1 00:00:00,240 --> 00:00:02,440 Speaker 1: This is Bloomberg Wall Street Week. 2 00:00:02,560 --> 00:00:04,480 Speaker 2: We may not have an overall recession, We're having a 3 00:00:04,559 --> 00:00:06,840 Speaker 2: rolling recession. To KNYE, roll looks pretty strongly. It is 4 00:00:06,840 --> 00:00:09,440 Speaker 2: when it comes to jobs, the financial stories that shape 5 00:00:09,440 --> 00:00:12,840 Speaker 2: our world. Three major regional bank failures send shockwaves through 6 00:00:12,880 --> 00:00:14,960 Speaker 2: the banking system. We're all trying to figure out what 7 00:00:15,040 --> 00:00:17,000 Speaker 2: to make of generative AI. 8 00:00:16,920 --> 00:00:19,320 Speaker 1: Through the eyes of the most influential voices. 9 00:00:19,440 --> 00:00:22,400 Speaker 2: Welcome down, Doctor Paul Krugman, Ryan moynihan, Bank of America, 10 00:00:22,560 --> 00:00:25,320 Speaker 2: deebro Laiir of the Paulson Institute, Welen Hubbard of the 11 00:00:25,360 --> 00:00:26,280 Speaker 2: Columbia Business School. 12 00:00:26,280 --> 00:00:30,160 Speaker 1: Bloomberg Wall Street Week with David Weston from Bloomberg Radio. 13 00:00:30,320 --> 00:00:34,040 Speaker 2: Are we going forward backward or just marching in place? 14 00:00:34,479 --> 00:00:38,400 Speaker 2: This is Bloomberg Wall Street Week. I'm David Weston. This 15 00:00:38,479 --> 00:00:41,839 Speaker 2: week's special contributor Larry Summers On renewing his campaign with 16 00:00:41,960 --> 00:00:44,760 Speaker 2: Bono on behalf of the world's poorest nations. 17 00:00:45,840 --> 00:00:49,640 Speaker 3: This is a moment when show me the money. 18 00:00:50,680 --> 00:00:53,520 Speaker 2: Marty schav has a Six Street Partners on what AI 19 00:00:53,640 --> 00:00:55,640 Speaker 2: could mean for the world of finance. 20 00:00:56,520 --> 00:01:00,520 Speaker 4: At some fundamental level, it's just math. This time it 21 00:01:00,640 --> 00:01:02,360 Speaker 4: actually is different. 22 00:01:03,000 --> 00:01:06,040 Speaker 2: And former President of Brown Ruth Simmons, I'm making sure 23 00:01:06,120 --> 00:01:09,880 Speaker 2: we're giving our future workforce what they need to succeed. 24 00:01:10,760 --> 00:01:16,880 Speaker 5: I believe firmly that corporations have to be along with 25 00:01:16,959 --> 00:01:19,400 Speaker 5: the universities leading this struggle. 26 00:01:33,200 --> 00:01:35,399 Speaker 2: It was a week of movement, but not always in 27 00:01:35,400 --> 00:01:38,520 Speaker 2: the same direction, and sometimes not in any particular direction 28 00:01:38,760 --> 00:01:42,240 Speaker 2: at all. ECV took a modest step forward in its 29 00:01:42,280 --> 00:01:45,400 Speaker 2: battle with inflation by raising another twenty five basis points. 30 00:01:45,880 --> 00:01:48,480 Speaker 6: The focus is probably going to move a bit more 31 00:01:48,560 --> 00:01:53,040 Speaker 6: to the duration, but it is not to say, because 32 00:01:53,080 --> 00:01:56,320 Speaker 6: we can't say that now that we are at peak. 33 00:01:57,120 --> 00:01:59,280 Speaker 2: ARM took a big step forward as it went to 34 00:01:59,320 --> 00:02:04,040 Speaker 2: market with it. You can't really run AI without ARM. 35 00:02:04,400 --> 00:02:07,120 Speaker 2: The auto industry took a big step backward as the 36 00:02:07,160 --> 00:02:09,840 Speaker 2: UAW failed to come to terms of Detroit Big three 37 00:02:09,919 --> 00:02:11,880 Speaker 2: automakers and moved to strike. 38 00:02:12,400 --> 00:02:14,959 Speaker 3: If we need to go all out, we will. 39 00:02:15,520 --> 00:02:16,840 Speaker 7: Everything is on the table. 40 00:02:17,080 --> 00:02:20,240 Speaker 5: From job security, we have jobs for all of our 41 00:02:20,320 --> 00:02:22,040 Speaker 5: people as we make this transformation. 42 00:02:22,800 --> 00:02:25,840 Speaker 2: The US economy more or less continue to march in place. 43 00:02:26,040 --> 00:02:28,920 Speaker 2: As CPI numbers came in just about as expected, with 44 00:02:29,000 --> 00:02:31,959 Speaker 2: inflation higher than the FED once but lower than some 45 00:02:32,120 --> 00:02:32,639 Speaker 2: ad feared. 46 00:02:33,200 --> 00:02:36,560 Speaker 8: The level of inflation that we're going to be forecasting 47 00:02:36,680 --> 00:02:41,480 Speaker 8: for the year is well above what people forecast at 48 00:02:42,280 --> 00:02:42,919 Speaker 8: the beginning. 49 00:02:43,680 --> 00:02:46,080 Speaker 2: And maybe the biggest step of all this week, at 50 00:02:46,160 --> 00:02:48,880 Speaker 2: least for New York football and for a future Hall 51 00:02:48,960 --> 00:02:51,440 Speaker 2: of Famer, was the one Aaron Rodgers took on Monday 52 00:02:51,480 --> 00:02:54,119 Speaker 2: Night's game against the Bills when he tore his achilles 53 00:02:54,200 --> 00:02:57,960 Speaker 2: tendon and ended his season only three plays into his 54 00:02:57,960 --> 00:03:00,359 Speaker 2: New York Jets career were all. 55 00:03:00,360 --> 00:03:03,560 Speaker 5: His contract was about one hundred and twelve millions, so 56 00:03:03,600 --> 00:03:05,480 Speaker 5: we even if you can't play like he's going to be. 57 00:03:05,400 --> 00:03:10,079 Speaker 2: Okay in the end. The equity Americans took a step 58 00:03:10,120 --> 00:03:12,000 Speaker 2: back this week, with the S and P five hundred 59 00:03:12,000 --> 00:03:14,840 Speaker 2: giving up just under two tenths of percent, ending the 60 00:03:14,840 --> 00:03:17,480 Speaker 2: week at forty four fifty. That is still nicely above 61 00:03:17,480 --> 00:03:19,920 Speaker 2: where our Bloomberg Elves predicted will end up the year. 62 00:03:20,320 --> 00:03:23,079 Speaker 2: The Nasdaq had a bit rougher time, down nearly four 63 00:03:23,200 --> 00:03:26,040 Speaker 2: tenths of percent. For her views on what the market 64 00:03:26,080 --> 00:03:28,760 Speaker 2: action told us about our investments, we welcome back now 65 00:03:28,800 --> 00:03:31,520 Speaker 2: one of our Bloomberg Alves, Savina Supermania, and she's Blank 66 00:03:31,560 --> 00:03:35,200 Speaker 2: of America's Securities, Head of US Equity and quantitative strategy. 67 00:03:35,280 --> 00:03:37,320 Speaker 2: So Cee Vidiot, welcome. Great to have you here in 68 00:03:37,400 --> 00:03:38,320 Speaker 2: the studio. 69 00:03:38,160 --> 00:03:41,839 Speaker 9: To be here, and I love the moniker, I love 70 00:03:41,880 --> 00:03:42,240 Speaker 9: having you. 71 00:03:42,920 --> 00:03:45,320 Speaker 2: So give us your take on where we are right now. 72 00:03:45,400 --> 00:03:47,960 Speaker 2: The actually was doing pretty well till Friday and then 73 00:03:48,080 --> 00:03:50,520 Speaker 2: backed off some. But did we learn anything this week 74 00:03:50,520 --> 00:03:52,160 Speaker 2: about exactly where we think we're headed? 