Podcasts about TSMC

Hosts Bart Diehl and Carson Yogan break down key shifts in the Arizona real estate market and what they mean for investors. In this episode:  Phoenix Demand Skyrockets: Record apartment absorption outpaces new supply for the first time since 2021, signaling rising rents and potential dividend growth. Debt Distresses Great Assets: How rising interest rates are creating opportunities to buy high-occupancy properties at significant discounts. Prescott's New Boom: The $262 billion TSMC plant is driving housing demand north, sparking new master-planned communities in Prescott.  Tune in for an insider's look at navigating today's commercial real estate landscape!

In der heutigen Folge sprechen die Finanzjournalisten Nando Sommerfeldt und Holger Zschäpitz über Broadcoms Schuldenplan, Georgs geniale Gas-Idee und die historischen Auswüchse am Anleihemarkt. Außerdem geht es um Walmart, Advance Auto Parts, AutoZone, O'Reilly Automotive, Home Depot, Lowe's, TJX Companies, Coty, JD Sports Fashion, Adidas, Puma, Fresenius, Fresenius Medical Care, Sartorius, UBS, Tonies, Korea Gas Corporation, Vontobel, Alibaba, Deere, Broadcom, Apollo Global Management, Blackstone, Goldman Sachs, Bank of America, Nvidia, Apple, Microsoft, Amazon, Alphabet, TSMC, Meta Platforms, Samsung Electronics, ASML, SK Hynix, Deutsche Börse, Moderna, BioNTech, Eli Lilly, Novo Nordisk, Mini Future Long auf den TTF-Gaspreis von Vontobel, (WKN: VY80G4), Vanguard FTSE Global All-Cap ETF thesaurierend (WKN: A42B1M), Vanguard FTSE Global All-Cap ETF ausschüttend (WKN: A42B1N), Vanguard FTSE Global Small-Cap ETF thesaurierend (WKN: A42B1P), Vanguard FTSE Global Small-Cap ETF ausschüttend (WKN: A42B1Q), Vanguard FTSE All-World ex-U.S. UCITS ETF thesaurierend (WKN: A42B1R), Vanguard FTSE All-World ex-U.S. ETF ausschüttend (WKN: A42B1S), SPDR MSCI ACWI IMI ETF (WKN: A1JJTD), Vanguard ESG Global All Cap ETF thesaurierend (WKN: A2QL8U), Vanguard FTSE All-World ETF thesaurierend (WKN: A2PKXG), Vanguard FTSE All-World ETF ausschüttend (WKN: A1JX52). Am 2. Oktober findet unser „Alles auf Aktien“-Summit in Berlin statt. Mit dem Code „AAAFRIENDS“ sparst du 50 Prozent auf dein Ticket – aber nur unter folgendem Link: https://veranstaltung.businessinsider.de/event/financesummit26/summary?rp=c6dc55d6-6f4f-4fb4-b75f-3f3501d84859 Wir freuen uns an Feedback über aaa@welt.de. Holt euch jetzt den exklusiven NordVPN-Deal inkl. 4 Bonusmonaten mit dem Code Allesaufaktien oder unter https://nordvpn.com/allesaufaktien Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Anzeige: Eight Sleep: Der Pod 5 reguliert die Temperatur im Bett automatisch, trackt Schlaf- und Gesundheitswerte ohne Wearable und kann so zu besserem Schlaf beitragen. Mit dem Code ALLESAUFAKTIEN erhaltet ihr auf https://www.eightsleep.com/allesaufaktien bis zu 350 Euro Rabatt. Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

After looking at companies that tanked in recent months in the last episode, Daniel Mahncke and Shawn O'Malley now take a look at the best-performing stocks of the pitches of the last two years. Google, Amazon, and Reddit are companies that generated great returns for the Intrinsic Value Portfolio, but there were also companies on the watchlist that turned into multibaggers in the past year. Daniel and Shawn discuss the patterns of the stocks that gained most in value, what one can learn from that, and how they think about selling and holding positions that went up past their fair value estimate. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:02:35) About Google's stock rise and valuation (00:14:09) How the massive capex changes the Mag7 (00:26:41) Why Amazon might be more attractive than Google (00:38:54) Why Daniel and Shawn decided to sell some Reddit (00:56:54) Why Remitly wasn't added to the Portfolio (01:05:35) How TSMC, Dell, and Comfort Systems became multibaggers (01:18:02) What the future holds for the AI trade Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about how to join us in NYC for our ⁠⁠⁠⁠⁠⁠⁠Intrinsic Value Conference⁠⁠⁠⁠⁠⁠⁠. Portfolio Review ⁠⁠⁠⁠Submit Tool⁠⁠⁠⁠. Pitch on ⁠⁠⁠Google.⁠⁠⁠ Pitch on ⁠⁠⁠Reddit⁠. Pitch on ⁠⁠⁠Crocs⁠. Pitch on ⁠⁠⁠Amazon⁠⁠⁠. Pitch on ⁠⁠⁠Remitly⁠⁠⁠. Pitch on ⁠TSMC⁠. Pitch on ⁠Dell⁠. Pitch on ⁠Comfort Systems⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠sponsors⁠⁠⁠: ⁠⁠⁠Fiscal.AI⁠⁠⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

My guest today is Ben Thompson, the founder and author of Stratechery. Ben is one of my favorite business thinkers and I love talking to him about everything happening in markets and technology.  We go through every important company, including OpenAI, Nvidia, Intel, Apple, Microsoft, Google, and Amazon. We also discuss why he thinks it would be dangerous for the United States to win the AI race outright, what container shipping and the railroads of the 1870s tell us about the buildout, and why the binding constraint on all of this may be capital rather than compute.  Please enjoy my conversation with Ben Thompson. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:16) Winning the AI Race With China (00:08:28) Timing, Capital, and the Railroads (00:11:34) Berkshire, Google, and Absolute Profits (00:14:23) Verifiable and Unverifiable Domains (00:20:20) Aggregation Theory in the AI Era (00:22:06) The Real Cost of Inference (00:25:40) Why Consumer AI Needs Advertising (00:30:08) Compute Shortages and Commodity Markets (00:35:46) Memory Cycles and Boom Bust Dynamics (00:42:08) TSMC, Intel, and Where Risk Goes (00:44:51) The Best Setups in Big Tech (00:52:14) The Frontier Model Contenders (00:54:27) Microsoft's IBM Playbook (01:00:45) Meta, Attention, and Advertising (01:07:29) NVIDIA, Commodities, and Power

In der heutigen Folge sprechen die Finanzjournalisten Nando Sommerfeldt und Holger Zschäpitz über das Aschenbrenner-Paradoxon, die Zeitenwende bei Geely und eine AAA-Idee, die keine Auto-Aktie ist. Außerdem geht es um S&P Global, Sandisk, Micron Technology, Bloom Energy, TSMC, Nebius Group, CoreWeave, STMicroelectronics, Applied Digital, Riot Platforms, SharonAI Holdings, ServiceNow, Autodesk, Adobe, Elastic, Shopify, Workday, Quantum Computing, Ondas Holdings, indie Semiconductor, SoundHound AI, Centrus Energy, SpaceX, Nvidia, OHB, Airbus, Geely Automobile Holdings, Strategy, Tesla, Home Depot, Baidu, Klarna, Xiaomi, Pony AI, Keysight Technologies, SK Hynix, Intel, Redeia, Schroders, Daimler Truck, Mercedes-Benz Group, Porsche AG, Global X Data Center REITs & Digital Infrastructure (WKN: A2QPB0), First Trust Nasdaq Clean Edge Smart Grid Infrastructure (WKN: A3DGK5), Global X European Infrastructure Development (WKN: A40E7B), iShares Global Infrastructure (WKN: A0LEW9), BNP Paribas Easy ECPI Global ESG Infrastructure (WKN: A3EWYS), SPDR Morningstar Multi-Asset Global Infrastructure (WKN: A12EAR). Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

After looking at companies that tanked in recent months in the last episode, Daniel Mahncke and Shawn O'Malley now take a look at the best-performing stocks of the pitches of the last two years. Google, Amazon, and Reddit are companies that generated great returns for the Intrinsic Value Portfolio, but there were also companies on the watchlist that turned into multibaggers in the past year. Daniel and Shawn discuss the patterns of the stocks that gained most in value, what one can learn from that, and how they think about selling and holding positions that went up past their fair value estimate. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:03:51) About Google's stock rise and valuation (00:15:32) How the massive capex changes the Mag7 (00:29:08) Why Amazon might be more attractive than Google (00:40:48) Why Daniel and Shawn decided to sell some Reddit (01:03:07) Why Remitly wasn't added to the Portfolio (01:11:57) How TSMC, Dell, and Comfort Systems became multibaggers (01:24:09) What the future holds for the AI trade Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about how to join us in NYC for our ⁠⁠⁠⁠⁠Intrinsic Value Conference⁠⁠⁠⁠⁠. Portfolio Review ⁠⁠⁠⁠Submit Tool⁠⁠⁠⁠. Pitch on ⁠⁠Google.⁠⁠ Pitch on ⁠⁠Reddit. Pitch on ⁠⁠Crocs. Pitch on ⁠⁠Amazon⁠⁠. Pitch on ⁠⁠Remitly⁠⁠. Pitch on TSMC. Pitch on Dell. Pitch on Comfort Systems. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠⁠⁠sponsors⁠⁠⁠⁠⁠: ⁠Plaud⁠ ⁠Plus500⁠ ⁠Netsuite⁠ ⁠Scribe⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

AGENDA: 05:00 Canva's Growth Gets Slashed as AI Costs Explode 20:00 The Great Software Reset: Why 2026 Will Punish AI Hesitancy 29:00 Canva's Valuation Reality Check—and the LP Liquidity Trap 35:00 Google's AI Brain Drain: Jeff Dean Leaves, Demis Steps Back 43:00 Why Google Can't Afford to Cure Alzheimer's 50:00 The Data-Centre Revolt: Can AI Survive the Political Backlash? 55:00 Elon Musk's $55BN Terrafab Bet to Break Free from TSMC 57:00 Revolut's $50BN CEO Pay Package—and the Return of Founder Control 01:13 AI Shopping, Atlassian's Revival, and Airtable's Brutal Sale Reality  

Bitcoin lleva un año 50% abajo de sus máximos y todo el mundo lo da por muerto: la inteligencia artificial se llevó el capital que antes iba a los activos digitales. En este episodio te explico por qué esa narrativa puede estar equivocada y qué tendría que pasar para que el ciclo se revierta. ──────────────

