1
00:00:02,759 --> 00:00:10,240
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. We want to welcome

2
00:00:10,360 --> 00:00:14,080
Speaker 1: our global audience across Bloomberg Radio and television Open AI.

3
00:00:14,320 --> 00:00:17,919
Speaker 1: It's released GPT five. It's most advanced model yet. The

4
00:00:17,920 --> 00:00:22,639
Speaker 1: company says it offers key improvements in major areas like reliability, accuracy,

5
00:00:22,800 --> 00:00:24,800
Speaker 1: and there's the strongest generator of AI model yet in

6
00:00:24,880 --> 00:00:26,160
Speaker 1: coding and writing and health.

7
00:00:26,760 --> 00:00:27,160
Speaker 2: For more.

8
00:00:27,280 --> 00:00:30,639
Speaker 1: We bring in Brad lightcap Open AI COO and this

9
00:00:30,720 --> 00:00:34,319
Speaker 1: feels so primed for enterprise adoption. Brad, when I think

10
00:00:34,360 --> 00:00:36,280
Speaker 1: of writing, when I think of coding, what is the

11
00:00:36,280 --> 00:00:37,160
Speaker 1: opportunity there?

12
00:00:38,560 --> 00:00:42,680
Speaker 3: Yeah, well, good morning, thanks for having me. GBT five

13
00:00:42,760 --> 00:00:45,760
Speaker 3: is a significant step forward in a few different domains.

14
00:00:45,800 --> 00:00:49,080
Speaker 3: So you mentioned coding, you mentioned writing in health for

15
00:00:49,200 --> 00:00:53,760
Speaker 3: consumers and medical professionals, and we think that opportunity unlocks

16
00:00:54,040 --> 00:00:55,880
Speaker 3: an amazing set of things in the enterprise that now

17
00:00:55,880 --> 00:00:56,680
Speaker 3: become possible.

18
00:00:57,600 --> 00:00:58,920
Speaker 4: It's a much more reliable model.

19
00:00:58,960 --> 00:01:01,640
Speaker 3: So it's better at things like calling tools, it's better

20
00:01:01,640 --> 00:01:05,320
Speaker 3: at things like structured thinking and reasoning, problem solving. And

21
00:01:05,360 --> 00:01:07,360
Speaker 3: what we see in the enterprise is when you make

22
00:01:07,400 --> 00:01:11,080
Speaker 3: these core capabilities better, the number of use cases enterprises

23
00:01:11,120 --> 00:01:12,240
Speaker 3: can adopt these models for.

24
00:01:12,280 --> 00:01:15,080
Speaker 4: Increases and so coding being significant.

25
00:01:15,520 --> 00:01:19,120
Speaker 3: It really is the language of computers and that was

26
00:01:19,120 --> 00:01:21,560
Speaker 3: a significant area of demand for us when we were

27
00:01:21,560 --> 00:01:23,559
Speaker 3: talking to customers about what they wanted in this model.

28
00:01:23,760 --> 00:01:26,479
Speaker 1: I mean, PhD level is what many are calling it,

29
00:01:26,560 --> 00:01:28,800
Speaker 1: well what Sam is calling it, and I'm sure yourself.

30
00:01:29,160 --> 00:01:32,160
Speaker 1: What's interesting is when you've got seven hundred million weekly

31
00:01:32,280 --> 00:01:34,920
Speaker 1: users of chatchipt, how much is that a funnel a

32
00:01:34,959 --> 00:01:36,920
Speaker 1: read across into enterprise? How much you could get the

33
00:01:36,959 --> 00:01:39,360
Speaker 1: inbound because ultimately the people in the old workforce are

34
00:01:39,400 --> 00:01:40,039
Speaker 1: already using it.

35
00:01:42,080 --> 00:01:44,520
Speaker 3: Well, really early on when we launched chat GPT, what

36
00:01:44,560 --> 00:01:47,680
Speaker 3: we found is I think, you know, a number of

37
00:01:47,720 --> 00:01:50,360
Speaker 3: months after we launched it, I think something like ninety

38
00:01:50,400 --> 00:01:53,880
Speaker 3: two percent of the Fortune five hundred we're actively using

39
00:01:53,920 --> 00:01:55,840
Speaker 3: chatchipt or people at ninety two percent of the Fortune

40
00:01:55,880 --> 00:01:57,920
Speaker 3: five hundred were actively using it. And so it was

41
00:01:58,040 --> 00:01:59,600
Speaker 3: very obvious for us we needed to go build a

42
00:01:59,600 --> 00:02:02,880
Speaker 3: work product because I think chatchapt as a product is

