Platforms Reshape Labor Economy Paydays and Job Mobility

1 00:00:06,080 - > 00:00:09,519 Narrator: This is Wage to Wallet, a podcast by PYMNTS. 2 00:00:09,519 - > 00:00:12,480 In collaboration with Ingo Money and WorkWhile. 3 00:00:12,480 - > 00:00:15,919 We break down how America's hourly workforce powers the real 4 00:00:15,919 - > 00:00:18,480 economy, from paycheck to GDP. 5 00:00:18,480 - > 00:00:23,760 In this episode, PYMNTS CEO Karen Webster sits down with 6 00:00:23,760 - > 00:00:29,440 Ingo Payments CEO Drew Edwards and WorkWhile COO Simon Khalaf 7 00:00:29,440 - > 00:00:33,520 to discuss the Wage to Wallet Index Making Ends Meet, Side 8 00:00:33,520 - > 00:00:35,840 Work Patterns in the Labor Economy. 9 00:00:39,119 - > 00:00:41,439 Karen Webster: Hey Simon, hey Drew, thanks for joining me 10 00:00:41,439 - > 00:00:45,039 today on another one of our podcasts to break down the 11 00:00:45,039 - > 00:00:49,119 latest wage-to-wallet report, which is all about the labor 12 00:00:49,119 - > 00:00:55,520 economy, job mobility, timing of pay, and the contribution to 13 00:00:55,520 - > 00:00:58,159 the economy and GDP that all that represents. 14 00:00:58,159 - > 00:01:00,399 Those thank thanks so much for taking the time. 15 00:01:01,759 - > 00:01:02,960 Simon Khalaf: Thanks for having us. 16 00:01:03,759 - > 00:01:04,159 Karen Webster: All right. 17 00:01:04,159 - > 00:01:07,519 Well, let's put to let me put together a little context here. 18 00:01:07,519 - > 00:01:11,120 The data suggest, and I I love this report because I think it 19 00:01:11,120 - > 00:01:12,719 is so insightful. 20 00:01:12,719 - > 00:01:17,760 It covers so many areas of the workforce that others really 21 00:01:17,760 - > 00:01:18,560 overlook. 22 00:01:18,560 - > 00:01:22,480 But the data suggest and really concludes that we're not 23 00:01:22,480 - > 00:01:24,480 operating in a single labor market. 24 00:01:24,480 - > 00:01:29,359 There's the labor economy, and then there is the non-labor 25 00:01:29,359 - > 00:01:30,000 economy. 26 00:01:30,000 - > 00:01:36,719 Both feel that their job security is okay, but the 27 00:01:36,719 - > 00:01:40,480 financial confidence for those two different workforces 28 00:01:40,480 - > 00:01:42,000 couldn't be more different. 29 00:01:42,000 - > 00:01:46,000 Financial confidence is rising for the non-labor economy 30 00:01:46,000 - > 00:01:50,319 workers, yet labor economy workers really remain 31 00:01:50,319 - > 00:01:52,239 financially constrained. 32 00:01:52,239 - > 00:01:57,680 So it suggests that employment alone, having a job isn't the 33 00:01:57,680 - > 00:01:58,719 full story. 34 00:01:58,719 - > 00:02:04,000 Stability of income, mobility, and obviously liquidity for the 35 00:02:04,000 - > 00:02:08,879 worker and their households is really more the dividing lines 36 00:02:08,879 - > 00:02:10,159 than just having a job. 37 00:02:10,159 - > 00:02:12,240 So, Simon, I want to start with you. 38 00:02:12,240 - > 00:02:16,240 Do you see based on your platform that labor economy 39 00:02:16,240 - > 00:02:21,840 workers are changing jobs more, or do they just wish they could? 40 00:02:24,080 - > 00:02:25,680 Simon Khalaf: Great question, uh Karen. 41 00:02:25,680 - > 00:02:29,599 I would say if there's any headline we're getting from our 42 00:02:29,599 - > 00:02:35,280 platform, is that our workers uh are working a little bit more, 43 00:02:35,280 - > 00:02:37,199 not changing jobs. 44 00:02:37,199 - > 00:02:42,159 So I'd say they've traded wage growth with more working hours. 45 00:02:42,159 - > 00:02:46,960 So if I look at kind of like last year, uh for the vast 46 00:02:46,960 - > 00:02:50,560 majority of the time, wage growth was higher than 47 00:02:50,560 - > 00:02:51,280 inflation. 48 00:02:51,280 - > 00:02:55,360 But in January, wage growth dipped below inflation. 49 00:02:55,360 - > 00:02:59,120 And the trend recovered a little bit in February, but it's 50 00:02:59,120 - > 00:03:01,439 but we're still collecting the data, data. 51 00:03:01,439 - > 00:03:04,159 So what happened was our workers decided to work more. 52 00:03:04,159 - > 00:03:07,199 As in life, I made less per hour, right? 53 00:03:07,199 - > 00:03:10,960 In an aggregate basis, it doesn't matter that much, but on 54 00:03:10,960 - > 00:03:14,719 an individual basis, given kind of the the upcoming uh 55 00:03:14,719 - > 00:03:18,800 expenses, whether it's taxes or lack of a tax return, they 56 00:03:18,800 - > 00:03:20,000 decided to work more. 57 00:03:20,000 - > 00:03:26,080 So that is something that we we believe is a healthy indicator 58 00:03:26,080 - > 00:03:29,039 because they didn't decide to dip into debt. 59 00:03:29,039 - > 00:03:31,599 They just decided to simply work more. 60 00:03:32,240 - > 00:03:34,639 Karen Webster: So job mobility is redefined. 61 00:03:34,639 - > 00:03:38,719 It's not taking leaving this job to go to another one, it's 62 00:03:38,719 - > 00:03:43,520 actually adding more shifts or more opportunities to the 63 00:03:43,520 - > 00:03:44,319 household income. 64 00:03:44,319 - > 00:03:47,039 And that's really job mobility, as you as you see it on your 65 00:03:47,039 - > 00:03:47,520 platform. 66 00:03:47,520 - > 00:03:52,400 Um there is access to work, right? 67 00:03:52,400 - > 00:03:56,800 So you're seeing that there is there is supply for the demand. 68 00:03:57,759 - > 00:03:58,800 Simon Khalaf: Absolutely, right. 69 00:03:58,800 - > 00:04:02,800 I mean, I always say, you know, you look at our allure index, 70 00:04:02,800 - > 00:04:05,280 which is the continuation rate, is very high. 71 00:04:05,280 - > 00:04:09,680 So our continuation rate is is, I think in February averaged 72 00:04:09,680 - > 00:04:13,680 over 90 98%, and January was around 97%. 73 00:04:13,680 - > 00:04:17,279 So I would say, and of course there's circumstances that 74 00:04:17,279 - > 00:04:20,560 people do not want to work, but I'd say whoever wants to work, 75 00:04:20,560 - > 00:04:22,720 we have found them the corresponding job. 76 00:04:22,720 - > 00:04:29,360 So right right now we we have 40% of our workers, so it's 1.2 77 00:04:29,360 - > 00:04:34,160 million roughly, uh uh, they're actually deciding to work what I 78 00:04:34,160 - > 00:04:36,399 would say more than full time. 79 00:04:36,399 - > 00:04:40,800 So they they're taking on effectively working on the 80 00:04:40,800 - > 00:04:46,079 platform like 36 plus hours and then and then more in order to 81 00:04:46,079 - > 00:04:49,199 make up for any salary depletion. 