Rassegna stampa economico-finanziaria del 13 Agosto 2026, strutturata per macro-temi e basata sulle principali testate giornalistiche nazionali.Fisco, Manovra e finanza pubblicaTestate: Corriere della Sera / La Stampa / Il Sole 24 Ore / MF- La Manovra 2027 entra nella fase decisiva con un fabbisogno indicato in oltre 30 miliardi di euro e, nelle ricostruzioni più ampie, con un perimetro di spesa vicino a 36 miliardi nel triennio. Il dossier più sensibile resta il nuovo taglio dell'IRPEF al ceto medio, con l'ipotesi di ridurre l'aliquota dal 43% al 33% per i redditi tra 50.000 e 60.000 euro, per un costo stimato intorno a 2,7 miliardi.- Sul tavolo figurano anche contratti pubblici, social card, energia, pensioni, sanità e difesa. Il Governo punta a utilizzare circa 14 miliardi di nuova capacità di spesa per energia e difesa, mentre il resto delle coperture dovrà essere ricomposto nel quadro europeo.- La componente sociale resta rilevante: vengono indicati almeno 5 miliardi per la sanità e circa 2 miliardi per il rinnovo dei contratti pubblici, insieme alla conferma della tassazione al 5% sui rinnovi e alla possibilità di introdurre misure una tantum per giovani e donne.- Sul fronte delle entrate torna l'ipotesi di un contributo straordinario del 5% sugli utili delle principali banche. Intesa Sanpaolo e UniCredit avrebbero generato insieme una base di circa 12 miliardi di utili semestrali, mentre per l'intero sistema degli intermediari la stima citata si colloca tra 30 e 40 miliardi.- Il vero punto di attenzione per mercati e investitori non è quindi la dimensione nominale della Manovra, ma la qualità delle coperture e la capacità di finanziare gli interventi senza compromettere credito, deficit e credibilità fiscale.Inflazione, energia e potere d'acquistoTestate: Corriere della Sera / La Stampa / Il Sole 24 Ore / MF- L'inflazione italiana di luglio viene rivista al rialzo al 2,9% su base annua e allo 0,3% mensile. L'inflazione acquisita per il 2026 raggiunge il 2,7%, con una dinamica dei prezzi meno uniforme rispetto ai mesi precedenti.- Il carrello della spesa mostra segnali di rallentamento, mentre servizi, trasporti ed energia continuano a esercitare una pressione più significativa sui bilanci familiari.- L'energia regolamentata aumenta del 14,8% su base annua e del 6,5% nel solo mese di luglio, mentre sul mercato libero l'elettricità registra un incremento del 9,7%.- Benzina e gasolio salgono rispettivamente di circa 8% e 18,8% su base annua, mentre i trasporti aumentano del 4,3%. Per una famiglia di quattro persone viene stimato un aggravio complessivo di circa 1.020 euro.- La pressione è particolarmente concentrata nei servizi turistici. Alberghi e ristorazione registrano aumenti superiori al 3%, mentre gli affitti brevi salgono del 9% in Liguria e del 7,6% in Sardegna.- Sul territorio, la Calabria registra l'inflazione regionale più elevata con +3,5%, seguita da Campania +3,3%, Puglia e Sicilia +3,2%. Tra le grandi città spiccano Reggio Calabria +4%, Napoli e Catania +3,4%.Energia, petrolio e rischio HormuzTestate: Repubblica / Corriere della Sera- Il mercato petrolifero resta strutturalmente teso. L'Agenzia Internazionale dell'Energia stima un deficit di circa 1,8 milioni di barili al giorno, mentre la produzione russa viene indicata in forte contrazione.- Le scorte globali hanno recuperato soltanto parzialmente. Il quadro combina quindi consumi in ripresa e un'offerta relativamente poco elastica, aumentando la sensibilità delle quotazioni a ogni nuovo shock geopolitico.- Il gas europeo resta intorno ai 60 euro per MWh sul TTF di Amsterdam, livello che continua a incidere direttamente sulla competitività delle imprese energivore e sui costi di trasporto.- Resta inoltre il rischio di un prolungamento della chiusura dello Stretto di Hormuz. In uno scenario persistente, l'impatto si trasferirebbe rapidamente su greggio, benzina, gasolio, prodotti raffinati e margini industriali.- Per le imprese, il tema energetico non è quindi soltanto macroeconomico: coperture, diversificazione geografica degli approvvigionamenti e gestione dei contratti diventano vere decisioni di tesoreria.Banche, M&A e corporate Italia-GermaniaTestate: Corriere della Sera / La Stampa- L'Italia continua a rafforzare la propria presenza industriale e finanziaria in Germania. Da inizio 2023 a luglio 2026 vengono contabilizzate 100 operazioni tra M&A e accordi commerciali, per un valore complessivo di 17,8 miliardi di euro.- Oltre l'80% del valore è riconducibile alla scalata UniCredit-Commerzbank, pari a circa 14,9 miliardi di euro per il 47,6% dell'istituto tedesco.- Il confronto storico è significativo: tra 2015 e 2022 le operazioni italiane in Germania valevano 9,5 miliardi, contro 7,7 miliardi di operazioni tedesche in Italia. Il rapporto si sta quindi progressivamente ribaltando.- La pipeline comprende anche l'accordo da 3,6 miliardi di euro tra Italo e Siemens per 26 nuovi treni ad Alta Velocità, l'espansione di MFE in ProSiebenSat.1, l'acquisizione di Centrotec Climate Systems da parte di Ariston e quella di Heidelberg Engineering da parte di EssilorLuxottica.- La BCE non porrebbe un veto all'operazione UniCredit-Commerzbank, ma sottolinea la complessità dell'integrazione e la necessità di preservare un coefficiente patrimoniale vicino al 12,5%.- Il quadro complessivo è positivo: la maggiore solidità patrimoniale di alcune imprese e banche italiane consente oggi di trasformare la relativa debolezza tedesca in un'opportunità di consolidamento continentale.Intelligenza Artificiale, mercati e portafogli obbligazionariTestate: Il Sole 24 Ore / MF / Corriere della Sera- La corsa all'Intelligenza Artificiale comincia a produrre effetti rilevanti anche sul mercato obbligazionario. Per il 2026 vengono stimate emissioni di debito legate all'AI per circa 360 miliardi di dollari, di cui circa 300 miliardi riconducibili alle Big Tech.- L'aumento dell'offerta di obbligazioni può esercitare pressione sulla curva dei Treasury. Il rendimento del decennale potrebbe aumentare di circa 10 punti base, mentre le scadenze più lunghe risultano ancora più sensibili.- Il Treasury a 20 anni viene indicato intorno al 5,23%, contro circa il 4,5% di fine giugno. La crescita del capex AI viene quindi finanziata sempre più attraverso debito di lunga durata.- Per gli investitori emerge un rischio specifico: la vita economica degli asset AI può essere molto più breve della durata dei finanziamenti utilizzati per costruirli. La selezione della duration diventa quindi centrale.- Sul mercato azionario la tecnologia continua però a guidare la crescita: Nvidia sale del 2,5%, Micron del 6,5%, mentre il Nasdaq risulta il migliore tra i principali indici americani. In Italia, il FTSE MIB supera per la prima volta quota 53.000 punti.- Il quadro resta quindi favorevole all'AI, ma cresce il rischio di concentrazione sia nel mercato azionario sia nel credito corporate.Fondo sovrano norvegese e gestione del capitaleTestate: Il Sole 24 Ore / MF / Corriere della Sera- Il Fondo sovrano norvegese registra nel primo semestre 2026 circa 160 miliardi di euro di utili, con un rendimento del 9,4%.- Il patrimonio complessivo supera 2.380 miliardi di dollari, con il 72,1% investito in azioni globali, circa il 26% in obbligazioni governative e il restante 2% in immobili commerciali e infrastrutture.- Tra le esposizioni tecnologiche figurano Nvidia, Alphabet e TSMC, confermando come anche un investitore estremamente diversificato mantenga una presenza significativa nei principali vincitori del ciclo AI.- La regola fiscale norvegese consente prelievi annuali intorno al 3% del patrimonio. Circa il 20-25% del bilancio dello Stato viene così finanziato dai trasferimenti del fondo.- Il modello norvegese resta uno dei principali esempi di disciplina intergenerazionale: trasformare una rendita temporanea in capitale permanente attraverso diversificazione, regole di prelievo e orizzonte di lungo periodo.Immobiliare, wealth e protezione del patrimonioTestate: Repubblica- Nel mercato residenziale italiano emerge un problema crescente di sostenibilità economica delle operazioni di demolizione e ricostruzione. In molti Comuni, i costi degli interventi superano i ricavi potenzialmente ottenibili dalla vendita.- Milano rappresenta un'eccezione grazie a valori immobiliari molto elevati. Il prezzo medio di vendita viene indicato intorno a 6.663 euro al metro quadrato, mentre il costo di ristrutturazione è vicino a 1.300 euro e quello di demolizione e ricostruzione può raggiungere 2.200 euro.- Nelle città medie e piccole, invece, agevolazioni fiscali e Piano casa non sono sempre sufficienti a rendere economicamente sostenibili le operazioni.- Il rischio è una progressiva obsolescenza immobiliare nelle aree con domanda più debole, mentre le città con infrastrutture, servizi e domanda stabile continueranno ad attrarre capitali e interventi.- Sul segmento ultra-high-net-worth emerge inoltre una crescente domanda di asset legati a sicurezza e protezione patrimoniale. In Svizzera alcune grotte e bunker della Guerra Fredda vengono offerte in leasing per 99 anni a circa 1 milione di franchi.

Een kwartaal om door een ringetje te halen! ABN maakte meer winst, zag het rendement op het eigen vermogen toenemen, de buffers stijgen en de kosten zakken. Het was bovendien allemaal veel beter dan waarop analisten hadden gerekend. En de aandeelhouder? Die is blij, want het dividend gaat omhoog. Deze aflevering hebben we het over het succes van ABN. Waar doet de bank het goed en waar kunnen ze het nóg beter doen? Ook gaat het over de buffers van de bank. Die staan er zo goed voor, dat ze op overnamepad kunnen. Moeten ze dat ook doen? Verder duiken we in de wereld van cloudverhuur. We hebben het over een Amerikaanse hit, CoreWeave. De omzet verdubbelde en beleggers duiken op het aandeel. Een klein probleempje: het maakt nog altijd geen winst. Winst was er ook voor het Noorse staatsfonds, de grootste belegger van de wereld. In de eerst helft van het jaar een keurige 186 miljard dollar winst! Ook laten de Noren weten hoeveel aandelen ze precies in SpaceX hebben. Hello Kitty, jawel, komt ook nog voorbij deze aflevering. Je hoort wat de link tussen dit schattige wezentje en president Trump is. Te gast: Martine Hafkamp van Fintessa Vermogensbeheer BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

OpenAI estrena la aplicación oficial de ChatGPT para Linux; Nvidia presenta Nemotron 3.5 Lightning y Switchyard para repartir tareas entre modelos; investigadores reconstruyen parte de las trazas de razonamiento cifradas de grandes modelos; Meta promete nuevos modelos Muse abiertos; y TSMC y Sony invertirán 4.690 millones de dólares en sensores de imagen para los móviles de 2029.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

T. Rowe Price technology portfolio manager Dom Rizzo joins Jack Forehand and Kai Wu to break down the AI investment cycle, hyperscaler capital spending, semiconductor demand, and why the recent tech selloff may look more like 1998 than the end of the boom. They discuss AI return on investment, OpenAI and Anthropic, open versus closed models, financing the data center buildout, the future of software, labor productivity, and how to construct a global technology portfolio.Topics coveredWhy Dom sees similarities between the 2026 semiconductor correction and the 1998 selloffWhy hyperscaler AI CapEx could accelerate from already historic levelsWhat cloud revenue growth and operating margins say about AI return on invested capitalWhy end-user productivity is the key test for sustainable AI demandOpen-weight models versus frontier labs and where AI economic value may accrueWhy chips, memory, logic semiconductors, TSMC and ASML sit at critical points in the AI value chainHow equity, debt and operating cash flow could finance the next stage of the data center buildoutWhy semiconductors remain cyclical even in a structurally capital-intensive AI boomWhy AI agents could turn traditional enterprise software into data pipesAI productivity, labor displacement and the case for faster GDP growthHow Dom thinks about technology portfolio construction, risk factors and global stock selectionTimestamps00:00 AI, the tech correction and the 1998 comparison04:07 Why the AI capital spending cycle may only be halfway12:33 The real test for AI demand: end-user ROI17:00 Why frontier models may capture most of the economic value21:23 Where the biggest AI moats and profit pools could emerge28:12 Financing the AI buildout with equity and debt36:03 Are semiconductors in a supercycle or still cyclical?41:43 What AI agents mean for traditional software companies46:03 AI productivity versus labor displacement51:01 Building a portfolio for a technology revolution56:06 Global tech opportunities and Dom's stock-picking frameworkLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Esta semana la tecnología está entrando en un terreno cada vez más extraño: campañas contra la inteligencia artificial, polémicas alrededor de las smart glasses, posibles nuevas regulaciones, publicidad dentro del gaming y una batalla tecnológica global que no para de crecer.En este CuriosiMartes analizamos qué hay detrás de algunas de las historias que están circulando sobre la IA y por qué muchas veces una parte real de una noticia puede terminar convertida en un relato completamente diferente. Además: Sony estudia nuevas formas de monetización, TSMC expande su producción en Estados Unidos, Samsung prepara la próxima generación de dispositivos expandibles, Apple enfrenta nuevas dudas del mercado y China sigue acelerando en tecnología, investigación y recursos humanos.Y hay más: Boston Dynamics actualiza Atlas para hacerlo todavía más autónomo, mientras nuevas investigaciones exploran el cerebro, la plasticidad neuronal, el desarrollo de medicamentos mediante IA y tecnologías capaces de recuperar parcialmente la visión.

