1 00:00:02,480 --> 00:00:06,359 Speaker 1: Bloomberg Audio Studios, podcasts, radio. 2 00:00:06,640 --> 00:00:10,280 Speaker 2: News for our Bloomberg audiences worldwide. I'm David Western. I'm 3 00:00:10,280 --> 00:00:12,520 Speaker 2: delighted to be joined now by Brian moynihan. He is 4 00:00:12,560 --> 00:00:15,319 Speaker 2: indeed the chair and CEO of Mecca America. So, first 5 00:00:15,320 --> 00:00:17,160 Speaker 2: of all, happy holidays, Brian. Great to be with you. 6 00:00:18,880 --> 00:00:20,800 Speaker 1: Happy holidays. David. Good to see you again. 7 00:00:21,400 --> 00:00:24,320 Speaker 2: So let's start with the year. It's been a very 8 00:00:24,360 --> 00:00:26,040 Speaker 2: strong year for the markets, by the way, a very 9 00:00:26,040 --> 00:00:28,520 Speaker 2: strong year for Bank of America. In the markets. The 10 00:00:28,560 --> 00:00:31,760 Speaker 2: economy's done well, not as well perhaps as the markets 11 00:00:31,840 --> 00:00:34,720 Speaker 2: might indicate. Do you think the economy is in better 12 00:00:34,760 --> 00:00:37,839 Speaker 2: shape because the markets are really over predicting what's going on. 13 00:00:40,040 --> 00:00:42,080 Speaker 3: Look, as you look at our team that we have 14 00:00:42,120 --> 00:00:44,879 Speaker 3: a great research team, as you know, David. Their projection 15 00:00:45,040 --> 00:00:47,760 Speaker 3: for twenty six is a strong economy two point four 16 00:00:47,840 --> 00:00:51,479 Speaker 3: percent us GDP growth. And that's strong not only on 17 00:00:51,520 --> 00:00:54,440 Speaker 3: a relative based to trend above two percent, which is 18 00:00:54,440 --> 00:00:56,600 Speaker 3: the trend. It's strong that way, but it's also strong 19 00:00:56,680 --> 00:00:58,400 Speaker 3: relative to the rest of the world in the sense 20 00:00:58,440 --> 00:01:02,840 Speaker 3: that you see that basically, whether it's Euroa or Japan, 21 00:01:03,280 --> 00:01:05,200 Speaker 3: other parts of the economy, you predict those to be 22 00:01:05,319 --> 00:01:09,160 Speaker 3: flatted down. And so it's strong on an absolute basis 23 00:01:09,160 --> 00:01:11,280 Speaker 3: relative of the United States history, and strong on a 24 00:01:11,319 --> 00:01:14,400 Speaker 3: relative basis to the other economies. And that's because, frankly, 25 00:01:14,520 --> 00:01:18,840 Speaker 3: the great American engine of capitalism, consumers are driving and 26 00:01:18,880 --> 00:01:21,240 Speaker 3: the markets are valuing that future growth rate, and that's 27 00:01:21,240 --> 00:01:24,920 Speaker 3: why they've been very, very constructive this year. 28 00:01:25,560 --> 00:01:27,720 Speaker 2: How much of that engine that you talk about is 29 00:01:27,920 --> 00:01:30,759 Speaker 2: AI investment, because that certainly is kicked in this year. 30 00:01:31,520 --> 00:01:33,800 Speaker 2: How much of the growth is really attributable only to 31 00:01:33,880 --> 00:01:35,199 Speaker 2: the AI investment. 32 00:01:36,840 --> 00:01:38,679 Speaker 3: You know, I think you'd have to get people to 33 00:01:38,720 --> 00:01:40,840 Speaker 3: parts of it. But for this year, frankly, the A 34 00:01:41,000 --> 00:01:43,200 Speaker 3: investment has been building during the year. It is probably 35 00:01:43,280 --> 00:01:45,560 Speaker 3: a bigger contributor next year in the years beyond. And 36 00:01:45,600 --> 00:01:47,960 Speaker 3: so if you look at the data center build out, 37 00:01:47,960 --> 00:01:50,040 Speaker 3: which is one of the ways that evidence itself, that's 38 00:01:50,080 --> 00:01:53,120 Speaker 3: a big deal. If you look at customer or client spending, 39 00:01:53,200 --> 00:01:56,160 Speaker 3: like US spending on AI that's hired it was last year, 40 00:01:56,200 --> 00:02:00,600 Speaker 3: but frankly, overall spending levels are shifting towards that necessarily 41 00:02:00,640 --> 00:02:04,760 Speaker 3: growing at a mid single digit rate type of numbers. 42 00:02:04,800 --> 00:02:07,440 Speaker 3: So I think that's part why the reason we feel 43 00:02:07,440 --> 00:02:10,120 Speaker 3: constructive for next year. We think AI spending continues. We 44 00:02:10,160 --> 00:02:15,880 Speaker 3: think there's benefits the American taxpayer from tax rebates, lower 45 00:02:15,919 --> 00:02:18,639 Speaker 3: taxes due to the tax bill going through and being 46 00:02:18,680 --> 00:02:22,160 Speaker 3: effective for next year. And we think the expense expensing 47 00:02:22,200 --> 00:02:25,080 Speaker 3: and other bonuses for businesses are good. So all that 48 00:02:25,160 --> 00:02:27,440 Speaker 3: leads to our confidence that we go from basically at 49 00:02:27,480 --> 00:02:31,040 Speaker 3: two percent type of growth letter level this year plus 50 00:02:31,080 --> 00:02:33,720 Speaker 3: or minus up to two point four percent, which is 51 00:02:33,760 --> 00:02:35,640 Speaker 3: all due to that. And AI is kicking and more 52 00:02:35,680 --> 00:02:37,960 Speaker 3: and more, and so it's not all attributable to AI, 53 00:02:38,080 --> 00:02:40,400 Speaker 3: but that's having a marginal impact that's pretty strong. 