82 00:04:49,199 - > 00:04:55,439 So but yes, the the uh the the there's no question that I mean 83 00:04:55,439 - > 00:04:59,439 with a platform and not a company in terms of who they're 84 00:04:59,439 - > 00:05:00,079 working for. 85 00:05:00,079 - > 00:05:04,000 So I think they're they're finding that job mobility is uh 86 00:05:04,000 - > 00:05:08,079 and the elasticity as in when I want to work more, it is 87 00:05:08,079 - > 00:05:08,639 available. 88 00:05:08,639 - > 00:05:12,079 And when I want to work less, I'm not penalized. 89 00:05:12,319 - > 00:05:12,639 Karen Webster: Yeah. 90 00:05:13,040 - > 00:05:16,560 Simon Khalaf: It's still early to tell that this is a trend, 91 00:05:16,560 - > 00:05:19,279 but that's kind of like the early indicators of what's going 92 00:05:19,279 - > 00:05:19,439 on. 93 00:05:19,920 - > 00:05:21,839 Karen Webster: Well, I mean, you're you're providing options, 94 00:05:21,839 - > 00:05:22,240 right? 95 00:05:22,240 - > 00:05:26,319 So all you can do is provide the options, and then it's up to 96 00:05:26,319 - > 00:05:28,240 the individual to take advantage of those options. 97 00:05:28,240 - > 00:05:31,279 Um, I want to I want to kick it to you. 98 00:05:31,279 - > 00:05:35,600 So if if unemployment isn't an issue, if having a job is really 99 00:05:35,600 - > 00:05:44,319 not the defining characteristic of worker um job security, but 100 00:05:44,319 - > 00:05:48,240 their financial confidence really is, what does that tell 101 00:05:48,240 - > 00:05:49,199 you about the need? 102 00:05:49,199 - > 00:05:51,519 What's broken for that workforce? 103 00:05:53,360 - > 00:05:54,319 Drew Edwards: Ooh, broken. 104 00:05:54,319 - > 00:05:58,000 See, I was sitting here listening to Simon's answer to 105 00:05:58,000 - > 00:06:02,959 that question, and I'm thinking to myself, it's always been like 106 00:06:02,959 - > 00:06:03,199 that. 107 00:06:03,199 - > 00:06:09,279 Like everybody I know uh from the early days of this company 108 00:06:09,279 - > 00:06:12,639 when we were serving Hispanic immigrants and physical branches 109 00:06:12,639 - > 00:06:15,519 to family members I have. 110 00:06:15,519 - > 00:06:20,240 Um unfortunately, hourly workers a lot of times struggle 111 00:06:20,240 - > 00:06:23,519 to make ends meet and work more than one job. 112 00:06:23,519 - > 00:06:29,199 I think what's magical about Uber and that whole economy, and 113 00:06:29,199 - > 00:06:34,079 then what Simon is doing for everybody else beyond those 114 00:06:34,079 - > 00:06:40,399 digital platforms is he can see what's going on and then he can 115 00:06:40,399 - > 00:06:43,040 create product around what's going on, right? 116 00:06:43,040 - > 00:06:46,720 So I I I'm not answering your question yet, but I just could 117 00:06:46,720 - > 00:06:52,240 not respond to it's unfortunate, but I think these people work 118 00:06:52,240 - > 00:06:55,519 extra hours all the time, more than a full-time job, more than 119 00:06:55,519 - > 00:06:58,319 one job to make ends meet, especially if they're a single 120 00:06:58,319 - > 00:07:02,000 mom raising kids or whatever the scenario happens to be. 121 00:07:02,000 - > 00:07:05,199 And it's beautiful that that now there's a platform where 122 00:07:05,199 - > 00:07:09,600 they can go and scale up their hours or scale up their jobs, 123 00:07:09,600 - > 00:07:09,920 right? 124 00:07:09,920 - > 00:07:13,680 To me, this the stress you're talking about, the confidence 125 00:07:13,680 - > 00:07:19,120 that this consumer has and its impact, I think your question on 126 00:07:19,120 - > 00:07:24,480 what does that mean to how they think about work, this is a 127 00:07:24,480 - > 00:07:30,639 pure uh speculation for me being in and around this, I guess, my 128 00:07:30,639 - > 00:07:38,160 whole career, but I believe it leans folks to entrepreneurial 129 00:07:38,160 - > 00:07:43,040 gig-like work, um work where they're in control of how often 130 00:07:43,040 - > 00:07:45,680 they work and how much they work, because in that world, 131 00:07:45,680 - > 00:07:49,600 because I employ over a hundred of hourly employees, uh, if they 132 00:07:49,600 - > 00:07:54,879 we set their hours, we control when they work, we control how 133 00:07:54,879 - > 00:07:57,439 many hours they get, we control what they get paid. 134 00:07:57,439 - > 00:08:01,600 That's a scary place to be if you're a worker, right? 135 00:08:01,600 - > 00:08:08,319 And once they venture out into side gigs or full-time gigging, 136 00:08:08,319 - > 00:08:11,199 you know, full-time transactional work on a platform 137 00:08:11,199 - > 00:08:13,920 like Simon's, they get more control. 138 00:08:13,920 - > 00:08:18,319 They get more certainty, they get the ability to manage when 139 00:08:18,319 - > 00:08:20,560 they work, how much they work, where they work. 140 00:08:20,560 - > 00:08:25,439 And that's not been the case for a factory worker 141 00:08:25,439 - > 00:08:27,680 traditionally, for a field worker, right? 142 00:08:27,680 - > 00:08:31,439 They're they're a um, I don't want to use the bad word there, 143 00:08:31,439 - > 00:08:34,080 but they're captive to that employer. 144 00:08:34,080 - > 00:08:38,320 And the platform that Simon's built is taking what I think has 145 00:08:38,320 - > 00:08:45,600 always been the world of the labor economy and and giving it 146 00:08:45,600 - > 00:08:46,480 possibilities. 147 00:08:46,480 - > 00:08:47,519 And that's what I'm saying. 148 00:08:48,639 - > 00:08:51,440 Karen Webster: So the the the ag the aggregators, I mean the the 149 00:08:51,440 - > 00:08:57,360 platforms that that provide lots of optionality for a worker 150 00:08:57,360 - > 00:09:01,120 that may need more work this week and less work the following 151 00:09:01,120 - > 00:09:03,759 week, have that, you know, have that ability. 152 00:09:03,759 - > 00:09:08,960 But but but still there is this there is this ability to get 153 00:09:08,960 - > 00:09:13,600 paid when those shifts are over that creates the financial 154 00:09:13,600 - > 00:09:17,360 confidence that I think the labor economy lacks, at least, 155 00:09:17,360 - > 00:09:19,440 at least from from our data's perspective. 156 00:09:19,440 - > 00:09:23,440 I mean, it isn't as if they're not employed or they don't have 157 00:09:23,440 - > 00:09:25,440 the ability to get work. 158 00:09:25,440 - > 00:09:28,559 It's it's how they're paid which creates the stress. 