1 00:00:01,520 --> 00:00:06,800 Speaker 1: From Bahard where Innovation, Money and power Collie in Silicon Valley, NBN. 2 00:00:07,160 --> 00:00:11,360 Speaker 1: This is Bloomberg Technology with Caroline Hyde and ed Love Love. 3 00:00:25,960 --> 00:00:28,840 Speaker 2: I met Lovelow in San Francisco. Caroline Hyde is off today. 4 00:00:28,880 --> 00:00:32,600 Speaker 2: This is Bloomberg Technology coming up full coverage of the 5 00:00:32,680 --> 00:00:35,800 Speaker 2: second biggest bank collapse in US history. We'll discuss the 6 00:00:35,840 --> 00:00:40,200 Speaker 2: outlook for funding with Jojao a Millennia Capital. Plus another 7 00:00:40,240 --> 00:00:43,080 Speaker 2: big week for tech earnings ahead with AMD, Quilcom and 8 00:00:43,200 --> 00:00:46,640 Speaker 2: Apple all reporting. We'll get a preview of what's to come. 9 00:00:46,760 --> 00:00:50,720 Speaker 2: And soft banks chip design firm ARM filing confidentially for 10 00:00:50,800 --> 00:00:53,239 Speaker 2: an IPO in the US over the weekend. More in 11 00:00:53,280 --> 00:00:56,640 Speaker 2: the offering later this app There is just one story 12 00:00:56,680 --> 00:00:59,400 Speaker 2: that we're talking about, and that is JP Morgan stepping 13 00:00:59,440 --> 00:01:03,120 Speaker 2: in to acquire First Republic, taking on portions of its 14 00:01:03,160 --> 00:01:07,720 Speaker 2: loan book and deposit base in a deal negotiated with regulators. 15 00:01:07,760 --> 00:01:10,440 Speaker 2: You see JP Morgan high by two point seven percent. 16 00:01:10,520 --> 00:01:13,080 Speaker 2: Some kind of feel good at bank, the biggest bank 17 00:01:13,240 --> 00:01:15,479 Speaker 2: that's got even bigger. Let's get more details on First 18 00:01:15,480 --> 00:01:18,720 Speaker 2: Republic bringing Bloomberg Shnali Bassack, who's out in Los Angeles 19 00:01:18,720 --> 00:01:21,240 Speaker 2: at the Milking Conference. And I imagine this is the 20 00:01:21,280 --> 00:01:23,720 Speaker 2: topic of the day, Shinali. But bring us the details 21 00:01:23,920 --> 00:01:26,760 Speaker 2: of this JP Morgan deal to acquire First Republic. 22 00:01:26,880 --> 00:01:28,760 Speaker 3: It certainly is because, as you say, we are watching 23 00:01:28,800 --> 00:01:31,240 Speaker 3: the biggest US bank get bigger. As we know that 24 00:01:31,280 --> 00:01:34,640 Speaker 3: there were other bidders behind the scenes. We had reported 25 00:01:34,680 --> 00:01:39,080 Speaker 3: that Apollo and black Rock had supported, for example, PNC's 26 00:01:39,120 --> 00:01:41,680 Speaker 3: efforts to buy the company as well. Yet the reason 27 00:01:41,760 --> 00:01:44,480 Speaker 3: JP Morgan had emerged as the biggest bidder here and 28 00:01:44,520 --> 00:01:47,280 Speaker 3: the most successful is because, as we know it that 29 00:01:47,360 --> 00:01:50,600 Speaker 3: the cost of the deposed insurance fund for the fdiit 30 00:01:51,520 --> 00:01:54,760 Speaker 3: was the least under a JP Morgan acquisition. 31 00:01:55,200 --> 00:01:56,200 Speaker 4: Now, any other. 32 00:01:56,080 --> 00:01:59,080 Speaker 3: Politics that come out beyond this is up for debate. 33 00:01:59,320 --> 00:02:02,720 Speaker 3: But the question here is does this stem the banking crisis. 34 00:02:02,720 --> 00:02:05,360 Speaker 3: We have seen a lot of California lenders now go 35 00:02:05,560 --> 00:02:08,880 Speaker 3: through a series of struggles as we have been talking about, 36 00:02:09,000 --> 00:02:13,239 Speaker 3: have failed. This one now is another major deal and 37 00:02:13,360 --> 00:02:17,600 Speaker 3: it certainly gives JP Morgan and even more attract it 38 00:02:18,120 --> 00:02:21,880 Speaker 3: the base of customers high net worth individuals. Remember, when 39 00:02:21,880 --> 00:02:23,840 Speaker 3: we look at First Republic, there has been a lot 40 00:02:23,880 --> 00:02:25,919 Speaker 3: of bleeding already for the last couple of weeks and 41 00:02:26,000 --> 00:02:30,240 Speaker 3: months but this deal and an iconic franchise is certainly 42 00:02:30,320 --> 00:02:31,560 Speaker 3: a co for JP Morgan. 43 00:02:33,240 --> 00:02:35,840 Speaker 2: You go back to when SBB collapse in one of 44 00:02:35,880 --> 00:02:38,639 Speaker 2: the beneficiaries was JP Morgan right snapping up some of 45 00:02:38,639 --> 00:02:42,000 Speaker 2: the venture capital and startup customers or depositors. 46 00:02:42,720 --> 00:02:43,280 Speaker 4: You pose a. 47 00:02:43,280 --> 00:02:46,440 Speaker 2: Question there sheanali about whether this fixes the sort of 48 00:02:46,480 --> 00:02:49,320 Speaker 2: systemic risk? Are there any answers to that on the 49 00:02:49,320 --> 00:02:54,679 Speaker 2: ground yet? In milkn conference in LA. 50 00:02:53,520 --> 00:02:55,640 Speaker 3: Yeah, you can kind of put it in their two buckets. 51 00:02:55,639 --> 00:02:58,160 Speaker 3: Here you have a lot of banking titans that believe 52 00:02:58,480 --> 00:03:00,840 Speaker 3: that there could be some pain among a couple of 53 00:03:00,840 --> 00:03:04,280 Speaker 3: more regional lenders. We just talked to Julian Salisbury, for example, 54 00:03:04,400 --> 00:03:06,760 Speaker 3: the CIO over at Coleman, who believes it's almost death 55 00:03:06,800 --> 00:03:09,680 Speaker 3: by a million cuts, where you see sustained pressure on 56 00:03:09,720 --> 00:03:13,120 Speaker 3: the industry but not wholesale failures or systemic wrist. On 57 00:03:13,160 --> 00:03:15,400 Speaker 3: the other hand, you talk to the likes of a 58 00:03:15,480 --> 00:03:18,200 Speaker 3: Mark Rowan, the CEO of Apollo, which was behind that 59 00:03:18,280 --> 00:03:21,160 Speaker 3: P and C bid, and there are les there's a 60 00:03:21,200 --> 00:03:23,639 Speaker 3: second wave. Really, there's a lot of problems under the 61 00:03:23,720 --> 00:03:26,320 Speaker 3: surface when you look at other asset classes like commercial 62 00:03:26,360 --> 00:03:29,840 Speaker 3: real estate, so to where the pain will be felt 63 00:03:30,080 --> 00:03:34,320 Speaker 3: further is a huge open question ed and the fact 64 00:03:34,480 --> 00:03:37,320 Speaker 3: is is that there could still be more pain among 65 00:03:37,400 --> 00:03:39,600 Speaker 3: this industry as well as other parts of the market, 66 00:03:39,880 --> 00:03:42,240 Speaker 3: as we continue to see the pressure high interest rates 67 00:03:42,240 --> 00:03:43,440 Speaker 3: on the systems. 68 00:03:44,320 --> 00:03:46,800 Speaker 2: All right, Bloomboagtion, Ali Bassack out at Milking, go work 69 00:03:46,840 --> 00:03:47,360 Speaker 2: the corridors. 70 00:03:47,400 --> 00:03:48,520 Speaker 4: We'll see you later in the week. 71 00:03:48,720 --> 00:03:50,480 Speaker 2: Let's stick with his story and for more, let's bring 72 00:03:50,520 --> 00:03:53,840 Speaker 2: in Lenny a cap to investor Joe Chow. Joe, I 73 00:03:53,840 --> 00:03:57,760 Speaker 2: would frame you as an avench capitalist with a knowledge 74 00:03:57,760 --> 00:04:00,720 Speaker 2: base in fintech the banking sector, but you also have 75 00:04:00,800 --> 00:04:04,280 Speaker 2: your own exposure to First Republic. Can you just tell 76 00:04:04,280 --> 00:04:05,800 Speaker 2: our audience what that exposure is? 77 00:04:07,040 --> 00:04:09,280 Speaker 5: Yeah, So, as a VC fund, we have profolio companies 78 00:04:09,280 --> 00:04:11,760 Speaker 5: and our operations which run on both our Socing Value 79 00:04:11,760 --> 00:04:12,640 Speaker 5: Bank and for Republic. 80 00:04:14,480 --> 00:04:17,560 Speaker 2: So, Joe, this outcome, what do you make of it? 81 00:04:17,640 --> 00:04:21,200 Speaker 2: I mean, JP Morgan becomes much bigger, but they've stepped 82 00:04:21,240 --> 00:04:24,320 Speaker 2: in and saved First Republic essentially. 83 00:04:25,200 --> 00:04:27,680 Speaker 5: Yeah, I would say this Republic incident is a little 84 00:04:27,680 --> 00:04:30,000 Speaker 5: bit different than the Local Value Bank incident in a 85 00:04:30,040 --> 00:04:33,920 Speaker 5: few ways. CD was largely unanticipated, but in the case 86 00:04:33,920 --> 00:04:37,280 Speaker 5: of b FC, most vcs and founders have already made 87 00:04:37,360 --> 00:04:40,280 Speaker 5: content continuous planted in the last few weeks. So in fact, 88 00:04:40,279 --> 00:04:44,719 Speaker 5: most founders, entrepreneurs vcs now have several bank accounts with 89 00:04:44,760 --> 00:04:47,920 Speaker 5: different banks and so you know, so you know, we're 90 00:04:47,960 --> 00:04:51,800 Speaker 5: not unprepared for sort of the transition here. Now our 91 00:04:51,839 --> 00:04:53,919 Speaker 5: you know, accounts at efforts will roll over to a 92 00:04:53,960 --> 00:04:58,120 Speaker 5: new acquirers hipping workings account, so that would ensure continuity. 