1 00:00:00,720 --> 00:00:03,960 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, alongside 2 00:00:03,960 --> 00:00:06,840 Speaker 1: my co host Matt Miller. Every business day, we bring 3 00:00:06,880 --> 00:00:11,440 Speaker 1: you interviews from CEOs, market pros, and Bloomberg experts, along 4 00:00:11,440 --> 00:00:15,480 Speaker 1: with essential market moving news. Find the Bloomberg Markets Podcast 5 00:00:15,520 --> 00:00:18,360 Speaker 1: on Apple Podcasts or wherever you listen to podcasts, and 6 00:00:18,400 --> 00:00:21,040 Speaker 1: at Bloomberg dot com slash podcast. Let's get to this 7 00:00:21,120 --> 00:00:23,200 Speaker 1: job Stata, let's get to this economy. Let's get to 8 00:00:23,239 --> 00:00:25,880 Speaker 1: this federal reserve. Jeff Cleveland, chief US economists have paid 9 00:00:25,880 --> 00:00:29,520 Speaker 1: in in regal Jeff. I don't know what your forecast 10 00:00:29,680 --> 00:00:34,080 Speaker 1: was for the job's number, but good morning. I can't imagine, Jeff, 11 00:00:34,080 --> 00:00:37,000 Speaker 1: he has five seventeen thousand dollars in your model five 12 00:00:37,400 --> 00:00:40,360 Speaker 1: seventeen thousand jobs. I had plus two twenty three. I'll 13 00:00:40,400 --> 00:00:43,760 Speaker 1: admit that, so I goes a little above consensus. But wow, 14 00:00:43,760 --> 00:00:46,960 Speaker 1: this just blew me away. I think my initial reaction was, wait, 15 00:00:47,120 --> 00:00:51,159 Speaker 1: what exactly is that an error? I mean, I love 16 00:00:51,200 --> 00:00:54,880 Speaker 1: reports like this. It completely up ends the narrative I've 17 00:00:54,880 --> 00:00:57,080 Speaker 1: been getting from clients and colleagues, you know, the talk 18 00:00:57,120 --> 00:01:01,320 Speaker 1: of an imminent recession. Uh, it's hard to find something 19 00:01:01,360 --> 00:01:04,520 Speaker 1: that I don't like in this job's report, it's hard 20 00:01:04,560 --> 00:01:06,720 Speaker 1: to I guess what I've been saying, maybe just because 21 00:01:06,720 --> 00:01:10,360 Speaker 1: I don't know. It's tough to forecasts of recession when 22 00:01:10,600 --> 00:01:14,080 Speaker 1: everybody's got a job, isn't it. Absolutely? I mean, at 23 00:01:14,080 --> 00:01:15,560 Speaker 1: the end of the day, when we look at things, 24 00:01:15,640 --> 00:01:18,319 Speaker 1: if if people are employed, they're working more hours average, 25 00:01:18,319 --> 00:01:20,640 Speaker 1: I only earned is still growing at a pretty decent clip, 26 00:01:20,680 --> 00:01:24,119 Speaker 1: plus over four percent year on year. Uh, that's your 27 00:01:24,120 --> 00:01:28,120 Speaker 1: spending power and to consume the economy of seventy consumer. 28 00:01:28,280 --> 00:01:31,360 Speaker 1: So ultimately you can't be too barished in that situation. 29 00:01:31,440 --> 00:01:34,080 Speaker 1: The time where we would get much more bearished would 30 00:01:34,080 --> 00:01:37,479 Speaker 1: be if aggregate incomes are dropping. And you know, that's 31 00:01:37,520 --> 00:01:40,320 Speaker 1: what you saw before two thousand and eight, and we're 32 00:01:40,360 --> 00:01:44,240 Speaker 1: not seeing that here in this data. So I mean, 33 00:01:44,319 --> 00:01:46,080 Speaker 1: what does it mean to the Fed? What does this 34 00:01:46,120 --> 00:01:49,800 Speaker 1: mean to Jerome Powell, who like doved out on Wednesday, Well, 35 00:01:49,840 --> 00:01:51,680 Speaker 1: it kind of explains a lot. Like he strode out 36 00:01:51,680 --> 00:01:53,360 Speaker 1: to the podium, right, and he and he was getting 37 00:01:53,360 --> 00:01:55,600 Speaker 1: pushed back right for some of the reporters, and he's like, okay, 38 00:01:55,600 --> 00:01:57,680 Speaker 1: you have your forecast, I have mine, all right, we'll 39 00:01:57,680 --> 00:02:00,040 Speaker 1: see And he was sort of you know, he and 40 00:02:00,120 --> 00:02:02,200 Speaker 1: pushed back heavily, and the market took that as devilishness. 41 00:02:02,720 --> 00:02:04,880 Speaker 1: But you know, now that you see this data report, 42 00:02:05,000 --> 00:02:08,320 Speaker 1: it's like, oh, okay, yeah, he has his forecast and 43 00:02:08,960 --> 00:02:12,000 Speaker 1: he they said ongoing increases, So a couple of more 44 00:02:12,080 --> 00:02:15,200 Speaker 1: rate hikes get you up over five. I think with 45 00:02:15,280 --> 00:02:18,200 Speaker 1: this data that's a slam dunk. I mean, so he 46 00:02:18,280 --> 00:02:21,240 Speaker 1: was he kind of knew, Um, the economy is so strong, 47 00:02:21,240 --> 00:02:23,600 Speaker 1: we're really going to continue. We'll say we're data dependent 48 00:02:23,800 --> 00:02:27,080 Speaker 1: so we don't freak the markets out. Yeah. I mean, 49 00:02:27,120 --> 00:02:28,440 Speaker 1: I don't know if you had this in hand, but 50 00:02:28,480 --> 00:02:30,839 Speaker 1: I'm just saying, like, now in retrospect, it seems much 51 00:02:30,880 --> 00:02:34,160 Speaker 1: more like, you know, he didn't need to aggressively push 52 00:02:34,240 --> 00:02:36,480 Speaker 1: back and try to you know, try to lecture anyone. 