1 00:00:02,520 --> 00:00:21,640 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:22,960 --> 00:00:26,200 Speaker 2: This is Wall Street Week. I'm David Weston bringing you 3 00:00:26,480 --> 00:00:30,160 Speaker 2: stories of capitalism. This week, David Goura takes us through 4 00:00:30,200 --> 00:00:33,199 Speaker 2: the state of the world as investors look forward to 5 00:00:33,240 --> 00:00:36,640 Speaker 2: the new year, and we learn how generative AI could 6 00:00:36,720 --> 00:00:39,120 Speaker 2: be used to give us healthy snacks that are just 7 00:00:39,159 --> 00:00:43,040 Speaker 2: as tasty as the unhealthy kind. But we begin with 8 00:00:43,120 --> 00:00:46,279 Speaker 2: a story about tenure, not the kind that guarantees you 9 00:00:46,320 --> 00:00:48,760 Speaker 2: a job, but the kind that you have to earn 10 00:00:49,120 --> 00:00:52,839 Speaker 2: day after day and year after year. The kind that, 11 00:00:53,159 --> 00:00:56,440 Speaker 2: looking back after fifteen years may look obvious, but that 12 00:00:56,520 --> 00:00:58,280 Speaker 2: at the beginning was anything. 13 00:00:58,480 --> 00:01:02,600 Speaker 3: But you know, I never really thought about I knew 14 00:01:02,600 --> 00:01:05,080 Speaker 3: I had to be here substantial time to work with 15 00:01:05,080 --> 00:01:07,040 Speaker 3: a team and drive the company what it could be. 16 00:01:07,080 --> 00:01:09,840 Speaker 3: And that's that's great, But it never was like to 17 00:01:09,880 --> 00:01:11,959 Speaker 3: do this job five years. I think you have. The 18 00:01:12,040 --> 00:01:15,120 Speaker 3: time has to be where you have the place from 19 00:01:15,160 --> 00:01:17,080 Speaker 3: where we were in a place's great condition. 20 00:01:17,840 --> 00:01:20,680 Speaker 2: When Brian moynihan took over as CEO of Bank of 21 00:01:20,760 --> 00:01:24,960 Speaker 2: America on January first, twenty ten, the place was definitely 22 00:01:25,000 --> 00:01:28,240 Speaker 2: not in great condition. It had just gone through a 23 00:01:28,280 --> 00:01:31,480 Speaker 2: series of mergers, including the one that brought moynihan to 24 00:01:31,560 --> 00:01:34,959 Speaker 2: the bank through its acquisition of Fleet Boston just six 25 00:01:35,080 --> 00:01:35,800 Speaker 2: years before. 26 00:01:36,840 --> 00:01:40,000 Speaker 3: Before Fleet Boston deal, Legacy Bank in America at that 27 00:01:40,080 --> 00:01:44,160 Speaker 3: point probably had one hundred thousand employees. Fleet Boss had 28 00:01:44,240 --> 00:01:47,600 Speaker 3: fifty sixty thousand, you know, then Merrill had sixty thousand. 29 00:01:47,800 --> 00:01:50,560 Speaker 3: Let's say, how fifteen thou twenty thousand country I'd had 30 00:01:50,560 --> 00:01:52,840 Speaker 3: forty fifty more. People came from the outside and not. 31 00:01:53,040 --> 00:01:54,840 Speaker 3: And so one of the things that the team and 32 00:01:54,880 --> 00:01:56,320 Speaker 3: I had to settle down is what does the culture 33 00:01:56,360 --> 00:01:57,240 Speaker 3: we want with the values? 34 00:01:58,520 --> 00:02:01,880 Speaker 2: Forming a single culture from such disparate parts would have 35 00:02:01,880 --> 00:02:05,360 Speaker 2: been a challenge all by itself. But this all happened 36 00:02:05,440 --> 00:02:08,919 Speaker 2: as the Great Financial Crisis was in full bloom, putting 37 00:02:09,040 --> 00:02:12,360 Speaker 2: enormous pressure on all the big banks. A year into 38 00:02:12,360 --> 00:02:15,320 Speaker 2: his tenure, moynihan faced a mountain of losses from the 39 00:02:15,360 --> 00:02:19,480 Speaker 2: subprime mortgage business he had inherited when his predecessor Ken 40 00:02:19,560 --> 00:02:24,840 Speaker 2: Lewis purchased Countrywide, leading to losses of over thirty billion dollars. 41 00:02:25,400 --> 00:02:28,960 Speaker 2: Its stock price had fallen nearly ten percent, its price 42 00:02:29,000 --> 00:02:32,560 Speaker 2: to book value was down too point four five, and 43 00:02:32,639 --> 00:02:35,800 Speaker 2: the government was putting into place fundamental changes in the 44 00:02:35,840 --> 00:02:40,520 Speaker 2: banking system overall, including upping the capital requirements for the banks, 45 00:02:41,040 --> 00:02:45,359 Speaker 2: requirements some doubted Bank of America could meet. You have 46 00:02:45,400 --> 00:02:47,160 Speaker 2: a lot of Hey, you also have a lot behind you. 47 00:02:47,200 --> 00:02:49,880 Speaker 2: And we think about what the banking world was where 48 00:02:49,880 --> 00:02:51,720 Speaker 2: Bank of America was in two and eight, two tho 49 00:02:51,880 --> 00:02:53,920 Speaker 2: and nineteen ten meters took a job. It was a 50 00:02:53,919 --> 00:02:55,080 Speaker 2: different world. 51 00:02:55,440 --> 00:02:57,400 Speaker 3: So a lot of people don't remember that in two 52 00:02:57,400 --> 00:02:59,680 Speaker 3: thousand and seven. In two thousand and six is actually 53 00:02:59,680 --> 00:03:01,960 Speaker 3: with the intolchrist started to come through, the property value 54 00:03:02,000 --> 00:03:05,320 Speaker 3: started going down, the overlanding and the consumer side became exposed, 55 00:03:05,560 --> 00:03:08,200 Speaker 3: the ripple effect through some of the CEOs and things 56 00:03:08,240 --> 00:03:10,720 Speaker 3: like that, the impact on the bear Stearns and Lehman 57 00:03:10,800 --> 00:03:14,600 Speaker 3: and Merrill and other companies, and the acquisitions that took place. 58 00:03:14,680 --> 00:03:14,720 Speaker 4: No. 59 00:03:14,840 --> 00:03:18,239 Speaker 3: Eight, what you had was non banks that weren't governed 60 00:03:18,240 --> 00:03:20,320 Speaker 3: by any rules that were all brought in the banking 61 00:03:20,360 --> 00:03:22,679 Speaker 3: system and the net was thrown over. The tent was 62 00:03:22,720 --> 00:03:24,639 Speaker 3: sort of a lot bigger group. That's what made it 63 00:03:24,680 --> 00:03:26,840 Speaker 3: different coming out the other side, because suddenly, you know, 64 00:03:26,919 --> 00:03:29,160 Speaker 3: the group of my peers were Golden Sacks, Morgan, Stanley, 65 00:03:29,840 --> 00:03:33,560 Speaker 3: JP City, ourselves, et cetera. It wasn't just the banks, 66 00:03:33,560 --> 00:03:35,960 Speaker 3: and then you know, the securities firms over here or 67 00:03:36,120 --> 00:03:38,560 Speaker 3: the insurance firms over here, and that then required all 68 00:03:38,600 --> 00:03:40,360 Speaker 3: the rules to change fast, and all the reach to 69 00:03:40,400 --> 00:03:42,920 Speaker 3: change fast. The stuff people hadn't thought about, you know, 70 00:03:42,960 --> 00:03:45,280 Speaker 3: the Vulcar Amendment, and you know this stuff, you know, 71 00:03:45,360 --> 00:03:47,080 Speaker 3: stuff that had people haven't thought about it. And that 72 00:03:47,200 --> 00:03:48,960 Speaker 3: was because suddenly he took all these people who were 73 00:03:48,960 --> 00:03:51,080 Speaker 3: governed by different regimes, different rules, threw them in a 74 00:03:51,080 --> 00:03:52,600 Speaker 3: pot and said we're going to put a tent overhaul. 75 00:03:53,520 --> 00:03:56,720 Speaker 2: Moyen's management team at the time knew that the challenges 76 00:03:56,760 --> 00:04:01,080 Speaker 2: could make or break his new leadership and Finukan served 77 00:04:01,120 --> 00:04:04,040 Speaker 2: as chief Marketing and strategy officer at Bank of America 78 00:04:04,040 --> 00:04:06,880 Speaker 2: in twenty eleven before becoming the vice chair of the 79 00:04:06,880 --> 00:04:09,800 Speaker 2: bank and chair of its European operations. 80 00:04:10,240 --> 00:04:14,840 Speaker 5: This was a moment, if you will, in financial services. 81 00:04:15,000 --> 00:04:20,279 Speaker 5: We were in a near recession that was the likes 82 00:04:20,279 --> 00:04:23,760 Speaker 5: of who we had never seen before, and biave A 83 00:04:24,400 --> 00:04:28,159 Speaker 5: had a million problems, and we had issues with regulators, 84 00:04:28,600 --> 00:04:32,599 Speaker 5: we had issues with analysts, we had issues with clients 85 00:04:32,600 --> 00:04:39,280 Speaker 5: and customers. Elected officials were questioning our viability and you know, 86 00:04:39,320 --> 00:04:41,839 Speaker 5: the stock price was very low and there were questions 87 00:04:41,880 --> 00:04:44,440 Speaker 5: about the company in general, and this is what Brian 88 00:04:44,480 --> 00:04:48,160 Speaker 5: Mornihan stepped into. And he stepped into it. I have 89 00:04:48,240 --> 00:04:51,599 Speaker 5: to say eyes wide open, because he had done almost 90 00:04:51,920 --> 00:04:57,880 Speaker 5: every executive job in the company, so he was prepared. 