1 00:00:03,000 --> 00:00:05,399 Speaker 1: Over the past quarter century, one of the most significant 2 00:00:05,400 --> 00:00:07,720 Speaker 1: companies in the world has been Google. It has led 3 00:00:07,760 --> 00:00:10,120 Speaker 1: today not by its founders, but by a personal who 4 00:00:10,119 --> 00:00:12,520 Speaker 1: started in two thousand and four, rose up the ranks, 5 00:00:12,560 --> 00:00:13,560 Speaker 1: and is now the CEO. 6 00:00:13,920 --> 00:00:15,320 Speaker 2: His name is Sundar Pashai. 7 00:00:15,760 --> 00:00:18,000 Speaker 1: I sat down with him recently and the Google officers 8 00:00:18,000 --> 00:00:20,200 Speaker 1: of New York to talk about the future of Google 9 00:00:20,239 --> 00:00:23,720 Speaker 1: and also importance of artificial intelligence to the company. When 10 00:00:23,760 --> 00:00:28,440 Speaker 1: you joined Google, the alphabet hadn't yet existed. It was 11 00:00:28,480 --> 00:00:31,480 Speaker 1: two thousand and four. The company was started in nineteen 12 00:00:31,520 --> 00:00:34,280 Speaker 1: ninety eight, I believe. So how big was the company 13 00:00:34,280 --> 00:00:35,200 Speaker 1: in two thousand and four? 14 00:00:35,560 --> 00:00:37,640 Speaker 3: Two thousand and four, we were about just totally one 15 00:00:37,640 --> 00:00:40,760 Speaker 3: thousand people, I think, but we are growing pretty fast. Yeah, 16 00:00:40,800 --> 00:00:41,920 Speaker 3: we were about thousand people. 17 00:00:42,479 --> 00:00:44,960 Speaker 1: So when you why did you come to the company 18 00:00:45,000 --> 00:00:45,599 Speaker 1: in two thousand four? 19 00:00:45,640 --> 00:00:46,959 Speaker 2: Who attracted you to come here? 20 00:00:47,200 --> 00:00:48,920 Speaker 1: A small, little search engine company? 21 00:00:49,240 --> 00:00:50,720 Speaker 2: Search wasn't that big a deal then? 22 00:00:50,800 --> 00:00:54,640 Speaker 3: Probably I was using the product from the outside, and 23 00:00:54,840 --> 00:00:58,160 Speaker 3: you know, I clearly noticed how much better it was 24 00:00:58,200 --> 00:01:00,440 Speaker 3: as a product. And I was a what I call 25 00:01:00,440 --> 00:01:02,800 Speaker 3: a power user of the product, right, and I had 26 00:01:02,800 --> 00:01:04,600 Speaker 3: a lot of ideas in my head on how the 27 00:01:04,600 --> 00:01:08,960 Speaker 3: product could be made better. But more importantly, you know, 28 00:01:09,160 --> 00:01:13,400 Speaker 3: I gaining access to technology made a big difference in 29 00:01:13,400 --> 00:01:16,600 Speaker 3: my life, and so I always tell the power of 30 00:01:16,640 --> 00:01:20,000 Speaker 3: giving access to technology. The thing about Google which appealed 31 00:01:20,040 --> 00:01:23,800 Speaker 3: to me was you could be somewhere in rural Indonesia, 32 00:01:23,959 --> 00:01:27,039 Speaker 3: or you could be a professor at Stanford, and you 33 00:01:27,080 --> 00:01:29,840 Speaker 3: would get access to that information as long as you 34 00:01:29,880 --> 00:01:31,280 Speaker 3: had a computer and connectivity. 35 00:01:31,360 --> 00:01:36,119 Speaker 1: With respect to the company today, you obviously have had 36 00:01:36,160 --> 00:01:40,160 Speaker 1: some legal challenges. One legal challenge by the federal government. 37 00:01:40,800 --> 00:01:44,000 Speaker 1: They charged that your I guess your search engine was 38 00:01:44,920 --> 00:01:46,720 Speaker 1: I hate to use the word monopolistic, but I guess 39 00:01:46,760 --> 00:01:49,240 Speaker 1: they probably used that word. It's running the company more 40 00:01:49,280 --> 00:01:53,800 Speaker 1: complicated because you have this lawsuit you have to deal with. 41 00:01:53,400 --> 00:01:56,280 Speaker 3: With our scale and size, I think scrutiny is inevitable, 42 00:01:56,360 --> 00:02:00,480 Speaker 3: and you know we've always engaged very respect fully and 43 00:02:00,520 --> 00:02:05,600 Speaker 3: responsibly through these processes. Even in the current current ruling, 44 00:02:05,640 --> 00:02:08,880 Speaker 3: I mean, the ruling goes to great length to point 45 00:02:08,919 --> 00:02:14,280 Speaker 3: out that we have achieved success by innovating. Our competitors 46 00:02:14,360 --> 00:02:18,200 Speaker 3: acknowledge that we had the best searching search engine in 47 00:02:18,240 --> 00:02:21,480 Speaker 3: the world. We definitely disagree with the ruling but it'd 48 00:02:21,520 --> 00:02:25,400 Speaker 3: been still in the middle of the remedies phase. And 49 00:02:25,639 --> 00:02:27,960 Speaker 3: you know, we will appeal and this process will likely 50 00:02:28,000 --> 00:02:32,639 Speaker 3: take many years. And you know, I'm confident given that 51 00:02:33,240 --> 00:02:39,000 Speaker 3: you know, we are focused on innovating using technology, will 52 00:02:39,000 --> 00:02:41,840 Speaker 3: do well in the long run. And just last week 53 00:02:41,880 --> 00:02:46,160 Speaker 3: there was a European court ruling which overruled the earlier decision. 54 00:02:46,200 --> 00:02:49,120 Speaker 3: But the process had taken you almost ten years, right, 55 00:02:49,160 --> 00:02:50,600 Speaker 3: So these things take take a while. 