00:42:33,179
But I think he's our only game in town right
now.

913
00:42:33,179 --> 00:42:34,259
And he's in a position of power

914
00:42:34,259 --> 00:42:36,699
and he's already done some pretty awesome
things.

915
00:42:37,579 --> 00:42:39,219
Yeah, no, Whitney's a good friend.

916
00:42:39,219 --> 00:42:41,379
She's been on the show many, many times
throughout the years.

917
00:42:41,379 --> 00:42:45,699
And I think that healthy skepticism is
important.

918
00:42:45,699 --> 00:42:48,659
But like you said, last helicopter out of
Saigon,

919
00:42:49,539 --> 00:42:50,980
the USAID stuff alone,

920
00:42:53,619 --> 00:42:54,739
you've been working on it.

921
00:42:54,739 --> 00:42:57,420
Obviously, Mike Benz, who's been on the show
as well,

922
00:42:57,420 --> 00:43:00,819
he went on Rogue and sort of made that
audience

923
00:43:00,819 --> 00:43:05,819
aware of USAID's place in the supply chain

924
00:43:07,859 --> 00:43:09,619
of corruption in the US

925
00:43:09,619 --> 00:43:11,619
and how they are essentially the front

926
00:43:11,619 --> 00:43:16,619
for all this black market or black funding
that goes on,

927
00:43:17,139 --> 00:43:21,779
whether it's via NGOs or a censorship
industrial complex.

928
00:43:21,779 --> 00:43:25,219
And yeah, I'm hopeful that all this stuff

929
00:43:25,219 --> 00:43:29,579
is getting laid bare and we'll have some form
of clarity

930
00:43:29,579 --> 00:43:31,699
at least and the conspiracy theorists

931
00:43:31,699 --> 00:43:34,299
will be proven right yet again.

932
00:43:35,339 --> 00:43:37,339
And then Adam Schiff, like you just look at
him

933
00:43:37,339 --> 00:43:39,339
and say, I don't trust that guy at all.

934
00:43:40,980 --> 00:43:43,779
Adam Schiff found my research partner,

935
00:43:43,780 --> 00:43:46,500
ex-cop woman named Jenny Moore,

936
00:43:46,500 --> 00:43:48,860
now deceased, suspicious death.

937
00:43:49,700 --> 00:43:51,340
But we were just sitting all alone

938
00:43:51,340 --> 00:43:56,340
in the Longworth cafeteria, completely empty
cafeteria.

939
00:43:56,340 --> 00:43:58,780
Who comes in, Adam Schiff gets a salad,

940
00:43:58,780 --> 00:44:00,540
comes and sits right next to us

941
00:44:00,540 --> 00:44:02,100
and is listening to our conversation.

942
00:44:02,100 --> 00:44:05,340
But we were talking about us using encrypted
communications

943
00:44:05,340 --> 00:44:08,780
on Capitol Hill for covert operations.

944
00:44:08,780 --> 00:44:10,500
So maybe that's why he was sitting next to
us.

945
00:44:10,500 --> 00:44:13,220
But shifty Schiff is shifty

946
00:44:13,259 --> 00:44:15,699
and that's where you need to start looking.

947
00:44:15,699 --> 00:44:17,859
That's why they gave him pardons, right?

948
00:44:17,859 --> 00:44:21,899
Who issues a pardon to Fauci back to 2014

949
00:44:21,899 --> 00:44:23,899
if he's not involved in gain of function in
Wuhan

950
00:44:23,899 --> 00:44:25,779
since 2014.

951
00:44:25,779 --> 00:44:28,059
The metadata of the pardons almost tell you

952
00:44:28,059 --> 00:44:30,019
who the criminals are, right?

953
00:44:30,019 --> 00:44:31,259
That's kind of where to focus.

954
00:44:31,259 --> 00:44:34,139
And that's why I say because of the
preemptive pardons,

955
00:44:36,579 --> 00:44:39,299
let them have their pickleball

956
00:44:39,299 --> 00:44:41,539
or whatever Hillary Clinton does these days.

957
00:44:41,820 --> 00:44:44,219
But we need to move on.

958
00:44:44,219 --> 00:44:46,820
Benz has worked at the State Department, I
think,

959
00:44:46,820 --> 00:44:49,820
for a while and so he knows that from the
inside.

960
00:44:50,659 --> 00:44:53,619
And I really kind of focused in on PREDICT

961
00:44:53,619 --> 00:44:57,139
and DARPA ADAPT and the cross section of
DARPA

962
00:44:57,139 --> 00:45:01,579
with USAID PREDICT or P3 PREVENT

963
00:45:01,579 --> 00:45:05,579
and all the other things around cancer
vaccines

964
00:45:05,579 --> 00:45:06,420
and so forth.

