1 00:00:00,120 --> 00:00:09,520 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to Bloomberg 2 00:00:09,640 --> 00:00:15,080 Speaker 1: Business Week with Carol Masser and Tim Stenovek on Bloomberg Radio. Well, 3 00:00:15,120 --> 00:00:17,960 Speaker 1: Alphabet Shares, the parent company of Google, hitting a new 4 00:00:18,040 --> 00:00:20,720 Speaker 1: all time high today. You'll remember that last month the 5 00:00:20,760 --> 00:00:25,680 Speaker 1: company reported Google Cloud revenue and operating income topped analysts projections. 6 00:00:26,079 --> 00:00:29,040 Speaker 1: Our own Bloomberg Intelligence is Mandeep sing writing after earnings 7 00:00:29,040 --> 00:00:31,800 Speaker 1: that Google's increase capex you buy ten billion dollars for 8 00:00:31,840 --> 00:00:34,519 Speaker 1: the full year suggests cloud segment growth is likely to 9 00:00:34,520 --> 00:00:38,760 Speaker 1: remain above thirty percent through the second half of the year. 10 00:00:39,159 --> 00:00:41,479 Speaker 1: We've got with us a man Deep Singh's Global head 11 00:00:41,479 --> 00:00:45,519 Speaker 1: of Technology Research at Bloomberg Intelligence, also joining us Yasmin Ahmed, 12 00:00:45,680 --> 00:00:49,000 Speaker 1: Managing Director of Data Cloud at Google Cloud. Both of 13 00:00:49,080 --> 00:00:52,160 Speaker 1: them join us here in the Bloomberg Interactive Broker Studio. 14 00:00:52,159 --> 00:00:54,640 Speaker 1: I should note Yasmin will be featured on an upcoming 15 00:00:54,680 --> 00:00:58,240 Speaker 1: episode of the Tech Disruptors podcast with man Deep Seing. 16 00:00:58,320 --> 00:01:02,640 Speaker 1: Be sure to check that out where you get your podcasts. 17 00:01:02,760 --> 00:01:04,400 Speaker 1: I want to actually start with you, Man Deep, and 18 00:01:04,680 --> 00:01:07,160 Speaker 1: just set the scene for us a little bit about 19 00:01:07,319 --> 00:01:10,120 Speaker 1: why you want to speak to someone like Yasmine on 20 00:01:10,200 --> 00:01:12,680 Speaker 1: the podcast. I mean, look, she's sitting right here by 21 00:01:12,680 --> 00:01:14,400 Speaker 1: the way she is and so she can hear you. 22 00:01:14,760 --> 00:01:18,320 Speaker 2: I thank you for the shout out on the tech 23 00:01:18,360 --> 00:01:22,640 Speaker 2: Test Disruptors episode. Look, Google Cloud, I mean, if it 24 00:01:22,959 --> 00:01:26,440 Speaker 2: was a separate company, you know, fifty billion dollar rund 25 00:01:26,520 --> 00:01:30,840 Speaker 2: rate segment growing at over thirty percent, just think about it, 26 00:01:30,880 --> 00:01:33,440 Speaker 2: they could exceed you know, one hundred billion dollars in 27 00:01:33,520 --> 00:01:37,280 Speaker 2: revenue over the next two three years. So clearly there 28 00:01:37,319 --> 00:01:40,319 Speaker 2: is a lot of exciting things that are going on. 29 00:01:40,480 --> 00:01:45,120 Speaker 2: And talking to Yasmin, it was pretty obvious that, you know, 30 00:01:45,560 --> 00:01:48,840 Speaker 2: they have a lot of products at the database level 31 00:01:49,040 --> 00:01:51,760 Speaker 2: that are doing very well when it comes to AI 32 00:01:51,960 --> 00:01:56,680 Speaker 2: agents and just how companies are looking at deploying generative AI. 33 00:01:56,880 --> 00:01:58,960 Speaker 1: So let's go right there. Yeah, I mean, come on 34 00:01:59,000 --> 00:02:01,080 Speaker 1: in to the conversation and talk a little bit about 35 00:02:01,120 --> 00:02:03,880 Speaker 1: what Mandeep was saying when it comes to the AI 36 00:02:04,000 --> 00:02:07,200 Speaker 1: agents and how they're utilizing Google Cloud right now. 37 00:02:08,160 --> 00:02:11,919 Speaker 3: So AI agents are transforming for our customers their business 38 00:02:12,040 --> 00:02:16,480 Speaker 3: as they deploy. Now, these highly sophisticated agents that can 39 00:02:16,840 --> 00:02:22,080 Speaker 3: optimize entire supply chains, that can transform how customer experience looks. 