54 00:02:41,000 --> 00:02:43,400 Speaker 2: So much of the American counomy is supported by the consumer, 55 00:02:43,440 --> 00:02:45,840 Speaker 2: and you at Back of America have a really powerful 56 00:02:46,000 --> 00:02:49,280 Speaker 2: viewpoint into the American consumer. How's the American consumer doing? 57 00:02:49,320 --> 00:02:51,480 Speaker 2: Because it has been very strong. There have been some 58 00:02:51,560 --> 00:02:53,320 Speaker 2: people saying it's starting to slow down. 59 00:02:55,200 --> 00:02:57,799 Speaker 1: Yeah, and so I think you have to step back. 60 00:02:57,840 --> 00:03:01,040 Speaker 3: We look at American consumers seventy million consumer putting four 61 00:03:01,040 --> 00:03:03,760 Speaker 3: and a half trillion dollars plus into the American economy 62 00:03:03,760 --> 00:03:06,240 Speaker 3: every year, and we've tracked the way that goes in 63 00:03:06,240 --> 00:03:08,640 Speaker 3: the American countmy for many years. And so in the 64 00:03:08,680 --> 00:03:10,639 Speaker 3: third quarter it was up about five percent of the 65 00:03:10,760 --> 00:03:13,160 Speaker 3: last year. As we look at the fourth quarter here 66 00:03:13,240 --> 00:03:15,079 Speaker 3: so far in October November, I'd say it in a 67 00:03:15,120 --> 00:03:16,720 Speaker 3: four to four and a half percent, which is very 68 00:03:16,760 --> 00:03:20,400 Speaker 3: consistent with a very solid growing economy. You know, at 69 00:03:20,400 --> 00:03:23,160 Speaker 3: the end of the day, it's going to work against 70 00:03:23,160 --> 00:03:25,720 Speaker 3: wage growth, and we see in the underlying consumers we 71 00:03:25,760 --> 00:03:28,200 Speaker 3: have wage growth. Iw eive their paychecks are going up, 72 00:03:28,240 --> 00:03:30,760 Speaker 3: and so the labor markets flattened out a little bit 73 00:03:30,760 --> 00:03:33,160 Speaker 3: in terms of job growth and things like that. It's 74 00:03:33,240 --> 00:03:36,520 Speaker 3: normalizing in terms of unemployment, but you still see underlying 75 00:03:36,560 --> 00:03:39,920 Speaker 3: wage growth. So the American consumer spending at four percent 76 00:03:40,040 --> 00:03:43,240 Speaker 3: more November this year versus November last year is a 77 00:03:43,320 --> 00:03:47,080 Speaker 3: very solid backdrop. The credit quality American consumer is strong. 78 00:03:47,480 --> 00:03:50,040 Speaker 3: And then you hear a lot about this discussion about 79 00:03:50,240 --> 00:03:53,360 Speaker 3: different rates of growth among different income tursiles or third. 80 00:03:53,440 --> 00:03:54,240 Speaker 1: So we look at. 81 00:03:54,120 --> 00:03:57,040 Speaker 3: The bottom third, middle third, and top third American income 82 00:03:57,760 --> 00:03:59,440 Speaker 3: people in the Bank of American customer base. 83 00:03:59,480 --> 00:04:00,480 Speaker 1: We do see difference. 84 00:04:00,520 --> 00:04:03,360 Speaker 3: Is I either higher income in middlecome are growing faster, 85 00:04:04,400 --> 00:04:07,760 Speaker 3: but even the lower income third is still growing. 86 00:04:07,800 --> 00:04:08,840 Speaker 1: And that's all good. 87 00:04:08,880 --> 00:04:11,560 Speaker 3: And that means why is that true companies are employing 88 00:04:11,600 --> 00:04:14,360 Speaker 3: people they're paying people now. The labor markets got a 89 00:04:14,400 --> 00:04:16,599 Speaker 3: little soft, and as we look forward to a four 90 00:04:16,640 --> 00:04:20,920 Speaker 3: point five four point six unemployment that is gotten worse, 91 00:04:20,960 --> 00:04:22,719 Speaker 3: so to speak, than it was the beginning year. 92 00:04:22,760 --> 00:04:25,119 Speaker 1: But frankly, this goes back to normalization question. 93 00:04:25,200 --> 00:04:27,719 Speaker 3: If you look at the tenure average unemployment the twenty 94 00:04:27,760 --> 00:04:29,839 Speaker 3: year to thirty year or forty year, it's five and 95 00:04:29,920 --> 00:04:32,200 Speaker 3: six percent, you know, as you go back through time, 96 00:04:32,240 --> 00:04:34,279 Speaker 3: and so a four and a half to four point 97 00:04:34,360 --> 00:04:38,520 Speaker 3: six unemployment rate is a very strong relative unemployment rate. 98 00:04:38,520 --> 00:04:40,680 Speaker 3: It's just a lot of years it's been below four 99 00:04:40,720 --> 00:04:42,440 Speaker 3: and a half percent, has actually been in the last 100 00:04:42,440 --> 00:04:44,680 Speaker 3: ten years. So people are very used to numbers now 101 00:04:45,000 --> 00:04:47,359 Speaker 3: which were part of the tightness and labor in the 102 00:04:47,480 --> 00:04:51,479 Speaker 3: twenty seventeen eighteen nineteen era, and