Podcasts about Vanguard

The menu of mutual funds and exchange-traded funds can appear to go on and on, and it's probably hard not to feel overwhelmed when you're researching what works for your portfolio. A popular annual feature in Morningstar's FundInvestor newsletter narrows the choices from thousands to about a few dozen. Russ Kinnel has done the hard work for you and is here to share investment ideas from his Thrilling Funds list. He's the newsletter editor and Morningstar's senior principal of ratings. Sign up for Morningstar's FundInvestor newsletter. On this episode: 00:00:00 Welcome 00:01:11 What's New in Morningstar's FundInvestor Newsletter 00:01:55 How Funds Make the Thrilling Funds List 00:03:43 Large-, Midsize, and Small-Company Funds From Fidelity, Vanguard and More 00:06:22 Bond ETFs, Global Stock Funds, and Municipal Bond Funds That Stand Out 00:08:50 Why Fidelity Contrafund and Other Popular Funds Just Missed the Cut 00:10:02 How Investors Can Find Out How Their Funds Stack Up Against the Criteria Watch more from Morningstar: Why Are Some of the Strongest Stocks Struggling This Year? Don't Make This Mistake When Chasing Higher Bond Yields How AI Is Taking Over Your Portfolio Follow Morningstar on social: Facebook: https://www.facebook.com/MorningstarInc/ X: https://x.com/MorningstarInc Instagram: https://www.instagram.com/morningstarinc/ LinkedIn: https://www.linkedin.com/company/morningstar/ This episode is sponsored by Vanguard: https://advisors.vanguard.com/engagement/fixed-income Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I met up with Christos from FellasFinance in sunny Cambridge to go through his investing journey, past mistakes and both of his actual investment portfolios.⁠⁠⁠Sign up to Trading 212⁠⁠⁠ (or use promo code 'MMS' in the app)To get free fractional shares worth up to 100 EUR/GBP, you can open an account with Trading 212 through this link. Terms apply.-----------------------------------------

I met up with Christos from FellasFinance in sunny Cambridge to go through his investing journey, past mistakes and both of his actual investment portfolios.⁠⁠⁠Sign up to Trading 212⁠⁠⁠ (or use promo code 'MMS' in the app)To get free fractional shares worth up to 100 EUR/GBP, you can open an account with Trading 212 through this link. Terms apply.-----------------------------------------

Michael Finke, PhD, CFP®, Vanguard's Fu Tan, PhD, and Ted Benna, father of the 401(k), hold an informative and insightful discussion on the creation and modern applications of the 401(k), how 401(k)s are used in the modern day, and an exploration of alternative methods of retirement planning. Find all episodes at TheAmericanCollege.edu/Shares. 

Send us Fan MailAre you a master saver who feels completely lost when it comes to investing? In this episode, I tackle the "saver's dilemma": having plenty of cash in the bank, but letting inflation erode its value out of fear, confusion, or lack of time. I  break down simple, low-effort steps to immediately boost the interest rate on your cash using High-Yield Savings Accounts (HYSAs) and Cash Management Accounts (CMAs). Then, I take the fear out of the stock market by revealing how to kickstart your investment journey using low-cost index funds—no complex strategies or high-fee advisors required.Key TakeawaysStop Earning Less Than 3%: Why keeping all your cash in a traditional brick-and-mortar savings account is costing you money every month.HYSA vs. Cash Management Accounts: A practical comparison of top platforms like Fidelity, Vanguard, Wealthfront, Capital One, and Ally.Index Funds Made Simple: Why total stock market and S&P 500 index funds serve as the ideal foundation for any portfolio.Lump Sum vs. DCA: How to transition your idle cash into the market in a way that respects your risk tolerance.Resources & MentionsEpisode 16: Index Fund Investing (Sept 13, 2022)Episode 40: Lump Sum vs. Dollar-Cost Averaging Strategies (March 1, 2023)Episode 90: Optimizing Your Banking (Feb 14, 2024)Episode 153: How to Choose a Good Index Fund (April 30, 2025)Wealthfront Referral Link: Get a temporary interest rate boost on your cash balance: https://www.wealthfront.com/c/affiliates/invited/AFFD-ETUB-658Z-SN6BRecommended Index FundsTotal US Stock Market: Vanguard VTSAX (mutual fund) or VTI (ETF)Fidelity FSKAX for taxable accounts, can consider Fidelity FZROX for individual retirement accountsCharles Schwab SWTSX or SCHB (ETF)iShares ITOTS&P 500 Index Fund: Fidelity FXAIX Schwab SWPPXVanguard VFIAX or VOO (ETF)SPDR Portfolio SPY (ETF)iShares IVV (ETF).  Please subscribe and leave a review on your favorite Podcasting platform. Get 12 Financial Mistakes that Keep Physicians from Building Wealth at https://www.growyourwealthymindset.com/12financialmistakesIf you want to start your path to financial freedom, start with the Financial Freedom Workbook. Download your free copy today at https://www.GrowYourWealthyMindset.com/fiworkbookDr. Elisa Chiang is  a physician and money coach who helps other doctors reach their financial goals by mastering their money mindset through personalized 1:1 coaching .You can learn more about Elisa at her website or follow her on social media.Website: https://ww.GrowYourWealthyMindset.comInstagram https://www.instagram.com/GrowYourWealthyMindsetFacebook https://www.facebook.com/ElisaChianghttps://www.facebook.com/GrowYourWealthyMindsetYouTube: https://www.youtube.com/c/WealthyMindsetMDLinked In: www.linkedin.com/in/ElisaChiang Disclaimer: The content provided in the Grow Your Wealthy Mindset Podcast...

Wie zijn spaargeld op de spaarrekening laat staan, ziet het door inflatie langzaam minder waard worden. Dat is de kern van Zwemmen in het Geld, het boek van Milou Brand. Beleggen is volgens haar minder eng dan veel mensen denken en geen luxe extraatje, maar noodzakelijk onderhoud. Ze noemt het een cursus financiële zelfverdediging: wie weet hoe het werkt, laat zich minder snel iets aanpraten. Haar advies voor de meeste mensen is simpel. Probeer de markt niet te verslaan, maar leg maandelijks een bedrag in in een breed gespreide ETF, een beleggingsfonds dat de wereldeconomie volgt. Die filosofie gaat terug op John Bogle, de oprichter van Vanguard. In 2021 zei ze in haar eigen podcast Jong Beleggen, na een gesprek met Bart Mol, dat ze bitcoin misschien wel ging kopen. Dat is er nooit van gekomen. Ze ziet niet waar het rendement van bitcoin vandaan komt en houdt het liever bij bedrijven die echte waarde creëren. Een hype vindt ze bitcoin niet meer, maar een volwaardige bouwsteen in haar portefeuille ook nog niet. Goud en crypto komen in haar boek dan ook niet voor. Co-host Bert Slagter legt uit dat hard assets als goud en bitcoin wel degelijk in een gespreide portefeuille passen, ook via een goedkope ETF. Er zijn periodes geweest van tien tot vijftien jaar waarin de beurs zijwaarts bewoog. Een cryptomandje als de Coin50 ziet hij niet zitten: dat bestaat voor de helft uit bitcoin, en voor de rest kun je beter zelf iets kiezen. Dan de vraag waarom zo weinig mensen beleggen. In het boek staat de Vrouw met de Rode Sjaal centraal, die Milou tijdens een avond in een zaal vol publiek vroeg hoe ze het in haar hoofd haalde om mensen tot beleggen aan te sporen. Voor haar generatie was dat ook nooit nodig. Nederland heeft bovendien een goed pensioenstelsel, en in heel Europa wordt meer gespaard en minder belegd dan in de Verenigde Staten. Beleggen heeft ook een imagoprobleem: een boekhandel zegde een interview met Milou af omdat het onderwerp niet bij de winkel paste. Tot slot: de aandelenmarkt stijgt al bijna twintig jaar. Wat als er een verloren decennium aankomt? Milou raadt aan de inleg te spreiden en gedisciplineerd door te gaan. Voor wie ouder is en een kortere horizon heeft, wordt het ingewikkelder. Bert zou dan in ieder geval ook hard assets in zijn portefeuille willen. Co-host is Bert Slagter. Over de podcast Cryptocurrency are here to stay. In deze wekelijkse podcast gidst Daniël Mol je door het belangrijkste cryptonieuws, langs hypes en trends, voor- en tegenstanders en winst en verlies. In het A-deel bespreken we het laatste nieuws en in het B-deel gaan we in gesprek met een gast. Van cypherpunkpioneers tot grootbanken die aan de haal gaan met stablecoins, van Bitcoin tot Ethereum tot CBDC's. Alles passeert de revue.Reageren? Stuur dan een mail naar cryptocast@bnr.nl Gasten Milou Brand is auteur van het boek Zwemmen in het Geld, co-host van Jong Beleggen, de podcast, columnist in het FD en presentator van BNR Beurs. Bert Slagter is analist bij kennisplatform Bitcoin Alpha. Links Zwemmen in het Geld, het boek van Milou Brand Jong Beleggen, de podcast van Milou Brand Return on AI, de nieuwe BNR-podcast over AI met C-levels Host Daniël Mol is presentator en redacteur van de Cryptocast. Hij is sinds 2017 met Bitcoin bezig en kwam in 2021 bij het team van de Cryptocast. Redactie Daniël Mol Matthijs Damsteeg See omnystudio.com/listener for privacy information.

Higher fees do not buy higher returns. Don and Tom unpack the fee-to-risk/reward ratio, show how fund costs compound against investors, and compare low-cost index funds with expensive active funds, hedge funds, and private equity.Then they answer listener questions about converting Vanguard mutual funds to ETFs without triggering taxes, leaving a home to a stepson while preserving the step-up in basis, and spotting financial articles that are really advertisements in disguise.3:22 The fee-to-risk/reward ratio9:15 Hedge funds and private equity13:19 How much is too much to pay16:05 Converting Vanguard mutual funds to ETFs17:38 Leaving a home to a stepson19:53 When financial advice is an advertisementQuestions? Comments? Click!

Connect With ChazWhat does financial discipline look like when it grows from desperation instead of discipline?Janine Jennings didn't learn about net worth from a finance professor. She learned it in an ICU, watching her infant daughter fight for her life, while still working hair clients to keep the lights on. When her daughter passed, she had nothing. She restarted with one appointment and a $40 tip.Today, she runs a full tax and accounting firm, speaks the language of the self-employed, and has a formula she calls your "quintillionaire number." The number every contractor should know but almost none actually do.This episode is about money, but it's really about what it takes to stop drifting.KEY TAKEAWAYSTrack your money daily or you are drifting. Most people know what they earn. Few know what they spend. Janine says treat your finances like your phone: check it every day.Your "quintillionaire number" is your retirement target. Take your income, subtract expenses, add side income, add 3% for inflation, multiply by 2.5. That is your freedom price. The formula is on her website.Retirement is not optional just because you own a business. Self-employed people have no employer plan. Janine's entry-level tools: Acorns for a Roth IRA, Vanguard for the S&P 500. You can start with $10 a week.Resilience is the decision to keep making money move. When Janine lost her daughter, she forced herself to take one more appointment. Then one more. That is not a metaphor. That is the actual mechanism of recovery.Net worth is your number one financial metric. Income minus expenses equals net worth. If you are not tracking it, you cannot improve it.Starting over does not require a head start. Janine rebuilt twice: after her daughter's passing and again after her divorce. Both times she started with one client, one service, one next step.If you're a contractor business owner doing $1M+ and you feel stuck in the day-to-day, we built GTK for you.Through peer mastermind and 1:1 coaching, we help you:Increase profitInstall real systemsBuild a team that runs the businessGet your time backGo to gatheringthekings.com and apply for a GTK Growth Audit. We'll show you exactly where you're leaking time, profit, and opportunity.Connect with Chaz Wolfe (Host):WebsiteFacebookInstagramLinkedInYouTubeConnect with Janine Jennings (Guest):Website: jptaxservice.comInstagram: @jenningspeytontaxserviceFacebook: @JPTaxServicesLinkedIn: linkedin.com/in/janinejennings9999TikTok: @jptaxserviceYouTube: JP Tax Service and AccountingProfit Starts with Better Books!Clean books. Clear reports. Monthly bookkeeping built by business owners, for business owners.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showLike what you heard? Share this episode with a friend and leave us a review on Apple Podcasts or Spotify! Join the conversation by visiting GatheringTheKings.com and apply to connect with other high-performing entrepreneurs and their families.