965
00:45:06,420 --> 00:45:10,940
But he has a bigger picture of all the other
programs too.

966
00:45:11,900 --> 00:45:14,659
So that's exciting to see what Mike Benz is
doing.

967
00:45:14,659 --> 00:45:16,300
It's exciting to see what Whitney's doing.

968
00:45:16,300 --> 00:45:20,139
She's been following the industrial
surveillance state

969
00:45:20,139 --> 00:45:21,059
for a long time.

970
00:45:21,059 --> 00:45:23,659
I think she's the one to watch as well.

971
00:45:23,659 --> 00:45:26,179
So I think there's a combination of people
here.

972
00:45:26,179 --> 00:45:29,780
It's kind of like the golden age of Athens.

973
00:45:29,780 --> 00:45:32,700
We've all got people on our side

974
00:45:32,700 --> 00:45:34,220
helping us with documents now

975
00:45:34,220 --> 00:45:37,260
and it's kind of like a golden age.

976
00:45:37,260 --> 00:45:39,740
Yeah, it has been incredible to see.

977
00:45:39,779 --> 00:45:41,219
Bringing back to Adam Schiff,

978
00:45:41,219 --> 00:45:42,859
have you ever heard the theory about

979
00:45:44,619 --> 00:45:46,500
Anthony Bourdain was a witness to something

980
00:45:46,500 --> 00:45:47,819
that Adam Schiff was involved in

981
00:45:47,819 --> 00:45:49,939
at the Standard Hotel in LA?

982
00:45:49,939 --> 00:45:51,779
I went to the Standard Hotel.

983
00:45:51,779 --> 00:45:53,739
I went to Sunset Boulevard

984
00:45:53,739 --> 00:45:56,859
and I went not far down the street in
Chateau-Montmartre

985
00:45:56,859 --> 00:45:59,019
with Conor Biden and all the crack and so
forth.

986
00:45:59,019 --> 00:46:01,379
And I think all these things are spun up

987
00:46:01,379 --> 00:46:04,059
to get away from what we're talking about
now.

988
00:46:05,219 --> 00:46:08,339
And no, I didn't find any evidence there.

989
00:46:08,340 --> 00:46:10,660
I talked to everybody wearing shoes there.

990
00:46:10,660 --> 00:46:15,660
And no, and I'm not trying to devalue any
other person

991
00:46:16,579 --> 00:46:19,579
who's made contributions and says that's the
truth.

992
00:46:19,579 --> 00:46:23,539
I just, I'm just saying I found no evidence
to support that.

993
00:46:23,539 --> 00:46:24,500
All right, that's good to know.

994
00:46:24,500 --> 00:46:25,340
So I see that one floating around

995
00:46:25,340 --> 00:46:27,660
and that one pops up from time to time on X.

996
00:46:27,660 --> 00:46:30,019
I'm like, yeah, that'd be interesting.

997
00:46:30,019 --> 00:46:32,260
The, this is fascinating.

998
00:46:34,180 --> 00:46:35,420
And like you said,

999
00:46:35,420 --> 00:46:38,380
the beauty of the modern age double-edged
sword

1000
00:46:38,380 --> 00:46:43,019
with all this high-tech AI could be used for
bad

1001
00:46:43,019 --> 00:46:44,059
but could also be used for good.

1002
00:46:44,059 --> 00:46:47,099
Like being able to find you on X, DM you,

1003
00:46:47,099 --> 00:46:50,740
have you come on similarly with Whitney,
Mike.

1004
00:46:50,740 --> 00:46:54,019
And I think this distributed independent
media

1005
00:46:54,019 --> 00:46:57,159
of people who really care about getting to
the truth

1006
00:46:57,159 --> 00:46:59,059
at the end of the day and surfacing

1007
00:46:59,059 --> 00:47:02,260
what's actually happening is having a
profound impact

1008
00:47:02,260 --> 00:47:07,260
on what we know, like the truth actually
getting

1009
00:47:08,340 --> 00:47:11,540
to the hearts and minds and the ears

1010
00:47:11,540 --> 00:47:14,300
of your average day common man.

1011
00:47:14,300 --> 00:47:15,140
Yeah, yeah.

1012
00:47:15,140 --> 00:47:17,060
And it's an exciting time to be doing that.