93 00:04:58,360 --> 00:05:00,520 Speaker 5: But I think on a broader sense, the different between 94 00:05:01,640 --> 00:05:04,480 Speaker 5: First C and slolicon value bank was FIRC has also 95 00:05:04,560 --> 00:05:08,480 Speaker 5: a larger non tech consumer base, and I think this 96 00:05:08,680 --> 00:05:12,320 Speaker 5: outcome helping shore the continuity of that. But for businesses 97 00:05:12,320 --> 00:05:14,480 Speaker 5: and tech tech business in the VCS, we've already been 98 00:05:14,560 --> 00:05:16,680 Speaker 5: largely prepared for this outcome. 99 00:05:18,120 --> 00:05:18,600 Speaker 4: Prepared. 100 00:05:18,720 --> 00:05:21,800 Speaker 2: How are you talking about diversification of banking. 101 00:05:22,800 --> 00:05:24,920 Speaker 5: Yeah, since, like you know, a few weeks ago, most 102 00:05:25,000 --> 00:05:27,640 Speaker 5: vcs and most founders that leads in our portfolio and 103 00:05:27,640 --> 00:05:30,840 Speaker 5: the ones we've we've connected with and the ones we're 104 00:05:30,839 --> 00:05:34,160 Speaker 5: seeing in the market have opened several new accounts at 105 00:05:34,160 --> 00:05:38,520 Speaker 5: different banks, even instant banks multiple accounts. And there's also 106 00:05:38,560 --> 00:05:41,800 Speaker 5: fintech players which are offering a higher deplit insurance than 107 00:05:41,800 --> 00:05:45,640 Speaker 5: to fifty because they're parking their opening accounts with multiple banks, 108 00:05:45,680 --> 00:05:47,599 Speaker 5: so that ensure you can actually, lets say, add four 109 00:05:47,839 --> 00:05:50,680 Speaker 5: to fifty case to give to a million. So that's 110 00:05:50,680 --> 00:05:53,520 Speaker 5: been underway for you know, since the SVB fallout, and 111 00:05:53,600 --> 00:05:56,200 Speaker 5: so when you know, this incident kind of occurred with FC, 112 00:05:57,000 --> 00:05:59,719 Speaker 5: vcs and founders have already kind of been making continuous 113 00:05:59,720 --> 00:06:02,400 Speaker 5: a plant plus even if the ones that happened, you know, 114 00:06:02,520 --> 00:06:05,000 Speaker 5: I guess they're the positive for being short by the 115 00:06:05,040 --> 00:06:06,719 Speaker 5: large bank in the US. So I think, you know, 116 00:06:06,720 --> 00:06:10,680 Speaker 5: founders and vcs feel much safer this time and securely on. 117 00:06:10,760 --> 00:06:12,679 Speaker 4: The last time, Joe. 118 00:06:12,680 --> 00:06:15,640 Speaker 2: Even for private market participants in vcs. There are some 119 00:06:15,680 --> 00:06:18,880 Speaker 2: big picture questions, one of which is what does the 120 00:06:18,920 --> 00:06:20,200 Speaker 2: Fed do this Wednesday? 121 00:06:21,640 --> 00:06:25,000 Speaker 5: Yeah, you know, financial stability was always sort of a 122 00:06:25,040 --> 00:06:29,240 Speaker 5: third objective by the Fed after the inflation and unemployment rate. 123 00:06:29,560 --> 00:06:31,120 Speaker 5: You know, if you if you look at sort of 124 00:06:31,120 --> 00:06:33,920 Speaker 5: effas of terms from Marks, you know, I think he 125 00:06:33,960 --> 00:06:36,120 Speaker 5: did to say in the pressure that we expect to 126 00:06:36,120 --> 00:06:37,920 Speaker 5: inflict some pains, and I think, you know, what was 127 00:06:37,960 --> 00:06:41,760 Speaker 5: happening here within the broader financial system is not inconsistent 128 00:06:41,800 --> 00:06:44,200 Speaker 5: with what they would anticipate. I think the real question 129 00:06:44,279 --> 00:06:48,400 Speaker 5: in my mind is to what extent will the banking 130 00:06:49,120 --> 00:06:52,240 Speaker 5: crisis and followouts sort of deter the FED from from 131 00:06:52,320 --> 00:06:55,200 Speaker 5: raising rates. In my view, you know, they're probably gonna 132 00:06:55,360 --> 00:06:58,200 Speaker 5: raise race another twenty five pips at this meeting. 133 00:06:58,480 --> 00:06:59,840 Speaker 6: But but you know, in my own. 134 00:06:59,800 --> 00:07:02,279 Speaker 5: View, holding rates at you know, five and a half, 135 00:07:02,520 --> 00:07:04,560 Speaker 5: five hun quarts five and a half is also very 136 00:07:04,720 --> 00:07:08,640 Speaker 5: very restrictive and so and so financials abilities, you know, 137 00:07:08,960 --> 00:07:10,960 Speaker 5: become a large concern for the FED and might actually 138 00:07:10,960 --> 00:07:13,400 Speaker 5: deser the FED from raising riks further. But by keeping 139 00:07:13,480 --> 00:07:16,440 Speaker 5: race at parent levels, there's still being very restrictive on 140 00:07:16,520 --> 00:07:18,680 Speaker 5: financial conditions and that could really kind of temper and 141 00:07:18,720 --> 00:07:19,640 Speaker 5: non inflation over time. 142 00:07:21,200 --> 00:07:24,080 Speaker 2: What what is the environment like right now, Joe? For 143 00:07:24,680 --> 00:07:28,280 Speaker 2: vcs that yourself considering writing checks for startups trying to 144 00:07:28,320 --> 00:07:29,920 Speaker 2: raise money, you. 145 00:07:29,880 --> 00:07:31,160 Speaker 6: Know, there's two schools of thought. 146 00:07:31,400 --> 00:07:32,800 Speaker 5: You know, my own school of thought is this is 147 00:07:32,800 --> 00:07:35,800 Speaker 5: actually the best comething best because you know, because values 148 00:07:35,880 --> 00:07:38,120 Speaker 5: start down and we believe this is a help you 149 00:07:38,200 --> 00:07:41,640 Speaker 5: reset for valuations and companies. And but there's another school 150 00:07:41,680 --> 00:07:44,960 Speaker 5: of thought, which is many found many vcs are actually 151 00:07:45,440 --> 00:07:49,080 Speaker 5: uh kind of holding back from investing, and so you know, 152 00:07:49,120 --> 00:07:51,960 Speaker 5: but within the broader system. You know, there's you know, 153 00:07:52,080 --> 00:07:54,680 Speaker 5: amongst kind of this period of weakness, I would I 154 00:07:54,680 --> 00:07:57,680 Speaker 5: would call uh, the whole AI kind of a genet 155 00:07:57,760 --> 00:08:01,440 Speaker 5: AI sort of a uh you know, innovation has been 156 00:08:01,480 --> 00:08:04,560 Speaker 5: introducing some new life back into the ecosystem. 157 00:08:04,840 --> 00:08:07,280 Speaker 6: So I think broader quality speaking, most. 158 00:08:07,120 --> 00:08:10,480 Speaker 5: Vcs are slowing down not only on investing, but lpis 159 00:08:10,480 --> 00:08:13,160 Speaker 5: are slowing down on investing in funds well, when bright 160 00:08:13,160 --> 00:08:15,840 Speaker 5: Spot is general AI, which seems to offer a lot. 161 00:08:15,760 --> 00:08:19,440 Speaker 6: Of new blood and excitement back into this ecosystem. 162 00:08:19,640 --> 00:08:22,400 Speaker 2: Hey, Joe, there was a guest on Bluemoll television earlier 163 00:08:22,440 --> 00:08:26,480 Speaker 2: saying that banks and markets don't have crystal balls, they 164 00:08:26,520 --> 00:08:29,360 Speaker 2: don't have clairvoyance. But as you look into the future, 165 00:08:30,080 --> 00:08:33,800 Speaker 2: does the JP Morgan intervention kind of settle things a. 166 00:08:33,720 --> 00:08:34,480 Speaker 4: Bit for now? 167 00:08:35,720 --> 00:08:38,480 Speaker 5: I think in the short term yes, because kaping Morgan 168 00:08:38,520 --> 00:08:41,480 Speaker 5: and I looked at the assets. They're fifteen to twenty 169 00:08:41,520 --> 00:08:44,520 Speaker 5: some larger than evarsas assets, and that should really contain 170 00:08:45,200 --> 00:08:47,680 Speaker 5: sort of the spread of the of the of the 171 00:08:47,760 --> 00:08:48,400 Speaker 5: bad assets. 172 00:08:48,559 --> 00:08:50,080 Speaker 6: But also I think you know, having. 173 00:08:51,679 --> 00:08:54,400 Speaker 5: And there's you know, debates on whether it's politically viable 174 00:08:54,480 --> 00:08:56,480 Speaker 5: or not, but I think having the largest band to 175 00:08:56,520 --> 00:09:00,160 Speaker 5: kind of step in and contain sort of this this 176 00:09:00,320 --> 00:09:03,079 Speaker 5: usual kind of health restore system and not very much 177 00:09:03,080 --> 00:09:05,840 Speaker 5: like an eight and nine. But you know, I guess 178 00:09:05,840 --> 00:09:09,040 Speaker 5: the question is, are we are we fully through the banking. 