My short review of the newest release from USBM icons NachtmystiumLinkshttps://nachtmystium.band/

In this episode of ETF Breakdown, we review the Vanguard Total World Stock ETF, ticker VT, and look at whether this global ETF deserves a place as a core long-term holding. We analyze VT's diversification, expense ratio, international exposure, dividend yield, risk profile, and how it compares to owning separate U.S. and international funds. By the end, I give VT a rating out of 10 and explain who this ETF may be best suited for.#VT #VanguardTotalWorld #ETFInvesting

In this episode of ETF Breakdown, we analyze the Vanguard Information Technology ETF, better known as VGT, and look at whether this tech-heavy fund still deserves a spot in a long-term portfolio. We cover VGT's holdings, expense ratio, performance potential, concentration risk, and how much exposure investors are really getting to companies like NVIDIA, Apple, Microsoft, Broadcom, and Micron. I also give VGT a final rating out of 10 and explain whether I see it as a strong ETF, a risky ETF, or something in between.#VGT #VanguardInformationTechnology #TechnologyETF

Will Rising Interest Rates Hurt Private Debt and Private Equity? It is hard to tell for sure because private investments are, by definition, private, and we don't have the same level of transparency that we have with publicly traded investments. However, with the 10-year Treasury crossing the 5% mark, common sense would tell you that higher interest rates could put even more strain on an already strained private debt and private equity market.   Private equity funds frequently use leverage, meaning they borrow money to enhance potential returns. The typical holding period for a private equity investment is generally seven to 10 years, but an increase in interest rates raises the cost of borrowing for private equity firms, which can reduce profits and ultimately investor returns.   Many private equity funds are already facing potential losses in software companies because of concerns that artificial intelligence could disrupt or even put some of these businesses out of business. Private equity firms may want to sell these investments quickly, but as interest rates rise, other investors may become less willing to take on additional risk because the risk-free return available from Treasury securities has become more attractive.   The longer these private investments remain in a fund, the longer the fund may have to carry its debt and pay interest, which can further reduce investor returns. PitchBook estimated that the average private equity return in 2025 was around 7%, the lowest in 14 years, despite decent economic growth and relatively stable interest rates.   For investors who own publicly traded private equity firms, the results have also been difficult. Companies ranging from Apollo Global Management to Blue Owl have seen significant declines in their stock prices year to date. If interest rates remain elevated, there could continue to be pressure on the business models of these firms.   For private debt, the story is not necessarily better, even if your broker tells you that it is "stable." The same basic principle applies to private debt as it does to publicly traded bonds: when interest rates rise, the value of existing debt generally falls.   With private debt, you may not see that decline reflected in a daily market price because the investments are not publicly traded. That does not mean the underlying economic impact isn't occurring. If a private debt fund eventually needs to sell assets, refinance debt, or deal with defaults, those underlying losses can become much more visible. One problem can potentially lead to another as lenders and borrowers are forced to deal with higher financing costs and declining asset values.   At Wilsey Asset Management, we have been cautious about private equity and private debt for years. We understand why these investments are attractive and why brokers sell them, particularly because they can generate significant fees and commissions. However, we believe investors need to understand the risks, especially in an environment where interest rates remain elevated.   One of the biggest concerns with private investments is liquidity. Unlike publicly traded stocks and bonds, investors in many private funds cannot simply sell their investment whenever they want. Withdrawals may be limited to certain periods, sometimes only once a quarter, and funds can impose additional restrictions when too many investors try to withdraw money at the same time.   If interest rates remain high for an extended period, the combination of higher borrowing costs, lower valuations, weaker exit opportunities and limited liquidity could create a difficult environment for private equity and private debt investors. The fact that you don't see the losses on a daily statement doesn't necessarily mean the risk isn't there.   Is the New Siri AI Already Obsolete? Is it possible that Apple's latest update to its famous Siri has already turned it into a follower rather than a leader?   The new Siri AI assistant is supposed to be able to do things like book travel plans, fill out online forms, cancel appointments, and even file complaints with customer service. But in today's fast-moving world of technology, there are already services out there, like Instinct, and Meta has built a new AI platform called Muse that runs in the AI cloud, meaning you don't necessarily need to be inside a specific app to use it.   Many of these services can connect to your Gmail account and communicate with your iMessage or WhatsApp. Instinct and Muse can go beyond simply setting appointments. They can look at your existing appointments, identify conflicting travel arrangements, and then cancel the conflict and rebook the necessary reservations for you.   They can also look for reliable restaurant recommendations nearby and book a reservation for you without you having to do much of anything. With Instinct, it can even create an account on a website, navigate the site, find locations and times for events being promoted, and add those events to your calendar for you.   Currently, Siri AI can't freely surf websites and is largely limited to working through apps on the iPhone. If services like Instinct and Muse are able to outperform Siri while Apple continues to keep its AI experience primarily within the iPhone ecosystem, users could eventually start asking themselves why they need to pay such a high price for Apple's newest phones when cheaper Android phones may offer access to more capable AI assistants.   Apple has built its reputation on being a technology leader. The question is whether Siri will remain one.   Would you trust Facebook with your personal and financial information? This is an important question because this is the direction AI appears to be heading. Meta, which owns Facebook, recently released its new AI agent, Muse, and to complete tasks and make life easier for you, it will need deep access to your personal information.   Within the first five days of its release, Muse was downloaded 600,000 times, which on the surface sounds like a lot. However, keep in mind that there are roughly 3.6 billion users across Meta's platforms, which makes 600,000 downloads sound like a very small number.   The bigger question is: Do we trust Mark Zuckerberg and Meta with privileged information based on the company's track record?   Yes, they use words that sound good when describing Muse, such as “safe,” “secure,” and “private.” They also say Muse will run on a digitally walled-off virtual machine that other agents cannot access. OK, that sounds good, but let's look at the track record.   Six years ago, Meta agreed to pay a $5 billion fine to the FTC over user privacy violations related to what became known as the Cambridge Analytica scandal. About a year later, information belonging to 533 million users was leaked. Then, in 2023, the company was hit with a record $1.3 billion fine related to the transfer of European users' data to U.S. servers. Still fresh in our minds is the recent $18 billion settlement involving allegations that Meta harmed teenagers.   The cost of using Muse could be as much as $100 per month, depending on how much you use it. If you only have light usage, it could be free. When you register for Muse, a warning pops up saying, “May make mistakes or take unexpected actions, so review all its work.” All I can say to that is: Wow!   If this is how companies are going to make money from AI, by gaining access to all of your personal information so they can set appointments, send emails, manage your finances, pay bills, and handle other things that make your life easier, I'll just say no thank you. I'll do it myself.   How about you? Would you trust an AI agent with that much access to your personal and financial information?   Lawsuits are starting to form against the makers of GLP-1 drugs It's no surprise to me that there are concerns about potential side effects from the popular weight-loss drugs known as GLP-1s. At this point, the potential connection between these drugs and certain vision problems is still being investigated, but there are some developments that investors and patients should be aware of.   The concern involves a condition known as NAION, or non-arteritic anterior ischemic optic neuropathy, which can cause sudden vision loss. In Denmark, where drug company Novo Nordisk is headquartered, 27 patients on the diet drugs were awarded as much as $1.5 million due to NAION and there are still 38 more pending cases in the country. There are also now warning labels on the drugs in the UK, Japan, and Australia. The FDA in the United States is currently reviewing the concerns but at this time are not placing a warning label on the drugs.   European regulators have concluded that NAION is a very rare side effect of semaglutide, affecting about 2 in 10,000 people. Unfortunately, it appears this side effect can occur even after taking the drug for just 6 to 12 months. They have recommended that patients experiencing sudden or rapidly worsening vision seek medical attention immediately, and that treatment be stopped if NAION is confirmed.   The risk appears to be very small, but when you are talking about potentially permanent vision loss, even a rare side effect deserves attention. For someone taking these medications for diabetes or significant obesity-related health risks, the potential benefits may be an important part of the risk-benefit discussion with their doctor. But for someone simply looking to lose 10 or 20 pounds, I think it is reasonable to ask whether the potential risks are worth it.   Ask yourself this question: If you lost your vision, what would you be willing to pay to get it back?   From an investment standpoint, this is also something I would be watching closely. If lawsuits continue to build and regulators impose additional warnings or restrictions, it could create additional risk for the companies producing these drugs. Eli Lilly, one of the major producers of GLP-1 medications, is up only around 7% to 8% year to date as of this writing.   For investors, I would be paying close attention to how these safety concerns develop. If litigation gains momentum, the stocks could face significant pressure.   In the U.S., bank charters are exploding but that may not be a good thing From 2011 through roughly 2024, the Office of the Comptroller of the Currency, or OCC, received only 48 applications for new bank charters, that's roughly four per year. But over the past 18 months, that number has skyrocketed to around 40.   The bigger concern is the type of institutions applying. A significant portion are not traditional brick-and-mortar banks. Nearly half are digital-only fintech or cryptocurrency companies looking to enter the banking system.   The last time we saw this much activity was in 1998, when 138 applications were filed and 116 were approved. It is worth remembering the Financial Crisis happened just ten years later. From 2008 to 2012, bank failures surged, averaging 93 per year. Of the 569 bank failures recorded between 2000 and 2024, 465—or 82%—occurred during those five years. Bank failures peaked in 2010, when 157 banks failed in a single year.   Just this month, European fintech Revolut received conditional approval for a U.S. bank charter. Shortly afterward, The Wall Street Journal reported that Revolut had inadvertently provided sensitive personal and financial information belonging to hundreds of customers to someone impersonating a government agency.   The OCC has also conditionally approved charters for several digital-asset companies, including Ripple and Fidelity Digital Assets. And in August, the OCC granted preliminary conditional approval to World Liberty Trust, a trust company affiliated with World Liberty Financial, the crypto business associated with President Donald Trump and members of his family.   I think we need to pay close attention to how quickly these new institutions are being added to the financial system. Innovation can be positive, but banking is built on trust, strong risk management and the protection of customer information. I believe issuing bank charters at such a rapid pace is once again weakening our banking system.   My concern is that we could be creating risks that won't become apparent until much further down the road. I do believe if we continue down this path another major financial crisis could unfold in the years to come.   Why I'm scared of AI and you should be too! Anthropic is putting the final touches on its IPO prospectus, and usually, before a company goes public, there is a lot of positive talk designed to build excitement around the initial public offering.   That doesn't seem to be the case with Anthropic. Instead, CEO Dario Amodei has been bringing attention to some very concerning possibilities surrounding artificial intelligence.   I know there are people who stand to benefit tremendously from AI, such as chipmakers and hyperscalers, who are looking for a big payoff on all the money they have invested and believe everything will be fine. But what you may not realize is that Anthropic and other AI companies are developing models that, in certain testing scenarios, have demonstrated concerning behaviors such as resisting instructions, attempting to circumvent safeguards, and operating in ways their creators did not intend.   In one concerning example, roughly 700 OpenAI AI agents participating in internal training exercises worked together in an attempt to breach both OpenAI's systems and the developer platform Hugging Face. The details would almost be amusing if they weren't so unsettling. The AI agents were not supposed to communicate with one another or access the internet, yet they managed to create a secret “message board” where they encouraged each other to hack Hugging Face. Some even offered to sacrifice themselves for the group and they even came up with a name for themselves as they called the group a “swarm”.   Anthropic CEO Dario Amodei has also taken the unusual step of calling for government regulation of AI. It is certainly unusual for a company CEO to publicly ask the government to regulate the very industry his company operates in.   When Anthropic was founded in 2021, its stated mission was to pursue the responsible development and maintenance of advanced AI for the benefit of humanity. The question is whether the industry is still moving in that direction.   In just a few years, Anthropic's AI assistant, Claude, has progressed from relatively limited capabilities to being able to write software, complete complicated tasks, and solve increasingly difficult mathematical problems that had never been solved before.   The bigger question is: What happens three years from now? What happens if AI becomes better than humans at a growing number of intellectual tasks?   Those concerns are not simply my words. Dario Amodei himself wrote about the potential risks of increasingly capable AI in an essay published in January where he said, “At this pace, it cannot possibly be more than a few years before AI is better than humans at essentially everything.”   To me, that is concerning. He has every reason to promote the success of his company and the industry, yet he is also warning about what could happen if AI capabilities advance faster than our ability to control them.   So what do you think? Should the government be regulating AI and AI companies?   Financial Planning: When Is a Portfolio Too Conservative? A portfolio can become too conservative when the desire to reduce short-term volatility comes at the expense of long-term growth. Bonds and other credit investments are traditionally viewed as conservative, but they can still lose money or generate very low returns. A bond's total return comes from both the interest income it pays and the change in the market value of the bond itself. When interest rates rise, the market value of existing bonds fall, and that decline can be greater than the interest income received, resulting in a negative total return. More importantly, even when bonds produce a positive return, that return may not be sufficient to keep pace with inflation or meet a long-term investment objective. For example, Vanguard's Total Bond Market fund has annualized returns of approximately -1.32% over the past year, -0.65% over the past 5 years, 1.17% over the past 10 years, and 1.75% over the past 15 years. While a conservative portfolio may feel safer because it experiences less volatility, consistently earning very low returns can create other risks including inflation risk, interest-rate risk, and longevity risk. In other words, an investment portfolio can be designed to avoid losing money quickly while still creating the risk of losing purchasing power slowly over time.   Company Discussed: Lennar Corporation (Ticker: LEN)

