1 00:00:14,160 --> 00:00:17,599 Speaker 1: Imed Ludlow in San Francisco. Caroline Hyde off today this 2 00:00:18,000 --> 00:00:21,320 Speaker 1: is Bloomberg Technology and coming up. Tech stocks drop as 3 00:00:21,320 --> 00:00:24,200 Speaker 1: a hot jobs report, adds pressure on the FED to 4 00:00:24,400 --> 00:00:28,920 Speaker 1: keep raising rates. Plus warnings from Amazon and Alphabet way 5 00:00:28,960 --> 00:00:32,160 Speaker 1: on shares as big tech gets hit by the slowdown. 6 00:00:32,200 --> 00:00:35,159 Speaker 1: Even so, then has that one rises for a fifth 7 00:00:35,440 --> 00:00:39,360 Speaker 1: straight week as Apple gains and the cyber tag which 8 00:00:39,400 --> 00:00:42,479 Speaker 1: sent derivative trading back to the nies. We've got the 9 00:00:42,479 --> 00:00:46,120 Speaker 1: Bloomberg scoop on how vulnerability at a little known software 10 00:00:46,120 --> 00:00:49,960 Speaker 1: firm lead to Sunbroker's going manual. But first we've got 11 00:00:49,960 --> 00:00:53,400 Speaker 1: to talk about these markets are really hot jobs print 12 00:00:53,440 --> 00:00:55,880 Speaker 1: for the month of January, putting pressure on the FED 13 00:00:56,080 --> 00:00:58,640 Speaker 1: to look at raising rates. That's the market take on 14 00:00:58,680 --> 00:01:00,680 Speaker 1: all of this, and then we digest starnings and that's 15 00:01:00,680 --> 00:01:03,240 Speaker 1: that one hundred down one point eight percent, although as 16 00:01:03,240 --> 00:01:06,160 Speaker 1: we said, it ended the week up on the week, 17 00:01:06,280 --> 00:01:09,920 Speaker 1: meaning five straight consecutive weeks of games. The Philadelphia Sundy 18 00:01:09,920 --> 00:01:12,080 Speaker 1: Conductor Index also kind of in line with that, down 19 00:01:12,080 --> 00:01:14,520 Speaker 1: one point nine percent. Is yields continue to push higher. 20 00:01:14,600 --> 00:01:17,920 Speaker 1: Bitcoin essentially did nothing in reaction to what we saw 21 00:01:18,280 --> 00:01:21,080 Speaker 1: from the data. It kind of traded sideways around twenty 22 00:01:21,120 --> 00:01:25,520 Speaker 1: three thousand, five hundred dollars per token. There's still momentum 23 00:01:25,520 --> 00:01:28,360 Speaker 1: in the earning story in both directions. Switched up the 24 00:01:28,360 --> 00:01:30,200 Speaker 1: board to look at the names that we're thinking about, 25 00:01:30,560 --> 00:01:33,919 Speaker 1: particularly Apple. It was so interesting because the post market 26 00:01:33,959 --> 00:01:37,880 Speaker 1: reaction Thursday to Apple's miss was negative, and yet we're 27 00:01:37,920 --> 00:01:41,520 Speaker 1: up two point five percent. There was some optimism around 28 00:01:41,520 --> 00:01:44,039 Speaker 1: what Tim Cook had to say on reopening in China. 29 00:01:44,160 --> 00:01:46,800 Speaker 1: Not so for Alphabet, the parent of Google or Amazon. 30 00:01:47,040 --> 00:01:52,080 Speaker 1: Those shares really dropping and then accelerating their declines following 31 00:01:52,120 --> 00:01:54,000 Speaker 1: that hot job sprints. Of course, their earnings were not 32 00:01:54,080 --> 00:01:58,080 Speaker 1: impressive to investors, worrying sounds about the trajectory three, which 33 00:01:58,080 --> 00:02:00,960 Speaker 1: will get into. And then Meta actually had been high, 34 00:02:01,040 --> 00:02:04,200 Speaker 1: continuing its momentum from its earnings that beat earlier in 35 00:02:04,240 --> 00:02:06,960 Speaker 1: the week. But then we had that jobs print as well, 36 00:02:07,080 --> 00:02:09,160 Speaker 1: and that's where we got to go because the focus 37 00:02:09,200 --> 00:02:11,720 Speaker 1: on data is back, the focus is on the FED 38 00:02:12,080 --> 00:02:14,679 Speaker 1: is back. To Boomberg's repick, it joins me from Mount 39 00:02:14,720 --> 00:02:16,560 Speaker 1: in d C. Read hit me with the numbers that 40 00:02:16,560 --> 00:02:20,320 Speaker 1: we got from the eco sphere on Friday. Well, first 41 00:02:20,360 --> 00:02:23,200 Speaker 1: of all, I'm always here to report good economic news. 42 00:02:23,240 --> 00:02:25,720 Speaker 1: So that's that's a great way to end of Friday. UM. 43 00:02:25,760 --> 00:02:28,120 Speaker 1: But what we saw was we saw that employers added 44 00:02:28,200 --> 00:02:31,200 Speaker 1: about a half a million jobs in January, which blew 45 00:02:31,320 --> 00:02:34,120 Speaker 1: past estimates. And it was this really broad based game 46 00:02:34,560 --> 00:02:37,840 Speaker 1: that really kind of showed this this restrengthening in the 47 00:02:37,919 --> 00:02:40,359 Speaker 1: labor market when we didn't really know it could get 48 00:02:40,360 --> 00:02:43,280 Speaker 1: any stronger. So you saw the unemployment rate fall to 49 00:02:43,560 --> 00:02:47,639 Speaker 1: three point four percent, which is a fifty three year low. UM. 50 00:02:47,680 --> 00:02:50,080 Speaker 1: We saw this broad based hiring. We saw the average 51 00:02:50,120 --> 00:02:53,280 Speaker 1: work week tick up, showing that there's still kind of 52 00:02:53,280 --> 00:02:57,040 Speaker 1: that robust labor demand. And we saw wages, while decelerating 53 00:02:57,040 --> 00:03:00,800 Speaker 1: from the prior month, we saw them still continue to 54 00:03:00,840 --> 00:03:05,040 Speaker 1: climb at a robust rate. Read. You're the only person 55 00:03:05,120 --> 00:03:07,720 Speaker 1: that I know that can go through all of that 56 00:03:07,800 --> 00:03:11,520 Speaker 1: was such beaming joy on a Friday after what has 57 00:03:11,560 --> 00:03:16,160 Speaker 1: been a busy day in markets, volatility driven by the numbers. Here, 58 00:03:16,200 --> 00:03:19,160 Speaker 1: I am broken and exhausted. You just keep going, fine, 59 00:03:19,240 --> 00:03:21,359 Speaker 1: let's go with it. I think that the narrative is 60 00:03:21,400 --> 00:03:23,519 Speaker 1: I framed it. We go back towards the FED right. 61 00:03:23,800 --> 00:03:27,400 Speaker 1: Good economic data, bad news for markets, and that's because 62 00:03:27,400 --> 00:03:29,320 Speaker 1: of what we think the FED will now have to 63 00:03:29,360 --> 00:03:32,400 Speaker 1: reconsider what is the market saying about the Federal Reserve 64 00:03:32,639 --> 00:03:34,600 Speaker 1: in response to the numbers that we have on the screen. 65 00:03:36,120 --> 00:03:38,520 Speaker 1: So especially whenever you get a number like today in 66 00:03:38,560 --> 00:03:41,840 Speaker 1: the week of other strong data that we've seen, Um, 67 00:03:41,920 --> 00:03:46,520 Speaker 1: you saw job openings rally back above eleven million. You've 68 00:03:46,560 --> 00:03:50,360 Speaker 1: seen jobless claims, so applications for unemployment you've seen them fall. 69 00:03:50,480 --> 00:03:53,480 Speaker 1: And the last four out of the last five weeks, 70 00:03:53,960 --> 00:03:58,480 Speaker 1: UM you've seen kind of susteemed strength and the consumer UM, 71 00:03:58,800 --> 00:04:02,520 Speaker 1: it really makes the a the Fed's job more difficult. 72 00:04:02,600 --> 00:04:04,480 Speaker 1: And you know, some of my colleagues had a great 73 00:04:04,480 --> 00:04:07,320 Speaker 1: story out today about how when you see jobs numbers 74 00:04:07,360 --> 00:04:10,800 Speaker 1: like today's, it you know, really strengthens the Fed's resolve 75 00:04:11,120 --> 00:04:16,279 Speaker 1: to you know, push rates above five percent. Alright, bloom 76 00:04:16,320 --> 00:04:20,160 Speaker 1: Bloomgog's re pick. It just fantastic rapporting on that ECO data. 77 00:04:20,400 --> 00:04:22,200 Speaker 1: That's Friday. I want to hone in on the tech 78 00:04:22,279 --> 00:04:26,279 Speaker 1: sector and put it in the context of job cuts, 79 00:04:26,320 --> 00:04:29,840 Speaker 1: all of those that we've seen, particularly in two Boomberg's 80 00:04:29,920 --> 00:04:32,120 Speaker 1: m Live team has been asking if these cuts have 81 00:04:32,200 --> 00:04:36,560 Speaker 1: been too large, too small, or even too premature. Here's 82 00:04:36,560 --> 00:04:38,800 Speaker 1: what some of Bloomberg Television's guests have had to say 83 00:04:38,839 --> 00:04:43,600 Speaker 1: about that question. Even after all of the recent layoffs 84 00:04:43,600 --> 00:04:46,000 Speaker 1: that you've seen for many of the large tech companies, 85 00:04:46,040 --> 00:04:49,080 Speaker 1: we still see tech, as you know, relatively bloated in 86 00:04:49,160 --> 00:04:51,200 Speaker 1: terms of if you look at how much it's you know, 87 00:04:51,520 --> 00:04:55,240 Speaker 1: employment counts have grown relative to its real sales growth 88 00:04:55,279 --> 00:04:58,120 Speaker 1: over the past several years. Even after you adjust for 89 00:04:58,160 --> 00:05:02,520 Speaker 1: the recent layoffs. For every big tech company that's having layoffs, 90 00:05:02,600 --> 00:05:05,760 Speaker 1: there are many small organizations who are eager to gobble 91 00:05:05,800 --> 00:05:08,560 Speaker 1: those employees up. So I wouldn't say that there's a 92 00:05:08,640 --> 00:05:12,120 Speaker 1: dearth right now. What we're seeing, however, is CEO is 93 00:05:12,160 --> 00:05:14,520 Speaker 1: taking advantage of fear in the headlines. So I think 94 00:05:14,560 --> 00:05:17,640 Speaker 1: that these layoffs put these companies in a position where 95 00:05:17,640 --> 00:05:20,640 Speaker 1: it's more difficult for them to show better than expected 96 00:05:20,680 --> 00:05:23,479 Speaker 1: sales and profits, because if they had better than expected 97 00:05:23,480 --> 00:05:26,520 Speaker 1: sales and profits, why are they cutting head council significantly. 