The Wall Street Journal reported that an investment firm named Situational Awareness committed $400 million to a stealth chip startup after a crash. The report did not identify the company or disclose terms, but the size of the commitment highlights growing investor interest in capital-intensive semiconductor ventures. Policy support from the CHIPS and Science Act and buyer demand from cloud, automotive, telecom, and defense are creating openings for new entrants. Chip development requires long lead times, significant design and manufacturing costs, and access to foundries such as TSMC or Intel Foundry Services. Investors typically use milestone-based tranches, board oversight, and strategic partners to manage risk. Founders that secure anchor customers, developer support, and supply chain capacity improve their chances of converting large financings into revenue.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

AI is creating a new cybersecurity arms race - and investors are taking notice. Michelle Martin looks at why CrowdStrike and Palo Alto Networks have hit record highs after Black Hat put the growing threat from AI agents firmly in focus. Could cybersecurity be the next major leg of the AI trade? Then, Microsoft is charging back, with Bernstein seeing a path to another 30% upside. TSMC’s sales surge 45% as AI hardware demand stays hot, while Intel turns to a US$15 billion stock offering to fund its ambitions. Also on Market View: Greg Abel begins putting Berkshire Hathaway’s enormous cash pile to work, Indonesian equities approach bull-market territory, and Singapore raises its 2026 growth forecast to 4.5–5.5%. Plus, what the latest DBS, OCBC and UOB results tell investors about the outlook for Singapore banks - and why Christopher Nolan’s The Odyssey has become a US$1.1 billion blockbuster. Hosted by Michelle Martin.See omnystudio.com/listener for privacy information.

Andrew, Ben, and Tom discuss Berkshire Hathaway finally deploying its cash pile with the $6.8 billion purchase of Taylor Morrison Homes, a $10 billion new stake in Google now among its top five holdings, and $4.5 billion in buybacks, prompting the question of whether Buffett's years of caution are giving way to optimism, operating earnings up 24% and utilities profit up 27% even as BNSF and Geico continue to lag, TSMC's strong July sales supporting Q3 guidance for 46-48% revenue growth, a rough stretch for agriculture with Taylor Farms recalling jalapeño products in a salmonella outbreak following an earlier Cyclospora scare, and Treasury Wine Estates taking a $500 million charge and retreating from its US "masstige" strategy as global wine consumption hits its lowest level since the 1960sJoin our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Hour 2 opens with the hosts addressing studio audio troubleshooting and evaluating former NBA center Enes Kanter Freedom's public declaration for the WNBA draft. On The Shortlist, the hosts review statements from U.S. Representative Alexandria Ocasio-Cortez and Vice President Kamala Harris debating proof-of-citizenship provisions in the SAVE America Act. In the St. Louis Morning Brief, local reporting focuses on a St. Louis City police investigation into a false vehicle theft and Amber Alert report, Kirkwood small business reactions to import tariffs, and Boeing's $75 million U.S. Air Force contract expansion for JDAM-LR precision kits at its St. Charles facility.In Segment 3, business anchor Nicole Murray (This Morning with Gordon Deal) delivers a national market update, reporting on Taiwan Semiconductor's (TSMC) $14.5 billion July revenue report, a CDC study on Cannabis Hyperemesis Syndrome (CHS), Taylor Fresh Foods' jalapeño recall, and the passing of Midwest auto group founder Frank Leta. Hour 2 concludes with Segment 4's In Other News, covering a New Jersey python capture, a Fandango promotional ticket discount for Spider-Man: Brand New Day, and a commuter train disablement in New York City. Hour Hashtags#StLouisBrief #InOtherNews #BoeingStCharles #NicoleMurray #TariffPolicy #AmberAlert #SpiderManBrandNewDay #TSMCHour Guest ListNicole Murray — Business Anchor, This Morning with Gordon Deal (Hour 2, Segment 3)

The August 10, 2026 edition opens in Hour 1 with the host returning to the morning show following a short leave after taking his youngest daughter to college. The hosts examine media commentator Clay Travis's recent $10 million challenge and former NBA players Enes Kanter Freedom and Royce White declaring for the WNBA draft to highlight league gender eligibility policies. On The Shortlist, the hosts react to campaign statements from Michigan Democratic U.S. Senate nominee Abdul El-Sayed and U.S. Representative Alexandria Ocasio-Cortez, alongside remarks from Senator Marsha Blackburn on the SAVE America Act. In Kim on a Whim, the hosts analyze the $124 trillion "Great Wealth Transfer" from Baby Boomers to Millennials, reviewing estate planning, Medicaid look-back rules, and Variable Universal Life (VUL) policies, before analyzing President Trump's designation of former Assistant U.S. Attorney Will Scharf as White House Counsel.Hour 2 addresses studio audio troubleshooting and Enes Kanter Freedom's public declaration for the WNBA draft. On The Shortlist, the hosts review statements from Vice President Kamala Harris debating proof-of-citizenship provisions in the SAVE America Act. In the St. Louis Morning Brief, local reporting covers a St. Louis City police investigation into a false vehicle theft and Amber Alert report, Kirkwood small business reactions to import tariffs, and Boeing's $75 million U.S. Air Force contract expansion for JDAM-LR precision kits in St. Charles. Business anchor Nicole Murray delivers a national market update on TSMC's $14.5 billion July revenue report, a CDC study on Cannabis Hyperemesis Syndrome (CHS), Taylor Fresh Foods' jalapeño recall, and the passing of Midwest auto group founder Frank Leta. Hour 2 concludes with In Other News, covering a New Jersey python capture, a Fandango ticket discount for Spider-Man: Brand New Day, and a commuter train disablement in New York City.Hour 3 evaluates WNBA draft policy and public health messaging exchanges between CBS News anchor Margaret Brennan and NIH Director Dr. Jay Bhattacharya. Senior Legal Fellow Hans von Spakovsky (Advancing American Freedom) analyzes Article III legal standing standards following a U.S. Court of Appeals for the D.C. Circuit ruling on White House construction projects, while commenting on Will Scharf's White House Counsel appointment. Fox News Radio foreign correspondent Jonathan Savage provides an operational update on Middle East maritime security along the Strait of Hormuz. Hour 3 concludes with Kim on a Whim, where Kim St. Onge and the host discuss Senator Mitch McConnell's discharge from rehab to home recovery and congressional term limits.Hour 4 opens with an analysis of Missouri primary election results on Amendments 4 and 5, evaluating public messaging strategy for Amendment 3 on the November general election ballot. On The Shortlist, the hosts review red state census population migration trends, campaign statements from Abdul El-Sayed, and comments from Lindy Lee on DNC financial debt. KMOX Sports Director Tom Ackerman joins the studio to analyze WNBA eligibility policy debates and St. Louis Cardinals roster developments, highlighting rookie infielder JJ Weatherholt and minor league prospect acquisitions. The broadcast concludes with an evaluation of WNBA economic expansion, Indiana Fever attendance growth, and league merchandise sales for Caitlin Clark and Sophie Cunningham. Full Show Guest ListNicole Murray — Business Anchor, This Morning with Gordon Deal (Hour 2, Segment 3)Hans von Spakovsky — Senior Legal Fellow, Advancing American Freedom (Hour 3, Segment 2)Jonathan Savage — Foreign Correspondent, Fox News Radio (Hour 3, Segment 3)Tom Ackerman — Sports Director, KMOX (Hour 4, Segment 3)

"The demand is there" for AI, says Sam Vadas when discussing TSMC's (TSM) monthly revenue. Figures for the Taiwanese chip manufacturer continue to rise even as the reaction in the stock appears muted. Sam turns to analyst updates through Morgan Stanley's upgrade for HPE (HPE) and Well Fargo's upgrade for Dick's Sporting Goods (DKS). ======== 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

US President Trump told Axios that they are only semi-negotiating with Iran but said it will work out and that it always works out.Iran's Supreme National Security Council issued six demands to the US, including total force withdrawal, end to proxy warfare.Iran's Foreign Ministry Spokesperson said Iran is currently focused on the Strait of Hormuz rather than resuming negotiations with the US, while adding that talks with Oman are constructive and positiveUS equity futures are mixed, with the NQ outperforming as strong TSMC July revenue lifts tech names.DXY slightly firmer; JPY reverses post-NFP gains despite hawkish BoJ Summary of Opinions.Fixed income flat; Energy benchmarks slightly firmer as Iran issues demands.Looking ahead, highlights include comments from Fed's Hammack.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Het grootste winstmomentum in jaren, zo wordt het al genoemd. Er komen weer beleggers naar Europa toe. Die zien hier een gemiddelde winststijging van 22 procent, lage brandstofprijzen én immuniteit voor volatiele tech-aandelen en weten niet hoe snel ze in moeten stappen. Maar hoe houdbaar is dat winstmomentum? En ben je te laat als je nu nog mee wilt doen met het feest? Dat hoor je in deze aflevering. Verder hebben we het over een kantelpunt bij Berkshire Hathaway. Het investeringsvehikel van Warren Buffett geeft z'n geld weer uit. Zowel aan eigen aandelen als die van anderen. Eindelijke slinkt de cashberg weer. Betekent het dat jij zelf ook weer aan de bak moet? We vertellen je ook nog over mogelijk een van de laatste keren dat we van Ebusco horen. De elektrische bussenbouwer waarschuwde al dat het na dit jaar einde verhaal zou zijn als er geen nieuwe orders binnenkomen, maar wat als bestaande orders afhaken? We praten je bij over het nieuwste gok-doelwit in Zuid-Korea. En we leren je een nieuwe term: Moneymaxxing. Te gast: Justin Blekemolen, van Lynx Beleggen BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

En Capital Intereconomía repasamos las claves del día y la evolución de los mercados en Asia, Wall Street y Europa, pendientes de la tecnología, las expectativas sobre los próximos movimientos de la Reserva Federal y las tensiones geopolíticas. En Asia, Sony y TSMC preparan una joint venture valorada en más de 6.000 millones de dólares, en un momento de fuerte competencia internacional por el liderazgo en semiconductores. En Wall Street, SpaceX se dispara mientras el S&P 500 cierra su mejor semana desde abril, prolongando el buen tono de la renta variable estadounidense. En el primer análisis de la mañana conversamos con Eduardo Bolinches, analista de Invertia, para conocer qué podemos esperar de los mercados esta semana y hasta dónde puede llegar el Ibex 35 después de su fuerte rally. Analizamos también el último dato de empleo de Estados Unidos y sus implicaciones para los próximos movimientos de la Reserva Federal, así como el impacto que puede tener sobre las bolsas y el petróleo la decisión de Donald Trump de mantener un perfil más bajo en el conflicto con Irán. Además, buscamos oportunidades de inversión en la Bolsa española y repasamos los valores que presentan mayor potencial en el actual escenario. Ampliamos el foco para conocer qué sectores internacionales resultan más atractivos y analizamos el mercado de divisas una semana después de la intervención sobre el yen. Terminamos la hora con el repaso a las principales portadas de la prensa económica nacional e internacional para conocer las noticias que marcarán la actualidad financiera, empresarial y macroeconómica de la jornada.