you had the pandemic 103 00:04:51,560 --> 00:04:54,400 Speaker 3: and it retightened, and so it's it's normalizing. But we 104 00:04:54,440 --> 00:04:56,799 Speaker 3: feel good about all that, and the consumers in pretty 105 00:04:56,800 --> 00:04:59,839 Speaker 3: good shape, spending more money, employed and earning more money, 106 00:05:00,160 --> 00:05:03,000 Speaker 3: differences between different types of consumers and then being wise 107 00:05:03,040 --> 00:05:05,800 Speaker 3: and how they spend if they trade down, and all 108 00:05:05,839 --> 00:05:07,880 Speaker 3: the things you're hearing the retailers talk about. True, but 109 00:05:07,920 --> 00:05:10,440 Speaker 3: the agurd amount of moving in the economy is still growing. 110 00:05:10,760 --> 00:05:14,680 Speaker 2: So you mentioned flattening out in the employment growth. There 111 00:05:14,680 --> 00:05:16,960 Speaker 2: are always risks to the upside and the downside, and 112 00:05:17,000 --> 00:05:19,360 Speaker 2: any of the numbers that we project, how concerned are 113 00:05:19,400 --> 00:05:21,520 Speaker 2: you let the downside, because certainly the FED is very 114 00:05:21,520 --> 00:05:24,560 Speaker 2: focused on a softening a labor market and whether that 115 00:05:24,600 --> 00:05:25,680 Speaker 2: may be a problem for us. 116 00:05:27,640 --> 00:05:30,160 Speaker 3: So if you look at the predictions for our team 117 00:05:30,240 --> 00:05:33,440 Speaker 3: and the other economists for unemployment, we're kind of peeking 118 00:05:33,480 --> 00:05:36,320 Speaker 3: out at this level now, in the mid to four 119 00:05:36,480 --> 00:05:38,760 Speaker 3: five four six range, and it sort of flattens out. 120 00:05:39,320 --> 00:05:41,560 Speaker 3: But the FED has to be wary because they have 121 00:05:41,600 --> 00:05:44,159 Speaker 3: a mandate to maintain full employment and we're at full 122 00:05:44,160 --> 00:05:46,480 Speaker 3: employment now, and they have a mandate to main strip 123 00:05:46,560 --> 00:05:49,800 Speaker 3: price stability and inflation fighting, and those two mandates can 124 00:05:49,880 --> 00:05:52,800 Speaker 3: work together sometimes and against each other sometimes. Right now, 125 00:05:52,800 --> 00:05:55,320 Speaker 3: we're in an unusual case where the labor market is 126 00:05:55,320 --> 00:06:00,240 Speaker 3: getting has softened, and yet the inflation because of to 127 00:06:00,240 --> 00:06:03,240 Speaker 3: build up inflation after COVID and stuff, is now still 128 00:06:03,240 --> 00:06:05,960 Speaker 3: working its way down. Our team predicts that the inflation 129 00:06:06,080 --> 00:06:08,680 Speaker 3: rate will get to the FEDCE target through the end 130 00:06:08,720 --> 00:06:11,320 Speaker 3: of twenty seven, but it's continuing to work in the 131 00:06:11,360 --> 00:06:14,760 Speaker 3: right direction, which gives the FED, in our view, latitude 132 00:06:14,800 --> 00:06:18,080 Speaker 3: to keep cutting rates next year, especially in the first half. 133 00:06:18,120 --> 00:06:20,080 Speaker 3: That will then provide a stimulus in the second half 134 00:06:20,120 --> 00:06:23,599 Speaker 3: of the year. Those rate cuts do a benefit the 135 00:06:23,600 --> 00:06:27,640 Speaker 3: consumer somewhat, but in fact benefit small businesses because in 136 00:06:27,720 --> 00:06:30,279 Speaker 3: mid sized business they borrow on lines of credit, which 137 00:06:30,279 --> 00:06:32,760 Speaker 3: are our floating rate into season. As rates come down, 138 00:06:32,800 --> 00:06:36,000 Speaker 3: they get instantaneous benefit. And if you hear our small 139 00:06:36,080 --> 00:06:39,160 Speaker 3: medium sized businesses, they're pretty optimistic about next year, telling 140 00:06:39,240 --> 00:06:42,640 Speaker 3: us they expect to grow, they expect to hire more people, 141 00:06:43,440 --> 00:06:45,920 Speaker 3: and those small businesses are backbone of the US economy. 142 00:06:45,920 --> 00:06:48,239 Speaker 3: We're the largest small business letter in the US economy, 143 00:06:48,320 --> 00:06:49,800 Speaker 3: so we see them being very active. 144 00:06:50,400 --> 00:06:51,880 Speaker 2: This year has been good for the markets. It's also 145 00:06:51,960 --> 00:06:54,240 Speaker 2: been good for back of America. I believe you hit 146 00:06:54,279 --> 00:06:56,600 Speaker 2: an all time high in your stock price today intra 147 00:06:56,760 --> 00:06:59,320 Speaker 2: day at least, what do you need in twenty six 148 00:06:59,640 --> 00:07:00,799 Speaker 2: totinue the growth? 149 00:07:02,920 --> 00:07:04,920 Speaker 3: Look, we just need our team that's done a great 150 00:07:05,000 --> 00:07:07,600 Speaker 3: job for us over the last several decades. 