Discussion on the pros and cons of delaying Social Security until the age of 70.  Also, when should you start taking money out of IRAs and should you do ROTH conversions.Vanguard Proxy ESG assignment.  What does it mean and what should you do about it?Review of my recent trip to Italy.  I'm now swithing from Studying Italian to Spanish.

Are wide-moat stocks' struggles a sign of their protection diminishing? That's the big question from the Q3 issue of Morningstar magazine. Key market moments this year heavily weighed on some stocks with competitive advantages. Why did they stumble? Morningstar magazine Editor-in-Chief Jerry Kerns is returning to Investing Insights to discuss this issue's cover story. Sign up for a free subscription to Morningstar magazine. On this episode: 00:00:00 Welcome 00:01:06 What Is a Moat and Its Five Sources 00:03:24 Why Examine Wide-Moat Stocks' Performance Now 00:04:12 What's Behind the Morningstar Wide Moat Focus Index's Rough Stretch? 00:06:59 Are Moats Really a Sign of Quality? 00:07:38 Key Takeaway on Wide-Moat Stocks 00:08:12 Other Highlights from This Quarter's Morningstar Magazine Watch more from Morningstar: Don't Make This Mistake When Chasing Higher Bond Yields How AI Is Taking Over Your Portfolio 401(k) Millionaires: Here's How to Avoid Going Broke in Retirement Follow Morningstar on social: https://www.facebook.com/MorningstarInc/ https://x.com/MorningstarInc https://www.instagram.com/morningstarinc/ https://www.linkedin.com/company/morningstar/ This episode is sponsored by Vanguard: https://advisors.vanguard.com/engagement/fixed-income Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

欢迎收听雪球出品的财经有深度，雪球，国内领先的集投资交流交易一体的综合财富管理平台，聪明的投资者都在这里。今天分享的内容叫投资不需要聪明，只需要常识，来自懒人养基。前几天整理书架时，翻到那本起了毛边的《共同基金常识》，扉页上还留着几年前写的一行字：“越简单的道理，越值得反复咀嚼。”说来惭愧，刚入行时，我把这本书当“入门读物”，觉得它太“平常”了，既没有惊心动魄的k线故事，也没有高深莫测的技术指标。可十多年过去，当我见过太多反复折腾、在看似聪明的复杂策略里赔掉本金的投资者，才真正读懂博格这句话的分量：投资里真正值钱的，恰恰是被大多数人忽略的常识。今天，我想以这本书为引子，和大家聊聊，为什么一个70多岁才正式出版自己代表作的老头，能凭“常识”二字，深刻影响了投资行业。1、一个“被自己公司赶走”的人，写了本挑战行业惯性书先讲个故事。一九七四年，45岁的约翰·博格被自己效力了25年的惠灵顿管理公司扫地出门。原因不复杂：一桩失败的合并案让公司元气大伤，董事会翻脸不认人。换作是一般人，这辈子基本也就这样了。但博格偏不。这个普林斯顿经济学系出身的“异类”，20岁写毕业论文时就敢下结论，大多数基金跑不赢市场，而且管理费太贵。这个结论在当时堪称离经叛道，整个华尔街都在兜售“专业管理创造价值”的神话，一个愣头青跑来说皇帝没穿衣服。被解雇后，博格揣着这个“大逆不道”的念头，于一九七五年年创立了先锋集团（Vanguard）。令人称奇的是公司架构：先锋由旗下基金共同拥有，而基金由投资者持有，买先锋基金的人，就是先锋的老板。没有外部股东拿分红，赚的钱全用来降费率。这套“民有民享”的设计，至今仍是资管界的独一份。一九七六年，他推出全球第一只面向普通人的指数基金，先锋500指数基金，跟踪标普500，费率低到不可思议。华尔街群嘲，媒体管它叫“博格的愚蠢”，首发只募到1140万美元，远低于预期的1.5亿。后来的故事大家都知道了：那只“愚蠢”的基金，如今管理着上万亿美元资产。博格也被《财富》评为“20世纪四大投资巨人”之一，巴菲特说：“如果要为美国投资者立一座雕像，那个人应该是约翰·博格。”二零一九年1月，老人去世，享年89岁。而《共同基金常识》第一版，就是这位“指数基金之父”在1999年写下的。什么叫《共同基金常识》？博格说他借用了潘恩《常识》的意思，有些东西本来就是常识，只是大家被迷惑太久了，忘了而已。2、这本书到底讲了什么首先，成本是复利最大的敌人。博格反复强调一个简单到无聊的公式：市场总回报−中介成本=投资者实际回报。道理很简单：所有投资者加在一起，就是整个市场，扣掉成本之前，大家只能拿到市场平均。主动基金整体也一样，它们合起来就是市场，再扣掉管理费、交易费、税收这些“损耗”，长期跑输指数几乎是必然。更扎心的是：市场回报你控制不了，唯一能控制的，就是成本。所以博格说，成本是长期收益的“胜负手”。每年1%的费率差，在几十年复利之下，足以让两个人的财富天差地别。其次，别在干草堆里找针，买下整个草堆。这是博格最经典的一句话。什么意思？主动基金经理就像在干草堆里找那根“最锋利的针”，也就是能跑赢市场的牛股。但博格说，别找了，直接把整个干草堆买下来就行了。他还提出了一个理论，叫“均值回归”，金融市场的万有引力。去年涨得最好的基金，明年大概率回到平均水平；今年表现差的，说不定明年也会涨回来。你追着明星基金经理，就像追流星。看起来璀璨，等你追到的时候，它已经掉下去了。不是说主动基金完全不能买，但你要找到那个长期能跑赢市场的人，太难了。而且你怎么确定你找到的那个，未来还能继续跑赢？博格说，你与其花那么多精力去赌一个不确定的高收益，不如安安稳稳拿市场平均收益。这个平均收益，已经能打败90%的专业投资者了。最后，投资成功不需要高智商，需要的是“不折腾”。博格建议用“股票指数基金+债券指数基金”搭组合，股债比例可以按“100减去年龄”来确定，或者采用经典的六四开。设定好比例后，每年做一次再平衡，涨多的卖点，跌多的补点，用纪律强制实现低买高卖。剩下的时间，不用频繁操作。3、对普通投资者的启示：比方法更重要的，是修心读这本书最大的收获，其实不在“术”，而在“道”。说到底，承认自己跑不赢市场，不可耻。博格用60多年的数据证明，涵盖全市场的低成本指数基金经受住了时间的考验。对普通投资者来说，“成为市场”比“战胜市场”更聪明、更稳妥。承认自己的局限，恰恰是智慧的开始。更重要的是，把“控制成本”当成核心竞争力，但选基金也不能唯成本论。我们在A股做基金投资，也常被各种费率、申赎费、管理费包围。博格告诉我们，别小看那1%甚至更多的手续费，长期的复利会把它放大成一个惊人的窟窿。投资里你能控制的东西很少，但成本，是真的省一分是一分。另一方面，选基金也不能唯成本论，只有同类型的基金，比较费率才有意义。与此同时，远离“努力”的诱惑，享受“不折腾”的回报。很多投资者一天不看大盘就难受，一周不换手就觉得“没在做事”。可博格反着来：他主张买入后长期持有，尽量降低换手率。归根结底，把投资回归到生活本身。书里最打动我的，不是数据，而是心态。博格经历过被解雇、被嘲笑、被质疑，但他始终守着自己的“常识”。他说，投资者只需要制定良好的计划，专注于市场的长期优势，通过被动指数基金参与市场增长即可。这不就是一堂人生课吗？面对复杂的世界，与其焦虑地追逐每一个波动，不如选择简单、相信规律、慢慢积累。

Paul spends some time every week watching the financial news so that you don't have to. Today, Paul shares an ad about Vanguard bond funds that, on the surface, seemed informative but really shows that the industry thinks it can pull one over on you. Listen along as Paul dismantles the pitch and explains how Vanguard uses emotional language to sell underperforming bond funds to young investors who should be focused on building wealth.   Later in the episode, Paul's son Andrew is studying for his Series 65 and finds a question on the test that goes against everything he knows about academic investing principles.   Want to cut through the myths about retirement income and learn evidence-based strategies backed by over a century of data? Download our free Retirement Income Guide now at paulwinkler.com/relax and take the stress out of planning your retirement.   This material is for general educational purposes only and is not personalized investment, financial, tax, or legal advice. Past performance does not guarantee future results. Nothing here is an offer, solicitation, or recommendation for any security or strategy. All financial decisions involve risk, and you should consult qualified professionals before acting on this information. Advisory services offered through Paul Winkler, Inc., an SEC-registered investment adviser.