56 00:02:50,960 --> 00:02:53,520 Speaker 1: So were you surprised that the US government after they 57 00:02:53,639 --> 00:02:56,959 Speaker 1: won that case, they filed another case against you? Were 58 00:02:57,000 --> 00:02:59,200 Speaker 1: you surprised that that happened? And you expect that'll go 59 00:02:59,280 --> 00:03:00,000 Speaker 1: on for a while too. 60 00:03:00,400 --> 00:03:02,440 Speaker 3: This one is not to do with our advertising on 61 00:03:02,480 --> 00:03:05,880 Speaker 3: our search engine. The other one is we provide an 62 00:03:06,040 --> 00:03:12,920 Speaker 3: ad platform for publishers to run advertising on their sites, 63 00:03:13,000 --> 00:03:16,200 Speaker 3: and so we provide a platform, and so this is 64 00:03:16,240 --> 00:03:17,960 Speaker 3: focused on that part of our business. 65 00:03:19,160 --> 00:03:20,080 Speaker 2: Part of our business. 66 00:03:20,600 --> 00:03:23,399 Speaker 3: Again, we are you know, that case is just getting underway, 67 00:03:23,480 --> 00:03:26,360 Speaker 3: so I expected to take take some time. And you know, 68 00:03:26,440 --> 00:03:31,440 Speaker 3: but look where we can figure out you know, constructive solutions. 69 00:03:32,120 --> 00:03:35,360 Speaker 3: I think we will where we think it really harms 70 00:03:35,400 --> 00:03:39,200 Speaker 3: our ability to innovate on behalf of our users or uh, 71 00:03:39,240 --> 00:03:41,440 Speaker 3: you know, we are going to be vigorous in defending 72 00:03:41,480 --> 00:03:43,560 Speaker 3: ourselves and it's going to take time for it to 73 00:03:43,560 --> 00:03:43,960 Speaker 3: play out. 74 00:03:44,080 --> 00:03:49,360 Speaker 1: Silicon Valley has been enamored with artificial intelligence AI. Everybody 75 00:03:49,400 --> 00:03:51,880 Speaker 1: thinks that they need to have AI starts, attach their 76 00:03:51,960 --> 00:03:54,760 Speaker 1: name or or be a leader in it. Chat GBT 77 00:03:55,080 --> 00:03:57,400 Speaker 1: got out to the public first, but you've been working 78 00:03:57,400 --> 00:03:59,520 Speaker 1: on it for a long time, maybe before open AI did. 79 00:04:00,240 --> 00:04:02,320 Speaker 1: Are you going to go more public with what you're 80 00:04:02,360 --> 00:04:06,320 Speaker 1: doing in artificial intelligence? And how is artificial intelligence going 81 00:04:06,360 --> 00:04:08,280 Speaker 1: to change your company? 82 00:04:09,040 --> 00:04:12,080 Speaker 3: I mean it is. It's been a big focus for 83 00:04:12,160 --> 00:04:14,440 Speaker 3: us as a company. One of the first things I 84 00:04:14,440 --> 00:04:17,799 Speaker 3: did as CEOs to pivot the company to be focused 85 00:04:17,800 --> 00:04:22,600 Speaker 3: on AI AI first. You know, we've developed a lot 86 00:04:22,600 --> 00:04:25,760 Speaker 3: of the core underlying technology, a lot of its powers 87 00:04:25,760 --> 00:04:28,799 Speaker 3: search today. So part of what has helped us keep 88 00:04:28,880 --> 00:04:31,560 Speaker 3: search about everyone else is by incorporating a lot of 89 00:04:31,600 --> 00:04:35,840 Speaker 3: AI in how we do search. The current moment around 90 00:04:35,920 --> 00:04:41,159 Speaker 3: generator AI is what's captured people's imagination. We are incorporating 91 00:04:41,200 --> 00:04:43,479 Speaker 3: that in search in a deep way. So today if 92 00:04:43,520 --> 00:04:45,480 Speaker 3: we go to Google and type a query in we 93 00:04:45,920 --> 00:04:48,640 Speaker 3: give for many queries, we give something called an AI 94 00:04:48,839 --> 00:04:53,320 Speaker 3: overview and you get a nice summary on top which 95 00:04:53,400 --> 00:04:56,960 Speaker 3: we are summarizing the top results and giving context around 96 00:04:57,000 --> 00:05:01,040 Speaker 3: it using AI. It's been very well just people love it, 97 00:05:01,240 --> 00:05:03,760 Speaker 3: and so it's you know, it's one direction in which 98 00:05:03,800 --> 00:05:07,280 Speaker 3: we are using AI. Table of our product we have. 99 00:05:07,800 --> 00:05:10,200 Speaker 3: Our models are called Gemini, and people can also talk 100 00:05:10,240 --> 00:05:13,120 Speaker 3: to Gemini directly. And I just feel like we're at 101 00:05:13,240 --> 00:05:16,160 Speaker 3: very very early stages of what is probably the most 102 00:05:16,160 --> 00:05:19,440 Speaker 3: profound shift in technology we will ever see as humanity. 103 00:05:19,480 --> 00:05:20,960 Speaker 2: So it's an exciting time today. 104 00:05:21,000 --> 00:05:23,039 Speaker 1: If I want to do a search, I suppose I 105 00:05:23,160 --> 00:05:26,000 Speaker 1: look your name up on a search and it'll come 106 00:05:26,080 --> 00:05:29,000 Speaker 1: up with your bio and so forth, and then maybe 107 00:05:29,120 --> 00:05:32,320 Speaker 1: articles written about you will appear on this and then 108 00:05:32,600 --> 00:05:35,680 Speaker 1: maybe those articles will guide me to let's say, an 109 00:05:35,680 --> 00:05:37,040 Speaker 1: advertising site or something. 110 00:05:37,920 --> 00:05:39,560 Speaker 2: But with AI, some. 111 00:05:39,480 --> 00:05:43,720 Speaker 1: People are worried that your AI will basically have all 112 00:05:43,760 --> 00:05:47,840 Speaker 1: the information, and therefore they won't they the advertisers won't 113 00:05:47,960 --> 00:05:49,920 Speaker 1: have anybody coming to their site any longer. 114 00:05:49,960 --> 00:05:51,320 Speaker 2: So how do you address that issue? 