40 00:02:22,400 --> 00:02:25,920 Speaker 3: If you take Wayfair for example, they're doing visual search, 41 00:02:26,320 --> 00:02:29,360 Speaker 3: so as a customer, you can upload a picture. That 42 00:02:29,480 --> 00:02:32,520 Speaker 3: picture is used to find similar products in the catalog 43 00:02:32,960 --> 00:02:36,280 Speaker 3: and bring those back. So Wayfair are seeing a fifteen 44 00:02:36,320 --> 00:02:40,000 Speaker 3: percent improvement in conversion from that kind of visual search. 45 00:02:40,200 --> 00:02:43,120 Speaker 3: That's why you see customers looking at how can we 46 00:02:43,160 --> 00:02:46,440 Speaker 3: deploy more of these agents and new AI apps across 47 00:02:46,440 --> 00:02:51,680 Speaker 3: the enterprise. So how has Jenny I also impacting the 48 00:02:51,680 --> 00:02:55,040 Speaker 3: work for data scientists and data developers On that end, 49 00:02:55,960 --> 00:02:58,520 Speaker 3: we see huge impact. I'll actually go back to my 50 00:02:58,680 --> 00:03:01,600 Speaker 3: experience when I first came out of university. I was 51 00:03:01,639 --> 00:03:04,720 Speaker 3: a software engineer and I got given all of the 52 00:03:04,800 --> 00:03:10,280 Speaker 3: mundane tasks refactoring the TECHTA, writing code, lambs, code tests, 53 00:03:10,680 --> 00:03:13,560 Speaker 3: and it felt like a penance you had to pay. Well, 54 00:03:13,639 --> 00:03:18,240 Speaker 3: with code agents and data engineering agents and data science agents, 55 00:03:18,320 --> 00:03:21,320 Speaker 3: we can take all of that mundane task away and 56 00:03:21,440 --> 00:03:25,720 Speaker 3: allow developers, data scientists, data engineers to actually focus on 57 00:03:25,800 --> 00:03:30,600 Speaker 3: solving business problems. In fact, Honeywell have said the use 58 00:03:30,639 --> 00:03:34,040 Speaker 3: of these coding agents from Google have enabled their developers 59 00:03:34,080 --> 00:03:38,920 Speaker 3: to operate seventy percent faster, so huge amounts of efficiency gain, 60 00:03:39,080 --> 00:03:41,200 Speaker 3: but also just an elevated role. 61 00:03:41,040 --> 00:03:44,040 Speaker 2: For these humans, So yes, Ben, can I jump in there? 62 00:03:44,120 --> 00:03:47,800 Speaker 2: So what do you think customers are replacing here? Given 63 00:03:48,000 --> 00:03:50,960 Speaker 2: you know, I mean all these sounds great, but if 64 00:03:51,000 --> 00:03:55,200 Speaker 2: you're an enterprise, you have to find budgets to you know, 65 00:03:55,320 --> 00:03:58,400 Speaker 2: deploy all this at scale, and generative AI, as we know, 66 00:03:58,760 --> 00:04:01,440 Speaker 2: is quite expensive. So what is it that they are 67 00:04:01,440 --> 00:04:04,280 Speaker 2: displacing to invest in generative AI? 68 00:04:05,440 --> 00:04:09,240 Speaker 3: I think generatively I is not just about augmenting now 69 00:04:09,280 --> 00:04:13,160 Speaker 3: new technology and bolting on more costs. In fact, the 70 00:04:13,200 --> 00:04:16,040 Speaker 3: most transformative use cases actually get to the heart of 71 00:04:16,040 --> 00:04:20,120 Speaker 3: a business and rewire the way work gets done. So 72 00:04:20,200 --> 00:04:23,359 Speaker 3: if we look at some of our customers and use cases. 