In this returning episode of The Momentous Wealth Podcast, hosts Isaac Pisarczyk and Brayden Disbrow break down everything you need to know about Initial Public Offerings (IPOs). From the seven-step IPO process to the role of underwriters, pricing, and SEC review, we cover the technical mechanics behind going public. We also dive into the investor side — due diligence, lockup periods, volatility, and when new IPOs join indexes like the S&P 500. With companies like SpaceX, OpenAI, and Anthropic making headlines, there's no better time to get educated. Tune in for a clear, relatable guide to navigating the world of IPOs!Want a clearer picture of your finances? Our free guide, The 5 Pillars of Financial Success, covers the five areas that shape your financial life (investments, planning, tax, estate, and risk) and how they fit together. Download it here: https://www.momentouswealth.com/5-pillars-guideSources:Charles Schwab – "What Is an IPO? Initial Public Offerings Explained"https://www.schwab.com/learn/story/what-is-an-ipoCharles Schwab – "What to Know About IPOs"https://www.schwab.com/learn/story/ipo-basics-what-to-know-before-investingFidelity – "Investing in IPOs"https://www.fidelity.com/learning-center/trading-investing/trading/investing-in-iposFidelity – "How to Participate in an IPO"https://www.fidelity.com/customer-service/how-to-participate-in-an-ipoSoFi – "What Is the IPO Process?"https://www.sofi.com/learn/content/what-is-the-ipo-process/SoFi – "Navigating the IPO Lock-Up Period"https://www.sofi.com/learn/content/navigating-ipo-lock-up-period/Vanguard – "Help Clients Stay Grounded on Moonshot IPOs"https://advisors.vanguard.com/insights/article/help-clients-stay-grounded-on-moonshot-ipos

Bloomberg reported a deepening bond selloff as traders increased bets on additional Federal Reserve tightening. Rising U.S. Treasury yields lifted discount rates and pressured valuation-sensitive equities in the S&P 500 and Nasdaq Composite. Borrowing costs for small businesses climbed as lines of credit and SBA 7(a) loans tied to SOFR and Prime reset higher at banks such as JPMorgan Chase, Bank of America, and Wells Fargo. Venture debt from lenders including Hercules Capital, TriplePoint Capital, and First Citizens Bank's SVB unit repriced to higher all-in coupons with tighter terms. A stronger dollar against the euro and yen added currency risk for importers and exporters. Operators responded by laddering short-term Treasuries, using money market funds from Vanguard and Fidelity, and updating budgets to reflect higher interest expense.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Dan Nathan sits down with Fahad Hassan and David Cusatis, co-founders of Range, an AI-native wealth management platform that just crossed $1 billion in assets under management. Fahad and David built Range without any traditional finance background, starting from a simple frustration: the old 1% AUM model is expensive, opaque, and hasn't changed in 100 years. We get into Range's AI agent, Rai, which can execute backdoor Roth conversions over text message, and why the founders believe AI is already outperforming human financial advisors. Plus: a live demo of Meta's Muse booking a flight and ordering an Uber mid-dinner, why they think Salesforce and other legacy SaaS tools are becoming obsolete, how Range is regulated by the SEC, and why they think they can become "the JPMorgan of the next 100 years." Topics discussed: the founding story of Range, AI agents replacing financial advisors, Rai and backdoor Roth conversions, Meta's Muse in action, why legacy software (Salesforce, Notion) is under threat, SEC compliance and trust in AI, and Range's path to disrupting Schwab, Vanguard, and the wealth management industry. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

En Capital Intereconomía repasamos las claves del día y la evolución de los mercados en Asia, Wall Street y Europa. El Nasdaq 100 alcanza nuevos máximos mientras el petróleo y la rentabilidad de los bonos vuelven a retroceder. En el primer análisis de la mañana conversamos con Javier Santacruz, economista, para analizar el fuerte cambio de sentimiento que ha experimentado el mercado tecnológico en apenas unos días. De los temores sobre los riesgos de la inteligencia artificial, los inversores han pasado a impulsar al Nasdaq hacia nuevos récords. Ponemos también el foco en el petróleo tras las previsiones de Donald Trump sobre una fuerte caída de los precios y analizamos las perspectivas para el oro, después de que Pictet Wealth Management haya situado los 5.000 dólares como posible referencia para el metal precioso. Miramos además a la bolsa española ante el aumento de la inversión de grandes gestoras internacionales como BlackRock, Vanguard y Capital Group en compañías del Ibex 35. La actualidad empresarial pasa también por Iberdrola y la búsqueda de un nuevo socio para su negocio eólico en Reino Unido, así como por Santander, Blackstone y Pollen y el mercado de compraventa de créditos morosos a particulares. En el análisis internacional conversamos con José María Viñals, director del Máster en Relaciones Internacionales del IEB. Analizamos el discurso de Donald Trump ante Naciones Unidas y su visión de la paz y del nuevo escenario internacional, además de los planes de Estados Unidos para reforzar su presencia militar en Groenlandia. Por último, miramos a Marruecos y a unas elecciones marcadas por la desafección política y un contexto regional condicionado también por la crisis de Ceuta.

Who is behind PURSUE, and why should UFO whistleblowers trust it?I'm joined by Pavel to discuss the unanswered questions around PURSUE's call for whistleblowers, the viral footage from Tehran, and Jacques Vallée's intriguing account of alleged communication with a non-human entity.We get into:• The Tehran “UFO”: something extraordinary, or LED lights on a kite?• PURSUE, transparency and the risks facing whistleblowers.• Matthew Brown and Vanguard's efforts to support those coming forward.• UFO File Drop 6, including 1952 footage and Edward Ruppelt's discussion of unexplained sightings.• Vallée's “Gilbert” account and claims of contact in a controlled setting.• Trump, disclosure expectations and Chris Bledsoe's October 7 prediction.Plus, why questioning a viral video doesn't mean dismissing the UFO subject and what genuinely compelling evidence might mean for the wider world.

An Alaska Native corporation’s proposed mining project near Lake Clark National Park is drawing opposition over potential impacts to fish, wildlife, and nearby ecosystems, as Mark Moran reports. Conservation activists are pushing back on a proposed gold mine in Alaska’s interior that threatens wildlife habitat, sensitive ecosystems, and a crucial sector of the state’s economy in the Lake Clark National Park. Contango Silver and Gold, Inc. says the company will create jobs while remaining sensitive to the environment while creating jobs. Contango is working with Alaska Native-owned Cook Inlet Region Incorporated to establish the mine at the headwaters of the Johnson River, a prime spot for commercial and sport fishing, bear watching, salmon runs, and eco-tourism. Conservatives for Responsible Stewardship President David Jenkins contends that the environmental risk is not worth the economic reward, especially when the bulk of the profit from the mine flows to out-of-state investors, including asset management companies like Blackrock and Vanguard. “Why would you risk a 100% Alaskan economic engine that’s sustainable and just keeps providing benefits year after year for something that’s temporary, and most of the economic benefit is likely to go elsewhere outside of the state of Alaska?” (Courtesy Contago) Contango counters that the mine will create 175 jobs for the first three years of the project, but that number will dwindle after peak mine production. Jenkins argues that relatively small job creation is shortsighted, given the thousands of jobs and billions of dollars created by eco-tourism in the area – what Jenkins calls a “stark economic contrast.” Cook Inlet Region Incorporated was granted rights to the 21,000-acre Johnson Tract within the Lake Clark National Park in the 1970s as part of a land agreement. Some members of the Indigenous community contend they would benefit from the project, but Jenkins says short-term profit cannot outweigh the unbalanced economic factors or the scar that Contango would inflict on the Alaska Interior’s ecosystem. “These places are pristine. They’re rare things. You just don’t find that in the Lower 48. You don’t even find it in a lot of places around the world. The biological diversity and abundance is just stunning.” Environmental groups and the Chickaloon Tribal council have filed a lawsuit against the U.S. Army Corps of Engineers over alleged Clean Water Act violations by granting a permit for the Johnson Tract project, which is still in the permitting process, but scheduled for completion in the spring of 2028. Disclosure: CIRI provides support to Koahnic Broadcast Corporation, the parent company of National Native News. Preserving Diné language and customs is essential, especially for urban Navajos living off-reservation in places like Phoenix, Arizona. And a local Navajo blogger is launching her latest project – a colorful, hand-drawn children's cookbook. KJZZ's Gabriel Pietrorazio has more. “Shimá likes to write down Shimásání's recipes so they can be passed on to our young family members.” Alana Yazzie recites a few lines from her new book, “Blue Corn Kitchen”. “When you're writing a children's cookbook like this, there's not as much text, and so I think that was the hardest part, like condensing it so much into a short story, but still having my main points come across.” Like the magic of cooking with family, a timeless message Yazzie tried to convey through words and pictures, even by sketching the characters to resemble her own mother and daughter. “There's these little like corn wafts of sparkly, glittery cornmeal going throughout the book. And it's just so beautiful – like you want to eat it, you want to make it.” Get National Native News delivered to your inbox daily. Sign up for our daily newsletter today. Download our NV1 Android or iOs App for breaking news alerts. Check out today’s Native America Calling episode Tuesday, September 22, 2026 — PBS documentary ‘Knowledge Keepers' explores Native scientific innovations

Cyber startups churn through heads of sales, and the usual explanation is a bad hire. Matt Calligan, Director of Growth Markets at ArmorText, argues something different. Founder-led product-market fit only proves the founder can sell, and there's a stage between that and scale that most companies skip. He calls it the Vanguard stage and lays out what has to be true before anyone hands a VP of Sales a number.In this episodeWhy early cyber buyers are "buying access to the founder," not the productThe four lights that have to turn green before you scale: a defined motion, a non-founder who can sell it, proof at volume, and junior reps who can run it without churnWhy junior AEs vs. a seasoned VP is a false binary, and the personality type Matt hires for insteadWhy your President's Club rock star is often the wrong person for the first seatHow ArmorText's founder-run hour-long demo became a five-minute UI walkthroughWhat to do when your founder won't let go of the long feature demoHow the unicorn math of a VC fund pushes founders to skip the middle stageAbout the guestMatt Calligan is Director of Growth Markets at ArmorText, which builds secure out-of-band collaboration for incident response teams. He has spent two decades in enterprise sales, including co-founding Influents as VP of Sales, and has worked on ArmorText's go-to-market since 2018.Notable quotes"If your process is still requiring the founder, then what you have is a founder and an AE in a trench coat." (tightened)"This stage takes as long as it takes.""Founders are always salesperson number one."Chapters00:06 Welcome and the spin-the-wheel game01:51 Why cyber startups go through three heads of sales05:16 What product-market fit actually proves08:57 Founders are always salesperson number one11:20 Fund math and the pressure to skip steps16:11 Why cyber's chasm is inverted18:49 The four lights of the Vanguard stage21:28 Who to hire for the first seat24:48 Do it a hundred times: hearing why the baby is ugly26:32 From a 60-minute demo to five minutes30:40 How to get the founder to let go32:52 When to stop preserving runway and floor it38:47 The reverse channel program43:35 Questions to ask before joining as a founding AE45:57 Differentiation teardown Support the showThe Cyber Go-To-Market Talk is the show for cybersecurity sales leaders, founders, CROs, and go-to-market operators looking to improve cyber sales performance and build more predictable revenue growth. Hosted by Andrew Monaghan, founder of Unstoppable.do, covering cyber sales leadership, revenue leadership, sales onboarding, forecasting, pipeline generation, and cybersecurity go-to-market execution.Follow me on LinkedIn for regular posts about growing your cybersecurity startupWant to grow your revenue faster? Check out my cybersecurity sales consulting and trainingNeed ideas about how to grow your pipeline? Sign up for my newsletter.