91 00:04:58,080 --> 00:05:00,159 Speaker 5: I think he was prepared in a way almost no 92 00:05:00,160 --> 00:05:02,960 Speaker 5: one else could have been. And he was also willing 93 00:05:03,480 --> 00:05:07,080 Speaker 5: and he was certainly able. So this was a moment 94 00:05:07,520 --> 00:05:13,000 Speaker 5: for this company. And he assembled a crew around him, 95 00:05:13,040 --> 00:05:15,920 Speaker 5: an executive management team, and we were going to work 96 00:05:16,240 --> 00:05:19,880 Speaker 5: twenty four to seven for what turned out to be 97 00:05:20,680 --> 00:05:24,360 Speaker 5: at least a few years, and we recovered. 98 00:05:24,120 --> 00:05:27,279 Speaker 2: To bring his bank back from the brink. Moyinhand focused 99 00:05:27,320 --> 00:05:30,400 Speaker 2: on what was working, where it could win, and what 100 00:05:30,560 --> 00:05:34,320 Speaker 2: it could do without cutting billions and expenses and tens 101 00:05:34,360 --> 00:05:36,320 Speaker 2: of thousands of jobs we. 102 00:05:36,360 --> 00:05:39,280 Speaker 3: Gotten with three hundred million dollars assets, fifty operating businesses, 103 00:05:39,680 --> 00:05:42,880 Speaker 3: we're down to operating efficient made sense, and we said, 104 00:05:42,880 --> 00:05:44,920 Speaker 3: now we've got to grow. And so the first part 105 00:05:44,920 --> 00:05:47,720 Speaker 3: of response was grow no excuses. But what you had 106 00:05:47,760 --> 00:05:49,919 Speaker 3: in the base that was these eight lines of business. 107 00:05:49,920 --> 00:05:51,960 Speaker 3: We talked about Jack Welsh or somebody say, you got 108 00:05:52,000 --> 00:05:53,159 Speaker 3: to be top one two three in the business. We 109 00:05:53,160 --> 00:05:54,960 Speaker 3: were top one two three in all the businesses across 110 00:05:55,000 --> 00:05:56,880 Speaker 3: the eight lines of business. But well, he had a 111 00:05:56,920 --> 00:05:58,640 Speaker 3: lot of other stuff that was not as interesting, and 112 00:05:58,640 --> 00:06:00,640 Speaker 3: then we had the regulatory issue, and then we had 113 00:06:00,680 --> 00:06:03,200 Speaker 3: the mortgage issues, so we had just push all that away. 114 00:06:03,600 --> 00:06:06,440 Speaker 3: But all those acquisitions left us with a franchise that 115 00:06:06,520 --> 00:06:08,760 Speaker 3: is a top consumer banking fans with the top wealth 116 00:06:08,760 --> 00:06:12,120 Speaker 3: manager franchise, the top commercial banker in the United States, 117 00:06:12,240 --> 00:06:15,200 Speaker 3: a global large corporate banker, and a global markets business 118 00:06:15,520 --> 00:06:17,320 Speaker 3: that we were able then to start to invest in 119 00:06:17,360 --> 00:06:18,400 Speaker 3: growing them organically. 120 00:06:18,880 --> 00:06:20,680 Speaker 5: We had to decide what we were going to do 121 00:06:20,800 --> 00:06:24,200 Speaker 5: in terms of everything from customer care, if you will, 122 00:06:24,800 --> 00:06:30,000 Speaker 5: employee morale, dealing with elected officials, dealing with regulators, and 123 00:06:30,040 --> 00:06:34,760 Speaker 5: then what businesses were going to lean in on and 124 00:06:34,800 --> 00:06:40,320 Speaker 5: those that we were going to perhaps exit. So Brian 125 00:06:40,720 --> 00:06:43,560 Speaker 5: is got a bit of the engineering, and that he 126 00:06:43,760 --> 00:06:49,279 Speaker 5: is an exacting person. We laid out every line of business, 127 00:06:49,480 --> 00:06:52,520 Speaker 5: we laid out every geography we were in. When you 128 00:06:52,520 --> 00:06:54,720 Speaker 5: look back on it, you can be a bit nostalgic, 129 00:06:54,760 --> 00:06:57,680 Speaker 5: but at the moment it was very difficult, and he 130 00:06:57,920 --> 00:07:01,360 Speaker 5: was so determined. He's a very good leader because he's calm, 131 00:07:01,839 --> 00:07:09,040 Speaker 5: and he's thorough, and he's also human. And what I 132 00:07:09,080 --> 00:07:11,680 Speaker 5: mean by that is he got when everybody was kind 133 00:07:11,720 --> 00:07:15,520 Speaker 5: of frazzled, and we just kept at it. But there 134 00:07:15,560 --> 00:07:17,720 Speaker 5: was a camaraderie around it that I think would be 135 00:07:19,120 --> 00:07:20,160 Speaker 5: maybe unexpected. 136 00:07:21,000 --> 00:07:24,040 Speaker 2: Even as moynihan and his team were rebuilding Bank of 137 00:07:24,080 --> 00:07:27,160 Speaker 2: America from the ground up, concerns about the bank led 138 00:07:27,200 --> 00:07:30,280 Speaker 2: others to make sure its problems didn't further risk the 139 00:07:30,480 --> 00:07:33,920 Speaker 2: entire financial system, leading the government to insist Bank of 140 00:07:33,960 --> 00:07:37,240 Speaker 2: America and the other money center banks take large infusions 141 00:07:37,240 --> 00:07:41,400 Speaker 2: of cash investments, and even for mega investor Warren Buffett 142 00:07:41,520 --> 00:07:44,360 Speaker 2: to pitch in with a five billion dollar investment. 143 00:07:45,080 --> 00:07:47,520 Speaker 3: So in twenty eleven August, when the United States had 144 00:07:47,520 --> 00:07:49,880 Speaker 3: been downgraded was close to the faulty. At that point, 145 00:07:49,960 --> 00:07:52,240 Speaker 3: the US was short and didn't have enough money. It 146 00:07:52,280 --> 00:07:54,280 Speaker 3: was starting to run out of money. We are also 147 00:07:54,280 --> 00:07:55,760 Speaker 3: Gett knocked him out at the same time, and he 148 00:07:55,800 --> 00:07:57,720 Speaker 3: came in in August and said, here's five billion. I 149 00:07:57,720 --> 00:07:59,640 Speaker 3: know you don't need it, but it'll stabilize in the 150 00:07:59,680 --> 00:08:01,880 Speaker 3: two fifty thousand plus people we had at that time 151 00:08:02,120 --> 00:08:03,760 Speaker 3: came to work the next day and stayed. The smartest 152 00:08:03,760 --> 00:08:06,280 Speaker 3: investor put five billion dollars on a company in twenty 153 00:08:06,280 --> 00:08:06,880 Speaker 3: four hours. 154 00:08:07,200 --> 00:08:10,240 Speaker 5: That was an interesting moment. At that point, we had 155 00:08:10,280 --> 00:08:15,400 Speaker 5: actually recovered enough that we had sufficient capital. And by 156 00:08:15,400 --> 00:08:18,280 Speaker 5: the way, he knew that, and in fact, that's what 157 00:08:18,360 --> 00:08:20,440 Speaker 5: he said to Brian, I know it, That's why I'm investing. 158 00:08:20,920 --> 00:08:26,240 Speaker 5: It helped us enormously with the analyst community and elected 159 00:08:26,280 --> 00:08:30,160 Speaker 5: officials and even our own employees. If Warren Buffett was 160 00:08:30,240 --> 00:08:33,920 Speaker 5: investing in us, it was such a boost of confidence 161 00:08:33,920 --> 00:08:36,800 Speaker 5: that we didn't maybe otherwise have at the point. So 162 00:08:37,440 --> 00:08:40,839 Speaker 5: it was reputationally really beneficial. 