115 00:05:51,440 --> 00:05:56,239 Speaker 3: Today we've past twenty five years. People come to Google 116 00:05:57,360 --> 00:06:00,159 Speaker 3: because they're not only looking for what they want, but 117 00:06:00,200 --> 00:06:03,840 Speaker 3: they enjoy the richness and the diversity of what exists 118 00:06:03,880 --> 00:06:06,000 Speaker 3: on the bat right, So people are clicking through and 119 00:06:06,040 --> 00:06:09,280 Speaker 3: going to a lot of sites. That is important for us. 120 00:06:09,720 --> 00:06:13,840 Speaker 3: That's what makes this thriving ecosystem. So even if as 121 00:06:13,920 --> 00:06:16,640 Speaker 3: we are evolving the product with AI, one of our 122 00:06:16,680 --> 00:06:20,880 Speaker 3: core design principles has been making sure it works that way, right, 123 00:06:20,920 --> 00:06:25,239 Speaker 3: and so we are prioritizing approaches which will send traffic 124 00:06:25,279 --> 00:06:29,640 Speaker 3: to publishers and in the case of commercial queries, advertisers 125 00:06:29,680 --> 00:06:33,360 Speaker 3: will also benefit as well. But that's you know, we 126 00:06:33,400 --> 00:06:37,680 Speaker 3: think about it holistically because I think that's what allows 127 00:06:37,680 --> 00:06:40,599 Speaker 3: people to create great content and create that virtual cycle. 128 00:06:40,839 --> 00:06:44,800 Speaker 1: So advertisers are they concerned that somehow the AI search 129 00:06:44,880 --> 00:06:47,680 Speaker 1: function you have might get them bypass. 130 00:06:47,760 --> 00:06:49,720 Speaker 3: The way we think about it is users, when they 131 00:06:49,720 --> 00:06:52,719 Speaker 3: come looking for information, there is an aspect of it 132 00:06:52,760 --> 00:06:56,120 Speaker 3: where they're looking for commercial information. It's naturally in the 133 00:06:56,240 --> 00:07:01,520 Speaker 3: user's intent, and when their intent is commercial, you know, 134 00:07:01,560 --> 00:07:04,800 Speaker 3: advertising turns out to be very very relevant information. There 135 00:07:04,839 --> 00:07:07,520 Speaker 3: was a core insight behind how we monetize Google Search 136 00:07:08,160 --> 00:07:11,800 Speaker 3: that adds a valuable information when users have a commercial intent. 137 00:07:12,280 --> 00:07:16,040 Speaker 3: That doesn't change just because there's a new underlying technology. Right, 138 00:07:16,280 --> 00:07:20,080 Speaker 3: people are always looking for commercial information and providers of 139 00:07:20,120 --> 00:07:24,240 Speaker 3: that information. Merchants, businesses are trying to reach users, and 140 00:07:25,040 --> 00:07:27,080 Speaker 3: so that dynamic will continue to exist. 141 00:07:27,240 --> 00:07:29,960 Speaker 1: So Google is more or less the search engine part 142 00:07:29,960 --> 00:07:32,720 Speaker 1: of your company. Alphabet has other parts. Let me ask 143 00:07:32,720 --> 00:07:33,480 Speaker 1: you about some of them. 144 00:07:33,520 --> 00:07:36,200 Speaker 3: I could, so maybe if I could step back. You know, 145 00:07:36,280 --> 00:07:40,320 Speaker 3: Google has many businesses beyond search, right, So you have 146 00:07:40,360 --> 00:07:43,800 Speaker 3: Google Search, but think about Google Cloud, where we provide 147 00:07:43,800 --> 00:07:48,080 Speaker 3: software at all enterprises. It's YouTube, there is YouTube and 148 00:07:48,480 --> 00:07:51,160 Speaker 3: so on. There's Android and so on. So think of 149 00:07:51,200 --> 00:07:56,360 Speaker 3: Google as our you know, Internet related businesses. Okay, and 150 00:07:56,520 --> 00:08:00,880 Speaker 3: then we're using technology still. But we have other long 151 00:08:01,000 --> 00:08:05,640 Speaker 3: term bets. Weimo was a bet on autonomous self driving 152 00:08:05,760 --> 00:08:09,720 Speaker 3: cars and that's been a long term bet. We have 153 00:08:09,800 --> 00:08:15,080 Speaker 3: Wing which is a drone delivery company. We have Calico, 154 00:08:15,320 --> 00:08:22,400 Speaker 3: which is pursuing long term drug discovery for difficult to 155 00:08:22,440 --> 00:08:25,720 Speaker 3: treat diseases, etcetera. So these are longer term bets, and 156 00:08:25,760 --> 00:08:27,480 Speaker 3: that's what we call as other bets. 157 00:08:27,640 --> 00:08:31,000 Speaker 1: Okay, So Waimo is a long term bet, I assume, 158 00:08:31,080 --> 00:08:33,520 Speaker 1: But have you been in one of those cars. 159 00:08:33,480 --> 00:08:33,959 Speaker 2: All the time? 160 00:08:33,960 --> 00:08:34,120 Speaker 1: You know? 161 00:08:34,320 --> 00:08:38,680 Speaker 3: I make sure to go periodically and take a ride 162 00:08:38,720 --> 00:08:42,360 Speaker 3: in Wemo is phenomenal the progress I've seen every six months. 163 00:08:42,880 --> 00:08:45,240 Speaker 3: The last time I was in the car, the third 164 00:08:45,280 --> 00:08:47,480 Speaker 3: time I was in it. You know, I was on 165 00:08:47,520 --> 00:08:49,520 Speaker 3: my phone in the back seat. Once in a while, 166 00:08:49,520 --> 00:08:51,320 Speaker 3: I tell myself, you know, look, I'm in a self 167 00:08:51,360 --> 00:08:54,800 Speaker 3: driving car. It's amazing even how people sometimes look in 168 00:08:54,840 --> 00:08:57,800 Speaker 3: Some people are curious, some people are kind of. 169 00:08:57,720 --> 00:08:59,560 Speaker 2: Like they used to it. You wear a helmet when 170 00:08:59,600 --> 00:09:01,640 Speaker 2: you're in there. Your confect. 171 00:09:03,240 --> 00:09:05,360 Speaker 3: Not at all, you know, quite the contrary. It's it's 172 00:09:05,400 --> 00:09:06,240 Speaker 3: pretty relaxing. 173 00:09:07,320 --> 00:09:07,480 Speaker 2: You know. 174 00:09:07,480 --> 00:09:10,439 Speaker 3: We've been surprised at how much okay, people like the 175 00:09:10,520 --> 00:09:14,040 Speaker 3: experience we are now. We've scaled up towe hundred thousand 176 00:09:14,080 --> 00:09:18,280 Speaker 3: paid rights every week in the In the US, we 177 00:09:18,400 --> 00:09:20,920 Speaker 3: recently announced a partnership with Uber, so you can use 178 00:09:20,960 --> 00:09:26,000 Speaker 3: the Uber app, for example in Atlanta to Haila waymocard. 