151 00:07:07,160 --> 00:07:08,000 Speaker 1: To keep doing it. 152 00:07:08,040 --> 00:07:09,520 Speaker 3: And what they need to do is go out and 153 00:07:09,520 --> 00:07:11,120 Speaker 3: get one more client and do one more thing with 154 00:07:11,160 --> 00:07:13,080 Speaker 3: every client we have, and they know that across all 155 00:07:13,120 --> 00:07:15,600 Speaker 3: of business, whether it's our markets teams, whether it's our 156 00:07:15,600 --> 00:07:18,320 Speaker 3: global corporate investment bank teams, our consumer teams, are wealth 157 00:07:18,400 --> 00:07:20,320 Speaker 3: managed teams, all of them need to go out and 158 00:07:20,360 --> 00:07:22,679 Speaker 3: just drive more business and at a granite growth engine, 159 00:07:22,680 --> 00:07:25,920 Speaker 3: which we described our Investor Day a while back, that 160 00:07:26,160 --> 00:07:28,760 Speaker 3: is intact, it's been growing with the market and now 161 00:07:29,080 --> 00:07:31,200 Speaker 3: because of some dynamics and a repricing and balance sheet, 162 00:07:31,240 --> 00:07:32,880 Speaker 3: we'll get more earnings left out of it like we 163 00:07:32,920 --> 00:07:36,400 Speaker 3: did in the third quarter. But that organic growth engine, 164 00:07:36,440 --> 00:07:39,160 Speaker 3: more customers and war with each customers been driving a 165 00:07:39,200 --> 00:07:40,960 Speaker 3: Bank of America, and our team just need to do 166 00:07:41,000 --> 00:07:41,320 Speaker 3: more of that. 167 00:07:41,360 --> 00:07:42,200 Speaker 1: And they know that. 168 00:07:42,440 --> 00:07:44,760 Speaker 2: How sensitive is your performance at back of the RecA 169 00:07:44,960 --> 00:07:47,360 Speaker 2: to what the Fed does on interest rates? I mean, 170 00:07:47,640 --> 00:07:50,480 Speaker 2: if it actually cuts faster than people think or doesn't 171 00:07:50,520 --> 00:07:51,160 Speaker 2: cut as fast. 172 00:07:53,320 --> 00:07:55,400 Speaker 3: Look at the end of the day, if our team 173 00:07:55,440 --> 00:07:58,600 Speaker 3: predicts and most of the economists out there predict that 174 00:07:58,680 --> 00:08:00,800 Speaker 3: the Fed funds rate will go from where it is 175 00:08:00,840 --> 00:08:03,360 Speaker 3: now down closer to three percent, and the debate might 176 00:08:03,360 --> 00:08:06,000 Speaker 3: be two and three quarters three three in a quarter, 177 00:08:06,040 --> 00:08:09,080 Speaker 3: but it's a debate that nominal rate environment from say 178 00:08:09,160 --> 00:08:11,240 Speaker 3: three percent Fed funds rate to maybe a four to 179 00:08:11,320 --> 00:08:14,800 Speaker 3: four and a half percent ten year treasury rate. That's 180 00:08:14,840 --> 00:08:18,200 Speaker 3: a good normal interest rate curve where banks can do well, 181 00:08:18,240 --> 00:08:19,960 Speaker 3: and our bank can do very well. We have a 182 00:08:20,480 --> 00:08:23,080 Speaker 3: two trillion dollars of positis and a tradeon of those 183 00:08:23,440 --> 00:08:26,600 Speaker 3: are low interest, low interest accounts, and if the nominal 184 00:08:26,680 --> 00:08:29,680 Speaker 3: rate environment sits in around three percent, it's a very 185 00:08:29,720 --> 00:08:32,680 Speaker 3: good environment for our company to grow it's earning streams 186 00:08:32,720 --> 00:08:35,040 Speaker 3: while it's grown a customer and its balances and its 187 00:08:35,240 --> 00:08:36,200 Speaker 3: deposits and loans. 188 00:08:36,440 --> 00:08:38,760 Speaker 2: You originally had an investor's day and you went through 189 00:08:38,800 --> 00:08:41,319 Speaker 2: in that your investment in tech, which as I recall 190 00:08:41,440 --> 00:08:44,240 Speaker 2: is ten billion dollars over ten years, one billion dollars 191 00:08:44,280 --> 00:08:46,360 Speaker 2: in this year alone. What did you get for that? 192 00:08:46,440 --> 00:08:48,400 Speaker 2: How do you monitor the return on investment of that 193 00:08:48,520 --> 00:08:49,480 Speaker 2: kind of tech investment? 194 00:08:51,280 --> 00:08:54,000 Speaker 3: Yeah, So each year we have about thirteen billion dollars 195 00:08:54,040 --> 00:08:57,719 Speaker 3: in total tech spending, about nine to ten nine or 196 00:08:57,800 --> 00:09:01,520 Speaker 3: so of it is the running of thevironment, protecting the environment, 197 00:09:01,600 --> 00:09:03,440 Speaker 3: and sort of what they call the infrastructure, both the 198 00:09:03,480 --> 00:09:09,079 Speaker 3: systems and protecting those systems, and new software purchases about 199 00:09:09,080 --> 00:09:12,120 Speaker 3: four billions what we call initiatives, and that's the new products, 200 00:09:12,200 --> 00:09:14,840 Speaker 3: new services, the things my teammates love to talk about 201 00:09:15,080 --> 00:09:16,640 Speaker 3: and what do we get for how do we measure? 202 00:09:16,720 --> 00:09:18,760 Speaker 3: We literally look at every project we do and say, 203 00:09:18,840 --> 00:09:22,600 Speaker 3: what's the payback in terms of additional revenue, additional expense 204 00:09:22,720 --> 00:09:26,280 Speaker 3: or simplicit expense taken out of either reducing work or 205 00:09:26,559 --> 00:09:28,200 Speaker 3: simplicity new systems environment. 