1013
00:47:17,060 --> 00:47:22,060
And the whole Peter Thiel thing, yeah,

1014
00:47:23,620 --> 00:47:25,060
the whole PayPal mafia thing.

1015
00:47:25,060 --> 00:47:29,740
I'm not saying Whitney isn't hitting
something there

1016
00:47:29,739 --> 00:47:32,419
with the PayPal mafia and Reid Hoffman is in
there

1017
00:47:32,419 --> 00:47:35,859
and David Sacks, the new AI guy for Trump is
there.

1018
00:47:35,859 --> 00:47:37,419
And they're all always there.

1019
00:47:37,419 --> 00:47:39,539
It's always the DARPA kids that seem to come
out on top

1020
00:47:39,539 --> 00:47:40,379
as billionaires.

1021
00:47:40,379 --> 00:47:43,859
I get all that, but it's for me right now,

1022
00:47:43,859 --> 00:47:46,579
it's last helicopter out of Saigon.

1023
00:47:46,579 --> 00:47:48,139
I'm just enjoying the ride.

1024
00:47:50,379 --> 00:47:51,539
Yeah, yeah.

1025
00:47:51,539 --> 00:47:56,059
It seems like, I mean, again, with what's
been laid bare

1026
00:47:56,059 --> 00:47:57,859
in the first two weeks alone, it's like, all
right,

1027
00:47:57,860 --> 00:48:00,579
this is pretty good to know.

1028
00:48:01,780 --> 00:48:04,260
It's only been like two weeks.

1029
00:48:04,260 --> 00:48:06,980
Yeah, yeah, yeah.

1030
00:48:06,980 --> 00:48:10,660
Like literally this is, I think this hour,

1031
00:48:10,660 --> 00:48:15,019
it was two weeks to the hour when he was
inaugurated.

1032
00:48:15,019 --> 00:48:15,860
And that's another thing.

1033
00:48:15,860 --> 00:48:19,500
Like he seems like he's on a war path

1034
00:48:19,500 --> 00:48:22,740
after what they did in 2020, how they dragged
him

1035
00:48:22,740 --> 00:48:27,740
through all these lawsuits and tried to
literally kill him

1036
00:48:27,779 --> 00:48:28,859
a couple of times.

1037
00:48:28,859 --> 00:48:32,459
It seems like Trump is, I've got nothing to
lose.

1038
00:48:32,459 --> 00:48:35,459
I'm gonna send it and really drain the swamp

1039
00:48:35,459 --> 00:48:37,099
this time around.

1040
00:48:37,099 --> 00:48:37,939
I love it.

1041
00:48:37,939 --> 00:48:38,759
I love it.

1042
00:48:38,759 --> 00:48:39,779
This has been good stuff.

1043
00:48:39,779 --> 00:48:42,819
And I'd love to come back and follow up.

1044
00:48:42,819 --> 00:48:44,939
I'm working on the cancer vaccines really
hard,

1045
00:48:44,939 --> 00:48:46,699
the mRNA cancer.

1046
00:48:46,699 --> 00:48:48,099
That was always the plan.

1047
00:48:49,139 --> 00:48:51,939
Are you talking to Kevin McKernan a lot about
that?

1048
00:48:53,899 --> 00:48:56,179
We got off on the wrong foot.

1049
00:48:56,179 --> 00:48:57,699
I respect Kevin.

1050
00:48:58,619 --> 00:49:00,139
But I think there's some things that Kevin
does

1051
00:49:00,139 --> 00:49:03,699
that fall short.

1052
00:49:03,699 --> 00:49:06,379
I think there was a private cancer project

1053
00:49:06,379 --> 00:49:09,019
being run by the CIA and the people doing

1054
00:49:09,019 --> 00:49:12,119
the Human Genome Atlas project where they
held,

1055
00:49:12,119 --> 00:49:14,299
they withheld the centromeres

1056
00:49:14,299 --> 00:49:16,899
so the things were around cell division.

1057
00:49:16,899 --> 00:49:19,179
And then they held back information on the
telomeres,

1058
00:49:19,179 --> 00:49:21,539
which is the cancer oriented stuff.

1059
00:49:21,539 --> 00:49:23,299
And they released it 10 years later.

1060
00:49:23,299 --> 00:49:25,139
And that didn't go unnoticed with me

1061
00:49:25,139 --> 00:49:28,139
because what that means is let's do the
cancer genome

1062
00:49:28,139 --> 00:49:32,980
atlas, but I'll reserve parts for private
tiers

1063
00:49:32,980 --> 00:49:35,619
so that we can go develop mRNA cancer
vaccines.