179 00:09:26,280 --> 00:09:30,120 Speaker 1: You have a business, that's the cloud business you mentioned earlier. 180 00:09:30,200 --> 00:09:33,160 Speaker 1: I think Amazon got to the cloud first and other 181 00:09:33,200 --> 00:09:36,680 Speaker 1: big tech companies got there later. How significant is cloud 182 00:09:36,720 --> 00:09:39,200 Speaker 1: to you now? And are you the person kind of 183 00:09:39,240 --> 00:09:40,720 Speaker 1: drove the cloud business here? 184 00:09:41,080 --> 00:09:43,440 Speaker 3: I mean it was one of the big areas. 185 00:09:44,160 --> 00:09:44,360 Speaker 2: You know. 186 00:09:44,480 --> 00:09:47,360 Speaker 3: I bet On, I say, became CEO in twenty fifteen, 187 00:09:47,480 --> 00:09:52,040 Speaker 3: and you know, we realized Google was built in the cloud. 188 00:09:52,120 --> 00:09:55,200 Speaker 3: Google Search, Google Maps, Gmail, everything works. We are a 189 00:09:55,280 --> 00:09:58,200 Speaker 3: native cloud company and we are one of the best 190 00:09:58,200 --> 00:10:00,560 Speaker 3: cloud infrastructure in the world. So we made it decision 191 00:10:00,640 --> 00:10:05,120 Speaker 3: to really provide it to everyone else. And you know, 192 00:10:05,480 --> 00:10:09,160 Speaker 3: we brought in CEOs who had real background to do that. 193 00:10:09,800 --> 00:10:13,079 Speaker 3: Our current CEO, Thomas Korean, has really helped scale the business. 194 00:10:13,240 --> 00:10:16,199 Speaker 3: Last quarter we had over ten billion dollars in revenues. 195 00:10:16,880 --> 00:10:22,200 Speaker 3: I think we're probably the number fourth largest software company 196 00:10:22,240 --> 00:10:25,240 Speaker 3: in the enterprise now, so it's one of our most 197 00:10:25,320 --> 00:10:29,199 Speaker 3: robust businesses. And in fact, I announced in one of 198 00:10:29,240 --> 00:10:32,320 Speaker 3: our earnings called recently that we will exit twenty twenty 199 00:10:32,360 --> 00:10:37,520 Speaker 3: four in YouTube and cloud at one hundred billion dollars runderrate. 200 00:10:37,640 --> 00:10:40,680 Speaker 3: And these were businesses which we've built from scratch over 201 00:10:40,760 --> 00:10:41,600 Speaker 3: the past decade. 202 00:10:41,800 --> 00:10:44,360 Speaker 1: So we've talked about cloud, which was a novelty ten 203 00:10:44,440 --> 00:10:46,720 Speaker 1: years ago or so, and now AI is a bit 204 00:10:46,760 --> 00:10:49,720 Speaker 1: of a novelty. What the areas in the future that 205 00:10:49,760 --> 00:10:54,760 Speaker 1: you think will be the next great tech interest for 206 00:10:55,160 --> 00:10:57,720 Speaker 1: Solicon Valley and other people is quantum computing? 207 00:10:57,720 --> 00:10:59,040 Speaker 2: One of them, you know. 208 00:10:59,040 --> 00:11:03,439 Speaker 3: Would view quantum one of those foundational technologies like AI 209 00:11:03,720 --> 00:11:07,679 Speaker 3: over time, you know, quantum, you know, you know, it's 210 00:11:07,880 --> 00:11:12,479 Speaker 3: fundamentally thinking about how to design different computers than classical computers. 211 00:11:13,040 --> 00:11:14,920 Speaker 3: So I would view it as just like aids are 212 00:11:15,000 --> 00:11:20,000 Speaker 3: underlying technologies, but these technologies are going to hopefully enable many, 213 00:11:20,040 --> 00:11:23,280 Speaker 3: many amazing applications on top of them, you know AI. 214 00:11:24,120 --> 00:11:27,320 Speaker 3: You know, that's why compared AI to fire or electricity. 215 00:11:27,640 --> 00:11:30,120 Speaker 3: You know, it's going to cut across every sector everywhere, 216 00:11:30,160 --> 00:11:31,720 Speaker 3: and people are going to be able to read think 217 00:11:32,320 --> 00:11:36,920 Speaker 3: think of AI as you're getting really intelligent decision making 218 00:11:36,960 --> 00:11:40,319 Speaker 3: systems to deploy everywhere. 219 00:11:40,559 --> 00:11:43,640 Speaker 1: Let's talk about your background for a moment. So where 220 00:11:43,640 --> 00:11:44,200 Speaker 1: were you born. 221 00:11:44,880 --> 00:11:47,080 Speaker 3: I was born in India in a city called Chennai 222 00:11:47,120 --> 00:11:48,040 Speaker 3: in the south of India. 223 00:11:48,160 --> 00:11:51,480 Speaker 1: And did you grow up you know, in middle class 224 00:11:51,559 --> 00:11:51,880 Speaker 1: kind of. 225 00:11:52,360 --> 00:11:54,720 Speaker 3: You know, it was a comfortable middle class life in India, 226 00:11:54,800 --> 00:11:58,480 Speaker 3: you know. But one of the things was technology wasn't 227 00:11:58,520 --> 00:12:00,800 Speaker 3: always around and we had to wait for a while 228 00:12:00,880 --> 00:12:03,600 Speaker 3: before you know, we were on a waiting list. At 229 00:12:03,600 --> 00:12:05,920 Speaker 3: that time, only the government made phones and this was 230 00:12:05,920 --> 00:12:08,600 Speaker 3: a rotary phone. So it was a five year wait 231 00:12:08,640 --> 00:12:11,480 Speaker 3: list to get a phone, right, and so we got 232 00:12:11,520 --> 00:12:14,640 Speaker 3: the rotary phone changed our lives we were one of 233 00:12:14,640 --> 00:12:17,360 Speaker 3: the few people in our neighborhood to have it. People 234 00:12:17,360 --> 00:12:19,040 Speaker 3: would come to our homes to make calls to the 235 00:12:19,080 --> 00:12:21,840 Speaker 3: loved ones. I recall, you know, I would have to 236 00:12:22,320 --> 00:12:24,559 Speaker 3: it would before our trip to find the black test 237 00:12:24,600 --> 00:12:26,880 Speaker 3: results for my mother. Sometimes I would go all the 238 00:12:26,920 --> 00:12:29,439 Speaker 3: way to the hospital and they would say, no, it's 239 00:12:29,440 --> 00:12:31,520 Speaker 3: not ready, come back tomorrow. And with the phone, I 240 00:12:31,559 --> 00:12:34,959 Speaker 3: could call and get that information right away. So it really, 241 00:12:35,520 --> 00:12:37,040 Speaker 3: you know, showed me the part of technology. 