Die Wall Street startet verhalten in die neue Woche. Im Fokus steht Intel mit einer geplanten Kapitalerhöhung über 15 Milliarden US-Dollar, zuzüglich einer möglichen Mehrzuteilung von 2,25 Milliarden US-Dollar, was wegen der Verwässerung zunächst belastet. Berkshire Hathaway meldet mit 12,98 Milliarden US-Dollar einen 16 Prozent höheren operativen Gewinn und 101,8 Milliarden US-Dollar Umsatz, allerdings profitiert das Ergebnis stark von sonstigen Erträgen, während GEICO schwächelt. Der Cashbestand sinkt auf rund 365 Milliarden US-Dollar, zugleich kaufte Berkshire für etwa 4,5 Milliarden US-Dollar eigene Aktien zurück. Im Tech-Sektor bleibt die KI-Nachfrage robust, TSMC meldet für Juli rund 45 Prozent Umsatzwachstum, während Nvidia bis zu 3 Milliarden US-Dollar in den Rechenzentrumsentwickler Lancium investieren will. Geopolitisch bleibt die Straße von Hormus ein Unsicherheitsfaktor, der Ölpreis zieht entsprechend an. Im Wochenverlauf stehen vor allem die Verbraucherpreise am Mittwoch, die Erzeugerpreise am Donnerstag und die Einzelhandelsumsätze am Freitag im Fokus. Ein Podcast - featured by Handelsblatt. ► Entdecke den exklusiven NordVPN Deal! Jetzt risikofrei testen mit einer 30-Tage-Geld-zurück-Garantie: https://nordvpn.com/wallstreet * ► Erhalte einen exklusiven 15% Rabatt auf Saily eSIM Datentarife! Lade die Saily-App herunter und benutze den Code wallstreet beim Bezahlen: https://saily.com/wallstreet * +++ Alle Rabattcodes und Infos zu unseren Werbepartnern findet ihr hier: https://linktr.ee/wallstreet_podcast +++ ► Mehr Einblicke: https://bit.ly/360wallstreetpc * Impressum: https://www.360wallstreet.de/impressum *Werbung

Die Wall Street startet verhalten in die neue Woche. Im Fokus steht Intel mit einer geplanten Kapitalerhöhung über 15 Milliarden US-Dollar, zuzüglich einer möglichen Mehrzuteilung von 2,25 Milliarden US-Dollar, was wegen der Verwässerung zunächst belastet. Berkshire Hathaway meldet mit 12,98 Milliarden US-Dollar einen 16 Prozent höheren operativen Gewinn und 101,8 Milliarden US-Dollar Umsatz, allerdings profitiert das Ergebnis stark von sonstigen Erträgen, während GEICO schwächelt. Der Cashbestand sinkt auf rund 365 Milliarden US-Dollar, zugleich kaufte Berkshire für etwa 4,5 Milliarden US-Dollar eigene Aktien zurück. Im Tech-Sektor bleibt die KI-Nachfrage robust, TSMC meldet für Juli rund 45 Prozent Umsatzwachstum, während Nvidia bis zu 3 Milliarden US-Dollar in den Rechenzentrumsentwickler Lancium investieren will. Geopolitisch bleibt die Straße von Hormus ein Unsicherheitsfaktor, der Ölpreis zieht entsprechend an. Im Wochenverlauf stehen vor allem die Verbraucherpreise am Mittwoch, die Erzeugerpreise am Donnerstag und die Einzelhandelsumsätze am Freitag im Fokus. Abonniere den Podcast, um keine Folge zu verpassen! ____ Folge uns, um auf dem Laufenden zu bleiben: • X: http://fal.cn/SQtwitter • LinkedIn: http://fal.cn/SQlinkedin • Instagram: http://fal.cn/SQInstagram

Meta brengt Muse Code uit, een eigen codeertool die vanaf nu beschikbaar is in bèta en draait op Muse Spark 1.2, het nieuwste AI-model van het bedrijf. Daarmee neemt Meta het op tegen Claude Code van Anthropic en Codex van OpenAI, na een lange periode van achterstand op het gebied van kunstmatige intelligentie. Verder gaat Anthropic eigen AI-chips ontwerpen. Rosanne Peters vertelt erover in deze Tech Update. De tool is bedoeld voor het schrijven en verbeteren van software en werkt volgens Meta vooral goed bij langdurige, complexe taken en projecten. Voor Muse Code betaal je per gebruik, dus zonder verplicht abonnement. Wie Meta toestemming geeft de eigen code te gebruiken voor het trainen van toekomstige modellen, betaalt ruim tien keer minder. Het is het eerste grote project van AI-chef Alexandr Wang, die vorig jaar werd aangenomen om de AI-strategie van Mark Zuckerberg te versterken. Anthropic gaat eigen AI-chips ontwerpen Anthropic zet intern een team op dat eigen AI-chips gaat ontwerpen. Het bedrijf wil daarmee minder afhankelijk worden van hardware van onder andere Google en Amazon, maar zegt tegelijk gebruik te blijven maken van zijn huidige chipleveranciers. Persbureau Reuters meldde in april al dat Anthropic dit overwoog. Wanneer het ontwerp klaar is en wanneer de chip in productie gaat, laat het bedrijf niet weten. Ook is onbekend wie de chips gaat maken; eerdere berichten noemden Samsung als mogelijke partner. Anthropic is niet de enige: OpenAI werkt sinds vorig jaar met Broadcom en TSMC aan een eigen chip, die mogelijk al eerder in productie gaat. Volgens Reuters kost het ontwerpen van een geavanceerde AI-chip ongeveer een half miljard dollar, inclusief de kosten voor gespecialiseerde ingenieurs. Meta introduceert Muse Code en het model Muse Spark 1.2 Meta mengt zich met Muse Code in de strijd om AI-codeeragenten Muse Code is in bèta beschikbaar voor macOS en Linux Anthropic bevestigt eigen team voor het ontwerpen van chips Anthropic zoekt ingenieurs voor zijn nieuwe custom silicon team Over de maker:Rosanne Peters is techredacteur en maakt De Grote Tech Show en De Technoloog. Sinds 2025 doet ze redactie- en productiewerk en is zij te horen in de Tech Update tijdens De Ochtend- en Avondspits. See omnystudio.com/listener for privacy information.

Memory has been one of the strongest corners of the semiconductor industry, and strong returns invite hard questions. In the second part of this series, equity analyst Shan Rui Yeo examines the main risks to the memory thesis: rising competition from China's CXMT and YMTC, the technologies that could reduce AI's appetite for memory, and the wave of capacity investment that could eventually tip the industry back into oversupply. He weighs each risk against the constraints holding it back, from equipment export controls to limited EUV supply, and notes that memory companies already trade at three to five times forward earnings. The conversation closes on a working principle: treat the terminal value as a distribution, not a fixed number. Key Takeaways China's CXMT is expanding DRAM capacity aggressively, but export controls on sub-18 nanometre equipment and EUV keep its effective supply share (about 10%) below its capacity share (about 15%). YMTC is the more credible technological threat: NAND density comes from stacking layers, and its Xtacking hybrid bonding architecture is proprietary. Efficiency gains may grow memory consumption rather than reduce it; cheaper tokens get spent on larger context windows (the Jevons paradox). The deepest risk is architectural: if large language models are not the path to AGI, the next paradigm may not be memory hungry, so terminal value is a distribution, not a fixed number. Announced capex is enormous but back-loaded into the 2030s, and EUV and equipment capacity are the bottleneck to bringing it online. Memory companies trade at three to five times forward earnings; the market is not assuming supernormal profits forever, and the NAND supply outlook is better in the near term. Companies Mentioned: Samsung, SK Hynix, Micron, CXMT (ChangXin Memory), YMTC (Yangtze Memory), Apple, NVIDIA, Google, ASML, Applied Materials, KLA, Lam Research, TSMC, Intel, Kioxia Host: Rob Campbell, CFA, Institutional Portfolio Manager Guest: Shan Rui Yeo, CFA, Equity Analyst   This episode is available for download anywhere you get your podcasts.   Founded in 1974, Mawer Investment Management Ltd. (pronounced "more") is a privately owned independent investment firm managing assets for institutional and individual investors. Mawer employs over 250 people in Canada, U.S., and Singapore.    Visit us at: https://www.youtube.com/@MawerInvestment https://www.mawer.com https://www.linkedin.com/company/mawer-investment-management/ https://www.instagram.com/mawerinvestmentmanagement/

Het is een beetje een saai, ouderwets en gedateerd techbedrijf, maar tóch moet je erop letten, zegt onze gast, Jos Versteeg van InsingerGillisen. Infineon! Een Duits techbedrijf, volgens Jos in de verste verte niet vergelijkbaar met bijvoorbeeld TSMC, daar is de techniek te oud voor. Tóch vindt hij de cijfers interessant, omdat ze chips leveren voor datacenters. Daar hoopt hij meer over te horen. We kijken vooruit op de cijfers, en ook een klein beetje op die van AMD. In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Te gast: Jos Versteeg van InsingerGillisen BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

TSMC in Dresden. Foxconn in Bordeaux. A German chemicals giant in Kaohsiung. EU-Taiwan industrial ties are deepening fast, and it's mutual: EU states have invested more in Taiwan than the US and Japan combined. EETO head Lutz Güllner explains what Chips Act 2.0 changes, where Taiwan fits into Europe's industrial strategy beyond chips, and how Taiwan's civil society is countering foreign information manipulation.Episode highlights:07:23 How EU-Taiwan relations reached their most active point yet09:09 The EU Council's landmark Taiwan statement17:01 The Industrial Accelerator Act and the EU Chips Act 2.024:15 FIMI: naming foreign information manipulation and interference32:44 Why understanding Taiwanese politics matters for a diplomatHost: Kwangyin Liu, Deputy Managing Editor, CommonWealth MagazineGuest: Lutz Güllner, Head of the European Economic and Trade Office in TaipeiProducers: Yayuan Chang, Weiru Wang＊Read more：https://english.cw.com.tw＊Share your thoughts：bill@cw.com.tw Powered by Firstory Hosting