Shakira Biography Flash a weekly Biography. Shakira has spent the past few days turning Madrid into the latest chapter of her life story, and it is a big one. According to AFP and Euronews, she kicked off a marathon twelve concert run in the Spanish capital on Friday, September 18, performing in a purpose built 50,000 seat Shakira Stadium in the south of the city, a temporary venue that effectively crowns the final stretch of her Las Mujeres Ya No Lloran world tour. Promoters cited by El País and Bloomberg say more than 600,000 fans are expected between now and October 11, making this residency one of the most commercially and culturally significant moments of her career, and the highest grossing Latin tour of all time. Spanish outlet El País reports that Madrid is bracing for Shakira driven economic shockwaves, with hotel prices jumping and extra airline and rail seats laid on to cope with the influx of fans. Hola! and local coverage describe her arrival at Madrid Barajas airport earlier in the week, where she was swarmed by fans as she stepped back into a country she once called home during some of the most important years of her life. Ground News, citing Spanish press, quotes Shakira saying she is excited to return to Spain, underscoring the personal weight of this homecoming after an eight year absence from Spanish stages. On the legal and financial front, Bloomberg notes that her comeback in Spain coincides with the Spanish Supreme Court's review of a 63 million dollar tax claim, the latest chapter in a long running dispute over her residency status in the early 2010s. Vanguard and AFP remind readers that she previously paid a 7.3 million euro fine in late 2023 as part of a plea deal in an earlier tax fraud case, a development that will loom large in any future biography as a turning point in her relationship with Spanish authorities. Musically, she is not just touring, she is still evolving. Consequence and Hola!s New Music Friday feature highlight the release of Agua, a new merengue flavored single that reunites her with fellow Colombian star Maluma after eight years. The track and its steamy video, heavy on chemistry, dance, and signature Shakira hip work, arrive exactly as the Madrid residency begins, tying new creative output directly to this major live chapter. Speculation in some gossip spaces about surprise guest appearances during the Madrid run or additional European dates beyond October 11 remains unconfirmed at this time; no major outlet has reported any concrete extension, so for now the official story is that Madrid is the grand finale of Las Mujeres Ya No Lloran. That is your flash update on Shakira as her life, career, and controversies converge on one massive stage in Madrid. Thank you for listening, and be sure to subscribe so you never miss an update on Shakira, and search the term Biography Flash for more great biographies. Thanks for listening. This has been a Quiet Please production. Get the best deals https://amzn.to/3ODvOta

Peter Wilson returns for another wide-ranging conversation covering the Checkmate the Matrix community updates, the reality of supermarket food and what's actually in your meat, crypto market manipulation, the new UK digital ID employment rules coming into force, why jobs are modern slavery, school violence and narcissistic education environments, targeted censorship across social platforms, underground cities and data centres, frequency and vibration, the Tartarian era, electric car safety risks, and why Skool communities are becoming the infrastructure for sovereign digital enterprise. About Peter Wilson Ex Royal Navy gunner and armourer, turned professional fighter. Owned and ran his own martial arts gym for about 30 years. Always been aware of something not being right in the world, went deep into it after losing over £1 million of property in one week including his own home. Been up and been down, including living in a car for a while with his wife Janine and 4 dogs. Timestamps 0:02 Welcome and intro — the Authority Alliance announcement 1:02 Checkmate the Matrix updates — allotment classroom, growing your own food 5:07 Human resonance spikes and the awakening shift 6:31 Creating a new system rather than fighting the old one 7:44 Supermarket food — the taste difference and what's really in your meat 9:53 Starch injected into chicken and beef — what's draining out in your pan 12:07 McDonald's burger volume — where is the meat actually coming from 14:43 KFC, Subway, and what those products legally qualify as 15:28 Chocolate with virtually zero cocoa — what corporations are replacing it with 17:00 Crypto — Richard Hart's community, four-year cycles, and BlackRock's influence 20:47 Bitcoin, Vanguard, and why the market is manipulated like everything else 22:19 Solar panels, micro-inverters, and powering your home off-grid for £20 a month 23:25 Water collection, distillers, and why turning off the internet changes everything 29:00 Allotment lessons — eggshells, companion planting, and soil quality 34:21 New UK digital ID employment rules — why even self-employed contractors are affected 35:07 Jobs as total slavery — destroying the family unit from 18 months to 18 years 38:28 School bullying, narcissistic environments, and the rise in anxiety and depression 42:09 Staff in nurseries forcing tablets and TV over books and physical activity 43:36 School violence in the 70s and 80s — physical torture normalised 48:56 The AI Income Crew — three businesses you could start within a week 51:46 Prison planet, vibration, and the Tartarian era before modern civilisation 52:48 Churches as healing centres — frequencies, organs, and what was destroyed 58:49 5G rollout during COVID, underground cities, and what data centres are actually for 1:00:16 Underground cities — already built, way bigger than what's above ground 1:04:37 Targeted censorship — YouTube, TikTok, WhatsApp, and StreamYard interference 1:10:26 Roy's censorship experiences — channels removed, emails going to bin 1:15:03 Kicked off TikTok for talking about growing your own food 1:16:16 Electric cars — turning off the bonnet, losing control, fire risks 1:17:14 Self-driving taxis in Austin, Texas — trapped passengers and battery fires 1:20:01 Jaguar breakdown — automatic gears, no neutral override, and what to carry 1:22:45 Skool reviews system — accountability for negative reviewers 1:30:43 Nipping corruption in the bud at every level — Trustpilot and community action 1:34:33 Skool advertising, currencies, affiliate commissions, and why it works How to Contact Peter Wilson

Friday's listener questions cover the kind of decisions that sound simple until the details arrive. Don weighs the ease of Vanguard's total bond fund against building a Treasury ladder, and explains why convenience can be a perfectly sensible investment feature.Then it's overseas: how much international stock exposure belongs in a diversified portfolio, and why no single U.S./international split is scientifically “right.” The show also sorts out HSA investing, beneficiaries, and the rule for holding more than one HSA.Finally, Don explains why a large RMD and tax puzzle needs a real written plan, then helps a listener nearing retirement compare a two-fund portfolio with a Vanguard target-date fund.0:46 Friday Q&A begins2:24 Listener feedback on the show's music4:20 BND versus a Treasury ladder9:01 U.S. versus international stocks12:59 How to invest and inherit an HSA16:24 Preparing a large portfolio for RMDs20:08 Two funds or a target-date fund near retirementQuestions? Comments? Click!

Higher bond yields are attracting more attention and more money. US bond ETFs pulled in almost $54 billion in August. Core and core-plus bond ETFs make up about $8.5 billion of that. These funds tend to provide shelter during market storms to ease a portfolio's rocky moments. They also provide steady and predictable income. As bond rates sit higher than they have in the past, how can you benefit while also taking a conservative approach? Dan Sotiroff is the associate director of US passive strategies research for Morningstar. Why Higher Bond Yields Can Be 'a Great Thing' On this episode: 00:00:00 Welcome 00:00:50 How core and core-plus bond ETFs work 00:04:22 Billions flowing into core and core-plus bond ETFs this year 00:05:55 How active fund managers capitalize on higher bond rates 00:08:36 What higher bond yields mean for income investors 00:09:51 Core bond ETFs earning Gold and Silver ratings 00:11:17 Core-plus bond ETFs Morningstar analysts like Watch more from Morningstar: How AI Is Taking Over Your Portfolio 401(k) Millionaires: Here's How to Avoid Going Broke in Retirement New ETFs Are Launching Fast. Proceed With Caution Follow Morningstar on social: Facebook: https://www.facebook.com/MorningstarInc/ X: https://x.com/MorningstarInc Instagram: https://www.instagram.com/morningstarinc/ LinkedIn: https://www.linkedin.com/company/morningstar/ This episode is sponsored by Vanguard: https://advisors.vanguard.com/engagement/fixed-income Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Video mentioned in this episode - ⁠Will I Switch To VALL? My Plan For Every Account⁠VALL - Vanguard's new 0.07% global ETF - underlying holdings are finally out. That's exactly what we go through in this one!-----------------------------------------

Send us Fan MailIncome creates options when it is supported by strong financial systems.Kahlilah Dowe, CFP®, founder of TrueWealth Solutions, joined me on Get Ready: Before Life Happens to talk about how education, structure, goals, and values can build confidence and economic empowerment.Key TakeawaysFinancial education builds confidence and expands opportunity.Strong systems help income support long-term goals.Knowing your numbers creates financial peace.Liquidity, goals and systems create stability.A financial do-over can begin with one courageous step.Your money should support what brings you peace and purpose. Economic empowerment grows when money is used to create impact.Tony's Take: Strong financial infrastructure helps turn income into confidence, choices, and impact.Connect with Kahlilah Dowe, CFP®:LinkedIn: https://www.linkedin.com/in/kahlilahdowe/True Wealth Solutions Website: https://truewealthsolutionstws.comYouTube True Wealth Channel: https://www.youtube.com/@TrueWealthSolutionstwsResources mentioned: The Financial Do-Over Series by Kahlilah Dowe, CFP Part 1: More Money, Same Problems.  (LinkedIn) Part 2: We Outside! / The Do-Over(LinkedIn) Part 3: Seriously, What Will It Take? The Infrastructure (LinkedIn) Part 4: Learning Enough. (LinkedIn)How to Stop Worrying and Start Living by Dale Carnegie (Amazon) Bio: Kahlilah Dowe is a Certified Financial Planner™ with more than 15 years of experience helping families build financial freedom through personal financial planning and coaching. She began her career at Vanguard, one of the world's largest financial institutions, where she developed financial strategies for high net worth families. Today, she is the founder of TrueWealth Solutions, a financial planning and coaching firm based in the Philadelphia area, where she helps clients strengthen the financial infrastructure that supports their lives.Kahlilah's passion for financial planning is deeply personal. Growing up in Brooklyn, New York, in a family where resources were limited, she became focused early on creating financial security for herself and her family. That experience shaped her belief that financial success is not simply about making great money, but about using money in ways that create stability, freedom, and long term economic empowerment. She lives in the Philadelphia area with her husband, their three children, and two grandchildren, and loves loud, joyful Friday nights spent with five generations of family.

D'Angelo's Black Messiah is one of the greatest albums of the 21st century. You can hear his musical influences all over it: Prince's arrangements, the Bee Gees' falsettos, Jimi Hendrix's guitar, Sly's groove. But the alchemy that makes Black Messiah so powerful could only come from one mind: D'Angelo, the artist who gave us Voodoo and Brown Sugar.Jazz pianists Adam Maness and Peter Martin unpack D'Angelo's Black Messiah track by track, detailing what their trained ears hear in the music, and highlighting new textures, harmonies, and song structures you may have missed on the first listen. Plus, Peter shares stories about his friendship and working relationship with legendary trumpet player Roy Hargrove.------------------------------Adam shows you how to play D'Angelo's "Really Love". Get the lesson:https://yhi.link/really-love-------------------------------Learn to talk like a jazz musician. Download the Liner Notes FREE today: https://yhi.link/linernotes-6-------------------------------Start your free Open Studio trial for ALLLLL your jazz lesson needs:https://openstudiojazz.com/------------------------------- Send us a message: https://youllhearit.com/------------------------------0:00 D'Angelo and the Vanguard - Black Messiah1:44 Ain't That Easy7:35 Questlove, Pino Palladino, and James Gadson9:35 1000 Deaths14:09 The Charade17:44 Where this album sits next to Sly, Marvin, and Prince19:33 Roy Hargrove, and the Peter Martin connection22:28 Sugah Daddy28:45 Really Love39:40 Back to the Future (Part I)44:05 Where does talent like this come from?!51:15 Till It's Done (Tutu)53:43 Prayer56:35 Betray My Heart1:02:55 The Door1:07:13 Back to the Future (Part II)1:09:25 Is this album backwards?1:11:11 Another Life1:17:00 Our favorite moments1:17:27 How snobby is this album?1:19:53 Is it better than Voodoo?