242 00:12:37,200 --> 00:12:38,600 Speaker 1: So where did you go to college? 243 00:12:38,920 --> 00:12:43,120 Speaker 3: I went to a college called the Indian Institute of Technology. 244 00:12:43,320 --> 00:12:45,920 Speaker 3: It's a set of you know, a few institutions which 245 00:12:45,960 --> 00:12:49,120 Speaker 3: are primarily focused on engineering. And what did you major in, 246 00:12:49,840 --> 00:12:52,040 Speaker 3: you know, what's today called material science? 247 00:12:52,520 --> 00:12:55,160 Speaker 2: And I astribute that, Okay, I did. 248 00:12:55,000 --> 00:12:58,000 Speaker 3: Okay, you know I had a chance to you know, 249 00:12:58,040 --> 00:13:03,200 Speaker 3: I really got interested in computers and semiconductors through my degree, 250 00:13:03,280 --> 00:13:06,920 Speaker 3: and so I really, you know, was motivated by what 251 00:13:07,000 --> 00:13:09,400 Speaker 3: was happening in Silicon Valley. I literally wanted to be 252 00:13:09,559 --> 00:13:12,320 Speaker 3: in the place where semi connectors were developed, and that's 253 00:13:12,360 --> 00:13:13,840 Speaker 3: why I had a chance to come to Stanford. 254 00:13:14,000 --> 00:13:17,000 Speaker 1: You went to Stanford get a master's degree in engineering. 255 00:13:17,360 --> 00:13:19,600 Speaker 1: That's right, And did you get a scholarship or how 256 00:13:19,640 --> 00:13:20,440 Speaker 1: to get you afford that? 257 00:13:20,840 --> 00:13:23,839 Speaker 3: I mean, it was very fortunate to get a full scholarship. 258 00:13:24,720 --> 00:13:26,240 Speaker 3: You know, I was a teaching assistant. 259 00:13:26,360 --> 00:13:28,520 Speaker 1: Had you left the United had you left the India 260 00:13:28,520 --> 00:13:29,000 Speaker 1: before that? 261 00:13:29,120 --> 00:13:29,840 Speaker 2: Had you ever been. 262 00:13:29,679 --> 00:13:32,320 Speaker 3: Out coming to the US was the first time I 263 00:13:32,360 --> 00:13:33,200 Speaker 3: was ever on a plane? 264 00:13:33,920 --> 00:13:36,120 Speaker 1: Okay, So you got your master's degree and then you 265 00:13:36,200 --> 00:13:38,040 Speaker 1: went to Wharton to get your MBA. 266 00:13:38,280 --> 00:13:40,079 Speaker 2: I had only seen the West coast. I wanted to 267 00:13:40,120 --> 00:13:41,160 Speaker 2: see more of the US. 268 00:13:41,840 --> 00:13:46,079 Speaker 3: I through fall, I came to the East coast, loved 269 00:13:46,080 --> 00:13:48,880 Speaker 3: the seasons, so I just wanted to spend time back 270 00:13:49,000 --> 00:13:53,200 Speaker 3: back east and so you know, it was also you know, 271 00:13:53,240 --> 00:13:56,000 Speaker 3: my goal with it was to get exposed to you know, 272 00:13:56,679 --> 00:14:00,640 Speaker 3: I had a deep engineering and scientific background, so chance 273 00:14:00,720 --> 00:14:02,640 Speaker 3: to get exposed to other walks of life out does 274 00:14:02,640 --> 00:14:07,600 Speaker 3: a business work, get exposed to people from finance, from economics, 275 00:14:07,720 --> 00:14:09,160 Speaker 3: and so that was all part of what I was 276 00:14:09,200 --> 00:14:09,600 Speaker 3: looking for. 277 00:14:09,640 --> 00:14:11,480 Speaker 1: So you got your MBA from Whorton, that's them. 278 00:14:11,520 --> 00:14:11,960 Speaker 2: What did you do? 279 00:14:12,760 --> 00:14:12,960 Speaker 1: You know? 280 00:14:13,600 --> 00:14:15,880 Speaker 3: Pretty much very soon after that, I ended up at Google, 281 00:14:15,880 --> 00:14:18,520 Speaker 3: but I spent a short time at mckinzi before that. 282 00:14:18,679 --> 00:14:20,560 Speaker 1: Yeah, so are your parents alive. 283 00:14:21,200 --> 00:14:23,320 Speaker 2: They are, and you know, I think they must be 284 00:14:23,360 --> 00:14:24,080 Speaker 2: pretty proud of you. 285 00:14:24,080 --> 00:14:26,400 Speaker 1: You're the CEO of Google and Alphabet that they call 286 00:14:26,440 --> 00:14:29,360 Speaker 1: you all the time with advice or what do they do? 287 00:14:29,400 --> 00:14:29,840 Speaker 2: You know? Both? 288 00:14:30,000 --> 00:14:32,760 Speaker 3: You know, I'm fortunate. I'm very close to them. I 289 00:14:32,840 --> 00:14:37,800 Speaker 3: see them almost every week. But my dad is you know, 290 00:14:37,920 --> 00:14:41,960 Speaker 3: kind of it shows me amazingly. He's eighty two. He 291 00:14:42,080 --> 00:14:46,400 Speaker 3: relentlessly reads everything about you know, he's tried to keep up. 292 00:14:46,680 --> 00:14:49,800 Speaker 3: He's not a software engineered by training. But a few 293 00:14:49,880 --> 00:14:53,720 Speaker 3: years ago you wanted to know what a API is, 294 00:14:53,880 --> 00:14:56,400 Speaker 3: and he has learned it. So he tracks news closely, 295 00:14:56,560 --> 00:14:58,000 Speaker 3: and so he just talked to me about it. 296 00:14:58,240 --> 00:15:01,640 Speaker 1: Eighty two is young. The present United States is still young. 297 00:15:01,720 --> 00:15:04,520 Speaker 1: There you go and your mother, you talked to her. 298 00:15:04,560 --> 00:15:08,200 Speaker 3: All the time, and you know she's I got my 299 00:15:08,280 --> 00:15:11,000 Speaker 3: love of reading from a very young age from from her. 300 00:15:11,400 --> 00:15:14,800 Speaker 3: Due to financial circumstances, you know, she she was working 301 00:15:14,840 --> 00:15:17,600 Speaker 3: as a stenographer. She stopped working to support the family. 