179 00:09:10,200 --> 00:09:10,640 Speaker 6: Fallouts? 180 00:09:10,760 --> 00:09:14,640 Speaker 5: I think for large banks which have regularly, uh you know, 181 00:09:14,679 --> 00:09:17,920 Speaker 5: regularly passed the first card test, the stress testing, I think, 182 00:09:17,960 --> 00:09:20,079 Speaker 5: personally think they will be fun. I think mis sized 183 00:09:20,120 --> 00:09:23,160 Speaker 5: banks management who haven't been on top of it would 184 00:09:23,160 --> 00:09:25,960 Speaker 5: be on top of it now. My actually natural concern 185 00:09:26,080 --> 00:09:29,800 Speaker 5: would be with the smaller banks, which you know, maybe 186 00:09:29,960 --> 00:09:31,719 Speaker 5: I would expect to be to see a little more 187 00:09:31,720 --> 00:09:34,040 Speaker 5: stress in the bankings on the smaller side. I think 188 00:09:34,040 --> 00:09:38,360 Speaker 5: from this larger banks, I think the crisis that uh 189 00:09:38,480 --> 00:09:39,640 Speaker 5: in the first term content. 190 00:09:39,600 --> 00:09:42,560 Speaker 2: All right, Joe Chow, Millennia Capital Goods, catch up, Thank 191 00:09:42,600 --> 00:09:45,760 Speaker 2: you for your time. Now, another busy week ahead for 192 00:09:45,880 --> 00:09:49,920 Speaker 2: earnings with Apple, a m D and qualcom Or reporting numbers. 193 00:09:49,960 --> 00:09:51,560 Speaker 2: Let's get a preview of what to expect from the 194 00:09:51,600 --> 00:09:55,520 Speaker 2: iPhone maker with our own Mark German. Good morning to you, Mark. 195 00:09:55,760 --> 00:09:56,880 Speaker 2: What's on deck for Apple? 196 00:09:56,960 --> 00:09:58,720 Speaker 1: Yeah, good morning, thank you for having me. So this 197 00:09:58,840 --> 00:10:01,360 Speaker 1: quarter is likely to play out a bit similarly the 198 00:10:01,440 --> 00:10:05,120 Speaker 1: last quarter. Right, Bloomberg consensus at this point anticipates about 199 00:10:05,120 --> 00:10:08,880 Speaker 1: a five percent annual decline. Last year for the second quarter, 200 00:10:08,920 --> 00:10:12,880 Speaker 1: Apple reported revenue of about ninety seven billion. For this 201 00:10:12,960 --> 00:10:15,080 Speaker 1: year twenty twenty three, the second quarter will come in 202 00:10:15,120 --> 00:10:19,880 Speaker 1: at around ninety two billion. That's according to a summary 203 00:10:19,920 --> 00:10:24,320 Speaker 1: of analyst estimates. Now, within that ninety two billion, it appears, 204 00:10:24,320 --> 00:10:27,400 Speaker 1: according to these estimates, that all of the Apple major 205 00:10:27,440 --> 00:10:31,400 Speaker 1: product categories aside from services, will see a bit of 206 00:10:31,440 --> 00:10:34,800 Speaker 1: a decline. That includes the iPhone, the iPad, the Mac, 207 00:10:34,920 --> 00:10:38,640 Speaker 1: as well as wearables. The iPhone decline is likely to 208 00:10:38,720 --> 00:10:42,880 Speaker 1: be less significant than in the previous quarter because in 209 00:10:42,920 --> 00:10:45,240 Speaker 1: the previous quarter we had the supply chain challenges that 210 00:10:45,320 --> 00:10:49,040 Speaker 1: have now been essentially fully resolved. But you'll still see 211 00:10:49,320 --> 00:10:52,840 Speaker 1: the iPad and the math go down quite significantly compared 212 00:10:52,880 --> 00:10:53,839 Speaker 1: to the year ago quarter. 213 00:10:54,320 --> 00:10:55,560 Speaker 4: Services, however, are. 214 00:10:55,440 --> 00:10:58,400 Speaker 1: The estimates indicate that should be up a little bit, 215 00:10:58,480 --> 00:11:02,720 Speaker 1: which obviously is news and positive growth somewhere for Apple 216 00:11:02,760 --> 00:11:03,240 Speaker 1: on this quarter. 217 00:11:03,520 --> 00:11:05,839 Speaker 2: All right, Bluemos, Mark Gurhman, We're all waiting for Apple 218 00:11:05,920 --> 00:11:07,680 Speaker 2: later in the week. And by the way, Mark's power 219 00:11:07,679 --> 00:11:10,480 Speaker 2: On column was out over the weekend. If you don't subscribe, 220 00:11:10,679 --> 00:11:13,120 Speaker 2: do so. He's focused on Apple watching software. Thank you 221 00:11:13,559 --> 00:11:16,600 Speaker 2: to Mark. Now sticking with earnings. Another stock we're watching 222 00:11:16,760 --> 00:11:20,760 Speaker 2: is Checkpoint Software, out with earnings that frankly disappointed to 223 00:11:20,800 --> 00:11:24,520 Speaker 2: the downside as revenue missed expectation. Shares at one point 224 00:11:24,520 --> 00:11:27,640 Speaker 2: in the session down by the most since February of 225 00:11:27,679 --> 00:11:31,280 Speaker 2: twenty twenty one. Now, coming up, how the AI startup 226 00:11:31,400 --> 00:11:34,280 Speaker 2: pine Cone managed to get a seven hundred and fifty 227 00:11:34,280 --> 00:11:38,080 Speaker 2: million dollars valuation in a one hundred dollars million dollar 228 00:11:38,240 --> 00:11:39,000 Speaker 2: funding round. 229 00:11:39,240 --> 00:11:42,240 Speaker 4: We'll talk to the CEO Ido Liberty. That's next. 230 00:11:42,760 --> 00:11:46,000 Speaker 2: Meantime, here's what Wing Venture partner Jake Flomenberg, who took 231 00:11:46,040 --> 00:11:48,320 Speaker 2: part in that round, had to say about. 232 00:11:48,080 --> 00:11:48,680 Speaker 4: It last week. 233 00:11:50,200 --> 00:11:54,080 Speaker 7: And so, if you're building a generative AI startup or 234 00:11:54,120 --> 00:11:56,600 Speaker 7: a generative AI application and you want to marry that 235 00:11:56,920 --> 00:11:59,040 Speaker 7: with your own company's data, or if you want to 236 00:11:59,080 --> 00:12:03,840 Speaker 7: remember things from one question to the next, you need 237 00:12:03,840 --> 00:12:05,400 Speaker 7: a vector database like Han Kong. 238 00:12:16,240 --> 00:12:19,200 Speaker 2: The White House is probing how companies use AI to 239 00:12:19,280 --> 00:12:23,280 Speaker 2: monitor and manage workers. The Biden administration says such practices 240 00:12:23,320 --> 00:12:26,440 Speaker 2: are on the rise and can inflict significant harm. Just 241 00:12:26,520 --> 00:12:30,000 Speaker 2: last week, the Minnesota Statehouse actually passed a bill to 242 00:12:30,080 --> 00:12:34,040 Speaker 2: require companies like Amazon to provide warehouse workers with copies 243 00:12:34,080 --> 00:12:37,360 Speaker 2: of data they collected on their pace of work. California 244 00:12:37,440 --> 00:12:40,480 Speaker 2: and New York had passed similar legislation in the past 245 00:12:40,720 --> 00:12:44,440 Speaker 2: couple of years. Let's keep talking about AI with pine Cone, 246 00:12:44,480 --> 00:12:47,520 Speaker 2: an AI powered platform. We've just raised one hundred million 247 00:12:47,600 --> 00:12:50,080 Speaker 2: dollars in a funding round last week, valuing the company 248 00:12:50,280 --> 00:12:53,400 Speaker 2: at seven hundred and fifty million dollars. Founder and CEO 249 00:12:53,480 --> 00:12:57,360 Speaker 2: Eedo Liberty joins us. Now, we would describe pine Cone 250 00:12:57,400 --> 00:13:01,400 Speaker 2: Edo as the long term memory for AI models, right, 251 00:13:01,520 --> 00:13:02,720 Speaker 2: Explain what pine Cone. 252 00:13:02,559 --> 00:13:06,000 Speaker 8: Does provides exactly that. I mean when. 253 00:13:07,559 --> 00:13:12,120 Speaker 9: We think about AI models as being smart, they might 254 00:13:12,160 --> 00:13:13,520 Speaker 9: not be very knowledgeable. 255 00:13:13,760 --> 00:13:15,119 Speaker 8: And if you see. 256 00:13:14,880 --> 00:13:19,880 Speaker 9: All the issues that people have had with what's called illucinations, 257 00:13:19,920 --> 00:13:24,720 Speaker 9: basically AI models giving very smart sounding answers but with 258 00:13:24,840 --> 00:13:29,199 Speaker 9: wrong information. That is solved by equipping them with long 259 00:13:29,280 --> 00:13:32,360 Speaker 9: term memory, which is provided by fine. 260 00:13:32,200 --> 00:13:36,200 Speaker 2: Con interesting timing one hundred million dollar round a seven 261 00:13:36,280 --> 00:13:39,960 Speaker 2: hundred and fifty million dollar valuation. You did a much 262 00:13:40,000 --> 00:13:42,840 Speaker 2: smaller round a sort of a year ago. How difficult 263 00:13:42,920 --> 00:13:45,200 Speaker 2: was it to raise those funds? And why did you 264 00:13:45,280 --> 00:13:46,199 Speaker 2: raise the fund. 265 00:13:46,440 --> 00:13:47,960 Speaker 8: It wasn't difficult at all. 266 00:13:48,280 --> 00:13:54,079 Speaker 9: In fact, the fact that sector databases really asserted themselves 267 00:13:54,200 --> 00:13:58,280 Speaker 9: as a critical piece of generative AI and the ability 268 00:13:58,320 --> 00:14:04,040 Speaker 9: to do that escale correctly and pine Cone being the 269 00:14:04,120 --> 00:14:07,720 Speaker 9: leader in that space and made that a no brainer 270 00:14:07,840 --> 00:14:13,440 Speaker 9: to be honest, And so it was. Yeah, it was 271 00:14:14,040 --> 00:14:16,840 Speaker 9: needed for us to really fuel the future research and 272 00:14:16,920 --> 00:14:20,880 Speaker 9: development and future investment in the field. 