163 00:08:41,720 --> 00:08:44,920 Speaker 2: Under moynihan, Bank of America has not only survived, it 164 00:08:44,960 --> 00:08:48,840 Speaker 2: has thrived, earning over fifteen billion dollars a year over 165 00:08:48,920 --> 00:08:52,199 Speaker 2: each of the last ten years, increasing its assets from 166 00:08:52,240 --> 00:08:55,120 Speaker 2: two point three to three point three trillion dollars and 167 00:08:55,160 --> 00:08:58,439 Speaker 2: its share price from thirteen dollars to forty four dollars, 168 00:08:58,920 --> 00:09:01,720 Speaker 2: and that price to book ratio it's gone from point 169 00:09:01,720 --> 00:09:05,160 Speaker 2: four to five to over one point twenty five. But 170 00:09:05,559 --> 00:09:07,920 Speaker 2: even as it has come back strong, it has not 171 00:09:08,080 --> 00:09:11,720 Speaker 2: grown as fast as its biggest rival, JP Morgan, whose 172 00:09:11,760 --> 00:09:14,160 Speaker 2: share price has led the pack of the big banks, 173 00:09:14,480 --> 00:09:18,080 Speaker 2: while Bank of America remains somewhere in the middle. Some 174 00:09:18,240 --> 00:09:22,319 Speaker 2: have suggested that moyni Hand's steadfast commitment to responsible growth 175 00:09:22,720 --> 00:09:27,560 Speaker 2: may have held it back. Did you leave any money 176 00:09:27,600 --> 00:09:29,679 Speaker 2: on the table because of the responsible part? And does 177 00:09:29,679 --> 00:09:32,679 Speaker 2: responsible growth look the same today in twenty five as 178 00:09:32,679 --> 00:09:33,800 Speaker 2: it did in twenty ten. 179 00:09:34,000 --> 00:09:35,840 Speaker 3: So one of the things we always talk to our 180 00:09:35,880 --> 00:09:38,440 Speaker 3: team it's about is response growth. Was a call to growth, 181 00:09:38,679 --> 00:09:40,760 Speaker 3: not a call to berresponded. We already pretyre responsible think 182 00:09:40,760 --> 00:09:43,360 Speaker 3: from ten to fifteen, and the idea was we've done 183 00:09:43,360 --> 00:09:45,200 Speaker 3: all the work, we reshaped the company. We said, now 184 00:09:45,200 --> 00:09:47,319 Speaker 3: we've got to grow and so that you never disrupt 185 00:09:47,320 --> 00:09:49,640 Speaker 3: the company's ability to keep going. And we have businesses 186 00:09:49,640 --> 00:09:52,800 Speaker 3: which offset each other. So the Marcus business has done 187 00:09:52,880 --> 00:09:54,920 Speaker 3: well and the interest rate environment change, and now the 188 00:09:54,920 --> 00:09:57,600 Speaker 3: consumer and global banking business will kick in. But the 189 00:09:57,640 --> 00:09:59,679 Speaker 3: idea of responsive growth is that you got to grow, 190 00:09:59,679 --> 00:10:01,560 Speaker 3: no oaks uses, You've got to win in a marketplace. 191 00:10:02,160 --> 00:10:06,079 Speaker 5: Responsible growth is like a dual thought. If you will, 192 00:10:06,880 --> 00:10:11,440 Speaker 5: demonstrating responsibility which is sort of an extension of good governance, 193 00:10:12,440 --> 00:10:15,680 Speaker 5: and also the contract you make with your clients and 194 00:10:15,720 --> 00:10:18,720 Speaker 5: your employees that You're going to be responsible to them 195 00:10:18,760 --> 00:10:21,959 Speaker 5: first and foremost all along the way, and remember now 196 00:10:21,960 --> 00:10:24,840 Speaker 5: where we came from. And then the second thing was 197 00:10:24,920 --> 00:10:30,040 Speaker 5: that we would grow, so responsible growth, I mean responsible growth. 198 00:10:30,080 --> 00:10:33,079 Speaker 5: Growth can be a modifier to growth. But it's actually 199 00:10:33,480 --> 00:10:36,679 Speaker 5: two thoughts. We will be responsible in all the ways 200 00:10:36,720 --> 00:10:42,160 Speaker 5: that clients and regulators and the community would want you 201 00:10:42,200 --> 00:10:46,880 Speaker 5: to be, and we will grow, maybe more conservatively than others. 202 00:10:46,920 --> 00:10:51,679 Speaker 5: But I just repeat again, Biave has had ten years 203 00:10:51,720 --> 00:10:56,280 Speaker 5: of fifteen billion plus net income consistently. 204 00:10:57,240 --> 00:10:59,080 Speaker 2: Where is back of America today? Give us a sense 205 00:10:59,120 --> 00:11:00,120 Speaker 2: of how you grade your. 206 00:11:00,720 --> 00:11:03,080 Speaker 3: Well, we have great potential. 207 00:11:03,120 --> 00:11:05,360 Speaker 4: But there's a phrase. 208 00:11:05,040 --> 00:11:07,960 Speaker 3: That Jim Collins, a great business writer, uses and what 209 00:11:08,040 --> 00:11:10,760 Speaker 3: I say, so, I got a lot of people asking me, 210 00:11:10,920 --> 00:11:12,959 Speaker 3: I just had our market presence all the time. You know, 211 00:11:13,040 --> 00:11:14,000 Speaker 3: what about fifteen years? 212 00:11:14,240 --> 00:11:15,160 Speaker 4: What are you proud of? What do you do? 213 00:11:15,200 --> 00:11:18,040 Speaker 3: I said, it's a nice start. What this company has 214 00:11:18,080 --> 00:11:20,800 Speaker 3: ahead of us is a lot because of what we've. 215 00:11:20,640 --> 00:11:22,760 Speaker 4: Done for the last two other years. 216 00:11:22,800 --> 00:11:24,240 Speaker 3: But we'll be done in the last fifteen years of 217 00:11:24,280 --> 00:11:24,600 Speaker 3: a team. 218 00:11:24,800 --> 00:11:26,400 Speaker 2: As you look forward to the next fifteen years, what 219 00:11:26,440 --> 00:11:28,599 Speaker 2: do you think that may hold for beg of America 220 00:11:28,640 --> 00:11:31,160 Speaker 2: and what kind of person that would need to lead 221 00:11:31,200 --> 00:11:33,400 Speaker 2: it going forward as opposed to what's lead of the best. 222 00:11:33,520 --> 00:11:36,439 Speaker 3: I think if you look ahead, the changes will come 223 00:11:36,480 --> 00:11:39,960 Speaker 3: from the data of the technology, the ability to transmit 224 00:11:40,000 --> 00:11:43,319 Speaker 3: that data, of the compute power, the ability to imitate 225 00:11:43,360 --> 00:11:46,679 Speaker 3: our humans thinking those are ahead of us. That implication 226 00:11:46,840 --> 00:11:53,520 Speaker 3: of voice recognition, computational power, algorithmic analysis, mathematical analysis, combined 227 00:11:53,600 --> 00:11:56,680 Speaker 3: with an understanding how humans can drive it makes the 228 00:11:56,679 --> 00:11:59,040 Speaker 3: next fifteen years a whole nother sea change and how 229 00:11:59,080 --> 00:12:01,719 Speaker 3: we operate and a person that will be operating that 230 00:12:02,200 --> 00:12:07,480 Speaker 3: has to be that wise and that current, that balanced 231 00:12:07,480 --> 00:12:09,520 Speaker 3: and that aggressive. And that's the key, which is how 232 00:12:09,559 --> 00:12:10,319 Speaker 3: do you push. 233 00:12:10,600 --> 00:12:14,200 Speaker 2: As moynihand positions Bank of America for the next fifteen years, 234 00:12:14,679 --> 00:12:16,920 Speaker 2: one of his priorities is to ensure that it is 235 00:12:16,960 --> 00:12:20,320 Speaker 2: both a global bank and a local one, that it 236 00:12:20,360 --> 00:12:23,480 Speaker 2: is thoroughly integrated into the communities it serves. 237 00:12:24,280 --> 00:12:28,480 Speaker 5: The whole idea was that we would set up a 238 00:12:28,520 --> 00:12:33,280 Speaker 5: situation where you'd have top line executives, but you'd also 239 00:12:33,360 --> 00:12:36,480 Speaker 5: have market presidents. And the idea behind that was that 240 00:12:36,520 --> 00:12:41,960 Speaker 5: market president would have a beat on that community, that economy. 241 00:12:42,400 --> 00:12:44,360 Speaker 5: How we were going to contribute to it, and by 242 00:12:44,360 --> 00:12:45,880 Speaker 5: the way, how we were going to get our fair 243 00:12:45,880 --> 00:12:49,640 Speaker 5: share of business from that community. So he's been very 244 00:12:49,640 --> 00:12:51,960 Speaker 5: focused and he takes it all the way down to 245 00:12:52,600 --> 00:12:57,200 Speaker 5: the community level to make sure that not only is 246 00:12:57,200 --> 00:12:59,240 Speaker 5: the reputation good, but the numbers are good. 247 00:13:01,360 --> 00:13:05,360 Speaker 2: Coming up, having your cake and eating it too. Literally 248 00:13:05,880 --> 00:13:09,080 Speaker 2: the promise of AI for healthy snack foods that taste 249 00:13:09,400 --> 00:13:12,880 Speaker 2: just as good as the other kind. That's next on 250 00:13:12,960 --> 00:13:24,760 Speaker 2: Wall Street Week. This is a story about having it all, 251 00:13:25,280 --> 00:13:28,959 Speaker 2: our favorite snack food, our trim figure, our healthy body, 252 00:13:29,040 --> 00:13:32,280 Speaker 2: and the health of the planet as well, all thanks 253 00:13:32,320 --> 00:13:36,280 Speaker 2: to the wonders of Generative AI, or at least that's 254 00:13:36,320 --> 00:13:41,000 Speaker 2: the plan. Why does the world need another snack food? 255 00:13:41,080 --> 00:13:42,120 Speaker 2: There are a lot of them out there. 256 00:13:42,200 --> 00:13:44,640 Speaker 6: The world needs another snack food because the world needs 257 00:13:44,640 --> 00:13:47,280 Speaker 6: a healthy snack food. They need a lot of healthy 258 00:13:47,320 --> 00:13:48,040 Speaker 6: snack foods. 