75 00:03:52,920 --> 00:03:54,880 Speaker 9: I think this week was well. 76 00:03:55,040 --> 00:03:57,360 Speaker 10: This week was a week where we heard a lot 77 00:03:57,360 --> 00:04:01,880 Speaker 10: of really negative headlines around put potentially sticky wage inflation, 78 00:04:02,040 --> 00:04:06,160 Speaker 10: I mean, the the auto worker strike. We're seeing signs of, 79 00:04:06,800 --> 00:04:09,480 Speaker 10: you know, kind of trends that we were expecting to 80 00:04:09,760 --> 00:04:13,640 Speaker 10: mean revert very quickly remaining high. So our view is 81 00:04:14,360 --> 00:04:17,080 Speaker 10: we're not necessarily out of the woods yet in terms 82 00:04:17,240 --> 00:04:22,080 Speaker 10: of the FED controlling inflation, and we think that the 83 00:04:22,080 --> 00:04:25,559 Speaker 10: the mistake to be made at this point is betting 84 00:04:25,640 --> 00:04:28,680 Speaker 10: that the FED will start cutting aggressively next year. We 85 00:04:28,680 --> 00:04:31,840 Speaker 10: we don't necessarilyssarily see that as a slam dunk, and 86 00:04:31,880 --> 00:04:34,159 Speaker 10: that's what a lot of investors that we speak to 87 00:04:34,360 --> 00:04:37,839 Speaker 10: are expecting. So there's this this theme around resumption to 88 00:04:38,560 --> 00:04:40,800 Speaker 10: what happened over the last ten years, and our view 89 00:04:40,839 --> 00:04:42,720 Speaker 10: is that we're at a break point where we may 90 00:04:42,800 --> 00:04:45,760 Speaker 10: never see these low rates of the last ten years 91 00:04:45,839 --> 00:04:48,760 Speaker 10: ever again. And in fact, I think it's important to 92 00:04:48,800 --> 00:04:50,680 Speaker 10: remember that what we saw over the last ten years 93 00:04:50,800 --> 00:04:54,000 Speaker 10: was an anomaly. Negative real rates are not the norm, 94 00:04:54,000 --> 00:04:56,320 Speaker 10: they're the exception, and where we are now just feels 95 00:04:56,360 --> 00:04:57,840 Speaker 10: a little bit more like firm footing. 96 00:04:58,160 --> 00:04:59,880 Speaker 2: What does that tell you at equity valuation? 97 00:05:00,240 --> 00:05:02,839 Speaker 10: So I feel like the SMP five hundred right now 98 00:05:02,920 --> 00:05:06,039 Speaker 10: is kind of a tale of two cities, and there's 99 00:05:06,400 --> 00:05:09,279 Speaker 10: you know, the benchmark isn't a broad market composite like 100 00:05:09,320 --> 00:05:12,599 Speaker 10: it normally is. It's a top heavy benchmark where thirty 101 00:05:12,640 --> 00:05:16,560 Speaker 10: percent of it roughly is made up of seven big 102 00:05:16,680 --> 00:05:20,800 Speaker 10: companies that play this megacab tech growth theme, and that 103 00:05:21,000 --> 00:05:24,800 Speaker 10: area of the market is relatively pricey. Perhaps that's where 104 00:05:24,839 --> 00:05:26,520 Speaker 10: we will see the greatest growth over. 105 00:05:26,400 --> 00:05:27,159 Speaker 9: The long term. 106 00:05:27,440 --> 00:05:29,839 Speaker 10: But I think what has yet to happen as these 107 00:05:29,880 --> 00:05:33,159 Speaker 10: companies need to recalibrate to an environment where the hurdle 108 00:05:33,240 --> 00:05:36,520 Speaker 10: rate is no longer zero, it's a lot higher than zero. 109 00:05:36,600 --> 00:05:38,599 Speaker 10: We just saw the FED move us from zero to 110 00:05:38,640 --> 00:05:41,840 Speaker 10: five very quickly. I think that when you take the 111 00:05:41,880 --> 00:05:45,760 Speaker 10: broader market index, you see a very different valuation story, 112 00:05:46,040 --> 00:05:49,719 Speaker 10: a very different growth expectation story. And I feel really 113 00:05:49,800 --> 00:05:53,120 Speaker 10: bullish about the equal weighted SMP five hundred because I 114 00:05:53,120 --> 00:05:55,440 Speaker 10: think what a lot of companies are doing right now 115 00:05:56,000 --> 00:05:58,320 Speaker 10: is smart. They've done the right things over the last 116 00:05:58,320 --> 00:06:01,320 Speaker 10: ten years. They haven't necessarily taken on a lot of 117 00:06:01,360 --> 00:06:05,720 Speaker 10: floating rate risk, They've paid down debt in many industries, 118 00:06:05,960 --> 00:06:10,480 Speaker 10: energy industrials, materials, a lot of these manufacturing exposed sectors 119 00:06:10,800 --> 00:06:13,880 Speaker 10: have been starved of capital and have gotten leader and 120 00:06:14,000 --> 00:06:18,480 Speaker 10: meaner and now look like they're focused on efficiency, which 121 00:06:18,520 --> 00:06:22,279 Speaker 10: I think is the best bowl case for the next 122 00:06:22,360 --> 00:06:22,960 Speaker 10: ten years. 123 00:06:23,000 --> 00:06:24,280 Speaker 9: And we can talk more about that. 124 00:06:24,400 --> 00:06:26,680 Speaker 2: Well, does that suggest, perhaps, Sevita, that there will be 125 00:06:26,720 --> 00:06:28,840 Speaker 2: some separation of the companies who can really get their 126 00:06:28,839 --> 00:06:32,000 Speaker 2: head around increasing productivity, being more efficient, as you say, 127 00:06:32,040 --> 00:06:34,680 Speaker 2: cutting costs, and ones that may have a tougher time 128 00:06:34,720 --> 00:06:36,840 Speaker 2: at that because some people do that better than others. 129 00:06:37,080 --> 00:06:37,320 Speaker 3: Yeah. 130 00:06:37,360 --> 00:06:40,480 Speaker 10: Absolutely, it's a story of the haves and the have nots, 131 00:06:40,520 --> 00:06:42,479 Speaker 10: who gets it right and who doesn't. And I think 132 00:06:42,520 --> 00:06:46,160 Speaker 10: that's where fundamental analysis, which hasn't really. 133 00:06:45,960 --> 00:06:47,480 Speaker 9: Mattered for a very long time. 134 00:06:47,800 --> 00:06:49,719 Speaker 10: It feels like all you wanted to do is buy 135 00:06:49,760 --> 00:06:52,120 Speaker 10: the rip, focus on the index, and now I think 136 00:06:52,120 --> 00:06:55,440 Speaker 10: it's the time to get in the weeds into the fundamentals, 137 00:06:55,720 --> 00:06:58,599 Speaker 10: and that's where you can figure out which CEOs and 138 00:06:58,680 --> 00:07:02,680 Speaker 10: CTOs are figured out, how to use technology to replace 139 00:07:02,800 --> 00:07:07,320 Speaker 10: more expensive workers, how to make markets more efficient. We're 140 00:07:07,320 --> 00:07:10,840 Speaker 10: seeing this already in a lot of industries, like, for example, restaurants. 141 00:07:10,840 --> 00:07:15,240 Speaker 10: Our analysts have been writing about how today's kitchen looks 142 00:07:15,400 --> 00:07:18,000 Speaker 10: so different from the kitchen ten years ago in terms 143 00:07:18,000 --> 00:07:21,800 Speaker 10: of automating and getting a lot more labor light, if 144 00:07:21,840 --> 00:07:22,200 Speaker 10: you will. 145 00:07:22,680 --> 00:07:25,080 Speaker 2: So that's automation. What about labor? I mean, we did 146 00:07:25,120 --> 00:07:28,640 Speaker 2: have that UAW strike yees specific plants. Yes, we don't 147 00:07:28,680 --> 00:07:31,040 Speaker 2: know where that's going exactly, but it's against a backdrop 148 00:07:31,080 --> 00:07:35,360 Speaker 2: of larger pattern of labor unrest and demands for workers. 149 00:07:35,480 --> 00:07:38,760 Speaker 2: Could that cut into some of those margins that maybe productivity. 150 00:07:38,160 --> 00:07:40,960 Speaker 10: Would give you, Oh absolutely, I think that right now 151 00:07:41,000 --> 00:07:46,080 Speaker 10: there is a tension between real inflation and financial asset inflation. 152 00:07:46,320 --> 00:07:48,600 Speaker 10: And what we saw for the last you know, during 153 00:07:48,760 --> 00:07:52,560 Speaker 10: post financial crisis, was this wonderful frictionless environment where there 154 00:07:52,640 --> 00:07:56,160 Speaker 10: was barely any inflation to speak of. If something got 155 00:07:56,200 --> 00:07:58,520 Speaker 10: expensive in the US, we could move it somewhere else, 156 00:07:58,640 --> 00:08:01,160 Speaker 10: or we could import from anywhere we wanted to, and 157 00:08:01,200 --> 00:08:04,720 Speaker 10: now we're in a very different setup. The disparity between 158 00:08:04,720 --> 00:08:06,040 Speaker 10: the wealthy and and the. 159 00:08:06,000 --> 00:08:07,520 Speaker 9: Poor has just grown larger. 