53 00:02:36,480 --> 00:02:39,000 Speaker 1: He's like the way of one other question that I'll 54 00:02:39,040 --> 00:02:44,920 Speaker 1: put to you, uh, Jeffrey Um, as an economist, how 55 00:02:44,960 --> 00:02:47,880 Speaker 1: accurate are these jobs numbers? I mean I have heard 56 00:02:47,919 --> 00:02:50,840 Speaker 1: that there is a margin of error of around two 57 00:02:50,919 --> 00:02:53,160 Speaker 1: hundred and fifty thousand. I haven't heard anyone say that 58 00:02:53,200 --> 00:02:56,320 Speaker 1: for ten years. But is that the case or you know, 59 00:02:56,600 --> 00:02:58,960 Speaker 1: in the tents, we would say, you know, the number 60 00:02:59,000 --> 00:03:01,720 Speaker 1: could come out anywhere between plus one hundred thousand and 61 00:03:01,760 --> 00:03:04,520 Speaker 1: plus three hundred thousand, and it would be within the 62 00:03:04,560 --> 00:03:06,400 Speaker 1: realm of possibly that would be accurate. So there are 63 00:03:06,440 --> 00:03:09,840 Speaker 1: pretty pretty decent error bars on this UM. But you know, 64 00:03:09,919 --> 00:03:12,920 Speaker 1: they revised these things annually. They mark them to tax 65 00:03:13,040 --> 00:03:15,959 Speaker 1: uh tax rolls. So I think this is uh the 66 00:03:16,320 --> 00:03:19,360 Speaker 1: as far as the economic data goes, this is pretty solid. 67 00:03:19,400 --> 00:03:23,640 Speaker 1: I'd rather relye on non foreign payrolls than purchasing manager 68 00:03:23,639 --> 00:03:26,560 Speaker 1: and disease, you know, surveys like that, So I think 69 00:03:26,600 --> 00:03:29,080 Speaker 1: this is I think this is pretty good. All right, Jeff, 70 00:03:29,080 --> 00:03:31,239 Speaker 1: thanks so much. We appreciate that. Jeffrey Cleveland, he's the 71 00:03:31,280 --> 00:03:37,160 Speaker 1: chief EOS economist of Payton and Regal. Our C suite 72 00:03:37,240 --> 00:03:41,120 Speaker 1: conversation today, Matt Calkin, CEO of apien UH joins us 73 00:03:41,160 --> 00:03:43,960 Speaker 1: to talk about his company, talk about tech, talk about 74 00:03:43,960 --> 00:03:48,200 Speaker 1: the tough year two that was for tech stocks. UM. Matt, 75 00:03:48,240 --> 00:03:52,760 Speaker 1: thanks so much for joining us here. Apien nasdac happien 76 00:03:53,160 --> 00:03:56,560 Speaker 1: ap up on your phone, okay, cool? Appien app N 77 00:03:57,000 --> 00:03:59,520 Speaker 1: is the ticker to put into your Bloomberg terminal of 78 00:03:59,520 --> 00:04:02,160 Speaker 1: the stock trade on NASAC. UH. Just give us the 79 00:04:03,120 --> 00:04:05,480 Speaker 1: thirty second kind of elevator pitch. What is apping? What 80 00:04:05,520 --> 00:04:07,320 Speaker 1: are you guys doing? Where do you play in this 81 00:04:07,640 --> 00:04:10,720 Speaker 1: tech stack? Sure it's good to be on. Hey, we 82 00:04:10,800 --> 00:04:15,080 Speaker 1: do process automation. Process automation means that we are organizing 83 00:04:15,120 --> 00:04:17,599 Speaker 1: a process from the very beginning to the end through 84 00:04:17,600 --> 00:04:20,960 Speaker 1: an organizations. You plant, you program it, you automated, you 85 00:04:21,000 --> 00:04:23,400 Speaker 1: execute it, and you revise it. Everything to do with 86 00:04:23,440 --> 00:04:27,120 Speaker 1: the process, and that's that's how organizations make the changes 87 00:04:27,160 --> 00:04:30,200 Speaker 1: and automate the behaviors that they do. So processes are 88 00:04:30,200 --> 00:04:34,600 Speaker 1: really central to the way large organizations behave and automation, well, 89 00:04:34,640 --> 00:04:37,080 Speaker 1: that's just uh, that's just when software helps you do 90 00:04:37,120 --> 00:04:39,120 Speaker 1: the work. Usually software is kind of a tool, like 91 00:04:39,160 --> 00:04:41,839 Speaker 1: if you use a product, it's helping you. But automation 92 00:04:41,960 --> 00:04:44,400 Speaker 1: is when software literally does the work. So we're talking 93 00:04:44,440 --> 00:04:49,279 Speaker 1: about artificial intelligence, robotic process automation, tools and rules that 94 00:04:49,360 --> 00:04:53,200 Speaker 1: allow you to delegate your work to software. So, uh, 95 00:04:53,360 --> 00:04:57,320 Speaker 1: last year was tough for tech companies and as we've 96 00:04:57,320 --> 00:05:00,520 Speaker 1: been saying, there's a real turnaround this year. Stock is 97 00:05:00,560 --> 00:05:04,200 Speaker 1: no different. It just goes up and almost a straight 98 00:05:04,240 --> 00:05:06,400 Speaker 1: line at the beginning of this year. What do you 99 00:05:06,440 --> 00:05:10,080 Speaker 1: think has made investors reconsider was it just like tax loss, 100 00:05:10,160 --> 00:05:13,679 Speaker 1: harvesting and December that they're coming back to buy now. 101 00:05:15,120 --> 00:05:16,680 Speaker 1: It's kind of funny. And I heard you talking about 102 00:05:16,680 --> 00:05:18,680 Speaker 1: Apple a moment ago, and I think the same phenomena 103 00:05:18,720 --> 00:05:21,280 Speaker 1: is happening across the tech sector right now. We're seeing 104 00:05:21,320 --> 00:05:24,159 Speaker 1: the prices have been driven more by mood than by 105 