1 00:00:01,520 --> 00:00:06,760 Speaker 1: From Marhart where Innovation, money and power Collie in Silicon Valley, NBN. 2 00:00:07,120 --> 00:00:11,160 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlove. 3 00:00:24,880 --> 00:00:27,280 Speaker 3: I'm Caroline Heidel, Bloomberg's world headquarters in New York and 4 00:00:27,360 --> 00:00:30,640 Speaker 3: Ludlow He's off. This is Blomberg Technology coming up. As 5 00:00:30,640 --> 00:00:33,600 Speaker 3: analysts call out a perfect storm in the markets amid 6 00:00:33,600 --> 00:00:36,200 Speaker 3: sapping sentiment, look at the outlook for the tech sector. 7 00:00:36,240 --> 00:00:39,960 Speaker 3: Helen Fishes with US clear Bridge Research plus Crypto's calm. 8 00:00:40,040 --> 00:00:43,480 Speaker 3: It shatters liquidations exceeding a billion dollars in twenty four 9 00:00:43,479 --> 00:00:46,000 Speaker 3: hours as price as they fall. Will break down what's 10 00:00:46,040 --> 00:00:49,920 Speaker 3: behind bitcoins volatile moves and it's gearing up to be 11 00:00:49,960 --> 00:00:52,000 Speaker 3: the biggest IPO of the year. Chip maker arm is 12 00:00:52,040 --> 00:00:54,360 Speaker 3: said to line up not one, not two, but twenty 13 00:00:54,480 --> 00:00:56,920 Speaker 3: eight banks for its public offering. We'll bring you the 14 00:00:56,960 --> 00:00:59,560 Speaker 3: latest of the company and who else is eyeing the 15 00:00:59,640 --> 00:01:04,160 Speaker 3: IPO window. But first, this is rather extraordinary earnings coming 16 00:01:04,240 --> 00:01:06,920 Speaker 3: after the bell today as well. We want to talk 17 00:01:06,920 --> 00:01:09,639 Speaker 3: about all of this with Henry Fresh, senior research analyst 18 00:01:09,680 --> 00:01:12,200 Speaker 3: for Technology Software over at clear Bridge. Of course, decades 19 00:01:12,200 --> 00:01:14,520 Speaker 3: of experience really that we turn to in these moments 20 00:01:14,600 --> 00:01:18,759 Speaker 3: because it is kind of a perfect storm. Berkley's countering 21 00:01:18,800 --> 00:01:21,600 Speaker 3: that talking for liquidity was about China. We, of course 22 00:01:21,800 --> 00:01:24,280 Speaker 3: then also see the bond market selling off. Is it 23 00:01:24,319 --> 00:01:26,319 Speaker 3: okay to stick with your technology holdings right now? 24 00:01:26,880 --> 00:01:29,200 Speaker 4: That's a really good question, Caroline, thanks for having me. 25 00:01:30,440 --> 00:01:30,720 Speaker 5: Yes. 26 00:01:30,920 --> 00:01:35,120 Speaker 4: Well, with the tenure rising as rapidly as it is currently, 27 00:01:35,200 --> 00:01:38,959 Speaker 4: it makes it hard for technology to outperform, especially the 28 00:01:39,040 --> 00:01:42,280 Speaker 4: long bond esque technology that we think about with cloud 29 00:01:42,360 --> 00:01:45,600 Speaker 4: and SaaS applications. But at the end of the day, 30 00:01:45,680 --> 00:01:49,200 Speaker 4: the fundamentals seem to be set to improve, So I 31 00:01:49,240 --> 00:01:51,720 Speaker 4: would look to the current environment to build positions, to 32 00:01:51,760 --> 00:01:52,600 Speaker 4: add positions. 33 00:01:52,760 --> 00:01:55,000 Speaker 3: Okay, so talk about the fundamentals that will improve. Are 34 00:01:55,000 --> 00:01:58,880 Speaker 3: you talking artificial intelligence, syncratic elements or you're talking that 35 00:01:59,160 --> 00:02:01,680 Speaker 3: generally on me? In the US? Isn't quite as dogs 36 00:02:02,160 --> 00:02:02,760 Speaker 3: thought it might be. 37 00:02:03,120 --> 00:02:06,360 Speaker 4: Well, there are a few factors. Artificial intelligence absolutely plays 38 00:02:06,400 --> 00:02:09,880 Speaker 4: into it. I'm of the view of rolling recessions. I 39 00:02:09,919 --> 00:02:13,000 Speaker 4: believe that tech has been in a very mild recession 40 00:02:13,000 --> 00:02:15,200 Speaker 4: over the course of the better part of the last year, 41 00:02:16,080 --> 00:02:19,320 Speaker 4: and without that recession actually having materialized, I think that 42 00:02:19,440 --> 00:02:22,920 Speaker 4: tech was poised to improve. Enterprise tech was poised to 43 00:02:22,919 --> 00:02:25,839 Speaker 4: see an improvement overall, as budgets had really been set 44 00:02:25,880 --> 00:02:30,760 Speaker 4: for a recessionary environment. In addition, in terms of future earnings, 45 00:02:30,800 --> 00:02:33,600 Speaker 4: there were a few sort of green shoots. Some of 46 00:02:33,600 --> 00:02:37,800 Speaker 4: the companies talked about the optimization, workload optimization, cost reduction 47 00:02:37,880 --> 00:02:41,320 Speaker 4: focus of customers starting to wane, starting to show signs 48 00:02:41,320 --> 00:02:45,080 Speaker 4: of waning. There were new project starts and new project 49 00:02:45,440 --> 00:02:48,800 Speaker 4: pickups and new workload starts, so that's actually quite encouraging. 50 00:02:50,320 --> 00:02:53,600 Speaker 4: And then you saw tech layoffs starting to wane. They 51 00:02:53,639 --> 00:02:56,320 Speaker 4: really slowed a lot over the last three quarters. Management 52 00:02:56,760 --> 00:03:00,240 Speaker 4: teams talked about increased visibility and improvement in their busininess 53 00:03:00,320 --> 00:03:02,480 Speaker 4: is through the quarter, so I think overall, and then 54 00:03:02,520 --> 00:03:04,280 Speaker 4: on top of it, we have AI, which which should 55 00:03:04,320 --> 00:03:04,880 Speaker 4: be a big driver. 56 00:03:05,280 --> 00:03:07,320 Speaker 3: How much is China or'll worry? 57 00:03:07,360 --> 00:03:11,160 Speaker 4: From real perspective, it's a worry only in so far 58 00:03:11,280 --> 00:03:17,919 Speaker 4: as the geopolitical backdrop creates shocks to the economy which 59 00:03:17,960 --> 00:03:21,000 Speaker 4: can delay recovery. But I don't view it as a 60 00:03:21,120 --> 00:03:26,600 Speaker 4: direct concern for particularly cloud and SaaS names. I think corporate. 61 00:03:27,800 --> 00:03:29,960 Speaker 4: I think enterprise and corporate customers around the globe have 62 00:03:30,000 --> 00:03:33,560 Speaker 4: imperatives which they need to pursue. They've delayed those for 63 00:03:33,880 --> 00:03:37,320 Speaker 4: essentially nine months plus, and I think they're ready to 64 00:03:37,320 --> 00:03:41,480 Speaker 4: get underway with them, you know, more or less unless 65 00:03:41,480 --> 00:03:42,960 Speaker 4: we have a massive shock to the economy. 66 00:03:43,040 --> 00:03:45,880 Speaker 3: It's interesting that using the green shoots coming from these endings, 67 00:03:45,960 --> 00:03:50,080 Speaker 3: some might say Apple is a bit of underwhelming Microsoft 68 00:03:50,120 --> 00:03:54,840 Speaker 3: to from real perspective, how these companies proving themselves that 69 00:03:55,040 --> 00:03:58,640 Speaker 3: just we as investors or indeed analysts, when they've got 70 00:03:58,640 --> 00:04:00,800 Speaker 3: all the by ratings out and the as have risen 71 00:04:00,840 --> 00:04:02,960 Speaker 3: so far with just too much price to perfection at 72 00:04:02,960 --> 00:04:04,920 Speaker 3: this moment, as Abigail was outlining. 73 00:04:05,400 --> 00:04:09,680 Speaker 4: So yes, you're outlining it well. So I think what 74 00:04:09,720 --> 00:04:11,600 Speaker 4: happened is we had a very very strong firstaff in 75 00:04:11,640 --> 00:04:14,240 Speaker 4: the group. It was compelling from a price performance perspective. 76 00:04:14,560 --> 00:04:18,039 Speaker 4: Investors got very enthused about AI, but particularly with respect 77 00:04:18,080 --> 00:04:21,360 Speaker 4: to Microsoft's results. I viewed the reaction as more of 78 00:04:21,400 --> 00:04:26,120 Speaker 4: a mismatch of timing mismatch versus anything. Microsoft's quarter was 79 00:04:26,279 --> 00:04:30,000 Speaker 4: inline and their commentary on AI was a little underwhelming, 80 00:04:30,040 --> 00:04:33,560 Speaker 4: a little more tepid than investors expected, but they proceeded 81 00:04:33,560 --> 00:04:36,919 Speaker 4: to explain that they hadn't actually introduced the majority of 82 00:04:36,960 --> 00:04:40,720 Speaker 4: A enabled applications which investors were very excited about customers 83 00:04:40,720 --> 00:04:43,120 Speaker 4: are getting excited about. In addition, some of the key 84 00:04:43,520 --> 00:04:46,320 Speaker 4: semiconductor chips that are needed to train and run these 85 00:04:46,400 --> 00:04:49,360 Speaker 4: large language models weren't actually available yet. In fact, only 86 00:04:49,400 --> 00:04:51,880 Speaker 4: a small swath of them became available only very recently. 87 00:04:52,279 --> 00:04:56,680 Speaker 4: So I viewed the results as not a demand indicator. 88 00:04:56,680 --> 00:04:59,000 Speaker 4: I think demand continues to build, and these companies are 89 00:04:59,040 --> 00:05:01,080 Speaker 4: doing extraordinarily well. They have a plan of what they 90 00:05:01,120 --> 00:05:03,080 Speaker 4: plan to deliver of the course of the year. It 91 00:05:03,160 --> 00:05:05,160 Speaker 4: was just a timing mismatch in terms of expections. 92 00:05:05,160 --> 00:05:07,040 Speaker 3: And so when you've been talking about the fact that 93 00:05:07,040 --> 00:05:08,680 Speaker 3: in this sort of environment where I'm looking at a 94 00:05:08,720 --> 00:05:11,640 Speaker 3: Nasdaq one hundred of by two percent on the week, 95 00:05:11,680 --> 00:05:15,120 Speaker 3: we've had three straight weeks of losses on that benchmark, 96 00:05:15,760 --> 00:05:17,800 Speaker 3: why do you make additions? You were saying, maybe you 97 00:05:17,880 --> 00:05:19,760 Speaker 3: might be buying in this dipmoket. 