98 00:05:26,600 --> 00:05:29,520 Speaker 1: You're at the place where layoffs are the most likely 99 00:05:29,800 --> 00:05:32,560 Speaker 1: catalyst to give FED confidence inflation. We'll get back to 100 00:05:32,560 --> 00:05:34,520 Speaker 1: two percent, so I expect sort of towards q t 101 00:05:34,640 --> 00:05:37,800 Speaker 1: Q three you'll see the margin improvements and bottom line 102 00:05:37,839 --> 00:05:41,360 Speaker 1: improvements on earnings that resolve from the layoffs and then 103 00:05:41,440 --> 00:05:42,919 Speaker 1: the cost cutting. But we're not going to see it 104 00:05:42,960 --> 00:05:44,440 Speaker 1: just yet, so I think it'll trickle in and then 105 00:05:44,440 --> 00:05:47,840 Speaker 1: in the coming months. So you've had the ECO data, 106 00:05:47,839 --> 00:05:50,240 Speaker 1: you've had the earnings. Let's get to the macro and 107 00:05:50,279 --> 00:05:53,159 Speaker 1: the earning season at large with Alex tap Scott, Managing 108 00:05:53,200 --> 00:05:56,360 Speaker 1: director over at nine point Partners, the Digital Asset Group 109 00:05:56,440 --> 00:05:59,240 Speaker 1: over six billion dollars US and assets under management or 110 00:05:59,320 --> 00:06:02,840 Speaker 1: eight billion Canadian dollars and Alex, it's quite the week 111 00:06:02,880 --> 00:06:05,400 Speaker 1: to digest. I'm grateful to have you with me. I 112 00:06:05,640 --> 00:06:08,520 Speaker 1: guess let's start on the jobs data, really hot print 113 00:06:08,560 --> 00:06:11,720 Speaker 1: here in the US for January. You know the logic here, 114 00:06:11,760 --> 00:06:14,920 Speaker 1: I guess from the market's reaction is the FED will 115 00:06:14,920 --> 00:06:17,680 Speaker 1: have to rethink about the timing of a policy pivot 116 00:06:18,000 --> 00:06:21,240 Speaker 1: and then in the context of higher rates, discounting present value, 117 00:06:21,279 --> 00:06:25,719 Speaker 1: future profits, tech, etcetera. Is this a think again moment 118 00:06:25,839 --> 00:06:29,040 Speaker 1: or is this just a blip? Well, I guess we're 119 00:06:29,040 --> 00:06:31,800 Speaker 1: back in the zone where good news is bad news. UM. 120 00:06:31,839 --> 00:06:34,839 Speaker 1: I think that the market reaction was pretty predictable given 121 00:06:34,920 --> 00:06:37,960 Speaker 1: the much harder than expected non farm perils. But as 122 00:06:38,000 --> 00:06:40,080 Speaker 1: you pointed out in your opening segment. You know, the 123 00:06:40,080 --> 00:06:42,080 Speaker 1: market has been up five weeks in a row, and 124 00:06:42,120 --> 00:06:45,679 Speaker 1: the NASDACK, which is obviously highly sensitive to interest rates, 125 00:06:46,040 --> 00:06:47,840 Speaker 1: UM is set to close up or I said, I 126 00:06:47,839 --> 00:06:50,719 Speaker 1: should say recently just closed up over three percent for 127 00:06:50,880 --> 00:06:52,960 Speaker 1: the week. So I think we probably do for a 128 00:06:52,960 --> 00:06:57,000 Speaker 1: pullback after a pretty blistering hot period. And as I 129 00:06:57,040 --> 00:07:00,200 Speaker 1: see it, I don't think the number today UM, as 130 00:07:00,200 --> 00:07:03,279 Speaker 1: one isolated data point is enough to derail this risk 131 00:07:03,360 --> 00:07:04,920 Speaker 1: on trade that we've seen to pick up the last 132 00:07:04,920 --> 00:07:11,160 Speaker 1: a while. So you yourself personally do hold Apple, Amazon, Alphabet, 133 00:07:11,200 --> 00:07:13,000 Speaker 1: the parent company of Google. I think you also hold 134 00:07:13,000 --> 00:07:17,560 Speaker 1: Microsoft as well. Is we reflect on the week of earnings, 135 00:07:17,800 --> 00:07:19,880 Speaker 1: you know, one of the takeaways that that other guests 136 00:07:19,880 --> 00:07:23,760 Speaker 1: have said is that essentially this is definitive proof megacat 137 00:07:23,880 --> 00:07:26,280 Speaker 1: tech is not immune to what we see in the 138 00:07:26,280 --> 00:07:30,360 Speaker 1: global economy. Was that your takeaway as well? Well? I don't. 139 00:07:30,440 --> 00:07:32,920 Speaker 1: I think that's been obvious for the past few months. 140 00:07:32,960 --> 00:07:35,280 Speaker 1: You know, I think going into two thousand and twenty three, 141 00:07:35,320 --> 00:07:38,600 Speaker 1: investors had really been pricing in the worst case scenario. 142 00:07:38,840 --> 00:07:41,440 Speaker 1: For the start of this year. I think expectations were 143 00:07:41,480 --> 00:07:43,640 Speaker 1: that inflation was going to keep running hot, and that 144 00:07:43,720 --> 00:07:45,960 Speaker 1: was going to force the FED and other central banks 145 00:07:45,960 --> 00:07:48,320 Speaker 1: to continue to jack up braids, and that we were 146 00:07:48,320 --> 00:07:50,280 Speaker 1: going to be doing at some point for you know, 147 00:07:50,400 --> 00:07:53,440 Speaker 1: some kind of economic um slow down, maybe even a 148 00:07:53,520 --> 00:07:57,800 Speaker 1: major recession. So far, the data has not borne that out. Now. 149 00:07:57,800 --> 00:07:59,640 Speaker 1: I wouldn't say that we're out of the woods yet, 150 00:08:00,000 --> 00:08:02,200 Speaker 1: but I think that the worst case scenario is really 151 00:08:02,240 --> 00:08:04,559 Speaker 1: no longer the base case. And I think a soft 152 00:08:04,680 --> 00:08:06,840 Speaker 1: landing went from something that was kind of a pipe 153 00:08:06,920 --> 00:08:09,480 Speaker 1: dream for a lot of balls to something that's increasingly 154 00:08:09,520 --> 00:08:12,240 Speaker 1: being priced into the market, and when that happens, I 155 00:08:12,320 --> 00:08:16,200 Speaker 1: think that that's obviously going to cycle capital back into stocks. 156 00:08:16,320 --> 00:08:18,280 Speaker 1: I think a lot of managers started the year very 157 00:08:18,360 --> 00:08:21,720 Speaker 1: underweight um, looking to wait and see how the FED reacted, 158 00:08:22,040 --> 00:08:23,840 Speaker 1: and you get this foe mode getting the year as 159 00:08:23,880 --> 00:08:25,440 Speaker 1: simple as it might sound, where they have to play 160 00:08:25,480 --> 00:08:27,600 Speaker 1: catch up to make sure they don't miss their benchmarks. 161 00:08:27,640 --> 00:08:29,120 Speaker 1: And I think we're starting to see that trade playing 162 00:08:29,120 --> 00:08:32,240 Speaker 1: it too right. There's this idea of tough comps right 163 00:08:32,440 --> 00:08:36,560 Speaker 1: or unfair comparisons between Q four two and Q four 164 00:08:36,600 --> 00:08:39,560 Speaker 1: twenty one. Apple was a good example of that in 165 00:08:39,640 --> 00:08:42,480 Speaker 1: their commentary around the difficulties they had in the last 166 00:08:42,520 --> 00:08:46,319 Speaker 1: three months two, but also the idea that it wasn't 167 00:08:46,360 --> 00:08:50,040 Speaker 1: fair to compare with the year the period a year 168 00:08:50,040 --> 00:08:52,960 Speaker 1: ago because of the different product release timings. You know, 169 00:08:53,320 --> 00:08:55,800 Speaker 1: how good was Q four twenty one compared to what 170 00:08:55,880 --> 00:08:59,760 Speaker 1: we saw in the most recent earnings period. Well, as 171 00:08:59,800 --> 00:09:02,360 Speaker 1: you pointed out, every company is different, and an Apple's case, 172 00:09:02,400 --> 00:09:04,920 Speaker 1: you know, the product cycle can weigh on earnings and 173 00:09:04,960 --> 00:09:07,560 Speaker 1: on revenue. But I think in general Q four two 174 00:09:07,600 --> 00:09:09,880 Speaker 1: thousand twenty one, so a little over a year ago 175 00:09:10,440 --> 00:09:12,560 Speaker 1: was kind of like the Goldilocks zone. You know, we 176 00:09:12,600 --> 00:09:15,719 Speaker 1: had all this deferred consumer spending because people have been 177 00:09:15,760 --> 00:09:18,520 Speaker 1: walked up during covid um. You had an economy that 178 00:09:18,600 --> 00:09:20,760 Speaker 1: was still growing in a very fast flip, and the 179 00:09:20,760 --> 00:09:23,840 Speaker 1: inflation concerns weren't quite as you know acute, I guess 180 00:09:23,840 --> 00:09:26,959 Speaker 1: as they are today. And so you saw that transferred 181 00:09:26,960 --> 00:09:30,280 Speaker 1: into consumer spending, you know, business spending on advertising, spending 182 00:09:30,280 --> 00:09:32,800 Speaker 1: on cloud and so forth. That translates into the earnings 183 00:09:32,800 --> 00:09:34,880 Speaker 1: for these companies. So I would say that looking at 184 00:09:35,000 --> 00:09:38,640 Speaker 1: you know, most investors were looking at this earnings prints 185 00:09:38,679 --> 00:09:41,840 Speaker 1: from these different companies hoping to avoid the worst and 186 00:09:42,000 --> 00:09:44,880 Speaker 1: to be clear, like tech earnings were mediocre, and I 187 00:09:44,880 --> 00:09:47,160 Speaker 1: would say kind of a mixed bag. You know, Facebook 188 00:09:47,160 --> 00:09:50,479 Speaker 1: beat on earnings and revenue but had exceptionally low expectations. 