1
00:00:02,520 --> 00:00:07,040
Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

2
00:00:07,800 --> 00:00:09,119
Speaker 2: Let's head over to Davos.

3
00:00:09,160 --> 00:00:13,040
Speaker 3: Salesforce CEO Mark Benyoff speaks with Bloomberg's Bradstone and Mustapha.

4
00:00:13,080 --> 00:00:15,560
Speaker 3: We're not getting along well on panels that they were

5
00:00:15,680 --> 00:00:17,680
Speaker 3: on here at Davos. You can go back and see that.

6
00:00:18,320 --> 00:00:21,440
Speaker 3: I remember one specifical panel that I was in and

7
00:00:21,520 --> 00:00:24,000
Speaker 3: it was thought they were going to come to blows,

8
00:00:24,000 --> 00:00:25,120
Speaker 3: but you know, it didn't happen.

9
00:00:25,360 --> 00:00:28,240
Speaker 1: And so has Larry Ellison, an oracle you think, stepped

10
00:00:28,240 --> 00:00:32,800
Speaker 1: in as a technology partner or a source of funding

11
00:00:32,880 --> 00:00:33,520
Speaker 1: for open Ai.

12
00:00:35,000 --> 00:00:37,959
Speaker 3: I can't speak to those specifics, but what I can

13
00:00:38,000 --> 00:00:40,920
Speaker 3: tell you is that I think it's a big moment

14
00:00:40,960 --> 00:00:43,280
Speaker 3: in the history of open ai that they're not running

15
00:00:43,320 --> 00:00:44,480
Speaker 3: on a Microsoft data center.

16
00:00:44,520 --> 00:00:48,680
Speaker 1: And how does it impact I guess Microsoft's competitive competitiveness

17
00:00:48,920 --> 00:00:52,159
Speaker 1: on AI going forward. We'll talk about Copilot and the like.

18
00:00:52,240 --> 00:00:56,200
Speaker 1: But did you feel like, you know, because of these developments,

19
00:00:57,000 --> 00:01:00,480
Speaker 1: Satya and Microsoft are in an unfavorable strategic position.

20
00:01:00,840 --> 00:01:03,360
Speaker 3: Well, I think, as you know, Microsoft doesn't have today

21
00:01:03,400 --> 00:01:06,759
Speaker 3: their own frontier model, so they rely on open Ai.

22
00:01:06,920 --> 00:01:09,240
Speaker 3: And now open Ai is saying that they're going to

23
00:01:09,319 --> 00:01:12,800
Speaker 3: another data center structure. So it's a big moment.

24
00:01:12,720 --> 00:01:17,360
Speaker 1: Right, and to just close the loop on Stargate. You know,

25
00:01:17,600 --> 00:01:20,759
Speaker 1: part of this is obviously driven by national security concerns

26
00:01:20,760 --> 00:01:24,000
Speaker 1: and the idea that you know that it is vitally

27
00:01:24,040 --> 00:01:26,399
Speaker 1: important for the US to maintain this lead. I mean,

28
00:01:26,440 --> 00:01:28,760
Speaker 1: do you share the concern and the urgency and do

29
00:01:28,800 --> 00:01:32,959
Speaker 1: you think China is close to AI parody?

30
00:01:33,160 --> 00:01:38,520
Speaker 3: Well, you have what six questions in there, let's kind

31
00:01:38,520 --> 00:01:41,320
Speaker 3: of peel it apart one by one. I think that

32
00:01:41,400 --> 00:01:46,520
Speaker 3: we're just seeing an incredible amount of investment in AI overall,

33
00:01:46,600 --> 00:01:50,400
Speaker 3: which is because it's really exciting. Look at what's happening

34
00:01:50,440 --> 00:01:52,480
Speaker 3: here at the conference I just showed you. You know that

35
00:01:52,560 --> 00:01:57,040
Speaker 3: Salesforce has run the information management for the World Economic

36
00:01:57,080 --> 00:02:01,160
Speaker 3: Forum for more than a decade, So all of everything

37
00:02:01,200 --> 00:02:05,120
Speaker 3: that's happening here is running on Salesforce. But and we've

38
00:02:05,200 --> 00:02:08,679
Speaker 3: used our Einstein platform here for years, but this is

39
00:02:08,720 --> 00:02:11,440
Speaker 3: the year that we're using Agent Force. So now you

40
00:02:11,560 --> 00:02:13,600
Speaker 3: have an agent that's run front end of your app.