98 00:05:19,520 --> 00:05:24,320 Speaker 4: Sure from a valuation perspective, an enterprise overall, we had 99 00:05:24,360 --> 00:05:27,160 Speaker 4: actually been within the pre pandemic five and ten yure 100 00:05:27,240 --> 00:05:29,560 Speaker 4: averages earlier in the year. When I was here in February, 101 00:05:30,160 --> 00:05:32,960 Speaker 4: we gained a few multiple turns. Since that point, we've 102 00:05:33,000 --> 00:05:36,240 Speaker 4: been giving up some of those multiple turns, but over time, 103 00:05:36,400 --> 00:05:39,960 Speaker 4: I do expect AI to contribute meaningfully to results and 104 00:05:40,040 --> 00:05:43,839 Speaker 4: actually to drive both growth and valuation over time. So 105 00:05:43,960 --> 00:05:46,359 Speaker 4: I would be looking to but again, the tenure is 106 00:05:46,400 --> 00:05:48,839 Speaker 4: the arbiter of everything. Near term, it feels like it 107 00:05:48,880 --> 00:05:51,720 Speaker 4: wants to go toward five percent. I'm not an economist. 108 00:05:51,880 --> 00:05:54,479 Speaker 4: If it does that, there's certainly more pain to come 109 00:05:54,520 --> 00:05:57,000 Speaker 4: in tech. But I would be adding incrementally as we 110 00:05:57,120 --> 00:05:59,680 Speaker 4: move forward, because it's anyone's guests as to where that 111 00:05:59,680 --> 00:06:00,840 Speaker 4: really would you. 112 00:06:00,760 --> 00:06:04,760 Speaker 3: Be adding to cybernames. We're looking at Palo Alto. We're 113 00:06:04,760 --> 00:06:06,240 Speaker 3: going to dig into that one in a moment as 114 00:06:06,279 --> 00:06:08,080 Speaker 3: to want if that coming off to the bell today. 115 00:06:09,480 --> 00:06:10,440 Speaker 5: I like cyber. 116 00:06:10,560 --> 00:06:14,680 Speaker 4: I think cyber fundamentals are generally resilient. We've seen some 117 00:06:14,760 --> 00:06:17,960 Speaker 4: weakness and firewalls and the firewall arena to date, and 118 00:06:18,000 --> 00:06:21,000 Speaker 4: I think that's partially a function of having been supply 119 00:06:21,120 --> 00:06:23,000 Speaker 4: constrained for so many years, and there was a lot 120 00:06:23,000 --> 00:06:26,000 Speaker 4: of exuberant customer ordering, let's call it that. So I 121 00:06:26,040 --> 00:06:30,000 Speaker 4: think we're normalizing after that. I tend near term to 122 00:06:30,040 --> 00:06:34,600 Speaker 4: favor some of the next generation cyber names, such as 123 00:06:34,640 --> 00:06:37,279 Speaker 4: a crowd striker or as the scaler, which were tied 124 00:06:37,360 --> 00:06:40,800 Speaker 4: very much to zero trust initiatives. Their government mandates behind 125 00:06:40,839 --> 00:06:43,920 Speaker 4: that part of the Inflation Reduction Act, has spend behind that. 126 00:06:44,240 --> 00:06:46,880 Speaker 3: But at the end of the day, Palo. 127 00:06:46,640 --> 00:06:50,760 Speaker 4: Alto itself is a is a leader in technology. I 128 00:06:50,760 --> 00:06:55,680 Speaker 4: think for today's you asked about today's announcement very conveniently 129 00:06:55,680 --> 00:06:57,320 Speaker 4: on a Friday in late August to go to two 130 00:06:57,360 --> 00:07:00,440 Speaker 4: hour conference call. I think with their it's time for 131 00:07:00,480 --> 00:07:03,000 Speaker 4: them to give their three year plan, which they did 132 00:07:03,040 --> 00:07:05,440 Speaker 4: three years ago. I've noted that when they do that, 133 00:07:05,480 --> 00:07:08,200 Speaker 4: they tend to be conservative. At the outset of the plan, 134 00:07:08,760 --> 00:07:10,920 Speaker 4: it seems like they also want to announce some amount 135 00:07:11,000 --> 00:07:12,880 Speaker 4: of change. It may be that there are shifting toward 136 00:07:12,880 --> 00:07:15,680 Speaker 4: to annual billions from multi r upfront billions that would 137 00:07:15,680 --> 00:07:19,320 Speaker 4: actually hurt pre cash flow and billions to some degree. 138 00:07:19,480 --> 00:07:22,600 Speaker 4: They may want to pursue certain areas such as a 139 00:07:22,680 --> 00:07:26,040 Speaker 4: zero trust more actively. It's hard to tell some of 140 00:07:26,080 --> 00:07:29,280 Speaker 4: this is anticipated by investors. It's hard to tell the 141 00:07:29,400 --> 00:07:31,680 Speaker 4: magnitude though, and exactly what they're going to announce. 142 00:07:31,960 --> 00:07:35,160 Speaker 3: You'll write the stock down significantly since they announced this date. 143 00:07:35,320 --> 00:07:37,840 Speaker 3: Henry Frish always great to have in the studio. Comeback scene, 144 00:07:37,880 --> 00:07:40,960 Speaker 3: we hope from Claarbridge. When the interesting takes on individual names, 145 00:07:41,000 --> 00:07:43,120 Speaker 3: individual valuations, and let's dig in on that name. We 146 00:07:43,120 --> 00:07:45,960 Speaker 3: were just talking about Palo Alto Networks because August the 147 00:07:45,960 --> 00:07:48,280 Speaker 3: second was when we really started to see the stock 148 00:07:48,360 --> 00:07:50,120 Speaker 3: sell off, because they said that they were going to 149 00:07:50,160 --> 00:07:53,840 Speaker 3: be reporting results on a Friday today after the bell. 150 00:07:54,080 --> 00:07:56,560 Speaker 3: Highly unusual. This is a company that usually gives us 151 00:07:56,560 --> 00:07:59,200 Speaker 3: its numbers on a Monday or a Tuesday. It's giving 152 00:07:59,200 --> 00:08:01,840 Speaker 3: investors and calls concern. I'm pleased to say that Bluemog's 153 00:08:01,920 --> 00:08:05,000 Speaker 3: run Rostellek is Prastellica is with us. Of course, who's 154 00:08:05,560 --> 00:08:09,280 Speaker 3: looking at sort of what pr disaster this could be. 155 00:08:09,320 --> 00:08:11,360 Speaker 3: If indeed they are trying to bury some sort of 156 00:08:11,360 --> 00:08:13,480 Speaker 3: bad news, everyone's brace for it. What the stocks off 157 00:08:13,480 --> 00:08:15,720 Speaker 3: by about twenty percent since they first announced this date. 158 00:08:16,880 --> 00:08:20,080 Speaker 6: Yeah, absolutely, this is extremely unusual. This is the first 159 00:08:20,160 --> 00:08:22,960 Speaker 6: Friday afternoon report that we've seen since Nike in late 160 00:08:23,000 --> 00:08:25,920 Speaker 6: twenty twenty. So think about all those different quarterly reports 161 00:08:25,920 --> 00:08:27,640 Speaker 6: we've had all those earning seasons. It is the first 162 00:08:27,640 --> 00:08:31,040 Speaker 6: time we've had it since then. So very unusual, very atypical. 163 00:08:31,600 --> 00:08:33,680 Speaker 6: I spoke with the company and a spokesperson told me 164 00:08:33,760 --> 00:08:36,920 Speaker 6: that between everything they have going on, they're doing results, 165 00:08:36,920 --> 00:08:40,199 Speaker 6: they're giving an outlook. Look. I just mentioned they are 166 00:08:40,200 --> 00:08:43,080 Speaker 6: giving a three year medium term targets, they're announcing a 167 00:08:43,080 --> 00:08:46,400 Speaker 6: strategy review, and they have a sales networking meeting next week. 168 00:08:46,480 --> 00:08:48,880 Speaker 6: So they have a lot going on. And the company 169 00:08:48,920 --> 00:08:51,120 Speaker 6: told me they just want to give investors and analysts 170 00:08:51,160 --> 00:08:54,760 Speaker 6: time to digest all the news that's coming out, which 171 00:08:54,920 --> 00:08:56,280 Speaker 6: sounds like it's going to be a lot, but again 172 00:08:56,320 --> 00:08:58,320 Speaker 6: it is very atypical as far as the timing goes. 173 00:08:58,640 --> 00:09:01,960 Speaker 3: I think what's also added to concern is fort neet right, 174 00:09:02,240 --> 00:09:06,880 Speaker 3: another cyber company that saw well less big gains than 175 00:09:06,880 --> 00:09:09,480 Speaker 3: many had anticipated. What are the headwinds facing some of 176 00:09:09,520 --> 00:09:10,800 Speaker 3: these cyber companies at the moment. 177 00:09:11,160 --> 00:09:13,240 Speaker 6: Yeah, absolutely so. Paul Botzel comes out and says it's 178 00:09:13,240 --> 00:09:14,480 Speaker 6: going to be a Friday, the stock calls out. You 179 00:09:14,480 --> 00:09:16,080 Speaker 6: can see in your chart there. A couple of days later, 180 00:09:16,080 --> 00:09:18,440 Speaker 6: Fortnete comes out and they cut their outlook. They're basically 181 00:09:18,440 --> 00:09:21,480 Speaker 6: talking about weaker it spending and cybersecurity has always been 182 00:09:21,520 --> 00:09:24,600 Speaker 6: an area that people feel like is pretty secure, pretty 183 00:09:24,679 --> 00:09:26,640 Speaker 6: durable as far as the demand goes. There's always going 184 00:09:26,679 --> 00:09:29,600 Speaker 6: to be demand for improving cyber you know, against any 185 00:09:29,640 --> 00:09:31,320 Speaker 6: kind of packs or threats like that. So the fact 186 00:09:31,320 --> 00:09:34,240 Speaker 6: that people might be seeing weakness in this very defensive 187 00:09:34,240 --> 00:09:36,600 Speaker 6: area of software, which is seen as another kind of 188 00:09:36,600 --> 00:09:39,120 Speaker 6: headwind for the stock coming after this unusual date. 189 00:09:39,760 --> 00:09:41,920 Speaker 3: And what did we hear in particular in terms of 190 00:09:42,360 --> 00:09:45,600 Speaker 3: just the macro environment weighing on client's desire to be 191 00:09:45,800 --> 00:09:50,840 Speaker 3: upping their spend or thinking about curtailing or fine tuning 192 00:09:50,840 --> 00:09:53,000 Speaker 3: their cyber spend. Is that what we heard from some 193 00:09:53,080 --> 00:09:53,960 Speaker 3: of the others in the space. 