159 00:09:28,960 - > 00:09:32,080 Simon Khalaf: It it is so rewarding to hear Drew, I'd say, 160 00:09:32,080 - > 00:09:37,440 long experience and our data, kind of like I would say to me, 161 00:09:37,440 - > 00:09:41,039 with analytics showing showing us exactly what do Drew is 162 00:09:41,039 - > 00:09:41,679 suggesting. 163 00:09:41,679 - > 00:09:44,879 As an it's always been like that. 164 00:09:44,879 - > 00:09:45,759 Right. 165 00:09:45,759 - > 00:09:49,360 Uh now we're seeing it, and the good news is that with AI, we 166 00:09:49,360 - > 00:09:50,559 can do something about it. 167 00:09:50,559 - > 00:09:53,519 So I'll just I'll just give you a couple of examples. 168 00:09:53,519 - > 00:09:57,279 Uh the first one is as you go into these companies, they're 169 00:09:57,279 - > 00:09:58,960 wired to avoid overtime. 170 00:09:58,960 - > 00:10:00,080 They're wired. 171 00:10:00,080 - > 00:10:05,039 I mean, they have, I'd say, rules engines, not compliance 172 00:10:05,039 - > 00:10:07,279 engines, rules engines to avoid overtime. 173 00:10:07,279 - > 00:10:08,080 Great. 174 00:10:08,320 - > 00:10:10,720 Karen Webster: And and and and some of that climate is because 175 00:10:10,720 - > 00:10:13,360 of regulations, labor laws. 176 00:10:14,480 - > 00:10:14,879 Simon Khalaf: And cost. 177 00:10:14,879 - > 00:10:15,360 Yes. 178 00:10:15,360 - > 00:10:19,759 But you look at our platform, there's no such thing as 179 00:10:19,759 - > 00:10:20,320 overtime. 180 00:10:20,320 - > 00:10:22,799 If you want to work more, it's your discretion. 181 00:10:22,799 - > 00:10:23,759 You will make more. 182 00:10:23,759 - > 00:10:25,440 We would make more. 183 00:10:25,440 - > 00:10:28,320 So you do the alignment. 184 00:10:28,320 - > 00:10:31,279 The second one, you're absolutely right, Karen, is same 185 00:10:31,279 - > 00:10:31,919 day pay. 186 00:10:31,919 - > 00:10:32,399 It's huge. 187 00:10:32,399 - > 00:10:33,679 Let me give you some stats. 188 00:10:33,679 - > 00:10:38,080 We did we we conduct surveys without workers and say, hey, if 189 00:10:38,080 - > 00:10:40,559 you're short of cash, what do you do? 190 00:10:40,559 - > 00:10:41,440 Right. 191 00:10:41,440 - > 00:10:46,320 It used to be the number one, which is around like 40 to 50 192 00:10:46,320 - > 00:10:50,159 percent, is go get a loan from either Chime or Cash App, right? 193 00:10:50,159 - > 00:10:56,960 Uh or payday loans, which are the the best one is 65% APR, and 194 00:10:56,960 - > 00:11:01,759 then the worst one is 400 to a thousand percent APR. 195 00:11:01,759 - > 00:11:05,919 Right now, the vast majority is take more shifts. 196 00:11:05,919 - > 00:11:08,320 Number two is take a loan. 197 00:11:08,320 - > 00:11:13,600 So not only are we giving them the opportunity to avoid that, 198 00:11:13,600 - > 00:11:14,879 but the same day pay. 199 00:11:14,879 - > 00:11:17,279 Look, we all have emergency expenses. 200 00:11:17,279 - > 00:11:18,480 We all have, right? 201 00:11:18,480 - > 00:11:22,080 You don't need an AI engine or a lose engine to tell you what 202 00:11:22,080 - > 00:11:23,840 is expected and what's not expected. 203 00:11:23,840 - > 00:11:27,840 Rent is expected, but you need four uh, you know, there's a 204 00:11:27,840 - > 00:11:29,840 storm and you need snow tires, right? 205 00:11:29,840 - > 00:11:32,799 That's an unexpected expense, right? 206 00:11:32,799 - > 00:11:36,799 Right now, I think we're providing the flexibility uh and 207 00:11:36,799 - > 00:11:40,879 the opportunity not to turn that unexpected event into a 65% 208 00:11:40,879 - > 00:11:44,879 APR short-term loan that you're eventually going to default on. 209 00:11:44,879 - > 00:11:49,039 I think that gives a lot more stability than just finding a 210 00:11:49,039 - > 00:11:49,919 job, right? 211 00:11:49,919 - > 00:11:52,559 It is immediately right, okay. 212 00:11:52,559 - > 00:11:53,279 I'll work. 213 00:11:53,279 - > 00:11:56,320 We've had so many people during the storm in Newark, all right. 214 00:11:56,320 - > 00:11:59,360 I was there, that came in and worked because their main job 215 00:11:59,360 - > 00:12:00,159 they couldn't get to. 216 00:12:00,159 - > 00:12:02,559 They came and worked and they got paid Saturday night. 217 00:12:02,559 - > 00:12:03,600 Yeah, yeah. 218 00:12:03,600 - > 00:12:05,120 Bank was not open. 219 00:12:05,840 - > 00:12:08,639 Karen Webster: Simon, do you do you see the workers on your 220 00:12:08,639 - > 00:12:12,879 platform doing the same jobs for different companies? 221 00:12:12,879 - > 00:12:16,000 Or do they do different jobs for different companies? 222 00:12:16,000 - > 00:12:21,120 So are they driving and working in a warehouse and and 223 00:12:21,120 - > 00:12:25,519 supporting an event, or are they just going from a warehouse job 224 00:12:25,519 - > 00:12:27,840 to another warehouse job to another warehouse job? 225 00:12:28,559 - > 00:12:35,120 Simon Khalaf: So uh that's changing uh fast as we enter 226 00:12:35,120 - > 00:12:37,519 more uh more verticals. 227 00:12:37,519 - > 00:12:39,440 So there's a lot of fungibility. 228 00:12:39,440 - > 00:12:44,559 Example, like, and we we we've announced our new product coach, 229 00:12:44,559 - > 00:12:47,679 which actually looks for these soft skills that you didn't know 230 00:12:47,679 - > 00:12:48,159 you have. 231 00:12:48,159 - > 00:12:51,840 And you'd be surprised how many bartenders we have discovered 232 00:12:51,840 - > 00:12:52,879 among drivers. 233 00:12:52,879 - > 00:12:59,279 So uh uh so uh it is hopefully not while they're driving. 234 00:12:59,279 - > 00:13:00,639 Right. 235 00:13:00,639 - > 00:13:05,120 So but I would I would say that that uh the uh uh recurrence 236 00:13:05,120 - > 00:13:09,759 rate is very high, as in if somebody takes a job, both 237 00:13:09,759 - > 00:13:14,799 parties want them to return, except if that job is no longer 238 00:13:14,799 - > 00:13:15,120 there. 239 00:13:15,120 - > 00:13:20,320 Uh but I haven't seen any statistically significant data 240 00:13:20,320 - > 00:13:27,919 like uh uh pointing to more fluidity and fungibility, except 241 00:13:27,919 - > 00:13:32,399 when the job they were called on to do the first day did not 242 00:13:32,399 - > 00:13:33,120 materialize. 