43
00:02:03,280 --> 00:02:06,200
Speaker 3: as useful in an enterprise environment, in a work environment

44
00:02:06,600 --> 00:02:10,359
Speaker 3: as it is in your personal life. It's an amazing

45
00:02:10,400 --> 00:02:14,240
Speaker 3: companion if you do anything from marketing to software engineering

46
00:02:14,919 --> 00:02:17,519
Speaker 3: to data analysis and research and I think there was

47
00:02:17,560 --> 00:02:20,120
Speaker 3: a lot of organic adoption when people discovered the tool,

48
00:02:20,400 --> 00:02:23,080
Speaker 3: realizing that it could make people much better at their

49
00:02:23,160 --> 00:02:25,600
Speaker 3: jobs and able to do more. And so we really

50
00:02:25,680 --> 00:02:27,800
Speaker 3: leaned in with that and we're trying to build the

51
00:02:27,800 --> 00:02:28,560
Speaker 3: best product we can.

52
00:02:29,320 --> 00:02:32,480
Speaker 1: It's like one tenth of the planet using chatchypt Brad.

53
00:02:32,480 --> 00:02:36,120
Speaker 1: But I'm interested in some analysis that Meno Ventures has done.

54
00:02:36,280 --> 00:02:38,919
Speaker 1: They've analyzed the LLLM market, particularly in the enterprise space,

55
00:02:39,040 --> 00:02:40,840
Speaker 1: and they've just tried to push back saying, look, you

56
00:02:41,000 --> 00:02:43,519
Speaker 1: lost the market share. Open Ai went from fifty percent

57
00:02:43,520 --> 00:02:45,720
Speaker 1: in the enterprise market share down to twenty five percent,

58
00:02:46,160 --> 00:02:49,000
Speaker 1: and funny enough, the company that they back, which is anthropic,

59
00:02:49,240 --> 00:02:51,919
Speaker 1: took the lead. What do you say to those of statistics,

60
00:02:52,000 --> 00:02:53,840
Speaker 1: Is it something you're seeing within your own numbers.

61
00:02:55,919 --> 00:02:58,120
Speaker 3: It's hard to measure these things. You know, you can

62
00:02:58,160 --> 00:03:01,520
Speaker 3: find you can find measurements that that's the opposite. But

63
00:03:01,560 --> 00:03:03,519
Speaker 3: what we really focus on is value for customers, Like

64
00:03:03,560 --> 00:03:05,720
Speaker 3: we've got to deliver the absolute best models and then

65
00:03:05,760 --> 00:03:10,280
Speaker 3: the absolute best products for developers, for startups, for enterprises

66
00:03:10,760 --> 00:03:13,520
Speaker 3: large and small, and that's that's been our focus. I

67
00:03:13,520 --> 00:03:15,440
Speaker 3: think you know, our API is a good example of

68
00:03:15,480 --> 00:03:18,240
Speaker 3: where we've really invested lately. We've got over four million

69
00:03:18,280 --> 00:03:21,640
Speaker 3: developers now actively using the API every day to build

70
00:03:21,680 --> 00:03:25,560
Speaker 3: new products. We support thousands and thousands of startups that

71
00:03:25,600 --> 00:03:28,400
Speaker 3: are building with us, that we work deeply with on

72
00:03:28,680 --> 00:03:31,040
Speaker 3: trying to improve our product so that they can ultimately

73
00:03:31,040 --> 00:03:33,639
Speaker 3: build a better product. And in the enterprise, I think,

74
00:03:33,680 --> 00:03:36,200
Speaker 3: you know, the demand that we see there is really unabated.

75
00:03:36,240 --> 00:03:39,400
Speaker 3: We grew chat GPT enterprise seats from three million seats

76
00:03:39,440 --> 00:03:42,040
Speaker 3: to five million seats in a matter of two months,

77
00:03:42,040 --> 00:03:45,840
Speaker 3: and so that growth is accelerating and we're just starting

78
00:03:45,880 --> 00:03:47,600
Speaker 3: to scratch the surface I think on the impact that

79
00:03:47,600 --> 00:03:50,480
Speaker 3: we can have both for developers and for enterprises. So

80
00:03:50,760 --> 00:03:52,720
Speaker 3: we see it as a long game and you know,

81
00:03:52,720 --> 00:03:54,600
Speaker 3: we're here to just do our best for customers.

82
00:03:55,480 --> 00:03:58,040
Speaker 1: For that, BOYD, you need infrastructure.