194 00:09:54,720 --> 00:09:57,160 Speaker 6: Yeah, absolutely, so, just some signs of some weaker spending. 195 00:09:57,160 --> 00:09:59,000 Speaker 6: Maybe it's seeking longer for deals to close, that the 196 00:09:59,000 --> 00:10:01,560 Speaker 6: deals aren't as big as me might have been expected before. 197 00:10:01,600 --> 00:10:03,560 Speaker 6: Is all these kinds of issues just kind of heightening 198 00:10:03,720 --> 00:10:06,560 Speaker 6: the kind of concerns about the global macroeconomic backdrop as 199 00:10:06,559 --> 00:10:09,120 Speaker 6: it pertains to security software spinning in particular. 200 00:10:09,800 --> 00:10:12,440 Speaker 3: Really great to get your take. I hope it's not 201 00:10:12,440 --> 00:10:14,200 Speaker 3: too long a day for you this Friday to be 202 00:10:14,200 --> 00:10:16,720 Speaker 3: working after the bell. Ryan Lastelliko is going to be 203 00:10:16,720 --> 00:10:20,880 Speaker 3: there with the other investors keenly awaiting that number to drop. 204 00:10:20,960 --> 00:10:32,000 Speaker 3: We really appreciate it. An unusual calm in the crypto 205 00:10:32,040 --> 00:10:35,080 Speaker 3: markets when it's over this week at least some bitcoin 206 00:10:35,160 --> 00:10:38,080 Speaker 3: sell off sparked by, well, maybe it's concerns over interest rates, 207 00:10:38,080 --> 00:10:41,040 Speaker 3: maybe it's potential big sales of certain types of the asset. 208 00:10:41,200 --> 00:10:43,640 Speaker 3: Let's talk about that route that pushed bitcoin below twenty 209 00:10:43,679 --> 00:10:45,680 Speaker 3: six thousand for the first time in two months. Please 210 00:10:45,720 --> 00:10:48,600 Speaker 3: to say Stephen Agias with us. He's got plenty of insight. 211 00:10:48,679 --> 00:10:51,120 Speaker 3: Co found a CEO of give Packed, its crypto fundraising 212 00:10:51,120 --> 00:10:55,240 Speaker 3: platform focused on social impact metaverse economy. But Stephen, do 213 00:10:55,280 --> 00:10:57,679 Speaker 3: you buy some of the rumors in the market that 214 00:10:57,720 --> 00:11:00,280 Speaker 3: perhaps this is sparked by one key big seller, that 215 00:11:00,400 --> 00:11:02,200 Speaker 3: is SpaceX. So were the reports coming from the Wall 216 00:11:02,240 --> 00:11:04,880 Speaker 3: Street Journal? Or is this just thin markets? 217 00:11:05,240 --> 00:11:07,600 Speaker 7: I think it's just market structure. I think the SpaceX 218 00:11:07,679 --> 00:11:10,920 Speaker 7: per selling probably already happened. What we saw in the 219 00:11:10,960 --> 00:11:13,199 Speaker 7: last few months was bitcoin the theory. I'm trading at 220 00:11:13,240 --> 00:11:16,240 Speaker 7: all time low volatility. Meanwhile, market makers were pulling out 221 00:11:16,280 --> 00:11:18,959 Speaker 7: of the ecosystem, so we had a very liquid market 222 00:11:19,040 --> 00:11:22,280 Speaker 7: and open interest on futures was ramping up significantly, So 223 00:11:22,320 --> 00:11:24,560 Speaker 7: the move was expected. I think we saw there had 224 00:11:24,559 --> 00:11:25,960 Speaker 7: to be a flush out. We didn't know if it 225 00:11:26,000 --> 00:11:27,720 Speaker 7: was going to be to the upside or to the downside. 226 00:11:27,840 --> 00:11:30,480 Speaker 7: But as the dollar has been getting strong recently, a 227 00:11:30,480 --> 00:11:32,120 Speaker 7: little bit of a sell off in crypto meant that 228 00:11:32,160 --> 00:11:34,199 Speaker 7: longs were liquidated and we saw a sharp, quick move 229 00:11:34,280 --> 00:11:34,959 Speaker 7: to the downside. 230 00:11:35,000 --> 00:11:37,400 Speaker 3: I mean, everyone's now eyeing the twenty five thousand dollars 231 00:11:37,440 --> 00:11:40,440 Speaker 3: level as some sort of technical line in the sand. 232 00:11:40,960 --> 00:11:43,680 Speaker 3: But is there any catalysts to the upside because many 233 00:11:43,720 --> 00:11:46,679 Speaker 3: had started to desire to get into bitcoin because there 234 00:11:46,679 --> 00:11:50,000 Speaker 3: were hopes to some sort of spot bitcoin ETF for example, 235 00:11:50,960 --> 00:11:51,480 Speaker 3: I think there are. 236 00:11:51,400 --> 00:11:52,520 Speaker 1: A lot of catalysts to the upside. 237 00:11:52,520 --> 00:11:55,640 Speaker 7: I think you had a spot BTC ETF, which Mike 238 00:11:55,679 --> 00:11:58,400 Speaker 7: Novograts on the Galaxy call basically says as a matter 239 00:11:58,440 --> 00:12:01,560 Speaker 7: of when not if, you have the having next year, 240 00:12:01,720 --> 00:12:04,079 Speaker 7: which typically there's a bull run after the having, but 241 00:12:04,120 --> 00:12:04,600 Speaker 7: it could. 242 00:12:04,400 --> 00:12:05,920 Speaker 1: Be something where we see a rally into it. 243 00:12:06,480 --> 00:12:09,720 Speaker 7: We have the sec case against Coinbase looking shaky after 244 00:12:09,840 --> 00:12:12,560 Speaker 7: Judge Torres basically said that, you know, token straight on 245 00:12:12,640 --> 00:12:17,440 Speaker 7: secondary exchanges aren't investment contracts. And lastly you have the 246 00:12:17,440 --> 00:12:20,040 Speaker 7: FED tightening cycle, you know, it stopping out. So I 247 00:12:20,040 --> 00:12:22,480 Speaker 7: think there's a lot of catalysts that you know, basically 248 00:12:22,520 --> 00:12:25,160 Speaker 7: are laying the narrative for what the future crypto bill run. 249 00:12:25,040 --> 00:12:25,600 Speaker 1: Could look like. 250 00:12:25,880 --> 00:12:28,960 Speaker 3: I'm interested that you talk force about the Bitcoin spot 251 00:12:29,000 --> 00:12:31,640 Speaker 3: ETF and what Mike said about it on the Call 252 00:12:31,679 --> 00:12:35,440 Speaker 3: for Galaxy. What about this talk around an eth ETF 253 00:12:35,480 --> 00:12:38,480 Speaker 3: at least anyth futures one. How much would that galvanize 254 00:12:38,480 --> 00:12:40,760 Speaker 3: interest in some of the alternative coins. 255 00:12:42,000 --> 00:12:42,920 Speaker 1: I think it's huge. 256 00:12:43,000 --> 00:12:46,320 Speaker 7: I think you know that that news happening within minutes 257 00:12:46,520 --> 00:12:50,160 Speaker 7: or hours of this liquidation is really interesting. But I 258 00:12:50,160 --> 00:12:53,160 Speaker 7: think the main thing for me with the Eth futures ETF, 259 00:12:53,360 --> 00:12:56,480 Speaker 7: it basically means that Ethereum won't be a security. I 260 00:12:56,480 --> 00:12:59,640 Speaker 7: think that ship has sailed and it's going to allow 261 00:12:59,679 --> 00:13:01,960 Speaker 7: people to a lot more exposure to Eth than they 262 00:13:02,000 --> 00:13:04,480 Speaker 7: would have the same way the people get exposure to 263 00:13:04,520 --> 00:13:05,840 Speaker 7: Bitcoin through the future's ETF. 264 00:13:06,440 --> 00:13:07,320 Speaker 1: So I think it's huge. 265 00:13:07,840 --> 00:13:10,720 Speaker 7: I don't know how deep down sort of the coin 266 00:13:10,800 --> 00:13:13,160 Speaker 7: market cap we can go as far as what's going 267 00:13:13,200 --> 00:13:15,640 Speaker 7: to be considered as security or not. But I think 268 00:13:15,720 --> 00:13:19,360 Speaker 7: for sure Ethereum, that ship has sailed, and Ethereum will 269 00:13:19,400 --> 00:13:22,120 Speaker 7: be considered a commodity regulated by a CFC moving forward. 270 00:13:22,280 --> 00:13:23,760 Speaker 3: Just go back to what you said at the start 271 00:13:23,800 --> 00:13:27,080 Speaker 3: of that answer, and the fact that these timings happened 272 00:13:27,320 --> 00:13:30,319 Speaker 3: almost hours apart from each other, what do you mean 273 00:13:30,360 --> 00:13:33,280 Speaker 3: by that? Was there any way that you think that 274 00:13:33,280 --> 00:13:35,319 Speaker 3: that was catalyzing your sell off in bitcoin? 275 00:13:36,400 --> 00:13:40,120 Speaker 7: I don't think there was any specific timing or reason. 276 00:13:40,320 --> 00:13:41,839 Speaker 7: I mean, I'm not really sure. I don't want to 277 00:13:41,840 --> 00:13:45,120 Speaker 7: be conspiratorial again. I think that the cause for the 278 00:13:45,120 --> 00:13:47,199 Speaker 7: liquidation was more market structure than anything. 279 00:13:47,280 --> 00:13:48,480 Speaker 1: I think it might have been an. 280 00:13:48,520 --> 00:13:52,120 Speaker 7: Interesting coincidence to see that that eth futures ETF was 281 00:13:52,120 --> 00:13:54,920 Speaker 7: approved within hours of that liquidation event yesterday. 282 00:13:55,000 --> 00:13:56,440 Speaker 3: Can you just talk a little bit about who the 283 00:13:56,440 --> 00:13:59,360 Speaker 3: marginal buyer is at the moment or indeed where we're 284 00:13:59,360 --> 00:14:02,840 Speaker 3: seeing people getting interested if you do say there's catalyst 285 00:14:02,920 --> 00:14:05,000 Speaker 3: to the upside a Bitcoin and indeed some of the 286 00:14:05,040 --> 00:14:08,920 Speaker 3: other alternative coins. Who is buying into this market regionally speaking, 287 00:14:09,120 --> 00:14:10,960 Speaker 3: institutionally speaking. 