302 00:15:18,440 --> 00:15:23,240 Speaker 3: But you know she was a voracious reader, and that's 303 00:15:23,240 --> 00:15:24,760 Speaker 3: where I got my love of reading from. 304 00:15:24,800 --> 00:15:28,280 Speaker 1: And so and did you meet your wife at Wharton 305 00:15:28,480 --> 00:15:29,680 Speaker 1: Stanford or where? 306 00:15:30,040 --> 00:15:30,280 Speaker 2: Back? 307 00:15:30,360 --> 00:15:33,880 Speaker 3: You know, we both went to I at Indian Industry 308 00:15:33,920 --> 00:15:36,320 Speaker 3: of Technology. She was you know, at the time they 309 00:15:36,320 --> 00:15:38,600 Speaker 3: were in that many women, So she was she was 310 00:15:38,640 --> 00:15:41,680 Speaker 3: a pioneer in her own way of breaking through. And 311 00:15:41,720 --> 00:15:44,080 Speaker 3: so I met my wife and undergraduate. 312 00:15:44,640 --> 00:15:46,560 Speaker 1: Okay, and so now we're married. 313 00:15:46,600 --> 00:15:49,440 Speaker 2: How long over twenty five years? Five years? And you 314 00:15:49,480 --> 00:15:51,320 Speaker 2: have two children? Two children, that's right? 315 00:15:51,360 --> 00:15:54,080 Speaker 1: And do they run around Silicon Valley bragging saying my 316 00:15:54,240 --> 00:15:56,560 Speaker 1: father is the CEO of Google or they don't? 317 00:15:57,200 --> 00:15:59,160 Speaker 3: Quite the contrary. I think, you know, they're trying to 318 00:15:59,200 --> 00:16:02,640 Speaker 3: find their own boys. And you know, we hardly talk 319 00:16:02,680 --> 00:16:05,040 Speaker 3: about my role or anything in the context of my family. 320 00:16:05,240 --> 00:16:07,400 Speaker 1: Let's say you go at a restaurant Silicon Valley with 321 00:16:07,440 --> 00:16:09,920 Speaker 1: your wife. Do people come up with resumes all the 322 00:16:09,960 --> 00:16:11,960 Speaker 1: time or ideas for funding things? 323 00:16:11,960 --> 00:16:15,760 Speaker 2: How does that work? Ah? Yeah? People less less that 324 00:16:15,880 --> 00:16:16,680 Speaker 2: as part. 325 00:16:16,720 --> 00:16:18,720 Speaker 3: You know, maybe people want to picture or want to 326 00:16:18,760 --> 00:16:21,920 Speaker 3: say hello, and so that does happen, particularly in Silicon Valley, 327 00:16:23,120 --> 00:16:27,640 Speaker 3: but you know, doesn't always happen. I've kept a reasonably 328 00:16:28,440 --> 00:16:29,760 Speaker 3: private life, so that helps. 329 00:16:29,760 --> 00:16:33,600 Speaker 1: I think, how do you avoid being sclerotic when you 330 00:16:33,640 --> 00:16:35,360 Speaker 1: have a company that now has one hundred and eighty. 331 00:16:35,200 --> 00:16:36,320 Speaker 2: Two thousand employees. 332 00:16:36,720 --> 00:16:39,400 Speaker 1: How do you get innovative ideas to get to the 333 00:16:39,440 --> 00:16:41,040 Speaker 1: top without bureaucracy killing it. 334 00:16:41,880 --> 00:16:45,320 Speaker 3: Look, I think you have to enable uh, you know, 335 00:16:45,520 --> 00:16:48,040 Speaker 3: you have to empower small teams to move fast and 336 00:16:48,080 --> 00:16:48,840 Speaker 3: get things done. 337 00:16:49,120 --> 00:16:49,320 Speaker 2: Right. 338 00:16:49,400 --> 00:16:52,600 Speaker 3: You have your big products, they are constantly improving, and 339 00:16:52,640 --> 00:16:55,000 Speaker 3: you have to make sure you're constantly investing in and 340 00:16:55,040 --> 00:16:59,040 Speaker 3: innovating on our core products. But you have to allow 341 00:16:59,040 --> 00:17:02,400 Speaker 3: in the system away for small teams to build new things, 342 00:17:02,760 --> 00:17:04,919 Speaker 3: and sometimes you have to be deliberate about it. Early 343 00:17:04,960 --> 00:17:08,600 Speaker 3: on at Google, we had a structure called Google Labs, 344 00:17:09,400 --> 00:17:11,560 Speaker 3: and you know, in which you could have small teams 345 00:17:11,560 --> 00:17:15,480 Speaker 3: with resources to go build new things. We are doing 346 00:17:15,520 --> 00:17:17,959 Speaker 3: that again now and we again have created a new 347 00:17:17,960 --> 00:17:20,520 Speaker 3: Google Labs team, And just recently they launched a product 348 00:17:20,520 --> 00:17:23,320 Speaker 3: called Google Notebook, in which you can go put a 349 00:17:23,320 --> 00:17:28,000 Speaker 3: set of documents and then AI kind of learns it. 350 00:17:28,160 --> 00:17:30,480 Speaker 3: You can ask questions, you can even tell it to 351 00:17:31,880 --> 00:17:35,639 Speaker 3: summarize it as a podcast, conversational podcast back to you, right, 352 00:17:35,720 --> 00:17:38,960 Speaker 3: and you know it's breadtaking if you use it. So, 353 00:17:39,040 --> 00:17:42,880 Speaker 3: but these are entirely new product built from scratch. And 354 00:17:42,960 --> 00:17:46,320 Speaker 3: so Google Photos was a product built from scratch too, 355 00:17:46,359 --> 00:17:50,800 Speaker 3: So you have to empower small teams to move the 356 00:17:50,880 --> 00:17:53,760 Speaker 3: velocity of a startup and cut through the processes you 357 00:17:53,800 --> 00:17:54,840 Speaker 3: have in a large company. 358 00:17:55,359 --> 00:17:56,800 Speaker 2: Let's talk about the company today. 359 00:17:56,920 --> 00:17:59,159 Speaker 1: So, as I mentioned earlier, you have I think it's 360 00:17:59,160 --> 00:18:02,159 Speaker 1: one hundred and eighty three one thousand employees, and the 361 00:18:02,240 --> 00:18:06,359 Speaker 1: company has a market capitalization today of about two and 362 00:18:06,400 --> 00:18:07,360 Speaker 1: a half trillion or something. 