41
00:02:13,720 --> 00:02:18,960
Speaker 3: Probably that is okay, Well, what it is is you

42
00:02:19,080 --> 00:02:22,480
Speaker 3: have the ability now to use an agent, a kind

43
00:02:22,520 --> 00:02:25,280
Speaker 3: of almost like a digital worker to help you to

44
00:02:25,680 --> 00:02:30,160
Speaker 3: kind of get your job done. So here I am,

45
00:02:30,240 --> 00:02:33,000
Speaker 3: I have my app, I'm on my phone. I'm normally

46
00:02:33,080 --> 00:02:35,400
Speaker 3: just bringing up my World Economic Forum app. I'm going

47
00:02:35,480 --> 00:02:37,600
Speaker 3: through trying to find all my sessions. So when is

48
00:02:37,639 --> 00:02:40,840
Speaker 3: Brad speaking? How do I know Brad? Can I contact

49
00:02:40,840 --> 00:02:43,480
Speaker 3: Brad instead? Now I can just say to the agent, hey,

50
00:02:43,800 --> 00:02:46,480
Speaker 3: when is Brad speaking? And let leave contact him for

51
00:02:46,639 --> 00:02:49,760
Speaker 3: me and help me make this happen. At Salesforce, we

52
00:02:49,919 --> 00:02:54,040
Speaker 3: just deployed our Agentforce technology on help dot salesforce dot com,

53
00:02:54,040 --> 00:02:58,040
Speaker 3: which is how we deliver help to our customers. Thirty

54
00:02:58,080 --> 00:03:01,160
Speaker 3: six thousand customers a week come to help dot salesforce

55
00:03:01,240 --> 00:03:03,680
Speaker 3: dot com. If you had come there a month or

56
00:03:03,680 --> 00:03:07,440
Speaker 3: so or ago, about ten thousand of those customers have

57
00:03:07,520 --> 00:03:10,080
Speaker 3: to move off and get support from human beings, and

58
00:03:10,120 --> 00:03:13,200
Speaker 3: about twenty six thousand got resolved by our software. Now

59
00:03:13,240 --> 00:03:16,959
Speaker 3: with agent Force, only five thousand are going off to

60
00:03:17,040 --> 00:03:19,280
Speaker 3: human beings. So our nine thousand support agents have a

61
00:03:19,320 --> 00:03:21,920
Speaker 3: little bit less to do because the software is able

62
00:03:21,919 --> 00:03:23,600
Speaker 3: to resolve a lot of this on its own. And

63
00:03:23,639 --> 00:03:27,400
Speaker 3: that's a very exciting moment that AI and agents can

64
00:03:27,440 --> 00:03:29,840
Speaker 3: kind of give us this incredible productivity that we couldn't

65
00:03:29,880 --> 00:03:32,760
Speaker 3: have whether we're trying to navigate the World Economic Form

66
00:03:32,760 --> 00:03:36,400
Speaker 3: and Davos or where Mark Benioffice trying to answer his customers'

67
00:03:37,200 --> 00:03:40,160
Speaker 3: support issues. Agents are making a huge difference in our

68
00:03:40,160 --> 00:03:41,200
Speaker 3: ability to be successful.

69
00:03:41,240 --> 00:03:42,840
Speaker 1: I want to come back to that, but I just

70
00:03:42,880 --> 00:03:45,480
Speaker 1: want to make sure we answered the previous question on

71
00:03:45,640 --> 00:03:49,280
Speaker 1: kind of AI parody between China and the US.

72
00:03:49,360 --> 00:03:51,280
Speaker 3: I don't think there is AI parody today. No. I

73
00:03:51,320 --> 00:03:54,360
Speaker 3: think that the US has a definitive lead, and I

74
00:03:54,400 --> 00:03:57,560
Speaker 3: think most of the analysis and I think obviously you

75
00:03:57,560 --> 00:03:59,600
Speaker 3: have a big shootout going on at the very top

76
00:03:59,680 --> 00:04:02,960
Speaker 3: end of the aron tier models between Google, Open Ai,

77
00:04:03,960 --> 00:04:09,240
Speaker 3: and Anthropic. Salesforce is obviously an investor in Anthropic. We

78
00:04:09,280 --> 00:04:14,720
Speaker 3: obviously have relationships with open ai as and also Google.

79
00:04:16,080 --> 00:04:20,440
Speaker 3: The Google technology I think is amazing. Neil Ferguson just

80
00:04:20,480 --> 00:04:23,120
Speaker 3: wrote a tremendous analysis of the three models and kind

81
00:04:23,160 --> 00:04:26,560
Speaker 3: of said it appears at Google. So Gemini as a player,

82
00:04:26,920 --> 00:04:29,320
Speaker 3: I think Gemini is more than a player. It's probably ahead.

83
00:04:30,279 --> 00:04:32,400
Speaker 1: Okay, And by the way, Dario proceeded you on stage

84
00:04:32,400 --> 00:04:33,400
Speaker 1: earlier this afternoon.

85
00:04:33,920 --> 00:04:35,920
Speaker 3: He has an outstanding model and is doing a great

86
00:04:35,960 --> 00:04:36,760
Speaker 3: job in his company.

87
00:04:37,040 --> 00:04:38,320
Speaker 2: Okay, So back to agent forest.

88
00:04:38,320 --> 00:04:42,840
Speaker 1: He said, few less things for your employees to do

89
00:04:42,920 --> 00:04:45,360
Speaker 1: with agent force, But does it also.

90
00:04:45,160 --> 00:04:46,400
Speaker 2: Mean fewer employees.