288 00:14:11,800 --> 00:14:14,719 Speaker 7: We're seeing long term holders really taking over right now, 289 00:14:14,720 --> 00:14:17,040 Speaker 7: and short term holders are the ones that are getting 290 00:14:17,240 --> 00:14:19,760 Speaker 7: sort of fleeced out at the moment. I think the 291 00:14:19,800 --> 00:14:22,280 Speaker 7: marginal buyer, one big catalyst for me is going to 292 00:14:22,320 --> 00:14:25,640 Speaker 7: be coinbase and their base chain. I think what they're 293 00:14:25,640 --> 00:14:28,040 Speaker 7: doing really is disrupting themselves with that, and they're going 294 00:14:28,120 --> 00:14:31,200 Speaker 7: to do a lot to bring retail and their assets 295 00:14:31,240 --> 00:14:33,640 Speaker 7: on chain. So I think what we saw is sort 296 00:14:33,640 --> 00:14:35,680 Speaker 7: of the demise of the last crypto ball run, was 297 00:14:36,000 --> 00:14:39,080 Speaker 7: these blow ups of centralized entities. And I think Coinbase 298 00:14:39,120 --> 00:14:42,360 Speaker 7: is really laying the groundwork for retail to put their 299 00:14:42,360 --> 00:14:47,440 Speaker 7: assets in decentralized entities and defied NFTs decentralized apps, and 300 00:14:47,480 --> 00:14:50,320 Speaker 7: I think that's gonna be a huge narrative shift and 301 00:14:50,360 --> 00:14:53,040 Speaker 7: a big rotation and where the marginal buyer comes and 302 00:14:53,080 --> 00:14:54,560 Speaker 7: where they where they park their assets. 303 00:14:55,040 --> 00:14:57,600 Speaker 3: Steven Angya, thank you very much for joining us today. 304 00:14:57,640 --> 00:15:00,640 Speaker 3: Give pat coo co founder as well. On all that 305 00:15:00,800 --> 00:15:04,200 Speaker 3: recent return, we say to volatility when it comes to crypto. Now, 306 00:15:04,200 --> 00:15:07,080 Speaker 3: talking about volatility, it's been a volatile week for Addie 307 00:15:07,120 --> 00:15:09,480 Speaker 3: and of course the Dutch payment process in company when 308 00:15:09,480 --> 00:15:12,320 Speaker 3: it released its disappointing first half results. It's tricking an 309 00:15:12,320 --> 00:15:14,560 Speaker 3: absolute meltdown in the stock. Remember it raised about twenty 310 00:15:14,600 --> 00:15:17,400 Speaker 3: billion dollars in market value yesterday. Now, in an interview 311 00:15:17,440 --> 00:15:20,760 Speaker 3: with Blueboat Television, it's Cohed says, look on back of 312 00:15:20,760 --> 00:15:23,200 Speaker 3: this weakness, there's not going to be shared buybacks just 313 00:15:23,200 --> 00:15:23,760 Speaker 3: take a listen. 314 00:15:24,640 --> 00:15:28,280 Speaker 2: We're focused on building a business and we've always had 315 00:15:28,400 --> 00:15:33,880 Speaker 2: a policy where we continue to invest our funt in 316 00:15:33,920 --> 00:15:36,920 Speaker 2: the business, and that what we continue to do. It's 317 00:15:37,000 --> 00:15:42,520 Speaker 2: clear that we lost some trust yesterday and I think 318 00:15:42,520 --> 00:15:45,920 Speaker 2: the best approach to this is now very carefully listening 319 00:15:46,000 --> 00:15:49,920 Speaker 2: to our investors and see how we can get back some. 320 00:15:49,960 --> 00:15:53,240 Speaker 3: Of that trusture Adien's co CEO feeling the need to 321 00:15:53,240 --> 00:15:55,640 Speaker 3: come and talk to the market post those earnings. Meanwhile, 322 00:15:56,120 --> 00:15:59,520 Speaker 3: coming up, are preparing for the biggest IPO of the year, 323 00:15:59,840 --> 00:16:03,440 Speaker 3: no less than twenty eight banks for bringing the details. Next, 324 00:16:03,600 --> 00:16:18,200 Speaker 3: there's a Bluebog technology time now for talking tech. First up, 325 00:16:18,240 --> 00:16:21,440 Speaker 3: look American trade groups comprising the biggest players in technology 326 00:16:21,440 --> 00:16:24,120 Speaker 3: and manufacturing. While they've asked the US government to urge 327 00:16:24,120 --> 00:16:27,400 Speaker 3: India to reconsider a policy introduced on tech imports that 328 00:16:27,480 --> 00:16:30,320 Speaker 3: would impose a new license requirement. The coalition includes some 329 00:16:30,360 --> 00:16:34,760 Speaker 3: America's largest businesses, including Apple and Intel. Meanwhile, the largest 330 00:16:34,840 --> 00:16:37,440 Speaker 3: US maker of chip making Machinery, gave a pretty bullish 331 00:16:37,480 --> 00:16:40,920 Speaker 3: forecast for the current quarter, indicating the industry slump maybe fading. 332 00:16:41,000 --> 00:16:44,320 Speaker 3: Applied Materials sees the shift towards artificial intelligence of course, 333 00:16:44,520 --> 00:16:48,000 Speaker 3: and the rise of Internet connected devices helping bolster its results. 334 00:16:48,480 --> 00:16:51,280 Speaker 3: Plus it's on track to be the biggest IPO of 335 00:16:51,280 --> 00:16:54,520 Speaker 3: the year soft bank backed ARM when it's wild. We 336 00:16:54,640 --> 00:16:58,600 Speaker 3: understand a whole roster of underwriters for its initial public offering, 337 00:16:58,880 --> 00:17:01,760 Speaker 3: twenty eight banks and all for the deal. Now the 338 00:17:01,760 --> 00:17:03,880 Speaker 3: company is aiming to be valued in a listening, we 339 00:17:03,960 --> 00:17:07,160 Speaker 3: understand to be well sixty to seventy billion dollar valuation. 340 00:17:07,680 --> 00:17:09,800 Speaker 3: Let's stick into it. Leanna Baker is the person who 341 00:17:09,840 --> 00:17:13,119 Speaker 3: knows so much about this potential listing here at Bloomberg 342 00:17:13,240 --> 00:17:17,359 Speaker 3: and just tell us why twenty eight banks are needed 343 00:17:17,480 --> 00:17:19,639 Speaker 3: in all their various tiers of importance. 344 00:17:20,600 --> 00:17:24,760 Speaker 8: So it sounds like it's too many banks, but remember 345 00:17:24,960 --> 00:17:28,160 Speaker 8: that Meta or Facebook when they went public many years ago, 346 00:17:28,200 --> 00:17:31,679 Speaker 8: they had about thirty five underwriters. So when you have 347 00:17:32,640 --> 00:17:35,960 Speaker 8: a large amount of shares that you need to get 348 00:17:35,960 --> 00:17:39,159 Speaker 8: out to the market, it helps to have different banks 349 00:17:39,160 --> 00:17:42,520 Speaker 8: to syndicate how the shares to clients. ARM could raise 350 00:17:42,600 --> 00:17:44,920 Speaker 8: several billion dollars. It's supposed to be in the top 351 00:17:44,960 --> 00:17:48,600 Speaker 8: five IPOs in tech of all time, so they're going 352 00:17:48,680 --> 00:17:51,240 Speaker 8: to need all the banks to get to work. However, 353 00:17:51,400 --> 00:17:55,080 Speaker 8: it could also be a relationship building exercise for ARM. 354 00:17:55,440 --> 00:17:57,760 Speaker 8: They want to keep good relationships with all the banks, 355 00:17:57,760 --> 00:17:59,840 Speaker 8: and that's why you'll see you know, the who's who 356 00:17:59,840 --> 00:18:00,760 Speaker 8: are Wall Street on. 357 00:18:00,720 --> 00:18:03,720 Speaker 3: This one, Sunny Is and international in that nature of course, 358 00:18:03,760 --> 00:18:06,040 Speaker 3: because they want an international investor base. I mean, I'm 359 00:18:06,080 --> 00:18:08,240 Speaker 3: interested as to basically how they're going to be eyeing 360 00:18:08,240 --> 00:18:11,600 Speaker 3: this current market volatility and whether that really matters when 361 00:18:11,600 --> 00:18:13,880 Speaker 3: they're trying to be getting this pretty hefty valuation. 362 00:18:15,000 --> 00:18:18,040 Speaker 8: There's certainly been a dead IPO market and there's a 363 00:18:18,040 --> 00:18:20,600 Speaker 8: lot of hopes that this will open things up. We 364 00:18:20,680 --> 00:18:24,439 Speaker 8: also reported yesterday that instacart is looking to go public 365 00:18:24,480 --> 00:18:27,439 Speaker 8: in September, so it feels like ARM is kind of 366 00:18:27,480 --> 00:18:30,880 Speaker 8: warming things up for everyone. Instacar won't be as big 367 00:18:30,920 --> 00:18:34,600 Speaker 8: of an IPO as ARM and doesn't have the global implications, 368 00:18:34,760 --> 00:18:38,520 Speaker 8: but there's still others to look forward to because it's 369 00:18:38,600 --> 00:18:41,879 Speaker 8: just really has been such a dreary year for IPOs 370 00:18:41,960 --> 00:18:44,960 Speaker 8: and the bankers working on these kind of transactions. 371 00:18:45,000 --> 00:18:47,600 Speaker 3: What is it only about fourteen billion IPOs? This see 372 00:18:47,600 --> 00:18:49,879 Speaker 3: a competitives like two hundred and forty back in twenty 373 00:18:49,920 --> 00:18:52,680 Speaker 3: twenty one. Leanna Baker, we thank you so much for 374 00:18:53,000 --> 00:18:55,439 Speaker 3: all the inside track. We wait with baited breath on 375 00:18:55,520 --> 00:18:58,840 Speaker 3: the arm didn't well intricacies and we also do for instacart. 376 00:18:58,840 --> 00:19:00,520 Speaker 3: That was a great scoot. We just had a talking 377 00:19:00,520 --> 00:19:03,240 Speaker 3: about it. How it's planning for an initial public offering 378 00:19:03,240 --> 00:19:06,120 Speaker 3: as soon as September. All according to sources, company could 379 00:19:06,160 --> 00:19:08,479 Speaker 3: publicly file its plans with the SEC as soon as 380 00:19:08,520 --> 00:19:09,080 Speaker 3: next week. 381 00:19:09,359 --> 00:19:09,479 Speaker 9: Now. 382 00:19:09,520 --> 00:19:13,160 Speaker 3: Inscott previously considered a direct listing remember which were made 383 00:19:13,320 --> 00:19:15,960 Speaker 3: cool where they likes to Spotify, But now it's understanding 384 00:19:15,960 --> 00:19:18,880 Speaker 3: that it plans to do a traditional IPO on the Nasdaq. 385 00:19:19,280 --> 00:19:30,600 Speaker 3: Representative for instacrat declined to comment. Welcome back to Blomberg Technology. 