00:05:24,279 --> 00:05:27,920 Speaker 1: the underlying value of the organizations, and so Apple can 106 00:05:27,960 --> 00:05:30,640 Speaker 1: deliver a result which you could question, and yet the 107 00:05:30,680 --> 00:05:34,719 Speaker 1: stock is up and Appen stock has been rising on well, 108 00:05:34,720 --> 00:05:37,360 Speaker 1: not very much news, and I think it's just an 109 00:05:37,360 --> 00:05:40,520 Speaker 1: alleviation of some of the mood that's been holding holding 110 00:05:40,520 --> 00:05:44,200 Speaker 1: stocks where they were. So give us your sense of 111 00:05:44,279 --> 00:05:47,440 Speaker 1: kind of what you're hearing from your customers in terms 112 00:05:47,480 --> 00:05:50,040 Speaker 1: of how they view kind of their their tech spend. 113 00:05:50,080 --> 00:05:52,480 Speaker 1: Here there's you know where it definitely concerned about a 114 00:05:52,520 --> 00:05:56,000 Speaker 1: recession in the general economy in twenty three and what 115 00:05:56,040 --> 00:05:58,000 Speaker 1: a discussions you're having to your clients about what they're 116 00:05:58,000 --> 00:06:01,120 Speaker 1: doing with their tech spend. Yeah, that's right, Well, there 117 00:06:01,160 --> 00:06:04,240 Speaker 1: is concerned about a recession, and I think that particularly 118 00:06:04,360 --> 00:06:06,360 Speaker 1: when there's a recession, that the high rates to come 119 00:06:06,400 --> 00:06:09,960 Speaker 1: with that are a weight on tech stocks, particularly because 120 00:06:10,240 --> 00:06:11,800 Speaker 1: so much of the value of the tech stock is 121 00:06:11,839 --> 00:06:13,960 Speaker 1: out in the distance, so you have to depreciate it 122 00:06:14,000 --> 00:06:16,040 Speaker 1: according to whatever interest rates do you think we're going 123 00:06:16,040 --> 00:06:21,960 Speaker 1: to prevail. We see customers right now trying to moderate 124 00:06:22,000 --> 00:06:25,279 Speaker 1: their risk, trying to plan for change, and and also 125 00:06:25,400 --> 00:06:28,200 Speaker 1: they're worried about productivity in the labor market. There's a 126 00:06:28,200 --> 00:06:30,320 Speaker 1: lot of volatility right now in that and of course 127 00:06:30,360 --> 00:06:33,039 Speaker 1: we've got this big job's number just recently, but we 128 00:06:33,120 --> 00:06:35,480 Speaker 1: don't know where the labor market is going. We don't 129 00:06:35,520 --> 00:06:37,840 Speaker 1: know about the supply of jobs right which is how 130 00:06:37,839 --> 00:06:40,080 Speaker 1: many jobs the employers are willing to create. But we 131 00:06:40,120 --> 00:06:42,120 Speaker 1: also don't know how about the supply of labor, which 132 00:06:42,120 --> 00:06:45,000 Speaker 1: has been unusually volatile since COVID, and it's still up 133 00:06:45,040 --> 00:06:47,320 Speaker 1: in the air. And we don't know about the productivity 134 00:06:47,360 --> 00:06:51,800 Speaker 1: which may change post COVID, according to people working in 135 00:06:51,839 --> 00:06:54,880 Speaker 1: a dispersed way or people being augmented by other technologies 136 00:06:54,960 --> 00:06:58,080 Speaker 1: like AI. AI is a huge topic right now here. 137 00:06:58,120 --> 00:07:00,000 Speaker 1: Everybody talking about it, and I think that the last 138 00:07:00,120 --> 00:07:02,839 Speaker 1: cold months has been a unique to break through in 139 00:07:02,880 --> 00:07:06,560 Speaker 1: the history of this technology. It's it's still a demo, honestly, 140 00:07:06,600 --> 00:07:09,040 Speaker 1: it's just a really good demo at this point, things 141 00:07:09,040 --> 00:07:12,480 Speaker 1: like chat, GPT and UH and Dolly are showing that 142 00:07:12,600 --> 00:07:16,400 Speaker 1: AI is extraordinarily powerful. And the other shoe that's waiting 143 00:07:16,440 --> 00:07:19,680 Speaker 1: to drop, that everybody's asking about talking about is when 144 00:07:19,720 --> 00:07:22,880 Speaker 1: will this move human productivity numbers? When are we going 145 00:07:22,920 --> 00:07:26,440 Speaker 1: to see this effected businesses instead of just just a 146 00:07:26,440 --> 00:07:28,480 Speaker 1: great demonstration of its power. When is it going to 147 00:07:28,560 --> 00:07:32,440 Speaker 1: be truly meaningful? Uh to the to the income statement 148 00:07:32,960 --> 00:07:36,000 Speaker 1: and and it's coming. So when will it beat you 149 00:07:36,040 --> 00:07:39,560 Speaker 1: at a board game? I can do it already had 150 00:07:39,600 --> 00:07:43,280 Speaker 1: a good really, so listeners may not know, but Matt 151 00:07:43,400 --> 00:07:47,840 Speaker 1: is um. He's authored several award winning board games, and 152 00:07:47,840 --> 00:07:50,040 Speaker 1: he's often the top finisher. Have you won the World 153 00:07:50,080 --> 00:07:54,360 Speaker 1: Board Gaming Championships? My best finish was third? Okay, So, 154 00:07:54,560 --> 00:07:56,840 Speaker 1: but there's there are apps out there that can beat 155 00:07:56,880 --> 00:07:59,520 Speaker 1: you and complicated I mean, is there AI out there? 