243 00:13:33,120 - > 00:13:36,720 Now, driving specifically, and then in the state of California, 244 00:13:36,720 - > 00:13:41,679 right, because of Prop 22, which we comply with, right, is 245 00:13:41,679 - > 00:13:46,399 makes it hard to actually take a driver, which is an independent 246 00:13:46,399 - > 00:13:50,240 contractor, and give them what I would say jobs that required a 247 00:13:50,240 - > 00:13:51,440 W-2, uh, right. 248 00:13:51,440 - > 00:13:55,360 So we do not do that because that then we will be uh in in 249 00:13:55,360 - > 00:13:56,960 violation of Prop 22. 250 00:13:56,960 - > 00:14:03,039 But I'd say uh within the the compliance and the laws, we 251 00:14:03,039 - > 00:14:04,000 definitely do that. 252 00:14:05,120 - > 00:14:05,440 Karen Webster: True. 253 00:14:05,440 - > 00:14:09,600 You mentioned side hustles and and you know it's it's broader 254 00:14:09,600 - > 00:14:13,440 than just gig work, which I think we associate with the Uber 255 00:14:13,440 - > 00:14:16,080 drivers and the delivery drivers, but this is really 256 00:14:16,080 - > 00:14:19,840 about um different things that people are doing using platforms 257 00:14:19,840 - > 00:14:24,399 to generate extra extra income, platforms where you can resell 258 00:14:24,399 - > 00:14:27,519 stuff that you've got in your garage or your closet, shift 259 00:14:27,519 - > 00:14:31,360 work like what Simon is offering through work while um we know 260 00:14:31,360 - > 00:14:33,440 that different people use different platforms for 261 00:14:33,440 - > 00:14:34,080 different reasons. 262 00:14:34,080 - > 00:14:38,399 I need to pay the bills, this is extra money for discretionary 263 00:14:38,399 - > 00:14:40,320 purposes, maybe I'm saving it. 264 00:14:40,320 - > 00:14:45,039 Um, how does that affect Drew from your perspective? 265 00:14:45,039 - > 00:14:48,159 Um, the different use cases and the different types of side 266 00:14:48,159 - > 00:14:53,039 hustles, how does that affect how money in and money out looks 267 00:14:53,039 - > 00:14:54,240 on your platform? 268 00:14:55,039 - > 00:14:56,240 Drew Edwards: That's an excellent question. 269 00:14:56,240 - > 00:15:01,840 And honestly, I uh we've never sliced and diced the data around 270 00:15:01,840 - > 00:15:06,879 our consumers based on the type of work they're doing. 271 00:15:06,879 - > 00:15:11,120 So whatever I'm gonna answer here is is is probably more 272 00:15:11,120 - > 00:15:12,000 anecdotal. 273 00:15:12,000 - > 00:15:19,919 But what we know is that um the the segment of the consumers 274 00:15:19,919 - > 00:15:26,080 where speed matters and where um good funds, so certainty of 275 00:15:26,080 - > 00:15:26,960 funds matters. 276 00:15:26,960 - > 00:15:31,120 So let's say you've you've had an unexpected expense, like I 277 00:15:31,120 - > 00:15:33,120 think one of y'all mentioned, right? 278 00:15:33,120 - > 00:15:37,360 My car broke down and now I literally can't do my job, or um 279 00:15:37,360 - > 00:15:40,480 I have to fix it today, and that's a thousand dollars, and 280 00:15:40,480 - > 00:15:43,279 these workers don't tend to have a lot in savings, right? 281 00:15:43,279 - > 00:15:48,080 These are the triggers that we see affecting whether or not 282 00:15:48,080 - > 00:15:51,840 they're willing to pay for speed or or wait on their funds, 283 00:15:51,840 - > 00:15:55,600 affecting whether or not they go looking for that payday loan. 284 00:15:55,600 - > 00:15:58,639 So I would I would offer, if you add on to what you're 285 00:15:58,639 - > 00:16:00,720 saying, Simon, when they're sitting there with that 286 00:16:00,720 - > 00:16:04,879 unexpected expense and they're evaluating, do I go, do I go 287 00:16:04,879 - > 00:16:10,480 find a loan or can I do more work tonight or tomorrow and 288 00:16:10,480 - > 00:16:11,600 solve this problem? 289 00:16:11,600 - > 00:16:15,519 Inherent in that is when will I get paid, right? 290 00:16:15,519 - > 00:16:18,399 Am I gonna get the money in time to solve this problem, 291 00:16:18,399 - > 00:16:18,639 right? 292 00:16:18,639 - > 00:16:22,000 And so I I feel like we've, you and I have talked about this. 293 00:16:22,000 - > 00:16:26,000 We've been seeing an upswing in this economy we're in right 294 00:16:26,000 - > 00:16:32,159 now, where workers are leaning into more hours based on what 295 00:16:32,159 - > 00:16:36,480 Simon's saying, but also a higher percentage of them are 296 00:16:36,480 - > 00:16:40,960 paying for different payment types or speed or types where 297 00:16:40,960 - > 00:16:43,919 they know it's going to give them real funds that can't be 298 00:16:43,919 - > 00:16:44,639 clawed back. 299 00:16:44,639 - > 00:16:48,720 We all know a check is not only slow, but it bounces, right? 300 00:16:48,720 - > 00:16:50,320 I mean, it gets taken away. 301 00:16:50,320 - > 00:16:52,879 So it's the worst of all instruments. 302 00:16:52,879 - > 00:16:57,840 And so modern payment rails are getting a lot of traction right 303 00:16:57,840 - > 00:17:02,399 now in this segment, I believe, because it's certain, it's 304 00:17:02,399 - > 00:17:06,480 instant, and sometimes it comes at a cost and sometimes it's 305 00:17:06,480 - > 00:17:11,039 free, but depending on their situation, the data says, and 306 00:17:11,039 - > 00:17:15,200 our experience is they're not stupid for paying for that. 307 00:17:15,200 - > 00:17:19,680 They're managing a cash flow situation, not unlike we do in 308 00:17:19,680 - > 00:17:20,240 business. 309 00:17:20,559 - > 00:17:21,039 Karen Webster: Right. 310 00:17:21,039 - > 00:17:25,920 So Simon, a question for you Do you observe on the platform 311 00:17:25,920 - > 00:17:30,240 when when workers are looking for work and they actually need 312 00:17:30,240 - > 00:17:30,640 the work? 313 00:17:30,640 - > 00:17:34,160 Like I need to pay that bill, or if you're living in Boston, 314 00:17:34,160 - > 00:17:36,400 you've probably gone through a set of snow tires already 315 00:17:36,400 - > 00:17:39,680 because we've had so much snow, need to get new tires for my 316 00:17:39,680 - > 00:17:39,920 car. 317 00:17:39,920 - > 00:17:44,160 Do they do they go wherever the job is, or do they go wherever 318 00:17:44,160 - > 00:17:46,160 the job is going to pay them the most? 319 00:17:46,799 - > 00:17:47,200 Simon Khalaf: Yeah. 320 00:17:47,200 - > 00:17:53,440 So we've seen a lot of uh folks are that are looking at the the 321 00:17:53,440 - > 00:17:58,640 aggregate pay versus uh the per hourly rate. 322 00:17:58,640 - > 00:18:04,160 So they're trying to make the most amount of money versus 323 00:18:04,160 - > 00:18:08,480 negotiating an hourly rate, which is a very interesting 324 00:18:08,480 - > 00:18:08,960 change. 