386 00:19:30,600 --> 00:19:33,320 Speaker 3: I'm Caroen heard in New York. Let's talk about more cybersecurity, 387 00:19:33,359 --> 00:19:35,360 Speaker 3: because of course Palo al To Networks is one of them. 388 00:19:35,640 --> 00:19:38,240 Speaker 3: There's a relatively new kid on the blocks as twenty 389 00:19:38,240 --> 00:19:41,919 Speaker 3: eighteen Abnormal Security, but as an AI native cybersecurity company 390 00:19:42,080 --> 00:19:46,360 Speaker 3: protects organizations from whole host of email attacks, business email compromise, 391 00:19:46,480 --> 00:19:50,000 Speaker 3: vendor fraud, malware and credential fishing. And pleased to say 392 00:19:50,000 --> 00:19:53,200 Speaker 3: that we can speak with Evan Riser, he's Abnormal Security CEO, 393 00:19:53,440 --> 00:19:56,720 Speaker 3: to discuss well, how you're using the joyous world degenerative 394 00:19:56,720 --> 00:20:00,560 Speaker 3: AI within cybersecurity, how cyber attackers are using it? But first, like, 395 00:20:01,200 --> 00:20:04,800 Speaker 3: how are we seeing risks dial up because of the 396 00:20:04,840 --> 00:20:06,600 Speaker 3: new large language model era we're in. 397 00:20:07,800 --> 00:20:09,160 Speaker 10: Well, first of all, thank you, thank you so much 398 00:20:09,160 --> 00:20:11,000 Speaker 10: for having me. I think AI is really exciting because 399 00:20:11,000 --> 00:20:12,640 Speaker 10: it is going to have a profound effect on the way 400 00:20:12,640 --> 00:20:16,119 Speaker 10: everyone uses technology. Unfortunately, criminals are also very excited to 401 00:20:16,200 --> 00:20:18,560 Speaker 10: use this technology. There's really kind of three things we 402 00:20:18,600 --> 00:20:21,000 Speaker 10: see criminals using to take a vaage jennerit to AI. 403 00:20:21,359 --> 00:20:24,080 Speaker 10: So one is that even petty criminals that they don't 404 00:20:24,080 --> 00:20:27,280 Speaker 10: have cybersecurity skills, they can now use things like chat 405 00:20:27,280 --> 00:20:30,480 Speaker 10: GPT to become very proficient in kind of technology language 406 00:20:30,480 --> 00:20:32,160 Speaker 10: skills to help them latch phishing attacks. 407 00:20:32,400 --> 00:20:32,800 Speaker 1: We see the. 408 00:20:32,840 --> 00:20:35,679 Speaker 10: Automation abilities of some of these generative models, a lot 409 00:20:35,680 --> 00:20:38,040 Speaker 10: of people who have more attacks, maybe most scary of 410 00:20:38,040 --> 00:20:40,760 Speaker 10: all these models, a lot of people to create very 411 00:20:41,119 --> 00:20:44,520 Speaker 10: sophisticated nation state level attacks, even by the petty criminals. 412 00:20:44,560 --> 00:20:45,720 Speaker 10: So I think it's going to be a brave new 413 00:20:45,720 --> 00:20:46,680 Speaker 10: world for cybersecurity. 414 00:20:47,000 --> 00:20:48,919 Speaker 3: I mean, of course, you were founded in twenty eighteen 415 00:20:49,000 --> 00:20:52,280 Speaker 3: before the whole fear are around chat GPT and they're like, 416 00:20:52,440 --> 00:20:56,600 Speaker 3: but have you seen suddenly the scale, the wall, the 417 00:20:56,640 --> 00:20:59,239 Speaker 3: worry just dial up. Are you having to fight more 418 00:20:59,280 --> 00:21:00,000 Speaker 3: cyber attacks? 419 00:21:01,640 --> 00:21:02,120 Speaker 1: Absolutely? 420 00:21:02,119 --> 00:21:05,119 Speaker 10: I mean I think you know, overall cyber attacks contain 421 00:21:05,200 --> 00:21:07,760 Speaker 10: to go up into the right with generative AI and 422 00:21:07,800 --> 00:21:11,040 Speaker 10: especially easy easy to access tools like chat GBT. Now 423 00:21:11,080 --> 00:21:13,680 Speaker 10: criminals are using easy to send, more targeted phishing attacks, 424 00:21:13,920 --> 00:21:16,639 Speaker 10: more complex types of fraud, and more sophisticated forms of 425 00:21:16,680 --> 00:21:19,919 Speaker 10: social engineering. So I expect, you know, unfortunately it's going 426 00:21:19,960 --> 00:21:21,560 Speaker 10: to be go up into the right for a while, 427 00:21:21,640 --> 00:21:23,600 Speaker 10: but we're hoping to make that curve go down. 428 00:21:24,000 --> 00:21:26,160 Speaker 3: And I don't think that's a bad feel revenue run 429 00:21:26,240 --> 00:21:29,040 Speaker 3: rates right, because of course more demand is no bad 430 00:21:29,080 --> 00:21:30,600 Speaker 3: thing for you to a certain extent. Evan, can you 431 00:21:30,640 --> 00:21:32,399 Speaker 3: talk us through some of the milestones you've been heading 432 00:21:32,760 --> 00:21:34,560 Speaker 3: and how you're taking that to the market at the 433 00:21:34,560 --> 00:21:36,800 Speaker 3: moment in terms of potentially even fundraising. 434 00:21:38,600 --> 00:21:40,600 Speaker 10: Yes, So I'm an all security you know, we're only 435 00:21:40,600 --> 00:21:41,639 Speaker 10: about a five year company. 436 00:21:41,640 --> 00:21:43,000 Speaker 1: We launch our product four years ago. 437 00:21:43,240 --> 00:21:45,200 Speaker 10: We announced this week we hit one hundred million dollars 438 00:21:45,200 --> 00:21:48,200 Speaker 10: in recurring revenue, so we've had a lot of success. 439 00:21:48,480 --> 00:21:50,720 Speaker 10: I really actually that success to the importance of kind 440 00:21:50,720 --> 00:21:53,840 Speaker 10: of email security as our first product. Email security is 441 00:21:53,880 --> 00:21:56,359 Speaker 10: the number one cause of financial loss, number one cybercrime, 442 00:21:56,600 --> 00:21:59,160 Speaker 10: biggest attack vector, So I think that speaks to both 443 00:21:59,200 --> 00:22:02,400 Speaker 10: the opportunity unity we have to protect customers, but also 444 00:22:02,400 --> 00:22:04,639 Speaker 10: the powers of the AA technology we use internally to 445 00:22:04,680 --> 00:22:07,440 Speaker 10: go help stop things like phishing, fraud, and social engineering. 446 00:22:08,040 --> 00:22:11,520 Speaker 3: What's interesting is your relationship with CrowdStrike, for example, and 447 00:22:11,560 --> 00:22:13,760 Speaker 3: the fact that you've sort of raised money from them 448 00:22:13,840 --> 00:22:17,600 Speaker 3: and got a clear partner going forward. What do you 449 00:22:17,640 --> 00:22:21,000 Speaker 3: see your future as evolving as with Abnormal Security. Will 450 00:22:21,040 --> 00:22:23,399 Speaker 3: you remain an independent company and we fold into another. 451 00:22:24,720 --> 00:22:26,719 Speaker 10: I know we definitely build a company from day one 452 00:22:26,760 --> 00:22:28,960 Speaker 10: to be independent company. We do expect an IPO in 453 00:22:28,960 --> 00:22:31,760 Speaker 10: the future, but the exact time of that is not 454 00:22:31,800 --> 00:22:34,399 Speaker 10: super important to us. We're probably focused on just helping 455 00:22:34,400 --> 00:22:36,199 Speaker 10: our customers and really taking advantage of some of these 456 00:22:36,280 --> 00:22:37,760 Speaker 10: new AI technologies to. 457 00:22:37,720 --> 00:22:39,400 Speaker 1: Stop the next duration of cyber attacks. 458 00:22:39,600 --> 00:22:41,639 Speaker 10: And we're very fortunate to work with great customers like 459 00:22:41,760 --> 00:22:45,000 Speaker 10: our great partners like CrowdStrike that really share our our 460 00:22:45,320 --> 00:22:49,480 Speaker 10: mission and values around protecting customers and building deep platforms 461 00:22:49,520 --> 00:22:51,159 Speaker 10: to integrate cleaning together to. 462 00:22:51,160 --> 00:22:52,280 Speaker 1: Create the best customer outcomes. 463 00:22:52,359 --> 00:22:55,320 Speaker 10: So very fortunate to all of our employees, partners, and 464 00:22:55,359 --> 00:22:56,960 Speaker 10: customers that help us get to where we are today. 465 00:22:57,800 --> 00:23:00,720 Speaker 3: Where are you in terms of global demand? What who 466 00:23:00,760 --> 00:23:02,560 Speaker 3: are your key customers? For example? 467 00:23:03,640 --> 00:23:04,600 Speaker 1: Yeah, that's a great question. 468 00:23:04,640 --> 00:23:07,680 Speaker 10: We're primarily focused in North America today, we will be 469 00:23:07,720 --> 00:23:10,239 Speaker 10: expanding a lot more internationally. And even though we've only 470 00:23:10,280 --> 00:23:11,919 Speaker 10: been in the market for about four years, today we 471 00:23:11,960 --> 00:23:14,359 Speaker 10: protect more than twelve percent of Fortune five hundred. We 472 00:23:14,400 --> 00:23:17,160 Speaker 10: expect that to grow substantial over the next couple of years. 473 00:23:17,680 --> 00:23:20,600 Speaker 10: And really we see email security and more generally cloud 474 00:23:20,640 --> 00:23:23,560 Speaker 10: security a global problem. So our ambitions to go protect 475 00:23:23,800 --> 00:23:25,480 Speaker 10: every organization, every person in the world. 476 00:23:26,280 --> 00:23:30,080 Speaker 3: You an AI optimist, you worried? I mean it feels 477 00:23:30,080 --> 00:23:32,120 Speaker 3: an age and eternity. It goes since all these letters 478 00:23:32,160 --> 00:23:35,200 Speaker 3: were being written about existential issues to do with AI. 479 00:23:35,359 --> 00:23:36,879 Speaker 3: But where do you sit on that? 480 00:23:38,240 --> 00:23:38,320 Speaker 1: So? 481 00:23:38,600 --> 00:23:40,359 Speaker 10: I think, you know, AI is a tool, and like 482 00:23:40,400 --> 00:23:42,520 Speaker 10: all tools, can be used for good and bad. I 483 00:23:42,560 --> 00:23:44,120 Speaker 10: think in the grand scheme of things, you know, AA 484 00:23:44,160 --> 00:23:45,520 Speaker 10: is going to have a huge impact in the world, 485 00:23:45,800 --> 00:23:48,280 Speaker 10: and I'm very bullish in the long term prospects. I 486 00:23:48,280 --> 00:23:49,680 Speaker 10: do think the next year or two are going to 487 00:23:49,720 --> 00:23:51,760 Speaker 10: be a little bit scary, especially if criminals get access 488 00:23:51,760 --> 00:23:54,600 Speaker 10: to only generative AI tools. In the last two months, 489 00:23:54,600 --> 00:23:58,240 Speaker 10: we've seen more sophistic attacks coming from generative AI, phishing 490 00:23:58,240 --> 00:24:00,960 Speaker 10: and social engineering we've ever seen the last five years. 