206 00:09:28,440 --> 00:09:30,040 Speaker 1: So we measured all that. It's got to be above 207 00:09:30,040 --> 00:09:30,800 Speaker 1: our cost of capital. 208 00:09:30,800 --> 00:09:32,640 Speaker 3: That's a technical way of measure, but what you really 209 00:09:32,679 --> 00:09:36,480 Speaker 3: see is that deployment of lots of capabilities to help 210 00:09:36,520 --> 00:09:40,520 Speaker 3: our customers do much greater things with AI, with agents 211 00:09:40,520 --> 00:09:42,560 Speaker 3: and all the things working on but also our teammates 212 00:09:42,559 --> 00:09:43,320 Speaker 3: to do great things. 213 00:09:43,320 --> 00:09:47,360 Speaker 1: So this year we deployed our three sixty five co pitt. 214 00:09:47,400 --> 00:09:48,960 Speaker 3: We'll finish the end of the year with two hundred 215 00:09:49,000 --> 00:09:51,600 Speaker 3: thousand people up and operating on AI and it's just 216 00:09:51,640 --> 00:09:54,240 Speaker 3: tremendous And that's all part of that major tech budget. 217 00:09:54,280 --> 00:09:56,080 Speaker 3: But also as part of that is just the nuts 218 00:09:56,120 --> 00:09:59,840 Speaker 3: and bolts of having a better trading system or enhancing 219 00:09:59,840 --> 00:10:03,080 Speaker 3: that trading system our data. So that four billion plus 220 00:10:03,160 --> 00:10:05,400 Speaker 3: a year is really important to drive the initiatives that 221 00:10:05,440 --> 00:10:06,880 Speaker 3: we have as an innovation company. 222 00:10:07,440 --> 00:10:09,760 Speaker 2: So we are in the early stages of AI, even 223 00:10:09,760 --> 00:10:11,600 Speaker 2: though you've been doing at Bank of America for a while. 224 00:10:11,600 --> 00:10:13,319 Speaker 1: Now, how far. 225 00:10:13,280 --> 00:10:15,360 Speaker 2: Will this go for Bank of America? What will Bank 226 00:10:15,400 --> 00:10:18,360 Speaker 2: of America look like five years down the road because 227 00:10:18,360 --> 00:10:20,720 Speaker 2: of AI? How transformed will it become? 228 00:10:22,800 --> 00:10:25,920 Speaker 3: Well, I think to answer that question, no one knows 229 00:10:25,960 --> 00:10:28,240 Speaker 3: precisely for sure. But what you will know is we'll 230 00:10:28,280 --> 00:10:31,680 Speaker 3: be applying more and more of automated intelligence or augmented 231 00:10:31,720 --> 00:10:35,840 Speaker 3: intelligence as we call it, with a person using AI 232 00:10:36,080 --> 00:10:38,880 Speaker 3: using that to be more effective, and that'll affect all 233 00:10:38,920 --> 00:10:42,000 Speaker 3: the businesses. So if you look historically, we deployed AI 234 00:10:42,280 --> 00:10:45,439 Speaker 3: more than five years ago with a product called Erica. 235 00:10:45,679 --> 00:10:48,959 Speaker 3: Erica was a agent, bought a small language model, and 236 00:10:49,000 --> 00:10:50,920 Speaker 3: all the words we use today back then we didn't. 237 00:10:51,040 --> 00:10:54,280 Speaker 3: And over the last twenty four hours, Erica interfaced with 238 00:10:54,320 --> 00:10:57,720 Speaker 3: two million Bank of America consumers and answer the questions. 239 00:10:57,840 --> 00:11:00,280 Speaker 3: It went from two hundred questions that could answer about 240 00:11:00,280 --> 00:11:02,760 Speaker 3: seven hundred question can answer about twenty million people use it. 241 00:11:02,800 --> 00:11:05,000 Speaker 1: So we have great experience of how that worked. 242 00:11:05,120 --> 00:11:06,960 Speaker 3: But what part of that experience was you have to 243 00:11:06,960 --> 00:11:09,400 Speaker 3: have your data perfect, you have to have the controls 244 00:11:09,400 --> 00:11:11,680 Speaker 3: around it, so answer the question right. So the customer 245 00:11:11,760 --> 00:11:13,640 Speaker 3: is getting a good answer, and you have to be 246 00:11:13,640 --> 00:11:18,600 Speaker 3: able to transmit that answer instantaneously on an inquiry across 247 00:11:18,640 --> 00:11:20,679 Speaker 3: the one hundred systems that touch our mobile bank or 248 00:11:20,720 --> 00:11:24,199 Speaker 3: digital banking out to the customers. So it's a lot 249 00:11:24,240 --> 00:11:24,800 Speaker 3: harder to do. 250 00:11:25,000 --> 00:11:25,200 Speaker 1: Now. 251 00:11:25,679 --> 00:11:27,520 Speaker 3: The interesting thing is we took that Erica and we 252 00:11:27,559 --> 00:11:30,040 Speaker 3: put it in our institutional side business, so cash pro 253 00:11:30,160 --> 00:11:34,559 Speaker 3: with Erica and hundreds of thousands of interactions with institutional 254 00:11:34,600 --> 00:11:37,480 Speaker 3: customers using it to answer questions about their accounts. Then 255 00:11:37,520 --> 00:11:39,360 Speaker 3: you took it and put it in an internal environment 256 00:11:39,400 --> 