156 00:07:59,560 --> 00:08:01,400 Speaker 1: Excuse me? They can beat you in a complicated board 157 00:08:01,400 --> 00:08:05,920 Speaker 1: game at chess or go absolutely and I think at 158 00:08:05,920 --> 00:08:08,080 Speaker 1: poker at this point there and then much better. And 159 00:08:08,120 --> 00:08:10,160 Speaker 1: it's the other games. The only reason why it can't 160 00:08:10,200 --> 00:08:12,440 Speaker 1: beat me is that no programmer has bothered to put 161 00:08:12,440 --> 00:08:15,240 Speaker 1: the time into it. AI at this point is exceptionally 162 00:08:15,280 --> 00:08:18,600 Speaker 1: good at systems in which the rules don't change quickly, 163 00:08:18,920 --> 00:08:23,560 Speaker 1: like driving or playing chess, or interpreting a photograph to 164 00:08:23,560 --> 00:08:25,840 Speaker 1: see how much damage there is to your car or 165 00:08:25,840 --> 00:08:28,520 Speaker 1: whether there's a kitten in the picture. AI is really 166 00:08:28,520 --> 00:08:31,200 Speaker 1: good when the rules don't change. The only hope for 167 00:08:31,280 --> 00:08:34,000 Speaker 1: us humans is that that that the rules in our 168 00:08:34,080 --> 00:08:36,839 Speaker 1: world change pretty fast. So, Matt, you've got we've got 169 00:08:36,840 --> 00:08:39,400 Speaker 1: just thirty seconds here, But are you putting this to 170 00:08:39,520 --> 00:08:43,240 Speaker 1: work at Appian? Are you using AI as well? Oh? 171 00:08:43,280 --> 00:08:47,680 Speaker 1: Absolutely absolutely, we were already integrating uh chat yv team. 172 00:08:47,720 --> 00:08:51,840 Speaker 1: We've had a AI for years, particularly for interpreting documents, 173 00:08:51,880 --> 00:08:54,440 Speaker 1: but it's one of the core things that our customers 174 00:08:54,440 --> 00:08:56,640 Speaker 1: are asking of us now. All right, Matt, thanks so 175 00:08:56,720 --> 00:08:59,560 Speaker 1: much for joining us. Really appreciate it. Uh, Matt Calkins, 176 00:08:59,640 --> 00:09:02,559 Speaker 1: He's see you on founder of Appian, the nastack symbols 177 00:09:02,640 --> 00:09:06,040 Speaker 1: a P P and plug into your Bloomberg terminal. Uh, 178 00:09:06,320 --> 00:09:10,520 Speaker 1: really interesting company. All right, let's get back to the uh. 179 00:09:11,040 --> 00:09:13,839 Speaker 1: The job's number, the payroll number again, the raw number, 180 00:09:13,840 --> 00:09:17,360 Speaker 1: the top line number, seventeen thousand jobs added. That gets 181 00:09:17,360 --> 00:09:19,959 Speaker 1: your attention. Unemployment rate down the three point four percent, 182 00:09:20,080 --> 00:09:23,800 Speaker 1: another headline number, that gets your attention and average hourly 183 00:09:23,840 --> 00:09:26,600 Speaker 1: earnings up four point four percent. Let's break it all 184 00:09:26,640 --> 00:09:28,959 Speaker 1: down like we like to do when we get these 185 00:09:29,040 --> 00:09:32,120 Speaker 1: numbers with Tom Gibble, founder and CEO of Lasal Network. 186 00:09:32,160 --> 00:09:37,680 Speaker 1: Lassal Network is a national staffing recruiting firm. Uh, Tom, 187 00:09:38,040 --> 00:09:40,880 Speaker 1: just blow out numbers. Help us find some perspective here. 188 00:09:41,040 --> 00:09:44,199 Speaker 1: Everybody's wrong and nobody gets it. My man, there you go. 189 00:09:44,480 --> 00:09:47,320 Speaker 1: What what did you take away? I took away that 190 00:09:47,360 --> 00:09:49,880 Speaker 1: the economy is extremely healthy, and I think what you 191 00:09:49,920 --> 00:09:51,680 Speaker 1: see with the stock market going down is that what 192 00:09:51,720 --> 00:09:54,560 Speaker 1: everybody knows is that the Fed's gonna raise interest rates. 193 00:09:54,640 --> 00:09:57,439 Speaker 1: And what nobody's saying is the Fed's gonna raise raise 194 00:09:57,520 --> 00:10:01,280 Speaker 1: interest rates because they can because the economy will tolerate it. 195 00:10:01,720 --> 00:10:06,240 Speaker 1: Because this economy is is so strong with labor and 196 00:10:06,400 --> 00:10:11,439 Speaker 1: jobs and wages increasing that companies can afford to borrow 197 00:10:11,520 --> 00:10:15,480 Speaker 1: money and actually pay for it versus zero percent. And 198 00:10:15,600 --> 00:10:18,520 Speaker 1: I think that raising interest rates is making up for 199 00:10:18,559 --> 00:10:22,360 Speaker 1: what wasn't done during the last decade. And we've got 200 00:10:22,600 --> 00:10:25,840 Speaker 1: a really strong economy. So you go back into Oh 201 00:10:26,000 --> 00:10:30,240 Speaker 1: recession was based purely on academic analytics. Analytics of two 202 00:10:30,280 --> 00:10:33,840 Speaker 1: consecutive negative GDP quarters, but you were comparing it against 203 00:10:34,840 --> 00:10:37,720 Speaker 1: the best year ever, because it was compared to the 204 00:10:37,760 --> 00:10:41,000 Speaker 1: worst year ever, And so you know, we're really just 205 00:10:41,120 --> 00:10:44,200 Speaker 1: at a really strong standpoint and a half a million 206 00:10:44,200 --> 00:10:49,480 Speaker 1: new jobs is awesome, And does that seem in line 207 00:10:49,520 --> 00:10:52,959 Speaker 1: with what you expect to keep happening? Are we I