91
00:04:46,440 --> 00:04:49,960
Speaker 1: What are the ramifications of this kind of new age

92
00:04:50,000 --> 00:04:54,960
Speaker 1: of agentic models going to unsupervised do our tasks for us?

93
00:04:55,040 --> 00:04:57,960
Speaker 3: No, no question, I mean, of course, in the history, look,

94
00:04:58,000 --> 00:05:01,160
Speaker 3: technology is always getting lower costs in these yer to use.

95
00:05:01,200 --> 00:05:03,720
Speaker 3: There's not any you know, horses out on the streets

96
00:05:03,800 --> 00:05:06,760
Speaker 3: right now going out, and now we have cars now.

97
00:05:07,040 --> 00:05:08,960
Speaker 3: And in the same way, I think we should just

98
00:05:09,000 --> 00:05:11,800
Speaker 3: think about the analogy I just gave you, which is

99
00:05:11,800 --> 00:05:17,000
Speaker 3: that we have a lot less work for our support

100
00:05:17,040 --> 00:05:19,839
Speaker 3: agents to do because we've deployed agent force at salesforce,

101
00:05:20,240 --> 00:05:23,840
Speaker 3: so I can redeploy that resource into other parts of

102
00:05:23,880 --> 00:05:26,480
Speaker 3: the company. That's very exciting for me. I have plenty

103
00:05:26,520 --> 00:05:29,960
Speaker 3: to do, so I can kind of say, Okay, these

104
00:05:29,960 --> 00:05:32,320
Speaker 3: folks can work on this now instead of being over here.

105
00:05:32,440 --> 00:05:36,000
Speaker 1: Has it changed the arc of your hiring plans going forward?

106
00:05:36,000 --> 00:05:39,280
Speaker 1: And what is the widespread deployment of this technology mean

107
00:05:39,360 --> 00:05:41,840
Speaker 1: for you a full employment labor market?

108
00:05:42,960 --> 00:05:45,560
Speaker 3: Well, that is really the powerful thing. We're in a

109
00:05:45,640 --> 00:05:48,080
Speaker 3: labor market where it's really hard to hire people. There

110
00:05:48,120 --> 00:05:51,600
Speaker 3: aren't people to hire I want to radically expand sales

111
00:05:51,640 --> 00:05:54,479
Speaker 3: and service and marketing and salesforce because we are seeing

112
00:05:54,880 --> 00:05:58,960
Speaker 3: a huge amount of demand in moving and deploying our

113
00:05:59,000 --> 00:06:02,520
Speaker 3: new technologies. Finding those people incredibly difficult. That I have

114
00:06:02,600 --> 00:06:06,360
Speaker 3: agents at my disposal is tremendous. So look, I want

115
00:06:06,360 --> 00:06:10,720
Speaker 3: an unlimited workforce. I think everybody does agent force AI agents.

116
00:06:11,240 --> 00:06:15,320
Speaker 3: That's beginning of an unlimited workforce. Giveing an example. We

117
00:06:15,360 --> 00:06:18,919
Speaker 3: have a customer called Wiley. They're a bookseller in the US.

118
00:06:19,000 --> 00:06:20,840
Speaker 3: You probably have picked up some of their books when

119
00:06:20,839 --> 00:06:24,360
Speaker 3: you're in college. You know them well, and you know

120
00:06:24,560 --> 00:06:27,800
Speaker 3: they have this moment every year called back to school,

121
00:06:27,839 --> 00:06:29,560
Speaker 3: and they have to normally hire a lot of gig

122
00:06:29,600 --> 00:06:31,839
Speaker 3: workers to go into sales or into service or whatever

123
00:06:31,880 --> 00:06:33,720
Speaker 3: it is. This is a year they did not have

124
00:06:33,800 --> 00:06:36,359
Speaker 3: to do it. There were one of our launch customers

125
00:06:36,400 --> 00:06:39,440
Speaker 3: on Agent Force. They have the ability to just scale

126
00:06:39,640 --> 00:06:41,840
Speaker 3: their sales and service when they needed it and then

127
00:06:41,880 --> 00:06:45,720
Speaker 3: bring it back down when that demand spike ended. And

128
00:06:46,080 --> 00:06:49,160
Speaker 3: we have now you know, lots of examples of that.

129
00:06:49,200 --> 00:06:53,920
Speaker 3: So we've now released the code late October. We did

130
00:06:53,920 --> 00:06:56,080
Speaker 3: about two hundred deals in.

131
00:06:56,040 --> 00:06:57,360
Speaker 2: Our third quarter.

132
00:06:57,560 --> 00:07:02,120
Speaker 3: In our fourth quarter, we'll see thousands of agent Force deals,

133
00:07:02,400 --> 00:07:06,680
Speaker 3: and I've never seen anything go as fast at Salesforce

134
00:07:06,760 --> 00:07:10,160
Speaker 3: as this. I've never been as excited about the technology

135
00:07:10,160 --> 00:07:11,360
Speaker 3: industry as I am right now.