83
00:03:58,640 --> 00:04:00,880
Speaker 2: Tell us a little bit about the costs of training

84
00:04:00,880 --> 00:04:03,680
Speaker 2: this model and how you're looking to expand with stargate

85
00:04:03,720 --> 00:04:06,040
Speaker 2: the project, and just been talking about how SoftBank's been

86
00:04:06,040 --> 00:04:08,320
Speaker 2: teaming up with fox Con, for example, to take over

87
00:04:08,560 --> 00:04:12,920
Speaker 2: an ohio ev plant. How is that continuing to meet

88
00:04:12,960 --> 00:04:15,400
Speaker 2: your demands or not meet them as the case may be.

89
00:04:17,040 --> 00:04:21,440
Speaker 3: Well, yeah, we've seen demand for AI increase at just

90
00:04:22,160 --> 00:04:25,320
Speaker 3: a torrid pace. Obviously, at the root of that is

91
00:04:25,440 --> 00:04:29,200
Speaker 3: getting right the infrastructure equation, and we think ultimately that's

92
00:04:29,200 --> 00:04:32,000
Speaker 3: going to be a critical input to the US and

93
00:04:32,040 --> 00:04:33,200
Speaker 3: its allies.

94
00:04:33,040 --> 00:04:34,279
Speaker 4: Being competitive in this area.

95
00:04:34,320 --> 00:04:36,720
Speaker 3: And so Stargate for US was a five hundred billion

96
00:04:36,760 --> 00:04:40,400
Speaker 3: dollar project to invest here in the United States to

97
00:04:40,480 --> 00:04:44,960
Speaker 3: build AI infrastructure for open AI and ultimately for the country.

98
00:04:45,560 --> 00:04:47,400
Speaker 3: We're working with a lot of great partners on that

99
00:04:47,440 --> 00:04:50,320
Speaker 3: project and hope to bring in more and I think,

100
00:04:50,440 --> 00:04:52,640
Speaker 3: you know, that's just the beginning. We're going to continue

101
00:04:52,640 --> 00:04:55,520
Speaker 3: it to invest aggressively. We've always found ourselves somewhat on

102
00:04:55,560 --> 00:04:59,000
Speaker 3: the wrong side of the demand curve for AI, you know,

103
00:04:59,080 --> 00:05:01,960
Speaker 3: despite the investment, significant investment to date, and so for

104
00:05:02,000 --> 00:05:03,280
Speaker 3: as long as we see demand, we're going.

105
00:05:03,200 --> 00:05:04,440
Speaker 4: To continue to invest aggressively.

106
00:05:05,000 --> 00:05:07,599
Speaker 3: AI is interesting in that the more you invest and

107
00:05:07,640 --> 00:05:09,200
Speaker 3: the more you make it available, the more you make

108
00:05:09,240 --> 00:05:14,240
Speaker 3: it you know, cost cost approachable for enterprises and for consumers,

109
00:05:14,440 --> 00:05:16,240
Speaker 3: the more people want to use it. So it's an

110
00:05:16,279 --> 00:05:18,920
Speaker 3: amazing trend and we'll continue to invest behind it.

111
00:05:19,160 --> 00:05:22,480
Speaker 1: We're speaking with Brad Lightcap of open Ai, the CEO

112
00:05:22,600 --> 00:05:25,280
Speaker 1: for our radio and TV audience is Brad. How has

113
00:05:25,320 --> 00:05:28,240
Speaker 1: the rollout ultimately been Do you think because so many

114
00:05:28,240 --> 00:05:30,560
Speaker 1: people wanted to use the app that maybe we hit

115
00:05:30,640 --> 00:05:32,320
Speaker 1: limits quicker than some anticipated.

116
00:05:33,880 --> 00:05:36,479
Speaker 3: Well, we're trying our best to keep up with demand.

117
00:05:37,240 --> 00:05:40,520
Speaker 3: Serving infrastructure at scale at seven hundred million users and

118
00:05:40,520 --> 00:05:43,839
Speaker 3: then millions of developers and many many billions of tokens

119
00:05:43,880 --> 00:05:47,000
Speaker 3: per minute that we process is not for the faint

120
00:05:47,000 --> 00:05:48,920
Speaker 3: of heart. Thankfully, I don't have to do that part

121
00:05:48,920 --> 00:05:51,080
Speaker 3: of it. But we're doing our best to make sure

122
00:05:51,080 --> 00:05:54,320
Speaker 3: the rollout is successful and we're hoping that by the

123
00:05:54,360 --> 00:05:56,200
Speaker 3: end of the week here everyone gets access.