273 00:14:21,040 --> 00:14:24,720 Speaker 2: You know, we're covering AI more than a daily basis 274 00:14:24,720 --> 00:14:26,680 Speaker 2: here on the show, right and we're talking to those 275 00:14:26,720 --> 00:14:29,880 Speaker 2: that are working on foundational models vcs that are investing 276 00:14:29,880 --> 00:14:32,200 Speaker 2: in the technology. We kind of break it down to 277 00:14:32,920 --> 00:14:37,560 Speaker 2: data prep, deep learning, and then inference. Where does pine 278 00:14:37,560 --> 00:14:40,280 Speaker 2: Cone sit within that development cycle. 279 00:14:40,840 --> 00:14:46,480 Speaker 9: Well, it's sort of a different component altogether. This is 280 00:14:46,560 --> 00:14:52,680 Speaker 9: about search and retrieval and access to your long term memories. 281 00:14:52,720 --> 00:14:55,640 Speaker 9: If you are trying to build a generative AI on 282 00:14:56,520 --> 00:15:00,480 Speaker 9: your own company data and you want your then you 283 00:15:00,560 --> 00:15:05,240 Speaker 9: want your application to not hallucinate or make gross mistakes, 284 00:15:06,240 --> 00:15:09,520 Speaker 9: then you have to equip it with that capability and 285 00:15:09,560 --> 00:15:12,960 Speaker 9: that's what prime Cone provides. That could be either on 286 00:15:13,040 --> 00:15:17,080 Speaker 9: training side or data cleaning, or in production at real 287 00:15:17,160 --> 00:15:18,720 Speaker 9: time or customer facing. 288 00:15:19,640 --> 00:15:22,240 Speaker 8: This happens everywhere up and down the stack. It's just 289 00:15:22,280 --> 00:15:23,520 Speaker 8: a whole different component. 290 00:15:24,120 --> 00:15:26,680 Speaker 9: And in some sense, doubling down on what I said before, 291 00:15:26,800 --> 00:15:31,280 Speaker 9: this is the realization that this is a critical piece 292 00:15:31,360 --> 00:15:34,960 Speaker 9: and that it's here to stay was exactly what catalyzed that, 293 00:15:35,120 --> 00:15:37,119 Speaker 9: both the round and evaluation. 294 00:15:37,520 --> 00:15:42,440 Speaker 2: You have customers like Shopify, HubSpot, ZAPR, and Gong interesting 295 00:15:42,520 --> 00:15:46,040 Speaker 2: names in Layman's terms, What is it you're doing for them? 296 00:15:47,200 --> 00:15:51,960 Speaker 8: When when large language models. 297 00:15:54,240 --> 00:16:00,400 Speaker 9: Processed texts, they don't save it or represent then in 298 00:16:00,440 --> 00:16:04,680 Speaker 9: the same way. And when you want to search for data, 299 00:16:04,760 --> 00:16:07,480 Speaker 9: when a large language model search is for data, they 300 00:16:07,520 --> 00:16:10,800 Speaker 9: don't search through it with a regular search engine like 301 00:16:10,880 --> 00:16:13,240 Speaker 9: you would and I would like search on Google, but 302 00:16:13,320 --> 00:16:15,920 Speaker 9: in a very different pattern. And that pattern is support 303 00:16:16,080 --> 00:16:19,960 Speaker 9: supported by pine Cone. And so if you know, if 304 00:16:20,000 --> 00:16:21,760 Speaker 9: you're on Gong and you want to see what the 305 00:16:21,880 --> 00:16:27,760 Speaker 9: some salesperson you know offered some discount for some customer, uh, 306 00:16:28,080 --> 00:16:30,480 Speaker 9: that's much easier to do with the large language model 307 00:16:30,680 --> 00:16:33,080 Speaker 9: searching through the data with with pine Cone. 308 00:16:32,840 --> 00:16:35,320 Speaker 8: Than with keyword search for example. Right. 309 00:16:35,680 --> 00:16:40,600 Speaker 9: The same goes for Shopify, who built a shopping assist 310 00:16:40,960 --> 00:16:46,520 Speaker 9: app very quickly with those capabilities, surfacing the actual information 311 00:16:46,560 --> 00:16:47,720 Speaker 9: about their products and so on. 312 00:16:48,440 --> 00:16:50,360 Speaker 8: Uh, the customer's products. 313 00:16:52,520 --> 00:16:55,600 Speaker 9: You know, it's it's really retrieval and search in the 314 00:16:55,640 --> 00:16:58,400 Speaker 9: way that AI models expect them to happen. 315 00:17:00,080 --> 00:17:02,120 Speaker 8: And the daily power. 316 00:17:02,120 --> 00:17:06,680 Speaker 9: It's very power, very new kinds of applications, especially where 317 00:17:06,720 --> 00:17:07,840 Speaker 9: hallucinations is. 318 00:17:07,800 --> 00:17:11,119 Speaker 8: Something that you really try very strong to avoid. 319 00:17:12,440 --> 00:17:14,720 Speaker 2: You know, is there a risk that the pool of 320 00:17:14,840 --> 00:17:19,000 Speaker 2: data is finite that it just runs out for training 321 00:17:19,040 --> 00:17:20,160 Speaker 2: foundational models? 322 00:17:20,920 --> 00:17:23,080 Speaker 8: I'm not sure what you mean by that, to be honest. 323 00:17:24,359 --> 00:17:26,720 Speaker 8: There's a debate we are enable, Okay. 324 00:17:27,400 --> 00:17:29,920 Speaker 2: Well, there's debit from industry participants. Right, there are lots 325 00:17:29,920 --> 00:17:34,160 Speaker 2: of data inputs that go into training generative AI tools. Right, 326 00:17:35,600 --> 00:17:39,560 Speaker 2: if you take for example, Google and Bard, they clearly 327 00:17:39,640 --> 00:17:42,639 Speaker 2: have an advantage in the data that they are able 328 00:17:42,640 --> 00:17:46,320 Speaker 2: to draw on to train that model. Others have less access, 329 00:17:46,359 --> 00:17:48,440 Speaker 2: especially those of the training models where there are far 330 00:17:48,520 --> 00:17:52,560 Speaker 2: fewer parameters and inputs. I guess my question is, you know, 331 00:17:52,600 --> 00:17:55,280 Speaker 2: how are you helping and make data available and what 332 00:17:55,320 --> 00:17:57,680 Speaker 2: the risk is that there is not sufficient access to 333 00:17:57,800 --> 00:18:00,720 Speaker 2: data to train future models. 334 00:18:01,960 --> 00:18:06,160 Speaker 9: So, first of all, I don't think that that is 335 00:18:06,280 --> 00:18:11,280 Speaker 9: that is a serious risk for training better models, because 336 00:18:11,280 --> 00:18:14,600 Speaker 9: I think we have scarcely tapped the. 337 00:18:16,560 --> 00:18:19,080 Speaker 8: Single percents of what's available out there. 338 00:18:19,560 --> 00:18:25,000 Speaker 9: But what pinekone allows you to do is actually make 339 00:18:25,119 --> 00:18:28,000 Speaker 9: your AI models a lot more accurate and a lot 340 00:18:28,040 --> 00:18:31,760 Speaker 9: more actionable without retraining them, which is I think why 341 00:18:32,440 --> 00:18:35,239 Speaker 9: it has been so successful and why we're ramped up 342 00:18:35,280 --> 00:18:41,080 Speaker 9: thousands of customers even just in Q one, because you know, 343 00:18:41,160 --> 00:18:44,920 Speaker 9: with the large language model, even without being retrained, if 344 00:18:44,920 --> 00:18:48,560 Speaker 9: it's given the right context and the right information from 345 00:18:48,600 --> 00:18:52,080 Speaker 9: which to construct the answer, then you don't have to 346 00:18:52,200 --> 00:18:55,240 Speaker 9: bake into the model all the data in your company. 