491 00:24:01,200 --> 00:24:03,119 Speaker 10: So I think the next years are a little bit scary, 492 00:24:03,200 --> 00:24:05,720 Speaker 10: but I'm very bush long term the opportunity for AI 493 00:24:05,880 --> 00:24:09,560 Speaker 10: to not just help customers, help our customers protective organizations 494 00:24:09,840 --> 00:24:12,119 Speaker 10: also have a transformati effect for how we use technology. 495 00:24:13,440 --> 00:24:17,000 Speaker 3: Ultimately, are you seeing more interest from vcs. I know 496 00:24:17,040 --> 00:24:19,680 Speaker 3: you're saying you're eventually eyeing an IPO, and you only 497 00:24:19,720 --> 00:24:23,720 Speaker 3: did a series B what s receive and relatively recently, 498 00:24:23,760 --> 00:24:26,439 Speaker 3: but are you looking at opportunities as everyone wants to 499 00:24:26,440 --> 00:24:29,520 Speaker 3: be allocating some of their funds to AI winners. 500 00:24:30,720 --> 00:24:32,919 Speaker 10: I think there's no short of interest in companies that 501 00:24:32,960 --> 00:24:36,040 Speaker 10: are really AI native focusing on real customer problems at 502 00:24:36,080 --> 00:24:37,200 Speaker 10: the kind of one hundred million. 503 00:24:36,920 --> 00:24:39,160 Speaker 1: Dollars plus air our scale, so. 504 00:24:39,080 --> 00:24:42,520 Speaker 10: There's no shorter interest. But we operate a very sustainable, 505 00:24:42,600 --> 00:24:44,919 Speaker 10: durable business. We have no need to raise any more money, 506 00:24:45,440 --> 00:24:47,520 Speaker 10: so we're right now. We're just focused on expanding both 507 00:24:47,560 --> 00:24:50,440 Speaker 10: our technology platform and helping GO protect more customers around 508 00:24:50,440 --> 00:24:50,760 Speaker 10: the world. 509 00:24:51,520 --> 00:24:55,959 Speaker 3: You're seasoned technologist and been part of big teams. I've 510 00:24:56,000 --> 00:24:59,000 Speaker 3: got to ask you. You were pretty significant within Twitter 511 00:24:59,119 --> 00:25:01,800 Speaker 3: at one point, really thinking about how its own machine 512 00:25:01,880 --> 00:25:04,520 Speaker 3: learning was working within the advertising unit of Twitter. What 513 00:25:04,520 --> 00:25:06,840 Speaker 3: do you think of X Do you use it? How 514 00:25:06,880 --> 00:25:08,680 Speaker 3: are you feeling about the development of that company? 515 00:25:09,840 --> 00:25:12,200 Speaker 10: Yeah, I don't know too much about it. I primarily 516 00:25:12,240 --> 00:25:15,440 Speaker 10: focus now on cybersecurity. My background is in machine learning 517 00:25:15,440 --> 00:25:18,240 Speaker 10: and AI, coming from the ads world, but I've lost 518 00:25:18,280 --> 00:25:19,960 Speaker 10: a little bit of touch right today, we're taking some 519 00:25:20,000 --> 00:25:23,720 Speaker 10: of the same core technologies around understanding the behavior and 520 00:25:23,800 --> 00:25:27,320 Speaker 10: interests and kind of the way people respond to different interactions, 521 00:25:27,400 --> 00:25:30,560 Speaker 10: right and applying that to cybersecurity. So from a technology perspective, 522 00:25:30,560 --> 00:25:33,480 Speaker 10: we're very similar, but yeah, different different use case and 523 00:25:33,520 --> 00:25:36,240 Speaker 10: I'm really excited to start using some of the technologies 524 00:25:36,240 --> 00:25:38,320 Speaker 10: that we built in ads that were original designed to 525 00:25:38,320 --> 00:25:40,800 Speaker 10: get people to click on ads, to now use for 526 00:25:40,840 --> 00:25:43,080 Speaker 10: a much more worthy cause, which is helping go stop crime. 527 00:25:43,640 --> 00:25:50,399 Speaker 3: Well, you dodged it. Evan Normal Security CEO sticking very 528 00:25:50,480 --> 00:25:53,119 Speaker 3: much with his themous cybersecurity and his own company, and 529 00:25:53,160 --> 00:25:53,919 Speaker 3: we thank him for that. 530 00:26:01,560 --> 00:26:04,200 Speaker 11: We're in San Francisco and this is the world's first 531 00:26:04,240 --> 00:26:08,320 Speaker 11: fully anaerobic bacteria factory. It makes these cutting edge life 532 00:26:08,320 --> 00:26:11,560 Speaker 11: therapeutics or probiotics to you and me. Pendulum is the 533 00:26:11,600 --> 00:26:15,240 Speaker 11: metabolic health startup backed by VCS with Halle Berry on 534 00:26:15,280 --> 00:26:17,400 Speaker 11: the team. It chose sf because it was the best 535 00:26:17,400 --> 00:26:20,600 Speaker 11: city to build a high tech bacteria factory that needs 536 00:26:20,680 --> 00:26:24,320 Speaker 11: a one hundred percent oxygen free environment. His how it works. 537 00:26:24,480 --> 00:26:27,879 Speaker 11: Step one, take a frozen sample of your desired bacteria. 538 00:26:28,240 --> 00:26:31,240 Speaker 11: Then swab an agar dish with your sample and start 539 00:26:31,280 --> 00:26:35,200 Speaker 11: to culture. A specific colony of bacteria is picked, inoculated, 540 00:26:35,480 --> 00:26:38,119 Speaker 11: and the density is scaled up. At each stage, the 541 00:26:38,160 --> 00:26:42,200 Speaker 11: samples tested for contaminants. In anaerobic fermentation, the bacteria still 542 00:26:42,200 --> 00:26:42,760 Speaker 11: needs food. 543 00:26:43,080 --> 00:26:43,800 Speaker 1: This is the food. 544 00:26:43,840 --> 00:26:47,240 Speaker 11: It's blended with special water from this machine in a 545 00:26:47,280 --> 00:26:50,639 Speaker 11: super high tech vacuum bag and inert gases are added 546 00:26:50,800 --> 00:26:54,720 Speaker 11: for the perfect environment. Later, the bacteria starter is combined 547 00:26:54,720 --> 00:26:57,359 Speaker 11: with the food solution and allowed to ferment. Once at 548 00:26:57,359 --> 00:26:59,560 Speaker 11: the right density, the bacteria is drawn out to this 549 00:26:59,600 --> 00:27:03,040 Speaker 11: machine collected like a slurry or like a bacteria milkshake. 550 00:27:03,160 --> 00:27:05,480 Speaker 11: Then they pour it in this cake pan trade thing. 551 00:27:05,640 --> 00:27:08,920 Speaker 11: The paste is blast chilled in nitrogen, tested again, freeze dried, 552 00:27:09,080 --> 00:27:12,600 Speaker 11: and then stored in superfridgids. Finally, the freeze dried bacteria 553 00:27:12,680 --> 00:27:15,119 Speaker 11: cake is ground to a powder. It's shipped off and 554 00:27:15,160 --> 00:27:17,880 Speaker 11: put into those little capsules and bottled. 555 00:27:18,880 --> 00:27:21,280 Speaker 3: And you just know how much he loved geeking out 556 00:27:21,359 --> 00:27:24,520 Speaker 3: over that one. Meanwhile, let's stick with healthcare. In fact, 557 00:27:24,560 --> 00:27:27,760 Speaker 3: we want to talk about how advancements from artificial intelligence 558 00:27:27,800 --> 00:27:30,440 Speaker 3: technology is going to help revolutionize the space that wasn't 559 00:27:30,480 --> 00:27:34,080 Speaker 3: included in US clinical trials legally to nineteen ninety three. 560 00:27:35,320 --> 00:27:38,879 Speaker 3: It was women's health, it was minorities health. Let's talk 561 00:27:38,920 --> 00:27:41,159 Speaker 3: about all of this. Prianka Chane, the CEO and co 562 00:27:41,200 --> 00:27:43,800 Speaker 3: founder of Evy. It's a startup looking to leverage overlooked 563 00:27:43,840 --> 00:27:47,280 Speaker 3: biomarkers in the female body. You're back by general catalysts, 564 00:27:47,280 --> 00:27:50,000 Speaker 3: by BBG Ventures, G nine Ventures, a whole host of 565 00:27:50,080 --> 00:27:54,120 Speaker 3: big vcs. And how much you're seeing this whole wave 566 00:27:54,160 --> 00:27:57,800 Speaker 3: of interest inficial intelligence benefit You'll run rate at the 567 00:27:57,800 --> 00:27:58,200 Speaker 3: moment with. 568 00:27:58,320 --> 00:27:59,960 Speaker 5: V Yeah, definitely, it's a great question. 569 00:28:00,080 --> 00:28:02,919 Speaker 12: I mean, we started Evy because of the proliferation of 570 00:28:02,960 --> 00:28:05,720 Speaker 12: AI in healthcare right where you're seeing that so many 571 00:28:05,720 --> 00:28:08,840 Speaker 12: companies are finally leveraging data to make the healthcare experience 572 00:28:08,920 --> 00:28:12,080 Speaker 12: more effective. But what we've realized is that given what 573 00:28:12,119 --> 00:28:14,520 Speaker 12: you just said that women weren't even in clinical research 574 00:28:14,600 --> 00:28:18,080 Speaker 12: until nineteen ninety three, there's so much missing data on 575 00:28:18,119 --> 00:28:20,080 Speaker 12: the female body, right, And if we're going to train 576 00:28:20,160 --> 00:28:22,720 Speaker 12: all these algorithms to help us understand what's going wrong 577 00:28:22,760 --> 00:28:25,360 Speaker 12: at the doctor's office or what's most likely to help us, 578 00:28:25,520 --> 00:28:27,600 Speaker 12: but it didn't include anyone who looked like you and I, 579 00:28:27,640 --> 00:28:29,560 Speaker 12: who's to say that's going to be effective on us? 580 00:28:29,800 --> 00:28:31,680 Speaker 12: And so we really set out to say, what would 581 00:28:31,720 --> 00:28:34,720 Speaker 12: it look like to build a precision healthcare platform that 582 00:28:34,800 --> 00:28:36,639 Speaker 12: was actually centered on the female body? What are the 583 00:28:36,720 --> 00:28:39,560 Speaker 12: data sets that are missing in our understanding and ability 584 00:28:39,600 --> 00:28:39,960 Speaker 12: to do that? 585 00:28:40,560 --> 00:28:43,920 Speaker 3: So always do you cool yourself a healthcare company? Well, 586 00:28:44,080 --> 00:28:46,000 Speaker 3: you must have to go around getting all the samples 587 00:28:46,040 --> 00:28:47,840 Speaker 3: in the data, I mean, now just one sols that 588 00:28:47,920 --> 00:28:49,840 Speaker 3: to be able to then run your AI models on them. 589 00:28:50,040 --> 00:28:50,880 Speaker 5: Yeah, great question. 