160 00:08:07,560 --> 00:08:09,440 Speaker 10: We all talk about this, but I think what we're 161 00:08:09,440 --> 00:08:13,840 Speaker 10: seeing now is real evidence of closing that gap. So 162 00:08:13,880 --> 00:08:16,080 Speaker 10: that's going to be bad for some corporations. And I 163 00:08:16,120 --> 00:08:19,200 Speaker 10: think where it's going to get difficult is for companies 164 00:08:19,200 --> 00:08:24,120 Speaker 10: that haven't been able to really think about how to navigate. 165 00:08:23,680 --> 00:08:25,280 Speaker 9: A higher interest rate environment. 166 00:08:25,960 --> 00:08:29,560 Speaker 10: And again, when I think about the indices within the market, 167 00:08:30,080 --> 00:08:33,240 Speaker 10: the S and P five hundred is this top heavy benchmark, 168 00:08:33,480 --> 00:08:36,360 Speaker 10: and then the Russell two thousand, which are smaller companies. 169 00:08:36,800 --> 00:08:38,600 Speaker 10: A lot of the companies that are in the Russell 170 00:08:38,679 --> 00:08:43,760 Speaker 10: today aren't necessarily these economically sensitive growth companies. It's a 171 00:08:43,760 --> 00:08:46,480 Speaker 10: lot of these so called zombie companies that have you know, 172 00:08:46,520 --> 00:08:49,840 Speaker 10: we're iPod in a much friendlier environment and are no 173 00:08:49,920 --> 00:08:54,439 Speaker 10: longer able to handle this rising rate shock, if you will. 174 00:08:54,559 --> 00:08:56,720 Speaker 2: So, Vita, taking a look at all of that analysis, 175 00:08:56,880 --> 00:08:58,960 Speaker 2: what is it indicate to you about where we're going next? 176 00:08:59,040 --> 00:09:01,439 Speaker 2: Is we look toward twenty twenty four. I mean, as 177 00:09:01,440 --> 00:09:03,079 Speaker 2: one of our l's you're at forty three hundred, which 178 00:09:03,120 --> 00:09:06,440 Speaker 2: is by the way, right at the median number right now, Oh, 179 00:09:06,440 --> 00:09:09,080 Speaker 2: that's proverty good, probably right, But what can you tell 180 00:09:09,160 --> 00:09:10,640 Speaker 2: us what where you think we may be headed as 181 00:09:10,640 --> 00:09:11,680 Speaker 2: we go into twenty twenty four. 182 00:09:12,040 --> 00:09:14,560 Speaker 10: Yeah, so, I mean I think that we're we're what 183 00:09:14,600 --> 00:09:17,520 Speaker 10: we're facing right now is again a market that's likely 184 00:09:17,600 --> 00:09:22,079 Speaker 10: to right size technology. I think that tech shrinks as 185 00:09:22,120 --> 00:09:24,400 Speaker 10: a percentage of the S and P five hundred, and 186 00:09:24,440 --> 00:09:27,800 Speaker 10: we see leadership broadened out to other old economy sectors 187 00:09:28,200 --> 00:09:31,079 Speaker 10: that are actually using these new tools to. 188 00:09:31,040 --> 00:09:31,960 Speaker 9: Get more efficient. 189 00:09:32,600 --> 00:09:35,080 Speaker 10: Next year I think could be a good year for earnings. 190 00:09:35,080 --> 00:09:38,080 Speaker 10: It could be a good year for nomenal growth. I 191 00:09:38,120 --> 00:09:41,520 Speaker 10: think the trick is figuring out which companies are doing 192 00:09:41,600 --> 00:09:44,200 Speaker 10: it right and which companies are are going to become 193 00:09:44,559 --> 00:09:47,480 Speaker 10: you know, obsolete, and that's that's where the challenges remain. 194 00:09:47,520 --> 00:09:49,839 Speaker 10: So it's really a stock pickers market. But I think 195 00:09:49,840 --> 00:09:52,120 Speaker 10: that if you want to buy an index, the equal 196 00:09:52,120 --> 00:09:55,439 Speaker 10: weighted SMP five hundred to me looks pretty interesting. These 197 00:09:55,440 --> 00:09:58,200 Speaker 10: are companies that are relatively cheap. The trading value is 198 00:09:58,440 --> 00:10:01,920 Speaker 10: I think something like fifteen times earnings, which is, you know, 199 00:10:02,160 --> 00:10:08,240 Speaker 10: a relatively average valuation, not expensive, not cheap. And my 200 00:10:08,400 --> 00:10:10,920 Speaker 10: sense is that we've all gotten used to just one 201 00:10:11,000 --> 00:10:14,400 Speaker 10: sector and one theme driving equities, but we could be 202 00:10:14,440 --> 00:10:17,440 Speaker 10: in an environment where the themes broaden out and we 203 00:10:17,559 --> 00:10:19,960 Speaker 10: see different stories for different sectors. 204 00:10:20,080 --> 00:10:21,760 Speaker 2: One last quick one, if I could, are we as 205 00:10:21,760 --> 00:10:23,600 Speaker 2: divers about it as we think we are given going 206 00:10:23,600 --> 00:10:24,400 Speaker 2: on in the INDIAXES. 207 00:10:24,640 --> 00:10:26,160 Speaker 9: So this is where it gets interesting. 208 00:10:26,200 --> 00:10:29,280 Speaker 10: I think that portfolio managers and active managers are in 209 00:10:29,360 --> 00:10:32,200 Speaker 10: a tough spot because we've been in a market where 210 00:10:32,240 --> 00:10:36,280 Speaker 10: seven stocks have been driving the index and they're almost 211 00:10:36,360 --> 00:10:39,199 Speaker 10: forced to own these companies. We're now at a point 212 00:10:39,200 --> 00:10:43,880 Speaker 10: where one out of ten active managers has more than 213 00:10:44,080 --> 00:10:48,520 Speaker 10: forty percent of their portfolio in five companies, not diversified 214 00:10:48,559 --> 00:10:48,839 Speaker 10: at all. 215 00:10:49,040 --> 00:10:50,840 Speaker 2: Peter, thank you so much for being a CEVIA super 216 00:10:50,960 --> 00:10:53,480 Speaker 2: nion of Bank of America. Coming up, we're told artificial 217 00:10:53,520 --> 00:10:55,559 Speaker 2: intelligen will take over the world. What can we be 218 00:10:55,600 --> 00:11:00,600 Speaker 2: for finans? We talked on Martyschow as a Six Street partners. 219 00:11:01,600 --> 00:11:05,840 Speaker 1: This is Bloomberg Wall Street Week with David Weston from 220 00:11:05,960 --> 00:11:06,880 Speaker 1: Bloomberg Radio. 221 00:11:14,160 --> 00:11:17,200 Speaker 2: This is Wall Street Week. I'm David Weston. Generative AI 222 00:11:17,400 --> 00:11:20,200 Speaker 2: may be here already, but we're told that we ain't 223 00:11:20,240 --> 00:11:23,240 Speaker 2: seen nothing yet. That it will change much of our world, 224 00:11:23,320 --> 00:11:25,960 Speaker 2: including potentially the world of finance. Here to give us 225 00:11:25,960 --> 00:11:28,280 Speaker 2: a sneak peek of what that world might look like 226 00:11:28,520 --> 00:11:30,800 Speaker 2: is Marty Schavez. He is a vice chairman of Six 227 00:11:30,960 --> 00:11:33,160 Speaker 2: Street Partners. Marty, welcome back to Wall Street. 228 00:11:33,160 --> 00:11:35,040 Speaker 4: Great to have you here, pleasure to be here. Thank you, David. 229 00:11:35,120 --> 00:11:36,800 Speaker 2: And we're talking about changing the world. Start with the 230 00:11:36,800 --> 00:11:41,560 Speaker 2: world of Six Street Partners. How is potentially AI changing 231 00:11:41,640 --> 00:11:42,480 Speaker 2: your world? 232 00:11:43,000 --> 00:11:46,560 Speaker 4: The first thing I noticed about AI is that all 233 00:11:46,679 --> 00:11:51,280 Speaker 4: of our limited partners, our investors, and all of our 234 00:11:51,320 --> 00:11:57,320 Speaker 4: portfolio companies to first approximation, want to understand it, and 235 00:11:57,520 --> 00:12:00,920 Speaker 4: they're also looking to us to have some thoughts that 236 00:12:00,960 --> 00:12:04,440 Speaker 4: are differentiated, not just to understand it, but to understand 237 00:12:04,440 --> 00:12:07,079 Speaker 4: what to do about it and what to do with it. 238 00:12:07,160 --> 00:12:09,840 Speaker 4: So there's a lot of conversations going on. 239 00:12:10,320 --> 00:12:12,120 Speaker 2: So will help us a little bit as if we 240 00:12:12,120 --> 00:12:14,800 Speaker 2: were one of your clients. Okay, give us a sense 241 00:12:14,800 --> 00:12:15,880 Speaker 2: a little bit of understanding. 