325 00:18:08,960 - > 00:18:13,839 So uh the we have a volatility index, right? 326 00:18:13,839 - > 00:18:18,880 You look at it, it is very interesting that uh if you want 327 00:18:18,880 - > 00:18:22,319 to sit and arbitrage this, you'll make a lot of money. 328 00:18:22,319 - > 00:18:23,440 Give you an example. 329 00:18:23,440 - > 00:18:26,400 So let's say I'm just throwing numbers like in North Carolina, 330 00:18:26,400 - > 00:18:28,960 I say the hourly rate is 22 bucks, right? 331 00:18:28,960 - > 00:18:33,920 That rate could be uh $20 or even $28 for eight hours later. 332 00:18:33,920 - > 00:18:38,880 So if you build a histogram, right, and and you're smart, you 333 00:18:38,880 - > 00:18:43,039 you book your time when the hourly rate is 28, and you sit 334 00:18:43,039 - > 00:18:46,960 on the sideline or only book 12 hours worth of work when the 335 00:18:46,960 - > 00:18:48,559 rate is 20 bucks. 336 00:18:48,559 - > 00:18:53,599 So we're starting to see our workers kind of like outsmart 337 00:18:53,599 - > 00:18:57,359 us, and they sit on the sidelines until the rate 338 00:18:57,359 - > 00:18:58,480 materializes. 339 00:18:58,480 - > 00:18:59,519 And guess what? 340 00:18:59,519 - > 00:19:04,319 We're building a tool, hopefully we'll announce soon 341 00:19:04,319 - > 00:19:05,680 that will do this for them. 342 00:19:05,680 - > 00:19:06,319 Right. 343 00:19:06,319 - > 00:19:09,680 And at the same time, look, I mean, our market has become on 344 00:19:09,680 - > 00:19:11,599 the demand side very diversified. 345 00:19:11,599 - > 00:19:15,279 And it's not that like hiring managers are gonna sit and 346 00:19:15,279 - > 00:19:17,839 arbitrage, they have a need, they're gonna do it. 347 00:19:17,839 - > 00:19:20,799 Uh and and they will post whatever rate they feel 348 00:19:20,799 - > 00:19:21,759 comfortable paying. 349 00:19:21,759 - > 00:19:24,880 There's enough to supply to meet it. 350 00:19:24,880 - > 00:19:29,680 But but the short answer to your question is that they're 351 00:19:29,680 - > 00:19:34,720 smart, they they know about rate and I'd say volatility. 352 00:19:34,720 - > 00:19:39,599 And I was actually surprised how volatile rates are. 353 00:19:39,599 - > 00:19:44,079 I mean, to me, employment, you negotiate your wage once a year 354 00:19:44,079 - > 00:19:44,319 here. 355 00:19:44,319 - > 00:19:50,799 Honestly, if you trade for the lack of a better word, labor on 356 00:19:50,799 - > 00:19:54,640 a 24-hour basis, I mean, it's published, you can go 357 00:19:54,640 - > 00:19:58,160 workwell.ai/slash rise, makeshift and do daily, and you 358 00:19:58,160 - > 00:20:01,039 will see that it's plus or minus, you know, 5%. 359 00:20:01,039 - > 00:20:04,400 And that could make or kill an economy. 360 00:20:04,400 - > 00:20:08,880 If you book everything at the negative 5%, right, you 361 00:20:08,880 - > 00:20:12,079 basically agreed on an annual 5% salary decrease. 362 00:20:12,079 - > 00:20:15,920 If you wick it at 5%, you've actually secured yourself a 5% 363 00:20:15,920 - > 00:20:17,440 salary increase paid daily. 364 00:20:17,440 - > 00:20:19,519 So this is happening a lot. 365 00:20:20,079 - > 00:20:23,519 Karen Webster: But you have to align the demand on the part of 366 00:20:23,519 - > 00:20:25,839 the of the of the of the employer. 367 00:20:25,839 - > 00:20:31,279 Like I really need this to the availability of someone who's 368 00:20:31,279 - > 00:20:36,960 willing to work that shift or or is in a geography that's 369 00:20:36,960 - > 00:20:40,240 acceptable to um that that particular job. 370 00:20:40,240 - > 00:20:43,119 So so it's a it's a mat it's a matching problem, right? 371 00:20:43,119 - > 00:20:45,680 So you have to make sure that the matches are in the 372 00:20:45,680 - > 00:20:49,200 employer's interest as well as the workers' interest. 373 00:20:49,599 - > 00:20:52,720 Drew Edwards: I think every time we talk about a platform and 374 00:20:52,720 - > 00:20:57,599 and a marketplace, I think most people in our industry think 375 00:20:57,599 - > 00:21:01,839 about the power of two-sided marketplaces and the leverage 376 00:21:01,839 - > 00:21:02,880 you get and everything else. 377 00:21:02,880 - > 00:21:05,279 But but I'm a capitalist, right? 378 00:21:05,279 - > 00:21:07,039 I believe in the free market economy. 379 00:21:07,039 - > 00:21:14,480 And what you're describing uh has the opportunity to actually 380 00:21:14,480 - > 00:21:21,519 find market wage rates based on true supply and demand, right? 381 00:21:21,519 - > 00:21:24,960 So this whole debate around how much should I be paying, what 382 00:21:24,960 - > 00:21:28,960 should minimum wage be, etc., can go out the window in our 383 00:21:28,960 - > 00:21:34,960 future if there's actual supply side and demand side freedom to 384 00:21:34,960 - > 00:21:40,480 choose and let the market decide what a fair price is. 385 00:21:40,480 - > 00:21:41,200 Yep. 386 00:21:41,359 - > 00:21:42,799 Simon Khalaf: Yeah, we publish it. 387 00:21:42,799 - > 00:21:44,559 I mean, it is it's available. 388 00:21:44,559 - > 00:21:48,720 Like every every hiring manager can go on Rise and do mix shift 389 00:21:48,720 - > 00:21:52,400 or non-makeshift and buy state, and they see what is being 390 00:21:52,400 - > 00:21:52,960 traded. 391 00:21:52,960 - > 00:21:57,279 Like honestly, our product, the the the enterprise, we're 392 00:21:57,279 - > 00:21:59,680 building those tools to tell them what they need to pay in 393 00:21:59,680 - > 00:21:59,759 order. 394 00:21:59,759 - > 00:22:04,079 order to get like like our propensity algorithm can tell 395 00:22:04,079 - > 00:22:06,720 them when they're gonna open jobs, right? 396 00:22:06,720 - > 00:22:10,960 And say, look, if you book them today, you're gonna save X. 397 00:22:10,960 - > 00:22:11,599 Right. 398 00:22:11,599 - > 00:22:13,440 And and great question, Karen. 399 00:22:13,440 - > 00:22:16,640 Like you're talking about supply-demand imbalance in which 400 00:22:16,640 - > 00:22:17,839 somebody holds out. 401 00:22:17,839 - > 00:22:23,519 If everybody's smart, right, then no market actually trades. 