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Descubre un mundo infinito de historias

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Bloomberg


Bloomberg's Joe Weisenthal and Tracy Alloway explore the most interesting topics in finance, markets and economics. Join the conversation every Monday and Thursday.


Odd Lots

Protein seems to be everywhere these days, with brands from Starbucks to Pepsi jumping on the trend. But the obsession with protein may have started earlier — with a humble dairy product that defied the broader decline in US milk consumption. Fairlife, which uses a specialized filtering process to boost protein and cut sugar and lactose in its milk products, helped spark the modern protein craze that’s unfolded alongside the rise of Ozempic and other GLP-1 drugs. Since Coca-Cola acquired the brand in 2020, Fairlife has become one of the company’s biggest growth drivers. Yet its success also highlights deeper challenges facing the American dairy industry, where per capita milk consumption continues to fall. So how did Fairlife buck the trend? And what does its story reveal about the future of US dairy? On this episode, we speak with Corey Geiger, lead dairy economist at CoBank.
 
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The Viral Milk That Helped Set Off America's Protein Boom

If the government were open, we'd be getting a jobs report today. But as it is, we're in this blackout of official economic data. That's unfortunate, because the economy is already in a very confusing spot, and so any additional data right now would be very helpful in figuring out where things are heading. In the absence of Non-Farm Payrolls, we talked with Bloomberg Opinion columnist Conor Sen about the worsening state of the labor market, and why he thinks the Federal Reserve needs to step in before the unemployment rate deteriorates further. We also talk about the role AI is (or isn't) playing in the labor market.

Read more: 
US Companies Announce Most October Job Cuts in Over 20 Years
Fed’s Hammack Says Inflation a Bigger Concern Than Labor Market

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Lots More on the Worsening State of the US Labor Market

The news business isn't a great one these days. At least for a lot of legacy outlets (newspapers, cable networks, print magazines etc.), the Internet has obliterated their business model. And now AI is coming along and there's a risk that it makes conditions even worse, because unlike in the glory days of the search era, the user doesn't even have to click to get what they're looking for. So how does an AI company like Perplexity, which combines search and AI to create a real-time destination for learning about the news, affect industry economics? On this episode, which was recorded live at Lazard's Foursquare conference (an annual gathering for business leaders in sports, media, and entertainment), we speak with Perplexity's Chief Business Officer Dmitry Shevelenko. We talk about the company's relationship with the news business, a new battle against Amazon, and the general economic and finance environment for hot AI startups.
 
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Dmitry Shevelenko on Perplexity's Vision for Reshaping the Internet

New York City is about to elect a new mayor, and whoever it is will have to address growing challenges like unaffordability, homelessness, and other basic quality-of-life challenges. Very few of these challenges are distinct to NYC. San Francisco, in particular, has also faced this weird tension, where it's incredibly dynamic and wealthy, and also famous for its obvious symptoms of dysfunction. The city is one year into the new Daniel Lurie administration, and many of the quality-of-life statistics have been improving (which is the case in other cities as well). The city is ground zero for the world's AI industry, which is heavily concentrated in SF, rather than the Bay Area writ large. So we spoke with Lurie about his approach to city management, what he's learned in his first year on the job, his vision for improving zoning, and what, if any, advice he'd offer to the next NYC mayor.
 
Read more:
Uber to Take on Waymo in San Francisco With Lucid, Nuro Robotaxis
Trump Cancels San Francisco Raids After Benioff, Huang Calls
 
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San Francisco's New Mayor on Homelessness, Unaffordability, and AI

Unfortunately, it doesn't seem as though you can get great stock picks just by going to ChatGPT and asking it to recommend some investments. And yet financial firms of all sorts — including trading firms — say they're increasingly using AI. But are the tools actually being deployed? And how do these tools differ from traditional machine learning or algorithmic approaches to trading, the likes of which have been used by quant firms for decades now. On this episode of the podcast, we speak with Iain Dunning, the head of AI research at Hudson River Trading, a major US market maker. We discuss the firm's attempts to use AI not just for more efficient trading, but also to make short-term predictions about price, which further gives its traders an edge. Dunning walks us through his work, his views on the main constraints facing the space (labor, power, chips, etc.) and how his work is both different and similar to what's happening at the major cutting edge research labs like ChatGPT.