347 00:18:55,560 --> 00:18:58,400 Speaker 8: You just have to present it to the model at 348 00:18:58,440 --> 00:19:01,880 Speaker 8: the query time say hey, you know, I want. 349 00:19:01,720 --> 00:19:06,000 Speaker 9: To see what this customers are for a discount, and 350 00:19:06,119 --> 00:19:09,000 Speaker 9: here are you know, twenty snippets of texts that I 351 00:19:09,040 --> 00:19:11,359 Speaker 9: think contained the answer. Can you just steal that from 352 00:19:11,359 --> 00:19:13,640 Speaker 9: that and give it to me in a reasonable way. 353 00:19:14,080 --> 00:19:17,119 Speaker 9: That's a much easier problem and a much more manageable 354 00:19:17,440 --> 00:19:18,520 Speaker 9: sort problem to sort. 355 00:19:18,720 --> 00:19:21,199 Speaker 2: All right, pine Cone founder and Cooedo Liberty, thank you 356 00:19:21,240 --> 00:19:32,320 Speaker 2: so much for joining the show. Time for Talking Tech 357 00:19:32,440 --> 00:19:36,359 Speaker 2: India's most valuable startup by you look into reinsure employees 358 00:19:36,400 --> 00:19:39,719 Speaker 2: and partners. After a weekend raid of the company's offices 359 00:19:39,760 --> 00:19:43,000 Speaker 2: by the agency that investigates money laundering in the country. 360 00:19:43,000 --> 00:19:46,040 Speaker 2: The investigations come at a time when the education company, 361 00:19:46,119 --> 00:19:48,960 Speaker 2: valued the twenty two billion dollars, is in talks with 362 00:19:49,080 --> 00:19:52,919 Speaker 2: investors to raise funds to address a liquidity crunch, and 363 00:19:52,960 --> 00:19:55,760 Speaker 2: Ali Barba co founder Jack Maher is joining the University 364 00:19:55,800 --> 00:19:58,840 Speaker 2: of Tokyo as a visiting professor. Mar is set to 365 00:19:58,880 --> 00:20:01,800 Speaker 2: assume the new position to day and will provide advice 366 00:20:01,840 --> 00:20:04,800 Speaker 2: on research topics and conduct his own research. Will also 367 00:20:04,840 --> 00:20:09,320 Speaker 2: give seminars about entrepreneurship and innovation. Plus, Twitter co founder 368 00:20:09,400 --> 00:20:13,160 Speaker 2: Jack Dorsey is offering sharp criticism of the social media's 369 00:20:13,320 --> 00:20:16,160 Speaker 2: new owner, Elon Musk and his handling of the deal, 370 00:20:16,400 --> 00:20:20,080 Speaker 2: after previously being publicly in favor of the sale. When 371 00:20:20,119 --> 00:20:22,480 Speaker 2: asked if Musk has proven himself to be the best 372 00:20:22,520 --> 00:20:26,520 Speaker 2: possible steward for the platform, Dorsey said, quote not no, 373 00:20:26,520 --> 00:20:29,520 Speaker 2: nor do I think he acted right after realizing his 374 00:20:29,680 --> 00:20:32,359 Speaker 2: timing was bad, Nor do I think the board should 375 00:20:32,440 --> 00:20:43,159 Speaker 2: enforce the sale. Welcome back to Bloomberg Technology. I'm ed 376 00:20:43,200 --> 00:20:45,200 Speaker 2: Lovelow in San Francisco. We've got to talk a little 377 00:20:45,200 --> 00:20:47,720 Speaker 2: bit about AI. Now here's what some of our guests 378 00:20:47,720 --> 00:20:50,480 Speaker 2: have had to say about what they think of generative 379 00:20:50,520 --> 00:20:53,120 Speaker 2: AI's applications and where it's best suited. 380 00:20:53,640 --> 00:20:54,160 Speaker 4: Have a listen. 381 00:20:54,800 --> 00:20:59,160 Speaker 7: This technology really takes personalization to the next level when 382 00:20:59,160 --> 00:21:03,440 Speaker 7: it comes to any type of education, but specifically financial literacy, 383 00:21:03,440 --> 00:21:04,640 Speaker 7: which is so complicated. 384 00:21:04,760 --> 00:21:08,520 Speaker 10: Certain categories are more exciting than others right now. Obviously, 385 00:21:08,600 --> 00:21:11,560 Speaker 10: generative AI is a category that is very exciting to 386 00:21:12,200 --> 00:21:15,520 Speaker 10: startup founders, customers, and venture capitalists as well. 387 00:21:15,600 --> 00:21:18,920 Speaker 7: There'll be hundreds, if not thousands of opportunities to build 388 00:21:18,960 --> 00:21:21,679 Speaker 7: interesting applications powered by AI. 389 00:21:21,800 --> 00:21:26,160 Speaker 10: Technology makes it easier to create. Generative AI is accelerating 390 00:21:26,200 --> 00:21:28,879 Speaker 10: that trend by decades, maybe centuries. 391 00:21:29,000 --> 00:21:31,639 Speaker 11: To me, brainstorming is actually a perfect use case for 392 00:21:31,720 --> 00:21:32,320 Speaker 11: these tools. 393 00:21:32,520 --> 00:21:35,000 Speaker 8: I don't really care whether you're using AI or some 394 00:21:35,119 --> 00:21:37,720 Speaker 8: other technology. I care what are you delivering to, what 395 00:21:37,800 --> 00:21:39,919 Speaker 8: set are customers and how delighted are they going to be? 396 00:21:40,600 --> 00:21:43,400 Speaker 2: So I show you these two names Starbucks and Young 397 00:21:43,480 --> 00:21:49,040 Speaker 2: Brands reporting earnings Tuesday Wednesday. AI Artificial intelligence is absolutely 398 00:21:49,080 --> 00:21:52,680 Speaker 2: dominated earning statements and calls over the last ten days 399 00:21:52,720 --> 00:21:55,520 Speaker 2: or so, particularly for the technology sector. Do we see 400 00:21:55,560 --> 00:21:59,439 Speaker 2: that happen again this week when it comes to qsr's 401 00:21:59,520 --> 00:22:02,600 Speaker 2: Qui restaurants and some of the more consumer facing companies. 402 00:22:02,640 --> 00:22:05,679 Speaker 2: It's really big question if that carries on throughout this 403 00:22:05,760 --> 00:22:09,480 Speaker 2: earning season. For more, let's bring in Presto Automation Chairman 404 00:22:09,720 --> 00:22:13,240 Speaker 2: and Interim CEO Krishna Gupta. Presto one of the largest 405 00:22:13,240 --> 00:22:17,480 Speaker 2: providers of AI power tech for restaurants like McDonald Zapplebee's, 406 00:22:17,520 --> 00:22:20,560 Speaker 2: Chili's and much more. Krishna, what do you think is 407 00:22:20,600 --> 00:22:24,080 Speaker 2: AI going to dominate the earnings narrative for this sector 408 00:22:24,080 --> 00:22:24,520 Speaker 2: this week? 409 00:22:25,359 --> 00:22:26,320 Speaker 4: And great to be on. 410 00:22:26,400 --> 00:22:28,639 Speaker 11: Well, let me start by saying I completely agree with 411 00:22:28,760 --> 00:22:32,040 Speaker 11: Keith Ruboy, which is that I don't care whether it's 412 00:22:32,080 --> 00:22:34,600 Speaker 11: AI or not. All that matters is are you creating 413 00:22:34,640 --> 00:22:39,000 Speaker 11: something that's immediately actionable and valuable for customers today? And 414 00:22:39,240 --> 00:22:41,760 Speaker 11: we believe that there are a lot of opportunities to 415 00:22:41,800 --> 00:22:45,040 Speaker 11: do that using AI in the hospitality and the restaurant sectors. 416 00:22:45,080 --> 00:22:48,600 Speaker 11: So yes, I expect every QSR, frankly every corporation in 417 00:22:48,640 --> 00:22:50,960 Speaker 11: the world to be thinking about what are the most 418 00:22:51,000 --> 00:22:53,720 Speaker 11: immediately actionable opportunities to leverage AI today. 419 00:22:53,960 --> 00:22:57,120 Speaker 2: Press Automation Chairman and Interim CEO Christiana Guttu is with 420 00:22:57,160 --> 00:22:59,359 Speaker 2: me in New York we were talking about the role 421 00:22:59,560 --> 00:23:03,760 Speaker 2: of art official intelligence in the restaurant industry, the quick 422 00:23:03,800 --> 00:23:06,160 Speaker 2: serve restaurant industry, and I was about to ask you 423 00:23:06,560 --> 00:23:09,440 Speaker 2: why you're so focused on drive through. How is something 424 00:23:09,480 --> 00:23:12,199 Speaker 2: so manual complemented by artificial intelligence. 425 00:23:12,880 --> 00:23:16,200 Speaker 11: Well, it's not necessarily complemented. I think it actually ends 426 00:23:16,240 --> 00:23:19,680 Speaker 11: up being one of the most immediately actionable applications of 427 00:23:20,240 --> 00:23:24,040 Speaker 11: generative AI in the enterprise. Because you have labor costs 428 00:23:24,040 --> 00:23:27,280 Speaker 11: which are rising, you have a relatively confined problem set 429 00:23:27,320 --> 00:23:29,680 Speaker 11: in the drive through, and I don't think in three 430 00:23:29,760 --> 00:23:31,720 Speaker 11: years is going to be a single drive through having 431 00:23:31,720 --> 00:23:34,439 Speaker 11: a human take your orders. I think, you know, voice spots, 432 00:23:34,440 --> 00:23:38,240 Speaker 11: whether that's ours or someone else's, will be pervasive. They 433 00:23:38,320 --> 00:23:43,240 Speaker 11: will never get tired of delivering perfect service, upselling the customer, 434 00:23:43,280 --> 00:23:47,200 Speaker 11: and ultimately delivering a lower cost, higher revenue experience to customers. 435 00:23:48,400 --> 00:23:51,720 Speaker 2: Like many of your industry pays, you have looked to 436 00:23:52,040 --> 00:23:56,040 Speaker 2: open AI and GPT tools to complement the work you're 437 00:23:56,080 --> 00:23:56,640 Speaker 2: already doing. 