242 00:12:15,880 --> 00:12:16,120 Speaker 4: Here. 243 00:12:16,320 --> 00:12:18,400 Speaker 2: We hear a lot of things that are wonderful potentially 244 00:12:18,400 --> 00:12:20,800 Speaker 2: about gender, some things that are pretty scary about it 245 00:12:20,840 --> 00:12:23,720 Speaker 2: as well. What do you tell them about the upside potential? 246 00:12:24,040 --> 00:12:27,319 Speaker 4: So I tend to be rather calm about it, maybe 247 00:12:27,360 --> 00:12:30,480 Speaker 4: in part because I've been working on AI in one 248 00:12:30,559 --> 00:12:34,000 Speaker 4: form or another for a long time. My PhD from 249 00:12:34,000 --> 00:12:37,280 Speaker 4: a million years ago nineteen ninety one was on AI 250 00:12:37,400 --> 00:12:40,800 Speaker 4: and an early iteration of AI when we didn't really 251 00:12:40,880 --> 00:12:45,520 Speaker 4: get that far, and the techniques that have now become 252 00:12:45,679 --> 00:12:50,400 Speaker 4: really valuable were techniques that actually was pretty skeptical about 253 00:12:50,440 --> 00:12:52,960 Speaker 4: thirty years ago. But what happened is the computers got 254 00:12:53,040 --> 00:12:57,680 Speaker 4: way faster, doubling about every eighteen months, and enough doubling 255 00:12:57,800 --> 00:13:00,760 Speaker 4: between nineteen ninety one and now, and now you are 256 00:13:00,800 --> 00:13:05,680 Speaker 4: seeing some amazing things. And so yet I would also say, 257 00:13:06,320 --> 00:13:11,200 Speaker 4: at some fundamental level, it's just math. These neural networks 258 00:13:11,240 --> 00:13:14,520 Speaker 4: are doing some really interesting math, but they're just doing 259 00:13:14,559 --> 00:13:19,080 Speaker 4: some calculations. The calculations are letting us do pattern matching 260 00:13:19,360 --> 00:13:26,200 Speaker 4: statistical recognition in an unprecedented way, and that is really powerful. So, 261 00:13:26,440 --> 00:13:30,280 Speaker 4: having seen so many ups and downs of AI, I believe, 262 00:13:30,480 --> 00:13:33,040 Speaker 4: and I think many people share this belief that this 263 00:13:33,200 --> 00:13:36,480 Speaker 4: time it actually is different and you can see it. 264 00:13:36,679 --> 00:13:39,560 Speaker 4: You can go to bard or chat GPT and ask 265 00:13:39,600 --> 00:13:44,160 Speaker 4: it some questions and get some pretty amazing things coming back. Now, 266 00:13:44,280 --> 00:13:47,640 Speaker 4: it's also got problems. There's the hallucinations that are much 267 00:13:47,679 --> 00:13:50,080 Speaker 4: talked about, and so what are we going to do 268 00:13:50,360 --> 00:13:54,520 Speaker 4: to ground those hallucinations? I think is one interesting topic. 269 00:13:54,840 --> 00:13:59,560 Speaker 4: Another interesting topic is as the doubling continues, and I 270 00:13:59,559 --> 00:14:02,600 Speaker 4: can tell you that every three months, I'm seeing something 271 00:14:03,000 --> 00:14:05,760 Speaker 4: that's not more twice as interesting as what I saw 272 00:14:05,920 --> 00:14:09,439 Speaker 4: three months before, but ten times more interesting. We are 273 00:14:09,559 --> 00:14:14,520 Speaker 4: in some crazy exponential inflection point. So looking out into 274 00:14:14,559 --> 00:14:18,040 Speaker 4: the future, we're starting to ask ourselves, when are these 275 00:14:18,080 --> 00:14:21,040 Speaker 4: computers going to be as smart as we are. We 276 00:14:21,120 --> 00:14:23,400 Speaker 4: don't think they're going to be conscious in any way, 277 00:14:23,720 --> 00:14:26,880 Speaker 4: but they might be intelligent. They might be as intelligent, 278 00:14:27,120 --> 00:14:30,680 Speaker 4: they might be more intelligent. What kind of things will 279 00:14:30,720 --> 00:14:34,680 Speaker 4: happen once we can tell the AIS? Go read every 280 00:14:34,720 --> 00:14:38,360 Speaker 4: book ever written, especially all the textbooks. If that can 281 00:14:38,400 --> 00:14:41,760 Speaker 4: be done in a way that respects intellectual property. Go 282 00:14:41,800 --> 00:14:44,920 Speaker 4: read all the textbooks and learn everything in them, and 283 00:14:45,000 --> 00:14:48,320 Speaker 4: tell us everything that is implied by what's in them, 284 00:14:48,560 --> 00:14:51,440 Speaker 4: and feed that back into the AI and train the 285 00:14:51,480 --> 00:14:55,160 Speaker 4: next generation of yourself. As this sloop gets going, we 286 00:14:55,240 --> 00:14:59,200 Speaker 4: can see the prospects for some really interesting things. It 287 00:14:59,240 --> 00:15:02,360 Speaker 4: wouldn't surprise me if in a few years we can 288 00:15:02,360 --> 00:15:09,240 Speaker 4: tell the computers, please invent a commercially effective nuclear fusion 289 00:15:09,520 --> 00:15:13,400 Speaker 4: reactor that's safe, and they might just go and do it. 290 00:15:13,680 --> 00:15:16,160 Speaker 4: And you can imagine the computers doing the same thing. 291 00:15:16,560 --> 00:15:20,240 Speaker 4: For climate change, we're already seeing and they can talk 292 00:15:20,240 --> 00:15:24,560 Speaker 4: about a company I'm working with, Recursion Pharmaceuticals. We're already 293 00:15:24,600 --> 00:15:28,080 Speaker 4: seeing what the computers can do for really hard problems 294 00:15:28,120 --> 00:15:31,800 Speaker 4: such as drug discovery. So there's no question in my 295 00:15:31,840 --> 00:15:34,400 Speaker 4: mind that they're going to make a huge difference. There's 296 00:15:34,440 --> 00:15:38,240 Speaker 4: also some concerns, and so delivering AI in a way 297 00:15:38,280 --> 00:15:43,480 Speaker 4: that is bold and also responsible and safe and ethical 298 00:15:43,880 --> 00:15:45,280 Speaker 4: is a huge area of concern. 299 00:15:45,520 --> 00:15:49,000 Speaker 2: Put those two things together, the AI and its potential 300 00:15:49,080 --> 00:15:51,880 Speaker 2: for good and potentially some risks as well. With the 301 00:15:51,880 --> 00:15:54,720 Speaker 2: financial world. I mean, when you talk about, for example, hallucinations, 302 00:15:54,840 --> 00:15:57,040 Speaker 2: that doesn't sound very good to me if that's part 303 00:15:57,080 --> 00:15:58,600 Speaker 2: of what's running the financial systems. 304 00:15:59,080 --> 00:16:03,520 Speaker 4: Right. So I started working on Wall Street in ninety three, 305 00:16:03,840 --> 00:16:07,440 Speaker 4: and in ninety three, when people like me showed up 306 00:16:07,440 --> 00:16:11,000 Speaker 4: on Wall Street, I often got asked, could you help 307 00:16:11,040 --> 00:16:14,000 Speaker 4: me figure out how to print this document or turn 308 00:16:14,120 --> 00:16:17,520 Speaker 4: my computer on and off? R What is this kind 309 00:16:17,520 --> 00:16:20,960 Speaker 4: of quant math and software guy doing here? So it 310 00:16:21,000 --> 00:16:24,800 Speaker 4: took a little while where we people like me, could 311 00:16:24,800 --> 00:16:28,240 Speaker 4: find the problems that we could actually solve that would 312 00:16:28,320 --> 00:16:31,400 Speaker 4: help us make better markets for our clients and manage 313 00:16:31,440 --> 00:16:34,560 Speaker 4: our risk more effectively. That's something we've been working on 314 00:16:34,640 --> 00:16:37,880 Speaker 4: for a very long time, bringing math to Wall Street. 