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How Hudson River Trading Actually Uses AI

You hear a lot about AI safety, and this idea that sufficiently advanced AI could pose some kind of threat to humans. So people are always talking about and researching "alignment" to ensure that new AI models comport with human needs and values. But what about humans' collective treatment of AI? A small but growing number of researchers talk about AI models potentially being sentient. Perhaps they are "moral patients." Perhaps they feel some kind of equivalent of pleasure and pain -- all of which, if so, raises questions about how we use AI. They argue that one day we'll be talking about AI welfare the way we talk about animal rights, or humane versions of animal husbandry. On this episode we speak with Larissa Schiavo of Eleos AI. Eleos is an an organization that says it's "preparing for AI sentience and welfare." In this conversation we discuss the work being done in the field, why some people think it's an important area for research, whether it's in tension with AI safety, and how our use and development of AI might change in a world where models' welfare were to be seen as an important consideration.
 
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The Movement That Wants Us to Care About AI Model Welfare

Everyone has an opinion on why housing is so expensive in America -- and to be fair, there are probably a lot of reasons for it. But one simple factor is that homes are expensive to build. Unlike many other physical objects, they haven't gotten cheaper over time. So why is this? And why haven't we found a way to bring down the cost curve by building modular housing in factories or on assembly lines? On this episode, we speak with Brian Potter the author of the new book The Origins of Efficiency. Potter also worked at a modular homes startup that failed, and is also the author of the excellent Construction Physics newsletter. So we talk about what he's learned about housing, as well as broader questions about how operational efficiency is achieved over time across a range of industries.
 
Read more:
Austin, Salt Lake City Top Global List of Most Affordable Cities
Affordable Housing Left Vulnerable After Trump Fires Building Inspectors
 
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Why It's Still So Expensive to Build Homes in America

If you go out to eat at a restaurant, whether it's a fast food chain or a Michelin-starred bistro, there's a good chance the ingredients on your plate came from the same source. Sysco is the dominant foodservice distributor in the US, using its massive logistics network to quietly supply the food that goes into meals in thousands of restaurants across the US. Sysco's scale and product standardization have helped define what American dining tastes like -- sometimes literally. But critics say its power has gone too far, leaving chefs and diners with fewer choices and blander outcomes. In this episode, we talk with Austin Frerick, author of Barons: Money, Power, and the Corruption of America's Food Industry, about how Sysco became the middleman shaping America's menus.
 
 
 
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The Hidden Supply Chain Making Every Menu Feel Familiar

A few years ago, governments and corporations were brimming with optimism about the prospect of getting to net-zero in the efforts against climate change. Today, you hear a lot less about that. And while there's renewables getting added to the mix all the time, one energy source that's really booming is natural gas. And coal is booming too. So what happened? And is there any prospect of the world getting back on track? On this episode, we speak with acclaimed energy historian Daniel Yergin, current Vice Chairman of S&P Global. He's also, of course, the author of the Pulitzer Prize-winning book The Prize, as well as, more recently, The New Map. We talk about all of the factors that changed the energy picture in recent years and the energy priorities of nations right now.
 
Read more:
Russia’s Crude Shipments Climb Close to a Post-Invasion High
Trump to Buy 1 Million Barrels to Help Refill Oil Reserve
 
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Daniel Yergin on What Happened to the Energy Transition

Some economists expected that the dollar would strengthen when the tariffs were imposed. Instead, the opposite happened. The dollar fell sharply and it's been a poor performer all year. Concurrently, it's been a great year for a lot of dollar-denominated assets, like stocks. Even US Treasuries have rallied this year. So what's going on? On this episode, we speak with recurring Odd Lots guest Hyun Song Shin, Economic Adviser and Head of the Monetary and Economic Department at the Bank for International Settlements. Per his work, the world didn't abandon the US dollar, but it did start hedging exposure to it. We discuss this phenomenon, as well as other risks on the macro landscape.

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Why The World Started Hedging Its US Dollar Exposure

Multi-strategy hedge funds are still all the rage on Wall Street, but what does it actually mean to be a pod shop and how are they being set up? On this episode, we speak with Dan Morillo, co-founder of Freestone Grove Partners and formerly a partner and head of equity quantitative research at Citadel (one of the most successful multi-strats out there.) While lots of people tend to talk about multi-strategy hedge funds as one big blob, he argues that there are important differences in their business models. We talk about how he identifies top portfolio managers, managing crowding risk, and the math behind compensation, scale and returns.

Previously:
How Hedge Funds Discover the Next Superstar Trader 
How to Succeed at Multi-Strategy Hedge Funds

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The Math That Explains How Multi-Strategy Hedge Funds Make Money

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