438 00:23:56,680 --> 00:23:58,000 Speaker 4: How does that relationship work? 439 00:23:59,160 --> 00:24:02,720 Speaker 11: Yeah, well, look, I think LM technology and generative AI 440 00:24:02,840 --> 00:24:07,960 Speaker 11: broadly does enable better personalization, faster speed of service, etc. 441 00:24:08,640 --> 00:24:10,240 Speaker 11: For us, we have a little bit of a unique 442 00:24:10,840 --> 00:24:13,240 Speaker 11: entry into the relationship in the sense that the CEO 443 00:24:13,280 --> 00:24:15,679 Speaker 11: of Open Ai, Sam Altman, is a longtime investor in 444 00:24:15,680 --> 00:24:18,919 Speaker 11: our company, a longtime friend of our founders. So we 445 00:24:18,960 --> 00:24:22,600 Speaker 11: are very actively in discussions with them and incorporating some 446 00:24:22,640 --> 00:24:24,080 Speaker 11: of that technology into what we're doing. 447 00:24:25,280 --> 00:24:27,680 Speaker 2: What's it been like in the first four or five 448 00:24:27,720 --> 00:24:31,400 Speaker 2: months of this year. Are their QSR CEO's phony every 449 00:24:31,480 --> 00:24:34,080 Speaker 2: day saying get in here now and give us some 450 00:24:34,280 --> 00:24:36,800 Speaker 2: AI capability? Literally, what is demand for you? 451 00:24:37,119 --> 00:24:38,840 Speaker 11: Yeah? Look, I mean I think we're at a really 452 00:24:38,960 --> 00:24:42,560 Speaker 11: unique moment in time where the customer's business needs, which 453 00:24:42,600 --> 00:24:45,399 Speaker 11: is low margins and rising labor costs, are met with 454 00:24:45,600 --> 00:24:49,159 Speaker 11: the progress on the technology side. And yes, because of 455 00:24:49,160 --> 00:24:52,760 Speaker 11: how pervasive this term AI is and everything you're reading 456 00:24:52,880 --> 00:24:56,000 Speaker 11: or seeing or hearing, it no longer feels like science fiction. 457 00:24:56,000 --> 00:24:57,679 Speaker 11: I would say in twenty twenty two, some of what 458 00:24:57,720 --> 00:25:00,879 Speaker 11: we were doing felt like futuristic. Maybe it's a twenty 459 00:25:00,880 --> 00:25:04,200 Speaker 11: five initiative. Well, a lot of CEOs and a lot 460 00:25:04,200 --> 00:25:07,440 Speaker 11: of sea level people at our customers in general are 461 00:25:07,480 --> 00:25:10,000 Speaker 11: now thinking, well, maybe we can actually deploy this now, 462 00:25:10,040 --> 00:25:12,520 Speaker 11: Maybe we should, Maybe we need to for the benefit 463 00:25:12,560 --> 00:25:15,640 Speaker 11: of our shareholders deploy these kinds of technologies now, because 464 00:25:15,680 --> 00:25:18,000 Speaker 11: if we don't do so, we're going to be left 465 00:25:18,000 --> 00:25:20,520 Speaker 11: behind relative to our competitors. We're not going to be 466 00:25:20,520 --> 00:25:23,800 Speaker 11: able to have that margin benefit that others are enjoying. So, yes, 467 00:25:23,880 --> 00:25:26,720 Speaker 11: the tone of conversations across the board has changed pretty 468 00:25:26,760 --> 00:25:28,320 Speaker 11: dramatically over the last six months. 469 00:25:29,000 --> 00:25:32,440 Speaker 2: Does AI displace or eliminate jobs in this industry? 470 00:25:32,560 --> 00:25:35,840 Speaker 11: Krishna, I believe, well, it's hard for me to apply 471 00:25:35,960 --> 00:25:38,679 Speaker 11: on this industry specifically, but I do believe that the 472 00:25:38,720 --> 00:25:44,560 Speaker 11: adoption of AI overall will actually be relatively neutral on jobs. 473 00:25:44,720 --> 00:25:48,240 Speaker 11: I believe that's the case historically with any large technology revolution, 474 00:25:48,960 --> 00:25:51,040 Speaker 11: is that new jobs come up, and we're already seeing 475 00:25:51,080 --> 00:25:54,520 Speaker 11: that you need humans, not necessarily incredibly skilled humans, but 476 00:25:54,560 --> 00:25:57,040 Speaker 11: you need humans to be able to train the AIS 477 00:25:57,080 --> 00:26:00,400 Speaker 11: to help them make sure that specific edge cases are 478 00:26:00,440 --> 00:26:02,880 Speaker 11: not run into and so at the end of the day, 479 00:26:03,040 --> 00:26:06,000 Speaker 11: I think there's a lot of opportunity for us to 480 00:26:06,240 --> 00:26:10,159 Speaker 11: help find new jobs for people in the restaurant industry specifically. However, 481 00:26:10,200 --> 00:26:13,399 Speaker 11: you have a unique challenge which is labor availability. A 482 00:26:13,440 --> 00:26:15,880 Speaker 11: lot of people still haven't been able to find laborer 483 00:26:16,080 --> 00:26:18,840 Speaker 11: and we are helping them do that using Automation. 484 00:26:19,880 --> 00:26:22,800 Speaker 2: Press, their automation chairman an interim CEO, christ Na Gutta 485 00:26:23,119 --> 00:26:25,400 Speaker 2: bringing AI to a drive through near you, sing thank 486 00:26:25,440 --> 00:26:28,600 Speaker 2: you Now coming up, bridging the gap in venture capital 487 00:26:28,640 --> 00:26:31,199 Speaker 2: funding for women of color founders. We'll talk about that 488 00:26:31,280 --> 00:26:35,240 Speaker 2: and more with Fearless Fun CEO arian Simone. That's next. 489 00:26:35,560 --> 00:26:36,960 Speaker 2: So we had to bring a second look at shares 490 00:26:37,000 --> 00:26:39,639 Speaker 2: of Ribbean under pressure again, down another two and a 491 00:26:39,680 --> 00:26:42,680 Speaker 2: half percent. Well so, as investors have little faith left 492 00:26:42,880 --> 00:26:45,520 Speaker 2: in the ability of the company to compete in a 493 00:26:45,560 --> 00:26:48,720 Speaker 2: crowded EV market. At least six analists have cut price 494 00:26:48,760 --> 00:26:51,560 Speaker 2: targets since April, a lot of people pointing to its 495 00:26:51,640 --> 00:26:54,240 Speaker 2: market cap being exactly the same as the. 496 00:26:54,160 --> 00:27:10,280 Speaker 4: Cache that's on its balance sheet. This is Bloomberg feelers. 497 00:27:10,320 --> 00:27:12,800 Speaker 2: Fun, the Benure Capital Fund built by women of Color 498 00:27:12,960 --> 00:27:15,840 Speaker 2: for women of color, has recently announced a new industry 499 00:27:15,920 --> 00:27:19,679 Speaker 2: specific cohort of the Get Venture Ready program in partnership 500 00:27:19,720 --> 00:27:23,840 Speaker 2: with JP Morgan Chase. The program is designed exclusively for 501 00:27:23,920 --> 00:27:28,120 Speaker 2: black women business owners in fintech all the workforce development industry. 502 00:27:28,480 --> 00:27:30,560 Speaker 2: To tell us more about what this program entails, that's 503 00:27:30,560 --> 00:27:34,000 Speaker 2: bring in the fun CEO Arianne Simone. Arianna actually just 504 00:27:34,040 --> 00:27:36,400 Speaker 2: want to start on JP Morgan because you have announced 505 00:27:36,400 --> 00:27:39,480 Speaker 2: this partnership with the EN they have acquired First Republic, 506 00:27:40,280 --> 00:27:45,000 Speaker 2: both of them with significant VC relationships and startups that 507 00:27:45,080 --> 00:27:47,359 Speaker 2: adventure backed. What is your reaction to the news that 508 00:27:47,680 --> 00:27:50,520 Speaker 2: JP Morgan has stepped in to acquire First Republic. 509 00:27:52,520 --> 00:27:56,639 Speaker 12: I'm not surprised. JP Morgan has definitely been a leader 510 00:27:56,680 --> 00:28:00,240 Speaker 12: in the space as far as venture capital is concerned 511 00:28:00,280 --> 00:28:03,760 Speaker 12: with emerging fund managers as well as the startup ecosystem 512 00:28:03,800 --> 00:28:06,680 Speaker 12: as a whole, So I'm not surprised when it came 513 00:28:06,760 --> 00:28:09,840 Speaker 12: to even the whole SBB scare and everything. 514 00:28:09,440 --> 00:28:10,159 Speaker 8: That was going on. 515 00:28:10,920 --> 00:28:13,680 Speaker 12: They were as investors in our fund, they were one 516 00:28:13,680 --> 00:28:16,360 Speaker 12: of the first LPs to reach out to see how 517 00:28:16,359 --> 00:28:20,080 Speaker 12: they could be supportive in taking over any type of 518 00:28:20,160 --> 00:28:22,399 Speaker 12: losses we may have incurred or anything that may have 519 00:28:22,440 --> 00:28:26,440 Speaker 12: taken place. Luckily, of course, we didn't. We had very 520 00:28:26,440 --> 00:28:29,399 Speaker 12: low exposure to the issue, but we saw early on 521 00:28:29,560 --> 00:28:31,840 Speaker 12: that they were definitely being a leader in the space 522 00:28:31,920 --> 00:28:34,240 Speaker 12: and wanting to get more into the startup ecosystem. 