590 00:28:51,120 --> 00:28:54,680 Speaker 12: So we actually run a precision female healthcare service and 591 00:28:54,720 --> 00:28:57,680 Speaker 12: we're specifically focused on the vaginal microbiome because it's actually 592 00:28:57,720 --> 00:28:59,800 Speaker 12: the leading reason that women go to the doctor in 593 00:28:59,800 --> 00:29:02,960 Speaker 12: the and when we go to the doctor, we're more 594 00:29:03,040 --> 00:29:05,880 Speaker 12: likely to be misdiagnosed than correctly diagnosed, and we're more 595 00:29:05,960 --> 00:29:07,960 Speaker 12: likely not to get better than we are to get better. 596 00:29:08,200 --> 00:29:10,920 Speaker 12: And so we built a platform finally leveraging technology and 597 00:29:11,040 --> 00:29:14,160 Speaker 12: data to better characterize what is actually going on for somebody. 598 00:29:14,240 --> 00:29:16,880 Speaker 12: So we have first of its kind precision testing, and 599 00:29:16,880 --> 00:29:20,280 Speaker 12: then we actually use data technology AI to understand what's 600 00:29:20,360 --> 00:29:23,080 Speaker 12: most likely to help that specific person, because it turns 601 00:29:23,080 --> 00:29:25,280 Speaker 12: out obviously no two people are the same, no two 602 00:29:25,320 --> 00:29:28,680 Speaker 12: women are the same. And so by actually delivering much 603 00:29:28,680 --> 00:29:31,880 Speaker 12: better care that's powered by data, we're actually gathering the 604 00:29:31,960 --> 00:29:33,840 Speaker 12: data on the back end to be able to enable 605 00:29:34,040 --> 00:29:35,840 Speaker 12: a much better future of women's healthcare. 606 00:29:36,600 --> 00:29:38,960 Speaker 3: I'm kind of still stuck on the fat that more 607 00:29:39,000 --> 00:29:44,840 Speaker 3: women get worse than get better. Yes, fair, How who 608 00:29:44,920 --> 00:29:47,200 Speaker 3: is there for adopting this? Who are you managing to 609 00:29:47,200 --> 00:29:49,360 Speaker 3: get on board from the healthcare community to want to 610 00:29:49,360 --> 00:29:51,840 Speaker 3: start to use this within not just trials, but in 611 00:29:51,880 --> 00:29:52,760 Speaker 3: real world application. 612 00:29:52,840 --> 00:29:54,680 Speaker 5: Yeah, such a good question. So I think the first 613 00:29:54,720 --> 00:29:55,480 Speaker 5: person who's. 614 00:29:55,280 --> 00:29:57,760 Speaker 12: Always the most motivated to help things get better is 615 00:29:57,760 --> 00:29:59,680 Speaker 12: the patient, is the person who's suffering. And so what 616 00:29:59,720 --> 00:30:02,560 Speaker 12: we is that there's so many women who are suffering 617 00:30:02,560 --> 00:30:05,400 Speaker 12: with symptoms, not getting answers, not getting better, and they're 618 00:30:05,480 --> 00:30:08,240 Speaker 12: extremely excited to have new data on their own bodies, 619 00:30:08,320 --> 00:30:10,520 Speaker 12: right they're trying to understand and I think we're seeing 620 00:30:10,520 --> 00:30:13,280 Speaker 12: this in many industries right where consumers are finally being 621 00:30:13,320 --> 00:30:15,400 Speaker 12: able to take control of their health because of the 622 00:30:15,440 --> 00:30:20,000 Speaker 12: proliferation of technology that's made it more affordable and possible 623 00:30:20,040 --> 00:30:22,440 Speaker 12: to even happen. And so we find that first it 624 00:30:22,480 --> 00:30:24,240 Speaker 12: came from the women, the women who were like, we 625 00:30:24,360 --> 00:30:27,040 Speaker 12: demand better answers. We're going to buy this with our 626 00:30:27,080 --> 00:30:29,240 Speaker 12: own money because we want to know what's going on. 627 00:30:29,720 --> 00:30:32,200 Speaker 12: And what we find is actually that ninety eight percent 628 00:30:32,280 --> 00:30:35,600 Speaker 12: of our members actually choose to have their data become 629 00:30:35,640 --> 00:30:38,000 Speaker 12: part of research, and I think that speaks so deeply 630 00:30:38,040 --> 00:30:40,640 Speaker 12: to the frustration that we've felt in the healthcare system 631 00:30:40,920 --> 00:30:44,080 Speaker 12: and the desire for our daughters to have a better experience. 632 00:30:44,320 --> 00:30:47,000 Speaker 12: So we started very much with women, and now finally 633 00:30:47,040 --> 00:30:49,000 Speaker 12: a couple of years later, we're really seeing a lot 634 00:30:49,000 --> 00:30:53,080 Speaker 12: of excitement from doctors, whether it's PCPs, obgans, doctors who 635 00:30:53,120 --> 00:30:56,040 Speaker 12: frankly are also frustrated that they don't have better tools 636 00:30:56,080 --> 00:30:58,160 Speaker 12: or better information to help their patients. 637 00:30:58,360 --> 00:31:02,120 Speaker 3: So when people are saying, well, the existential risks the 638 00:31:02,120 --> 00:31:04,960 Speaker 3: cyber risks, the risk concerns around AI, but then we 639 00:31:05,120 --> 00:31:06,880 Speaker 3: try and look for really where we're going to see 640 00:31:07,000 --> 00:31:10,200 Speaker 3: dramatic change. You'd say healthcare is sort of just and 641 00:31:10,240 --> 00:31:12,200 Speaker 3: foremost out there in terms of industries that can be 642 00:31:12,280 --> 00:31:12,960 Speaker 3: disrupted by this. 643 00:31:13,120 --> 00:31:15,680 Speaker 12: I think absolutely healthcare will be disrupted by AI in 644 00:31:15,720 --> 00:31:17,760 Speaker 12: so many ways, not only in the ways that we 645 00:31:17,880 --> 00:31:20,120 Speaker 12: better diagnose people, that we help them come up with 646 00:31:20,160 --> 00:31:21,800 Speaker 12: better treatments. I think, at the end of the day, 647 00:31:21,880 --> 00:31:25,200 Speaker 12: how can you possibly in the human brain, understand every 648 00:31:25,240 --> 00:31:27,520 Speaker 12: single measurement of what's going right and wrong in your body, 649 00:31:27,840 --> 00:31:30,640 Speaker 12: understand what disease that's most likely to be, and then 650 00:31:30,720 --> 00:31:32,320 Speaker 12: how you're going to fix it. I mean, it's an 651 00:31:32,320 --> 00:31:35,040 Speaker 12: impossible to ask for the human brain, and given the 652 00:31:35,040 --> 00:31:38,080 Speaker 12: fact that every person is so so different. But I 653 00:31:38,080 --> 00:31:40,680 Speaker 12: also think it's really important that it's done carefully and 654 00:31:40,720 --> 00:31:43,280 Speaker 12: done well right and we think about who what data 655 00:31:43,320 --> 00:31:45,600 Speaker 12: are we using to train this algorithm? Does it include 656 00:31:45,640 --> 00:31:47,760 Speaker 12: the people that we're then going to use this algorithm on? 657 00:31:48,200 --> 00:31:49,960 Speaker 12: And so I think it's very important that we put 658 00:31:50,040 --> 00:31:52,640 Speaker 12: up the right guardrails, we ask the right questions, and 659 00:31:52,680 --> 00:31:55,240 Speaker 12: that we don't just assume that AI that's well designed 660 00:31:55,560 --> 00:31:57,560 Speaker 12: means it's going to be well designed for health care, 661 00:31:57,600 --> 00:31:58,880 Speaker 12: and I think health care needs. 662 00:31:58,680 --> 00:32:01,800 Speaker 5: Its own set of rules and thoughts as. 663 00:32:01,600 --> 00:32:04,840 Speaker 12: We bring this revolutionary technology into the space, so that 664 00:32:04,840 --> 00:32:07,080 Speaker 12: we don't actually widen the gap that already exists. 665 00:32:07,080 --> 00:32:09,880 Speaker 3: So who's thinking about that? I mean, already you've captured 666 00:32:09,920 --> 00:32:14,000 Speaker 3: the imaginations of vcs. You're clearly managing to get into 667 00:32:14,520 --> 00:32:16,920 Speaker 3: the world of healthcare. What about the administration? Are you 668 00:32:17,000 --> 00:32:19,640 Speaker 3: having discussions? I mean all people coming to some of 669 00:32:19,680 --> 00:32:22,400 Speaker 3: the startups and indeed the bigger companies within AI to 670 00:32:22,440 --> 00:32:24,880 Speaker 3: think about rules, god rails, safety. 671 00:32:24,960 --> 00:32:28,560 Speaker 12: Yeah, definitely. I think one the companies, hopefully this time around, 672 00:32:28,640 --> 00:32:30,240 Speaker 12: are being a little bit smarter about this. I know 673 00:32:30,280 --> 00:32:32,520 Speaker 12: it's something we think about all day every day. Maybe 674 00:32:32,520 --> 00:32:34,959 Speaker 12: that's because we're a female leadership team, but you know, 675 00:32:35,000 --> 00:32:37,600 Speaker 12: it could just be because we're a company started today. 676 00:32:38,280 --> 00:32:40,760 Speaker 12: I also think that we're seeing that regulatory bodies like 677 00:32:40,760 --> 00:32:44,320 Speaker 12: the FDA are looking. They're interested, they want to understand 678 00:32:44,440 --> 00:32:47,680 Speaker 12: what the new technologies are and how to set up guidelines. 