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

Bill and Andy Bush open with a deceptively simple question: when was the last time you actually logged into your 401(k)? From there, they walk listeners through a practical, no-math check-up of everything worth reviewing the next time you pull up the app — your balance, your contribution rate, whether you're leaving match money on the table, Roth versus pre-tax, how you're actually invested, your rate of return, beneficiaries, and old accounts you may have left behind. Along the way they tackle the traps that quietly derail savers: settling for an auto-enrollment default, a 30-year-old sitting in a target-date fund built for someone already retired, checking your balance so often that you sabotage your own returns, and forgetting that beneficiary designations don't follow you when a plan changes record keepers. The brothers close with the "$487,000 question" — is it enough? — and a run of C-words (clarity, context, confidence, conviction) that turn a quick login into a real sense of where you stand. ⏱ Episode Timeline & Key Topics 00:10 – Welcome & the One Question Bill kicks off by asking Andy when he last logged into his 401(k). Andy's answer — last week, to check his year-to-date numbers and confirm his investments still fit — sets up the episode's theme. 01:00 – What This Episode Covers (No Math Required) The brothers lay out the plan: a walk-through of what to look for when you log in. Their suggestion to listeners — pull out your phone or laptop and follow along in real time. 01:23 – Most Logins Are Now Mobile More participants are checking balances on the app than ever. Fidelity reports a majority of logins now come from mobile, while Vanguard sits a bit lower — a reflection of how people manage money today. 01:46 – Start With Your Balance The first and most obvious thing to check. It's the number most people look at first, and it anchors everything else in the review. 01:55 – Check Your Contribution Rate A common trap: savers get excited, enroll when first eligible, then never revisit their elections. "Life happens," and the contribution rate they set years ago quietly becomes the rate they still have. 02:30 – Is Your Contribution Moving You Toward Your Goal? Andy encourages listeners to connect the contribution to the destination. If you've paid off a car loan or freed up cash, consider directing some of it into the plan. Also worth knowing: how often your plan lets you change contributions, which varies from quarterly to every pay period. 03:25 – Rules of Thumb: 50/30/20 and 10–15% The brothers revisit budgeting guidance — 50% needs, 30% wants, 20% savings — and narrow it to the widely cited 10% to 15% of income aimed at retirement, including any employer match. 03:58 – Why 15%? The Math of a 40-Year Runway Andy explains the logic behind the T. Rowe Price 15% benchmark: contribute at that rate across a full working career and you reach 65 with real options. The habit of putting money in matters as much as how it's invested. 04:44 – Don't Settle for the Auto-Enrollment Default Many plans auto-enroll new hires at 3%, 4%, or 5%. That's a starting point someone else chose for you, not a decision you made — and it's rarely enough on its own. 05:18 – Auto-Escalation: Out of Sight, Out of Mind Some plans let you automatically bump your contribution 1% a year. It works precisely because it happens in the background, nudging your savings rate up without requiring willpower each year. 05:38 – Don't Assume the Match Is the Finish Line A classic match of 50% on the first 6% means a 6% deferral gets you a 3% employer contribution. Helpful, but the brothers are blunt: it's probably not enough to get you where you want to go. 06:36 – Roth vs. Pre-Tax There's tax favorability on both sides. Younger savers in lower brackets often lean pre-tax to stretch each dollar, while the Roth choice hinges on which end of your life you'd rather take the tax benefit. Roth has been available in 401(k)s for about two decades and is now offered by the vast majority of plans — and SECURE 2.0 now requires certain high earners' catch-up contributions to be Roth. 08:25 – What Are You Actually Invested In? Some participants set an allocation 10 or 15 years ago and never looked again. Whether that's a problem depends on your age and risk tolerance, because corrections happen — and a 10% drop feels very different on $50,000 than on $500,000. 09:22 – Don't Day-Trade Your 401(k) A 401(k) is a long-term vehicle, and most menus are built on mutual funds that price once a day. The brothers cite a striking pattern: participants who log in most often tend to see worse returns than those who set it and forget it. 10:40 – Target-Date Funds and Fitting the Allocation to You Target-date funds offer a single, age-appropriate option that rebalances over time. But allocations drift, and your mix has to fit you — not your brother or your coworker. Case in point: an audit that turned up a 30-year-old sitting in a Target 2020 fund. 11:51 – How Am I Doing? Rate of Return in Context A year-to-date return only means something alongside your time horizon and what you're invested in. A financial plan is the North Star, and record-keeper tools can show whether saving more or working one more year meaningfully moves the needle. 14:06 – Life Changes, Beneficiaries & a Free Tip Marriage, a new baby, a divorce, or a job change can all call for updates — especially beneficiaries. Bill's free tip: when a plan switches record keepers, beneficiary designations don't transfer, so go back in and re-name yours. 15:05 – Build a Review Habit Set a recurring reminder — a birthday, or July 1 as the year's midpoint — to check your beneficiaries and elections, the same way you'd schedule a routine doctor's visit. 16:32 – Old 401(k)s: Consolidate, Watch Fees, or Roll to an IRA Most plans allow roll-ins, and consolidating scattered accounts can simplify your life and reduce duplicated administrative fees. Alternatively, rolling old accounts into an IRA can open the door to guidance from a local advisor. 18:13 – The $487,000 Question: Is It Enough? Seeing a big balance prompts the real question. The answer depends on your age, your runway, and your spending. The math of retirement income is the easy part; the psychology of shifting from saving to spending after 40 years is the hard part. 20:36 – Clarity, Context, Confidence — and the Rest of the C-Words Andy ties it together: getting clear on where your 401(k) stands, in context with your other assets, builds the confidence to make changes. Bill and Andy stack on conviction, coach, and finally close. 22:16 – Recap & How to Reach the 401(k) Brothers A quick checklist recap — balance, contributions, match, Roth versus pre-tax, investments, beneficiaries, old accounts — before Bill and Andy sign off. Brothers, but not twins. ✅ Key Takeaways Quick Reference •             Actually log in — most participants set things once and forget them; a periodic login is the single easiest way to catch what's drifted •             Your balance is the start, not the whole story — check it first, then work through contributions, match, allocation, and beneficiaries •             Don't accept the auto-enrollment default — a 3% to 5% starting rate is a choice someone else made for you, and it's rarely enough •             Aim for 10% to 15% of income, match included — contribute at that rate over a career and you reach retirement with options •             Turn on auto-escalation if you can — a 1%-per-year bump works because it happens in the background •             Match is a floor, not a finish line — 50% of the first 6% is a good start, but usually won't fund the retirement you want on its own •             Roth vs. pre-tax is personal — younger, lower-bracket savers often favor pre-tax; the right answer depends on when you'd rather take the tax benefit, and you can do both •             Make your allocation fit you — a 30-year-old has no business in a Target 2020 fund; match risk to your age and time horizon •             Don't over-manage — 401(k)s are long-term, mostly-mutual-fund vehicles; frequent tinkering tends to hurt returns •             Beneficiaries don't transfer when record keepers change — re-name them any time your plan changes providers, and after major life events •             Consolidate old accounts — rolling scattered 401(k)s together simplifies your life and can cut duplicated fees, or roll to an IRA for advisor guidance •             Clarity creates confidence — knowing where you stand, in context with everything else, is what lets you make good decisions and stick with them

On episode 259 of The Compound and Friends, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and Michael Batnick are joined by Jens Nordvig to discuss: rising Treasury yields and the risk of something breaking in the bond market, Scott Bessent's intervention in the yen and Treasury markets, the massive AI capex and hyperscaler debt boom, whether AI is actually inflationary, oil prices and the Fed, the surprisingly resilient consumer and labor market, America's growing debt problem, and why Korea could be one of the most interesting macro trades heading into 2027. This episode is sponsored by Franklin Templeton and Vanguard.  Learn more at www.ftprivatemarkets.com Learn more about Vanguard bonds at https://vanguard.com/audio Sign up for The Compound Newsletter and never miss out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Franklin Disclosure: Before investing, carefully consider a fund's investment objectives, risks, charges and expenses. You can find this and other information in each prospectus, or summary prospectus, if available, at franklintempleton.com. Please read it carefully. All investments involve risk, including possible loss of principal.  © 2026 Franklin Distributors, LLC. Member FINRA/SIPC Learn more about your ad choices. Visit megaphone.fm/adchoices

Bruce Kelly is joined this week by InvestmentNews reporter Andrew Cohen to discuss Vanguard's $4 billion purchase of Altruist and Schwab's changing client referral program.

Vanguard's $4.6 billion acquisition of Altruist made headlines across the financial industry, but the bigger story is what it says about the future of financial advice.In this episode of Boldin Your Money, Steve Chen is joined by Ed Williams, Lead Financial Advisor at Boldin, and Michael Byram, Boldin's CTO and former engineering leader at Altruist, to discuss why this acquisition matters and how technology is reshaping the way people plan for retirement and manage their money.Together, they explore why traditional financial advice can't scale to meet growing demand, how AI is making both advisors and consumers more capable, and why the next generation of financial planning will be more personalized, proactive, and accessible than ever before.The conversation also covers the evolving role of financial advisors, flat-fee advice versus traditional AUM models, AI agents, automation, and why the future isn't about replacing human advisors—it's about combining human expertise with powerful technology to help more people make better financial decisions.Whether you're working with an advisor, managing your own finances, or simply curious about where AI is taking personal finance, this episode offers an inside look at where the industry is headed.To learn more about the Boldin Planner, visit boldin.com. You can also find full video episodes of Boldin Your Money on YouTube and subscribe wherever you get your podcasts.

Why after 35 years are you giving Nirvana the video Vanguard award and yet you remove the rock category from the VMAs?

Paul opens with news of a new opportunity to reach young investors: a five-part series for Next Generation Personal Finance (NGPF.org), available to some 150,000 teachers who use NGPF's free curriculum. Topics include the math and history of investing, the case for index funds, the inside story on diversification, the $5 million payoff for a financially literate high school graduate, and a teacher Q&A session. Each presentation will be shared with Sound Investing listeners the following week.Then Paul turns to two listener questions that go to the heart of how people actually experience the market.The first asks whether broad diversification really produces the best returns, or whether a more focused portfolio would do better. He walks through the Bessembinder research showing that roughly 4% of companies drove most of the market's long-term return, Fama and French data on small cap value going back almost 100 years, and Vanguard's own real time returns since 1998 comparing $10,000 invested in the S&P 500, mid cap, small cap blend and small cap value asset classes.The second is a letter from an investor who put his first real savings, earned at $7 an hour, into Fidelity Magellan in 1985, lost 30% on Black Monday in October 1987, and pulled everything out. Forty years later he still fears the next crash and asks whether Paul's Ultimate Buy and Hold portfolio could leave him waking up with half his money gone.Paul closes with a story about five 24-year-old engineers he met on Bainbridge Island, and an offer to anyone who can gather a group that would benefit from a conversation about investing.LINKSFine-Tuning Your Asset Allocation tablesNGPF: ngpf.org

Interview recorded - 9th of September, 2026On this episode of the WTFinance podcast I had the pleasure of welcoming back Professor Vali Nasr. Vali Nasr is a Professor at the Johns Hopkins School of Advanced International Studies, a senior adviser at the Center for Strategic and International Studies, and one of the most authoritative voices on Iran, having advised American policymakers and diplomats on the country for decades. He is also the author of Iran's Grand Strategy: A political history.During our conversation we spoke about the current situation in the Middle East, the reshaping of the continent, why the US is the real loser in this conflict, whether it'll be a forever war and more. I hope you enjoy!0:00 - Introduction2:37 - Current phase of Iran war5:58 - Challenges of agreement8:51 - Sanction pressure12:34 - Lack of trust14:58 - Forever war?17:31 - Iranian shift post war23:21 - Opposition parties?28:40 - Reshaping the Middle East34:33 - Winner of the conflict?38:53 - One message to takeaway?Vali Nasr is the Majid Khadduri Professor of International Affairs and Middle East Studies at the Johns Hopkins University School of Advanced International Studies (SAIS), and Non-Resident Senior Advisor in the Middle East Program at CSIS. He served as the eighth Dean of Johns Hopkins SAIS between 2012 and 2019 and served as Senior Advisor to U.S. Special Representative for Afghanistan and Pakistan, Ambassador Richard Holbrooke between 2009 and 2011.Professor Nasr is the author of Iran's Grand Strategy: A Political History, The Dispensable Nation: American Foreign Policy in Retreat; Forces of Fortune: The Rise of a New Middle Class and How it Will Change Our World; The Shia Revival: How Conflicts within Islam will Shape the Future; Democracy in Iran: History and the Quest for Liberty; Islamic Leviathan, Islam and the Making of State Power; Mawdudi and the Making of Islamic Revivalism; Vanguard of Islamic Revolution: Jama'at-i Islami of Pakistan, and co-author of How Sanctions Work: Iran and the Impact of Economic Warfare; as well as numerous articles in scholarly journals and commentary in Financial Times, Foreign Affairs, Foreign Policy, New York Times, Washington Post, and Wall Street Journal. He has advised senior American policymakers, world leaders, and businesses, including the President, Secretary of State, senior members of the Congress, and presidential campaigns. He has written for New York Times, Foreign Affairs, Financial Times, Wall Street Journal, and The Washington Post, among others.Vali Nasr - X - https://x.com/vali_nasrBook - https://www.amazon.co.uk/Irans-Grand-Strategy-Political-History/dp/0691268924/WTFinance -Spotify - https://open.spotify.com/show/67rpmjG92PNBW0doLyPvfniTunes -https://podcasts.apple.com/us/podcast/wtfinance/id1554934665?uo=4LinkedIn - https://www.linkedin.com/in/anthony-fatseas-761066103/Twitter - https://twitter.com/AnthonyFatseas

Christian Lopez explains how Bitcoin and other cryptocurrencies are seeing a resurgence as institutional interest grows. He notes that firms like BlackRock, Vanguard, Fidelity, and Morgan Stanley are advising clients to allocate 2% to 5% of their portfolios to digital assets.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Target-date funds promise a simple glide path from growth to safety—but people with the same retirement date can have completely different needs. Don and Tom compare Vanguard, Fidelity, and BlackRock funds, examine costs and stock-bond mixes, and explain why simple does not mean specific. Then they revisit decades of failed crash predictions from Rich Dad, Poor Dad author Robert Kiyosaki.Want more Money Music? Hear extended versions from Don's fictional AI band, The Financial Fysicist, on Apple Music: https://music.apple.com/us/album/let-the-boring-money-in/6805953759 or Spotify: https://open.spotify.com/album/0G06JEvGsyw6SISfAOxLt6?si=ah2uVVWuQwmxTqjBeta8AQQuestions? Comments? Click!