00:11:41,400 Speaker 3: so people could work with the IT department to get 257 00:11:41,440 --> 00:11:45,160 Speaker 3: their laptops replaced or passwords change. So we use that 258 00:11:45,240 --> 00:11:47,760 Speaker 3: model plus other models, and we believe it has a 259 00:11:47,760 --> 00:11:50,640 Speaker 3: big impact. Where exactly it goes we'll figure out, but 260 00:11:50,720 --> 00:11:52,880 Speaker 3: what we know there'll be more of it. It'll be 261 00:11:52,880 --> 00:11:56,280 Speaker 3: helping people be smart and more efficient, help us take 262 00:11:56,360 --> 00:11:58,120 Speaker 3: work out that we can get out and invest in 263 00:11:58,320 --> 00:12:01,120 Speaker 3: work that helps our customers and our teammate be more successful. 264 00:12:01,440 --> 00:12:03,679 Speaker 3: And we are after it every day, and we have 265 00:12:04,040 --> 00:12:07,280 Speaker 3: thirty proofs of concept going. We have money we're spending 266 00:12:07,320 --> 00:12:08,880 Speaker 3: and all that stuff, but a lot of this is 267 00:12:08,920 --> 00:12:12,240 Speaker 3: really fairly controlled, right now to make sure that we 268 00:12:12,240 --> 00:12:14,440 Speaker 3: can actually deliver what we say when we put a 269 00:12:14,520 --> 00:12:16,400 Speaker 3: and I, we just can't let it run and have 270 00:12:16,440 --> 00:12:18,720 Speaker 3: answers that customers won't have faith in. And that's why 271 00:12:19,160 --> 00:12:20,839 Speaker 3: it'll be a little slower build out there, I think 272 00:12:20,840 --> 00:12:23,160 Speaker 3: people see, but a very relentless build out. 273 00:12:23,200 --> 00:12:25,400 Speaker 2: From your experience so far, and once you project out, 274 00:12:25,559 --> 00:12:27,760 Speaker 2: when you look at the return on that investment AI, 275 00:12:27,960 --> 00:12:29,880 Speaker 2: how much it's from growing the top line that is 276 00:12:30,120 --> 00:12:32,839 Speaker 2: increasing revenue, and how much of it is from cost 277 00:12:32,880 --> 00:12:34,240 Speaker 2: savings because of the use of AI. 278 00:12:36,559 --> 00:12:38,680 Speaker 3: I think when you look at it in the very 279 00:12:38,720 --> 00:12:42,720 Speaker 3: near term, it's mostly about process engineering, so embedding AI 280 00:12:42,800 --> 00:12:45,720 Speaker 3: and the discrete process to take out work and that 281 00:12:45,760 --> 00:12:48,200 Speaker 3: we've been doing for years for several years, so we 282 00:12:48,240 --> 00:12:50,800 Speaker 3: see it. And before that we have machine learning and 283 00:12:50,840 --> 00:12:55,280 Speaker 3: other digital digitization, and that was a capability that we built, 284 00:12:55,280 --> 00:12:58,160 Speaker 3: but also the customer would use and that's an operative 285 00:12:58,240 --> 00:13:00,520 Speaker 3: term here, as a customer has to use the use 286 00:13:00,600 --> 00:13:03,720 Speaker 3: the techniques and things that we build. I think over 287 00:13:03,800 --> 00:13:08,760 Speaker 3: time it'll be much more about enhancing the revenue side. 288 00:13:08,800 --> 00:13:10,520 Speaker 3: And so if you look at our proofs to cons 289 00:13:11,040 --> 00:13:13,400 Speaker 3: our projects going in and out proofs the concept, then 290 00:13:13,480 --> 00:13:16,959 Speaker 3: more implemented. We have a relationship management preparation tools or 291 00:13:17,040 --> 00:13:19,560 Speaker 3: pitch book preparation tools that allow us to do more 292 00:13:21,040 --> 00:13:24,600 Speaker 3: faster turnaround, better preparation for meetings, better idea generation for 293 00:13:24,679 --> 00:13:26,760 Speaker 3: meetings with clients, and we know that I will generate 294 00:13:26,800 --> 00:13:30,800 Speaker 3: additional revenues. So there's equal parts revenue and expense in 295 00:13:30,840 --> 00:13:33,920 Speaker 3: the last twenty four months, twelve months. You know it 296 00:13:34,040 --> 00:13:35,920 Speaker 3: even go back to Erika starts three or four or 297 00:13:35,920 --> 00:13:38,439 Speaker 3: five years ago. Whatever it started, it was more about 298 00:13:38,480 --> 00:13:41,240 Speaker 3: removing tasks and costs. I think that's moving because people 299 00:13:41,280 --> 00:13:43,680 Speaker 3: are getting more comfortable with this ability to answer questions 300 00:13:43,679 --> 00:13:45,960 Speaker 3: and help them prepare for a meeting with a client, 301 00:13:46,320 --> 00:13:48,000 Speaker 3: and you know what they need to think about, whether 302 00:13:48,000 --> 00:13:50,559 Speaker 3: it's a wealth manager client or a commercial banking client. 