OpenAI files for a trillion-dollar IPO the same week a pre-release model breaches Hugging Face and 42 state attorneys general open a coordinated investigation. Patrick Moorhead and Daniel Newman also break down AMD's hyperscaler CPU numbers from Advancing AI, the Moonshot distillation accusations, a wave of coordinated AI governance moves in Washington, and a stacked earnings slate spanning TSMC, Alphabet, IBM, ServiceNow, and Intel. The handpicked topics for this week are: OpenAI compresses a trillion-dollar IPO filing, a 42-state AG investigation, and a Hugging Face breach into five days: OpenAI and Anthropic both filed S-1 paperwork the same week 42 state attorneys general opened a coordinated investigation into OpenAI's data handling and safety practices, and a pre-release model reportedly breached Hugging Face days later. Moorhead and Newman question the timing, noting a rogue-agent narrative surfacing in the same week as a trillion-dollar valuation push draws obvious scrutiny. (The Decode) AMD's Advancing AI event puts a number on its hyperscaler momentum: Lisa Su confirmed Helios ships at the end of Q3 with volume ramping in Q4, backed by two-gigawatt capacity commitments from Microsoft and Anthropic and a claimed 70 to 75 percent share of hyperscaler CPU deployments. Moorhead points to NVIDIA's multi-layer software stack as the harder barrier AMD still has to close. (The Decode) Washington accuses Moonshot of distilling Anthropic's models days after Xi Jinping's WAIC keynote: Xi launched a 29-country AI cooperation organization at the Shanghai World AI Conference, and US officials Kratsios and Bessent followed with claims that Moonshot's Kimi K3 model shows data overlap with Anthropic's Opus models. Newman points to NVIDIA hardware in the training runs as evidence the distillation question extends beyond software alone. (The Decode) Five layers of government moved on AI oversight in a single week: Congress drafted a breach-response framework in reaction to the Hugging Face incident, the White House's 30-day pre-release review framework nears finalization, and state attorneys general and statehouses continue advancing their own rules in parallel. Moorhead notes nearly two decades of prior Capitol Hill engagement compressed into a single week of coordinated action, with each branch pursuing a different definition of the problem. (The Decode) Chinese open-source models now account for roughly a third of US developer traffic, and Moorhead and Newman take opposite sides on what it means: Moorhead argues enterprises are de-risking away from frontier-lab dependency, pointing to demand for smaller, workflow-specific open models. Newman counters with Vercel data showing those models capture 29 percent of gateway tokens against just 4 percent of revenue, framing the shift as a price discount that enterprise dollars have yet to follow. (The Flip) TSMC sells out CoWoS packaging capacity through 2026 and confirms a 10 percent price increase for 2027: The company posted a record quarter and committed $100 billion to its Arizona expansion on top of the pricing move. Newman calls the sustained capital spending a signal that the broader AI buildout still has runway. (Bulls & Bears) Google Cloud grows 82 percent as Alphabet posts its first-ever negative free cash flow quarter: The company raised its capital expenditure guidance to $195 to $205 billion and beat on revenue and EPS once one-time gains from its SpaceX and Anthropic stakes are excluded. Newman frames the spending as evidence Alphabet is prioritizing long-term AI infrastructure position over near-term cash generation. (Bulls & Bears) IBM misses Q2 revenue at $17.16 billion and cuts its full-year growth guide to 4 to 5 percent: Mainframe revenue fell 42 percent as enterprises redirected budget toward GPU and AI infrastructure purchases. CEO Arvind Krishna says a portion of the delayed deal flow has already resumed into the current quarter, pointing toward a potential rebound. (Bulls & Bears) ServiceNow crosses $1 billion in agentic AI annual contract value and raises its full-year guide: Agentic AI usage in production climbed 9x in nine months, and the company reaffirmed a target of $1.5 billion in AI ACV by year end. Newman points to margin compression from recent acquisitions as the tradeoff behind the platform's push into workflow and security convergence. (Bulls & Bears) NetSuite's new agentic platform, Next, becomes part of Six Five's own back-office stack: Newman and Moorhead both confirmed their companies are testing NetSuite Next for finance and accounting workflows. Newman points to the rollout as evidence that established SaaS platforms are absorbing agentic features directly into existing systems.  Intel posts its fastest revenue growth since 2011 and lifts 2026 capital spending guidance: EPS came in near double consensus estimates, and CFO David Zinsner signaled a significant capex increase for 2027 tied to 14A demand. Moorhead reads the spending signal as confirmation of an anchor customer for the 14A node, ahead of any formal announcement. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. OpenAI's High-Stakes Week: https://www.npr.org/2026/07/23/g-s1-135085/openai-hacking-ai-models AMD's Advancing AI Push: https://blogs.microsoft.com/blog/2026/07/20/microsoft-expands-azure-ai-and-hpc-infrastructure-with-amd/ Moonshot and the Distillation Debate: https://x.com/mkratsios47/status/2079933645888880708 Five Layers of AI Governance: https://www.politico.com/news/2026/07/22/openai-hugging-face-congress-response-01009190 Chinese Open-Source Traffic Debate: https://www.cnbc.com/2026/07/07/chinese-ai-models-costs-us-openai-anthropic.html ; https://wyomingdatacenterfacts.com/2026/07/20/the-quiet-surge-how-chinese-open-weight-models-are-powering-u-s-ai/ TSMC's Pricing Power: https://www.bloomberg.com/news/articles/2026-07-21/tsmc-in-talks-to-raise-prices-by-up-to-10-in-2027-nikkei-says Alphabet's Cash Flow Turn: https://qz.com/alphabet-google-second-quarter-earnings-revenue-cloud-072226 IBM's Mainframe Miss: https://www.cnbc.com/2026/07/22/ibm-q2-earnings-report-2026.html ServiceNow and NetSuite's Agentic Push: https://newsroom.servicenow.com/pressreleases/details/2026/ServiceNow-Reports-Second-Quarter-2026-Financial-Results/default.aspx ; https://www.netsuite.com/portal/home.shtml Intel's Growth Signal: https://www.cnbc.com/2026/07/23/intel-intc-earnings-report-q2-2026.html  

BESI, ASML, ASM, alledrie gaan ze onderuit na een verhaal van The Information. Volgens de peperdure techsite is het nu echt zover: China is begonnen met de productie van eigen DUV-chipmachines. Een Chinees staatsbedrijf zou deze gemiddeld geavanceerde chipmachines kunnen leveren, een product waar ASML nu nog nagenoeg alleenheerser op is. Maar ja, we horen wel vaker dat China enigszins hoogwaardige chips kan produceren, terwijl de productieprocessen nagenoeg altijd inefficiënt blijken te zijn. Oftewel: duur, verspillend en amper concurrerend. Waarom beleggers dan toch in paniek raken en of dat wel terecht is, bespreken we deze aflevering. Een aflevering die sowieso vol zit met China. Want chipbedrijf CXMT kreeg het voor elkaar: van de ene op de andere dag het grootste beursbedrijf van China worden. CXMT schoot na de beursgang vannacht met meer dan 500 procent omhoog. Het bedrijf produceert geheugenchips en is daarmee een concurrent van Samsung en SK Hynix. Of het bedrijf die twee ook serieus kan beconcurreren, gaan we ook voor je uitzoeken. En het lijkt de Chinashow wel: we hebben het ook over de soort-van-Chinese bank HSBC, de Hong Kong & Shanghai Banking Corporation. Dankzij hun zetel en beursnotering in Londen, is het een Europees beursbedrijf. En sinds kort zelfs Europa's op-een-na grootste beursbedrijf, achter ASML. Ze naderen een beurswaarde van 300 miljard dollar. Hoe ze dat voor elkaar kregen, en waarom ze zo belachelijk veel winstgevender zijn dan ING en ABN, gaan we je ook vertellen. Hoor je ook Welke nieuwe horde in de overnamesoap van Warner Bros door Paramount nu weer krijgt Hoe Nvidia de koers van andere chipbedrijven omhoog helpt Waarom president Trump de beursgang van She-in in de weg zit Te gast: Robbert Manders, van het Antaurus Europe Fund BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

Welcome to another episode of The AZREIA Show! In this episode, Michael Del Prete sits down with Rob Leonard Jr., President of the Arizona Private Lenders Association (APLA) and partner at Colonial Capital, to discuss the evolving landscape of private lending in Arizona. Rob shares his journey from real estate investing into lending, explains why he chose financing over flipping, and reveals how Colonial Capital has grown since 2003 by helping investors fund everything from fix-and-flips to commercial real estate projects through hard money, bridge loans, and creative capital solutions. Drawing from decades of experience through both the Great Financial Crisis and the COVID-19 market disruption, Rob shares valuable lessons on risk management, disciplined underwriting, and avoiding deals that don't make financial sense. The conversation also explores today's biggest opportunities across Arizona, including industrial development, retail build-to-suit projects, land bridge financing, and the long-term impact of major economic drivers like TSMC, infrastructure expansion, and population growth. Rob also explains the mission of the Arizona Private Lenders Association and how it helps lenders, investors, and industry professionals stay informed on market trends and regulations. Whether you're looking for private capital, exploring commercial real estate opportunities, or simply wanting to understand where Arizona's market is headed, this episode delivers practical insights from one of the state's most experienced private lenders. Learn what makes a strong lending opportunity, where smart investors are focusing today, and how private lending continues to play a critical role in Arizona's real estate market. 01:21 – Rob's Real Estate Origin 03:18 – Choosing Lending Over Flipping 04:04 – Colonial Through the Housing Crash 05:37 – Early Career Lessons 06:40 – Modernizing Colonial Capital 08:18 – Colonial Capital Overview 11:53 – Passive Investing Advice 14:19 – COVID Deal Stress Test 18:44 – What's Hot in Arizona Today 20:29 – Retail Deals & Market Growth 21:13 – Arizona's Future Outlook 23:25 – Infrastructure & Economic Growth 25:17 – Investor Playbook 28:13 – What's Next for Phoenix 31:32 – Arizona Private Lenders Association (APLA) 34:56 – Who Should Join APLA 36:16 – How to Connect Online -- Contact Alden of Silver Crest Opportunity Fund at http://silvercrestopportunityfund.com "AZREIA does not endorse specific investments. Please do your own due diligence." Want to grow your real estate business?

In this episode of The Canadian Investor Podcast, we start with the latest Canadian inflation data and why the headline number may not fully reflect what households are feeling. We then look at TSMC’s huge quarter and what it says about the AI chip boom, before turning to Intuitive Surgical and Netflix, two high-quality businesses that sold off after earnings for very different reasons. We also discuss Cogeco’s latest results, the challenges facing legacy telecom companies, and wrap up with a cautionary discussion about a viral TFSA strategy that may be riskier than it looks. Tickers discussed: TSM, NVDA, AAPL, AMD, AVGO, INTC, ISRG, NFLX, CGO.TO, BCE.TO, RCI.B.TO, T.TO, QBR.B.TO Subscribe to Our New Youtube Channel! Check out our portfolio by going to Jointci.com Our Website Canadian Investor Podcast Network Twitter: @cdn_investing Simon’s twitter: @Fiat_Iceberg Braden’s twitter: @BradoCapital Dan’s Twitter: @stocktrades_ca Want to learn more about Real Estate Investing? Check out the Canadian Real Estate Investor Podcast! Apple Podcast - The Canadian Real Estate Investor Spotify - The Canadian Real Estate Investor Web player - The Canadian Real Estate Investor Asset Allocation ETFs | BMO Global Asset Management Sign up for Fiscal.ai for free to get easy access to global stock coverage and powerful AI investing tools. Register for EQ Bank, the seamless digital banking experience with better rates and no nonsense.See omnystudio.com/listener for privacy information.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI.Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself.This is a reference conversation on fast inference, AI chips, and the next compute bottleneck.(00:00) Cold open & Intro(01:31) Why speed became the AI bottleneck(02:32) Tokens per second per user, explained(03:16) AI's broadband moment and the Netflix analogy(04:35) The AI chip landscape: GPUs, TPUs, Trainium, ASICs(06:36) What is an ASIC?(08:08) Nvidia, Groq, and the fast inference war(09:16) OpenAI, Broadcom, and specialized silicon(12:10) China, power, and sovereign AI infrastructure(15:05) Is the AI infrastructure boom a bubble?(18:56) The hidden bottlenecks: HBM, CoWoS, and 3nm(22:57) Why agents are creating CPU demand(25:36) Andrew Feldman's path from SeaMicro to Cerebras(26:13) Why Cerebras bet on AI in 2016(31:14) SRAM vs. HBM: why inference is a memory problem(33:19) What wafer-scale computing actually means(34:28) The deep-tech “Everest” problem(36:07) The moment the first Cerebras system worked(36:49) Ringing the bell and surviving deep tech(39:08) How a giant chip handles failure(41:22) Why GPUs struggle with decode(42:17) Prefill vs. decode explained(44:01) The “100 HD movies” problem in AI inference(45:04) How fast inference changes RL and training(48:08) Reasoning models and why they cost more compute(50:08) Verification, guardrails, and small models checking big models(52:37) Multimodal AI and the path to video(53:51) Cerebras' business model: hardware, cloud, and API(55:14) OpenAI's 750MW inference deal(55:36) Why data centers are measured in megawatts(58:01) AWS Trainium + Cerebras decode(59:29) Fast tokens as a cloud product(01:00:52) Is CUDA still a moat?(01:03:53) How TSMC helped Cerebras build the giant chip(01:07:41) Why nobody cared in 2020(01:08:15) Why chip supply chains are hard to diversify(01:09:54) Why today's AI models will be the worst you ever use(01:10:38) What fast AI could do to SaaS

- TSMC Raising Chip Prices 5% to 10% - Report: Apple Teams with Klarna for New Upgrade Program - Apple Reportedly Nixing iPhone Upgrade Program - Apple Upgrade Will Reportedly Exclude Entry Level Devices - 9to5Mac: iOS 27 Code Indicates Lender Kill-Switch for Bad Contracts - Apple Addresses Hide My Email Vulnerability - Apple Ordered to Pay Masimo $634M in Patent Case - Apple TV Drops Trailer for "Women in Blue" Season-Two - Sponsored by Copilot Money: Get a two month free trial with Offer Code MACOSKEN at copilot.money/macosken - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

Take a Network Break! In this week’s episode our red alert highlights two critical vulnerabilities in RabbitMQ, and we dig into listener follow-up about data centers in space. This week’s news coverage considers Apple’s $30 billion spending pledge to Broadcom, TSCM promising another $100 billion to build even more chip fabs in the US, and... Read more »