136
00:07:11,520 --> 00:07:14,240
Speaker 1: So in the tech industry, we love a good old

137
00:07:14,240 --> 00:07:16,240
Speaker 1: fashioned feud. We used to have a lot of them,

138
00:07:16,360 --> 00:07:19,960
Speaker 1: like Larry and Bill Sparring. You know, Kendrick Lamar and

139
00:07:20,040 --> 00:07:23,280
Speaker 1: Drake had had nothing on the tech industry. But recently,

140
00:07:23,320 --> 00:07:27,320
Speaker 1: you know, you've been tweeting or posting on x as

141
00:07:27,360 --> 00:07:32,640
Speaker 1: you describe agent Force and drawing juxtapositions with Microsoft and

142
00:07:32,800 --> 00:07:36,720
Speaker 1: co Pilot. Not to do more Microsoft pashion here, but

143
00:07:37,040 --> 00:07:39,280
Speaker 1: I think you compared it to Yeah.

144
00:07:39,400 --> 00:07:41,640
Speaker 2: No, but I want you to because I don't know

145
00:07:41,680 --> 00:07:44,600
Speaker 2: that I really fully got my brain around it.

146
00:07:44,720 --> 00:07:48,960
Speaker 1: How the agent Force model is different than the coilet mind.

147
00:07:48,760 --> 00:07:51,000
Speaker 3: I can make it very similar. Well, first of all,

148
00:07:51,040 --> 00:07:54,280
Speaker 3: you're right, Microsoft has disappointed you know, a lot of

149
00:07:54,320 --> 00:07:56,560
Speaker 3: customers with Copilot, and the reason why is.

150
00:07:56,520 --> 00:07:57,880
Speaker 2: I'm not sure everybody agrees.

151
00:07:57,920 --> 00:08:00,520
Speaker 3: But okay, well, I mean you can read the Gartner analysis,

152
00:08:00,520 --> 00:08:03,040
Speaker 3: you can read a lot of the media, but I'm

153
00:08:03,040 --> 00:08:05,440
Speaker 3: sure you're a big Copilot user yourself, Is that right?

154
00:08:05,680 --> 00:08:07,800
Speaker 1: I try to moderate my Oh, I see you know

155
00:08:07,920 --> 00:08:09,680
Speaker 1: I don't want to, all right, but you're like most people,

156
00:08:09,760 --> 00:08:12,200
Speaker 1: you don't use it, all right, thank you, thank you

157
00:08:12,240 --> 00:08:13,040
Speaker 1: for making my case.

158
00:08:13,080 --> 00:08:17,520
Speaker 3: But anyway, the point of it is all Copilot is

159
00:08:17,520 --> 00:08:20,080
Speaker 3: is repackaged chat GPT. So in a chart I just

160
00:08:20,120 --> 00:08:23,560
Speaker 3: saw like the twenty most used apps currently on the Internet,

161
00:08:23,600 --> 00:08:26,440
Speaker 3: like open Ai was very much at the top. Co

162
00:08:26,560 --> 00:08:29,240
Speaker 3: Pilot was not on the list. I just retweeted it. Actually,

163
00:08:29,280 --> 00:08:32,600
Speaker 3: so when you look at that, why that is is

164
00:08:32,640 --> 00:08:36,880
Speaker 3: because why use it? It's not really offering me any value.

165
00:08:37,000 --> 00:08:41,040
Speaker 3: So agent force is really what AI was meant to be.

166
00:08:41,120 --> 00:08:44,040
Speaker 3: The ability to give a company, a user, a customer,

167
00:08:44,600 --> 00:08:47,520
Speaker 3: really the value of artificial intelligence. That's why I was

168
00:08:47,559 --> 00:08:51,400
Speaker 3: excited to bring it to the show. Excited that Davos

169
00:08:51,400 --> 00:08:54,439
Speaker 3: has deployed it and we can really show customers here

170
00:08:54,559 --> 00:08:57,520
Speaker 3: this is a really great opportunity. In an example, yesterday

171
00:08:57,559 --> 00:09:00,599
Speaker 3: we were in a session with Rollbank Canada who just

172
00:09:00,640 --> 00:09:03,559
Speaker 3: deployed it for their wealth management. Look their ability to

173
00:09:03,760 --> 00:09:06,320
Speaker 3: scale and compete against the big US banks when their

174
00:09:06,360 --> 00:09:09,480
Speaker 3: wealth management is augmented because they're using agent for us.

175
00:09:09,520 --> 00:09:12,840
Speaker 3: That's very exciting for them. They're CEO Dave McKay is here.

176
00:09:12,880 --> 00:09:15,520
Speaker 3: It was really a great, a great moment to hear

177
00:09:15,559 --> 00:09:16,599
Speaker 3: you know what they're doing with this.

178
00:09:17,600 --> 00:09:21,480
Speaker 1: So before you mentioned the abundant capital flowing into AI,

179
00:09:21,960 --> 00:09:24,920
Speaker 1: you know, anthropic to even today there was news of

180
00:09:24,960 --> 00:09:29,200
Speaker 1: another one hundred one billion dollars from Google. Is there

181
00:09:29,520 --> 00:09:33,079
Speaker 1: any case to be made that AI is over invested

182
00:09:33,120 --> 00:09:33,760
Speaker 1: at this point?