259 00:13:49,120 --> 00:13:53,200 Speaker 2: Harold Schmidtz spent decades at Mars, the company responsible for 260 00:13:53,280 --> 00:13:56,280 Speaker 2: some of the most popular snacks in the world. He 261 00:13:56,400 --> 00:13:59,480 Speaker 2: is now a founding partner of the venture capital firm 262 00:14:00,080 --> 00:14:04,400 Speaker 2: arch Group, targeting the seven hundred billion dollar global snack market, 263 00:14:04,800 --> 00:14:08,679 Speaker 2: putting artificial intelligence at the center of snack food companies 264 00:14:08,920 --> 00:14:13,560 Speaker 2: like rivals. What does artificial intelligence add to the recipe? 265 00:14:13,720 --> 00:14:18,560 Speaker 6: What AI does is it actually makes this incredibly complex 266 00:14:18,800 --> 00:14:22,760 Speaker 6: mixture of chemistry biochemistry that then goes through food processing. 267 00:14:23,240 --> 00:14:28,120 Speaker 6: It actually makes it understandable, so product development can happen 268 00:14:28,600 --> 00:14:34,440 Speaker 6: exponentially faster in order to create exponentially better products that 269 00:14:34,560 --> 00:14:38,000 Speaker 6: are more cost effective for consumers in the marketplace. 270 00:14:38,480 --> 00:14:42,520 Speaker 2: If Schmidz succeeds in making healthier snacks, it couldn't come 271 00:14:42,640 --> 00:14:46,320 Speaker 2: at a better time. Studies show people continue to eat 272 00:14:46,400 --> 00:14:49,920 Speaker 2: more snack food even as they struggle with weight gains 273 00:14:49,920 --> 00:14:51,000 Speaker 2: that affect their health. 274 00:14:51,360 --> 00:14:55,880 Speaker 6: Consumers, for whatever reason, have less time for traditional meals 275 00:14:55,920 --> 00:14:58,080 Speaker 6: as we think about them, and so snack foods play 276 00:14:58,600 --> 00:15:02,680 Speaker 6: a really important role in filling these dietary occasions. 277 00:15:02,840 --> 00:15:06,440 Speaker 2: At the same time, obesity is increasing at alarming rates. 278 00:15:06,800 --> 00:15:07,760 Speaker 2: Are those two things. 279 00:15:07,600 --> 00:15:11,960 Speaker 6: Connected, So it is quite possible they are connected. What 280 00:15:12,040 --> 00:15:18,080 Speaker 6: we certainly know is that obesity is high and it 281 00:15:18,240 --> 00:15:23,320 Speaker 6: has been increasing. And diabetes type two diabetes specifically it's 282 00:15:23,360 --> 00:15:26,120 Speaker 6: been called a global epidemic is high and it has 283 00:15:26,240 --> 00:15:30,040 Speaker 6: been increasing. It is really really interesting that for the 284 00:15:30,080 --> 00:15:34,440 Speaker 6: first time in history, thanks to the pharmaceutical industry, we 285 00:15:34,600 --> 00:15:39,920 Speaker 6: actually see hints that this curve is bending downwards. 286 00:15:40,360 --> 00:15:43,880 Speaker 2: JP Morgan predicts that by twenty thirty nine percent of 287 00:15:43,920 --> 00:15:48,680 Speaker 2: the US population thirty million Americans will be on GLP 288 00:15:48,800 --> 00:15:52,120 Speaker 2: one drugs like ozembic and we gov. When you talk 289 00:15:52,160 --> 00:15:56,000 Speaker 2: about the golp ones, we gov or zempic, those other 290 00:15:56,080 --> 00:15:59,440 Speaker 2: things we know about, they do affect the demand, do 291 00:15:59,480 --> 00:16:01,680 Speaker 2: they not? And that sounds like it wouldn't be good 292 00:16:01,680 --> 00:16:04,040 Speaker 2: for somebody in snack food business, But people are eating 293 00:16:04,160 --> 00:16:05,960 Speaker 2: less snacks. That's not good. 294 00:16:06,280 --> 00:16:10,640 Speaker 6: Now, for the first time in human history, we actually 295 00:16:10,680 --> 00:16:14,880 Speaker 6: have a class of pharmaceuticals that's relatively safe and it 296 00:16:15,120 --> 00:16:20,160 Speaker 6: causes a decrease in food consumption. So in terms of demand, 297 00:16:20,440 --> 00:16:24,040 Speaker 6: consumer demand will go down for certain types of foods 298 00:16:24,120 --> 00:16:27,880 Speaker 6: that are less healthy, but consumer demand is going to 299 00:16:27,920 --> 00:16:31,800 Speaker 6: go up for foods that are more healthy. And frankly, 300 00:16:31,960 --> 00:16:35,360 Speaker 6: it's pretty clear from a business context that there's a 301 00:16:35,480 --> 00:16:38,560 Speaker 6: gap in the marketplace for these healthy foods. 302 00:16:39,120 --> 00:16:42,119 Speaker 2: Seeking healthy foods to fill this gap in the marketplace 303 00:16:42,200 --> 00:16:46,800 Speaker 2: became a quest for UC Davis' computer science professor Ilius Tacopolis, 304 00:16:47,320 --> 00:16:49,760 Speaker 2: not because it was his job, but because of his 305 00:16:49,840 --> 00:16:52,720 Speaker 2: experience with his son, who had to drop out of 306 00:16:52,760 --> 00:16:56,680 Speaker 2: school due to a debilitating condition. Doctors had no answers. 307 00:16:56,880 --> 00:16:59,760 Speaker 2: So he looked at the food his son was eating. 308 00:17:00,000 --> 00:17:03,720 Speaker 7: And started with different nutritionists. We design the diet and 309 00:17:03,760 --> 00:17:07,520 Speaker 7: we see nine days my sign was cured and he's 310 00:17:07,560 --> 00:17:10,480 Speaker 7: now fifteen. He never had this issue again and said, okay, 311 00:17:10,560 --> 00:17:13,800 Speaker 7: I'm sorry. That's it. Everything we do in PIPA is 312 00:17:13,840 --> 00:17:14,720 Speaker 7: all about food. 313 00:17:15,119 --> 00:17:18,600 Speaker 2: To the couple of company, PIPPA targets uses of AI 314 00:17:18,800 --> 00:17:21,879 Speaker 2: for food, working with a range of companies from the 315 00:17:22,000 --> 00:17:26,240 Speaker 2: largest consumer packaged goods companies to startups like Rivals, where 316 00:17:26,280 --> 00:17:29,960 Speaker 2: the focus is on fiber and protein over savory snacks 317 00:17:29,960 --> 00:17:32,240 Speaker 2: full of sugar, fat and starch. 318 00:17:32,920 --> 00:17:36,359 Speaker 7: Food is something that is underserved in terms of AI. 319 00:17:36,760 --> 00:17:40,160 Speaker 7: We are at the precipice or something really big. We 320 00:17:40,359 --> 00:17:43,000 Speaker 7: have and need to change the paradigm and we have 321 00:17:43,040 --> 00:17:48,679 Speaker 7: to be more sustainable going to our health and for 322 00:17:48,800 --> 00:17:52,960 Speaker 7: healthy society. We need to generate a food that can 323 00:17:53,040 --> 00:17:55,200 Speaker 7: keep us healthy in the long term. 324 00:17:55,359 --> 00:17:58,359 Speaker 2: There's the food industry checually. Some of the large food companies, 325 00:17:58,359 --> 00:18:00,119 Speaker 2: because they're very big, do they know there are on 326 00:18:00,160 --> 00:18:01,320 Speaker 2: the brink of something really big? 327 00:18:01,920 --> 00:18:04,760 Speaker 7: They do they have realized that, hey, you know what, 328 00:18:04,960 --> 00:18:07,840 Speaker 7: like maybe like there's a recording that's coming. 329 00:18:08,040 --> 00:18:11,199 Speaker 2: If I am a food company and I want to 330 00:18:11,200 --> 00:18:15,399 Speaker 2: develop a new food, what does AI allow me to 331 00:18:15,400 --> 00:18:17,120 Speaker 2: do that? Otherwise I could not. 332 00:18:17,200 --> 00:18:22,800 Speaker 7: Do Yeah, excellent question. So what AI does is figuring 333 00:18:22,880 --> 00:18:30,200 Speaker 7: out where you should focus. So imagine having all these variables, 334 00:18:30,359 --> 00:18:33,760 Speaker 7: all these parameters. I mean, where do you start and 335 00:18:33,800 --> 00:18:36,480 Speaker 7: what do you explore? Based on the velocity you have 336 00:18:36,520 --> 00:18:39,840 Speaker 7: with your experiments, your formulations are doing and tasting right. 337 00:18:40,000 --> 00:18:43,040 Speaker 7: So what AI can do is quickly and dirtily, of course, 338 00:18:43,440 --> 00:18:46,280 Speaker 7: make you focus on what matters. 339 00:18:46,359 --> 00:18:50,240 Speaker 2: In the pursuit of the food industry's holy trinity affordability, 340 00:18:50,520 --> 00:18:55,040 Speaker 2: scalability and taste. Sorting through the billions of possible ingredient 341 00:18:55,119 --> 00:18:59,680 Speaker 2: combinations and process parameters is hard enough without worrying about 342 00:18:59,720 --> 00:19:04,320 Speaker 2: neutral trition, let alone sustainability. But rivals must also hit 343 00:19:04,359 --> 00:19:08,000 Speaker 2: the mark on that familiar, satisfying crunch. 