mean, 208 00:10:53,080 --> 00:10:55,839 Speaker 1: maybe we won't see five hundred thousand again, but are 209 00:10:55,880 --> 00:10:58,640 Speaker 1: we gonna keep seeing two D three D over the 210 00:10:58,679 --> 00:11:02,040 Speaker 1: next coming months? Yeah, I think you're gonna see see 211 00:11:02,080 --> 00:11:04,520 Speaker 1: you know, two hundred thousand, give or take, meaning you know, 212 00:11:04,640 --> 00:11:06,600 Speaker 1: maybe you have one fifty, maybe you have to fifty, 213 00:11:06,679 --> 00:11:09,000 Speaker 1: but but I think on average will be will be 214 00:11:09,040 --> 00:11:12,120 Speaker 1: in that that two area code. And I think that 215 00:11:12,200 --> 00:11:14,800 Speaker 1: the point of that is saying that small and medium 216 00:11:14,840 --> 00:11:18,400 Speaker 1: sized companies, which everybody has always said drives the economy 217 00:11:18,440 --> 00:11:20,839 Speaker 1: and it drives hiring, and now it really is. As 218 00:11:20,840 --> 00:11:23,520 Speaker 1: we see big tech laid people off, and we see 219 00:11:23,520 --> 00:11:25,880 Speaker 1: some other companies lay people off, and guess what we're 220 00:11:25,880 --> 00:11:28,360 Speaker 1: gonna see over the next six to nine months. We're 221 00:11:28,360 --> 00:11:31,600 Speaker 1: gonna see the beginning of the infrastructure package, and there's 222 00:11:31,640 --> 00:11:34,120 Speaker 1: gonna be those jobs that are gonna becoming good. So 223 00:11:34,160 --> 00:11:36,960 Speaker 1: I think we're gonna have a relatively healthy twenty three 224 00:11:37,480 --> 00:11:40,880 Speaker 1: tom Any regionality to the labor market here. Sometimes you know, 225 00:11:40,880 --> 00:11:43,520 Speaker 1: when you see you might see the Sunbelt do better 226 00:11:43,600 --> 00:11:45,600 Speaker 1: than you know, the Rust Belt in those types of things. 227 00:11:45,600 --> 00:11:48,400 Speaker 1: Are we've seen that this time around. Yeah, I think 228 00:11:48,400 --> 00:11:51,880 Speaker 1: we're we're we're getting We're continuing to see the rise 229 00:11:51,960 --> 00:11:56,240 Speaker 1: of the of the of the Southern states from Texas 230 00:11:56,320 --> 00:11:59,280 Speaker 1: to Florida and everything in between. And you're seeing you know, 231 00:11:59,320 --> 00:12:02,920 Speaker 1: big tech in California and and letting people go in 232 00:12:03,000 --> 00:12:05,400 Speaker 1: that way and where the laws are. And I think 233 00:12:05,440 --> 00:12:08,559 Speaker 1: we'll continue to see the evolution of Red States in 234 00:12:08,600 --> 00:12:12,040 Speaker 1: the service level economy. I think we're gonna see, uh, 235 00:12:12,080 --> 00:12:16,360 Speaker 1: really a growth in in in cities like Birmingham and 236 00:12:16,400 --> 00:12:19,040 Speaker 1: in that Raleigh, Durham and that Charlotte. You're gonna see 237 00:12:19,080 --> 00:12:21,839 Speaker 1: Miami continuing to get great. I mean, I'm seeing more 238 00:12:21,840 --> 00:12:24,600 Speaker 1: and more companies that are doing hiring in Tampa. I 239 00:12:24,640 --> 00:12:27,440 Speaker 1: think you're seeing that evolution that that those cities in 240 00:12:27,480 --> 00:12:30,520 Speaker 1: those in those Red States are are going to continue 241 00:12:30,559 --> 00:12:33,480 Speaker 1: to be where companies want to hire people in the 242 00:12:33,520 --> 00:12:39,640 Speaker 1: service business. And what has historically been a manufacturing car manufacturing, UH, 243 00:12:39,679 --> 00:12:43,120 Speaker 1: furniture manufacturing area, will we'll move into a white collar 244 00:12:43,200 --> 00:12:47,000 Speaker 1: service area. What's your take time? On wages, we saw 245 00:12:47,640 --> 00:12:50,839 Speaker 1: average hourly earnings four point three percent year over year, 246 00:12:50,920 --> 00:12:54,600 Speaker 1: so a slight climbs zero point from um last month? 247 00:12:55,640 --> 00:12:58,559 Speaker 1: Are workers getting enough to you run on the leading 248 00:12:59,080 --> 00:13:01,640 Speaker 1: placement firms in the country. Are the people you're placing 249 00:13:01,800 --> 00:13:06,439 Speaker 1: happy with the pay they're getting? Well? I think I think, 250 00:13:06,720 --> 00:13:10,520 Speaker 1: uh uh. If you ask an employee if they're happy 251 00:13:10,559 --> 00:13:13,440 Speaker 1: with their pay, no matter what the number is the 252 00:13:13,480 --> 00:13:17,280 Speaker 1: majority and we're gonna say no. Um, it's like a 253 00:13:17,320 --> 00:13:19,720 Speaker 1: divorce settlement that one party is always gonna think they 254 00:13:19,720 --> 00:13:21,760 Speaker 1: paid too much, one party is always gonna think they 255 00:13:21,760 --> 00:13:24,560 Speaker 1: got too little. And and I think that that's what 256 00:13:24,559 --> 00:13:27,800 Speaker 1: what wages are. However, what I mean, though, is do 257 00:13:27,880 --> 00:13:30,240 Speaker 1: they have enough money to keep up with inflation? Or 258 00:13:30,440 --> 00:13:33,800 Speaker 1: are they having to say, you know, cut money on 259 00:13:33,840 --> 00:13:37,079 Speaker 1: their food bill or you know, drive a loss. But 260 00:13:37,280 --> 00:13:39,480 Speaker 1: answer it this way and say, I think they're in 261 00:13:39,480 --> 00:13:42,800 Speaker 1: the exact same boat that they were three years ago 262 00:13:43,000 --> 00:13:46,679 Speaker 1: before inflation on lower wages. What we've done is is 263 00:13:46,720 --> 00:13:50,000 Speaker 1: just changed the narrative. So you're making more things cost more, 264 00:13:50,000 --> 00:13:52,240 Speaker 1: and before you were making less and things cost less. 265 00:13:52,440 --> 00:13:54,840 Speaker 1: People are in the exact same boat as they were before. 