402 00:22:23,519 - > 00:22:28,880 If buyers and sellers agree on on basically waiting for each 403 00:22:28,880 - > 00:22:30,240 other, you got a stalemate. 404 00:22:30,240 - > 00:22:32,000 But that's not how the world works, right? 405 00:22:32,000 - > 00:22:35,039 I mean if you've got an event for Taylor Swift and you need 406 00:22:35,039 - > 00:22:36,799 you need bartenders, you know what? 407 00:22:36,799 - > 00:22:38,079 You're gonna hire bartenders. 408 00:22:38,079 - > 00:22:40,319 You're not gonna sit and arbitrage labor. 409 00:22:40,319 - > 00:22:40,640 Right. 410 00:22:40,640 - > 00:22:45,119 And at the same time, right, I mean warehouse warehouse are are 411 00:22:45,119 - > 00:22:48,559 heavily impacted by what gets distributed and also by 412 00:22:48,559 - > 00:22:51,599 e-commerce demand, which is highly unpredictable. 413 00:22:51,599 - > 00:22:55,039 Even even with the most prediction engines, right, some 414 00:22:55,039 - > 00:22:57,039 something memes on TikTok, right? 415 00:22:57,039 - > 00:23:00,000 And like your warehouse demand is huge. 416 00:23:00,000 - > 00:23:04,240 So I I we have not hit at all the boundary of what I call a 417 00:23:04,240 - > 00:23:05,440 free market economy. 418 00:23:05,759 - > 00:23:09,119 Karen Webster: At all how how does this look like though on a 419 00:23:09,119 - > 00:23:14,000 local level where you know you're you're not just trading a 420 00:23:14,000 - > 00:23:18,400 worker that has capacity with an employer that wants to hire 421 00:23:18,400 - > 00:23:23,920 you're taking that worker from one potential job opportunity 422 00:23:23,920 - > 00:23:28,400 away from another job potential job opportunity because these 423 00:23:28,400 - > 00:23:30,799 are people who have to show up for work. 424 00:23:30,799 - > 00:23:38,160 How do you look at the um the impact on the local economy when 425 00:23:38,160 - > 00:23:44,240 you've got um laborers labor labor economy workers who are 426 00:23:44,240 - > 00:23:53,519 going to places that pay them the most people you bus people 427 00:23:53,519 - > 00:23:56,160 so people are willing to travel long distances to go. 428 00:23:56,559 - > 00:23:59,599 Simon Khalaf: Correct we yes so what uh we've introduced a 429 00:23:59,599 - > 00:24:02,640 product called I mean it's a bad name called a long-term 430 00:24:02,640 - > 00:24:07,279 assignment uh uh in order to specifically uh uh do something 431 00:24:07,279 - > 00:24:10,799 like that so give you an example inventory management it happens 432 00:24:10,799 - > 00:24:14,559 like it is scheduled like Walmart like hires a few 433 00:24:14,559 - > 00:24:17,680 companies to come and do uh uh inventory management that you're 434 00:24:17,680 - > 00:24:20,160 not gonna go to the local market and supply these people 435 00:24:20,160 - > 00:24:24,000 it's tap that right I mean even in think about it in the worst 436 00:24:24,000 - > 00:24:28,000 economy unemployment is 90% in the best economy unemployment is 437 00:24:28,000 - > 00:24:32,960 98% so let's say you have a city that has a hundred workers 438 00:24:32,960 - > 00:24:35,920 and you need 200 you're not gonna find it even if the 439 00:24:35,920 - > 00:24:36,880 economy is bad. 440 00:24:36,880 - > 00:24:40,400 So we do that all the time we call it long-term assignment and 441 00:24:40,400 - > 00:24:44,000 we put people on a bus and we put them in motels or hotels 442 00:24:44,000 - > 00:24:48,240 right and we we basically we create a surge in that market 443 00:24:48,240 - > 00:24:52,799 and they will come back Friday night and you know based on our 444 00:24:52,799 - > 00:24:56,240 agreement with a company we do that for events management we do 445 00:24:56,240 - > 00:24:59,279 that for inventory management and also without warehouse 446 00:24:59,279 - > 00:25:02,480 spikes that's really I mean that's really that's really 447 00:25:02,480 - > 00:25:03,039 interesting. 448 00:25:03,839 - > 00:25:06,559 Karen Webster: Drew does any of this show up in your in your 449 00:25:06,559 - > 00:25:08,880 data or how you think about.. 450 00:25:09,759 - > 00:25:11,279 Drew Edwards: I'm just sitting there thinking that's how 451 00:25:11,279 - > 00:25:15,519 immigrants got to Atlanta so that that's but I'm trying not 452 00:25:15,519 - > 00:25:20,640 to get political here but that's the very dynamic that drives 453 00:25:20,640 - > 00:25:26,480 that's the very dynamic that drives um influxes of labor 454 00:25:26,480 - > 00:25:30,400 specific you know the Olympics is being built or this is going 455 00:25:30,400 - > 00:25:35,599 on and it outstrips the supply it it doesn't show up in my data 456 00:25:35,599 - > 00:25:39,119 carin or at least we don't we don't look at that but it it 457 00:25:39,119 - > 00:25:40,240 makes total sense. 458 00:25:40,400 - > 00:25:44,160 Simon Khalaf: I have a daughter who's a nurse and she can make a 459 00:25:44,160 - > 00:25:49,279 whole lot more money being a traveling nurse which just makes 460 00:25:49,279 - > 00:25:53,519 no sense to me right that that a hospital is going to pay such 461 00:25:53,519 - > 00:25:58,079 a premium and put people in a hotel but there's a 462 00:25:58,079 - > 00:26:02,559 supply-demand mismatch and that's a that's a specialty 463 00:26:02,559 - > 00:26:08,319 market right um imagine now when you apply that to the labor 464 00:26:08,319 - > 00:26:12,559 economy and all of the different situations that go there it's 465 00:26:12,559 - > 00:26:17,839 just gets back to that free market and um kudos to you Simon 466 00:26:17,839 - > 00:26:22,319 for busing's a bad word here in the South but but putting 467 00:26:22,319 - > 00:26:27,359 people uh getting giving people the ability to go where the work 468 00:26:27,359 - > 00:26:30,720 is yeah that's very you're providing transportation called 469 00:26:30,720 - > 00:26:37,279 a free ride yes yeah it's the power of a free market never 470 00:26:37,279 - > 00:26:42,559 fails right I agree I mean the base again I go back to Adam 471 00:26:42,559 - > 00:26:47,680 Smith right that it is through labor that all wealth has been 472 00:26:47,680 - > 00:26:51,839 uh established it is not through gold nor silver right it is 473 00:26:51,839 - > 00:26:55,519 hard work of people right and you give them the opportunity 474 00:26:55,519 - > 00:26:59,359 they rise to that opportunity I mean I'm actually proud of our 475 00:26:59,359 - > 00:27:04,720 workers like honestly it is refreshing to see that