344 00:19:08,200 --> 00:19:11,800 Speaker 8: You can see that we got good filling in the center. 345 00:19:12,000 --> 00:19:15,560 Speaker 8: You got real nice cell structure so that the texture 346 00:19:15,640 --> 00:19:16,200 Speaker 8: is delicate. 347 00:19:16,680 --> 00:19:20,439 Speaker 2: Like Harold schmidtz Ralph Jerome spent decades at the Mars Company, 348 00:19:20,760 --> 00:19:23,840 Speaker 2: including as head of its chocolate research and development arm. 349 00:19:24,400 --> 00:19:27,239 Speaker 2: He's a co founder of rivals, and he showed us 350 00:19:27,240 --> 00:19:30,840 Speaker 2: the business end of applying AI to food processing, all 351 00:19:30,880 --> 00:19:33,520 Speaker 2: based around a machine called an extruder. 352 00:19:34,000 --> 00:19:38,800 Speaker 8: This machine will need, it will mix, it will hydrate, 353 00:19:39,000 --> 00:19:40,120 Speaker 8: it will cook. 354 00:19:40,080 --> 00:19:42,320 Speaker 4: It will cool, it will form. 355 00:19:42,200 --> 00:19:45,159 Speaker 8: It will shape. The problem with it is that a 356 00:19:45,200 --> 00:19:48,080 Speaker 8: lot of times it's more art than science. 357 00:19:48,600 --> 00:19:52,120 Speaker 2: It's been around since the nineteen thirties, a technology used 358 00:19:52,119 --> 00:19:55,520 Speaker 2: in some fifty percent of food products on grocery store 359 00:19:55,520 --> 00:19:56,440 Speaker 2: shelves today. 360 00:19:56,840 --> 00:20:00,960 Speaker 8: So typically in the industry, extruders have been run with 361 00:20:01,359 --> 00:20:05,399 Speaker 8: high starch components because it works, you know it. You 362 00:20:05,520 --> 00:20:08,000 Speaker 8: use starch and you get to the end of the 363 00:20:08,000 --> 00:20:11,320 Speaker 8: barrel and you get this beautiful expanded puff or chip 364 00:20:11,560 --> 00:20:16,000 Speaker 8: or whatever. The high carbill hydrate food d jore is. 365 00:20:16,960 --> 00:20:19,840 Speaker 2: It turns out that rival's objective to shift away from 366 00:20:19,840 --> 00:20:22,760 Speaker 2: snacks that are full of fat, unhealthy starches and sugars 367 00:20:22,880 --> 00:20:25,560 Speaker 2: to healthier ingredients is hard to do. 368 00:20:26,440 --> 00:20:30,520 Speaker 8: So the challenge is can you extrude high protein and 369 00:20:30,600 --> 00:20:35,520 Speaker 8: high fiber and get those textural characteristics. And by textual characteristics, 370 00:20:35,560 --> 00:20:39,280 Speaker 8: I mean the aeration, the delicate texture so that when 371 00:20:39,320 --> 00:20:41,320 Speaker 8: you bite on it you get nice crunch, so that 372 00:20:41,359 --> 00:20:43,640 Speaker 8: when you bite in it it's not a hard rock, 373 00:20:44,200 --> 00:20:48,520 Speaker 8: and that's what the aerration does. Starch out of an 374 00:20:48,560 --> 00:20:53,080 Speaker 8: extruder does that brilliantly, whereas protein and fiber do not. 375 00:20:53,359 --> 00:20:55,400 Speaker 8: Product is moving further and further down. 376 00:20:55,720 --> 00:20:58,800 Speaker 2: Jerome claims Rivals would have had to conduct five hundred 377 00:20:58,960 --> 00:21:02,879 Speaker 2: thousand experiments to develop its first products, so it turned 378 00:21:02,880 --> 00:21:05,760 Speaker 2: to AI to cut that number to only seventy one, 379 00:21:06,160 --> 00:21:09,560 Speaker 2: coming up with its Red Ranch, Tasty Taco, and late 380 00:21:09,680 --> 00:21:12,760 Speaker 2: Night Pizza flavors. With the data they've been collecting to 381 00:21:12,760 --> 00:21:17,560 Speaker 2: train their algorithms, Jerome and the Rivals AI engine produce affordable, 382 00:21:17,720 --> 00:21:19,240 Speaker 2: nutritious snacks. 383 00:21:19,760 --> 00:21:22,560 Speaker 7: If you have the right data to train it, it 384 00:21:22,640 --> 00:21:25,879 Speaker 7: will be able to figure out any complex relationship. So 385 00:21:25,920 --> 00:21:29,480 Speaker 7: what you're saying here is it's running a simulation with 386 00:21:29,840 --> 00:21:32,600 Speaker 7: some parameters. And what the AI is doing here is 387 00:21:32,600 --> 00:21:34,680 Speaker 7: actually it's going to optimize. It will take a mime 388 00:21:34,760 --> 00:21:37,640 Speaker 7: or two and it will run many many simulations and 389 00:21:37,680 --> 00:21:40,840 Speaker 7: it will say, okay, maybe you want to change to 390 00:21:40,960 --> 00:21:43,760 Speaker 7: die or you want to change the temperature. It gives 391 00:21:43,800 --> 00:21:48,080 Speaker 7: actually some new parameters, new value for the parameters for 392 00:21:48,200 --> 00:21:50,520 Speaker 7: you to test in the lab. So instead of you 393 00:21:50,640 --> 00:21:53,080 Speaker 7: trying to figure out okay, what should I do? What's 394 00:21:53,119 --> 00:21:56,160 Speaker 7: my next experiment? It actually does it for you by 395 00:21:56,240 --> 00:21:59,720 Speaker 7: running like thousands to millions of simulations in a matter 396 00:21:59,760 --> 00:22:00,040 Speaker 7: of me. 397 00:22:01,560 --> 00:22:05,000 Speaker 2: Rivals was created to apply AI technology to the manufacture 398 00:22:05,040 --> 00:22:08,400 Speaker 2: of snack foods, but it's far from the only food company, 399 00:22:08,600 --> 00:22:12,639 Speaker 2: big or small, that is pursuing the possibilities. As Alex 400 00:22:12,680 --> 00:22:14,200 Speaker 2: Frederick of Pitchbook told. 401 00:22:14,080 --> 00:22:17,359 Speaker 4: Us, from what I have seen, food companies are taking 402 00:22:17,440 --> 00:22:21,600 Speaker 4: quite a drastic approach to apply AI throughout their food production, 403 00:22:21,840 --> 00:22:26,760 Speaker 4: from concept development, consumer research R and D, and helping 404 00:22:27,080 --> 00:22:33,359 Speaker 4: to scale food production. Unilever, Craft Hends, PepsiCo, and Abmbev 405 00:22:33,400 --> 00:22:36,199 Speaker 4: are a number of examples of big companies who are 406 00:22:36,280 --> 00:22:40,080 Speaker 4: all heavily investing in and deploying AI models. 407 00:22:40,520 --> 00:22:44,600 Speaker 2: The leading international food companies have big advantages in size 408 00:22:44,640 --> 00:22:47,680 Speaker 2: and scale, in access to capital, and the amount of 409 00:22:47,720 --> 00:22:51,800 Speaker 2: proprietary data that they can use, but Schmidz thinks smaller 410 00:22:51,840 --> 00:22:55,280 Speaker 2: startups like Rivals also have some things going for them. 411 00:22:55,560 --> 00:23:00,199 Speaker 6: The beauty of Rivals with the AI component is the 412 00:23:00,280 --> 00:23:05,080 Speaker 6: AI actually enables Rivals to radically reduce its R and 413 00:23:05,160 --> 00:23:09,120 Speaker 6: D costs, its consumer insight, generation costs, and a whole 414 00:23:09,160 --> 00:23:12,520 Speaker 6: host of other costs. That in traditional food companies that 415 00:23:12,600 --> 00:23:15,959 Speaker 6: aren't using AI at the center, they have to put 416 00:23:16,000 --> 00:23:20,359 Speaker 6: a lot of money into supporting those operations in a 417 00:23:20,359 --> 00:23:23,480 Speaker 6: way that rivals actually can now put a lot less 418 00:23:23,680 --> 00:23:24,840 Speaker 6: exponentially less. 419 00:23:25,160 --> 00:23:28,200 Speaker 2: The road to better, healthier food is littered with new 420 00:23:28,240 --> 00:23:31,119 Speaker 2: inventions that have been overhyped or have fallen by the 421 00:23:31,119 --> 00:23:35,159 Speaker 2: wayside over the years. Most recently, food tech investors and 422 00:23:35,280 --> 00:23:38,680 Speaker 2: vcs lost billions of dollars on a promise that technology 423 00:23:38,720 --> 00:23:43,000 Speaker 2: could be used to replace animals with plants and cultivated cells. 