189 00:09:50,679 --> 00:09:54,239 Speaker 1: Apples you put it up mixed, mixed on both. But ultimately, 190 00:09:54,440 --> 00:09:57,000 Speaker 1: you know, A, we're comparing it to a period that 191 00:09:57,760 --> 00:09:59,840 Speaker 1: was you know, if not an lt lier, very difficult 192 00:10:00,040 --> 00:10:03,400 Speaker 1: ump compared to what we saw last year. Uh and B. 193 00:10:03,720 --> 00:10:06,079 Speaker 1: I think that really people are trying to look forward 194 00:10:06,400 --> 00:10:08,680 Speaker 1: um to see what these companies are going to do, 195 00:10:08,760 --> 00:10:12,599 Speaker 1: to try and write the ship and uh, yes, know, 196 00:10:12,760 --> 00:10:15,800 Speaker 1: well how zeroed in? Then is this market on the 197 00:10:15,800 --> 00:10:18,160 Speaker 1: bottom line? When we talk about what we learned from 198 00:10:18,160 --> 00:10:21,600 Speaker 1: earnings about three going forward, the answer seems to be 199 00:10:21,640 --> 00:10:26,320 Speaker 1: not that much other than the zeroed in on cost. Yeah, well, 200 00:10:26,360 --> 00:10:28,120 Speaker 1: in a zero interest rate environment, it's a grow at 201 00:10:28,160 --> 00:10:30,080 Speaker 1: all costs kind of market, right, And that's why you 202 00:10:30,120 --> 00:10:33,120 Speaker 1: saw you know, unprofitable tech companies trading at sky high 203 00:10:33,200 --> 00:10:36,320 Speaker 1: valuations and and that was true also for big cap 204 00:10:36,360 --> 00:10:39,600 Speaker 1: tech companies. And I think we're starting to migrate from 205 00:10:39,640 --> 00:10:42,280 Speaker 1: a top line market to a bottom line market. There's 206 00:10:42,280 --> 00:10:45,040 Speaker 1: an increasing focus on costs, all the inputs that go 207 00:10:45,120 --> 00:10:48,880 Speaker 1: into Uh, you know the financials for these companies, and 208 00:10:49,320 --> 00:10:51,679 Speaker 1: as as you've pointed out, there's been some job cuts 209 00:10:51,679 --> 00:10:53,160 Speaker 1: and some other trimming, and I think if you look 210 00:10:53,200 --> 00:10:55,920 Speaker 1: at the earnings calls for these companies the past week 211 00:10:55,960 --> 00:10:58,400 Speaker 1: and a half or so, that's something that they've really 212 00:10:58,480 --> 00:11:01,120 Speaker 1: drilled down on, and I think investors are pricing that 213 00:11:01,200 --> 00:11:03,760 Speaker 1: and they're thinking, Okay, well, if they can keep cutting 214 00:11:03,760 --> 00:11:07,480 Speaker 1: cost even if revenue is flat, then I'm going to 215 00:11:07,520 --> 00:11:09,520 Speaker 1: see bigger earnings and I want to buy into that trip. 216 00:11:10,800 --> 00:11:14,040 Speaker 1: All right, Alex Taps got managing director over at nine 217 00:11:14,040 --> 00:11:16,240 Speaker 1: Point Partners Digital Asset Groups. So good to have you 218 00:11:16,280 --> 00:11:28,560 Speaker 1: here with us. Tech is really doing quite terribly on 219 00:11:28,720 --> 00:11:32,520 Speaker 1: fundamental so well. Tech companies are ripping on their stock prices. 220 00:11:32,920 --> 00:11:38,840 Speaker 1: They're having an awful earning season. It's a pretty direct 221 00:11:38,920 --> 00:11:41,960 Speaker 1: and our assessment from Credit Swiss is Jonathan Golob on 222 00:11:42,280 --> 00:11:45,439 Speaker 1: how text faring. Let's continue the conversation a mega week 223 00:11:45,720 --> 00:11:49,600 Speaker 1: of megacap tech earnings. Just getting e commerce perspective. Rachel Typograph, 224 00:11:49,679 --> 00:11:53,000 Speaker 1: CEO and founder of mick Mac, a global econ commerce 225 00:11:53,200 --> 00:11:57,160 Speaker 1: enablement and analytics platform for multi channel brand. You heard 226 00:11:57,200 --> 00:12:00,560 Speaker 1: what Jonathan Golob had to say, about how tech he's doing. 227 00:12:00,920 --> 00:12:05,360 Speaker 1: In summary, not very well when you looked across Amazon 228 00:12:05,440 --> 00:12:08,400 Speaker 1: on the e commerce side, and also as it extends 229 00:12:08,440 --> 00:12:11,000 Speaker 1: to what Alphabet had to say about core search. What 230 00:12:11,080 --> 00:12:14,920 Speaker 1: was your takeaway from this week, Rachel, Listen, we all 231 00:12:15,000 --> 00:12:17,120 Speaker 1: knew this was going to be a tough week, but 232 00:12:17,240 --> 00:12:21,600 Speaker 1: we're talking about the three biggest players in advertising. We 233 00:12:21,679 --> 00:12:25,320 Speaker 1: have Google, we have Meta, we have Amazon. They each 234 00:12:25,360 --> 00:12:29,199 Speaker 1: play a fundamental role in the ecosystem, but times start changing. 235 00:12:29,760 --> 00:12:32,560 Speaker 1: With Google, they've had the stronghold in search, but they 236 00:12:32,600 --> 00:12:35,839 Speaker 1: also sit on this amazing asset YouTube, which is an 237 00:12:35,840 --> 00:12:40,040 Speaker 1: incredible place for brand awareness and engagement. You have Meta, 238 00:12:40,520 --> 00:12:43,960 Speaker 1: largest player and Social. They've done a phenomenal job of 239 00:12:44,000 --> 00:12:48,440 Speaker 1: building out full funnel ad solutions across Facebook and Meta. 240 00:12:49,080 --> 00:12:51,480 Speaker 1: And now you have Amazon, the largest player in e 241 00:12:51,600 --> 00:12:54,280 Speaker 1: com which is also another way of saying they're sitting 242 00:12:54,320 --> 00:12:57,640 Speaker 1: on a treasure chest of purchase data waiting to be monetized. 243 00:12:58,320 --> 00:13:02,480 Speaker 1: And what's changed in the no I'm starting instruction, Rachel, 244 00:13:02,480 --> 00:13:04,000 Speaker 1: I want to I want to zero in on that 245 00:13:04,080 --> 00:13:08,319 Speaker 1: because this is what surprised me about this earnings report 246 00:13:08,400 --> 00:13:13,760 Speaker 1: from Amazon. Last quarter, the upside surprise was advertising, and 247 00:13:13,880 --> 00:13:17,720 Speaker 1: this quarter the downside surprise was the AWS didn't come 248 00:13:17,760 --> 00:13:19,680 Speaker 1: in to save the day. But we didn't really even 249 00:13:19,679 --> 00:13:23,120 Speaker 1: talk about advertising, and you're basically saying that this is 250 00:13:23,800 --> 00:13:30,200 Speaker 1: some unlocked still locked up potential to the commerce giant. Absolutely, 251 00:13:30,559 --> 00:13:34,840 Speaker 1: AWS was an amazing way to offset margins. Advertising is 252 00:13:34,840 --> 00:13:38,160 Speaker 1: an amazing way to offset margins in the losses of 253 00:13:38,200 --> 00:13:43,680 Speaker 1: their traditional retail business. So Amazon's ad business grew in 254 00:13:43,760 --> 00:13:47,960 Speaker 1: Q four It was now becoming a very very close 255 00:13:48,000 --> 00:13:51,480 Speaker 1: player behind Meta. And the reason for this is because 256 00:13:51,480 --> 00:13:55,360 Speaker 1: of the headwinds that Google and Meta have experienced changes 257 00:13:55,400 --> 00:14:00,040 Speaker 1: in Apple's iOS fourteen cookie list Internet, they started to 258 00:14:00,120 --> 00:14:04,480 Speaker 1: undo the way that direct response advertising fundamentally worked in 259 00:14:04,520 --> 00:14:09,880 Speaker 1: platter platforms like Meta and Google. As a result, advertisers 260 00:14:10,440 --> 00:14:12,599 Speaker 1: want to be able to demonstrate the r o I 261 00:14:12,920 --> 00:14:16,520 Speaker 1: of their marketing investment, and Amazon is sitting on all 262 00:14:16,520 --> 00:14:19,120 Speaker 1: of this purchase data to be able to demonstrate to 263 00:14:19,200 --> 00:14:23,560 Speaker 1: advertisers direct attribution. And so Amazon is really writing the 264 00:14:23,560 --> 00:14:28,040 Speaker 1: tail winds that are in fact Google and Metas headwards. 