73 00:04:23,680 --> 00:04:30,320 Speaker 3: For example, Optis, they are Australia's second largest tech telecommunications company. 74 00:04:30,680 --> 00:04:34,680 Speaker 3: They've now deployed a networking agent that is actually scanning 75 00:04:35,120 --> 00:04:39,520 Speaker 3: all of the network signals, identifying potential issues in the future, 76 00:04:39,880 --> 00:04:43,200 Speaker 3: and creating service now tickets three weeks ahead of a 77 00:04:43,279 --> 00:04:47,520 Speaker 3: network issue actually happening. This is rewiring how an organization 78 00:04:47,640 --> 00:04:51,360 Speaker 3: works and it helps them avoid future cost future complexity 79 00:04:51,839 --> 00:04:53,160 Speaker 3: that they would have to deal with. 80 00:04:53,320 --> 00:04:56,119 Speaker 2: So you are convinced on the ROI of this, Jenny, 81 00:04:56,200 --> 00:04:59,240 Speaker 2: I spend that we debate every day whether there is enough. 82 00:05:00,680 --> 00:05:05,839 Speaker 1: This is the question we always ask you about. 83 00:05:02,320 --> 00:05:07,800 Speaker 2: Right, because she interfaces with the customers day in and 84 00:05:07,839 --> 00:05:09,240 Speaker 2: day out. Do you convince on that. 85 00:05:10,240 --> 00:05:13,520 Speaker 3: I am convinced because I heard the stories from our customers. 86 00:05:13,760 --> 00:05:17,360 Speaker 3: But also recently we published a blog six hundred plus 87 00:05:17,480 --> 00:05:22,080 Speaker 3: use cases from our customer ecosystem tangible use cases delivering 88 00:05:22,279 --> 00:05:27,160 Speaker 3: ROI and value. However, I also recognize many organizations are 89 00:05:27,279 --> 00:05:31,360 Speaker 3: super anxious. There's a lot of anxiety around the spend 90 00:05:31,600 --> 00:05:34,880 Speaker 3: of on Jenny I about how to get to that ROI. 91 00:05:35,360 --> 00:05:39,360 Speaker 3: The customers I see succeeding and moving the fastest are 92 00:05:39,400 --> 00:05:42,760 Speaker 3: those that are getting out into pilots and trying things out, 93 00:05:43,160 --> 00:05:45,720 Speaker 3: finding the low hanging fruit, and then building on topic. 94 00:05:45,839 --> 00:05:50,040 Speaker 2: Agents are not hallucinating for the customers that you have deployed. 95 00:05:50,880 --> 00:05:55,480 Speaker 3: Well, Hallucination a huge concern for CIOs as they deploy 96 00:05:55,560 --> 00:05:58,840 Speaker 3: this technology. But this is one of the core challenges 97 00:05:58,880 --> 00:06:01,560 Speaker 3: we recognize two years so go at Google. So when 98 00:06:01,600 --> 00:06:06,240 Speaker 3: we built AI into our data cloud, that's actually one 99 00:06:06,240 --> 00:06:09,160 Speaker 3: of the first challenges we set out to solve. Is 100 00:06:09,320 --> 00:06:12,599 Speaker 3: AI must be grounded in business context and business data, 101 00:06:13,200 --> 00:06:15,880 Speaker 3: i e. If it can't find the answer from the 102 00:06:15,920 --> 00:06:19,240 Speaker 3: business data it can't hallucinate, It actually needs to ask 103 00:06:19,279 --> 00:06:24,000 Speaker 3: you a question back and disambiguing. So solving for trust 104 00:06:24,160 --> 00:06:27,240 Speaker 3: and AI has been a core part of the data 105 00:06:27,279 --> 00:06:28,600 Speaker 3: and a cloud strategy. 