424 00:23:43,520 --> 00:23:47,199 Speaker 6: So these companies that we're talking about in terms of 425 00:23:47,280 --> 00:23:51,399 Speaker 6: the plant based and cell culture meat, those companies, the 426 00:23:51,520 --> 00:23:57,679 Speaker 6: taste was subpar, the nutrition in many cases is subpar, 427 00:23:58,160 --> 00:24:03,919 Speaker 6: the affordability is subpar, the scale ability, the ability to 428 00:24:03,960 --> 00:24:07,439 Speaker 6: manufacture for millions, tens of millions, hundreds of millions of 429 00:24:07,440 --> 00:24:10,720 Speaker 6: people's is subpar. And it actually turns out that the 430 00:24:10,800 --> 00:24:15,840 Speaker 6: environmental sustainability aspects are sometimes subpar as well. So it's 431 00:24:15,880 --> 00:24:19,439 Speaker 6: a miss on all of these key points that actually 432 00:24:19,520 --> 00:24:23,040 Speaker 6: a successful company in the food space needs to deliver on. 433 00:24:23,280 --> 00:24:27,840 Speaker 4: There's a long list of technology failures in food. One 434 00:24:27,960 --> 00:24:32,360 Speaker 4: good example is Olestra. Anyone who was around and snacking 435 00:24:32,359 --> 00:24:35,160 Speaker 4: on foods in the nineties is likely familiar with Olestra. 436 00:24:35,640 --> 00:24:38,560 Speaker 4: This was a fat replacement product that was used in 437 00:24:38,680 --> 00:24:41,800 Speaker 4: chips and other snack foods, and it was found to 438 00:24:41,840 --> 00:24:46,920 Speaker 4: cause significant gastro distress and was fairly quickly pulled off 439 00:24:46,960 --> 00:24:47,480 Speaker 4: the market. 440 00:24:48,040 --> 00:24:51,760 Speaker 2: But Rival's chief marketing officer, Erica Patney, who was the 441 00:24:51,800 --> 00:24:55,679 Speaker 2: marketing force behind the Kind Healthy Snacks brand, says that 442 00:24:55,840 --> 00:24:58,639 Speaker 2: Rivals is off to a strong start with its launch 443 00:24:58,720 --> 00:24:59,040 Speaker 2: on the. 444 00:24:58,960 --> 00:25:01,520 Speaker 9: West Coast, So we're going to be about three x 445 00:25:01,560 --> 00:25:04,760 Speaker 9: what we were last year this year in terms of sales. 446 00:25:04,960 --> 00:25:07,879 Speaker 9: Our repeat rate tells us that we're growing our velocity, 447 00:25:07,920 --> 00:25:10,480 Speaker 9: which is one of the key metrics at retail. So 448 00:25:10,520 --> 00:25:12,880 Speaker 9: we are now in about two thousand stores. 449 00:25:13,240 --> 00:25:15,800 Speaker 2: Do you market the AI piece? Because AI has so 450 00:25:16,000 --> 00:25:18,320 Speaker 2: much in the zeitgeist right now, people will try it 451 00:25:18,359 --> 00:25:19,280 Speaker 2: just because it's AI. 452 00:25:19,119 --> 00:25:23,720 Speaker 9: Generated, So to consumers, we do not, And our belief 453 00:25:23,800 --> 00:25:28,000 Speaker 9: is that the consumer one doesn't really need to know 454 00:25:28,840 --> 00:25:31,840 Speaker 9: how is it made in terms of what equipment is 455 00:25:31,960 --> 00:25:36,680 Speaker 9: used and what technology goes into R and D and innovation. 456 00:25:36,880 --> 00:25:39,359 Speaker 9: What they really care about is how does it taste, 457 00:25:39,520 --> 00:25:41,600 Speaker 9: what does it do for me, and what does it cost? 458 00:25:42,040 --> 00:25:46,480 Speaker 6: This is a multi billion dollar opportunity rivals is, and 459 00:25:46,520 --> 00:25:50,840 Speaker 6: it's a profitable, multi billion dollar opportunity that can be 460 00:25:50,960 --> 00:25:55,879 Speaker 6: profitable in the context of what the tech VC world 461 00:25:56,000 --> 00:25:59,280 Speaker 6: in Silicon Valley would sort of traditionally think of. I 462 00:25:59,280 --> 00:26:02,360 Speaker 6: think one way to be really successful in the consumer 463 00:26:02,400 --> 00:26:06,199 Speaker 6: products industry and food is to realize two things. People 464 00:26:06,280 --> 00:26:11,240 Speaker 6: want to live forever, and they want to feel great 465 00:26:11,440 --> 00:26:12,880 Speaker 6: while they're living forever. 466 00:26:14,480 --> 00:26:17,159 Speaker 2: Coming up, David Goura is here for a look forward 467 00:26:17,240 --> 00:26:20,159 Speaker 2: to what twenty twenty five has to offer to investors 468 00:26:20,640 --> 00:26:23,960 Speaker 2: and to the world. That's next on Wall Street Week 469 00:26:30,720 --> 00:26:33,200 Speaker 2: David Goura is here for a look forward to what 470 00:26:33,240 --> 00:26:36,879 Speaker 2: twenty twenty five has to offer to investors and to 471 00:26:36,960 --> 00:26:37,720 Speaker 2: the world. 472 00:26:37,840 --> 00:26:40,760 Speaker 10: This is a story about the intersection of geopolitical risks 473 00:26:40,840 --> 00:26:44,320 Speaker 10: and the global economy. Every year, Eurasia Group publishes a 474 00:26:44,359 --> 00:26:47,600 Speaker 10: report forecasting the biggest risks the world is likely to face. 475 00:26:48,119 --> 00:26:50,600 Speaker 10: Cliff Kupchen, chairman of eur Asia Group, says that the 476 00:26:50,640 --> 00:26:53,080 Speaker 10: world is entering a time of danger that is on 477 00:26:53,240 --> 00:26:56,040 Speaker 10: par with the nineteen thirties and the early Cold War. 478 00:26:57,520 --> 00:27:01,560 Speaker 11: Jizero means lack of a group of countries that can 479 00:27:01,640 --> 00:27:06,679 Speaker 11: impose our international order. The main reason that it is 480 00:27:07,520 --> 00:27:10,760 Speaker 11: reached a crescendo this year is the victory of Donald Trump. 481 00:27:10,880 --> 00:27:16,320 Speaker 11: He cements, he cements and sort of makes permanent much 482 00:27:16,320 --> 00:27:19,280 Speaker 11: more permanent, the victory of the G zero. It was 483 00:27:19,280 --> 00:27:24,880 Speaker 11: a long term trend. Retrenchment is a factive American life. 484 00:27:24,920 --> 00:27:27,960 Speaker 11: It's been around since since Trump won. But now we 485 00:27:28,000 --> 00:27:29,680 Speaker 11: have a Donald Trump that's much stronger than he was 486 00:27:29,760 --> 00:27:33,679 Speaker 11: last time. Is not going to support US alliances and 487 00:27:33,680 --> 00:27:36,120 Speaker 11: the US role in the world. If the US doesn't lead, 488 00:27:36,880 --> 00:27:40,000 Speaker 11: there's no alternative. China is the other great power. China 489 00:27:40,160 --> 00:27:42,200 Speaker 11: probably doesn't want to lead. From what I can tell, 490 00:27:42,200 --> 00:27:43,120 Speaker 11: it's not ready to lead. 491 00:27:43,440 --> 00:27:46,159 Speaker 10: You mentioned China. That's another risk the relationship between the 492 00:27:46,280 --> 00:27:49,119 Speaker 10: US and China, and you can forecast a decoupling of 493 00:27:49,119 --> 00:27:52,000 Speaker 10: that relationship. Do you see that as a return to 494 00:27:52,040 --> 00:27:54,000 Speaker 10: the way things were during the first Trump term or 495 00:27:54,000 --> 00:27:56,520 Speaker 10: does that look different under the second one? 496 00:27:56,840 --> 00:27:59,639 Speaker 11: It looks similar but worse, and that's a real problem. 497 00:28:00,200 --> 00:28:04,240 Speaker 11: Unmanaged a coupling or breakdown. The main trigger of it 498 00:28:04,280 --> 00:28:05,920 Speaker 11: is going to be the Trump tariffs, which are going 499 00:28:05,920 --> 00:28:07,520 Speaker 11: to be like a slap on the face of the Chinese. 500 00:28:07,520 --> 00:28:09,520 Speaker 11: I think it's going to be you know, big numbers 501 00:28:09,560 --> 00:28:11,440 Speaker 11: will come out with fifty to sixty pern top rate, 502 00:28:11,600 --> 00:28:14,359 Speaker 11: maybe a twenty five percent average rate, but they're going 503 00:28:14,440 --> 00:28:18,359 Speaker 11: to be big numbers. The supply chains hit to global 504 00:28:18,359 --> 00:28:22,320 Speaker 11: growth and more structural suspicion between the two sides, which 505 00:28:22,359 --> 00:28:23,760 Speaker 11: is kind of the last thing we need because it 506 00:28:23,800 --> 00:28:26,080 Speaker 11: makes inadvertent escalation that much more likely. 507 00:28:26,240 --> 00:28:29,440 Speaker 10: Markets have been so focused on artificial intelligence for several 508 00:28:29,480 --> 00:28:31,840 Speaker 10: years now. It's something that Erasier Group has warned about 509 00:28:31,920 --> 00:28:33,520 Speaker 10: as a risk in years past. 510 00:28:33,359 --> 00:28:36,360 Speaker 11: Well a boon and a risky, but a mixed back. 511 00:28:36,520 --> 00:28:41,600 Speaker 10: Here you're writing about AI unbound. There is a group 512 00:28:41,680 --> 00:28:44,600 Speaker 10: of President Lex supporters who are enthusiastic about not having 513 00:28:44,680 --> 00:28:45,880 Speaker 10: the kind of regulatory hurdles that. 514 00:28:45,880 --> 00:28:47,280 Speaker 11: Have been absolutely administration. 