183
00:09:34,679 --> 00:09:37,920
Speaker 3: No, there's not. I would say that we are really

184
00:09:38,000 --> 00:09:43,280
Speaker 3: at a point where everything is changing. I think the evidence,

185
00:09:43,360 --> 00:09:45,240
Speaker 3: like we just did a demo with your associate in

186
00:09:45,280 --> 00:09:47,600
Speaker 3: the back here where I just took out my phone

187
00:09:47,600 --> 00:09:50,360
Speaker 3: and we were demoing the World Economic Forum app. And

188
00:09:50,440 --> 00:09:52,240
Speaker 3: if we were here a year ago, we'd be looking

189
00:09:52,280 --> 00:09:54,200
Speaker 3: at a very similar app kind of going through what

190
00:09:54,360 --> 00:09:57,200
Speaker 3: sessions are we going through? Who's coming to the conference?

191
00:09:57,280 --> 00:09:59,600
Speaker 3: I need to contact this person, all the typical things,

192
00:10:00,040 --> 00:10:02,040
Speaker 3: and instead I have this agent that I just can

193
00:10:02,640 --> 00:10:05,200
Speaker 3: go right to and say, hey, do all this for me.

194
00:10:06,240 --> 00:10:10,360
Speaker 3: That level of productivity, that's why in the third quarter

195
00:10:10,480 --> 00:10:14,240
Speaker 3: of last year we saw productivity rise in the US

196
00:10:14,280 --> 00:10:18,319
Speaker 3: without any additional workers. That really hasn't happened in a while.

197
00:10:18,520 --> 00:10:22,120
Speaker 3: That very society margin there that I think that is

198
00:10:22,160 --> 00:10:24,400
Speaker 3: exactly what is going on. I think AI is kind

199
00:10:24,400 --> 00:10:27,000
Speaker 3: of kicking in Right now, I'm writing the business plan

200
00:10:27,040 --> 00:10:30,200
Speaker 3: for my next fiscal year, which starts February one, and

201
00:10:30,240 --> 00:10:31,640
Speaker 3: this will be the third year in a row that

202
00:10:31,679 --> 00:10:34,400
Speaker 3: I'm working with an AI to write my business plan

203
00:10:34,480 --> 00:10:38,240
Speaker 3: for the year. So I'm I have a colleague. I

204
00:10:38,440 --> 00:10:42,000
Speaker 3: will write different parts of the plan, and then I

205
00:10:42,040 --> 00:10:44,080
Speaker 3: will talk to the AI and I will say, hey,

206
00:10:44,080 --> 00:10:46,040
Speaker 3: what do you think of this? How does this compete?

207
00:10:48,720 --> 00:10:51,000
Speaker 3: We have our we have some great technology. And then

208
00:10:51,040 --> 00:10:54,520
Speaker 3: I'll say, hey, how does this How does this affect

209
00:10:54,600 --> 00:10:57,839
Speaker 3: this competitor? How does it affect that competitor? And I

210
00:10:57,880 --> 00:11:01,400
Speaker 3: have been shocked at how it's impact me. That it

211
00:11:01,440 --> 00:11:04,320
Speaker 3: has made me more productive, but also expands my own

212
00:11:04,360 --> 00:11:07,520
Speaker 3: consciousness on what I should be doing and leading and

213
00:11:07,559 --> 00:11:10,560
Speaker 3: moving forward. So AI is definitely a part of I

214
00:11:10,559 --> 00:11:11,160
Speaker 3: cat to be.

215
00:11:11,160 --> 00:11:13,280
Speaker 1: A little bit more skeptical, and when I are.

216
00:11:13,280 --> 00:11:14,880
Speaker 3: I didn't know that. How long do I know you for?

217
00:11:15,080 --> 00:11:17,960
Speaker 3: I never knew this, But I'm so happy to know

218
00:11:18,000 --> 00:11:18,880
Speaker 3: this about you.

219
00:11:18,920 --> 00:11:22,280
Speaker 1: When I when I hear things like Larry, your former boss,

220
00:11:22,320 --> 00:11:27,760
Speaker 1: Larry Ellison saying today that there will be targeted vaccines

221
00:11:27,800 --> 00:11:31,040
Speaker 1: for cancer using AI within forty eight hours, I mean

222
00:11:31,480 --> 00:11:33,440
Speaker 1: it just feels like the exuberance is.

223
00:11:34,040 --> 00:11:35,839
Speaker 3: I don't think he means in the next forty eight hours.

224
00:11:35,840 --> 00:11:37,119
Speaker 3: So I think what he's saying.

225
00:11:36,960 --> 00:11:39,320
Speaker 2: Is we understand forty eight hours within diego okay.