265 00:14:29,800 --> 00:14:33,040 Speaker 1: Let's zero and a Meta. That was the surprise of 266 00:14:33,080 --> 00:14:37,440 Speaker 1: the week, probably you know, a the response Metas shares 267 00:14:37,520 --> 00:14:41,080 Speaker 1: jumping by the most in a decade, Thursday. But the 268 00:14:41,320 --> 00:14:45,560 Speaker 1: other data point is Facebook at two billion daily users, 269 00:14:45,640 --> 00:14:49,240 Speaker 1: up seventy million from a year ago. What is Meta 270 00:14:49,360 --> 00:14:53,880 Speaker 1: done to make its platforms interesting again, because presumably the 271 00:14:53,880 --> 00:14:58,320 Speaker 1: market reaction is the investors saying this bodes quite well 272 00:14:58,360 --> 00:15:02,880 Speaker 1: for advertising revenue going forward. It Yeah. So the way 273 00:15:02,920 --> 00:15:06,280 Speaker 1: that people measure the efficacy of their advertising dollars is 274 00:15:06,280 --> 00:15:10,800 Speaker 1: through this mythical model called media mixed modeling, and what 275 00:15:10,960 --> 00:15:15,120 Speaker 1: it requires is that brands need full funnel marketing in 276 00:15:15,240 --> 00:15:17,880 Speaker 1: order for the math equation to work. They need to 277 00:15:17,920 --> 00:15:21,480 Speaker 1: have upper funnel media that's letting people know, hey, I 278 00:15:21,520 --> 00:15:25,240 Speaker 1: exist in the world, mid funnel media that's encouraging them 279 00:15:25,280 --> 00:15:28,160 Speaker 1: to buy, and then bottom of the funnel media that's 280 00:15:28,520 --> 00:15:31,320 Speaker 1: pushing them towards the path to purchase. Bottom of the 281 00:15:31,320 --> 00:15:35,240 Speaker 1: funnel media think Amazon, think Walmart, think Instacart, all of 282 00:15:35,320 --> 00:15:40,239 Speaker 1: retail media. It's typically expensive. Upper funnel media is more affordable. 283 00:15:41,000 --> 00:15:45,080 Speaker 1: Meta remains to to be a really strong platform for 284 00:15:45,200 --> 00:15:49,080 Speaker 1: upper funnel media. Mick mac We work across eight hundreds 285 00:15:49,080 --> 00:15:53,080 Speaker 1: of the biggest brands in the world, and we actually 286 00:15:53,120 --> 00:15:57,840 Speaker 1: saw paid traffic within Meta increase by eight. We saw 287 00:15:58,000 --> 00:16:01,960 Speaker 1: the client in Google by seven and what that demonstrates 288 00:16:02,040 --> 00:16:06,120 Speaker 1: is that advertisers still seeing Meta as an upper funnel channel. 289 00:16:06,600 --> 00:16:09,360 Speaker 1: And what's happening is that people are moving dollars out 290 00:16:09,360 --> 00:16:14,040 Speaker 1: of Google because primarily that's search, into Amazon because Amazon 291 00:16:14,200 --> 00:16:19,160 Speaker 1: is the new search. Hey, Rachel inaggregate, what did we 292 00:16:19,280 --> 00:16:22,440 Speaker 1: learn about the health of the advertising market for social 293 00:16:22,440 --> 00:16:27,160 Speaker 1: media companies this week? We're learning that people are continuing 294 00:16:27,200 --> 00:16:32,480 Speaker 1: to spend. Is it the gold rush of no? But 295 00:16:32,560 --> 00:16:36,320 Speaker 1: are billions of dollars still moving into platforms like Meta 296 00:16:36,480 --> 00:16:41,760 Speaker 1: and Pinterest and TikTok. Absolutely, and TikTok continues to be 297 00:16:41,800 --> 00:16:46,480 Speaker 1: a platform that everyone should be watching. We saw massive 298 00:16:46,520 --> 00:16:50,680 Speaker 1: amounts of dollars again shift away from platforms like YouTube 299 00:16:50,720 --> 00:16:56,160 Speaker 1: into TikTok. In two, Rachel, I finally want to go 300 00:16:56,200 --> 00:16:59,440 Speaker 1: to Twitter. Since Twitter has gone private, there's less fund 301 00:16:59,520 --> 00:17:02,560 Speaker 1: to be had. That's not true at all. You can 302 00:17:02,600 --> 00:17:04,640 Speaker 1: just go to Elon Musk Twitter, which we did today, 303 00:17:04,640 --> 00:17:08,320 Speaker 1: and Elon Musk is talking about sharing revenues from ads 304 00:17:08,359 --> 00:17:12,000 Speaker 1: with the creators. And I'm interesting your take on this 305 00:17:12,320 --> 00:17:16,280 Speaker 1: about Twitter's pivot to a created driven platform. Do you 306 00:17:16,280 --> 00:17:20,560 Speaker 1: think he can do that? Listen, Ellen is trying everything 307 00:17:20,640 --> 00:17:24,640 Speaker 1: right now to keep dollars within the platform. At MICMAC, 308 00:17:24,720 --> 00:17:30,000 Speaker 1: we've essentially seen a hundred decline in paid media traffic 309 00:17:30,080 --> 00:17:34,560 Speaker 1: and Twitter since he took ownership. So with creators, it's 310 00:17:34,600 --> 00:17:38,600 Speaker 1: actually an interesting play YouTube did a very similar move 311 00:17:38,880 --> 00:17:44,080 Speaker 1: where they share the ad revenue with creators. As a result, 312 00:17:44,480 --> 00:17:49,159 Speaker 1: many many influencers have built six figure businesses on YouTube 313 00:17:49,520 --> 00:17:52,600 Speaker 1: and it's encourage them to move away from platforms like 314 00:17:52,640 --> 00:17:56,200 Speaker 1: Instagram into YouTube for content creation. So I do think 315 00:17:56,200 --> 00:17:59,000 Speaker 1: there's an opportunity for Twitter to engage with creators in 316 00:17:59,040 --> 00:18:02,200 Speaker 1: this way. But it's not novel. It's a proven playbook 317 00:18:02,280 --> 00:18:07,280 Speaker 1: that works in environments like YouTube. Alright, Rachel Typograph, founder 318 00:18:07,320 --> 00:18:09,560 Speaker 1: and CEO of MCMAC, thank you so much for joining 319 00:18:09,680 --> 00:18:14,600 Speaker 1: us this Friday. Now, Rivian is developing an electric bike 320 00:18:15,119 --> 00:18:19,960 Speaker 1: that's potentially expanding the ev maker's product lineup. This is 321 00:18:19,960 --> 00:18:23,240 Speaker 1: something I was reporting earlier today for Bloomberg. CEO R J. 322 00:18:23,400 --> 00:18:26,680 Speaker 1: Scarings disclosed the e bike effort Friday at a company 323 00:18:26,720 --> 00:18:29,960 Speaker 1: wide meeting all hands. That's according to my sources, a 324 00:18:30,000 --> 00:18:33,720 Speaker 1: small group of engineers is already working on this project. 325 00:18:34,160 --> 00:18:36,960 Speaker 1: Are J. Scarings told his staff the company owns patterns 326 00:18:36,960 --> 00:18:42,640 Speaker 1: for electric bicycle components and designs now coming up. Derivatives 327 00:18:42,680 --> 00:18:46,240 Speaker 1: traders sent back to manual processing thanks to a cyber 328 00:18:46,280 --> 00:18:49,040 Speaker 1: attack earlier this week. We have the details on that 329 00:18:49,240 --> 00:19:07,160 Speaker 1: coming up next. This is Bloomberg. A cyber attack earlier 330 00:19:07,200 --> 00:19:11,640 Speaker 1: this week sent derivatives trading back to the nighties. Bear 331 00:19:11,680 --> 00:19:14,800 Speaker 1: with us joining us for more. Bloomberg's Katherine Doctor who 332 00:19:14,800 --> 00:19:17,520 Speaker 1: had that scoop, explained the basics of this story to 333 00:19:17,520 --> 00:19:21,399 Speaker 1: meet Catherine. So just this week, Ion Trading, it's a 334 00:19:21,440 --> 00:19:26,280 Speaker 1: software firm that is the key to many markets across 335 00:19:26,320 --> 00:19:30,960 Speaker 1: the globe, but this specific software was for derivatives trading, 336 00:19:31,440 --> 00:19:35,280 Speaker 1: and earlier it started in Europe. Some of the systems 337 00:19:35,520 --> 00:19:38,960 Speaker 1: were down and it came out that there was a 338 00:19:39,000 --> 00:19:44,720 Speaker 1: cyber attack, and ultimately that meant that derivatives trading was 339 00:19:45,280 --> 00:19:49,199 Speaker 1: up ended across the world. And it's really been a 340 00:19:49,280 --> 00:19:53,840 Speaker 1: disruption and this target which is ongoing. The systems are 341 00:19:53,920 --> 00:19:57,880 Speaker 1: still down across the globe and only just now either 342 00:19:57,960 --> 00:20:01,399 Speaker 1: coming back online or moving elsewhere are UM. There's been 343 00:20:01,440 --> 00:20:06,400 Speaker 1: a ripple effect and there has also been UM many 344 00:20:06,480 --> 00:20:10,679 Speaker 1: developments even even currently UM we're we're calling sources because 345 00:20:11,160 --> 00:20:17,240 Speaker 1: it has come come out on the UM cyber attack. 