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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

Alaska is one of the states in the Federal Reserve's 12th District, which is headquartered in San Francisco. For Mary Daly, the head of the SF Fed, the state right now is a leading indicator for the US economy overall. In an interview recorded on a trip to Anchorage, Daly tells us what she's learning from businesses in the state, how it relates to the rest of the US economy, and how she uses what she's learning on the ground in order to best guide monetary policy. We also get her big picture views on structural trends such as AI and an aging workforce, and why she's growing increasingly confident that the tariffs will not contribute to sustained upward pressure on inflation.

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Mary Daly on Why Alaska Is a Leading Indicator for the US Economy

The Chinese Communist Party is probably one of the most difficult entities in the world to grasp due to its opacity of its inner workings. A new book from Joseph Torigian, a research fellow at Stanford's Hoover History Lab and an associate professor at American University, sheds light on the entity by examining the life of Xi Zhongxun, the father of Xi Jinping. The elder Xi was one of the earliest and important players in the revolution, and his life trajectory -- having spent 16 years out of power during the Cultural Revolution -- offers a lens into how the party operates. On this episode, we discuss Xi Zhongxun's life, the constant pressure to stay on the correct line, and what his struggles might tell us about how his son rules China today.
 
Read more:
China Draws Red Lines on US Chip Tracking With Nvidia Meeting
The AI Showdown: How the US and China Stack Up
 
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Joseph Torigian on Xi Zhongxun and Elite Chinese Communist Party Politics

Late last week, Donald Trump shocked Wall Street by firing Erika McEntarfer, the head of the Bureau of Labor Statistics, the agency responsible for publishing some of America's most important economic data. The firing came after the BLS released a weaker than expected jobs report for July, with just 73,000 new jobs added for the month (compared to forecasts for 103,000). The bureau also revised jobs numbers for the prior two months down by nearly 260,000 jobs. Trump called the data "rigged." But why does the BLS make these revisions, and what does the firing of the BLS chief mean for anyone trying to gauge the direction of the US economy? In this episode, we speak to Bill Beach, a former BLS chief, about the latest drama in US economic statistics.
 
Read more:
Trump to Name New Fed Governor, BLS Head in Coming Days
S&P 500 Bounces 1% After Weak Jobs Data Stokes Rate-Cut Optimism
 
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Bill Beach on How Trump Just Politicized US Economic Data

When it comes to tech startups, you often hear about VCs making a ton of money, or founders experiencing life-changing exits. But something is changing in the world of AI. Now it's the engineers themselves getting pay packages that can be in the 9-figure range. Why is this? Why is it happening? How is it changing the culture of Silicon Valley and business more generally? On this episode, we speak with John Coogan and Jordi Hays, the co-hosts of TBPN, a daily show about technology, which covers the industry in a sports-like manner. We talk about the economics of these transactions, why they make sense, and who are the industry's top superstars.
 
Read more:
Meta Seizes Its Moment to Spend Aggressively in the AI Race
Apple Rebound Looks Elusive as AI Woes Draw Investor Scrutiny
 
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The AI Industry Is Becoming Like Professional Sports

Everybody knows that New York City has a ton of pizzerias. And yet, new ones are opening up all the time. Why do we need more? And how is there still money to be made? On this episode, we speak with Alex Xenopolous, Evan Xenopolous, and James Shields, three restaurant entrepreneurs that recently opened up Xeno's Pizza, a shop close to the Bloomberg offices in Manhattan. We talked about everything from location scouting, to the cost of ingredients, to oven technology, the state of New York City, and how any given establishment differentiates their brand in the hopes of making money by selling what on the surface looks like an abundant, commoditized market.
 
Read more:
 
https://www.bloomberg.com/news/articles/2025-08-01/5-top-new-york-city-restaurants-to-try-right-now-summer-2025
 
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How to Make Money Selling Pizza in New York City

Bakeries are great microcosms of the economy. There's lots of labor involved. You need commodities like flours and eggs, plus energy for your ovens. You need capital investment to get the ovens in the first place, and you need sustained consumer demand to keep you in business. Put it all together and you have a business that tells you a lot about what's going on right now. In this episode, we speak with Andy Kadin, who is the owner of the Los Angeles-based bakery Bub & Grandma's, as well as a sandwich shop and a soon-to-open pizzeria. We talk about what the bread business entails, how much money people are spending right now, and the impact of Trump's tariffs on food costs.
 