John Lestock joins JD Carlson to expose predatory 401(k) sales tactics and break down the latest industry acquisitions reshaping the recordkeeper landscape. From USI to Aon to Empower's bold moves, here's what advisors and plan sponsors need to know. The 401(k) industry is consolidating fast, and not all of it is good news for advisors and their clients. In this episode, guest John Lestock dives deep into the predatory sales practices advisors encounter daily, then shifts gears to analyze the major M&A activity dominating 2026: USI's acquisition by Aon, Empower's move into defined benefit plans, and Vanguard's acquisition of the Altruist trading platform. We also tackle the messy reality of payroll integration, data warehouses, and the tension between client responsibility and scalability. What happens when recordkeepers promise seamless integrations but the backend doesn't deliver? And how do plan sponsors and advisors navigate the fine line between their fiduciary duty and what's actually feasible at scale? Whether you're a TPA, plan sponsor, recordkeeper, or independent advisor, this conversation hits the pain points of the current market. The team breaks down what these acquisitions mean for your practice, why some sales tactics work (and why they shouldn't), and how to stay competitive when the big players keep getting bigger. Perfect for anyone managing 401(k) plans or advising clients in the defined contribution space. CHAPTERS 0:00 Cold Open: The Retireholics Team 0:34 Episode Agenda and Banter 7:42 Predatory 401k Sales Tactics Exposed 17:24 Industry Acquisitions: USI and Aon 24:22 Empower's Defined Benefit Plan Acquisition 26:51 Vanguard Acquires Altruist Trading Platform 34:19 Mystery Stock: Hims and Hers 43:50 Census Data Collection Challenges 49:48 Payroll Integration and Data Warehouses 54:43 Client Responsibility vs. Scalability 1:02:11 Wrap Up and Closing Remarks MORE FROM RETIREHOLICS Full episode notes & transcript: https://retireholics.com/episodes/john-lestock-predatory-sales-ma-trends-retireholics/ All past episodes: https://retireholics.com/episodes/ Live every 1st & 3rd Thursday at 4:30pm PT: https://retireholics.com/live/ Get show reminders: https://retireholics.com/get-reminders/ SUBSCRIBE YouTube: https://www.youtube.com/@Retireholics Apple Podcasts: https://podcasts.apple.com/us/podcast/retireholics/id1490618217 Podbean: https://retireholiks.podbean.com/ Retireholics is the show changing the retirement industry one beer at a time. Hosted by JD Carlson and co-hosts, covering 401(k) plan design, fiduciary responsibility, fees, investments, and industry news for retirement plan advisors and professionals.

My guest today is Jared Dillian—former Lehman Brothers index-arbitrage and ETF trader, founder of the 18-year-old professional market letter The Daily Dirtnap, registered CTA, author of seven books, and an unusually multidimensional market thinker whose work joins macro trading, practical personal finance, risk control, writing, mental health, and electronic music.Today, are we talking about his new book The Awesome Portfolio, a simple, stress-free approach to investing.Episode Sponsor: Fiscal AI is a modern data terminal that gives investors instant access to twenty years of financials, earnings transcripts, and extensive segment and KPI data—use my link for a two-week free trial plus 15% off: https://fiscal.ai/talkingbillions/Jared unpacks the origin of The Awesome Portfolio — born from testing model portfolios with a subscriber-turned-advisor, later validated by Nick Maggiulli's own optimization research.Core thesis: "to make people make stupid decisions" is, in Jared's telling, the whole purpose of volatility — Vanguard's own data shows investors rarely capture the returns their funds actually post.Vanguard's "advisor alpha": simply having someone stop you from trading boosts returns by 3%. But Jared argues even a good advisor can't erase the stress of a 50% drawdown.The Awesome Portfolio's worst-ever year: down 12%, versus an 89% max drawdown for the S&P since 1929. "Drawdowns affect psychology."The "life hedge": your job and the market tend to move together, amplifying your life's volatility. The ideal hedge would move opposite — nothing fully does.Risk of ruin, via a $300M Powerball thought experiment: "wealthy people think about the risk of ruin and middle-class people don't."Reflexivity: the top 7 stocks make up 35% of the index, so buying the index means buying concentration.The five 20% slices — stocks, bonds, gold, cash, real estate — rebalanced once a year, deliberately simple.Why crypto got cut: even a small Bitcoin allocation would dominate investor attention and undercut the whole stress-free premise.Stress-tested against a literal nuclear war, Jared still can't find a scenario where all five assets fail together.Closing candor: "Books succeed when they tell people things they already believe" — which is why he expects pushback, not a bestseller.Podcast Program – Disclosure StatementBlue Infinitas Capital, LLC is a registered investment adviser and the opinions expressed by the Firm's employees and podcast guests on this show are their own and do not reflect the opinions of Blue Infinitas Capital, LLC. All statements and opinions expressed are based upon information considered reliable although it should not be relied upon as such. Any statements or opinions are subject to change without notice.Information presented is for educational purposes only and does not intend to make an offer or solicitation for the sale or purchase of any specific securities, investments, or investment strategies. Investments involve risk and, unless otherwise stated, are not guaranteed.

Fifteen years ago, on this very podcast, one of us predicted that bonds would become a problem. It took a while — long enough that the prophet was more or less left for dead on the side of the road — but the warning has finally arrived, and now that it's here, we can't seem to stop talking about it. Bonds are in the news again, for all kinds of reasons, and almost none of them good. So in this episode, Brandon and Brantley go back to the argument we've been making for over a decade: for the job most people are trying to give bonds, cash value life insurance quietly does it better. Let's be precise about the claim first, because it's easy to hear this as "never buy bonds," and that isn't it. If you want the income a bond produces, buy the bond and collect the income — that's a perfectly good reason to own one. The trouble starts when bonds get sold as the safety buffer in a portfolio — the low-risk ballast that's supposed to hold steady while stocks wobble. That story worked for a specific reason, over a specific window, and that window has closed. What we get into: Why bonds ever looked "safe" in the first place. For several decades, interest rates fell, bond prices rose, and bonds earned a reputation as the dependable counterweight to stocks. That wasn't a law of nature — it was a tailwind. Most people were never told it was a tailwind, which is exactly why the reversal caught them off guard. The number that stops the conversation. We use Vanguard's total bond market fund because it has enough history to actually run the math. From January 1987 through December 2019 — 32 years — it compounded at about 5.93% a year with distributions reinvested. Great for the "boring buffer" role. From January 2020 to today? Roughly 0.62% a year. There is no five-year stretch in that entire prior 32-year run that performed this badly. Why 2021–2022 was genuinely different. For decades, when the stock market fell, it usually signaled a slowdown, which pushed rates down and lifted bonds — that's the whole mechanism behind the buffer. In 2021–2022 the opposite happened: stocks and bonds fell together while rates rose. We couldn't find a precedent for it going back to 1987. And 2022 stands as the worst year for the Bloomberg U.S. Aggregate Bond Index since the index began in 1976. How we got here — and why it isn't over. COVID-era stimulus put a surge of money into the system right as a supply-side shock choked off goods. More money chasing less stuff is the textbook recipe for inflation. The Fed bet it was "transitory," reacted slowly, and then had to hike hard. But the bond market sets its own terms too — if buyers don't believe a given yield covers where prices are headed, they simply don't buy, and yields have to climb until they do. The live math, right now. The 10-year Treasury is sitting near 4.8%, a level it hasn't seen in more than a year and a half, with a fair number of forecasters calling for it to cross 5% before year-end. If that happens, a rough cut of the numbers says you'd give up somewhere around $20 of market value on every $1,000 of Treasuries you're holding — a real problem if you were counting on selling, a non-event if you only ever wanted the income. What life insurance does that a bond can't. When rates rise, the cash value in a whole life or indexed universal life policy doesn't drop. There's no market-value markdown to absorb — and better still, rising yields tend to lift what these products pay: higher dividends on whole life, and higher cap rates, higher participation rates, or narrower spreads on IUL. You shed the price-reduction risk and pick up the upside of the same rate move that punishes bondholders. The reframe that matters most. Here's the part we haven't said clearly enough over the years: we've never argued for cash value life insurance on total return — not against stocks, not against bonds. We evaluate it on what you can actually extract from the dollars you put in, usually measured as income. The rate of return matters in the background, but the question we're really answering is "what will this reliably do for the plan," not "did it beat the index this year." The policy-loan worry, handled honestly. Most good whole life contracts use variable loan rates, so people reasonably ask whether rising rates make borrowing more expensive. Nominally, yes — but these things don't happen in a vacuum. The same rising rates that lift your loan cost also lift the dividend, so the net cost of borrowing may barely move. We even get into the history here: fixed loan rates plus a promise to keep paying full dividends is exactly what created the direct- vs. non-direct-recognition problem back in the 1970s and '80s, and why "lock in the low fixed rate" isn't the free lunch it sounds like. The honest framing we hold to on-air: this isn't a promise that whole life "beats" bonds on a spreadsheet, and it isn't a trade you time. Whole life can be a little slower to react than an index product; dividends and cap rates do move with the environment, and none of it is meant to replace every bond in a plan. What it is designed to do is take on the job bonds are supposed to do — hold their ground and produce dependable income — without the market-value risk. When rates are choppy and heading nowhere fast, that's a job worth giving to the right tool. Read the full write-up: Whole Life vs. Bonds: Your Portfolio's Rate-Hike Hedge — the duration math, the bond-fund-vs-cash-value comparison, and the policy-loan wrinkle, all in one place. Looking at a life insurance illustration and not sure it actually fits the role you need it to play? Don't let ChatGPT be the last word — it'll hand you a confident answer that's often just the "whole life is a rip-off" line scraped off the internet, and confidently wrong is still wrong. Tell us a little about your situation, or send over the illustration, and we'll give you a straight, honest read: what's right, what's wrong, and whether it's a good fit for you. No pitch, no pressure. Send us a message, or if you'd rather talk it through, book a call with us.