124
00:05:56,400 --> 00:05:59,120
Speaker 1: Your job description is more about building the enterprise relationships

125
00:05:59,120 --> 00:06:01,159
Speaker 1: and partnerships. I also think about the partnership you have

126
00:06:01,200 --> 00:06:03,560
Speaker 1: in Microsoft and Sati and Adela is out there really

127
00:06:03,600 --> 00:06:06,279
Speaker 1: talking about integrating already and how excited he was for

128
00:06:06,320 --> 00:06:09,120
Speaker 1: the product, but there's a tension in the relationship there.

129
00:06:09,560 --> 00:06:12,680
Speaker 1: I'm interested in what you think the progress being made.

130
00:06:12,680 --> 00:06:16,040
Speaker 1: Sam Alman was on Networks talking about progress being made

131
00:06:16,080 --> 00:06:18,560
Speaker 1: with a relationship going forward. Microsoft, can you give us

132
00:06:18,560 --> 00:06:21,080
Speaker 1: a timeline about when you think a deal will be

133
00:06:21,120 --> 00:06:24,480
Speaker 1: done in the future of how they interact with your product?

134
00:06:24,600 --> 00:06:25,520
Speaker 2: And were broadly how.

135
00:06:25,520 --> 00:06:27,760
Speaker 1: Much ownership they continue to have in the business as

136
00:06:27,800 --> 00:06:28,760
Speaker 1: a for profit one.

137
00:06:30,320 --> 00:06:32,800
Speaker 3: Yeah, Well, we feel really positive about the relationship with

138
00:06:32,839 --> 00:06:35,359
Speaker 3: Microsoft and they've they've been a great partner throughout the history.

139
00:06:35,200 --> 00:06:35,680
Speaker 4: Of open Ai.

140
00:06:35,800 --> 00:06:38,640
Speaker 3: They've been with us from the beginning really since before

141
00:06:38,720 --> 00:06:43,080
Speaker 3: chat Ept obviously have been a huge infrastructure partner for

142
00:06:43,160 --> 00:06:44,880
Speaker 3: us with Azure, and.

143
00:06:44,880 --> 00:06:47,040
Speaker 4: So we continue we expect that to continue.

144
00:06:47,040 --> 00:06:50,000
Speaker 3: We see we see no future that you know of

145
00:06:50,040 --> 00:06:52,520
Speaker 3: open ai that doesn't include Microsoft in a significant way.

146
00:06:53,440 --> 00:06:55,599
Speaker 3: We've got to work on what that future looks like together.

147
00:06:55,640 --> 00:06:57,600
Speaker 3: We're in that process with them right now. We feel

148
00:06:57,640 --> 00:07:00,840
Speaker 3: very good about it. But we think also ultimately there's

149
00:07:00,960 --> 00:07:03,560
Speaker 3: you know, the world is really big, and the demand

150
00:07:03,600 --> 00:07:06,240
Speaker 3: for these systems and these models in the enterprise and

151
00:07:06,360 --> 00:07:09,359
Speaker 3: consumer is significant, and so they represent not only an

152
00:07:09,400 --> 00:07:12,080
Speaker 3: infrastructure partner for us, but a great partner to be

153
00:07:12,080 --> 00:07:13,880
Speaker 3: able to help distribute and bring the benefits of the

154
00:07:13,880 --> 00:07:16,440
Speaker 3: technology to the world given the size of their footprint.

155
00:07:16,480 --> 00:07:18,880
Speaker 4: So, you know, more to work through there, But we

156
00:07:18,920 --> 00:07:19,680
Speaker 4: feel good about it.

157
00:07:19,720 --> 00:07:21,760
Speaker 3: And like I said, I think you know, when we're

158
00:07:21,760 --> 00:07:23,320
Speaker 3: standing at the finish line of all this, they'll be

159
00:07:23,320 --> 00:07:23,800
Speaker 3: there with us.

160
00:07:24,560 --> 00:07:27,640
Speaker 1: Meanwhile, the stuff of Suliman over at Microsoft is busy

161
00:07:27,640 --> 00:07:30,400
Speaker 1: in the tussle for talent, so to plenty of other

162
00:07:30,520 --> 00:07:34,040
Speaker 1: rivals that we understand, Mark Zuckerberg busy. And what's interesting

163
00:07:34,360 --> 00:07:37,920
Speaker 1: is while you, as one of those long tenured employees

164
00:07:38,080 --> 00:07:41,160
Speaker 1: and staff over at open Ai, remain committed because of

165
00:07:41,240 --> 00:07:44,120
Speaker 1: innovations such as this, But what about the liquidity that

166
00:07:44,120 --> 00:07:47,679
Speaker 1: we're talking about bringing to some of your well people

167
00:07:47,720 --> 00:07:50,160
Speaker 1: that you work alongside. How is that going? We understand

168
00:07:50,200 --> 00:07:52,200
Speaker 1: they might be even a five hundred billion dollar valuation

169
00:07:52,360 --> 00:07:55,280
Speaker 1: involved in what is a secondary sale of your shares

170
00:07:55,520 --> 00:07:56,920
Speaker 1: to the likes of Thrive Capital.