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What an LA Bakery Says About the Economy Right Now

Stablecoins are emerging as one of the most active areas of cryptocurrencies. The idea of using blockchain rails to transmit money has captured the attention of legacy financial institutions as well as policymakers, as evidenced by the recent passage of the GENIUS Act, which builds out a regulatory framework for that business. But what are the opportunities. And how do stablecoin providers actually make money? On this episode, we speak with Jeremy Allaire, the co-founder and CEO of Circle, which is the company that backs USDC, the second biggest stablecoin on the market. We discuss the company's business model, concerns about financial stability, and the prospects for stablecoins to open up entirely new avenues of payments and commerce.
 
Read more:
Trump Crypto Group Offers Proposals to Boost Digital Finance
FIS Partners With Circle to Offer Bank Stablecoin Payments
 
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Circle's CEO on the Booming Business of Stablecoins

President Trump has announced tariffs on basically every trading partner. However there is a real sense that the ultimate goal is to hamper the growing perceived economic threat from China. One vision, for how the trade war could be "won" in some sense is by isolating China from the rest of the world. But that's not happening. And in fact, if anything, China is deepening its relationship with other trading nations, particularly in Asia right now. On this episode we speak with Cameron Johnson, a partner at the consulting firm Tidalwave Solutions. Cameron is based in Shanghai, and has an on-the-ground perspective on the state of Chinese manufacturing, having worked alongside producers and end buyers. He talks about the scale of Chinese manufacturing dominance, what Chinese firms are doing to counteract the tariffs, and he argues that in artificial intelligence, China is already way ahead in many respects.
 
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This Is How Chinese Manufacturers Are Countering Trump's Trade War

Tension between the US and China has been building for some time. But so far this has been limited to issues of trade. The US has imposed tariffs on China. It's imposed restrictions on technology exports. In turn, China has imposed some of its own tariffs, and also limited the export of things like rare earth metals. But historically speaking, many hot wars have their roots in some kind of trade-related tensions between nations. So the risk exists that a trade war one day becomes a hot war. So how does this happen, and how can it be avoided? On this episode, we speak with Dale Copeland, a professor of international relations at the University of Virginia. He discusses his theories of trade, and we discuss his most recent book, A World Safe for Commerce: American Foreign Policy From the Revolution to the Rise of China, which specifically discusses the prospect for an outright US-China confrontation.
 
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How a Trade War With China Could Become a Hot War

Today, the cost of energy — things like electricity, or gas, or heating oil — is considered an essential piece of economic data. But it turns out that for much of America's history, we've been overlooking a crucial economic figure: the cost of firewood. For decades, firewood was the dominant energy source powering the US economy. And yet there aren't really any official statistics about firewood prices. After all, how would you even go about putting a price tag on something that's growing in a lot of people's backyards? On this episode, we speak with Nicholas Muller, a Carnegie Mellon University economist and author of the new paper, "Firewood in the American Economy: 1700 to 2010," which attempts to fill in this crucial gap in our economic data. We talk about how Nicholas went about finding 300-year-old firewood prices, and what the new data series can tell us about the development of the US economy and the relationship between growth and energy.

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What 300 Years of Firewood Prices Say About the Economy

Breakthroughs in generative AI have created enormous opportunities for humans to learn from computers. We can use them to explain the news, understand historical concepts, fix our coding errors, and so forth. But of course, AI also has to learn from human. The technology digests enormous amounts of written text, and often relies on human feedback to calibrate its models. Luis von Ahn has been at the forefront of these back and forth interactions for years. He is currently the CEO and co-founder of Duolingo, the language learning app, but prior to that, he was one of the original developers of CAPTCHAs, the little puzzles you solve to log into websites and prove that you're a human. And of course, in the process of proving your humanity, you're also training computers to get better at identifying red lights, cross walks, bicycles and wavy letters. On this episode, we discuss the history of his work, the future of CAPTCHAs, the success of Duolingo and how he is using today's advanced AI models in aid of language learnings.

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Luis von Ahn Explains How Computers and Humans Learn From Each Other

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