Take a Network Break! In this week’s episode our red alert highlights two critical vulnerabilities in RabbitMQ, and we dig into listener follow-up about data centers in space. This week’s news coverage considers Apple’s $30 billion spending pledge to Broadcom, TSCM promising another $100 billion to build even more chip fabs in the US, and... Read more »

Take a Network Break! In this week’s episode our red alert highlights two critical vulnerabilities in RabbitMQ, and we dig into listener follow-up about data centers in space. This week’s news coverage considers Apple’s $30 billion spending pledge to Broadcom, TSCM promising another $100 billion to build even more chip fabs in the US, and... Read more »

In this episode, Ben and Jay analyze the latest developments in the semiconductor industry, focusing on TSMC, ASML, Air, and the broader market implications of AI and chip manufacturing advancements. They explore how these trends impact supply chains, CapEx, and future growth prospects.Key Topics:TSMC's CapEx increase and demand signalsASML's capacity expansion and high NA EUV technologyAir's role in semiconductor testing and optical advancementsMarket sentiment and investor rotation in semiconductorsThe impact of AI on chip demand and manufacturing

Is Cerebras Systems the next great AI chip stock or a red-hot IPO priced for perfection? In this episode of 7investing Live, Simon Erickson and executive producer Heather Horton welcome back Nick Rossolillo, co-founder of Chip Stock Investor, to break down three of the market's biggest stories.First up: Cerebras Systems (NASDAQ:CBRS), the wafer-scale chip maker that just IPO'd at a $40+ billion market cap. With 44GB of SRAM embedded directly on the chip, Cerebras was purpose-built to solve AI's "memory wall" problem for inference workloads. Now it's reportedly landed a ~$10 billion order from OpenAI and a deal with Amazon Web Services that could top $20 billion. Simon and Nick dig into whether these massive orders are real, how Cerebras stacks up against NVIDIA's GPUs and hyperscaler custom silicon, the TSMC capacity bottleneck that could throttle its growth, and how to value a company trading near 20x sales without profits.Then the conversation turns to Rocket Lab (NASDAQ:RKLB), which has pulled back from $150 to around $70 per share. Simon shares the latest iteration of his discounted cash flow valuation, and the duo debates the proposed Iridium acquisition — a deal that could pull Rocket Lab to EBITDA-positive on a pro forma basis — plus what the long-awaited Neutron rocket launch means for the company's future.Finally: Netflix (NASDAQ:NFLX). After another quarter of decelerating revenue guidance, is the streaming giant now a value stock rather than a growth stock? Nick explains why the advertising business hasn't reaccelerated growth the way he expected, and what he'd need to see before buying the dip.Plus: Nick's take on the recent chip stock sell-off across NVIDIA, AMD, Broadcom, SanDisk, and Kioxia and why "stocks go up, stocks go down" might be the healthiest way to think about it.Subscribe for more deep dives on AI infrastructure, semiconductors, and innovative growth stocks!Start your FREE 7-day trial of 7investing: https://www.7investing.com/subscribeFollow Nick and Casey Rossolillo at Chip Stock Investor: https://chipstockinvestor.comRocket Lab Deep Dive videos mentionedPart 1 https://youtu.be/AMDd0-JKUH0 (Deep Dive)Part 2: https://youtu.be/Z76xTGFNwBA (Valuation)Companies MentionedPublicly Traded:Cerebras Systems (NASDAQ:CBRS)Rocket Lab (NASDAQ:RKLB)Netflix (NASDAQ:NFLX)NVIDIA (NASDAQ:NVDA)Advanced Micro Devices (NASDAQ:AMD)Broadcom (NASDAQ:AVGO)Micron Technology (NASDAQ:MU)Taiwan Semiconductor Manufacturing (NYSE:TSM)Amazon (NASDAQ:AMZN)Alphabet (NASDAQ:GOOGL)Meta Platforms (NASDAQ:META)Iridium Communications (NASDAQ:IRDM)SanDisk (NASDAQ:SNDK)Kioxia Holdings (TSE:285A)Globalstar (NASDAQ:GSAT)SpaceX (NASDAQ: SPCX)Private / Pre-IPO:OpenAIAnthropicVideos Mentioned:https://www.youtube.com/watch?v=Z76xTGFNwBA&t=3shttps://www.youtube.com/watch?v=AMDd0-JKUH0&t=987sHere's the shifted chapter list, with all timestamps moved back 55 seconds:0:00 Welcome to 7investing Live0:54 Cerebras Systems: IPO recap & the Wafer-Scale Engine2:31 Is NVIDIA even the right comparison for Cerebras?5:38 The memory wall: why bigger AI models need new chips8:52 Latency vs. throughput — and the new AI alliances10:46 Are the $10B OpenAI & $20B Amazon orders real?14:02 Cerebras risks: how do you value a hot IPO?17:27 The TSMC capacity bottleneck20:01 Heather's take on Cerebras20:41 Rocket Lab: the sell-off & Iridium acquisition24:34 Simon's DCF valuation & price target for RKLB29:05 Why Neutron changes everything30:12 Q&A: Does Peter Beck carry an "Elon premium"?31:36 Netflix: buying opportunity or cheap for a reason?36:57 Q&A: Is Netflix a growth stock or a value stock?39:03 Chip stocks selling off: normal volatility or a warning?42:57 Wrap-up & final thoughts#7investing #Simonerickson #Cerebras #CBRS #NVIDIA #AIinvesting #semiconductors #chipstocks #RocketLab #RKLB #Netflix #NFLX #AIinference #stocks #investing #stockmarket #TSMC #AIdatacenters