523 00:28:35,200 --> 00:28:36,879 Speaker 4: Let's get to your relationship with them. 524 00:28:36,880 --> 00:28:40,640 Speaker 2: I find this really interesting making CAPTO available to women 525 00:28:40,680 --> 00:28:44,520 Speaker 2: of color, but specifically in fintech. Why fintech. 526 00:28:46,440 --> 00:28:49,240 Speaker 12: Well, at the Fearless Fund, we actually invest in CpG 527 00:28:49,400 --> 00:28:52,720 Speaker 12: as well as technology, and CpG were heavy beauty and wellness, 528 00:28:52,960 --> 00:28:56,200 Speaker 12: heavy food and beverage, and in technology were heavy fintech, 529 00:28:56,280 --> 00:29:00,000 Speaker 12: SaaS marketplace and in the fintech sector. We also had 530 00:29:00,120 --> 00:29:03,760 Speaker 12: have investors LPs such as JP, Morgan, Chase and a 531 00:29:03,800 --> 00:29:06,880 Speaker 12: few others that are in the fintech business. For us, 532 00:29:06,920 --> 00:29:10,960 Speaker 12: this makes a very good strategic relationship. Anytime we invest 533 00:29:10,960 --> 00:29:13,360 Speaker 12: in a fintech company, we know that we have potential 534 00:29:13,440 --> 00:29:17,400 Speaker 12: acquirers that are invested in our fund that may acquire 535 00:29:17,440 --> 00:29:18,800 Speaker 12: them later on down the line. 536 00:29:19,120 --> 00:29:20,200 Speaker 8: So it just makes. 537 00:29:20,040 --> 00:29:22,520 Speaker 12: Good for business overall, and it sets up a very good, 538 00:29:22,560 --> 00:29:23,960 Speaker 12: strong pipeline. 539 00:29:24,840 --> 00:29:27,240 Speaker 2: For women of color in all of the industries that 540 00:29:27,240 --> 00:29:30,320 Speaker 2: you're investing in. What has the environment been like so 541 00:29:30,400 --> 00:29:32,360 Speaker 2: far in twenty twenty three are on. We've talked a 542 00:29:32,400 --> 00:29:36,720 Speaker 2: lot about tighter financial conditions on this program, but also 543 00:29:37,000 --> 00:29:41,560 Speaker 2: inequality frankly in how founders and startups are able to 544 00:29:41,640 --> 00:29:44,120 Speaker 2: navigate that those type of financial conditions. 545 00:29:45,360 --> 00:29:47,680 Speaker 12: That is very true. Women of color are the most 546 00:29:47,720 --> 00:29:52,120 Speaker 12: founded entrepreneur demographic, they are just the least funded, so 547 00:29:52,280 --> 00:29:54,920 Speaker 12: in any condition, whether there's a market correction or not, 548 00:29:55,480 --> 00:29:58,840 Speaker 12: they have the biggest gaps of disparity. We exist because 549 00:29:58,840 --> 00:30:01,280 Speaker 12: we do hope to change what the narrative is in 550 00:30:01,280 --> 00:30:04,480 Speaker 12: this landscape. As of right now, the profile of a 551 00:30:04,480 --> 00:30:07,720 Speaker 12: woman of color entrepreneur is somebody who you definitely want 552 00:30:07,760 --> 00:30:09,760 Speaker 12: to bet on if their cash burn rates are low. 553 00:30:10,040 --> 00:30:12,720 Speaker 12: They're not used to large sums of access to capital, 554 00:30:12,800 --> 00:30:16,160 Speaker 12: so they're very good stewards over their investment. Our portfolio 555 00:30:16,200 --> 00:30:18,520 Speaker 12: actually is even quite healthy at a time like this 556 00:30:18,680 --> 00:30:21,360 Speaker 12: because we chose to invest in women of color who 557 00:30:21,400 --> 00:30:23,800 Speaker 12: are very mindful of their investments. 558 00:30:24,800 --> 00:30:27,760 Speaker 2: You're coming to us from Atlanta, Georgia. On this program, 559 00:30:27,800 --> 00:30:31,160 Speaker 2: we talk a lot about San Francisco, Silicon Valley, Miami, 560 00:30:31,280 --> 00:30:35,320 Speaker 2: New York, sometimes ostin less about Atlanta, Georgia. What's the 561 00:30:35,440 --> 00:30:40,720 Speaker 2: environment like for raising venture funds founding a business in 562 00:30:40,760 --> 00:30:41,960 Speaker 2: that city and in that state. 563 00:30:43,200 --> 00:30:45,840 Speaker 12: One thing I can say that the whole VC tech 564 00:30:45,880 --> 00:30:50,960 Speaker 12: ecosystem in Atlanta, Georgia is definitely growing. It's definitely growing. 565 00:30:52,200 --> 00:30:55,680 Speaker 12: I would like to add, even just for transparency, that 566 00:30:56,120 --> 00:30:59,400 Speaker 12: is it easy. No, it's not easy to raise capital 567 00:30:59,560 --> 00:31:02,440 Speaker 12: in any environment, especially when you're an emerging fund manager. 568 00:31:02,800 --> 00:31:05,080 Speaker 12: And especially when your thesis is for women of color. 569 00:31:05,440 --> 00:31:08,240 Speaker 12: But we have been blessed by a very amazing LPs 570 00:31:08,280 --> 00:31:10,560 Speaker 12: such as you saw even on screen with JP Morrigan, 571 00:31:10,640 --> 00:31:15,200 Speaker 12: Bank of America, Allibank, Casco, MasterCard, and many others. So 572 00:31:15,480 --> 00:31:18,000 Speaker 12: for us, even though we are based in Atlanta, yes, 573 00:31:18,040 --> 00:31:22,240 Speaker 12: we have raised capital from many other areas including San Francisco, 574 00:31:22,440 --> 00:31:24,880 Speaker 12: New York, and all the other major areas that you 575 00:31:24,920 --> 00:31:26,920 Speaker 12: see a lot of financial institutions based. 576 00:31:27,320 --> 00:31:29,760 Speaker 2: We had a founder on the show earlier who just 577 00:31:29,920 --> 00:31:32,480 Speaker 2: closed one hundred million dollar round at seven hundred and 578 00:31:32,520 --> 00:31:36,280 Speaker 2: fifty million dollar valuation, but a year ago. You know, 579 00:31:36,640 --> 00:31:39,560 Speaker 2: even at their last stage there are about one hundred 580 00:31:39,600 --> 00:31:42,000 Speaker 2: and twenty eight million dollar valuation. I give you that 581 00:31:42,000 --> 00:31:43,680 Speaker 2: as an example because I want to know what kind 582 00:31:43,720 --> 00:31:46,440 Speaker 2: of activity you're seeing. You know, there is there a 583 00:31:46,560 --> 00:31:50,160 Speaker 2: desperation to go out and get rounds done. From the 584 00:31:50,160 --> 00:31:53,640 Speaker 2: startups perspective, is their willingness to deploy capital. 585 00:31:53,680 --> 00:31:55,520 Speaker 4: From the VC's perspective. 586 00:31:56,720 --> 00:31:57,640 Speaker 8: You are correct. 587 00:31:58,480 --> 00:32:02,320 Speaker 12: With this current market and vitce, you're seeing a lot 588 00:32:02,320 --> 00:32:05,240 Speaker 12: of tech founders raise what we call bridge rounds, which 589 00:32:05,280 --> 00:32:07,040 Speaker 12: is like an in between round before you get to 590 00:32:07,080 --> 00:32:09,920 Speaker 12: your next round. That's just pretty much what the market 591 00:32:09,920 --> 00:32:14,120 Speaker 12: has dictated in this current space. You are also correct 592 00:32:14,160 --> 00:32:15,840 Speaker 12: that a couple of years ago you could see a 593 00:32:15,880 --> 00:32:19,720 Speaker 12: company that was precede with the valuation of nine figures 594 00:32:19,760 --> 00:32:21,800 Speaker 12: and it was just like nothing. It was just something 595 00:32:21,800 --> 00:32:25,960 Speaker 12: that you could see. So valuations have gone down. Some 596 00:32:26,080 --> 00:32:28,720 Speaker 12: investors would like to say they're probably more realistic now 597 00:32:29,160 --> 00:32:31,120 Speaker 12: as far as valuations. So you have seen a drop 598 00:32:31,280 --> 00:32:34,080 Speaker 12: in those, and from a standpoint of the founder, their 599 00:32:34,120 --> 00:32:36,720 Speaker 12: approach to this environment is pretty much just been to 600 00:32:36,760 --> 00:32:39,600 Speaker 12: raise a bridge round to stay afloat while they continue 601 00:32:39,640 --> 00:32:40,800 Speaker 12: to build their technology. 602 00:32:41,760 --> 00:32:43,320 Speaker 2: Hey area, and the last question I want to go 603 00:32:43,400 --> 00:32:46,960 Speaker 2: to is CpG fintech. Where's the most activity right now? 604 00:32:47,000 --> 00:32:50,880 Speaker 2: Are there any specific areas thematically that you're looking at 605 00:32:50,880 --> 00:32:53,520 Speaker 2: going WHOA, there's a lot of activity. 606 00:32:53,080 --> 00:32:57,960 Speaker 12: Here for us, as I was sitting before, we're heavies. 