266 00:13:55,200 --> 00:13:58,360 Speaker 1: What they're complaining about is saying I should be able 267 00:13:58,400 --> 00:14:02,719 Speaker 1: to do more because I'm making more money what they did. 268 00:14:02,720 --> 00:14:06,360 Speaker 1: What the layman doesn't realize is when wages rise as 269 00:14:06,400 --> 00:14:09,559 Speaker 1: fast as they have, things are gonna cost more. This 270 00:14:09,600 --> 00:14:11,520 Speaker 1: is economics one O one that should be taught in 271 00:14:11,600 --> 00:14:14,000 Speaker 1: high school that people are now learning in real life. 272 00:14:14,240 --> 00:14:17,319 Speaker 1: And that's the problem, uh in the ballot box to 273 00:14:18,000 --> 00:14:21,280 Speaker 1: grocery aisle and what we're dealing with now. And I 274 00:14:21,320 --> 00:14:24,000 Speaker 1: think that that's the standard situation. And I think that 275 00:14:24,080 --> 00:14:26,720 Speaker 1: you're not gonna see wages increase. I I don't. I 276 00:14:26,800 --> 00:14:29,200 Speaker 1: don't think so. I think they're gonna level off. And 277 00:14:29,240 --> 00:14:31,720 Speaker 1: what we're seeing with the tech layoffs is that the 278 00:14:31,760 --> 00:14:34,120 Speaker 1: tech companies continue to hire. They got rid of their 279 00:14:34,160 --> 00:14:36,600 Speaker 1: poor performers, and if they bring people back, they're not 280 00:14:36,680 --> 00:14:39,400 Speaker 1: doing it at the exuberant wages that they were twelve 281 00:14:39,440 --> 00:14:42,720 Speaker 1: months ago. Tom, you're a proud graduate of the University 282 00:14:42,720 --> 00:14:45,600 Speaker 1: of Colorado. How fired up are you for coach Prime? 283 00:14:46,240 --> 00:14:48,880 Speaker 1: Let me tell you something. I have not had my 284 00:14:48,960 --> 00:14:52,040 Speaker 1: phone buzz more than it has in the past sixty days. 285 00:14:52,320 --> 00:14:55,000 Speaker 1: And we are not only gonna win the Pack twelve, 286 00:14:55,440 --> 00:14:58,720 Speaker 1: we are gonna be an elite power five and within 287 00:14:58,840 --> 00:15:01,920 Speaker 1: two years will be the playoff. There you go, and 288 00:15:01,960 --> 00:15:05,640 Speaker 1: that is a sentiment. I've never seen a coach is 289 00:15:05,800 --> 00:15:09,000 Speaker 1: hiring have more of an impact on an institution than 290 00:15:09,120 --> 00:15:11,960 Speaker 1: Dion Sanders at the University of Colorado. So, Tom, thanks 291 00:15:11,960 --> 00:15:13,720 Speaker 1: so much for joining us there. Tom is a founder 292 00:15:13,720 --> 00:15:17,600 Speaker 1: and CEO of LaSalle Network. UH, big big national staffing company. 293 00:15:17,600 --> 00:15:19,560 Speaker 1: And you know he's been bullish about this job market 294 00:15:19,600 --> 00:15:23,200 Speaker 1: for the longest time. Uh, and he's been right, been right. 295 00:15:25,640 --> 00:15:28,000 Speaker 1: Let's get to the story that we were talking about 296 00:15:28,040 --> 00:15:30,360 Speaker 1: a little bit last year. At one point, you know, 297 00:15:30,480 --> 00:15:32,600 Speaker 1: you think we had so many shortages of so many products. 298 00:15:32,680 --> 00:15:36,160 Speaker 1: One of the more serious ones was uh, baby formula. 299 00:15:36,360 --> 00:15:38,920 Speaker 1: And at the time we spoke to Laura Modi, CEO 300 00:15:39,000 --> 00:15:42,040 Speaker 1: and founder a baby formula company Bobby Uh. She joins 301 00:15:42,120 --> 00:15:44,080 Speaker 1: us again today, So Laura, thanks so much for joining 302 00:15:44,120 --> 00:15:46,200 Speaker 1: us here. Can you give us an update on kind 303 00:15:46,200 --> 00:15:50,680 Speaker 1: of the baby formula market supply, demand, availability? Where are 304 00:15:50,720 --> 00:15:52,880 Speaker 1: we right now? Yeah? Good to charge you guys again. 305 00:15:53,440 --> 00:15:55,600 Speaker 1: I look, it has been a long year. I mean, 306 00:15:55,640 --> 00:16:00,000 Speaker 1: you can coin last year's formula the shortage with severe 307 00:16:00,120 --> 00:16:02,120 Speaker 1: and it really was one of the most serious shortages 308 00:16:02,160 --> 00:16:04,400 Speaker 1: we've had in a long time. I would say, while 309 00:16:04,400 --> 00:16:08,920 Speaker 1: the shortages coming to a close, it still remains a crisis. Fundamentally. 