346 00:20:17,680 --> 00:20:23,439 Speaker 1: The the actual attacking website. UM that this uh, this 347 00:20:23,600 --> 00:20:26,880 Speaker 1: software is now not on their target lift, so we 348 00:20:27,040 --> 00:20:31,879 Speaker 1: might actually see some developments and some resolutions soon. Um. 349 00:20:31,920 --> 00:20:35,280 Speaker 1: This is a story, Catherine, of technology gone wrong, just 350 00:20:35,400 --> 00:20:38,639 Speaker 1: terrific repulsing from you and the team. My understanding is 351 00:20:38,680 --> 00:20:42,080 Speaker 1: that we've had responses from the US Treasury Department and 352 00:20:42,160 --> 00:20:46,840 Speaker 1: also some banks. What are they saying, Yes, so today, UM, 353 00:20:46,920 --> 00:20:51,080 Speaker 1: we put out some reporting on the banks that are 354 00:20:51,119 --> 00:20:54,320 Speaker 1: either directly impacted, and what they are saying is that 355 00:20:54,359 --> 00:20:57,680 Speaker 1: they are managing their trade, some of them manually, so 356 00:20:57,720 --> 00:21:01,119 Speaker 1: they're going back to UH. We do refer to the 357 00:21:01,200 --> 00:21:06,760 Speaker 1: nineteen eighties when this world of derivatives trading was more 358 00:21:06,840 --> 00:21:12,320 Speaker 1: manual and less automated, less dependent on computers and electronic 359 00:21:12,359 --> 00:21:16,240 Speaker 1: trading um than it is today. So they are trying 360 00:21:16,280 --> 00:21:19,520 Speaker 1: to resolve this issue. They are manually reporting so that 361 00:21:19,560 --> 00:21:23,600 Speaker 1: they can abide by their regulatory guidance, and the regulators 362 00:21:23,640 --> 00:21:27,520 Speaker 1: are are giving some flexibility as well. They've extended some 363 00:21:27,640 --> 00:21:31,680 Speaker 1: deadlines and some of their reporting requirements that are due 364 00:21:31,800 --> 00:21:34,439 Speaker 1: at the end of this week have been extended. It 365 00:21:34,440 --> 00:21:36,199 Speaker 1: feels like this is just the start of this one. 366 00:21:36,240 --> 00:21:48,480 Speaker 1: Bloomberg's Katherine Dougherty, Thank you very much. Welcome back to 367 00:21:48,480 --> 00:21:52,160 Speaker 1: Bloomberg Technology. I'm Ed Ludlow in San Francisco. Now, Google 368 00:21:52,320 --> 00:21:56,520 Speaker 1: has invested almost four million in the AI startup and Thropic, 369 00:21:56,560 --> 00:21:59,520 Speaker 1: which is testing a rival to open a eyes chat 370 00:21:59,560 --> 00:22:02,800 Speaker 1: GPS that according to a Bloomberg source, the deal gives 371 00:22:02,840 --> 00:22:06,920 Speaker 1: Google a stake in Anthropic, but it doesn't require Anthropic 372 00:22:07,160 --> 00:22:11,240 Speaker 1: to spend any of the funds buying cloud services from Google. 373 00:22:11,440 --> 00:22:14,639 Speaker 1: It's been a long week of AI headlines and I 374 00:22:14,640 --> 00:22:17,040 Speaker 1: want to break them all down now with Saga Shah, 375 00:22:17,040 --> 00:22:20,800 Speaker 1: head of Responsible AI at fract Or, a global AI 376 00:22:20,880 --> 00:22:26,240 Speaker 1: provider to fortune companies across finance, healthcare, and retail. Let's 377 00:22:26,240 --> 00:22:29,600 Speaker 1: start with that and Google, Sago, you you operate in 378 00:22:29,640 --> 00:22:31,639 Speaker 1: the world of AI. I know that you're following the 379 00:22:31,680 --> 00:22:36,840 Speaker 1: headlines and technological developments closely. You know the executives over 380 00:22:37,080 --> 00:22:40,200 Speaker 1: Alphabet and Google were ready for the questions when you're 381 00:22:40,240 --> 00:22:42,600 Speaker 1: going to pull back the curtain. But what did you, 382 00:22:42,720 --> 00:22:47,080 Speaker 1: as an industry participant, actually learned this week about Google's 383 00:22:47,320 --> 00:22:52,080 Speaker 1: latest in artificial intelligence? Shan, I think this is about 384 00:22:52,160 --> 00:22:56,719 Speaker 1: anything big phonol of us don't affect as we can understand. 385 00:22:57,600 --> 00:23:02,280 Speaker 1: Definitely you know doc after Google's learnings report, for sure, 386 00:23:02,359 --> 00:23:05,680 Speaker 1: but that also indicates a lot about the macro about environment, 387 00:23:06,240 --> 00:23:09,960 Speaker 1: not just about chagpt right, the digital business going down, 388 00:23:10,600 --> 00:23:13,560 Speaker 1: not not growing as fast as it was last two 389 00:23:13,640 --> 00:23:15,720 Speaker 1: years like that sort of sort of a micro trend. 390 00:23:16,560 --> 00:23:20,439 Speaker 1: But the most interesting part has been UH agupty and 391 00:23:20,840 --> 00:23:24,720 Speaker 1: this investment and Tropic as well as the internal arms 392 00:23:24,720 --> 00:23:28,080 Speaker 1: of Google all coming together, which was a great thing 393 00:23:28,119 --> 00:23:32,560 Speaker 1: to see. Uh. For example, the internal Lambda which is 394 00:23:32,600 --> 00:23:36,399 Speaker 1: the language models UM. Right. Uh. They are now using 395 00:23:36,440 --> 00:23:41,680 Speaker 1: that UM for the apprentice pat internally to test. They've 396 00:23:41,680 --> 00:23:44,800 Speaker 1: asked all their employees to start using it and sending feedback. Right, 397 00:23:44,880 --> 00:23:49,720 Speaker 1: that's an interesting move. The other deep mind UM which 398 00:23:49,760 --> 00:23:52,560 Speaker 1: also has you know the almost the best day algorithm 399 00:23:52,560 --> 00:23:55,200 Speaker 1: at two thousands ten right, That is a London based 400 00:23:55,240 --> 00:23:57,800 Speaker 1: company that they acquired, and it's sort of become the 401 00:23:58,200 --> 00:24:00,720 Speaker 1: you know, part of Google. They have also in UM. 402 00:24:00,920 --> 00:24:03,880 Speaker 1: I asked keep my indortional get involved in this. Uh. 403 00:24:04,280 --> 00:24:07,760 Speaker 1: In this charge is UH And of course the Tropic, 404 00:24:07,800 --> 00:24:11,800 Speaker 1: as everybody's noticing, it's just another one of those investments, 405 00:24:11,800 --> 00:24:16,119 Speaker 1: which is a significant investment, still less than Microsoft some 406 00:24:16,160 --> 00:24:19,480 Speaker 1: more d billion darted investment and open Aye. So I 407 00:24:19,520 --> 00:24:23,119 Speaker 1: feel Google is of course trying to match up the 408 00:24:23,160 --> 00:24:26,960 Speaker 1: speed as well as using all H A I methods 409 00:24:27,320 --> 00:24:30,440 Speaker 1: to catch up. And it's possible it may you wouldn't go, 410 00:24:30,960 --> 00:24:34,159 Speaker 1: you know, better than anything else out there, considering the 411 00:24:34,320 --> 00:24:38,119 Speaker 1: advances it has already made. Um in the world of 412 00:24:39,280 --> 00:24:42,359 Speaker 1: the other conversation has kind of emerged over the course 413 00:24:42,359 --> 00:24:46,040 Speaker 1: of the week is who's ahead and whose best position 414 00:24:46,119 --> 00:24:50,320 Speaker 1: to commercialize artificial intelligence? The stock market is also paying attention, 415 00:24:50,480 --> 00:24:52,920 Speaker 1: right we we We look at some interesting names, C 416 00:24:53,200 --> 00:24:57,000 Speaker 1: three AI, big Bear AI, even buzz feed whose stock 417 00:24:57,040 --> 00:24:59,560 Speaker 1: has been a w over the place because of their 418 00:24:59,640 --> 00:25:01,760 Speaker 1: relay s and ship with open ai for the use 419 00:25:01,800 --> 00:25:04,520 Speaker 1: of chat GPT. We're sharing just some of the jumps 420 00:25:04,520 --> 00:25:09,120 Speaker 1: on Friday, big Bear up for what was your conclusion 421 00:25:09,560 --> 00:25:12,679 Speaker 1: this week about who is leading in the field of 422 00:25:12,680 --> 00:25:17,680 Speaker 1: our official intelligence? Is it Microsoft? Is it Google? That's 423 00:25:17,680 --> 00:25:22,240 Speaker 1: a tough question, frankly, and I would I would reflect 424 00:25:22,320 --> 00:25:24,560 Speaker 1: upon it in a different way. I think the race 425 00:25:24,680 --> 00:25:29,199 Speaker 1: is much longer than this year, uh, Frankly. Chart deputy 426 00:25:29,320 --> 00:25:34,440 Speaker 1: generative AI, which is a larger umbrella in with synthetic data, 427 00:25:34,520 --> 00:25:37,080 Speaker 1: language models, all of these other types of algorithms are 428 00:25:37,080 --> 00:25:39,960 Speaker 1: also being used. Right. Uh, it is still a little 429 00:25:40,000 --> 00:25:44,080 Speaker 1: far away from being where let's say Google searches for today. 430 00:25:44,160 --> 00:25:46,960 Speaker 1: So it's I think it's out in the open where 431 00:25:47,000 --> 00:25:49,200 Speaker 1: the competition will land. And let's say Google use it's 432 00:25:49,240 --> 00:25:53,480 Speaker 1: all its data, all its algorithms, all its investments towards that. 433 00:25:54,160 --> 00:25:56,560 Speaker 1: And then Microsoft is doing the same. If it integrates 434 00:25:56,600 --> 00:25:59,600 Speaker 1: open air with let's say Microsoft teams and all of 435 00:25:59,640 --> 00:26:04,680 Speaker 1: it's A locations, will the search consumer search move from 436 00:26:05,400 --> 00:26:09,000 Speaker 1: Google to Amazon product searches or within the Microsoft perhaps, 437 00:26:09,440 --> 00:26:13,240 Speaker 1: or maybe Google will develop something much better. That's a 438 00:26:13,400 --> 00:26:17,120 Speaker 1: sort of a debate we keep having every day. Um, 439 00:26:17,480 --> 00:26:20,439 Speaker 1: but I think it's a little away from the stock market. 440 00:26:20,440 --> 00:26:24,119 Speaker 1: Like the stock market reactions are, I feel quite stark. 441 00:26:24,720 --> 00:26:28,160 Speaker 1: So this, uh, I would expect less stark behavior going forward, 442 00:26:28,200 --> 00:26:31,679 Speaker 1: because you will start seeing the improvements in the right 443 00:26:31,720 --> 00:26:35,040 Speaker 1: the way I currently it's only Uh, it's very infinite. 444 00:26:35,680 --> 00:26:39,000 Speaker 1: It still cannot be trusted to do many things. It 445 00:26:39,040 --> 00:26:41,880 Speaker 1: can do basic stuff like a replication of natural language 446 00:26:41,880 --> 00:26:47,280 Speaker 1: and you know copies media um from you know, NBAS contracts, etcetera. 