226
00:11:39,120 --> 00:11:41,800
Speaker 3: So what is okay? So I've actually talked to him

227
00:11:41,840 --> 00:11:45,400
Speaker 3: about what he's doing. It is pretty incredible. His vision,

228
00:11:46,120 --> 00:11:49,120
Speaker 3: you know, his vision is that, you know, there's these

229
00:11:49,160 --> 00:11:51,840
Speaker 3: amazing kind of cancer tests that have kind of been developed,

230
00:11:51,880 --> 00:11:54,400
Speaker 3: like GRAYIL and others, where you could get a blood

231
00:11:54,400 --> 00:11:56,839
Speaker 3: test and it shows you if you have these fragments

232
00:11:56,840 --> 00:11:58,800
Speaker 3: of cancer in your bloodstream and so forth, and then

233
00:11:58,880 --> 00:12:01,679
Speaker 3: they go to try to find the cant in this case.

234
00:12:01,920 --> 00:12:03,880
Speaker 3: And I'm not going to describe it as beautifully as

235
00:12:03,880 --> 00:12:07,840
Speaker 3: he did today, but because it's his vision, his idea

236
00:12:08,559 --> 00:12:10,800
Speaker 3: is that you're gonna be able to run that same

237
00:12:10,920 --> 00:12:15,000
Speaker 3: test and then use AI to have much more affinity

238
00:12:15,000 --> 00:12:18,640
Speaker 3: on what's really going on, and then be able to

239
00:12:18,760 --> 00:12:25,680
Speaker 3: use our mRNA technology to develop a highly personalized vaccine

240
00:12:26,040 --> 00:12:29,520
Speaker 3: for that cancer and eradicated from your body. That is

241
00:12:29,600 --> 00:12:34,160
Speaker 3: pretty extraordinary. Obviously it hasn't happened yet, but the vision

242
00:12:34,240 --> 00:12:36,480
Speaker 3: is awesome. And you know what, I think we should

243
00:12:36,480 --> 00:12:39,240
Speaker 3: be looking for innovation in the world, and it's exciting

244
00:12:39,280 --> 00:12:42,600
Speaker 3: to see someone like Larry Ellison having a huge vision

245
00:12:42,640 --> 00:12:46,920
Speaker 3: for cancer. He has invested lots of his own money

246
00:12:46,960 --> 00:12:50,720
Speaker 3: and cancer treatment and has centers in la and in

247
00:12:50,760 --> 00:12:53,120
Speaker 3: Oxford and it's been very impressive. Okay to see what

248
00:12:53,160 --> 00:12:53,560
Speaker 3: he's done.

249
00:12:53,600 --> 00:12:55,840
Speaker 1: I want to hit a couple more topics before we

250
00:12:55,880 --> 00:12:58,720
Speaker 1: are supplanted by a head of state. I think last

251
00:12:58,800 --> 00:13:01,040
Speaker 1: year we talked about social media and you sounded the

252
00:13:01,080 --> 00:13:04,520
Speaker 1: alarm on misinformation and you.

253
00:13:04,480 --> 00:13:06,280
Speaker 2: Know, the lack of proper oversight.

254
00:13:07,000 --> 00:13:09,920
Speaker 1: You've viewed some salty language, and in the last few

255
00:13:09,960 --> 00:13:14,400
Speaker 1: months we have seen a retreat on moderation. You know what,

256
00:13:14,400 --> 00:13:17,960
Speaker 1: what do you make recent moves at Meta and the

257
00:13:18,000 --> 00:13:21,240
Speaker 1: company formerly noticed Twitter, And do you think it's dangerous?

258
00:13:21,920 --> 00:13:25,240
Speaker 3: Well, I think, you know, my thoughts on social media

259
00:13:25,320 --> 00:13:27,680
Speaker 3: is really well known. But I think that you know,

260
00:13:27,720 --> 00:13:29,679
Speaker 3: we need to continue to look at from the fundamental

261
00:13:29,679 --> 00:13:33,040
Speaker 3: reshaping of you know, section two thirty. I think if

262
00:13:33,080 --> 00:13:36,840
Speaker 3: we are going to let these organizations go to every

263
00:13:36,880 --> 00:13:41,079
Speaker 3: possible extreme, then they should be held accountable and liable

264
00:13:41,160 --> 00:13:43,760
Speaker 3: for whatever the content is. And I think that when

265
00:13:43,760 --> 00:13:45,920
Speaker 3: we look at section two thirty, it wasn't written for

266
00:13:45,960 --> 00:13:48,959
Speaker 3: this kind of moment, and especially with the age of AI,

267
00:13:49,520 --> 00:13:50,840
Speaker 3: this is a time when that we have to go

268
00:13:50,920 --> 00:13:52,720
Speaker 3: back and do that. And I hope that the Trump

269
00:13:52,760 --> 00:13:55,920
Speaker 3: administration will evaluate that they've made a number of comments

270
00:13:55,960 --> 00:13:58,560
Speaker 3: along that along those lines, and I think that that

271
00:13:58,720 --> 00:14:02,480
Speaker 3: is appropriate to to get rid of that regulation so

272
00:14:02,520 --> 00:14:07,240
Speaker 3: that these organizations can be held accountable. I think you're

273
00:14:07,240 --> 00:14:09,439
Speaker 3: a journalist, you're held accountable for you what you write

274
00:14:09,480 --> 00:14:11,640
Speaker 3: and what you put on your platform, So should they.