310 00:16:09,080 --> 00:16:12,720 Speaker 1: We have not addressed the problem, which is domestic supply, 311 00:16:12,960 --> 00:16:18,120 Speaker 1: domestic manufacturing. So until we both stir our domestic manufacturing practices, 312 00:16:18,160 --> 00:16:21,960 Speaker 1: we may be in this crisis for a long time. So, um, 313 00:16:22,200 --> 00:16:25,480 Speaker 1: what does it look like then out there for mothers 314 00:16:25,560 --> 00:16:29,520 Speaker 1: who need formula? Are they able to get any formula? 315 00:16:29,760 --> 00:16:33,760 Speaker 1: Is it just um, you know, high quality formulas in 316 00:16:33,920 --> 00:16:38,160 Speaker 1: short supply or what's what's it look like? We're coming 317 00:16:38,240 --> 00:16:42,440 Speaker 1: up on a year since the Abbot recall happened, and 318 00:16:42,480 --> 00:16:46,000 Speaker 1: since the shortage started, and as of last week nearly 319 00:16:46,040 --> 00:16:49,000 Speaker 1: a heard of households with the baby under one that 320 00:16:49,200 --> 00:16:52,520 Speaker 1: they still had trouble finding formula. Yes, some high quality 321 00:16:52,560 --> 00:16:56,760 Speaker 1: ones and some very sensitive formulas as well. So where 322 00:16:56,800 --> 00:16:58,800 Speaker 1: are we kind of just give us the sense of 323 00:16:58,960 --> 00:17:02,840 Speaker 1: kind of domestic duction, domestics supply kind of where are 324 00:17:02,920 --> 00:17:08,800 Speaker 1: we today? Where should it be in your opinion? Yeah, Look, 325 00:17:08,880 --> 00:17:11,840 Speaker 1: this is I believe the cause of what is being 326 00:17:11,960 --> 00:17:15,919 Speaker 1: just concentrated and in many ways the complacency of a 327 00:17:16,000 --> 00:17:21,360 Speaker 1: concentrated industry. Of the market is dominated by two players, 328 00:17:21,920 --> 00:17:24,159 Speaker 1: and those two players have owned been conforming in the 329 00:17:24,160 --> 00:17:29,040 Speaker 1: markets for over forty years, very little innovation and no competition, 330 00:17:29,240 --> 00:17:32,120 Speaker 1: which has resulted in the complacency that we see today. 331 00:17:32,600 --> 00:17:36,000 Speaker 1: And your company, just to refreshes you, guys, are direct 332 00:17:36,040 --> 00:17:38,280 Speaker 1: to consumer. Talk to us about your company, Bobby, and 333 00:17:38,560 --> 00:17:41,679 Speaker 1: kind of how you're playing in this market, which I 334 00:17:41,680 --> 00:17:43,760 Speaker 1: guess a lot of us who don't have children maybe 335 00:17:43,760 --> 00:17:47,800 Speaker 1: forget how critical it is for so many families. That's 336 00:17:47,880 --> 00:17:51,199 Speaker 1: r right. Look, intanporta is an essential good. This is 337 00:17:51,240 --> 00:17:54,400 Speaker 1: not a typical pantry product. It's not a granola bar. 338 00:17:55,080 --> 00:17:57,679 Speaker 1: When you start your baby on formula, you need to 339 00:17:57,720 --> 00:18:02,280 Speaker 1: make sure that you have the product available. Bobby is 340 00:18:02,320 --> 00:18:05,560 Speaker 1: a direct to consumer formula company. We also now sit 341 00:18:05,600 --> 00:18:08,959 Speaker 1: on target shows. But we have built some models that 342 00:18:09,040 --> 00:18:12,000 Speaker 1: when you subscribe to if we subscribe to you, which 343 00:18:12,080 --> 00:18:15,200 Speaker 1: essentially say you come in in your first month, We're 344 00:18:15,240 --> 00:18:17,399 Speaker 1: going to make sure that we have the supply for 345 00:18:17,560 --> 00:18:21,600 Speaker 1: you for your entire journey. And just having that model 346 00:18:21,680 --> 00:18:24,160 Speaker 1: gives that peace of mind to parents this no matter 347 00:18:24,200 --> 00:18:26,640 Speaker 1: how bad the shortages get, we're going to make sure 348 00:18:26,640 --> 00:18:30,439 Speaker 1: that you have your supply. How concerned our parents. We 349 00:18:30,440 --> 00:18:33,719 Speaker 1: were just talking, uh Paul and I about how you know, 350 00:18:33,800 --> 00:18:36,480 Speaker 1: back in the day, kids would just walk to school 351 00:18:36,520 --> 00:18:40,040 Speaker 1: by themselves, but now you know, not until you're eighteen 352 00:18:40,200 --> 00:18:43,080 Speaker 1: are you allowed to be loose in the city. Parents 353 00:18:43,240 --> 00:18:46,920 Speaker 1: really care Now My parents just ignored me until it worked, 354 00:18:47,000 --> 00:18:50,200 Speaker 1: until I got a job. But you know, parents these 355 00:18:50,280 --> 00:18:53,520 Speaker 1: days are very serious about child well being in health, 356 00:18:53,600 --> 00:18:58,040 Speaker 1: and so is Bobby, you know, getting taking full advantage 357 00:18:58,040 --> 00:19:00,160 Speaker 1: of that. I mean, what's what's what's your say else? 358 00:19:00,160 --> 00:19:04,639 Speaker 1: Look like the sales are good? You know, we have 359 00:19:04,760 --> 00:19:07,160 Speaker 1: gotten to a place now where we're serving four percent 360 00:19:07,280 --> 00:19:11,080 Speaker 1: of the nonwick market in the gusts of two years. 361 00:19:11,119 --> 00:19:13,480 Speaker 1: So it has been a very fast growing two years, 362 00:19:13,560 --> 00:19:17,000 Speaker 1: the fastest growing formula since the eighties, and now the 363 00:19:17,080 --> 00:19:21,640 Speaker 1: fifth largest formula company in the US and hard to believe. 