303 00:13:51,400 --> 00:13:54,680 Speaker 2: Including banking. Going beyond that in general to our economy, 304 00:13:54,679 --> 00:13:57,319 Speaker 2: because AI appears to be being transformed, and goodness dooes, 305 00:13:57,320 --> 00:14:00,240 Speaker 2: there's hundreds of billions of dollars being invested. Can we 306 00:14:00,280 --> 00:14:03,240 Speaker 2: get the return investment as an economy without cutting a 307 00:14:03,240 --> 00:14:03,880 Speaker 2: lot of jobs. 308 00:14:06,600 --> 00:14:09,640 Speaker 3: I think the question will be, you know how fast 309 00:14:09,720 --> 00:14:11,720 Speaker 3: the jobs will grow around and the new jobs are 310 00:14:11,720 --> 00:14:13,280 Speaker 3: growing to shift of jobs, So if you look, in 311 00:14:13,320 --> 00:14:16,199 Speaker 3: the last fifty years or so, we had a lot 312 00:14:16,240 --> 00:14:18,400 Speaker 3: of technology come in, and we have twice as many 313 00:14:18,400 --> 00:14:20,880 Speaker 3: people work in the US and we did fifty five 314 00:14:20,960 --> 00:14:23,960 Speaker 3: years ago. So think about David, in our lifetimes, what 315 00:14:24,000 --> 00:14:27,000 Speaker 3: we've seen. You know, we've seen the desktop computer, to 316 00:14:27,000 --> 00:14:29,720 Speaker 3: the laptop computer, to the phone as a computer, et cetera. 317 00:14:29,760 --> 00:14:31,920 Speaker 3: And think of all that technology coming in and we 318 00:14:31,920 --> 00:14:34,840 Speaker 3: employed twice as many people. So the question will be, well, 319 00:14:34,840 --> 00:14:37,040 Speaker 3: you have more people doing different types of jobs. And 320 00:14:37,080 --> 00:14:39,840 Speaker 3: so my advice to myself and my advice to my 321 00:14:39,840 --> 00:14:42,600 Speaker 3: teammates is simple, learn it, harness it, make it your 322 00:14:44,080 --> 00:14:46,520 Speaker 3: agent to help you be more successful. And yes, does 323 00:14:46,520 --> 00:14:49,280 Speaker 3: it change the human content of work in areas like 324 00:14:49,360 --> 00:14:53,080 Speaker 3: finance and audit and legal that we hadn't done it before. Yes, 325 00:14:53,160 --> 00:14:55,360 Speaker 3: But the people use it effectively will be able to 326 00:14:55,400 --> 00:14:58,240 Speaker 3: do more and generate more revenue and then potentially generate 327 00:14:58,280 --> 00:15:00,760 Speaker 3: more jobs out there because people there are more people 328 00:15:00,760 --> 00:15:02,840 Speaker 3: are the more money, spending more money. America is making 329 00:15:02,920 --> 00:15:05,400 Speaker 3: more money. It should all be good, but it's going 330 00:15:05,440 --> 00:15:07,360 Speaker 3: to be an interesting transition. 331 00:15:08,120 --> 00:15:10,680 Speaker 2: Brian As, CEO of Bank of America. An important part 332 00:15:10,680 --> 00:15:13,160 Speaker 2: of your job, maybe the most important part is managing risk, 333 00:15:13,360 --> 00:15:16,280 Speaker 2: upside risk, downside risk. As you look at the risks 334 00:15:16,320 --> 00:15:18,760 Speaker 2: out there, how do you manage, if at all, for 335 00:15:18,960 --> 00:15:22,240 Speaker 2: the risk that in fact we're over investing in AI 336 00:15:22,520 --> 00:15:23,920 Speaker 2: as an economy. 337 00:15:25,560 --> 00:15:26,880 Speaker 1: Well, I think it's an economy. 338 00:15:26,960 --> 00:15:29,200 Speaker 3: The issue would be if it got overheated and it retracted, 339 00:15:29,800 --> 00:15:31,880 Speaker 3: what would the impact flow for us as a company 340 00:15:31,920 --> 00:15:35,520 Speaker 3: more into consumers, job loss, things like that. And right 341 00:15:35,520 --> 00:15:38,160 Speaker 3: now we see that as being relatively limited because it's 342 00:15:38,200 --> 00:15:40,440 Speaker 3: a narrow a group of companies and there a group 343 00:15:40,440 --> 00:15:43,560 Speaker 3: of spending and the companies are spending and have a 344 00:15:43,600 --> 00:15:45,480 Speaker 3: lot of money. As a lender, we look at the 345 00:15:45,560 --> 00:15:48,000 Speaker 3: leverage on these projects and make sure that we're comfortable 346 00:15:48,080 --> 00:15:50,240 Speaker 3: that in the duration of the contract by the person 347 00:15:50,280 --> 00:15:53,800 Speaker 3: who's going to commit to use the data the data 348 00:15:53,800 --> 00:15:56,440 Speaker 3: center and therefore that's the revenus room. You think about 349 00:15:56,840 --> 00:15:58,560 Speaker 3: the tenant, for lack of a better term, the quality 350 00:15:58,600 --> 00:16:00,280 Speaker 3: that tenant, the link to that tenant. 351 00:16:00,280 --> 00:16:02,560 Speaker 1: Those are dishes. So we look at all that. 352 00:16:02,840 --> 00:16:05,520 Speaker 3: It's the market valuations, you know, our team thinks that 353 00:16:05,600 --> 00:16:08,160 Speaker 3: you'll see, you know, you could could see a debate 354 00:16:08,200 --> 00:16:12,000 Speaker 3: about the over whether the AI stalks are overvalued undervalued. 