106 00:06:28,920 --> 00:06:32,800 Speaker 1: You mentioned anxiety around hallucinations, but there's also anxiety around 107 00:06:32,880 --> 00:06:36,320 Speaker 1: job displacement as a result of this technology. I hosted 108 00:06:36,320 --> 00:06:39,239 Speaker 1: a dinner in Atlanta at an event that we did recently, 109 00:06:39,279 --> 00:06:43,919 Speaker 1: Bloomberg Business Value of AI Event, and by far the 110 00:06:44,000 --> 00:06:47,360 Speaker 1: topic that dominated the conversation was like, what is the 111 00:06:47,520 --> 00:06:49,880 Speaker 1: labor landscape going to look like in the coming years 112 00:06:50,440 --> 00:06:54,000 Speaker 1: as these agents displace workers even more so than they 113 00:06:54,040 --> 00:06:55,840 Speaker 1: have now, how do you think about that? 114 00:06:57,200 --> 00:06:59,760 Speaker 3: Being part of Google, we're one hundred and eighty thousand 115 00:06:59,760 --> 00:07:02,159 Speaker 3: calls leagues across the globe, we actually get to see 116 00:07:02,200 --> 00:07:06,360 Speaker 3: those dynamics playing out. So on one hand, we have engineers. 117 00:07:06,680 --> 00:07:09,320 Speaker 3: Even at Google, we're seeing thirty to forty percent of 118 00:07:09,320 --> 00:07:11,680 Speaker 3: our code now being written by Gemini. 119 00:07:12,080 --> 00:07:14,760 Speaker 1: Yeah, so the entry level coders aren't needed anymore. 120 00:07:14,360 --> 00:07:17,480 Speaker 3: So we don't need to hire as many developers as 121 00:07:17,480 --> 00:07:20,160 Speaker 3: we potentially historically had to do to ramp up to 122 00:07:20,200 --> 00:07:23,280 Speaker 3: where the business is growing. However, on the other hand, 123 00:07:23,680 --> 00:07:28,200 Speaker 3: AI has a transformational impact on business outcomes. So IHD 124 00:07:28,560 --> 00:07:33,320 Speaker 3: the Hotels Group, they're actually hiring developers to build the 125 00:07:33,360 --> 00:07:38,280 Speaker 3: next generation of conversational booking experiences where consumers can come 126 00:07:38,320 --> 00:07:43,200 Speaker 3: and chat and book experience holiday experiences direct. So I 127 00:07:43,240 --> 00:07:48,080 Speaker 3: think this is what I see this as a job's shifting, 128 00:07:48,240 --> 00:07:52,480 Speaker 3: where skills are shifting, the types of roles are potentially shifting. 129 00:07:52,920 --> 00:07:56,720 Speaker 3: It's a recalibration across the enterprise. 130 00:07:56,840 --> 00:07:59,920 Speaker 1: I think recalibration is certainly fair to say Yasmin Ahma, 131 00:08:00,120 --> 00:08:03,920 Speaker 1: Managing Director Data Cloud at Google Cloud, Mandeep Singh, Global 132 00:08:03,960 --> 00:08:06,760 Speaker 1: Head of Technology Research at Bloomberg Intelligence. Both are here 133 00:08:06,800 --> 00:08:10,240 Speaker 1: in the Bloomberg Interactive Broker's studio. That just a taste 134 00:08:10,320 --> 00:08:13,000 Speaker 1: of what is to come on the Tech Disruptors podcast 135 00:08:13,080 --> 00:08:16,160 Speaker 1: with Mandeep Singh. For more insights from Mandy and the 136 00:08:16,320 --> 00:08:19,840 Speaker 1: entire Bloomberg Intelligence team, check out the Tech Disruptors podcast. 137 00:08:19,880 --> 00:08:23,800 Speaker 1: It features conversations with thought leaders and management teams on 138 00:08:23,920 --> 00:08:27,240 Speaker 1: disruptive trends in the tech world. It covers everything from 139 00:08:27,280 --> 00:08:30,680 Speaker 1: AI to EVS, to VR and beyond. You can find 140 00:08:30,720 --> 00:08:35,160 Speaker 1: it on Apple, Spotify, or anywhere you get your podcast