1064
00:49:35,619 --> 00:49:37,859
And basically what happened,

1065
00:49:37,859 --> 00:49:39,980
I think it was 20 years later that they
finally

1066
00:49:39,980 --> 00:49:43,019
published those is they kind of gave up.

1067
00:49:43,019 --> 00:49:46,980
Now they want the open source help on those
genes.

1068
00:49:46,980 --> 00:49:49,019
It didn't turn out to just BP53.

1069
00:49:49,019 --> 00:49:50,779
It didn't just turn out to be RAS.

1070
00:49:50,779 --> 00:49:53,940
It didn't turn out to be just a simple
pathway

1071
00:49:53,980 --> 00:49:55,780
that they could block with mRNA.

1072
00:49:55,780 --> 00:49:57,380
It's much more complicated.

1073
00:49:57,380 --> 00:49:59,579
And there's all kinds of biofeedback loops

1074
00:49:59,579 --> 00:50:03,179
where cancer finds a way to get around.

1075
00:50:03,179 --> 00:50:05,039
And now they want the help.

1076
00:50:05,039 --> 00:50:10,039
So I don't think Kevin necessarily goes and
exposes.

1077
00:50:11,220 --> 00:50:12,940
He had a gene sequencing company,

1078
00:50:12,940 --> 00:50:16,880
I think it was the Ag and Core.

1079
00:50:18,179 --> 00:50:23,179
I don't think he is fully or allowed to be
fully transparent

1080
00:50:23,500 --> 00:50:28,500
about the private nature of the cancer genome
atlas projects

1081
00:50:29,299 --> 00:50:31,460
and the cancer moonshots.

1082
00:50:31,460 --> 00:50:34,460
So I'm there to make sure that Kevin,

1083
00:50:34,460 --> 00:50:37,500
for the rest of the story, for the rest of
Kevin's story.

1084
00:50:37,500 --> 00:50:39,819
I love Kevin, but I just want to be there

1085
00:50:39,819 --> 00:50:41,059
to tell the rest of the story

1086
00:50:41,059 --> 00:50:42,699
about the genes that he forgot.

1087
00:50:44,099 --> 00:50:45,179
Interesting.

1088
00:50:45,179 --> 00:50:46,659
This is fascinating stuff, George.

1089
00:50:46,659 --> 00:50:48,579
I can't believe this is the first time

1090
00:50:48,579 --> 00:50:49,419
you've been on the show,

1091
00:50:49,419 --> 00:50:50,619
but hopefully it's the first of many.

1092
00:50:50,619 --> 00:50:54,019
I'm sure there will be plenty of stuff coming
to light

1093
00:50:54,019 --> 00:50:55,259
in the coming months and years

1094
00:50:55,259 --> 00:51:00,259
that will provide additional conversations

1095
00:51:00,299 --> 00:51:02,179
and the need for additional conversations

1096
00:51:02,179 --> 00:51:04,299
to keep adding context to the story.

1097
00:51:04,299 --> 00:51:06,859
Where can people find out more about your
work

1098
00:51:06,859 --> 00:51:10,299
and follow you to keep up with the research

1099
00:51:10,299 --> 00:51:11,699
that you're doing?

1100
00:51:11,699 --> 00:51:15,739
Well, there's georgewebb.substack.com.

1101
00:51:15,739 --> 00:51:18,179
That's my sole source of income is paid.

1102
00:51:18,179 --> 00:51:19,299
Most people are free.

1103
00:51:19,299 --> 00:51:21,619
99% of the people are free,

1104
00:51:21,619 --> 00:51:24,219
but some people say, oh, I want great
journalism.

1105
00:51:24,219 --> 00:51:26,059
I'll do $8 a month.

1106
00:51:26,059 --> 00:51:28,659
The other one is realgeorgewebb1.

1107
00:51:28,659 --> 00:51:33,019
So the number one on Twitter, and that's all
free.

1108
00:51:33,019 --> 00:51:34,179
It's not a subscriber model.

1109
00:51:34,179 --> 00:51:36,179
So those are the two places to find me.

1110
00:51:37,179 --> 00:51:39,899
All right, we'll link to that in the show
notes.

1111
00:51:39,899 --> 00:51:42,819
George, thank you so much for your time
today.

1112
00:51:42,819 --> 00:51:45,699
Thanks, I'm going to check out of this hotel
now.

1113
00:51:45,699 --> 00:51:47,579
Thanks very much. Peace and love.

1114
00:51:47,579 --> 00:51:48,420
Bye bye.