275
00:14:12,840 --> 00:14:16,720
Speaker 1: You have come to Davos in years past and made

276
00:14:16,880 --> 00:14:18,320
Speaker 1: some pretty strenuous.

277
00:14:17,920 --> 00:14:20,960
Speaker 3: One thing we said was yes, positive, Well.

278
00:14:20,840 --> 00:14:23,560
Speaker 1: We've still three and a half minutes and you've made

279
00:14:23,600 --> 00:14:28,800
Speaker 1: some pretty strenuous laid out some strenuous climate goals. I

280
00:14:28,840 --> 00:14:31,640
Speaker 1: think there was planting a trillion trees by twenty twenty.

281
00:14:31,920 --> 00:14:35,040
Speaker 3: I mean, how in fact today right now, actually we're

282
00:14:35,040 --> 00:14:43,400
Speaker 3: announcing that we're delivering the largest forest reserve in Earth's

283
00:14:43,440 --> 00:14:46,640
Speaker 3: history in the Congo, and President of the Congo's making

284
00:14:46,640 --> 00:14:49,680
Speaker 3: the announcement part of the trillion tree announcement that we

285
00:14:49,680 --> 00:14:52,520
Speaker 3: did five years ago. And we're almost at two hundred

286
00:14:52,560 --> 00:14:55,640
Speaker 3: billion trees, so we're not quite at a trillion, but

287
00:14:55,680 --> 00:14:58,960
Speaker 3: we got to keep going, I will se question twoga

288
00:14:59,000 --> 00:15:02,000
Speaker 3: cont Okay, I'm glad you're excited about that.

289
00:15:02,840 --> 00:15:04,200
Speaker 2: I love trees. We love trees.

290
00:15:04,520 --> 00:15:06,360
Speaker 3: If I'm not sure you do based on that, but

291
00:15:07,040 --> 00:15:09,760
Speaker 3: we spent exactly about five seconds on trees and a

292
00:15:09,800 --> 00:15:12,960
Speaker 3: lot on Larry Ellison. I think you like Larry Ellison

293
00:15:12,960 --> 00:15:18,040
Speaker 3: more than trees. Brad. My question is I'm just saying.

294
00:15:17,800 --> 00:15:22,280
Speaker 1: That is required to run these AI data centers. If

295
00:15:22,320 --> 00:15:26,240
Speaker 1: the industry is sort of conveniently waving it away talking

296
00:15:26,280 --> 00:15:31,160
Speaker 1: about nuclear if we've really accounted for the requirements that

297
00:15:31,160 --> 00:15:32,080
Speaker 1: these companies are.

298
00:15:31,920 --> 00:15:34,120
Speaker 2: Going to need as they scale up their operations.

299
00:15:34,800 --> 00:15:37,400
Speaker 3: Yeah, well, I think that that whole idea that energy

300
00:15:37,480 --> 00:15:40,040
Speaker 3: still needs to get rationalized against what's happening with these

301
00:15:40,120 --> 00:15:43,400
Speaker 3: data center demands and some of the stuff that's happening

302
00:15:43,480 --> 00:15:46,400
Speaker 3: I think is kind of there's some kind of indiscriminate

303
00:15:46,760 --> 00:15:50,280
Speaker 3: kind of development and people just using them as much

304
00:15:50,320 --> 00:15:52,560
Speaker 3: as they can and so forth. I think it's salesforce.

305
00:15:52,640 --> 00:15:54,800
Speaker 3: We continue to try to do our best to be

306
00:15:54,840 --> 00:15:57,400
Speaker 3: in that zero company, including in the work of our

307
00:15:57,440 --> 00:16:01,000
Speaker 3: AI will deliver about two trillion AIS actions this week.

308
00:16:01,880 --> 00:16:03,720
Speaker 3: You know we've been doing that for about a decade.

309
00:16:03,800 --> 00:16:07,760
Speaker 3: You mentioned Einstein now agent force, and even in development

310
00:16:07,760 --> 00:16:09,480
Speaker 3: of our own models and all of these things, we

311
00:16:09,520 --> 00:16:13,760
Speaker 3: have not had these same exhaustive energy demands. And part

312
00:16:13,760 --> 00:16:17,440
Speaker 3: of that's because of how we operate our own systems.

313
00:16:18,160 --> 00:16:20,320
Speaker 3: But I think for everybody there's probably an opportunity for

314
00:16:20,360 --> 00:16:21,560
Speaker 3: another level of efficiency.

315
00:16:21,920 --> 00:16:24,520
Speaker 2: And what is the solution? Is Is it nuclear? Is

316
00:16:24,560 --> 00:16:25,760
Speaker 2: it fuel cells?