Don’t Fade and Die in AI Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Matt Yanchyshyn, VP AWS Marketplace, Rekha Thangelapalita, Elastic GSI Leaders; Allison McFadden, Accenture AWS Leader; and James Kang of Nvidia join Ultimate Partner. In this panel discussion, leaders from Elastic, Accenture, Nvidia, and AWS dissect the urgent shifts in the ecosystem, emphasizing that partners must adapt to AI and agentic co-selling or risk fading away completely. The conversation explores the necessity of deep co-engineering, the power of multi-product solutions in the AWS marketplace, and how automated agents are now replacing traditional human sales pipeline progression. By embracing data readiness and strategic collaboration, organizations can survive the “token maxing” era, effectively scale their enterprise opportunities, and align with NVIDIA’s five-layer strategy to dominate the new cloud landscape. https://youtu.be/zUkL4Wqsa68 Key Takeaways AI agents will automate the majority of AWS partner co-selling attachments and opportunity progressions this year. Partners who fail to embrace agentic workflows and automated governance face the existential risk of fading into obsolescence. Successful multi-product offerings require a “blood to all organs” approach that benefits the client, the ISV, the GSI, and the hyperscaler simultaneously. Nvidia’s “five-layer cake” model emphasizes that successful outcomes at the application layer automatically drive growth for all underlying infrastructure. The “token maxing” phenomenon is forcing enterprises to seek cost-effective, open-model alternatives to scale their generative AI securely. Integrating GSIs and ISVs on the AWS marketplace significantly increases enterprise deal sizes and long-term customer renewal rates. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags strategic collaboration agreement, data readiness engine, agentic co-sell, semantic layer, token maxing, five layer cake, accelerated computing platform, open models, cloud consumption, multi-product solutions, partner central agents, propensity data, automated opportunity progression, generative AI governance Transcript Matt Y and Panel Audio Podcast [00:00:00] Vince Menzione: You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to Agen Co-sell, or you can fade and die. [00:00:11] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:22] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi. Own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:44] Vince Menzione: It is the strategy because [00:00:46] Vince Menzione: being in the room changes everything. Let’s start. [00:00:51] Vince Menzione: We’ve got some amazing leaders joining us. So I think probably for a little bit of context, maybe just start with Rika. You can introduce yourself, your role and, uh, what, what you’ve been doing at Elastic. Yeah. [00:01:03] Rekha Thangellapalli: Yeah, sounds great. [00:01:04] Rekha Thangellapalli: Hi everyone. I’m Reka and I lead GSI Alliances at Elastic. Um, for the past 14 years, I’ve had the pleasure of building different kinds of partner ecosystems across companies such as SAP. MuleSoft, Salesforce, Coupa, and now Elastic. Um, I wanna thank Ultimate partner and Vince for having us here today. Thank you and the panel of these incredible speakers for joining me on stage. [00:01:31] Rekha Thangellapalli: Um, very excited for the conversation today. [00:01:33] Vince Menzione: We love Elastic, and you’ve had some of your other leaders on stage at other events. As such, the quality of your leadership team is amazing. Thank you. [00:01:42] Rekha Thangellapalli: I wholeheartedly agree. [00:01:45] Allison McFadden: Excellent. Um, hello everyone. Allison McFadden. I lead our North America AWS practice at Accenture. [00:01:52] Allison McFadden: Uh, I’ve been there for five years, and truth be told, it was my first partnership role, my first formal partnership role. Uh, so I can take some tips from all of you in the room here today. Prior to that, I was 21 years with IBM, and I got into partnerships because my last role at IBM was actually trying to build. [00:02:14] Allison McFadden: Linux business on the mainframe, and I had to have partners. I had to have partners to help me with workloads to run there. So I kind of learned, uh, trial by fire. But I’m excited for the conversation today. Excited to be in this room and excited to talk about what we’re doing with, uh, elastic. Thank you. [00:02:34] James Kang: Uh, my name is James Kang. Nice to see and meet everyone here. Vince, thank you for the opportunity. Thank you [00:02:38] Vince Menzione: for being here. [00:02:39] James Kang: Um, I’m with Nvidia, so I help manage the AWS partnership at Nvidia all up. Um, I guess fun fact, I’m former AWS and so I see a lot of very familiar faces here in the front row. Uh, former colleagues and then current friends. [00:02:56] James Kang: And so, uh, looking forward to the conversation. [00:02:59] Vince Menzione: Great. Well, we’ll start with an easy tia. Matt. This is not directed to you, directed to the others. So what does a successful AWS partnership look like from your C? So we’ll start with Eureka. [00:03:09] Rekha Thangellapalli: Sure. So from an ISV perspective, I think we really are looking at three things. [00:03:15] Rekha Thangellapalli: Uh, mutual investment building together. And scaling together. So when we talk about mutual investment, elastic recently signed a five-year SCA or strategic collaboration agreement with AWS. And while that is a significant milestone in our partnership, for us, what matters more is what it represents, and that is really a long-term commitment from both companies. [00:03:39] Rekha Thangellapalli: Towards product engineering, um, and joint go to market initiatives to deliver value to customers over time. And that’s what we see is that the best partnerships really compound and they build upon each other every year. Um, they don’t necessarily kind of reset every year. Um, next we talk about building together. [00:03:59] Rekha Thangellapalli: So, um. When we talk about joint solutions, we want to deliver solutions that are better together and the customers have to see us that way. And so whether it’s search, observability, or security, we’re looking at taking to market solutions that we can’t or necessarily don’t wanna take on our own. And finally we talk about scaling together. [00:04:22] Rekha Thangellapalli: And this is where marketplace, for instance, plays a big role, um, when customers can draw down on their cloud commitments, transact online and go from, you know, pilot to enterprise scale adoption in hours, not days. Um, this is when really everyone wins. Um, and this is also where partners like Accenture play a critical role. [00:04:47] Rekha Thangellapalli: Um, you know, the incredible amount of expertise that they bring, uh, the managed services capabilities and, um, their data assets actually play a huge role in having our customers realize that value faster. And, um, like Vince mentioned, at the end of the day, best partnerships are all all about creating kind of that. [00:05:07] Rekha Thangellapalli: Self-sustaining flywheel. And so it starts with investing together, building something unique, and having the customers realize that success faster because that success is really the only thing that’s gonna keep that flywheel going for everyone involved. I [00:05:26] Vince Menzione: absolutely. [00:05:26] Allison McFadden: Okay, amazing. I’m gonna riff off a few things Ika said, but from a GSI perspective. [00:05:32] Allison McFadden: A relationship with a WSA successful relationship with AWS looks slightly different. Um, so I think the first thing that we think of in the GSI Community common thread is that the client outcome and delivering value for clients is what we, what we’re striving for. Um, and so the partnership with AWS in that case, um, um, it has to, it has to. [00:06:01] Allison McFadden: Look like one team in front of our clients. So we have to show up indistinguishable, and that’s with AWS and with an ISV partner, it has to look like one solution in front of the client, especially moments that matter. So board meetings, um, you know, the time we’re gonna sign a deal, like we have to look like one team, uh, and keep our our client outcome, um, first and foremost in mind. [00:06:24] Allison McFadden: The second thing, and this is I think where the magic of all the people in this room comes into play. We can have as many discussions at a CEO level as we want. And if our client teams on the ground are not working together, it falls apart. Falls apart directly in front of the client. Yes. And that is a really hard thing to do. [00:06:45] Allison McFadden: So I’m passionate about the alliance work because that that work is what makes it happen at the corporate level. [00:06:53] James Kang: Cool. Um. I’ll start here. So in Nvidia is a accelerated computing platform company. Um, if you asked. Anyone on the, on the street about a year ago, what is ai? A lot of times they would say AI is, is open ai, or it’s philanthropic. [00:07:12] James Kang: Um, Jensen and I’ll, I’ll reference Jensen a lot today, um, because he is our leader, um, but he also sets the strategy in the direction for Nvidia. He talks a lot about AI in the metaphor of a five layer cake. And in terms of the five layer cake, you start off with the foundational bottom layer being power and energy, which sustains. [00:07:32] James Kang: All of our data centers, you move up the stack in terms of chips. So things think of Foxconn, think of TSMC. Next you have the infrastructure layer. So obvious choice is AWS, and then you get to the models where you do have the philanthropics and the open ais. But finally in at the precipice, you have the application layer. [00:07:53] James Kang: Ultimately, the reason why I mentioned all different stacks of the layers, the five layer cake, is the fact that the application layer is the most important. And so when you think about. Partners like Elastic or ServiceNow Trend, ai, CrowdStrike. Every time you pull from the application layer and you see a success, it pulls all five different components of that layer up. [00:08:13] James Kang: And so ultimately, as I think about success, it’s it’s being able to develop these co-sell wins at the application layer and really demonstrating that through extreme co-engineering and co-design with all the different application. Infrastructure, power and energy layers in mind. Um, Jensen also likes to think of himself not only as the CEO and founder, but also as the, the chief Marketing Officer. [00:08:35] James Kang: We are a very event driven company, and so at our big events like GTC or at big industry events like CES or Computex, he likes to show up on the biggest stage, biggest stages and showcase the partnerships with not only ISVs and GSIs, but also with end customers. And so that’s what I think about when I think of SA success. [00:08:56] Vince Menzione: That’s a really good point. You talked about, Allison, you talked about having an alliance strategy, or at least you teed it up, so I thought maybe we would go there for a second. Right? Like, what does a great alliance strategy look like and why is it important to the success of the partnership? [00:09:11] Allison McFadden: Man, I, uh, I have so many opinions on this. [00:09:13] Allison McFadden: We could probably be up here all day. That’s [00:09:15] Vince Menzione: okay. [00:09:16] Allison McFadden: Um, no, I think. Uh, there, there are a couple things, and the first one that comes to mind is focus. We cannot be all things to all people. Um, so when it comes to think about some of the, the work we’re doing with Elastic, we have a very, very clear point of view on what client problem we’re solving, what clients we want to talk to. [00:09:38] Allison McFadden: It helps if, um, from an ISV perspective, if there’s a very clear fit in. The Accenture portfolio or whatever, you know, SI consulting partner. You’re working with a very clear fit in the portfolio and we know what we’re not gonna go after, what we’re not gonna spend our time on because we have, we have this tendency, there’s millions of people. [00:10:00] Allison McFadden: The ecosystem chart that, you know, Vince, you showed up there, there’s so many connections. There’s probably more connections there than there are atoms in the universe, right? So, um. Defining what we do together and what we don’t do together is the first thing that pops to my mind. [00:10:19] Vince Menzione: Reka, do you have a perspective on it since we’re gonna, we’re gonna talk next about what you’ve done together, but, and I also wanna get mass perspective as a hyperscaler partner here as well. [00:10:29] Rekha Thangellapalli: Yeah, I mean from my perspective, I, I’m gonna, you know, kinda echo what Allison said is to be just maniacally focused. Yep. Um, because, especially from my perspective, so Elastic has three different solutions, right? We’ve got search, we’ve got observability, we’ve got security that map to completely different business units within Accenture. [00:10:47] Rekha Thangellapalli: And of course Accenture does a lot of things. And so, you know, when we first came together it was like. Okay, what are we gonna focus on? What industries are we gonna go after? Which segments are we gonna go after? Which customers, you know, um, outcomes are we trying to solve? And I think that sort of maniacal focus is the number one contributing factor to, to the fact that I’m like, up here on stage today. [00:11:12] Rekha Thangellapalli: Great. [00:11:14] Vince Menzione: Matt? Perspective? [00:11:16] Matt Yanchyshyn: Yeah, I, I, I guess I was trying to. To add something, uh, additional from an AWS perspective, uh, when it comes to, you know, what does a great alliance look like? Uh, AWS is obsessed with data, you know, in data we trust. And, and so the best, um, and, and this goes sales business problem, and it’s not just the engineering teams. [00:11:34] Matt Yanchyshyn: And so, uh, you know, Accenture does a good job of this elastic, definitely. And if you can come to the table with, um, quantifiable proof of the value of customer outcomes and partnerships. Um, you’ll win all the time and it’ll be a durable relationship with AWS ’cause we really are this data obsessed company and, and even the most senior sales leaders. [00:11:54] Matt Yanchyshyn: Uh, and so what I mean by that specifically is like if you, if you can show like your a RR to land an a RR conversion ratio, like in in numerical format, it’ll light up our sales leaders and, and they’ll be all, and they will co-sell with you all day long. If you can show the, I mentioned this earlier, like the AWS service, uh, whether you’re consulting company or, um, elastic and, and how the shape of customer accounts change positively when we work together. [00:12:15] Matt Yanchyshyn: That type of sort of quantifiable data works particularly well from an alliance perspective. With AWS as a partner, we, we really are like this data in sort of results out company. Um, so I, yeah, that’s just adding to the great points that were already made. I would say specific to AWS that that’s key. [00:12:30] Matt Yanchyshyn: Yeah. And I’m gonna bring up one more thing. I want to dive in on the, the joint value proposition, but you mentioned something that made a lot of sense and resonated to me about the organizations once you get out of partner, the partner world that we all know and love. Mm-hmm. Once you get down into a field organization or account management organization. [00:12:49] Matt Yanchyshyn: Not as much understanding and really organizations do a bad job here, honestly, in terms of enabling the field organizations. Do you agree? [00:12:58] Allison McFadden: I agree because I, I agree. And, um, you know, I think that’s one of the things, and, and I, I, when I joined Accenture, what we had was a lot of wicked smart architects delivering programs to clients in the field. [00:13:15] Allison McFadden: Very smart, very deep in AWS knowledge. Um, and that was awesome for the 10 clients they were staffed on and to get that understanding of how AWS works and I dream about lar, right? Like, this is a good, you know, but that takes real effort and real work. Yeah. And it’s, it’s um, almost like being a language translator. [00:13:37] Allison McFadden: Yes. For me. Yeah. So, you know, I had to deeply learn AWS so that I could. [00:13:42] Rekha Thangellapalli: Sure. [00:13:42] Allison McFadden: Teach my account teams. My account teams are really smart. They know who they’re selling to. They know their customers. They know what their customers need. They do not know what AWS has to offer always because they’ve got 20 partners lining up to try to tell their stories. [00:13:57] Allison McFadden: Um, they don’t know how to ask of the AWS team or the elastic team or the Nvidia team. Yeah. What they need [00:14:02] Vince Menzione: this co-selling piece. Yeah. [00:14:04] Allison McFadden: And so that is where, um. We had to build that muscle even around our AWS practice, which was a huge practice at Accenture, but we didn’t necessarily surround it with that kind of enablement and um, almost deal coaching layer. [00:14:21] Vince Menzione: So Elastic and Accenture came together. I dunno which one of you wants to lead this part of the conversation, but you will, right? Yeah. So tell us about the genesis of this and why. And a lot of people dunno what Elastic does, but you do some really incredible work. Like I, somebody told me one day was like, oh, you know, Uber, like, that’s elastic, powering all that. [00:14:41] Vince Menzione: Like, we don’t