1 00:00:02,520 --> 00:00:08,240 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:12,880 --> 00:00:16,159 Speaker 2: A single best idea is to understand that we do 3 00:00:16,239 --> 00:00:20,040 Speaker 2: not plan, we do have a plan. I'm death on meetings. 4 00:00:20,200 --> 00:00:25,000 Speaker 2: I detest meetings that usually slow everything down. And you 5 00:00:25,000 --> 00:00:27,840 Speaker 2: would not believe how the team that puts Bloomberg Surveillance 6 00:00:27,880 --> 00:00:32,600 Speaker 2: together every day really doesn't do much in meetings. It's 7 00:00:32,640 --> 00:00:35,480 Speaker 2: cryptic back and forth. Lots of it's on your iPhone, 8 00:00:35,880 --> 00:00:38,800 Speaker 2: you know, text messages in that, and you know there's 9 00:00:38,880 --> 00:00:42,040 Speaker 2: a lot of machinery like can you bring in Detroit 10 00:00:42,080 --> 00:00:45,040 Speaker 2: online two or London Online three. There's a lot of 11 00:00:45,159 --> 00:00:48,280 Speaker 2: physical stuff that goes on that I take for granted, 12 00:00:48,320 --> 00:00:51,680 Speaker 2: which I shouldn't. Today was nuts. There's just no other 13 00:00:51,720 --> 00:00:54,520 Speaker 2: way to put it. A major shout out to Matthew 14 00:00:54,560 --> 00:00:57,360 Speaker 2: Miller who went out to Detroit to the Auto show 15 00:00:57,360 --> 00:01:01,080 Speaker 2: and had a piercing interview with four Word and Red Bull. 16 00:01:01,200 --> 00:01:03,360 Speaker 2: Is they jump into F one that interview. I'm sure 17 00:01:03,400 --> 00:01:07,640 Speaker 2: we'll go worldwide with the interest in F one. Patient 18 00:01:07,840 --> 00:01:11,640 Speaker 2: around all that was dominic constum at missouo constumme of course, 19 00:01:11,760 --> 00:01:16,120 Speaker 2: legendary credit suitee and doing brilliant synthesis at sixty thousand 20 00:01:16,160 --> 00:01:21,360 Speaker 2: feet of your economic system here is dominic constem of missuo. 21 00:01:21,840 --> 00:01:25,680 Speaker 1: So basically, consumer spending in nominal terms fell well short 22 00:01:25,800 --> 00:01:28,959 Speaker 1: of its trend during the last year because of when 23 00:01:29,000 --> 00:01:31,160 Speaker 1: the towers hit, and now the consumers are kind of 24 00:01:31,160 --> 00:01:33,160 Speaker 1: getting that back. This is if you remember one of 25 00:01:33,160 --> 00:01:35,840 Speaker 1: the problems with sequencing the stimulus before. You know, you 26 00:01:35,920 --> 00:01:38,200 Speaker 1: kind of wanted it up front before the towers hit 27 00:01:38,440 --> 00:01:40,160 Speaker 1: and you didn't get it, So now you're getting it. 28 00:01:40,280 --> 00:01:43,080 Speaker 1: So that kind of in our view, avoids any kind 29 00:01:43,120 --> 00:01:45,320 Speaker 1: of recession or anything like that. So the economy is fine, 30 00:01:45,440 --> 00:01:47,160 Speaker 1: but I wouldn't say it's going to sort of meaningfully 31 00:01:47,480 --> 00:01:50,280 Speaker 1: accelerate in a way that will deliver job growth and 32 00:01:50,400 --> 00:01:52,520 Speaker 1: acceleration in the labor input to. 33 00:01:52,480 --> 00:01:55,400 Speaker 2: Growth dominic constum And really can't say enough about the 34 00:01:55,440 --> 00:02:00,200 Speaker 2: work at MISSOO Stephen Rashudo and Jordan Rochester as well 35 00:02:00,400 --> 00:02:02,680 Speaker 2: in the blur of all of our back and forth 36 00:02:02,720 --> 00:02:05,880 Speaker 2: with London and with Detroit, with Matt Miller and A 37 00:02:05,960 --> 00:02:10,079 Speaker 2: Wong stopped by. She's running US economics for all of Bloomberg. 38 00:02:10,160 --> 00:02:14,800 Speaker 2: She's been absolutely brilliant nationally, even globally acclaimed last year 39 00:02:14,840 --> 00:02:17,840 Speaker 2: for her study of the American economy. She touched on 40 00:02:17,880 --> 00:02:21,400 Speaker 2: AI here, but just Anna Wong with a view forward. 41 00:02:21,840 --> 00:02:25,280 Speaker 3: We thought that the bottom was somewhere in July and 42 00:02:25,360 --> 00:02:29,400 Speaker 3: August last year. However, even though things are slowly improving, 43 00:02:29,480 --> 00:02:33,560 Speaker 3: its still very fragile. I think, and AI is doing 44 00:02:33,600 --> 00:02:36,079 Speaker 3: more damage to the labor market than many people. 45 00:02:36,080 --> 00:02:38,640 Speaker 2: I can describe that in what way do you see 46 00:02:38,680 --> 00:02:39,600 Speaker 2: that in your research? 47 00:02:39,760 --> 00:02:42,600 Speaker 3: Yes, in earnings. So we are entering into an earning 48 00:02:42,639 --> 00:02:45,040 Speaker 3: season right now, and even in the last earnings we 49 00:02:45,080 --> 00:02:48,440 Speaker 3: are seeing in sectors which do not think AI is 50 00:02:48,520 --> 00:02:51,560 Speaker 3: making a splash, but it is happening, even in construction 51 00:02:52,240 --> 00:02:55,800 Speaker 3: utility sector. We just thought, you know, we know retail, we. 52 00:02:55,720 --> 00:02:56,799 Speaker 2: Know in tech. 53 00:02:56,880 --> 00:03:01,919 Speaker 3: They are replacing new hires in construction in. 54 00:03:01,960 --> 00:03:08,840 Speaker 2: July, in September into twenty twenty seven. Remember that moment 55 00:03:09,160 --> 00:03:13,520 Speaker 2: Bronze that Ana Wong onto something there. It's her guests, 56 00:03:14,360 --> 00:03:17,880 Speaker 2: it's her probability, it's not her hope. It's just like, 57 00:03:17,960 --> 00:03:21,280 Speaker 2: what do you think about AI, doctor Wong. There's a 58 00:03:21,280 --> 00:03:24,360 Speaker 2: lot percolating on this, but all my radars up about 59 00:03:24,360 --> 00:03:28,840 Speaker 2: this mystery of AI, and our corporate leaders will adapt 60 00:03:28,880 --> 00:03:32,079 Speaker 2: to it. After the fourth of July. In the next year, 61 00:03:32,120 --> 00:03:35,440 Speaker 2: new FED Chairman and all that watch to see the 62 00:03:35,600 --> 00:03:42,320 Speaker 2: synthesis of computer process and technology into the American labor economy. 63 00:03:42,880 --> 00:03:46,720 Speaker 2: We're out on podcasts on Apple and Spotify. On YouTube podcasts, 64 00:03:46,920 --> 00:03:48,240 Speaker 2: single best idea