355 00:16:12,040 --> 00:16:14,680 Speaker 3: Our team talks about that, but what people are forgetting 356 00:16:14,720 --> 00:16:17,080 Speaker 3: is there's a lot of other companies are coming in 357 00:16:17,120 --> 00:16:19,480 Speaker 3: and producing more and more earnings growth. At the end 358 00:16:19,480 --> 00:16:21,240 Speaker 3: of the day, the earnings growth is what will drive 359 00:16:21,240 --> 00:16:23,640 Speaker 3: the market, and so it may move around. But as 360 00:16:23,920 --> 00:16:27,480 Speaker 3: Chris Heise, our teammates, has said on your UH Blueberg 361 00:16:27,520 --> 00:16:30,120 Speaker 3: and other places, you know it's a proud bull. He 362 00:16:30,160 --> 00:16:32,400 Speaker 3: thinks you know that it's a The market ahead is 363 00:16:32,480 --> 00:16:34,840 Speaker 3: very constructive and our team has you know, a mid 364 00:16:34,920 --> 00:16:37,560 Speaker 3: single digit S and P growth predicted for next year. 365 00:16:37,880 --> 00:16:39,760 Speaker 2: So, Brian, one last question is we head into the 366 00:16:39,800 --> 00:16:43,440 Speaker 2: holidays and into the new year, what's the biggest upside 367 00:16:43,520 --> 00:16:44,920 Speaker 2: risk that you see in the new year. 368 00:16:47,480 --> 00:16:50,840 Speaker 3: Well, I think the risk is that the upside put 369 00:16:50,880 --> 00:16:54,400 Speaker 3: potential is for the series of policies that have come 370 00:16:54,440 --> 00:16:59,400 Speaker 3: out taxation, UH, trade and tariff, immigration and deregulation. I 371 00:16:59,400 --> 00:17:01,240 Speaker 3: think the biggest one is still yet to come for 372 00:17:01,320 --> 00:17:04,439 Speaker 3: businesses a deregulation piece and it's falling into place, and 373 00:17:04,480 --> 00:17:06,920 Speaker 3: if that works the right way, then you'll see the 374 00:17:07,000 --> 00:17:10,680 Speaker 3: US kick another level of potential growth. And when the 375 00:17:10,800 --> 00:17:13,040 Speaker 3: US kicks and growth, the world will have growth. Because 376 00:17:13,080 --> 00:17:14,879 Speaker 3: the end of the day, our economy is the biggest 377 00:17:14,880 --> 00:17:17,679 Speaker 3: economy with the biggest consuming economy, and so if our 378 00:17:18,119 --> 00:17:21,480 Speaker 3: if our citizens are employed, working and earning more money, 379 00:17:21,480 --> 00:17:22,600 Speaker 3: that's good for the world. 380 00:17:23,200 --> 00:17:25,320 Speaker 2: Is there any downside at all because of the uncertaint 381 00:17:25,320 --> 00:17:27,000 Speaker 2: about policy because it's moved around a bit. 382 00:17:29,160 --> 00:17:32,199 Speaker 3: There has been a lot this year, honestly, and if 383 00:17:32,240 --> 00:17:35,240 Speaker 3: you talk to our small business customers for example, and 384 00:17:35,280 --> 00:17:39,719 Speaker 3: the most recent surveys, they're now getting an understanding how 385 00:17:39,720 --> 00:17:41,280 Speaker 3: the tariffs are going to affect them. It took a 386 00:17:41,280 --> 00:17:43,640 Speaker 3: while for them to settle in and they can see 387 00:17:43,640 --> 00:17:46,160 Speaker 3: the ten fifteen to twenty percent level and they're adjusting 388 00:17:46,200 --> 00:17:48,880 Speaker 3: for their adjusting supply chains. That's still work going on, 389 00:17:49,040 --> 00:17:51,880 Speaker 3: and the ability to pass through those costs and things 390 00:17:51,960 --> 00:17:55,480 Speaker 3: like that is coming through the system now. Their biggest worry, 391 00:17:55,560 --> 00:17:58,359 Speaker 3: what they tell us, is to get employees, and that 392 00:17:58,480 --> 00:18:01,000 Speaker 3: has a bit to do with settling in on the 393 00:18:01,000 --> 00:18:04,439 Speaker 3: immigration policy. And it's sort of getting more precise in 394 00:18:04,480 --> 00:18:06,919 Speaker 3: the minds of a small business owner a mid sized company, 395 00:18:06,920 --> 00:18:10,000 Speaker 3: can I have the labor availability that I need? And 396 00:18:10,040 --> 00:18:12,520 Speaker 3: so that issue I think is straightening out as the 397 00:18:13,200 --> 00:18:18,720 Speaker 3: precision of the work on immigration policy continues through, you know, 398 00:18:18,760 --> 00:18:21,840 Speaker 3: and that was probably more on the minds this year 399 00:18:21,920 --> 00:18:24,840 Speaker 3: as that settles in and the trade policies have settled 400 00:18:24,840 --> 00:18:27,440 Speaker 3: in during the course of the summer. I think they're 401 00:18:27,440 --> 00:18:30,040 Speaker 3: feeling better about understanding them, and they still have to 402 00:18:30,040 --> 00:18:32,840 Speaker 3: make some adjustments, but they're feeling better about understanding them. 403 00:18:33,160 --> 00:18:34,959 Speaker 2: Brian, as many a US head a new break, I 404 00:18:34,960 --> 00:18:37,359 Speaker 2: hope you and your colleagues have a wonderful holiday. Thank 405 00:18:37,359 --> 00:18:39,720 Speaker 2: you so much for being with us. That is Brian Moneyhand. 406 00:18:39,760 --> 00:18:42,800 Speaker 2: He is chair and CEO of Bank of America. Back 407 00:18:42,840 --> 00:18:43,800 Speaker 2: to you.