315 00:16:38,080 --> 00:16:41,240 Speaker 4: And so I've had an opportunity to see many iterations 316 00:16:41,280 --> 00:16:44,720 Speaker 4: of this movie, of this movie. So initially one of 317 00:16:44,760 --> 00:16:46,920 Speaker 4: the things I might work on would be, we've got 318 00:16:46,960 --> 00:16:49,560 Speaker 4: a complicated book of risk. How do we hedge it? 319 00:16:49,800 --> 00:16:53,080 Speaker 4: We've got thirty seconds to make a phone call and 320 00:16:53,280 --> 00:16:55,880 Speaker 4: construct the first order hedge of the book. All right, 321 00:16:56,000 --> 00:16:58,480 Speaker 4: so we do that lots of math and software. We 322 00:16:58,520 --> 00:17:01,160 Speaker 4: want to do it reliably, don't want to make mistakes. 323 00:17:01,360 --> 00:17:03,240 Speaker 4: We certainly don't want to hoose to me and get 324 00:17:03,240 --> 00:17:06,159 Speaker 4: the wrong hedge and maybe make the risk position worse. 325 00:17:06,520 --> 00:17:08,600 Speaker 4: And we got pretty good at that. But I remember 326 00:17:08,600 --> 00:17:12,080 Speaker 4: thinking even then, Okay, so now we're calculating this hedge 327 00:17:12,080 --> 00:17:14,560 Speaker 4: and the person next to me is calling the exchange 328 00:17:14,560 --> 00:17:17,840 Speaker 4: and saying buy or sell that many futures. I remember thinking, 329 00:17:17,880 --> 00:17:20,680 Speaker 4: even as a kid, well, we could do that part too, 330 00:17:21,160 --> 00:17:24,520 Speaker 4: But it took a long time. Eventually we got there, 331 00:17:24,760 --> 00:17:28,479 Speaker 4: especially in equities markets that were exchange traded where there 332 00:17:28,520 --> 00:17:31,080 Speaker 4: was a lot of data, and we could close that loop. 333 00:17:31,200 --> 00:17:33,520 Speaker 4: You could do some analysis, and then you could say 334 00:17:33,680 --> 00:17:35,439 Speaker 4: this is the trade we should do, and then you 335 00:17:35,440 --> 00:17:38,640 Speaker 4: could have the computers just do that trade. And at 336 00:17:38,640 --> 00:17:41,719 Speaker 4: the time that began, there was a lot of concerns, 337 00:17:41,800 --> 00:17:44,400 Speaker 4: how could that go wrong? What if the computer puts 338 00:17:44,440 --> 00:17:47,080 Speaker 4: in the wrong trade? And then the whole loop got 339 00:17:47,119 --> 00:17:51,080 Speaker 4: faster and faster, and computers were putting in orders with 340 00:17:51,160 --> 00:17:54,760 Speaker 4: a latency of sub one millisecond, much faster than a 341 00:17:54,800 --> 00:17:58,119 Speaker 4: trader could ever operate. And then we actually had some 342 00:17:58,920 --> 00:18:03,040 Speaker 4: train wrecks. There was a company night Trading where the 343 00:18:03,080 --> 00:18:07,000 Speaker 4: algos run amok and kept putting orders into the exchange 344 00:18:07,720 --> 00:18:10,119 Speaker 4: that were at the wrong price, and so all the 345 00:18:10,240 --> 00:18:13,720 Speaker 4: orders got taken out on the other side, and they 346 00:18:13,720 --> 00:18:16,800 Speaker 4: eroded their capital in forty five minutes, and then they 347 00:18:16,800 --> 00:18:20,399 Speaker 4: were bankrupt. So some things have gone horribly wrong we 348 00:18:20,560 --> 00:18:24,840 Speaker 4: learned from those episodes. I'm in the camp that sees 349 00:18:24,960 --> 00:18:29,440 Speaker 4: AI as essentially wonderful, but still more of the same, 350 00:18:29,840 --> 00:18:34,199 Speaker 4: So more math, more analysis, things going faster and faster, 351 00:18:34,720 --> 00:18:37,880 Speaker 4: and yet I think there are some principles that are 352 00:18:38,080 --> 00:18:43,000 Speaker 4: stable in time. So here's one. I remember a town 353 00:18:43,080 --> 00:18:46,560 Speaker 4: hall years ago where I was talking and I said, 354 00:18:46,600 --> 00:18:50,320 Speaker 4: there's really three strategies that I see when it comes 355 00:18:50,320 --> 00:18:53,800 Speaker 4: to computers. Number one is, you could be a person 356 00:18:53,880 --> 00:18:56,840 Speaker 4: who tells the computers what to do. That's my strategy, 357 00:18:56,880 --> 00:18:59,600 Speaker 4: and it's working pretty well for me, it's not for everybody. 358 00:19:00,080 --> 00:19:05,240 Speaker 4: Number Two, you could collaborate effectively with the computers and 359 00:19:05,440 --> 00:19:08,040 Speaker 4: the people who tell the computers what to do. And 360 00:19:08,280 --> 00:19:12,240 Speaker 4: I recommend that to everybody. Everybody can embrace that strategy 361 00:19:12,600 --> 00:19:16,080 Speaker 4: and use the computers to give yourself a force multiplier, 362 00:19:16,200 --> 00:19:19,280 Speaker 4: leverage a superpower, and then go on and do more 363 00:19:19,320 --> 00:19:23,760 Speaker 4: interesting things that the computers can't do. And you always worry, well, 364 00:19:23,800 --> 00:19:26,800 Speaker 4: maybe we won't be needed at all and the computers 365 00:19:26,800 --> 00:19:30,680 Speaker 4: will take over. I've never seen that happen. The third strategy, 366 00:19:30,800 --> 00:19:33,600 Speaker 4: which I have also seen, is stand in the way 367 00:19:33,600 --> 00:19:37,119 Speaker 4: of progress. See if, in the name of your job security, 368 00:19:37,400 --> 00:19:41,080 Speaker 4: you can stop the computers, and that is really dumb, 369 00:19:41,160 --> 00:19:44,200 Speaker 4: and don't do that, and some people do do that. 370 00:19:44,640 --> 00:19:48,840 Speaker 4: I think that that advice applies today. I think everybody 371 00:19:48,880 --> 00:19:52,639 Speaker 4: needs to be looking at computers and generative AI and 372 00:19:52,720 --> 00:19:56,520 Speaker 4: thinking how can I use this to be more productive. 373 00:19:56,680 --> 00:19:58,720 Speaker 2: Mario, It's such a treat you have here on Wall Street. 374 00:19:58,720 --> 00:20:01,280 Speaker 2: We thank you so much. As Marie Chavez of Six 375 00:20:01,320 --> 00:20:06,560 Speaker 2: Street Partners coming up. Is a college degree still a 376 00:20:06,600 --> 00:20:09,320 Speaker 2: ticket to a better life? And if so, what can 377 00:20:09,359 --> 00:20:11,760 Speaker 2: we do to make sure everyone has a shot at 378 00:20:11,800 --> 00:20:16,320 Speaker 2: that ticket? We asked Ruth Simmons, former president of Brown University. 379 00:20:17,440 --> 00:20:21,959 Speaker 5: I want those elite institutions to stop it, to stop 380 00:20:22,119 --> 00:20:23,960 Speaker 5: saying we are so much better. 381 00:20:24,560 --> 00:20:26,639 Speaker 2: This is Wall Street Week on Bloomberg. 382 00:20:27,840 --> 00:20:32,080 Speaker 1: This is Bloomberg Wall Street Week with David Weston from 383 00:20:32,160 --> 00:20:33,119 Speaker 1: Bloomberg Radio. 384 00:20:41,440 --> 00:20:45,160 Speaker 2: A college diploma. The GI Bill sent soldiers returning from 385 00:20:45,200 --> 00:20:47,960 Speaker 2: World War Two off to get their degrees, and ever 386 00:20:48,000 --> 00:20:51,159 Speaker 2: since that higher education has been seen as the ticket 387 00:20:51,359 --> 00:20:54,040 Speaker 2: to a better life in the United States, helping not 388 00:20:54,200 --> 00:20:57,639 Speaker 2: just those getting the opportunity, but driving the economic force 389 00:20:57,720 --> 00:20:59,280 Speaker 2: of the United States as well. 390 00:20:59,480 --> 00:21:03,160 Speaker 11: There's not an of college educated white men to drive 391 00:21:03,200 --> 00:21:06,119 Speaker 11: the economy in the next thirty or forty years as demographics, 392 00:21:06,240 --> 00:21:09,200 Speaker 11: so you need blacks to start businesses, you need Latinos 393 00:21:09,200 --> 00:21:10,800 Speaker 11: and women to be part of the economy. 