look I 476 00:27:04,720 - > 00:27:09,839 thought honestly that they are poor money managers they're not 477 00:27:09,839 - > 00:27:13,680 right they're actually great money managers because it 478 00:27:13,680 - > 00:27:17,920 matters right because if they miss a payment like no one is 479 00:27:17,920 - > 00:27:22,079 gonna bail them out like if my daughter misses her her cell 480 00:27:22,079 - > 00:27:25,279 phone payment I bail her out there's no one to bail them out 481 00:27:25,279 - > 00:27:28,160 so they're very good money managers so and they and they 482 00:27:28,160 - > 00:27:31,440 work hard and honestly you know you open up opportunity for them 483 00:27:31,440 - > 00:27:36,319 they jump out yeah well I I I think I mean to to wrap up the 484 00:27:36,319 - > 00:27:39,599 conversation which is which has been really these are always so 485 00:27:39,599 - > 00:27:42,960 interesting um the way we started was to talk about this 486 00:27:42,960 - > 00:27:48,319 idea of of job security and the the job security for the labor 487 00:27:48,319 - > 00:27:54,400 economy really being about this this job um financial security 488 00:27:54,400 - > 00:27:58,160 with respect to their job prospects not so much whether 489 00:27:58,160 - > 00:28:01,359 they'll lose their job but but whether they feel financially 490 00:28:01,359 - > 00:28:04,960 confident with the jobs that they have when you think about 491 00:28:04,960 - > 00:28:10,720 the possibilities of creating um products that actually meet the 492 00:28:10,720 - > 00:28:15,039 needs of the labor economy worker that provide not just the 493 00:28:15,039 - > 00:28:19,599 financial you know same-day pay but things that provide more 494 00:28:19,599 - > 00:28:24,319 ability to manage their money to manage their work environment 495 00:28:24,319 - > 00:28:30,319 to manage their schedules um to to really be that that labor 496 00:28:30,319 - > 00:28:34,480 economy worker that has that feels control of their financial 497 00:28:34,480 - > 00:28:37,359 and job situation what would that be if the two of you were 498 00:28:37,359 - > 00:28:41,359 designing something what would that look like well Simon you 499 00:28:41,359 - > 00:28:45,200 probably already have I mean in fact well it's unfair the guy is 500 00:28:45,200 - > 00:28:49,039 sitting right there I've got a visual I feel it was a softball 501 00:28:49,039 - > 00:28:52,000 question I didn't really mean it to be yeah well well I mean 502 00:28:52,000 - > 00:28:55,839 it's not that hard right I mean if you actually I always go back 503 00:28:55,839 - > 00:28:59,359 to to to movies are made by culture you look at Jerry 504 00:28:59,359 - > 00:29:02,799 Maguire it's all about show me the money right I mean if you've 505 00:29:02,799 - > 00:29:06,160 got an agent that's looking after you booking you right 506 00:29:06,160 - > 00:29:07,759 that's what AI is great at. 507 00:29:07,759 - > 00:29:12,000 It's predicting the job market and telling you right it's it's 508 00:29:12,000 - > 00:29:14,240 I hate to use the word agentic, right? 509 00:29:14,240 - > 00:29:18,400 It but but it is someone that is taking care of you and then 510 00:29:18,400 - > 00:29:23,680 also monitoring your expenses and and at the same time saying 511 00:29:23,680 - > 00:29:26,640 look you're welcome to take a loan. 512 00:29:26,640 - > 00:29:29,680 That's how much it's gonna cost you but you're also welcome to 513 00:29:29,680 - > 00:29:33,119 take these extra shifts and then that will give you a 30% 514 00:29:33,119 - > 00:29:36,400 discount technically on everything you're buying right I 515 00:29:36,400 - > 00:29:40,480 will make that calendar for you right and then it is up to you 516 00:29:40,480 - > 00:29:42,799 if you want to buy it or not. 517 00:29:42,799 - > 00:29:46,799 That's kind of what we what we technically have but having 518 00:29:46,799 - > 00:29:50,079 package with a name and a Jerry Maguire kind of show me the 519 00:29:50,079 - > 00:29:53,920 money attitude that is going to come very soon I should say in 520 00:29:53,920 - > 00:29:55,119 in in in Q2. 521 00:29:55,119 - > 00:29:59,519 We did launch our first uh uh uh first agent called coach 522 00:29:59,519 - > 00:30:02,480 which basically built continuously builds the resume 523 00:30:02,480 - > 00:30:06,799 for you nonstop right the second one so this one is basically to 524 00:30:06,799 - > 00:30:10,240 to make sure you're marketable and once you're marketable and 525 00:30:10,240 - > 00:30:12,720 you're earning money we want to make sure you're spending it 526 00:30:12,720 - > 00:30:16,960 wisely so that 360 is something that beautifully because of 527 00:30:16,960 - > 00:30:20,160 algorithms and uh reinforced learning and our prediction 528 00:30:20,160 - > 00:30:24,160 models has become doable versus you know you're gonna pay a 529 00:30:24,160 - > 00:30:29,119 Morgan Stanley money advisor with 300k in order to manage 530 00:30:29,119 - > 00:30:31,599 somebody's money who makes 58k a year. 531 00:30:31,599 - > 00:30:34,640 I think an agent cost me like seven tokens. 532 00:30:36,240 - > 00:30:38,720 Karen Webster: Well I I mean you you make a good point I I you 533 00:30:38,720 - > 00:30:43,519 know I think back to how before AI and before these kinds of 534 00:30:43,519 - > 00:30:46,880 innovations how hard it is to actually sit down and put 535 00:30:46,880 - > 00:30:50,640 together a spreadsheet to manage the you know the family budget. 536 00:30:51,279 - > 00:30:52,960 Simon Khalaf: Well the federal government hasn't been able to 537 00:30:52,960 - > 00:30:57,279 do it and look at how much how much they spend how many people 538 00:30:57,279 - > 00:30:59,279 they have working to try to do that. 539 00:31:00,240 - > 00:31:03,440 Karen Webster: But um exactly but but but Drew your your your 540 00:31:03,440 - > 00:31:05,119 thoughts I know you have many. 541 00:31:05,680 - > 00:31:11,039 Drew Edwards: I just got this vision of you know the old 542 00:31:11,039 - > 00:31:15,920 movies of lines down at the shipyards with workers trying to 543 00:31:15,920 - > 00:31:19,680 get on a boat for the day or on the ship for the day and that 544 00:31:19,680 - > 00:31:23,759 was their their whole day and they were totally captive to 545 00:31:23,759 - > 00:31:26,640 whether or not they could convince the person that the 546 00:31:26,640 - > 00:31:30,640 they were the right one for the job and the pay was whatever the 547 00:31:30,640 - > 00:31:35,680 pay was and think about they've probably got bills they got to 548 00:31:35,680 - > 00:31:39,039 pay and they actually have to go pay those bills with cash right 549 00:31:39,039 - > 00:31:42,400 and so they've got to go get some paycheck and get their 550 00:31:42,400 - > 00:31:45,200 hands on the actual cash the paycheck's not going to come for 551 00:31:45,200 - > 00:31:45,519 weeks. 