447 00:26:47,680 --> 00:26:50,720 Speaker 1: And so that those jobs may get you know, repurposed 448 00:26:50,720 --> 00:26:54,360 Speaker 1: to something more meaningful. Um. For some people, you know, 449 00:26:54,440 --> 00:26:57,440 Speaker 1: the upscaling, maybe requirement so and so, some of those 450 00:26:57,440 --> 00:26:58,960 Speaker 1: things will happen, but it is still not going to 451 00:26:59,000 --> 00:27:02,560 Speaker 1: be completely disrupt TiO as we are seeing, at least 452 00:27:02,560 --> 00:27:06,640 Speaker 1: for the next few months. We're still is far away. 453 00:27:06,960 --> 00:27:08,960 Speaker 1: So side it will give me then a taste of 454 00:27:09,040 --> 00:27:12,200 Speaker 1: the here and now, the reality, right and what fractals doing. 455 00:27:12,640 --> 00:27:14,760 Speaker 1: You know, we started this segment by saying, you are 456 00:27:14,880 --> 00:27:19,479 Speaker 1: providing AI right now to fourtune companies. What does that 457 00:27:19,520 --> 00:27:23,280 Speaker 1: look like? What does that even mean? Yeah? So practical 458 00:27:23,720 --> 00:27:25,760 Speaker 1: is uh, we are a set off you know, five 459 00:27:25,760 --> 00:27:28,879 Speaker 1: thousand people to believe we're providing text services in AI 460 00:27:29,000 --> 00:27:33,120 Speaker 1: to almost one fifty plus Fortune Friday companies across all 461 00:27:33,640 --> 00:27:39,040 Speaker 1: healthcare you know, financial services, insurance, consumer goods, retail, and 462 00:27:39,080 --> 00:27:44,600 Speaker 1: so on. And we for us, every strategic decision, tactical decision, 463 00:27:44,640 --> 00:27:50,119 Speaker 1: operational decision that corporate person or consumer is making right, 464 00:27:50,160 --> 00:27:53,080 Speaker 1: how can we make that better using AI? And when? 465 00:27:53,119 --> 00:27:55,280 Speaker 1: When when I say AI, it's so much broader term 466 00:27:55,400 --> 00:27:58,880 Speaker 1: to us. It is AI plus cloud engineering, which enables 467 00:27:58,920 --> 00:28:03,320 Speaker 1: a just human centered design, behavioral sciences which enable user adoption. 468 00:28:03,680 --> 00:28:06,920 Speaker 1: So all of those things come together to help companies 469 00:28:06,960 --> 00:28:13,000 Speaker 1: make better decisions. We also have products which help behavioral 470 00:28:13,040 --> 00:28:16,440 Speaker 1: sciences to solve complex problems, like one of our company's 471 00:28:16,440 --> 00:28:20,359 Speaker 1: final my works very closely to reduce human trafficking, to 472 00:28:20,520 --> 00:28:24,440 Speaker 1: reduce HIV in Africa in Initia, you know, and so on, 473 00:28:25,119 --> 00:28:28,000 Speaker 1: and other such products which help in let's say, training 474 00:28:28,320 --> 00:28:30,480 Speaker 1: analytics with There is one of those companies which we 475 00:28:30,560 --> 00:28:34,040 Speaker 1: invested in which helps create the data science professional pool 476 00:28:34,080 --> 00:28:38,120 Speaker 1: in the world. It's the largest training platform and so on. 477 00:28:38,200 --> 00:28:40,800 Speaker 1: So that is what we're trying to do with cultivating 478 00:28:40,800 --> 00:28:44,360 Speaker 1: a community of data scientists who can work, you know, 479 00:28:44,560 --> 00:28:47,920 Speaker 1: in this new environment because a lot of our skilling 480 00:28:48,000 --> 00:28:51,920 Speaker 1: is required in this environment and a lot of business 481 00:28:51,920 --> 00:28:55,320 Speaker 1: problems can be solved using AI, but there is always 482 00:28:55,320 --> 00:28:58,640 Speaker 1: a human also required. So how we believe is people 483 00:28:58,760 --> 00:29:02,400 Speaker 1: without AI will offer comparental people with the it is 484 00:29:03,320 --> 00:29:08,440 Speaker 1: was this human Sagasha just a really deep read on 485 00:29:08,480 --> 00:29:10,480 Speaker 1: what's happening in this space right now, a head of 486 00:29:10,760 --> 00:29:15,600 Speaker 1: responsible AI of fractal Thank you. Now coming up, Calls 487 00:29:15,600 --> 00:29:18,640 Speaker 1: are growing for banning TikTok in the US. Could it 488 00:29:18,720 --> 00:29:50,160 Speaker 1: actually happen though we'll discuss this is Bloomberg. Could TikTok 489 00:29:50,240 --> 00:29:54,000 Speaker 1: be banned in the US, just over two years after 490 00:29:54,040 --> 00:29:57,360 Speaker 1: the Donald Trump administration tried to force the Chinese parent 491 00:29:57,440 --> 00:30:01,040 Speaker 1: company Bike Dance to sell it's US business. There are 492 00:30:01,080 --> 00:30:04,760 Speaker 1: new legislative efforts to ban the social media app out right. 493 00:30:05,200 --> 00:30:07,720 Speaker 1: Two bills have been proposed, one in the House, one 494 00:30:07,760 --> 00:30:11,400 Speaker 1: in the Senate. But it's a tricky issue. Policymakers are 495 00:30:11,400 --> 00:30:13,720 Speaker 1: walking a really interesting line. Of course, they want to 496 00:30:13,800 --> 00:30:16,280 Speaker 1: keep American either status safe and make sure that there's 497 00:30:16,280 --> 00:30:19,880 Speaker 1: no influence from foreign governments on users in the US, 498 00:30:19,920 --> 00:30:23,360 Speaker 1: but they also don't want to upset a really important 499 00:30:23,360 --> 00:30:27,200 Speaker 1: and increasingly powerful voter base, being young Americans who are 500 00:30:27,640 --> 00:30:30,320 Speaker 1: loving and finding a lot of delight from the TikTok app. 501 00:30:30,920 --> 00:30:34,320 Speaker 1: That's reflected in the mood in d C. Daniel Flatley, 502 00:30:34,360 --> 00:30:36,960 Speaker 1: a reporter for Bloomberg News, was sitting in his car 503 00:30:37,000 --> 00:30:39,920 Speaker 1: a block away from the Capitol when we spoke. You 504 00:30:39,960 --> 00:30:43,640 Speaker 1: won't find very many lawmakers on Capitol Hill who are 505 00:30:43,720 --> 00:30:48,040 Speaker 1: unabashed supporters of TikTok. They say that they have some concerns. 506 00:30:48,320 --> 00:30:51,120 Speaker 1: My patients just run thin. If they've got a solution set, 507 00:30:51,480 --> 00:30:53,280 Speaker 1: they ought to lay it out. And if they don't, 508 00:30:53,360 --> 00:30:55,200 Speaker 1: I think you're going to see if they ever get 509 00:30:55,240 --> 00:30:57,560 Speaker 1: to a solution. That may be moot because individual states 510 00:30:57,640 --> 00:31:00,440 Speaker 1: or Congress may act. It's not totally clear or whether 511 00:31:00,600 --> 00:31:03,320 Speaker 1: they have the appetite to ban it completely, because that 512 00:31:03,360 --> 00:31:08,360 Speaker 1: would be taking things up pretty far and could potentially 513 00:31:08,360 --> 00:31:10,320 Speaker 1: be challenged in court. So we'll see how all that 514 00:31:10,480 --> 00:31:13,400 Speaker 1: works out. The app is already banned on all devices 515 00:31:13,480 --> 00:31:15,880 Speaker 1: issued by the federal government. A number of states have 516 00:31:15,920 --> 00:31:20,200 Speaker 1: followed suit, as have publicly funded universities in those states. 517 00:31:20,240 --> 00:31:23,000 Speaker 1: Some have advocated using the prospect of an outright ban 518 00:31:23,160 --> 00:31:27,280 Speaker 1: as a bargaining chip for negotiations with China over tech. Ultimately, 519 00:31:27,320 --> 00:31:30,400 Speaker 1: if Congress fails to make any progress, it will fall 520 00:31:30,440 --> 00:31:33,000 Speaker 1: to President Joe Biden to find a way to rein 521 00:31:33,080 --> 00:31:40,360 Speaker 1: in the Chinese tech giant that was Bloomberg's Alex Webb, 522 00:31:40,440 --> 00:31:44,120 Speaker 1: Alex Brinka, and Daniel Flatley. Now, let's talk a little 523 00:31:44,120 --> 00:31:47,360 Speaker 1: bit more about this, bringing gen c VCS found and 524 00:31:47,560 --> 00:31:53,080 Speaker 1: CEO Megan Loist for her take. Megan, you know VC founders, 525 00:31:54,040 --> 00:31:59,800 Speaker 1: You know gen Z tech TikTok is an app you 526 00:32:00,040 --> 00:32:04,600 Speaker 1: used by okay, broad demographic, but gen Z as well. 527 00:32:04,960 --> 00:32:07,240 Speaker 1: The other side of this debate is the use of 528 00:32:07,320 --> 00:32:11,360 Speaker 1: base and then saying we don't get it, we don't 529 00:32:11,400 --> 00:32:14,920 Speaker 1: get the risks, we don't get why you're taking this 530 00:32:15,000 --> 00:32:18,640 Speaker 1: away from us. Where do you sell on that? I 531 00:32:18,680 --> 00:32:21,680 Speaker 1: think it's interesting, right. I think of there's two two 532 00:32:21,680 --> 00:32:23,920 Speaker 1: sort of users you have to think about. There's the 533 00:32:23,960 --> 00:32:27,280 Speaker 1: people who consume TikTok content like me, and there's also 534 00:32:27,320 --> 00:32:29,920 Speaker 1: the creators who their livelihoods are dependent on TikTok and 535 00:32:29,920 --> 00:32:32,640 Speaker 1: their followings. So for users who are spending fifteen and 536 00:32:32,680 --> 00:32:34,720 Speaker 1: twenty hours a week on TikTok and it's their number 537 00:32:34,720 --> 00:32:38,239 Speaker 1: one sport of entertainment, uh, they're obviously concerned that's going 538 00:32:38,280 --> 00:32:41,120 Speaker 1: to be taken away. But for creators where they're actually 539 00:32:41,160 --> 00:32:45,080 Speaker 1: monetizing on TikTok, and seventy plus percent of creators their 540 00:32:45,160 --> 00:32:47,720 Speaker 1: number one sorts of income is actually through brand deals. 