171
00:07:58,840 --> 00:08:02,480
Speaker 3: Yeah, well, nothing there to share here, But we continue

172
00:08:02,520 --> 00:08:06,080
Speaker 3: to see very healthy demand for on the investors side

173
00:08:06,120 --> 00:08:08,120
Speaker 3: for wanting to be I think part of the Opening

174
00:08:08,120 --> 00:08:09,960
Speaker 3: Eye journey and mission and we're very grateful for that.

175
00:08:10,600 --> 00:08:12,160
Speaker 3: And on the talent side, look, I think you know,

176
00:08:12,240 --> 00:08:14,440
Speaker 3: Opening Eye was founded as a nonprofit. It was founded

177
00:08:14,440 --> 00:08:16,640
Speaker 3: as a mission driven company to be able to build

178
00:08:17,320 --> 00:08:20,400
Speaker 3: you know, general intelligence that's beneficial for all of humanity,

179
00:08:20,920 --> 00:08:23,200
Speaker 3: and we haven't strayed from that mission. I think ultimately

180
00:08:23,240 --> 00:08:26,080
Speaker 3: that's what attracts talent is people want to work for

181
00:08:26,120 --> 00:08:28,880
Speaker 3: a project that's bigger than themselves and something that's going

182
00:08:28,920 --> 00:08:31,320
Speaker 3: to be impactful for us and for you know, for

183
00:08:31,520 --> 00:08:34,480
Speaker 3: for for humanity and our species. And so I think

184
00:08:34,480 --> 00:08:37,960
Speaker 3: that's the thing that ultimately attracts people to where they work. Obviously,

185
00:08:38,000 --> 00:08:40,480
Speaker 3: like it's a competitive market and we continue to compete.

186
00:08:40,679 --> 00:08:42,040
Speaker 3: But at the end of the day, when we ask

187
00:08:42,120 --> 00:08:43,760
Speaker 3: people what it is that keeps them at opening, I

188
00:08:43,840 --> 00:08:44,400
Speaker 3: it's the mission.

189
00:08:44,800 --> 00:08:45,320
Speaker 4: The mission.

190
00:08:45,720 --> 00:08:49,440
Speaker 1: At the moment, you've got something that's generally intelligence, but

191
00:08:49,440 --> 00:08:51,400
Speaker 1: it's not artificial general intelligence.

192
00:08:51,440 --> 00:08:52,679
Speaker 4: Brad, when do you get there?

193
00:08:52,720 --> 00:08:57,520
Speaker 3: Briefly, you know, I've sworn off making predictions in AI.

194
00:08:57,600 --> 00:09:00,680
Speaker 3: It's too hard, the curves are too steep. But I think,

195
00:09:00,720 --> 00:09:02,680
Speaker 3: you know, we feel really good about the rate of progress.

196
00:09:02,760 --> 00:09:05,600
Speaker 3: GPT five is a great representation of how we start

197
00:09:05,640 --> 00:09:08,480
Speaker 3: to make progress on little things, you know, things like

198
00:09:08,520 --> 00:09:11,080
Speaker 3: for example, being able to have the model dynamically reason

199
00:09:11,440 --> 00:09:14,520
Speaker 3: and decide how much it wants to think about the

200
00:09:14,559 --> 00:09:17,200
Speaker 3: problem that you ask it to solve. That's something that

201
00:09:17,240 --> 00:09:20,560
Speaker 3: we do natively as humans that previously our models couldn't do.

202
00:09:20,679 --> 00:09:22,600
Speaker 4: So it's these little steps forward that we.

203
00:09:22,520 --> 00:09:25,080
Speaker 3: Think accumulated and ultimately get us to something that will

204
00:09:25,080 --> 00:09:26,199
Speaker 3: be truly remarkable.

205
00:09:26,800 --> 00:09:29,840
Speaker 1: I'd like cap talking about the latest GPT five. We

206
00:09:29,880 --> 00:09:32,360
Speaker 1: thank you so much, CEO of open Ai,