394 00:21:10,960 --> 00:21:13,640 Speaker 2: But over time, the cost of going to college has skyrocketed, 395 00:21:13,760 --> 00:21:16,560 Speaker 2: putting increased pressure on the system to make sure young 396 00:21:16,600 --> 00:21:17,640 Speaker 2: people can get there. 397 00:21:17,720 --> 00:21:19,280 Speaker 12: We've got to find a way to break the back 398 00:21:19,320 --> 00:21:22,479 Speaker 12: of the education system and make it more accessible, cheaper, 399 00:21:22,720 --> 00:21:26,160 Speaker 12: get more people into college, get higher graduation rates. That's 400 00:21:26,359 --> 00:21:28,040 Speaker 12: I think a societal thing that we have to face them. 401 00:21:28,040 --> 00:21:30,600 Speaker 12: It's going to be very critical in the next ten years. 402 00:21:30,640 --> 00:21:33,200 Speaker 2: And once in college that they can find the resources 403 00:21:33,200 --> 00:21:34,480 Speaker 2: they need to stay there. 404 00:21:34,760 --> 00:21:36,840 Speaker 13: People that are going to college today still have to 405 00:21:36,880 --> 00:21:40,440 Speaker 13: pay significant amount of tuition and they're going to need financing. 406 00:21:40,560 --> 00:21:42,440 Speaker 13: So far is one of the companies that can provide that, 407 00:21:42,840 --> 00:21:45,240 Speaker 13: But there's a number of companies in the ecosystem that 408 00:21:45,320 --> 00:21:48,440 Speaker 13: haven't been able to withstand what's happened over the last 409 00:21:48,480 --> 00:21:50,680 Speaker 13: three years, and they've dropped out in helping people pay 410 00:21:50,720 --> 00:21:52,080 Speaker 13: for their college or refinance the. 411 00:21:52,080 --> 00:21:55,719 Speaker 2: College, creating what Ray MacGuire of Lizard calls a pipeline 412 00:21:55,760 --> 00:21:59,000 Speaker 2: of talent, a pipeline as diverse as the nation it's 413 00:21:59,080 --> 00:22:00,480 Speaker 2: being prepared to lead. 414 00:22:00,800 --> 00:22:03,320 Speaker 14: You have to make sure that we have a robust pipeline. 415 00:22:03,480 --> 00:22:07,399 Speaker 14: That pipeline needs to have sponsorship, That pipeline needs to 416 00:22:07,480 --> 00:22:09,960 Speaker 14: be trained, it needs to be encouraged, and it also 417 00:22:10,119 --> 00:22:12,600 Speaker 14: needs to have visible examples of people who look like 418 00:22:13,280 --> 00:22:16,280 Speaker 14: the pipeline, so that it gives a pipeline some confidence 419 00:22:16,320 --> 00:22:17,960 Speaker 14: that they too can get there. 420 00:22:18,040 --> 00:22:20,840 Speaker 2: All of which feeds the American dream of enlisting all 421 00:22:20,880 --> 00:22:23,040 Speaker 2: the talent we have for the benefit of us. 422 00:22:23,080 --> 00:22:27,600 Speaker 15: All I benefited from living in a country that believed 423 00:22:27,800 --> 00:22:30,320 Speaker 15: in my potential, and even though I was a poor 424 00:22:30,400 --> 00:22:34,679 Speaker 15: kid living in a rural community, America cheered me on. 425 00:22:35,440 --> 00:22:40,560 Speaker 15: This country wanted me to win and succeed, and that 426 00:22:40,720 --> 00:22:43,840 Speaker 15: manifest in the ways in which all of this made 427 00:22:43,880 --> 00:22:47,080 Speaker 15: it possible for me to get on the mobility escalator 428 00:22:47,160 --> 00:22:49,679 Speaker 15: and write it as far as I could, and my 429 00:22:49,840 --> 00:22:51,120 Speaker 15: talent would bring. 430 00:22:51,000 --> 00:22:55,879 Speaker 2: Me and here to take us through the state of 431 00:22:55,960 --> 00:22:58,120 Speaker 2: higher education. Today, we welcome to somebody who really knows 432 00:22:58,119 --> 00:23:00,760 Speaker 2: it backwards and forwards. She's Ruth Set. She has served 433 00:23:00,800 --> 00:23:03,080 Speaker 2: as president of Smith, She's served as president of Brown 434 00:23:03,160 --> 00:23:06,320 Speaker 2: University and then as president of Prairie A and M. 435 00:23:06,520 --> 00:23:08,320 Speaker 2: Thank you so much for being with thous welcome roofs. 436 00:23:08,600 --> 00:23:10,359 Speaker 2: Thank you for having me a lot of debate right 437 00:23:10,400 --> 00:23:13,199 Speaker 2: now about the value of college education. There was a 438 00:23:13,240 --> 00:23:16,040 Speaker 2: time that was indispensable to really move forward and move 439 00:23:16,119 --> 00:23:18,560 Speaker 2: up in this country. Is it as important today as 440 00:23:18,560 --> 00:23:19,880 Speaker 2: it was a generation ago. 441 00:23:20,080 --> 00:23:23,399 Speaker 5: It's far more important today than it was when I 442 00:23:23,480 --> 00:23:26,719 Speaker 5: started out. When you think about the complexity of the world, 443 00:23:27,680 --> 00:23:31,719 Speaker 5: when you think about the mirriad things that people have 444 00:23:31,800 --> 00:23:35,800 Speaker 5: to absorb from an intellectual standpoint, when you think about 445 00:23:35,880 --> 00:23:41,840 Speaker 5: the difficulty of making a happy, fulfilling life, Oh my goodness, 446 00:23:42,240 --> 00:23:44,160 Speaker 5: do we ever need a higher education? 447 00:23:44,960 --> 00:23:46,919 Speaker 2: So it raises in my mind is the question, how 448 00:23:46,920 --> 00:23:49,760 Speaker 2: do we make sure that the people who can benefit 449 00:23:49,800 --> 00:23:51,919 Speaker 2: from that, the talent that we have in this country, 450 00:23:51,960 --> 00:23:54,680 Speaker 2: get the education because it's gotten harder, it's gotten more 451 00:23:54,680 --> 00:23:57,439 Speaker 2: expensive and more difficult, particularly for people who are not 452 00:23:57,480 --> 00:23:58,600 Speaker 2: from privileged backgrounds. 453 00:23:58,920 --> 00:24:02,760 Speaker 5: My own view, and I've espoused this for many, many years, 454 00:24:03,000 --> 00:24:08,679 Speaker 5: is that we've become too segmented in higher education in 455 00:24:08,720 --> 00:24:12,639 Speaker 5: this country. And that is to say, there are eleit 456 00:24:12,760 --> 00:24:18,520 Speaker 5: institutions that pull themselves up as being so much better, 457 00:24:18,640 --> 00:24:22,919 Speaker 5: so much more privileged than other institutions. There are the 458 00:24:23,040 --> 00:24:28,720 Speaker 5: haves and have nots. And furthermore, we've pulled apart significantly 459 00:24:29,119 --> 00:24:34,160 Speaker 5: because the rich have gotten extraordinarily richer and the poorer 460 00:24:34,359 --> 00:24:37,960 Speaker 5: have stayed poor or gotten much worse. But here's the thing, 461 00:24:38,400 --> 00:24:42,040 Speaker 5: and I so want in my book for people to 462 00:24:42,240 --> 00:24:46,600 Speaker 5: understand this, and that is I had the education that 463 00:24:46,720 --> 00:24:51,600 Speaker 5: I could get. I grew up at segnated country. I 464 00:24:51,640 --> 00:24:55,119 Speaker 5: grew up when we didn't have access as African Americans 465 00:24:55,160 --> 00:25:00,439 Speaker 5: to the quality of education that whites had. And I 466 00:25:00,480 --> 00:25:06,840 Speaker 