541 00:32:47,960 --> 00:32:49,960 Speaker 1: You take away their main platform, you take away a 542 00:32:50,000 --> 00:32:52,320 Speaker 1: lot of people's livelihoods. And so I think people are 543 00:32:52,400 --> 00:32:54,640 Speaker 1: up in arms, especially gen Zers who make up sixty 544 00:32:54,640 --> 00:32:58,000 Speaker 1: plus percent of TikTok's users, um, simply because you're taking 545 00:32:58,040 --> 00:33:00,920 Speaker 1: away either our main source of entertain men or our 546 00:33:00,960 --> 00:33:03,640 Speaker 1: actual source of income. And so and again, the number 547 00:33:03,640 --> 00:33:06,240 Speaker 1: one career path for gen z eears today, we want 548 00:33:06,240 --> 00:33:09,200 Speaker 1: to be YouTubers, bloggers, content readers, and so this is 549 00:33:09,200 --> 00:33:11,479 Speaker 1: going to continue to be a theme for a lot 550 00:33:11,520 --> 00:33:14,280 Speaker 1: of people. And TikTok is our platform of choice. So 551 00:33:14,720 --> 00:33:16,760 Speaker 1: and on the founder side as well, you'd see a 552 00:33:16,760 --> 00:33:20,200 Speaker 1: lot of consumer founders scaling their businesses on TikTok. It's 553 00:33:20,200 --> 00:33:23,600 Speaker 1: a great path for distribution and building an authentic organic 554 00:33:23,680 --> 00:33:25,960 Speaker 1: presence versually doing a lot of paid marketing. And so 555 00:33:26,000 --> 00:33:28,400 Speaker 1: you're actually taking away a lot of opportunity from founders 556 00:33:28,400 --> 00:33:33,880 Speaker 1: as well. Right colloquially, gen Z is anyone born in 557 00:33:33,920 --> 00:33:38,960 Speaker 1: the late nineties through I think until that's pretty wide 558 00:33:38,960 --> 00:33:42,200 Speaker 1: group of people that we're talking about. I'm interested in 559 00:33:42,360 --> 00:33:45,200 Speaker 1: gen Z fee cs the kind of community that you 560 00:33:45,320 --> 00:33:48,720 Speaker 1: founded and started because it is an ecosystem of both 561 00:33:48,760 --> 00:33:51,920 Speaker 1: founders and vcs. And the other debate that we that 562 00:33:52,000 --> 00:33:53,960 Speaker 1: we want to have you on to talk about is 563 00:33:54,000 --> 00:33:57,920 Speaker 1: how those people will fare in three. Are colleagues at 564 00:33:57,920 --> 00:34:01,360 Speaker 1: Bloomberg Opinion Allison shre you Go write this column this 565 00:34:01,440 --> 00:34:06,440 Speaker 1: week titled gen Z startups will face a tough sell 566 00:34:06,880 --> 00:34:10,120 Speaker 1: in three. In other words, they're entering an environment where 567 00:34:10,120 --> 00:34:13,480 Speaker 1: they're trying to raise money, get businesses off the ground. 568 00:34:14,320 --> 00:34:17,160 Speaker 1: But the world's changed. What are the conversations you're having 569 00:34:17,280 --> 00:34:21,440 Speaker 1: right now with the community that you founded. Yeah, I 570 00:34:21,440 --> 00:34:24,480 Speaker 1: think the funding environment is difficult for younger founders and 571 00:34:24,560 --> 00:34:26,719 Speaker 1: older founders. The average age of a founder is about 572 00:34:26,719 --> 00:34:30,600 Speaker 1: thirty five years old. But and I understand the conversation 573 00:34:30,640 --> 00:34:33,200 Speaker 1: with investors where it's like, Okay, maybe there's less risk 574 00:34:33,320 --> 00:34:36,360 Speaker 1: in backing experience founders who have done at once before. 575 00:34:36,640 --> 00:34:38,959 Speaker 1: But on the flip side of that coin, uh, there's 576 00:34:38,960 --> 00:34:41,839 Speaker 1: a generational opportunity in backing gen Z founders who are 577 00:34:41,840 --> 00:34:45,280 Speaker 1: building problems from themselves. And you see this with every generation. 578 00:34:45,320 --> 00:34:48,680 Speaker 1: It's not something that's necessarily unique with gen zum but 579 00:34:48,760 --> 00:34:51,319 Speaker 1: it's really important because when you look at the world 580 00:34:51,360 --> 00:34:54,879 Speaker 1: today and make the problems that we're facing and experiencing, uh, 581 00:34:54,960 --> 00:34:58,000 Speaker 1: gen Z founders have naturally they experienced the pain point 582 00:34:58,000 --> 00:35:00,759 Speaker 1: firsthand and they can build around that problem. Even for 583 00:35:00,840 --> 00:35:03,360 Speaker 1: me as a gen Z investor and formally as investor 584 00:35:03,400 --> 00:35:07,080 Speaker 1: at the Heppo General Atlantic Large funds, I tended to 585 00:35:07,120 --> 00:35:10,600 Speaker 1: back first time founders gen Z founders because I infinitely 586 00:35:10,640 --> 00:35:13,680 Speaker 1: understood their take as an investor, and so what I'm 587 00:35:13,680 --> 00:35:16,200 Speaker 1: seeing broadly is it really encouraging founders to go back 588 00:35:16,200 --> 00:35:19,440 Speaker 1: to the fundamentals and so thinking about their unique value 589 00:35:19,440 --> 00:35:21,839 Speaker 1: prop and why they're building the problem, what makes them 590 00:35:21,840 --> 00:35:24,439 Speaker 1: the best person to build that business. Really just thinking 591 00:35:24,440 --> 00:35:27,160 Speaker 1: about founder market fit, which is true for gen Z founders, 592 00:35:27,200 --> 00:35:31,239 Speaker 1: millennial founders, gen X founders, any founders, and so I think, 593 00:35:31,320 --> 00:35:33,759 Speaker 1: you know, broadly, I think there's still a massive opportunity 594 00:35:33,800 --> 00:35:36,439 Speaker 1: to be backing gen Z founders, young people who see 595 00:35:36,440 --> 00:35:40,520 Speaker 1: problems authentically from the gen Z perspective. Megan, when you 596 00:35:40,560 --> 00:35:43,040 Speaker 1: look at opportunities yourself as an investor, I know you 597 00:35:43,160 --> 00:35:46,239 Speaker 1: back a lot of gen Z founders. What are thematically 598 00:35:46,239 --> 00:35:49,239 Speaker 1: the areas you're most excited about? Twenty three? We just 599 00:35:49,280 --> 00:35:52,919 Speaker 1: had thirty seconds. Yeah, So we actually do a poll 600 00:35:53,000 --> 00:35:55,520 Speaker 1: every year. This year, the top three trends that gen 601 00:35:55,600 --> 00:35:58,480 Speaker 1: Z investors are tracting our climate, tech, AI and m 602 00:35:58,600 --> 00:36:01,200 Speaker 1: L which is a very through part hapic right now, 603 00:36:01,680 --> 00:36:04,160 Speaker 1: and then fintech as well, though those are three areas 604 00:36:04,160 --> 00:36:07,239 Speaker 1: that we're tracking, And even just this week, I've been 605 00:36:07,239 --> 00:36:09,560 Speaker 1: pitched on a bunch of generative AI companies and so 606 00:36:09,840 --> 00:36:12,880 Speaker 1: a lot of younger founders are building an AI. Probably 607 00:36:13,080 --> 00:36:16,359 Speaker 1: gen Z VCS founder and CEO Megan Lays. So great 608 00:36:16,400 --> 00:36:17,920 Speaker 1: to have you on the show. Thank you so much 609 00:36:17,960 --> 00:36:30,359 Speaker 1: for joining us. When you think of indoor cycling at home, 610 00:36:30,480 --> 00:36:33,520 Speaker 1: you probably think of Peloton. But there's a new kid 611 00:36:33,520 --> 00:36:37,239 Speaker 1: on the block, Swift, which some cyclists use when they 612 00:36:37,280 --> 00:36:40,279 Speaker 1: can't ride outside, and which takes you. Okay, bear with 613 00:36:40,360 --> 00:36:43,880 Speaker 1: me inside the metaverse Quick Takes. Tim Stenovic took a 614 00:36:44,000 --> 00:36:50,040 Speaker 1: ride with the company's co CEO, Eric Men. There you are. Now, 615 00:36:50,080 --> 00:36:54,880 Speaker 1: I gotta catch you and I'm cheasing you. Now, where's 616 00:36:54,920 --> 00:37:00,600 Speaker 1: the sprit? That's what I'm waiting for. Yeah, you left 617 00:37:00,600 --> 00:37:11,040 Speaker 1: me in the dust there. When it comes to indoor cycling, 618 00:37:11,160 --> 00:37:13,920 Speaker 1: pretty much everybody has heard of Peloton, but Swift is 619 00:37:13,960 --> 00:37:16,160 Speaker 1: what a lot of the serious cyclists use when they're 620 00:37:16,200 --> 00:37:18,680 Speaker 1: just not able to ride outside. Here's how it works. 