607 00:32:58,240 --> 00:33:02,800 Speaker 12: We're fifty percent CpG FI technology and the sectors that 608 00:33:02,840 --> 00:33:06,200 Speaker 12: we are in, Yes, there's definitely heavy activity. One thing 609 00:33:06,240 --> 00:33:08,040 Speaker 12: I can say even on the tech space of course, 610 00:33:08,120 --> 00:33:10,200 Speaker 12: right now we all know that AI is like the 611 00:33:10,280 --> 00:33:13,160 Speaker 12: new hot thing. We've been investing in AI now for 612 00:33:13,280 --> 00:33:16,480 Speaker 12: about I think about a couple of years. We invested 613 00:33:17,080 --> 00:33:19,360 Speaker 12: in a company by the name of Clever Aria Moore 614 00:33:19,440 --> 00:33:22,440 Speaker 12: is the founder on the AI space, but you'll see 615 00:33:22,440 --> 00:33:24,320 Speaker 12: a lot of activity there. One thing I can say, 616 00:33:24,360 --> 00:33:27,000 Speaker 12: even back to fintech is that a lot of the 617 00:33:27,160 --> 00:33:31,480 Speaker 12: febal founders that are of color, they are very hot 618 00:33:31,560 --> 00:33:33,920 Speaker 12: for acquisition in the area of fintech. 619 00:33:34,040 --> 00:33:36,640 Speaker 2: Bilis fun Ceo Arion Smoan. We'll get you back on. 620 00:33:36,680 --> 00:33:47,200 Speaker 2: Thank you so much for your time. Soft Banks Semiconductor 621 00:33:47,280 --> 00:33:50,280 Speaker 2: Design Unit ARM filed confidentially over the weekend for an 622 00:33:50,280 --> 00:33:54,560 Speaker 2: initial public offering. Joining us now is Bloomberg Semiconductor correspondent 623 00:33:54,680 --> 00:33:58,160 Speaker 2: Ian King. Valuation is really interesting here. We've been covering 624 00:33:58,200 --> 00:34:02,000 Speaker 2: this saga story for quite a long time. Where do 625 00:34:02,040 --> 00:34:02,920 Speaker 2: we settle out on that. 626 00:34:03,160 --> 00:34:05,440 Speaker 13: Well, if you remember in video, was trying to buy 627 00:34:05,520 --> 00:34:09,840 Speaker 13: them for forty billion dollars based upon where their revenue 628 00:34:09,880 --> 00:34:13,640 Speaker 13: is roughly a billion a quarter. That's a massive valuation. 629 00:34:13,760 --> 00:34:17,480 Speaker 13: If you were to compare that revenue level to publicly 630 00:34:17,480 --> 00:34:20,720 Speaker 13: traded companies, they'd be worth sort of ten billion, probably 631 00:34:20,719 --> 00:34:22,880 Speaker 13: well less than twenty So really hard to put a 632 00:34:22,960 --> 00:34:25,480 Speaker 13: valuation on it. Obviously, a lot of people really like 633 00:34:25,520 --> 00:34:27,759 Speaker 13: their technology and really like their prospects. 634 00:34:28,040 --> 00:34:29,960 Speaker 4: What does ARM do You say they like their technology? 635 00:34:29,960 --> 00:34:30,799 Speaker 4: I mean, what is ARM? 636 00:34:31,000 --> 00:34:33,239 Speaker 13: Yeah, I mean it's a very good question. It's hard 637 00:34:33,239 --> 00:34:35,760 Speaker 13: to understand the licensing of an instruction set. 638 00:34:35,800 --> 00:34:37,840 Speaker 4: I realize, yes, Niche. 639 00:34:38,360 --> 00:34:40,600 Speaker 13: The way to understand it is they produce a layer 640 00:34:40,640 --> 00:34:45,520 Speaker 13: of technology that is in every smartphone, increasingly in computers 641 00:34:46,080 --> 00:34:49,400 Speaker 13: and also into data centers as well, and they're moving 642 00:34:49,440 --> 00:34:51,640 Speaker 13: up the food chain. They're doing more of the design, 643 00:34:51,640 --> 00:34:53,560 Speaker 13: they're doing more of the kind of thing that Pollcom 644 00:34:54,000 --> 00:34:56,920 Speaker 13: broad Com would do, and that's a very high margin business. 645 00:34:57,239 --> 00:34:59,680 Speaker 2: Is this going to be a really really big payday 646 00:35:00,120 --> 00:35:00,839 Speaker 2: for soft Bank? 647 00:35:01,960 --> 00:35:04,480 Speaker 13: Initially No, in terms of the cash. They've said, look, 648 00:35:04,520 --> 00:35:07,439 Speaker 13: we're not going to dump our entire holdings into the market. 649 00:35:07,520 --> 00:35:09,839 Speaker 13: We're going to put an element of our holdings into 650 00:35:09,880 --> 00:35:13,000 Speaker 13: the market. But what it will do is let people 651 00:35:13,080 --> 00:35:15,360 Speaker 13: know what ARM is worth. They paid thirty two billion 652 00:35:15,440 --> 00:35:18,800 Speaker 13: dollars for this in twenty sixteen. People will be looking 653 00:35:18,840 --> 00:35:22,400 Speaker 13: for this as a kind of a cornerstone of soft 654 00:35:22,400 --> 00:35:26,839 Speaker 13: Banks holdings to kind of restore some faith and confidence 655 00:35:26,880 --> 00:35:30,359 Speaker 13: in that company's investing and try and get evaluation as well. 656 00:35:30,360 --> 00:35:31,440 Speaker 13: Above at thirty. 657 00:35:31,160 --> 00:35:34,680 Speaker 2: Two there's also a jurisdiction or territorial angle to this story, 658 00:35:34,680 --> 00:35:37,200 Speaker 2: which is ARM is a UK based firm, it's a 659 00:35:37,320 --> 00:35:39,640 Speaker 2: US listing soft banks in Japan. 660 00:35:40,480 --> 00:35:42,279 Speaker 4: That's quite complicated, it is. 661 00:35:42,320 --> 00:35:45,080 Speaker 13: I mean, the UK, as we've written, has really really 662 00:35:45,120 --> 00:35:48,280 Speaker 13: wanted them to come back to the LS and really, 663 00:35:48,520 --> 00:35:51,120 Speaker 13: as far as we understand, have failed to do that. 664 00:35:51,239 --> 00:35:53,439 Speaker 13: So it's going to be a US listing a lot 665 00:35:53,440 --> 00:35:57,640 Speaker 13: of ARMS operations. It's CEO here these days, headquarters is 666 00:35:57,680 --> 00:36:00,880 Speaker 13: still Cambridge and obviously the ownership and Japans whole a 667 00:36:00,880 --> 00:36:02,759 Speaker 13: lot of regulatory issues that needed to. 668 00:36:02,719 --> 00:36:05,600 Speaker 4: Be navigated there also give us the boiler plate. 669 00:36:05,680 --> 00:36:07,680 Speaker 2: There's a lot that we don't know, like timing, there 670 00:36:07,719 --> 00:36:10,120 Speaker 2: are lots of factors that need to happen before we 671 00:36:10,160 --> 00:36:11,280 Speaker 2: move forward with this listing. 672 00:36:11,440 --> 00:36:13,920 Speaker 13: Yeah, I mean the big thing is to focus on 673 00:36:13,960 --> 00:36:16,759 Speaker 13: this is going to be the biggest tech IPO this 674 00:36:16,920 --> 00:36:19,279 Speaker 13: year when you look at it from a valuation perspective. 675 00:36:20,000 --> 00:36:22,400 Speaker 13: They've said all along sort of second half of the 676 00:36:22,480 --> 00:36:27,799 Speaker 13: year typically. You know, we've got the filing now, so 677 00:36:28,400 --> 00:36:30,680 Speaker 13: that's say it's probably autumn. 678 00:36:30,360 --> 00:36:33,759 Speaker 2: Right, bloomberg Z and King on all things ARM. We're 679 00:36:33,840 --> 00:36:36,920 Speaker 2: rating that listing. That does it for this edition of 680 00:36:36,920 --> 00:36:39,919 Speaker 2: Bloomberg Technology come back tomorrow for a jam packed show. 681 00:36:40,160 --> 00:36:42,320 Speaker 4: Guests just for example, Uber. 682 00:36:42,160 --> 00:36:46,399 Speaker 2: CEO, Dara Kostra Shahi, Apple co founder Steve Wasne act 683 00:36:46,480 --> 00:36:49,840 Speaker 2: and so Fi CEO and Sinota. You don't want to 684 00:36:49,880 --> 00:36:52,239 Speaker 2: miss that one. And okay, look, we're just a day 685 00:36:52,239 --> 00:36:54,280 Speaker 2: into the week. We have earnings, we have a FED meeting. 686 00:36:54,280 --> 00:36:56,360 Speaker 2: But there's a lot to recap. Check out the podcast 687 00:36:56,600 --> 00:37:01,600 Speaker 2: wherever you get your podcasts, Apple, Spotify, iHeart and of 688 00:37:01,640 --> 00:37:05,560 Speaker 2: course on all of your Bloomberg platforms. Apple comes later 689 00:37:05,600 --> 00:37:07,880 Speaker 2: in the week. That is where a big portion of 690 00:37:07,880 --> 00:37:10,239 Speaker 2: our attention is. But don't forget the discussion we've had 691 00:37:10,239 --> 00:37:14,520 Speaker 2: in the show, which is the QSRs are reporting, Will 692 00:37:14,560 --> 00:37:17,640 Speaker 2: they talk about AI? AI has been the buzzword of 693 00:37:17,680 --> 00:37:21,040 Speaker 2: this earning season, not just the technology sector, but more broadly. 694 00:37:21,080 --> 00:37:22,759 Speaker 2: Go and check out that Bank of America data or 695 00:37:22,760 --> 00:37:25,440 Speaker 2: we will track it here on Bloomberg Technology. 696 00:37:25,520 --> 00:37:26,280 Speaker 4: This is Bloomberg