5: went to a black college that was still a wonderful 467 00:25:06,960 --> 00:25:10,080 Speaker 5: experience for me and beneficial for me. And I was 468 00:25:10,119 --> 00:25:13,560 Speaker 5: able to take that and go to Harvard to graduate 469 00:25:13,560 --> 00:25:16,760 Speaker 5: school and then make my way in the world. So 470 00:25:16,840 --> 00:25:21,800 Speaker 5: what I advocate is that people use every avenue for education, 471 00:25:22,119 --> 00:25:27,240 Speaker 5: and community colleges are a good place to start. So 472 00:25:27,359 --> 00:25:31,680 Speaker 5: are state colleges that have a teaching mission. You don't 473 00:25:31,720 --> 00:25:35,400 Speaker 5: have to go to a research university. You may want 474 00:25:35,440 --> 00:25:38,400 Speaker 5: to do that, but it isn't necessary at all. But 475 00:25:38,520 --> 00:25:43,000 Speaker 5: more than anything, I want those elite institutions to stop it, 476 00:25:43,440 --> 00:25:46,280 Speaker 5: to stop saying we are so much better. And if 477 00:25:46,280 --> 00:25:48,880 Speaker 5: you don't come to Brown and if you don't come 478 00:25:48,880 --> 00:25:52,119 Speaker 5: to Harvard, well, og, you're not going to be successful 479 00:25:52,160 --> 00:25:52,480 Speaker 5: in life. 480 00:25:52,560 --> 00:25:53,920 Speaker 4: It's just not true. 481 00:25:54,119 --> 00:25:57,439 Speaker 2: You grew up in a segregated environment. Yes, a very 482 00:25:57,440 --> 00:25:59,560 Speaker 2: different environment, I hope, than what we really have today. 483 00:26:00,119 --> 00:26:02,919 Speaker 2: Your book, you write, I believe about actually being with 484 00:26:03,080 --> 00:26:05,639 Speaker 2: white students for the first time in Mexico. He went 485 00:26:05,720 --> 00:26:07,440 Speaker 2: to Mexico to study Spanish. 486 00:26:07,520 --> 00:26:08,359 Speaker 5: Yes, isn't that odd? 487 00:26:08,600 --> 00:26:12,840 Speaker 2: You mentioned the exactly what a story, But you mentioned 488 00:26:12,880 --> 00:26:16,040 Speaker 2: the importance of teachers. What about your classmates? Does it 489 00:26:16,080 --> 00:26:18,879 Speaker 2: make a difference in your experience now to have a 490 00:26:18,960 --> 00:26:21,960 Speaker 2: diverse group of students around you, not just teachers who 491 00:26:21,960 --> 00:26:22,359 Speaker 2: believe in you. 492 00:26:22,520 --> 00:26:24,720 Speaker 5: Of course it does, and it doesn't know where you are. 493 00:26:25,080 --> 00:26:26,960 Speaker 4: You can be in a tribal college. 494 00:26:27,160 --> 00:26:33,680 Speaker 5: Or a minority serving institution or an HBCU. You need 495 00:26:33,880 --> 00:26:38,360 Speaker 5: to have experiences with people of all backgrounds. So let 496 00:26:38,359 --> 00:26:41,199 Speaker 5: me tell you this story. If I May, I was 497 00:26:42,280 --> 00:26:47,080 Speaker 5: did an exchange program with Wellesley when I was a junior, 498 00:26:47,920 --> 00:26:53,000 Speaker 5: and I was taking an ancient philosophy class. And in 499 00:26:53,040 --> 00:26:56,720 Speaker 5: the middle of this class, the question turned to apartheid, 500 00:26:57,280 --> 00:27:00,640 Speaker 5: which was still going on in South Africa, and so 501 00:27:00,720 --> 00:27:03,800 Speaker 5: we started a conversation, and of course everybody in the 502 00:27:03,800 --> 00:27:08,680 Speaker 5: classroom decried the situation in South Africa. But there was 503 00:27:08,800 --> 00:27:12,360 Speaker 5: one girl who raised her hand and who spoke up 504 00:27:12,560 --> 00:27:20,800 Speaker 5: to say that she wanted to defend apartheid. She was 505 00:27:20,920 --> 00:27:27,320 Speaker 5: South African and she talked about her life as a 506 00:27:27,359 --> 00:27:31,080 Speaker 5: white South African. Of course, she didn't convince me that 507 00:27:31,119 --> 00:27:37,119 Speaker 5: apartheid was a just system. But when I heard her. 508 00:27:37,080 --> 00:27:39,520 Speaker 4: Speak, I have never forgotten this girl. 509 00:27:40,040 --> 00:27:43,320 Speaker 5: I wish I could find her, because I cannot remember 510 00:27:43,359 --> 00:27:46,120 Speaker 5: a single other student in that class. But I remember 511 00:27:46,240 --> 00:27:49,520 Speaker 5: her and I remember the lesson she taught me, which is, 512 00:27:50,440 --> 00:27:53,560 Speaker 5: whenever you have an opportunity to hear different points of. 513 00:27:53,600 --> 00:27:56,840 Speaker 4: View, even if you don't agree with them, you. 514 00:27:56,960 --> 00:28:00,639 Speaker 5: Learn something from it. And I think she set me 515 00:28:00,680 --> 00:28:03,840 Speaker 5: on a course in my studies that I never would 516 00:28:03,840 --> 00:28:05,840 Speaker 5: have had without that encounter. 517 00:28:06,600 --> 00:28:10,480 Speaker 2: So finally, this is Bloomberg, this is Wall Street. We 518 00:28:10,520 --> 00:28:14,560 Speaker 2: speak to people in places of leadership in the financial 519 00:28:14,560 --> 00:28:18,560 Speaker 2: world and incorporations. What can those leaders in the financial 520 00:28:18,640 --> 00:28:21,680 Speaker 2: and business world do to make sure that we have 521 00:28:21,880 --> 00:28:24,720 Speaker 2: all the talent that we need that we can develop 522 00:28:24,880 --> 00:28:29,040 Speaker 2: wherever they come from, whatever their origin, really on the team, 523 00:28:29,080 --> 00:28:31,520 Speaker 2: as it were, helping move our economy forward. 524 00:28:31,880 --> 00:28:35,400 Speaker 5: Well, I certainly believe that universities have a special role 525 00:28:35,480 --> 00:28:40,960 Speaker 5: to play, but I believe firmly that corporations have to 526 00:28:41,000 --> 00:28:46,120 Speaker 5: be along with universities, leading this struggle to make sure 527 00:28:46,440 --> 00:28:49,520 Speaker 5: that we're tapping into the talent and ability of all 528 00:28:49,600 --> 00:28:50,960 Speaker 5: segments of the US population. 529 00:28:51,160 --> 00:28:52,960 Speaker 4: Imagine that we have. 530 00:28:53,000 --> 00:28:55,720 Speaker 5: A country so rich in diversity and so rich in 531 00:28:55,840 --> 00:28:59,800 Speaker 5: talent in all these areas, and we're not availing ourselves 532 00:28:59,800 --> 00:29:03,800 Speaker 5: of that. That doesn't make any sense. It's illogical. So 533 00:29:04,120 --> 00:29:06,040 Speaker 5: you know, as you may know, I've been on a 534 00:29:06,080 --> 00:29:09,400 Speaker 5: number of corporate boards and in the in the boardroom, 535 00:29:09,680 --> 00:29:17,520 Speaker 5: I'm often advocating for inclusion of executives from different perspectives, 536 00:29:17,560 --> 00:29:24,080 Speaker 5: including women, because it adds immeasurably to the corporation's understanding 537 00:29:24,320 --> 00:29:28,840 Speaker 5: of their customers, understanding what the opportunities will be for 538 00:29:28,960 --> 00:29:34,240 Speaker 5: them as a business going forward. So no CEOs need 539 00:29:34,280 --> 00:29:36,520 Speaker 5: to be playing a very visible role in this, and 540 00:29:36,560 --> 00:29:39,560 Speaker 5: I've been very fortunate in the last few years to 541 00:29:39,600 --> 00:29:44,040 Speaker 5: see many CEOs step into this space and say no, 542 00:29:44,480 --> 00:29:47,160 Speaker 5: I'm going to lead in this space because it's important 543 00:29:47,200 --> 00:29:47,800 Speaker 5: for the country. 