679 00:32:47,720 --> 00:32:50,080 Speaker 12: They have a variety of working groups with companies, with 680 00:32:50,200 --> 00:32:53,600 Speaker 12: industry and also with academics and ethicis. So I have 681 00:32:53,760 --> 00:32:57,000 Speaker 12: hope that we will build the right guardrails, and that 682 00:32:57,200 --> 00:32:59,240 Speaker 12: when we do that, we will unlock an age of 683 00:32:59,400 --> 00:33:02,080 Speaker 12: precision better and where women finally get better. Right where 684 00:33:02,480 --> 00:33:05,520 Speaker 12: right now we're diagnosed on average four years later than 685 00:33:05,560 --> 00:33:08,760 Speaker 12: men across over seven hundred diseases. Right, but imagine if 686 00:33:08,760 --> 00:33:11,040 Speaker 12: we could actually look at the data in your body 687 00:33:11,080 --> 00:33:13,080 Speaker 12: and understand what's going on like that gives me. 688 00:33:13,120 --> 00:33:14,000 Speaker 5: A lot of hope. 689 00:33:14,800 --> 00:33:17,640 Speaker 3: Great storytelling, great interest in the company. And we thank 690 00:33:17,640 --> 00:33:19,320 Speaker 3: you so much for coming on, and thank you Rob. 691 00:33:19,360 --> 00:33:21,880 Speaker 3: We probably telling us slightly depressing story, but one that 692 00:33:21,960 --> 00:33:24,240 Speaker 3: might be changing pretty soon. Every co found or a 693 00:33:24,320 --> 00:33:27,520 Speaker 3: CEO Preanka Jane there. Meanwhile, coming up can open AI, 694 00:33:27,720 --> 00:33:30,760 Speaker 3: crack the code for AI to oversee content and moderation. 695 00:33:31,320 --> 00:33:33,640 Speaker 3: We'll discuss that one next. Meanwhile, let's just check out 696 00:33:33,680 --> 00:33:35,920 Speaker 3: the shares of Uber and Lyft. We had to break. 697 00:33:35,920 --> 00:33:37,719 Speaker 3: We want to look at the companies and they're being threatened, 698 00:33:38,360 --> 00:33:40,880 Speaker 3: perhaps to stop doing business in Minneapolis as after the 699 00:33:40,880 --> 00:33:43,280 Speaker 3: city council adopted a new rual Thursday that would set 700 00:33:43,280 --> 00:33:46,120 Speaker 3: a minimum wage for ride shared drivers. This is all 701 00:33:46,120 --> 00:33:48,720 Speaker 3: being reported by CNN Shares, though on the huh side 702 00:33:48,800 --> 00:34:01,280 Speaker 3: on the percentage point, this has been about technology. What 703 00:34:01,720 --> 00:34:04,920 Speaker 3: could content moderation look like if you use chat gipt. 704 00:34:05,240 --> 00:34:07,680 Speaker 3: While open ai is testing such systems and has invited 705 00:34:07,680 --> 00:34:11,000 Speaker 3: customers to also experiment with it, open ai says its 706 00:34:11,040 --> 00:34:14,200 Speaker 3: tools can help businesses performed six months of work just 707 00:34:14,239 --> 00:34:17,080 Speaker 3: a day or so. New mega technologies. Rachel Metz joins 708 00:34:17,120 --> 00:34:19,160 Speaker 3: us now because I was left a little bit head 709 00:34:19,160 --> 00:34:21,080 Speaker 3: scratching on this one because I thought a lot of 710 00:34:21,080 --> 00:34:25,040 Speaker 3: content moderation was already using AI. How is generated generative 711 00:34:25,080 --> 00:34:26,680 Speaker 3: AI changing the game of tool? 712 00:34:27,520 --> 00:34:31,360 Speaker 9: You are correct, A lot of content moderation already uses AI, 713 00:34:32,080 --> 00:34:35,760 Speaker 9: uses it in concert with humans. It's a really tough job, 714 00:34:36,160 --> 00:34:38,840 Speaker 9: and it is not a job that open ai is 715 00:34:38,880 --> 00:34:42,640 Speaker 9: saying turnover entirely to AI. But what they're thinking is. 716 00:34:42,600 --> 00:34:43,279 Speaker 3: A couple of things. 717 00:34:43,320 --> 00:34:45,880 Speaker 9: One is, they think it can help with content moderation, 718 00:34:46,280 --> 00:34:49,160 Speaker 9: but they also think that it could help companies like 719 00:34:49,239 --> 00:34:51,120 Speaker 9: apply policy changes much. 720 00:34:51,000 --> 00:34:52,680 Speaker 5: More quickly than they might in the. 721 00:34:52,640 --> 00:34:56,359 Speaker 9: Past, which is which is an interesting way of looking 722 00:34:56,440 --> 00:34:58,160 Speaker 9: at it. So this is this is what they're testing 723 00:34:58,160 --> 00:35:01,640 Speaker 9: out there, inviting other company these to try it out. 724 00:35:01,480 --> 00:35:04,760 Speaker 3: As well via their API. So well, it'll be interesting 725 00:35:04,800 --> 00:35:07,600 Speaker 3: to say, can you dig into the refining policy making 726 00:35:07,640 --> 00:35:11,200 Speaker 3: how does that happen using a generative chapel? 727 00:35:12,040 --> 00:35:15,040 Speaker 9: Sure, So, like what they're saying, for instance, is maybe 728 00:35:15,080 --> 00:35:20,440 Speaker 9: they would make a maybe they refined their content moderation policy, 729 00:35:20,760 --> 00:35:23,320 Speaker 9: and they want to make it even better to exclude 730 00:35:23,320 --> 00:35:27,160 Speaker 9: certain kinds of things, like different kinds of talk about 731 00:35:27,200 --> 00:35:29,600 Speaker 9: different kinds of crime on a social network. For instance, 732 00:35:29,640 --> 00:35:31,799 Speaker 9: like if someone's saying, can you how do I steal 733 00:35:31,840 --> 00:35:33,160 Speaker 9: a car? Or can you help me steal a car? 734 00:35:33,239 --> 00:35:37,040 Speaker 9: This was an example. They gave it a blog post. Initially, 735 00:35:37,360 --> 00:35:40,359 Speaker 9: an AI system might not be able to spot that 736 00:35:40,480 --> 00:35:45,239 Speaker 9: as objectionable content because it might say, well, it's not 737 00:35:45,280 --> 00:35:47,920 Speaker 9: a violent crime, and maybe they were talking about violent 738 00:35:48,000 --> 00:35:50,680 Speaker 9: crime as being not okay on the social network before. 739 00:35:50,719 --> 00:35:54,799 Speaker 9: So they could use GPT four to sort of help 740 00:35:55,040 --> 00:35:58,960 Speaker 9: retrain their system really quickly with like a set of 741 00:35:59,480 --> 00:36:02,799 Speaker 9: data that would make that would help make it clear 742 00:36:02,840 --> 00:36:06,560 Speaker 9: to the system that this is something that's objectionable, and 743 00:36:06,600 --> 00:36:07,440 Speaker 9: then they could roll. 744 00:36:07,280 --> 00:36:07,880 Speaker 3: That out faster. 745 00:36:08,080 --> 00:36:10,360 Speaker 9: The idea would be to roll out these changes faster 746 00:36:10,719 --> 00:36:11,719 Speaker 9: than they might otherwise. 747 00:36:12,120 --> 00:36:16,400 Speaker 3: How about helping those poor people whose basically job it 748 00:36:16,520 --> 00:36:19,840 Speaker 3: is to look at all this objectionable content and analyze 749 00:36:19,840 --> 00:36:22,840 Speaker 3: it is there any path towards not needing the human 750 00:36:22,840 --> 00:36:24,760 Speaker 3: eye that can be so affected by these things. 751 00:36:25,600 --> 00:36:28,480 Speaker 9: So one of the things about content moderation, it's so tricky. 752 00:36:29,360 --> 00:36:32,239 Speaker 9: Besides having to me, it's awful that people have to 753 00:36:32,280 --> 00:36:34,719 Speaker 9: look at really horrible images and that can be really 754 00:36:34,800 --> 00:36:37,520 Speaker 9: damaging to people. We know that, I mean, we can 755 00:36:37,640 --> 00:36:40,759 Speaker 9: use machines to offload some of that, or to at 756 00:36:40,840 --> 00:36:44,120 Speaker 9: least make a top level decision that can then be 757 00:36:44,160 --> 00:36:47,239 Speaker 9: passed on to a human to double check. So that's 758 00:36:47,280 --> 00:36:51,360 Speaker 9: something that's already being done since GPT four. I'm not 759 00:36:51,400 --> 00:36:53,799 Speaker 9: sure if that's going to be useful for this kind 760 00:36:53,840 --> 00:36:57,319 Speaker 9: of machine vision task at this time. I think they're 761 00:36:57,360 --> 00:37:03,399 Speaker 9: talking more about written stuff. But in general, it's really 762 00:37:03,440 --> 00:37:06,800 Speaker 9: hard to use machines to offload this stuff entirely because 763 00:37:06,800 --> 00:37:09,160 Speaker 9: a lot of it is dependent on context, whether it's 764 00:37:09,160 --> 00:37:12,040 Speaker 9: written or it's an image or a video. What is 765 00:37:12,880 --> 00:37:14,239 Speaker 9: that what looks bad. 766 00:37:14,000 --> 00:37:14,760 Speaker 3: To some person? 767 00:37:15,000 --> 00:37:17,440 Speaker 9: Or if I post something and I mean one thing, 768 00:37:17,760 --> 00:37:19,920 Speaker 9: somebody who is looking at it in another time, in 769 00:37:20,000 --> 00:37:23,320 Speaker 9: other place might not see like my sort of coded message. 770 00:37:23,320 --> 00:37:25,239 Speaker 9: Because people are pretty clever on the Internet, and they 771 00:37:25,239 --> 00:37:27,839 Speaker 9: don't always just say or post something. 772 00:37:27,640 --> 00:37:30,759 Speaker 3: Obviously horrible this might be just one example of why 773 00:37:30,880 --> 00:37:33,359 Speaker 3: some people would quite like AI to take their job, 774 00:37:33,480 --> 00:37:36,279 Speaker 3: but for now not quite able to play Bag technologies 775 00:37:36,360 --> 00:37:39,759 Speaker 3: Rachel Metz. Thank you so much. Meanwhile, that this does 776 00:37:39,760 --> 00:37:42,400 Speaker 3: it for this edition and this whole week of big technology. 777 00:37:42,440 --> 00:37:43,839 Speaker 3: But you do not want to forget to check out 778 00:37:43,880 --> 00:37:46,680 Speaker 3: a podcast, find it on the terminal, you know, online 779 00:37:46,719 --> 00:37:49,800 Speaker 3: on Apple, Spotify, iHeart, and of course in the meantime, 780 00:37:49,920 --> 00:37:52,759 Speaker 3: we wish you a very wonderful weekend from New York. 781 00:37:52,920 --> 00:37:59,520 Speaker 3: This is new bag technology. 782 00:38:00,120 --> 00:38:00,160 Speaker 8: Z