515 00:28:47,720 --> 00:28:50,200 Speaker 10: What are the consequences potential consequences of not having them 516 00:28:50,200 --> 00:28:52,000 Speaker 10: in placement It comes to the development of AI. 517 00:28:52,160 --> 00:28:56,640 Speaker 11: What we're going to see this year is less attempt 518 00:28:56,640 --> 00:29:03,040 Speaker 11: at regulation while AI continues it's relentless march forward to 519 00:29:03,080 --> 00:29:06,320 Speaker 11: become more and more powerful. We do have Trump and 520 00:29:06,880 --> 00:29:10,200 Speaker 11: his friends from Silicon Valley, especially Elon Musk, who are 521 00:29:10,320 --> 00:29:12,920 Speaker 11: strong supporters of this, and that'll be one of the 522 00:29:13,000 --> 00:29:15,640 Speaker 11: jet skis that AI is going to be on this year. 523 00:29:15,920 --> 00:29:18,360 Speaker 10: Dovetail that with what we're talking about a few minutes ago, 524 00:29:18,400 --> 00:29:21,600 Speaker 10: that is the US's relationship with China, and how does 525 00:29:21,640 --> 00:29:25,480 Speaker 10: that binary play out in the world of AI. The 526 00:29:25,560 --> 00:29:28,120 Speaker 10: race continues between these two countries when it comes to 527 00:29:28,200 --> 00:29:30,360 Speaker 10: developing and improving this technology. 528 00:29:30,600 --> 00:29:35,680 Speaker 11: Probably the greatest risk from lack of regulation is the 529 00:29:35,760 --> 00:29:40,240 Speaker 11: US China relationship. They share a common interest in trying 530 00:29:40,280 --> 00:29:44,360 Speaker 11: to prevent autonomous weapons, in trying to prevent the intersection 531 00:29:44,480 --> 00:29:50,360 Speaker 11: of AI and military activity. But as the relationship gets worse, 532 00:29:50,520 --> 00:29:54,360 Speaker 11: even this nascent AI working group which are sharing this year, 533 00:29:54,640 --> 00:29:55,600 Speaker 11: is likely to disappear. 534 00:29:56,080 --> 00:29:59,840 Speaker 10: Risks to the global economy are growing along with geopolitical threats. 535 00:30:00,320 --> 00:30:04,000 Speaker 10: Morgan Stanley's chief Global Economists Seth Carpenter thinks these risks 536 00:30:04,040 --> 00:30:06,120 Speaker 10: will define the coming year in economics. 537 00:30:07,040 --> 00:30:09,960 Speaker 12: Yeah, I think risks are sort of the key theme 538 00:30:10,000 --> 00:30:11,680 Speaker 12: that we highlighted when we at the end of each 539 00:30:11,720 --> 00:30:14,680 Speaker 12: year we did this year ahead publication to try to 540 00:30:14,720 --> 00:30:17,040 Speaker 12: give a roadmap to markets. But this time I think 541 00:30:17,400 --> 00:30:19,680 Speaker 12: I realized early on we couldn't provide a roadmap. All 542 00:30:19,720 --> 00:30:22,240 Speaker 12: we could basically do is talk a little bit about 543 00:30:22,240 --> 00:30:24,760 Speaker 12: the risks, and it will be a year to forecast 544 00:30:24,800 --> 00:30:29,000 Speaker 12: early and forecast often. I think the geopolitical risks are manifold. 545 00:30:29,040 --> 00:30:32,760 Speaker 12: I think just the policy discussions that we've been talking 546 00:30:32,800 --> 00:30:36,000 Speaker 12: about coming out of the new administration, for trade policy, 547 00:30:36,040 --> 00:30:40,680 Speaker 12: for immigration policy, for fiscal policy, and maybe for regulatory policy, 548 00:30:40,720 --> 00:30:43,400 Speaker 12: all of those are big unknowns that I think give 549 00:30:43,480 --> 00:30:46,240 Speaker 12: lots of scope for lots of different outcomes for the 550 00:30:46,400 --> 00:30:48,160 Speaker 12: US economy and the global economy. 551 00:30:48,400 --> 00:30:51,360 Speaker 10: We're getting a sense of what President LEC. Trump's policies 552 00:30:51,400 --> 00:30:53,440 Speaker 10: might be when it comes to trade and immigration. So 553 00:30:53,520 --> 00:30:56,040 Speaker 10: let's take those two in kind, and I'll ask you, 554 00:30:56,080 --> 00:30:58,800 Speaker 10: from what we've heard about terrafty might have put in place, 555 00:30:58,800 --> 00:31:01,640 Speaker 10: the way he might approach trade, what effects could that 556 00:31:01,680 --> 00:31:03,120 Speaker 10: have on the global economy? 557 00:31:03,160 --> 00:31:05,320 Speaker 12: The way the tariffs are put into place. We've said 558 00:31:05,360 --> 00:31:08,400 Speaker 12: if all the tariffs were imposed right away on day one, 559 00:31:08,800 --> 00:31:11,120 Speaker 12: could probably shave off close to one and a half 560 00:31:11,160 --> 00:31:14,280 Speaker 12: percent from GDP growth for the year and probably push 561 00:31:14,360 --> 00:31:18,040 Speaker 12: up inflation nine tenths, so almost a full percentage point. 562 00:31:18,440 --> 00:31:21,680 Speaker 12: But looking at what happened when Trump was president, before 563 00:31:21,720 --> 00:31:25,080 Speaker 12: there were waves of tariffs being implemented, Listening to what 564 00:31:26,160 --> 00:31:31,160 Speaker 12: the designated Treasury Secretary Scott Besson has said maybe phase 565 00:31:31,240 --> 00:31:33,040 Speaker 12: it in over time, and so if you assume that's 566 00:31:33,120 --> 00:31:36,800 Speaker 12: what's going to happen, then it changes the calculus a bit. 567 00:31:36,920 --> 00:31:41,040 Speaker 12: So evidence from twenty eighteen, twenty seventeen, twenty eighteen, twenty 568 00:31:41,120 --> 00:31:44,680 Speaker 12: nineteen suggests that the inflationary effect of tariffs happens fairly quickly, 569 00:31:44,800 --> 00:31:48,360 Speaker 12: two or three months. The hit to growth, though, happens 570 00:31:48,400 --> 00:31:51,360 Speaker 12: over a longer period of time, maybe two or three quarters. 571 00:31:51,840 --> 00:31:54,480 Speaker 12: And so if we really do get that phased in approach, 572 00:31:54,520 --> 00:31:57,800 Speaker 12: then the real hit to growth probably doesn't happen until 573 00:31:57,800 --> 00:31:58,640 Speaker 12: twenty twenty six. 574 00:31:59,080 --> 00:32:01,720 Speaker 10: So justice there could beulf between the rhetoric that we've 575 00:32:01,720 --> 00:32:03,360 Speaker 10: heard from the president elect and the policies that might 576 00:32:03,360 --> 00:32:05,560 Speaker 10: have been in place when it comes to trade. Same 577 00:32:05,600 --> 00:32:07,840 Speaker 10: seems to be true of immigration policy as well. But 578 00:32:07,960 --> 00:32:09,600 Speaker 10: play that out for me. You talked a bit about 579 00:32:09,640 --> 00:32:12,960 Speaker 10: the effects that might have on inflation. Certainly the Fed's 580 00:32:12,960 --> 00:32:15,320 Speaker 10: focus right now is, if not squarely on mostly on 581 00:32:15,360 --> 00:32:18,480 Speaker 10: the labor market, on the jobs market. What effects could 582 00:32:18,840 --> 00:32:21,880 Speaker 10: kind of a more strict draconian immigration policy have on 583 00:32:22,040 --> 00:32:24,080 Speaker 10: for the US economy and the global economy as well. 584 00:32:24,200 --> 00:32:26,800 Speaker 12: I don't think there's as big a gulf between rhetoric 585 00:32:26,840 --> 00:32:30,520 Speaker 12: and likely policy. For immigration. I think it's another situation 586 00:32:30,560 --> 00:32:35,720 Speaker 12: where there's very clear and consistent views about restricting restraining 587 00:32:35,760 --> 00:32:38,760 Speaker 12: immigration in the United States. It's very tricky. So one 588 00:32:38,800 --> 00:32:42,440 Speaker 12: of the reasons why we had three eight percent growth 589 00:32:42,440 --> 00:32:44,600 Speaker 12: in twenty twenty three, close to three percent growth in 590 00:32:44,640 --> 00:32:47,880 Speaker 12: twenty twenty four, and yet inflation continuing to come down 591 00:32:48,000 --> 00:32:50,959 Speaker 12: was because we had this large increase in the labor supply. 