364 00:19:21,920 --> 00:19:24,360 Speaker 1: And yet I think it's also just another big wake 365 00:19:24,440 --> 00:19:27,240 Speaker 1: up call to how badly the formula industry needs to change. 366 00:19:27,600 --> 00:19:31,480 Speaker 1: And look as Terence ourselves, I've made this formula because 367 00:19:31,520 --> 00:19:34,400 Speaker 1: I wanted it for my own kids. We went with 368 00:19:34,520 --> 00:19:38,000 Speaker 1: a better for you infant formula, clean ingredients left out 369 00:19:38,040 --> 00:19:41,960 Speaker 1: the corn syrup, And you're totally right. Parents have changed 370 00:19:42,000 --> 00:19:44,919 Speaker 1: their own eating habits. They're buying organic for themselves, so 371 00:19:45,080 --> 00:19:48,720 Speaker 1: obviously they want to do that for their child. And frankly, 372 00:19:49,119 --> 00:19:51,680 Speaker 1: the US, as the first world country, should be putting 373 00:19:51,680 --> 00:19:54,399 Speaker 1: out the best intoint formula. We deserve to do it. 374 00:19:54,400 --> 00:19:58,040 Speaker 1: It's also great to have women taking control of this. 375 00:19:58,160 --> 00:20:02,800 Speaker 1: I followed the she media like crew and uh, you 376 00:20:02,800 --> 00:20:05,560 Speaker 1: know they're doing this future of helping at south By Southwest, 377 00:20:06,920 --> 00:20:10,000 Speaker 1: Is it is? It? Is it? Um? This is helpful, 378 00:20:10,119 --> 00:20:15,560 Speaker 1: this kind of networking for women in business. Yes, and 379 00:20:15,880 --> 00:20:18,000 Speaker 1: you know we need to get more women behind the 380 00:20:18,000 --> 00:20:23,200 Speaker 1: wheel driving businesses that represent their needs. You know, we're 381 00:20:23,280 --> 00:20:27,359 Speaker 1: we're selling an alternative to breast milk. I don't believe 382 00:20:27,400 --> 00:20:30,080 Speaker 1: anyone else should be behind the wheel. And yes, I'm 383 00:20:30,119 --> 00:20:33,119 Speaker 1: still the only female CEO of an infant formula company. 384 00:20:33,280 --> 00:20:37,800 Speaker 1: It's insane. That's insane. It's insane. It is absolutely insane. 385 00:20:37,880 --> 00:20:41,760 Speaker 1: So I'll challenge any of those male CEOs out there 386 00:20:41,800 --> 00:20:45,640 Speaker 1: against what we're doing. No, I I am very excited 387 00:20:45,680 --> 00:20:49,440 Speaker 1: to see see the rise of more female founders and CEOs, 388 00:20:49,560 --> 00:20:53,000 Speaker 1: especially in industry that they think and doctors right, right, 389 00:20:53,040 --> 00:20:57,399 Speaker 1: because um, Laura, there are so many issues that affect 390 00:20:57,440 --> 00:21:01,480 Speaker 1: women during pregnancy and as their nurse saying that are 391 00:21:01,560 --> 00:21:07,800 Speaker 1: just under researched. Um. You know, and I hope that 392 00:21:07,880 --> 00:21:11,120 Speaker 1: you work with doctors and and female doctors are able 393 00:21:11,119 --> 00:21:13,679 Speaker 1: to do research that that male doctors for decades or 394 00:21:13,680 --> 00:21:18,679 Speaker 1: centuries haven't done. That's exactly it. I mean, up until recently, 395 00:21:19,280 --> 00:21:22,240 Speaker 1: women were treated as smaller men when it came to 396 00:21:22,320 --> 00:21:26,479 Speaker 1: the research side of and anything in the world of 397 00:21:26,480 --> 00:21:29,920 Speaker 1: pharma and food and anything that women were consuming. Well, 398 00:21:30,880 --> 00:21:34,359 Speaker 1: we now need to look at women and gender very differently, 399 00:21:34,920 --> 00:21:37,640 Speaker 1: and I believe women are driving that movement. Laura, thank 400 00:21:37,640 --> 00:21:39,919 Speaker 1: you so much for joining us. Really appreciate chatting with 401 00:21:39,960 --> 00:21:42,479 Speaker 1: you again. You're really so helpful to us when this 402 00:21:42,840 --> 00:21:46,159 Speaker 1: story in this issue really became apparent last year. And 403 00:21:46,160 --> 00:21:48,280 Speaker 1: we're good to see some improvement in that side of 404 00:21:48,280 --> 00:21:51,160 Speaker 1: the market, but still more room UH to grow into 405 00:21:51,359 --> 00:21:53,840 Speaker 1: and to change. Laura Moody, she's a CEO and co 406 00:21:53,960 --> 00:21:57,959 Speaker 1: founder of Bobby. Bobby is a baby formula delivery startup 407 00:21:58,000 --> 00:22:00,560 Speaker 1: that sells direct to consumer and offers a sup sscription 408 00:22:00,640 --> 00:22:04,920 Speaker 1: service to parents across the US. Are really unique, UH, 409 00:22:04,920 --> 00:22:08,720 Speaker 1: an interesting company. Thanks for listening to the Bloomberg Markets podcast. 410 00:22:09,080 --> 00:22:12,280 Speaker 1: You can subscribe and listen to interviews with Apple Podcasts 411 00:22:12,440 --> 00:22:16,360 Speaker 1: or whatever podcast platform you prefer. I'm Matt Miller. I'm 412 00:22:16,359 --> 00:22:20,399 Speaker 1: on Twitter at Matt Miller, three pt on Fall Sweeney 413 00:22:20,400 --> 00:22:23,040 Speaker 1: I'm on Twitter at pt Sweeney Before the podcast. You 414 00:22:23,080 --> 00:22:25,480 Speaker 1: can always catch us worldwide at Bloomberg Radio