317
00:16:25,800 --> 00:16:25,880
Speaker 3: Like?

318
00:16:25,920 --> 00:16:29,240
Speaker 1: How does the industry catch up to the energy use

319
00:16:29,240 --> 00:16:30,120
Speaker 1: that will be required?

320
00:16:31,560 --> 00:16:33,480
Speaker 3: I think that it's going to How are we going

321
00:16:33,560 --> 00:16:35,520
Speaker 3: to get more energy in the world. I mean, I'm

322
00:16:35,560 --> 00:16:39,040
Speaker 3: not really an energy expert. I'm obviously a huge bowl

323
00:16:39,120 --> 00:16:42,680
Speaker 3: on fusion. I think what Commonwealth Energy is doing is incredible.

324
00:16:42,720 --> 00:16:44,720
Speaker 3: I'm an investor in the company and I hope that

325
00:16:45,160 --> 00:16:47,520
Speaker 3: it's a huge success. And then yes, I also think

326
00:16:47,920 --> 00:16:52,720
Speaker 3: these kind of SMR nuclear modular nuclear plants. I think

327
00:16:52,720 --> 00:16:55,440
Speaker 3: there's a new one just going in Texas for example,

328
00:16:55,520 --> 00:16:58,480
Speaker 3: right now in other places. I mean, if you think

329
00:16:58,520 --> 00:17:01,000
Speaker 3: about it, you know we've run small nuclear for a

330
00:17:01,040 --> 00:17:05,720
Speaker 3: long time. Everyone knows about nuclear submarines or nuclear aircraft carriers, right,

331
00:17:06,240 --> 00:17:08,800
Speaker 3: and that's basically what we're talking about. Why don't we

332
00:17:08,840 --> 00:17:10,480
Speaker 3: just take one of those and just plug it into

333
00:17:10,480 --> 00:17:13,680
Speaker 3: our grid. We just don't think that way. But that's

334
00:17:13,720 --> 00:17:16,960
Speaker 3: all we're really talking about, being able to just use

335
00:17:17,560 --> 00:17:19,520
Speaker 3: the energy that we have in the world that's available

336
00:17:19,520 --> 00:17:21,600
Speaker 3: to us, and deliver in the clean way. I think

337
00:17:21,760 --> 00:17:23,879
Speaker 3: obviously we have to think about the amount of carbon

338
00:17:23,880 --> 00:17:27,280
Speaker 3: we're putting in the atmosphere. It's very significant, not just

339
00:17:27,320 --> 00:17:30,080
Speaker 3: from our emissions, but as you mentioned, the trees. And

340
00:17:30,119 --> 00:17:31,479
Speaker 3: I hate to go back there because I know you

341
00:17:31,480 --> 00:17:34,080
Speaker 3: have it to test for them. But you know, we

342
00:17:34,160 --> 00:17:36,280
Speaker 3: used to have six trillion trees on the planet. Now

343
00:17:36,280 --> 00:17:38,840
Speaker 3: we have less than three trillion. Every trillion trees is

344
00:17:38,840 --> 00:17:43,080
Speaker 3: two hundred gigatons of carbon, so that's six hundred gigatons

345
00:17:43,119 --> 00:17:48,280
Speaker 3: of carbon that we emitted through deforestation. And connecting that

346
00:17:48,440 --> 00:17:53,520
Speaker 3: back to industrialization, we've only admitted about two hundred gigatons

347
00:17:54,000 --> 00:17:57,800
Speaker 3: through all industrializations, so you can see deforestation remains a

348
00:17:57,880 --> 00:17:58,520
Speaker 3: major issue.

349
00:17:58,560 --> 00:18:00,480
Speaker 1: Last question, you could give me a yes, Ordain, you're

350
00:18:00,600 --> 00:18:00,920
Speaker 1: the owner.

351
00:18:01,000 --> 00:18:02,640
Speaker 3: I know we've probably exited the trees now once.

352
00:18:03,560 --> 00:18:06,000
Speaker 1: Well, no, this is actually understand you're the owner of.

353
00:18:06,000 --> 00:18:07,920
Speaker 3: No one's going to accuse you of being the Laura.

354
00:18:08,080 --> 00:18:11,840
Speaker 1: You're the owner of Time magazine, a tree based publication

355
00:18:12,119 --> 00:18:14,760
Speaker 1: at a time where it's very uncomfortable.

356
00:18:14,119 --> 00:18:15,160
Speaker 2: For media owners.

357
00:18:15,400 --> 00:18:17,520
Speaker 1: There were reports that you're going to sell, are you?

358
00:18:17,640 --> 00:18:20,919
Speaker 3: Are you going to There's no deal on the table

359
00:18:20,960 --> 00:18:23,520
Speaker 3: for time, so if that we do sign something, I

360
00:18:23,520 --> 00:18:24,520
Speaker 3: will call you right away.

361
00:18:25,240 --> 00:18:26,640
Speaker 2: Mark Benieff, thank you very much.

362
00:18:26,680 --> 00:18:27,160
Speaker 3: Thank you,