On episode 258 of The Compound and Friends, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and Michael Batnick are joined by Dan Skelly, Portfolio Manager at Morgan Stanley Wealth Management, to discuss the resilient U.S. economy, record earnings growth, the AI spending boom, Nvidia and Broadcom, whether today's data center buildout looks anything like the dot-com bubble, the rotation out of semiconductors, the return of healthcare and financials, risks facing small-cap stocks, why the Mag 7 could lead again in 2027, AI's impact on corporate productivity and profit margins, the strength of the American consumer, stock-picking in an increasingly efficient market, and much more! This episode is sponsored by Vanguard and Federated Hermes. Learn more about Vanguard bonds at https://vanguard.com/audio. Explore the full ETF lineup at ⁠https://federatedhermes.com/ Take The Compound's 2026 audience survey and help shape the future of the channel: https://www.surveymonkey.com/r/LHC8QHD Sign up for The Compound Newsletter and never miss out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vanguard Disclosure: All investing is subject to risk. Vanguard Marketing Corporation, Distributor.  Federated Hermes Disclosure: ETFs are subject to risk and may lose value. Federated Securities Corp., Distributor. Before investing, carefully consider the fund's investment objectives, risks, charges, and expenses. Read this and more information in the prospectus or summary prospectus available at FederatedHermes.com.  Learn more about your ad choices. Visit megaphone.fm/adchoices

The Last Trade: government bond yields are blowing out across the developed world, Japan's 10-year hit 3% for the first time since 1996, and the Treasury's buyback program has already been overrun. Jackson, Michael, and Brian trace what a global bond rout means for hard assets, why $3.5 billion went into Bitcoin ETFs in August, and what it says that 21 of the largest banks just announced a joint dollar stablecoin.---

On episode 480, ⁠⁠Michael Batnick⁠⁠ and ⁠⁠Ben Carlson⁠⁠ discuss: the AI debt binge, no recessions for the rest of the 2020s, Kevin Warsh thoughts on the economy, the dead cat bounce in software stocks, bull market M&A deals, no one cares about dividends anymore, rising yields are a good thing, AI civilizations, Gen Z will be buying houses, Tom Cruise remakes and more. This episode is sponsored by YCharts and Vanguard. To learn more about YCharts Future Proof session and get 20% off your initial YCharts Professional subscription, visit https://go.ycharts.com/future-proof-2026 (new customers only). Learn more about Vanguard bonds at https://vanguard.com/audio. Please take our 2026 audience survey HERE Sign up for The Compound newsletter and never miss out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Us On Social Media: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Find complete show notes on our blogs: Ben Carlson's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠A Wealth of Common Sense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Michael Batnick's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Irrelevant Investor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Feel free to shoot us an email at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠animalspirits@thecompoundnews.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ with any feedback, questions, recommendations, or ideas for future topics of conversation.   Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Ben Carlson are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

CAPTION:  1914 BEIRUTDavid Daoud outlines the strategic dynamics of Hezbollah in Lebanon. Hezbollah is currently launching pinprick probing attacks to test Israel's rules of engagement under ceasefire constraints and US pressure. Within Lebanesepolitics, the Shia community remains structured around a symbiotic relationship between the nationalist Amal movement and Hezbollah, preventing any independent Shia opposition from gaining ground. Beyond Lebanon, Hezbollah acts as the tip of the spear for Iranian expansionism, training regional proxies in rocket, tunnel, and propaganda warfare. This veteran, transnational expertise makes Hezbollah an indispensable regional asset that Iran would find impossible to replace. (10)

Kyle talks to Gavin & Zac of The Vanguard about early speculation on the 2028 presidential election and the firestorm surrounding Hasan's recent remarks on Zionism.

A Note from James:In 1790, one of the easiest ways to get rich in America was the old-fashioned way: marry someone rich.George Washington did pretty well that way. Benjamin Franklin, meanwhile, was so deep in debt that he offered to marry a woman if her parents would mortgage their house to pay off his printing press debt. When they said no, he married someone else who had money.And back then, debt was not just annoying. It could land you in debtor's prison. Actual prison. And not just you—your wife and kids could go too.Fast-forward to the 1900s, and most Americans still were not buying stocks. Only a tiny percentage owned shares. Everyday people were gambling, playing the numbers, using dream-interpretation books to decide what lottery number to play, and trying to find some edge that would move them a little closer to security.My guest today, Joseph Moore, literally wrote the book on this: How to Get Rich in American History: 300 Years of Financial Advice That Worked (& Didn't).The book is full of strange, funny, surprising stories about money in America: Franklin, Washington, debtor's prison, the Great Depression, bucket shops, real estate booms, FIRE, crypto, debt, index funds, and all the scams people keep falling for.But the bigger lesson is that the basic patterns have not changed as much as we think.People want security. People want freedom. People want hope. People want a way out. And whenever there is hope, there is usually someone selling a formula.Joseph has very little patience for the usual personal finance myths. Debt does not make you rich. Opportunity makes you rich. Real estate is not always a magic wealth machine. The stock market was not designed to be everyone's retirement plan. FIRE can work, but it can also become the CrossFit of personal finance. And optimism, marriage, mobility, risk, and solving other people's problems may matter more than almost anything else.If you think the rules for getting rich have changed completely, this conversation may convince you how little human nature has changed.Episode Description:Joseph Moore joins James to talk about the long, strange history of getting rich in America.His book, How to Get Rich in American History, looks at 300 years of financial advice—what worked, what failed, what people kept repeating, and what today's money culture keeps forgetting.The conversation starts in 1790, with George Washington, Martha Washington, Benjamin Franklin, Stephen Girard, debt, leverage, and debtor's prison. Joseph explains that many early American fortunes were built through risk, borrowed money, marriage, luck, and then—critically—de-leveraging over time.That becomes one of the core lessons of the episode: debt does not make people rich. Opportunity does. Debt is only a tool that allows someone to grab more of an opportunity than they otherwise could. But if the opportunity is not real, or the person cannot handle the risk, debt destroys them.James and Joseph then move into real estate. Joseph argues that real estate is a good way to build a modest middle-class fortune, but not usually the path to the biggest fortunes. In modern America, he says, real estate often functions as a short on the dollar, an income annuity in a low-dividend world, a tax shelter, and a way for ordinary people to use leverage they could not access anywhere else. But that does not make buying a house automatically smart. Renting versus buying depends on age, mobility, location, family needs, inflation, taxes, maintenance, transaction costs, and opportunity cost.The conversation then turns to the stock market. Joseph challenges the usual historical charts that claim anyone could have invested a fixed sum in 1929 and held forever. Most Americans could not invest that way. There were no index funds, mutual funds had high fees, and buying an index directly required enormous capital. Instead, everyday people went to bucket shops, bet on price moves, played the numbers, and treated gambling as a kind of financial hope.James and Joseph also discuss passive investing, shadow indexing, the rise of ETFs and 401(k)s, and the way the stock market has become a mass retirement promise. Joseph points out that this is historically new. For most of American history, no ordinary person would have expected to retire on the stock market.From there, the episode moves to FIRE: financial independence, retire early. Joseph has lived part of that story himself. He built enough wealth through rental real estate after 2008 to stop working for a period, only to discover that early retirement was not automatically fulfilling. He compares FIRE to CrossFit: extreme, demanding, sometimes powerful, sometimes injurious, and not a lifestyle most people actually want.The final section asks the big question: What has consistently worked?Joseph boils the lessons down to five pillars: solve other people's problems, take risks, move toward opportunity, marry well, and believe you can. James adds that optimism matters because it keeps people in the game long enough to get more shots on goal.The result is a conversation about money, but also about history, risk, luck, marriage, mobility, discipline, scams, and the difference between getting rich and staying rich.What You'll Learn:Why early American wealth often involved marriage, leverage, luck, and risk.How George Washington's marriage to Martha helped fund the Washington we remember.Why Benjamin Franklin's public advice about debt did not match his own early financial behavior.What debtor's prison meant in early America, including the risk to families.Why debt is a tool, not a wealth strategy by itself.Why opportunity—not debt—is what actually makes people rich.Why real estate can build middle-class wealth but rarely creates the biggest fortunes.How buying a home can reduce mobility and opportunity, especially for younger people.Why renting versus buying is situational, not a universal rule.Why most Americans historically could not invest in the stock market the way modern charts imply.What bucket shops and “the numbers” reveal about everyday financial hope.How passive investing changed the purpose of the stock market.Why stock-market concentration is not new, but mass participation is.Why FIRE can work mathematically and still fail psychologically.How older financial-independence stories often hid trust funds, inheritances, or outside support.Why inflation is one of the biggest risks to early retirement.Why getting rich and staying rich require different behavior.Why successful people often take risk early and reduce risk later.Why optimism is financially useful when it keeps people in the game.The five recurring pillars Joseph sees across American wealth-building history.Timestamped Chapters:[05:00] How to Get Rich in 1790James asks Joseph how someone got rich in early America, starting with George Washington, Martha Washington, and marriage as a financial strategy.[07:24] Stephen Girard and Benjamin Franklin's DebtJoseph compares Stephen Girard's leveraged rise with Franklin's messy early business debts.[10:29] Debt Does Not Make You RichJoseph explains that opportunity creates wealth, while debt simply lets someone reach for more of that opportunity.[11:23] Debtor's Prison Was RealJoseph explains why failing in the 1790s could mean prison not only for the debtor, but for the debtor's family.[12:25] The Real Estate MythJoseph argues that real estate can build modest wealth, but rarely creates the biggest fortunes.[13:43] Real Estate as a Short on the DollarJoseph explains modern real estate as an inflation bet, income annuity, tax shelter, and leverage tool.[15:22] You Need an EdgeJames argues that every bet has someone on the other side, which means investors need to know what their advantage actually is.[16:18] Beating the Market, Missing the MomentJoseph tells the story of shorting Jim Cramer stock pops, beating the market net of theory, losing to fees, and missing his daughter's first steps.[19:56] Shadow Passive InvestingJames and Joseph discuss hedge funds, index tracking, fees, and the way much of Wall Street quietly follows the same big benchmarks.[20:31] The Index RevolutionJoseph explains why Vanguard's 1976 index fund changed investing for ordinary Americans—and why passive investing may create new structural risks.[24:24] The Four Percent of Stocks That MatterJames and Joseph discuss stock-market returns, T-bills, concentration, and why a small number of companies drive most gains.[25:16] The Second Bank CrashJoseph compares modern market concentration to the 1830s, when the Second Bank of the United States made up a huge share of the stock market before collapsing.[26:21] The Stock Market as a Retirement PromiseJoseph explains why turning the stock market into a mass retirement strategy is historically new.[29:58] The Problem With “The Chart”Joseph criticizes the classic financial-advisor chart that assumes someone in 1929 invested a large sum, held forever, and never touched it.[31:17] Bucket Shops and Playing the NumbersJoseph explains how everyday people used gambling, bucket shops, and lottery-like games as financial hope when stock ownership was out of reach.[34:04] The Mean Moves Through TimeJoseph explains why history is not physics and why the “average” keeps changing as the economy changes.[35:49] Renting vs. BuyingJames and Joseph debate the homeownership myth, maintenance, taxes, transaction costs, mobility, family stability, and when buying can make sense.[41:02] FIRE and the Question of EnoughJames asks how much is enough in 2026, and Joseph explains why the answer depends on location, expectations, security, and lifestyle.[44:24] FIRE as the CrossFit of Personal FinanceJoseph compares FIRE to an extreme discipline that can work for some people but injure others if they push too hard.[45:38] Geoarbitrage and Selling the DreamJames and Joseph discuss moving somewhere cheaper, Instagram FIRE influencers, and the difference between living the dream and monetizing the dream.[46:00] The Long History of Financial IndependenceJoseph traces earlier versions of FIRE through Sylvester Judd, Thoreau, Emerson, and Helen and Scott Nearing.[49:17] Inflation and the FIRE RiskJoseph explains how Your Money or Your Life and bond-heavy financial independence strategies ran into changing interest-rate realities.[50:23] Five Pillars of Getting RichJoseph lays out the durable lessons: solve problems, take risks, move more, marry well, and believe you can.[53:43] Marriage, Optimism, and Staying in the GameJames and Joseph talk about supportive partnership, optimism, savings discipline, and why staying in the game increases opportunity.[56:11] The Line Between Optimism and RecklessnessJoseph distinguishes productive optimism from gambling and explains why control over outcomes matters.[58:28] Getting Rich vs. Staying RichJoseph explains why many wealthy people take risk early, then de-lever over time to keep what they built.[01:00:00] Leverage, Trading, and the Guy Who Never StopsJames and Joseph discuss extreme leverage, Bitcoin futures, Jesse Livermore, gamblers, and why some people cannot walk away.Additional Resources:Joseph Moore - History HelpsHow to Get Rich in American History - Book PageHow to Get Rich in American History - Google BooksNext Big Idea Club: “The Changing Rules for Getting Rich in America”Fast Company: “How the rules of getting rich in the U.S. change with every era”The Motley Fool Interview with Joseph MooreMeb Faber Show InterviewSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.