544 00:29:48,240 --> 00:29:50,280 Speaker 2: Doctor Simmons, thank you so much for spending time with us. 545 00:29:50,280 --> 00:29:53,400 Speaker 2: As Ruth Simmons, she's the author of Uphome, a Girl's 546 00:29:53,520 --> 00:29:58,320 Speaker 2: Journey coming up. They did it once? Can they repeat 547 00:29:58,360 --> 00:30:03,000 Speaker 2: their performance? Likely duo of YouTube's Bono and Larry Summers 548 00:30:03,000 --> 00:30:06,080 Speaker 2: take on relief for the world's poorest countries once again. 549 00:30:06,640 --> 00:30:11,880 Speaker 16: We need to take a flow of capital to the 550 00:30:12,040 --> 00:30:12,880 Speaker 16: global South. 551 00:30:13,360 --> 00:30:15,400 Speaker 2: That's next on Wall Street Week on Bloomberg. 552 00:30:17,960 --> 00:30:22,200 Speaker 1: This is Bloomberg Wall Street Week with David Weston from 553 00:30:22,320 --> 00:30:25,880 Speaker 1: Bloomberg Radio. 554 00:30:29,520 --> 00:30:32,200 Speaker 2: Here to put in perspective what a major auto strike 555 00:30:32,280 --> 00:30:36,840 Speaker 2: can mean is Bloomberg's International Economics and Policy correspondent Michael McKee. 556 00:30:37,800 --> 00:30:41,120 Speaker 17: For the automakers and their employees, this may be as 557 00:30:41,320 --> 00:30:44,520 Speaker 17: existential a labor conflict as we've seen since the days 558 00:30:44,520 --> 00:30:48,120 Speaker 17: of Roger Smith. Gm Ford and Stilantis have made big 559 00:30:48,160 --> 00:30:51,040 Speaker 17: profits in recent years, and union members now want a 560 00:30:51,040 --> 00:30:53,760 Speaker 17: piece of those, given the concessions they made in two 561 00:30:53,800 --> 00:30:57,360 Speaker 17: thousand and eight to keep the automakers alive. Both sides 562 00:30:57,400 --> 00:30:59,680 Speaker 17: also want to set the work rules for the new 563 00:30:59,680 --> 00:31:03,800 Speaker 17: World electronic vehicles. That's why an lists say this could 564 00:31:03,800 --> 00:31:06,880 Speaker 17: be a long strike. We don't know who will win 565 00:31:06,960 --> 00:31:10,000 Speaker 17: the faceof but we can make a pretty educated guess 566 00:31:10,040 --> 00:31:14,520 Speaker 17: as to who loses equity investors. History shows auto strikes 567 00:31:14,560 --> 00:31:17,560 Speaker 17: don't have a big impact on the macroeconomy. This walkout 568 00:31:17,600 --> 00:31:20,640 Speaker 17: could effect up to one hundred forty six thousand workers, 569 00:31:20,720 --> 00:31:24,680 Speaker 17: which would represent only about one percent of manufacturing employment. 570 00:31:25,280 --> 00:31:28,040 Speaker 17: Go back to nineteen ninety eight when the numbers were similar. 571 00:31:28,280 --> 00:31:32,200 Speaker 17: Twelve seven hundred workers are picketing today, then it was 572 00:31:32,440 --> 00:31:35,760 Speaker 17: nine thousand, two hundred at two plants in Flint, Michigan. 573 00:31:36,320 --> 00:31:40,280 Speaker 17: Their walkout shut down GM production nationwide, putting two hundred 574 00:31:40,400 --> 00:31:43,600 Speaker 17: thousand in total out of work for fifty four days. 575 00:31:44,240 --> 00:31:46,840 Speaker 17: You can see the dramatic impact on the auto industry 576 00:31:46,880 --> 00:31:50,360 Speaker 17: production numbers and employment in the third quarter of nineteen 577 00:31:50,400 --> 00:31:53,480 Speaker 17: ninety eight, but you can't see it in the GDP numbers. 578 00:31:53,640 --> 00:31:56,120 Speaker 17: The losses were made up in the ensuing months, but 579 00:31:56,200 --> 00:31:58,800 Speaker 17: there can be a big ripple effect in the markets. 580 00:31:58,840 --> 00:32:01,600 Speaker 17: The nineteen ninety eight stri I cost GM two point 581 00:32:01,640 --> 00:32:05,280 Speaker 17: three billion dollars in profits, and of course the stock plunged. 582 00:32:06,040 --> 00:32:09,240 Speaker 17: Shares of parts makers like paid producer H. B. Fuller 583 00:32:09,360 --> 00:32:13,680 Speaker 17: also fell, as did steelmakers, but the impact was felt 584 00:32:13,720 --> 00:32:16,720 Speaker 17: far beyond the rust belt. The New York Times saw 585 00:32:16,800 --> 00:32:20,080 Speaker 17: revenue fall as auto advertising dried up. A lot of 586 00:32:20,080 --> 00:32:22,760 Speaker 17: the impact today will, of course depend on how many 587 00:32:22,920 --> 00:32:25,920 Speaker 17: end up off the job and for how long. Given 588 00:32:25,960 --> 00:32:28,600 Speaker 17: this strike is for the first time against all of 589 00:32:28,640 --> 00:32:31,360 Speaker 17: the Big Three. At the same time, there is reason 590 00:32:31,680 --> 00:32:33,160 Speaker 17: for investors to be concerned. 591 00:32:33,760 --> 00:32:38,520 Speaker 2: David finally, one more thought, getting the band back together. 592 00:32:38,920 --> 00:32:41,400 Speaker 2: That's what you two lead singer Bonow has proposed with 593 00:32:41,400 --> 00:32:44,200 Speaker 2: our special contributor Larry Summers. It turns out that they 594 00:32:44,240 --> 00:32:46,920 Speaker 2: worked together back when Larry was President Clinton's Secretary of 595 00:32:46,960 --> 00:32:49,360 Speaker 2: the Treasury, and Bonou got a meeting with him to 596 00:32:49,400 --> 00:32:52,000 Speaker 2: talk about relieving the debt of the poorest countries around 597 00:32:52,040 --> 00:32:52,440 Speaker 2: the world. 598 00:32:52,600 --> 00:32:57,240 Speaker 8: Bono is an unlikely but very close friend. I will 599 00:32:57,280 --> 00:33:00,960 Speaker 8: confess it. I'd never heard of him before war. We 600 00:33:01,040 --> 00:33:02,200 Speaker 8: had our meeting. 601 00:33:02,320 --> 00:33:04,640 Speaker 2: And so we turned to Larry Summers this week for 602 00:33:04,760 --> 00:33:05,600 Speaker 2: our one more thought. 603 00:33:06,880 --> 00:33:09,360 Speaker 8: Twenty three years ago, at the time of the Millennium, 604 00:33:10,120 --> 00:33:15,680 Speaker 8: Bno and I got together because at that time Africa's 605 00:33:16,120 --> 00:33:21,920 Speaker 8: economic prospects and poorest countries outside of Africa looked very 606 00:33:21,960 --> 00:33:25,520 Speaker 8: dim because of the debt burdens. 607 00:33:25,960 --> 00:33:31,880 Speaker 3: Today, the problems are much graver than they were. 608 00:33:33,240 --> 00:33:33,600 Speaker 4: Then. 609 00:33:34,200 --> 00:33:36,920 Speaker 3: The great news is that we have. 610 00:33:37,320 --> 00:33:43,000 Speaker 8: Staggering capacities with technology to address these problems of a 611 00:33:43,120 --> 00:33:47,840 Speaker 8: kind we could not have imagined a decade ago. 612 00:33:48,520 --> 00:33:52,080 Speaker 7: We couldn't have imagined that a vaccine could be created 613 00:33:52,280 --> 00:33:55,800 Speaker 7: in a year against a new disease. We could not 614 00:33:55,960 --> 00:34:00,960 Speaker 7: imagine that in many parts of the country's electricity was 615 00:34:01,000 --> 00:34:04,480 Speaker 7: going to be cheaper in the world solar electricity was 616 00:34:04,520 --> 00:34:09,160 Speaker 7: going to be cheaper than coal electricity. But if these 617 00:34:09,239 --> 00:34:12,759 Speaker 7: technologies are going to find application, we. 618 00:34:12,800 --> 00:34:18,920 Speaker 16: Need to take the flow of capital to the global South, 619 00:34:19,560 --> 00:34:27,759 Speaker 16: to developing countries up by a dimensional factor, not just 620 00:34:28,080 --> 00:34:30,240 Speaker 16: in an incremental way. 621 00:34:30,920 --> 00:34:36,080 Speaker 3: But this is a moment when show me the money, 622 00:34:36,840 --> 00:34:38,680 Speaker 3: when we need to move