363 00:18:07,440 --> 00:18:09,920 Speaker 2: Yeah, it's over two trillion dollars. Yeah, over two trillion dollars. 364 00:18:10,000 --> 00:18:13,159 Speaker 1: So the market capitalization is doubled since you've been the CEO, 365 00:18:13,760 --> 00:18:16,080 Speaker 1: and the stock prices more than doubled since you've been 366 00:18:16,080 --> 00:18:19,639 Speaker 1: the CEO. So do you tell the founders, look, you've 367 00:18:19,680 --> 00:18:21,600 Speaker 1: done a great job, and maybe they should give you 368 00:18:21,600 --> 00:18:22,960 Speaker 1: a big piece of the company or something. 369 00:18:24,400 --> 00:18:28,080 Speaker 3: I'm very fortunate to be compensated. Well, look, I think 370 00:18:28,960 --> 00:18:32,760 Speaker 3: for me, Look, as a technologist, you know, I feel 371 00:18:32,840 --> 00:18:37,680 Speaker 3: like I'm at the front friend, drow Of, you know, 372 00:18:37,840 --> 00:18:40,840 Speaker 3: dreamt about things like AI for a long time. To 373 00:18:40,960 --> 00:18:44,439 Speaker 3: actually see it getting built now and to see this 374 00:18:44,560 --> 00:18:47,560 Speaker 3: transition play out in front of us, to me, you know, 375 00:18:47,560 --> 00:18:49,320 Speaker 3: that's the most motivating thing I could ask for. 376 00:18:49,520 --> 00:18:51,720 Speaker 1: I also know that Google in the early days had 377 00:18:51,800 --> 00:18:54,800 Speaker 1: a policy of free food for its employees, and I 378 00:18:54,840 --> 00:18:56,680 Speaker 1: sampled some of your free food at lunch today. 379 00:18:56,680 --> 00:18:59,840 Speaker 2: It was very good. You obviously have to spend a 380 00:18:59,840 --> 00:19:00,320 Speaker 2: lot of money. 381 00:19:00,320 --> 00:19:02,679 Speaker 1: You've got one hundred and eighty two thousand employees, so 382 00:19:02,800 --> 00:19:05,840 Speaker 1: giving them lunch or breakfast or dinner every day, or 383 00:19:05,840 --> 00:19:09,080 Speaker 1: for many of them every day, you must have a calculation, says, 384 00:19:09,119 --> 00:19:11,439 Speaker 1: are we spending this much on food? We're getting this 385 00:19:11,560 --> 00:19:14,960 Speaker 1: much higher productivity? And how do you assess that? And 386 00:19:14,960 --> 00:19:16,960 Speaker 1: why do you think other companies don't give away food 387 00:19:17,040 --> 00:19:17,800 Speaker 1: as much as you do? 388 00:19:18,560 --> 00:19:20,919 Speaker 3: Look quite the contrar I think in technology, if you 389 00:19:21,760 --> 00:19:24,000 Speaker 3: go to the Bay Area, I think a set of 390 00:19:24,040 --> 00:19:26,480 Speaker 3: things which Google has done as part of now standard 391 00:19:26,680 --> 00:19:31,400 Speaker 3: modern workplaces in the area. But you know, people value 392 00:19:31,440 --> 00:19:37,640 Speaker 3: in person collaboration to us. You know, I can recalled 393 00:19:37,720 --> 00:19:40,760 Speaker 3: several times, you know, when I was working at Google 394 00:19:40,800 --> 00:19:45,320 Speaker 3: early on, being in cafes, meeting someone else, talking, getting 395 00:19:45,359 --> 00:19:46,760 Speaker 3: excited about something, and. 396 00:19:46,720 --> 00:19:48,679 Speaker 2: So it sparks creativity, It creates a. 397 00:19:48,680 --> 00:19:53,280 Speaker 3: Community, and I think the benefit that comes out of it, 398 00:19:53,359 --> 00:19:55,480 Speaker 3: you know, far dwarfs. 399 00:19:55,040 --> 00:19:56,159 Speaker 2: The costs associate with it. 400 00:19:56,200 --> 00:19:59,640 Speaker 1: Google is regularly considered be one of the most favorable 401 00:19:59,640 --> 00:20:03,879 Speaker 1: places to work for employees, always high end surveys, and 402 00:20:03,920 --> 00:20:05,879 Speaker 1: so I assume you've got lots of people who always 403 00:20:05,880 --> 00:20:08,919 Speaker 1: want to come here. How many people a year try 404 00:20:08,960 --> 00:20:10,200 Speaker 1: to apply for jobs at Google? 405 00:20:10,320 --> 00:20:10,399 Speaker 2: Is? 406 00:20:10,400 --> 00:20:12,680 Speaker 1: I assume it's a million people or something more than that. 407 00:20:12,880 --> 00:20:15,960 Speaker 3: What I'm proud of is the metric I look at 408 00:20:16,040 --> 00:20:19,560 Speaker 3: is when we make an offer, what percentage of people 409 00:20:20,480 --> 00:20:21,440 Speaker 3: accept the offer? 410 00:20:21,920 --> 00:20:25,359 Speaker 2: And you know it is percentages. It's almost close to 411 00:20:25,440 --> 00:20:26,240 Speaker 2: ninety percent, right. 412 00:20:26,280 --> 00:20:28,679 Speaker 1: So typically, if somebody wants to get a job, somebody's 413 00:20:28,720 --> 00:20:30,440 Speaker 1: watching this and says, I want to work at Google, 414 00:20:30,520 --> 00:20:33,520 Speaker 1: what are you looking for? High IQs, high work quotient? 415 00:20:33,840 --> 00:20:35,440 Speaker 1: And it used to be said that you had a 416 00:20:35,600 --> 00:20:38,120 Speaker 1: very complicated interview process. I don't know if you still 417 00:20:38,160 --> 00:20:39,920 Speaker 1: have that. But what's the best way to get a 418 00:20:40,000 --> 00:20:41,560 Speaker 1: job here for an entry level person? 