592 00:32:51,560 --> 00:32:54,040 Speaker 12: You'd have a restriction in labor supply, which has got 593 00:32:54,040 --> 00:32:56,760 Speaker 12: to be bad for growth. It's got a way on 594 00:32:56,800 --> 00:32:59,680 Speaker 12: growth a bit. And at the same time you'd have 595 00:32:59,720 --> 00:33:03,040 Speaker 12: to have of some reduction in demand I mean people 596 00:33:03,040 --> 00:33:05,520 Speaker 12: coming to the country by things. Both of those go 597 00:33:05,600 --> 00:33:08,440 Speaker 12: in the same direction in terms of overall economic growth. Now, 598 00:33:08,600 --> 00:33:10,680 Speaker 12: which of those would win out when it comes to inflation, 599 00:33:10,760 --> 00:33:13,800 Speaker 12: I think is a tricky question. We think the evidence 600 00:33:13,840 --> 00:33:16,120 Speaker 12: is reasonably clear than the US so far has been 601 00:33:16,120 --> 00:33:19,080 Speaker 12: more of a supply story than a demand story, and 602 00:33:19,160 --> 00:33:21,240 Speaker 12: so as a result, I would suspect we're going to 603 00:33:21,280 --> 00:33:24,719 Speaker 12: have an uplift to inflation. And so this disinflationary process 604 00:33:24,720 --> 00:33:28,320 Speaker 12: we've seen coming off those really highs of inflation that 605 00:33:28,400 --> 00:33:31,360 Speaker 12: everyone was worried about. We've come down dramatically from there. 606 00:33:31,400 --> 00:33:33,720 Speaker 10: Another trend that we've seen recently is democracy being challenged 607 00:33:33,720 --> 00:33:38,600 Speaker 10: in some major countries around the world, about France, South Korea, Canada. 608 00:33:38,720 --> 00:33:41,440 Speaker 10: Does that affect the investibility of those countries that kind 609 00:33:41,440 --> 00:33:43,480 Speaker 10: of political instability that we've seen so much as we. 610 00:33:43,440 --> 00:33:47,040 Speaker 12: Have, whether it's financial assets and sort of that kind 611 00:33:47,120 --> 00:33:51,680 Speaker 12: of financial investment, or if it's direct investment, capital spending 612 00:33:52,040 --> 00:33:56,920 Speaker 12: and development of factories, certainty is definitely more attractive than uncertainty. 613 00:33:57,400 --> 00:33:58,840 Speaker 12: And so I don't think you need to get to 614 00:33:58,880 --> 00:34:02,240 Speaker 12: any sort of extreme case where democracy is threatened the 615 00:34:02,240 --> 00:34:05,200 Speaker 12: way you phrase it, I think just a lack of 616 00:34:05,240 --> 00:34:08,239 Speaker 12: clarity about where policy is going, a lack of ability 617 00:34:08,280 --> 00:34:10,960 Speaker 12: to plan two, three, and five years down the road. 618 00:34:10,960 --> 00:34:14,719 Speaker 12: I think all of that does change the investability of 619 00:34:14,760 --> 00:34:20,239 Speaker 12: any given economy, but it doesn't necessarily have to be 620 00:34:20,280 --> 00:34:22,600 Speaker 12: a one zero kind of equation, right. Often what we 621 00:34:22,680 --> 00:34:26,200 Speaker 12: see is if there's more uncertainty, then investors require a 622 00:34:26,239 --> 00:34:29,080 Speaker 12: bit more of a risk premium in order to invest, 623 00:34:29,120 --> 00:34:31,120 Speaker 12: and so it just might mean that it's more expensive 624 00:34:31,200 --> 00:34:34,160 Speaker 12: or more costly to do that investment. One of the 625 00:34:34,280 --> 00:34:38,000 Speaker 12: risks we pointed to for the outlook for Europe and 626 00:34:38,040 --> 00:34:41,440 Speaker 12: to some extent the UK is with the geopolitical uncertainty 627 00:34:41,480 --> 00:34:45,120 Speaker 12: that's going on right there on Europe storstep how much 628 00:34:45,160 --> 00:34:48,640 Speaker 12: of an effect could that have on business capital formation 629 00:34:48,920 --> 00:34:51,399 Speaker 12: plans that caused people to say, hey, let's just wait 630 00:34:51,480 --> 00:34:53,919 Speaker 12: a minute. Too much is going on, it's hard to plan. 631 00:34:54,000 --> 00:34:55,920 Speaker 12: Let's do a little bit less. And if that happens, well, 632 00:34:55,960 --> 00:34:59,160 Speaker 12: then you get this reinforcing cycle. You get less investment spending, 633 00:34:59,200 --> 00:35:02,040 Speaker 12: which leads to less output, leads to less employment, which 634 00:35:02,080 --> 00:35:04,880 Speaker 12: then also ends up weighing on the economy. That's the 635 00:35:04,920 --> 00:35:05,880 Speaker 12: real tricky part. 636 00:35:06,200 --> 00:35:08,680 Speaker 10: I'll close just by asking you about AI. And we've 637 00:35:08,680 --> 00:35:12,200 Speaker 10: seen such fervor and enthusiasm for artificial intelligence since seeing 638 00:35:12,280 --> 00:35:14,040 Speaker 10: the progress that's been made over the last year or two. 639 00:35:15,160 --> 00:35:17,280 Speaker 10: You think a lot, I imagine just about the effects that'll 640 00:35:17,320 --> 00:35:21,040 Speaker 10: have on the economy broadly, particularly when it comes to productivity. 641 00:35:21,600 --> 00:35:24,000 Speaker 10: Are we closer now as we round the quarter to 642 00:35:24,000 --> 00:35:26,879 Speaker 10: twenty twenty five to having an understanding of the kind 643 00:35:26,880 --> 00:35:28,400 Speaker 10: of effects, the kind of impacts they're going to have 644 00:35:28,520 --> 00:35:29,520 Speaker 10: on the economy. 645 00:35:29,640 --> 00:35:31,319 Speaker 12: I think yes and no. So I think in the 646 00:35:31,320 --> 00:35:33,560 Speaker 12: short run. The answer is yes, in the sense that 647 00:35:34,440 --> 00:35:38,960 Speaker 12: we've seen a big boom in investment for power generation 648 00:35:39,160 --> 00:35:41,640 Speaker 12: and for data centers, and we are starting to see 649 00:35:41,680 --> 00:35:45,280 Speaker 12: more and more some use cases where there's actual application 650 00:35:45,320 --> 00:35:48,440 Speaker 12: of AI where it could start to help promote some 651 00:35:48,520 --> 00:35:50,719 Speaker 12: productivity on the one hand, and on the other hand 652 00:35:50,800 --> 00:35:54,160 Speaker 12: in terms of the demand for investment, well there's been 653 00:35:54,160 --> 00:35:56,120 Speaker 12: a little bit of a boost to GDP because of 654 00:35:56,160 --> 00:35:58,000 Speaker 12: that as well. So I think we're starting to get 655 00:35:58,000 --> 00:35:59,480 Speaker 12: a little bit of a handle on what's going on. 656 00:35:59,520 --> 00:36:02,360 Speaker 12: But that's the first wave. What I find interesting, and 657 00:36:02,360 --> 00:36:05,719 Speaker 12: the people who are true experts on technological development and 658 00:36:05,760 --> 00:36:09,239 Speaker 12: diffusion will tell you the real gains happen, the real 659 00:36:09,239 --> 00:36:14,480 Speaker 12: transformations happen when new industries, new business models are created 660 00:36:14,800 --> 00:36:18,040 Speaker 12: that couldn't have been conceived of before the new innovation happened. 661 00:36:18,040 --> 00:36:20,680 Speaker 12: And I think that's where we still don't have any 662 00:36:20,719 --> 00:36:23,880 Speaker 12: idea exactly what's going to happen. Think back to the 663 00:36:24,440 --> 00:36:29,200 Speaker 12: mid two thousands when smartphones were first created. If at 664 00:36:29,239 --> 00:36:31,200 Speaker 12: the time it was super interesting. You could look on 665 00:36:31,239 --> 00:36:33,120 Speaker 12: your phone, you can get the weather, you can get 666 00:36:33,160 --> 00:36:36,640 Speaker 12: email on your phone. We're here in Manhattan, and what 667 00:36:36,680 --> 00:36:39,560 Speaker 12: I find remarkable is you walk across any street and 668 00:36:39,560 --> 00:36:42,200 Speaker 12: you'll dodge four or five or six delivery people who 669 00:36:42,280 --> 00:36:45,360 Speaker 12: have at a smartphone on the handlebars of their bicycles 670 00:36:45,360 --> 00:36:48,399 Speaker 12: and doing it deliveries exactly. But those deliveries now are 671 00:36:48,400 --> 00:36:51,680 Speaker 12: optimized for efficiency across one dimension or another, and it's 672 00:36:51,719 --> 00:36:53,440 Speaker 12: all available in a way that no one could have 673 00:36:53,480 --> 00:36:55,960 Speaker 12: conceived of when we came into two thousands. So I 674 00:36:55,960 --> 00:36:58,480 Speaker 12: think there are lots of use cases that aren't even 675 00:36:58,520 --> 00:37:01,359 Speaker 12: conceivable yet that we remain to be seen. 676 00:37:02,520 --> 00:37:04,359 Speaker 2: That does it for us Here at Wall Street Week, 677 00:37:04,560 --> 00:37:07,759 Speaker 2: I'm David Weston. This is Bloomberg. See you next week 678 00:37:07,800 --> 00:37:22,840 Speaker 2: for more stories of capitalism.