552 00:31:45,519 - > 00:31:49,680 It's just it's just such a dark age when you think about how it 553 00:31:49,680 - > 00:31:53,759 used to be and I hear you talk about how this is so from from 554 00:31:53,759 - > 00:31:58,880 me we live in in this place we call you know empowering money 555 00:31:58,880 - > 00:32:02,319 mobility which all of us that are lucky enough to have 556 00:32:02,319 - > 00:32:06,720 multiple accounts and extra cash flow and lots of things we do 557 00:32:06,720 - > 00:32:07,039 with it. 558 00:32:07,039 - > 00:32:10,160 We move money in and out and around all the time and honestly 559 00:32:10,160 - > 00:32:14,000 we're used to doing it for free and we're used to it being fast 560 00:32:14,000 - > 00:32:21,279 right but it's not that way for a marginal financial person, 561 00:32:21,279 - > 00:32:21,519 right? 562 00:32:21,519 - > 00:32:25,200 Whatever Chase and B of A offer to one guy is not the same 563 00:32:25,200 - > 00:32:28,079 thing they offer to the next guy because they have their own AI 564 00:32:28,079 - > 00:32:31,279 agents determining whether they should trust you with that float 565 00:32:31,279 - > 00:32:35,039 or whether they should advance you the money and all and so the 566 00:32:35,039 - > 00:32:40,240 combination of this this beautiful free market uh for 567 00:32:40,240 - > 00:32:45,039 this vital part of our economy the these transactional workers 568 00:32:45,039 - > 00:32:48,960 with modern money mobility and modern credit options because 569 00:32:48,960 - > 00:32:54,000 you have visibility into what you can see um the marketplace 570 00:32:54,000 - > 00:32:58,559 that you're building or that we could do together if I have a 571 00:32:58,559 - > 00:33:05,039 small part in it is it we only work most people for the money. 572 00:33:05,039 - > 00:33:10,160 We only work for paying our bills and buying food and 573 00:33:10,160 - > 00:33:14,079 putting presents under the tree for our children and all of the 574 00:33:14,079 - > 00:33:18,240 things that go in between right so the financial side of this 575 00:33:18,240 - > 00:33:22,160 marketplace is equally as important right and the things 576 00:33:22,160 - > 00:33:26,319 you can do with the visibility and with the with the upward 577 00:33:26,319 - > 00:33:29,839 mobility that a market that a free market economy like that 578 00:33:29,839 - > 00:33:32,079 creates it's unlimited. 579 00:33:32,079 - > 00:33:37,200 So and think about Karen all of the businesses that have been 580 00:33:37,200 - > 00:33:40,720 built and sold to the credit bureaus and their efforts just 581 00:33:40,720 - > 00:33:45,680 to see these workers that don't necessarily because a lot of 582 00:33:45,680 - > 00:33:49,279 them to get their money from shift to shift either they were 583 00:33:49,279 - > 00:33:52,240 cash based or they would go cash a check putting them in the 584 00:33:52,240 - > 00:33:56,880 cash economy and they're invisible and so you know this 585 00:33:56,880 - > 00:34:02,720 thing you're building uh Simon is is is bringing them into the 586 00:34:02,720 - > 00:34:06,000 light which is where the better options are when people can 587 00:34:06,000 - > 00:34:08,400 compete for their economics. 588 00:34:09,280 - > 00:34:12,400 Karen Webster: I think Simon Simon's point was was really 589 00:34:12,400 - > 00:34:16,079 well made and I know Drew you've made a similar point too in 590 00:34:16,079 - > 00:34:19,119 conversations we've had it it these are these are people who 591 00:34:19,119 - > 00:34:22,400 understand how to manage money they just need better tools and 592 00:34:22,400 - > 00:34:26,480 they need they need better tools and they need opportunities to 593 00:34:26,480 - > 00:34:31,679 to work um because they want to and you know platforms like like 594 00:34:31,679 - > 00:34:35,440 Workwell make that so much easier but but think about the 595 00:34:35,440 - > 00:34:38,639 platform economy and how that has created so many 596 00:34:38,639 - > 00:34:42,800 opportunities for people you know in a digital and a physical 597 00:34:42,800 - > 00:34:46,960 world to really um earn income and to do it in a way that 598 00:34:46,960 - > 00:34:50,960 eliminates the frictions that were that always existed you 599 00:34:50,960 - > 00:34:54,239 know if you had to go to the classified to look to see what 600 00:34:54,239 - > 00:34:58,320 job openings there there were or you had to go door to door and 601 00:34:58,320 - > 00:35:02,000 knock on you know the fast food place to say are you looking for 602 00:35:02,000 - > 00:35:02,639 summer help. 603 00:35:02,639 - > 00:35:07,840 I mean I remember those days where it was not easy to find 604 00:35:07,840 - > 00:35:11,440 work even if you wanted it to and so now the ability to 605 00:35:11,440 - > 00:35:16,559 provide access to opportunities to provide same-day pay to 606 00:35:16,559 - > 00:35:21,840 provide environments for money management that is equivalent to 607 00:35:21,840 - > 00:35:25,119 what those with lots of money have available to them is is 608 00:35:25,119 - > 00:35:26,400 certainly exciting. 609 00:35:26,400 - > 00:35:30,320 You both always have great great conversations together and 610 00:35:30,320 - > 00:35:33,679 I thank you for this one I hope to talk to you both again soon 611 00:35:33,679 - > 00:35:34,079 thank you. 612 00:35:34,400 - > 00:35:35,840 Simon Khalaf: Thank you appreciate it. 613 00:35:35,840 - > 00:35:38,159 Bye now. 614 00:35:41,840 - > 00:35:45,119 Narrator: That's it for this episode of the PYMNTS Podcast 615 00:35:45,119 - > 00:35:48,960 the thinking behind the doing conversations with the leaders 616 00:35:48,960 - > 00:35:52,800 transforming payments commerce and the digital economy be sure 617 00:35:52,800 - > 00:35:55,599 to follow us on Spotify and Apple Podcasts. 618 00:35:55,599 - > 00:36:01,119 You can also catch every episode at PYMNTS.com/ podcasts. 619 00:36:01,119 - > 00:36:02,719 Thanks for listening.