621 00:37:18,920 --> 00:37:21,800 Speaker 1: A smart trainer connects to apps and it can adjust 622 00:37:21,920 --> 00:37:24,399 Speaker 1: to make it harder, like if you're going up a hill. 623 00:37:24,640 --> 00:37:27,520 Speaker 1: One of those apps is Swift. It helps you exercise, 624 00:37:27,600 --> 00:37:30,440 Speaker 1: but it's also a game. You're peddling your bike in 625 00:37:30,440 --> 00:37:33,160 Speaker 1: the real world, but you have this avatar. I wanted 626 00:37:33,200 --> 00:37:35,160 Speaker 1: to learn more about Swift, and I thought there was 627 00:37:35,200 --> 00:37:37,200 Speaker 1: no better way to do that than to go for 628 00:37:37,239 --> 00:37:42,480 Speaker 1: a ride with the company's CEO. I'm in New York City, 629 00:37:43,239 --> 00:37:48,000 Speaker 1: you're in Long Beach right now. We're three thousand miles away, 630 00:37:48,040 --> 00:37:53,319 Speaker 1: but we're we're actually riding together on screen. Is this 631 00:37:53,400 --> 00:37:57,120 Speaker 1: the metaverse to you? Absolutely, it's a net averse. It's 632 00:37:57,200 --> 00:38:01,400 Speaker 1: a virtual space where people come together. There, they engage, 633 00:38:02,000 --> 00:38:04,680 Speaker 1: they build communities. If you look at the clubs that 634 00:38:04,719 --> 00:38:08,680 Speaker 1: we have, we have well over twenty clubs. It's really interesting. 635 00:38:08,760 --> 00:38:10,680 Speaker 1: The power of the community is I think unique to 636 00:38:11,040 --> 00:38:14,160 Speaker 1: with how do you develop courses? What's the process there? 637 00:38:14,360 --> 00:38:17,760 Speaker 1: We have a fantastic creative team and what we've learned 638 00:38:17,760 --> 00:38:21,520 Speaker 1: that over the years, These magical places like Latopia and 639 00:38:21,520 --> 00:38:24,319 Speaker 1: now mccourie Islands are the kind of maps that our 640 00:38:24,360 --> 00:38:27,120 Speaker 1: community would like to spend more of their time on. 641 00:38:27,320 --> 00:38:30,480 Speaker 1: And that's partly because we can do crazy things right. 642 00:38:30,800 --> 00:38:33,920 Speaker 1: We can create tunnels under the water in the seabed, 643 00:38:34,120 --> 00:38:37,520 Speaker 1: we can create roads above Manhattan. There are other people 644 00:38:37,520 --> 00:38:40,279 Speaker 1: who can try to replicate and be a more of 645 00:38:40,280 --> 00:38:42,440 Speaker 1: a simulator. We want to be our own thing, and 646 00:38:43,040 --> 00:38:45,719 Speaker 1: I think we can enhance what the real world is 647 00:38:45,760 --> 00:38:48,120 Speaker 1: and and just making much more interesting to do things 648 00:38:48,160 --> 00:38:54,560 Speaker 1: that you otherwise can't do. We see a lot of 649 00:38:54,560 --> 00:38:57,600 Speaker 1: companies raising prices right now because of inflation. Am I 650 00:38:57,640 --> 00:39:01,719 Speaker 1: correct in thinking that you guys have not raised prices. 651 00:39:02,000 --> 00:39:04,960 Speaker 1: We did raise prices five years ago, but we have 652 00:39:05,080 --> 00:39:08,520 Speaker 1: not done so since then. But let's be honest, the 653 00:39:08,560 --> 00:39:11,399 Speaker 1: cost of running our business has gotten more expensive. It's 654 00:39:11,400 --> 00:39:14,680 Speaker 1: been really tough to supply. Chain issues are real. We 655 00:39:14,760 --> 00:39:17,560 Speaker 1: are trying to run a business that is profitable at 656 00:39:17,600 --> 00:39:20,360 Speaker 1: some point. It's not. At the moment. We're really focused 657 00:39:20,400 --> 00:39:24,520 Speaker 1: on trying to onboard as many customers as possible and 658 00:39:24,600 --> 00:39:29,200 Speaker 1: trying to make the pricing accessible. You've raised over half 659 00:39:29,200 --> 00:39:31,960 Speaker 1: a billion dollars. Your investors want to see a return. 660 00:39:32,200 --> 00:39:35,560 Speaker 1: What's the exit for them? I think we all have 661 00:39:35,680 --> 00:39:40,200 Speaker 1: aspirations of taking this company public. There's no doubt about it. 662 00:39:40,239 --> 00:39:43,160 Speaker 1: Our investors have invested in this business because they think 663 00:39:43,200 --> 00:39:45,920 Speaker 1: we will be the category leader. You know, we believe 664 00:39:45,960 --> 00:39:49,640 Speaker 1: we can do that through through product. Take us into 665 00:39:49,640 --> 00:39:52,839 Speaker 1: the decision to launch hand cycles and tell me about 666 00:39:52,840 --> 00:39:56,880 Speaker 1: the reception and what you've heard. Back in September, we 667 00:39:57,000 --> 00:40:00,359 Speaker 1: finally launched the hand cycle. It develops everyone, but those 668 00:40:00,400 --> 00:40:03,200 Speaker 1: who are using are really those who are disabled. This 669 00:40:03,239 --> 00:40:06,480 Speaker 1: is really about, you know, leaving up to our values, 670 00:40:06,560 --> 00:40:11,240 Speaker 1: which is about diversity and inclusivilty. We have some responsibility 671 00:40:11,280 --> 00:40:17,440 Speaker 1: and making sure we make those investments. What's the breakdown 672 00:40:17,440 --> 00:40:23,719 Speaker 1: those with mail versus female men women? So there's a 673 00:40:23,760 --> 00:40:26,040 Speaker 1: lot of work to be done there. And then this 674 00:40:26,120 --> 00:40:28,839 Speaker 1: is why the sponsorship of the Woman's sort of Fronts 675 00:40:28,920 --> 00:40:31,040 Speaker 1: is so important for us. Take me through the r 676 00:40:31,040 --> 00:40:33,480 Speaker 1: all I on that. How does that translate into more people, 677 00:40:34,160 --> 00:40:36,960 Speaker 1: you know, paying fifteen bucks a months with if you're 678 00:40:37,000 --> 00:40:40,520 Speaker 1: going to sponsor a bike race, the sponsor the Towtter Fronts. 679 00:40:41,400 --> 00:40:44,440 Speaker 1: So when the opportunity came up for us to really 680 00:40:44,480 --> 00:40:47,960 Speaker 1: help support and relaunch the Woman's sort of Fronts, we 681 00:40:48,120 --> 00:40:52,279 Speaker 1: jumped at it. We'll have to find a sprint to do. 682 00:40:56,000 --> 00:40:58,880 Speaker 1: That was last I feel so bad, I'm throwing my 683 00:40:59,000 --> 00:41:02,359 Speaker 1: colleague much so, why towel You used to work at 684 00:41:02,360 --> 00:41:05,799 Speaker 1: a bank? Yes, now you ride likes all day. I 685 00:41:05,840 --> 00:41:09,440 Speaker 1: started my career on Wall Street many years ago. One 686 00:41:09,440 --> 00:41:14,640 Speaker 1: of the inspirations for startings with was to recreate my community, 687 00:41:14,719 --> 00:41:19,400 Speaker 1: my experience, the competition to training that I had in 688 00:41:19,480 --> 00:41:22,120 Speaker 1: Central Park. What does with look like three or five 689 00:41:22,200 --> 00:41:24,879 Speaker 1: years from now? I think there will be a convergence. 690 00:41:25,080 --> 00:41:28,560 Speaker 1: It's our job to make the interplay between indoor and 691 00:41:28,560 --> 00:41:32,399 Speaker 1: outdoor as seamless as possible, so that what's not lost 692 00:41:32,480 --> 00:41:36,480 Speaker 1: in that interplay is a social connection and the ability 693 00:41:36,480 --> 00:41:39,280 Speaker 1: to share experiences. I think that is a big unlock 694 00:41:39,440 --> 00:41:44,960 Speaker 1: for how we can really contribute to the cycling industry. 695 00:41:46,600 --> 00:41:49,560 Speaker 1: I've never done anything like this before in terms of interviews. 696 00:41:49,600 --> 00:41:52,640 Speaker 1: You know, I've not done this before either, But I'm 697 00:41:52,680 --> 00:41:56,000 Speaker 1: told that there are some tech leaders who on a 698 00:41:56,080 --> 00:41:59,759 Speaker 1: monthly basis get on Swift and have their catch up. 699 00:42:00,600 --> 00:42:02,440 Speaker 1: I think they find it amusing to be able to 700 00:42:02,640 --> 00:42:05,600 Speaker 1: do some exercise at the same time. All right, Eric, 701 00:42:05,600 --> 00:42:13,439 Speaker 1: I'm gonna catch you. Oh who you are? Okay, that's 702 00:42:13,520 --> 00:42:18,040 Speaker 1: enough for me. That's not work out rutual high five. 703 00:42:18,640 --> 00:42:23,279 Speaker 1: That was great, Thanks very thanks him. See it again. Yeah, 704 00:42:23,360 --> 00:42:28,240 Speaker 1: let's do it again. I would love to quick takes, 705 00:42:28,239 --> 00:42:31,440 Speaker 1: Tim Semic There some breaking news on the Bloomberg terminal. 706 00:42:32,080 --> 00:42:36,040 Speaker 1: Elon Musk has defeated the Tesla shareholder suit on the 707 00:42:37,880 --> 00:42:41,759 Speaker 1: sweets about taking Tesla private. The juries found that Musk 708 00:42:41,800 --> 00:42:47,200 Speaker 1: did not mislead the public about the Tesla take private deal. 709 00:42:47,400 --> 00:42:49,560 Speaker 1: We'll have more in that in the coming days. That 710 00:42:49,680 --> 00:42:52,280 Speaker 1: does it for this edition of Bloomberg Technology. Don't forget 711 00:42:52,440 --> 00:42:55,719 Speaker 1: check out our podcast I Heeart, Apple, Spotify, wherever you 712 00:42:55,760 --> 00:43:00,280 Speaker 1: get your podcasts. What a week. This is Bloomberg Pick 713 00:43:02,360 --> 00:43:04,640 Speaker 1: who give a big too big