419 00:20:42,520 --> 00:20:44,440 Speaker 3: Look, we are you know, it depends on whether you're 420 00:20:44,480 --> 00:20:48,000 Speaker 3: an engineering or something else, but you know we are. 421 00:20:48,119 --> 00:20:50,000 Speaker 3: You know, if you're an engineering, we are looking for 422 00:20:51,440 --> 00:20:55,040 Speaker 3: really good programmers, people who understand computer science well and 423 00:20:55,320 --> 00:20:58,760 Speaker 3: you know, can be dynamically, you know, are willing to 424 00:20:58,800 --> 00:21:02,720 Speaker 3: learn and grow up themselves into new situations and do well. 425 00:21:03,200 --> 00:21:06,919 Speaker 3: But we are really looking for you know, you know, 426 00:21:07,520 --> 00:21:09,760 Speaker 3: superstar software engineers. 427 00:21:09,840 --> 00:21:10,760 Speaker 2: Right, So, when. 428 00:21:10,640 --> 00:21:12,639 Speaker 1: You're doing a search yourself, I suppose you want to 429 00:21:12,640 --> 00:21:15,560 Speaker 1: get information. Do you ever have frustrations that you can't 430 00:21:15,560 --> 00:21:17,760 Speaker 1: get what you want? I mean, sometimes I can't find 431 00:21:17,760 --> 00:21:20,000 Speaker 1: what I want, But I'm not the CEO of Google, 432 00:21:20,040 --> 00:21:22,119 Speaker 1: so I assume you can have better access to the 433 00:21:22,160 --> 00:21:23,080 Speaker 1: search than I can. 434 00:21:23,600 --> 00:21:27,000 Speaker 3: No, there are one of the reasons I'm so excited 435 00:21:27,000 --> 00:21:31,360 Speaker 3: about AI improving searches. You know, we're constantly people don't 436 00:21:31,359 --> 00:21:35,720 Speaker 3: always formulate the right queries. You know, we are constantly 437 00:21:35,760 --> 00:21:38,200 Speaker 3: working to make search easier to use. So for example, 438 00:21:38,880 --> 00:21:42,239 Speaker 3: you can now you know, speak with search as well 439 00:21:42,280 --> 00:21:46,200 Speaker 3: as take pictures and ask search questions. In many countries, 440 00:21:46,200 --> 00:21:49,480 Speaker 3: for example, in places like India, a large volume of 441 00:21:49,480 --> 00:21:52,520 Speaker 3: our queries actually come by people just talking to search. 442 00:21:53,320 --> 00:21:55,600 Speaker 3: And you know, because people get phone not everyone is 443 00:21:55,600 --> 00:21:58,800 Speaker 3: comfortable typing in these phones. They just talk talk to Google. 444 00:21:59,480 --> 00:22:01,120 Speaker 2: Similarly, what we call visual search. 445 00:22:01,160 --> 00:22:02,760 Speaker 3: We have a product called Google Lens and you can 446 00:22:02,800 --> 00:22:05,520 Speaker 3: take a picture and ask questions. We get billions of 447 00:22:05,600 --> 00:22:09,040 Speaker 3: queries through visual search. Humans interact with the world in 448 00:22:09,160 --> 00:22:12,920 Speaker 3: very natural ways. We see things, we hear things, we speak, 449 00:22:13,640 --> 00:22:16,280 Speaker 3: and so that's an example of how we can search, 450 00:22:16,560 --> 00:22:19,800 Speaker 3: make search better, and AI can play it all. I 451 00:22:19,960 --> 00:22:23,480 Speaker 3: constantly am challenging our product find things we could be 452 00:22:23,520 --> 00:22:25,679 Speaker 3: doing better than I'm emailing my teams. 453 00:22:25,720 --> 00:22:28,280 Speaker 1: So, leaving the US government aside, what do you see 454 00:22:28,280 --> 00:22:31,840 Speaker 1: as the biggest challenge to Google an alphabet going forward? 455 00:22:31,960 --> 00:22:35,760 Speaker 1: Is it competitors coming along, is it a new technology 456 00:22:35,800 --> 00:22:38,360 Speaker 1: that you're not in yet coming along and being very important, 457 00:22:38,840 --> 00:22:44,200 Speaker 1: or just keeping your organization efficient and not bureaucratic. 458 00:22:44,480 --> 00:22:45,880 Speaker 2: What do you see as the biggest challenges? 459 00:22:46,359 --> 00:22:51,400 Speaker 3: Look, one of the things I would say, because we've 460 00:22:51,440 --> 00:22:54,879 Speaker 3: always been a foundational technology company and we are working 461 00:22:54,920 --> 00:22:57,760 Speaker 3: on these technologies which can apply across many things. I 462 00:22:57,760 --> 00:22:59,440 Speaker 3: think one of the things which is unique to Google 463 00:22:59,480 --> 00:23:02,159 Speaker 3: and Alphabet is, you know, we have a chance to 464 00:23:02,160 --> 00:23:07,480 Speaker 3: go do many things, but being disciplined doing a few 465 00:23:07,520 --> 00:23:10,760 Speaker 3: things and doing them well and doing it with the 466 00:23:11,080 --> 00:23:14,920 Speaker 3: relentless focus on innovation and doing it as efficiently as possible. 467 00:23:14,960 --> 00:23:17,720 Speaker 3: As a company with discipline, you know what leads to 468 00:23:17,760 --> 00:23:21,200 Speaker 3: long term success. And so so I think that's what 469 00:23:21,240 --> 00:23:24,720 Speaker 3: I think about, right, you know, where all can we 470 00:23:24,760 --> 00:23:27,880 Speaker 3: do it ourselves as a company? Where do we partner 471 00:23:27,880 --> 00:23:30,280 Speaker 3: and enable others? That's why Google Cloud has been a 472 00:23:30,280 --> 00:23:33,160 Speaker 3: big part of our focus because there are many sectors 473 00:23:33,160 --> 00:23:35,119 Speaker 3: in which the best way we can help the world 474 00:23:35,200 --> 00:23:39,240 Speaker 3: and have a good business doing so is by providing 475 00:23:39,280 --> 00:23:42,639 Speaker 3: our technology and solutions to others, and so you know, 476 00:23:42,640 --> 00:23:43,679 Speaker 3: it's getting that balance. 477 00:23:43,760 --> 00:23:43,920 Speaker 2: Druck. 478 00:23:44,920 --> 00:23:47,439 Speaker 1: Thanks for listening to hear more of my interviews. You 479 00:23:47,480 --> 00:23:51,560 Speaker 1: can subscribe and download my podcast on Spotify, Apple, or 480 00:23:51,600 --> 00:23:52,439 Speaker 1: wherever you listen.