think about that. That the engines that you have and the, the backend to the customers, huge customers. [00:14:48] Rekha Thangellapalli: Yeah, absolutely. Um, so when AWS launched this feature last, um, reinvent where basically it allowed, you know, channel partners such as Accenture to be able to bundle up their services, their data assets with an ISV solution and put it on marketplace, um, you know, Accenture and Elastic immediately saw an opportunity. [00:15:09] Rekha Thangellapalli: Um, at the time most customers were doing gen ai. But they were running into the same challenge, which was that their data just was not ready. And by the way, this is a problem we were solving. Outside of marketplace. I think the, the feature that you guys launched just gave us a way to package it up and to be able to create this repeatable solution, which we call data readiness engine for gen ai and put it on marketplace. [00:15:40] Rekha Thangellapalli: And, um, this to me was a success because. Each company had a clear reason to invest. Um, so for Accenture, they were able to, you know, create a very differentiated services led offering. Uh, for Elastic, we were able to expand on our AI story. And for AWS, um, you know, it drives marketplace adoption, increases cloud consumption, all of that great stuff. [00:16:07] Rekha Thangellapalli: And customers, of course get. A solution to a very real problem that, that they were having. Um, and you know, the surprising part for me going through that journey was that, um. The pitching, the idea, getting the budget, getting the executive sponsorship was actually the easy part. The hard part was getting all three companies to come together, uh, to go from idea to launch in a very ambitious timeline of six weeks. [00:16:37] Rekha Thangellapalli: Nice. And so, you know, this was very much like. Doesn’t matter your title. We’re rolling up our sleeves and we are on this outcome together. Um, and so we literally built a RACI matrix, a project plan, and you know, we had daily standup calls for six weeks where literally. At least one person from each three of these companies called in, you know, got rid of any blockers and we made sure we were on target for that timeline. [00:17:07] Rekha Thangellapalli: Um, and you know, at the end we had a successful launch. But I think my favorite part about the story is the impact that we’re having and, um. My favorite story comes from a global pharmaceutical company that, you know, had basically nine petabytes of data spread across six different continents. Wow. And by working with Accenture and Elastic, they were able to build that trusted foundation that their AI and their agents can, you know, kind of safely tap into and be accessible at scale. [00:17:41] Rekha Thangellapalli: Um, so that’s my version. Allison. [00:17:44] Allison McFadden: Yeah. Well, I don’t have a lot to add. I just, I would say this is a good example of a couple of principles, right? One is having a forcing function is never a bad idea. Sign up for a big event, sign up. I’m like, I’m here with my, you know, Nvidia guys saying, sign up for the event. [00:17:58] Allison McFadden: It’ll make you move quick, right? [00:18:00] Audience Member: Yes. [00:18:00] Allison McFadden: Um, so that is one, but two, one of my mentors once told me, when you’re designing any kind of, you know, offering go to market motion, it has to get blood to all organs. If it does not get blood to all organs, it does not go [00:18:14] Vince Menzione: nice. [00:18:14] Allison McFadden: Um, [00:18:14] Vince Menzione: I love that analogy. [00:18:15] Allison McFadden: Oh, I love it. And I can talk all day. [00:18:17] Allison McFadden: That guy was brilliant. I love him. But, um, no, and, and so Elastic did a really nice job of bringing the tech to the table. Um, our team has to trust in that technology and its ability to scale, right? Um, because at Accenture we have to be able to deploy across 700,000 consultants. Um. And yeah, so I think those are the two, two things that really worked well here is we had, uh, trust in the technology solved a customer need. [00:18:50] Allison McFadden: Um, it drives, we don’t even talk about, like, yes, it drives marketplace revenue, but it unlocks work that we do that drives even more revenue to our AWS Friends. Right. So this is a, this is a, um, product that’s getting your data ready for AG agentic. It’s a messy problem that everyone’s dealing with, and it removes blockers for clients and it unlocks more, you know, ag agentic work on top of that. [00:19:15] Allison McFadden: So, blood to all organs. [00:19:17] Vince Menzione: So, was that the proposal going forward to say we need to have, we need to have trust in the solution. We need to drive significant revenue. It needs to be something all of our, you know, seven, 700,000 people. Can be a part of and help drive? Is that how you think about? [00:19:32] Allison McFadden: Yeah, and for us right now, um, it’s an interesting time for Accenture. [00:19:36] Allison McFadden: Our clients are asking a lot of us, and what it does is it having some of these accelerators helps us deliver cheaper, better, faster to our clients, which is what they’re demanding of us right now. Um, so it’s an accelerator to client outcomes. [00:19:55] Vince Menzione: James, what is NVIDIA’s role and how do, how do you enter the equation here? [00:20:00] James Kang: Yeah, it’s, um, it’s a good question. Um, I, I would say that Nvidia is probably one of the most misunderstood organizations in the world. Um, despite the, uh, the market capitalization in the valuation of the company, we have a very tiny organization. Um, what I mean by that is, um, if you think about. [00:20:20] James Kang: Salesforces and field sales organizations. Um, we’ll take Salesforce as the account or the customer. As an example, we have one account manager at NVIDIA that no, not only covers and is responsible for the relationship with Salesforce, um, but also manages. Automation Anywhere as well as DocuSign. Whereas at AWS, in contrast, like there are full armies and teams Yeah. [00:20:45] James Kang: That are supporting the Salesforce relationship. And so as you think about partnering and working with Nvidia, the focus has to be on really. Extreme co-design, but also being very prescriptive in terms of what are the very specific customer outcomes that we are solving for. And the guidance that I would give is bring in Nvidia into that equation and that conversation as early as possible because that [00:21:10] James Kang: co-engineering and co-design needs to be part of the foundational building blocks in order for you to come out with a end solution that checks all those different requirements. [00:21:20] James Kang: And so I think. Again, like going back to Nvidia, um, we like to talk about two different types of brains. A brain one and a brain two. Uh, brain One you think about the next quarter and making sure that you’re hitting the revenue targets for the next quarter. Brain two, you think about a long-term goals and potentials looking around corners and being very strategic. [00:21:41] James Kang: The saying internally is without Brain one, there is no oxygen, but without brain two, there is no future. And everyone at NVIDIA is trained to think in that brain two mentality. [00:21:52] Vince Menzione: Wow, Matt. [00:21:54] Matt Yanchyshyn: Yeah, I, I was just thinking I love the blood doll organs. Uh, and so just on, on that note, um, and, and, you know, the multi-product solutions that, that you, you built together, uh, that is a really good example of blood do organs because like we all know, that’s how customers buy. [00:22:07] Matt Yanchyshyn: They, they buy solutions and increasingly they’re looking for combinations of ISV, sometimes multiple products from multiple ISVs with services. Uh, often they’re buying it through a resell motion. You know, and they, and, and so that from a customer perspective, they want a single place to go. And so that’s the multi-product solution. [00:22:24] Matt Yanchyshyn: They wanna find everything they need, they need Accenture, they need Elastic to solve a specific solution. And I think where that’s headed is even more specific listings, like with AI powered listing experience, like, you know, elastic Plus Accenture for, I’ll make something up like a manufacturing workload. [00:22:37] Matt Yanchyshyn: And so this solution based. Uh, sort of buying is, is very customer centric. It’s what customers want. We all know that. But that’s, that’s the customer sort of organ, I guess. Um, but then, you know, you all have SCAs and those SCAs have marketplace commits. It helps if that gets transacted through marketplace helps the AWS relationship, you know that that’s an organ. [00:22:55] Matt Yanchyshyn: It’s the relationship. It’s, it’s the commercial construct and that you have, uh, that that’s another organ. You’re marketing people. They, that’s another organ. They don’t wanna land, uh, leads on a static marketing page. They wanna land a lead on a, a storefront with a multi-product solution that can actually convert and that you can actually buy it through that. [00:23:12] Matt Yanchyshyn: So the marketing person’s happy because they, they have less churn. Uh, and then, you know, our reps are happy ’cause guess how they get paid? They retire quota when they sell Marketplace. And they, we also, Jay McMain will tell you, that’s another organ called Jay or on, on you now. Um, [00:23:27] Matt Yanchyshyn: he’ll like that. I’ll call him up and tell him that. [00:23:29] Matt Yanchyshyn: Yeah, [00:23:30] Matt Yanchyshyn: but he, he’ll tell you, you know, don’t believe me. Obviously, never believe Matt, believe, believe the, the data and, and his data shows that. Those deals will close faster and larger if you use marketplace. So that’s, that’s a lot of organs. That’s the whole body. Um, but you know, when you have your customer happy ’cause that’s how they wanna buy your field happy. [00:23:45] Matt Yanchyshyn: Um, and, you know, the relationship happy and you know, your marketing team happy. Uh, and, and Jay happy. Um, and, and you know, I think that multi-product construct and, and the way you kind of use it to model a partnership and the way buyers ultimately wanna buy is, is really powerful. And so I, I think it’s, you know, it’s really a manifestation of how. [00:24:04] Matt Yanchyshyn: We kind of intend and to go to market anyway. Uh, so I think, you know, and thanks for leading the way, by the way. You’re, you’re amongst the very first, so that’s great to see. [00:24:11] Matt Yanchyshyn: So these storefronts are really helping this drive, drive this. Well, [00:24:13] Matt Yanchyshyn: that’s the next evolution. Like we’re talking about the multiproduct solution. [00:24:16] Allison McFadden: I’m JJ Accenture storefront. [00:24:17] Vince Menzione: Yeah. Oh, there you go. I mean, j and j Accenture storefront. [00:24:20] Allison McFadden: We’re gonna talk about that. [00:24:20] Matt Yanchyshyn: Yeah. I mean, [00:24:21] Matt Yanchyshyn: Accenture also leading the way yet again with storefronts. And so I think the combination of. You know, again, I was talking a lot about conversion. Yeah. And you know, buyers know sometimes they know what they wanna buy and, but if you really wanna convert that lead, you wanna land them again, something that combines, you know, elastic Accenture’s services plus software, but in a storefront that is, you know, surrounding with just the solutions they want so they don’t need to kind of go searching. [00:24:42] Matt Yanchyshyn: So, you know, ultimately reducing that time to close, I guess, really ’cause meeting the customer where they are with what they need. [00:24:51] Matt Yanchyshyn: So we talk about co-selling a little bit. We, Jay and I talk about this all the time. We gotta keep looping Jay in here, even though he is not even in town this week, but Reko, um, what does co-sell look like inside Elastic? [00:25:02] Matt Yanchyshyn: You’ve got, we talked about an incredible leadership team. I’ve gotten meet some of your leaders. Seems like you drive, you do a good job internally driving that. Let’s talk a little bit about it. [00:25:11] Rekha Thangellapalli: Yeah, and this is something I’m, I’m personally very passionate about. Um, co-sell is. Very much a journey, not a destination. [00:25:20] Rekha Thangellapalli: And I think step one for us is recognizing the different partner types that we have. Because at Elastic we work with, you know, OEMs, MSPs, resale distributors, GSIs, um, and they all bring something very unique. To the customer lifecycle and they all contribute very differently within, you know, our own sales cycle and sales process. [00:25:45] Rekha Thangellapalli: And so, you know, figuring out what is the unique benefit they bring, how do we enable them? So training and enablement is a huge piece of it, and so is making sure we’ve got the right metrics to measure success. Um, I know a lot of companies look at partner sourced as the north star, and that’s great, right? [00:26:06] Rekha Thangellapalli: Because that is undeniable. You can say, Hey, that would not exist if it wasn’t for my partner team. Um, but we’ve also noticed that when we bring in GSIs, it actually increases renewal rates. It significantly increases. Um, a RR over time. Um, it expands deal sizes and so these are very real metrics that we can point to, um, beyond just the co-sell and the partner sourced number. [00:26:32] Rekha Thangellapalli: Um, so for us it’s looking at it from a very holistic perspective, but also catering it towards that unique partner and making sure we’re doing everything we can to set them up for success and setting up the partnership for success. [00:26:47] Vince Menzione: So clo close win ratios, deal size and renewal rates? [00:26:52] Rekha Thangellapalli: Yes. For specifically for geos size. [00:26:54] Rekha Thangellapalli: Yeah. [00:26:55] Vince Menzione: Very interesting. Allison, uh, what had to change internally to produce these co-selling? We talked a little bit about the field organization and enabling a, a group of, and, you know, account sellers that are very customer focused and enabling them on the co-sell side. What had to change internally to drive that? [00:27:13] Vince Menzione: Yeah. [00:27:14] Allison McFadden: I, I might have already alluded to this a little bit in a previous answer, but, um, creating the capacity to develop, build, and sell these solutions, um, inside of a large GSI, where billable hours is kind of the number one metric on the table. Um. Is part of the investment that we had to make within Accenture to get this done? [00:27:36] Audience Member: Yeah, [00:27:36] Allison McFadden: so expert technology time. So we have technologists that understand the elastic technology. We do similar with Nvidia, by the way, we. We released some of their time to go co-develop the solution because it has to hold technical water, right? It can’t just be a marketing pitch. It can’t just be, it has to be a real, um, what’s the there, there. [00:27:59] Allison McFadden: So in order to actually do proper co-sell, we had to release some of that time. Um, to invest in those partnerships. Um, we’ve also done similar with some industry aligned business development leaders recently, so we have freed their time up to go. Uh. Open new conversations, educate client, account teams, go to clients, have conversations. [00:28:26] Allison McFadden: Um, so that, that’s a new motion that we, uh, have just kind of recently made, um, to allow them, I love this brain one, brain two also, right? So to allow them to focus on brain two, because a lot of our time. Typically spent delivery issues, you know, getting my hours, where am I charging my time? And so just freeing up a little of that capacity to do this work, um, helps get us in this brain two mode where we’re not just living to survive. [00:28:56] Vince Menzione: I. So, Matt, you’ve removed a lot. I mean, one of the things I admire, I admire AWS for being first to market and removing the most friction in marketplace of any of the vendors. Really, truly that. You talked about some of the announcements. How does some of, how does some of this tie PC central agents propensity sales plays, MCP, how does some of this tie to how, how you’re thinking about the future? [00:29:18] Vince Menzione: And how to enable more motions like this. [00:29:20] Matt Yanchyshyn: Yeah. Well, I, I think if you know my boss, UBA Borno, uh, you’ll know that she has a maniacal focus on automation. Yeah. Um, and, uh, co-sell is increasingly automated. You know, you were asking earlier about propensity data. You can get that propensity data in addition to sales plays and, uh, opportunity scores through the partner central agents. [00:29:38] Matt Yanchyshyn: So things that used to require multiple calls to A PDM, if you’re lucky to have one. Yeah. Or a p sm. Uh, you, you can now get through, through these agents, you know, uh, tech Systems, TGS, they, they manage what, over 5,500 customer opportunities with agents that they built on top of our partner Central APIs. [00:29:55] Matt Yanchyshyn: Um, and work Span has built a whole product and business that’s right on leveraging, uh, our APIs, our capabilities to sort of tie into your CRM. So, majority of all opportunities will be progressed and managed by agents. This year at AWS, we already have a majority of all customer opportunities, all app have a partner attached and I, I took a personal goal for a majority of those partner attachments, not to happen from a human. [00:30:22] Matt Yanchyshyn: But from our solution matching engine. And how do you get recommended by that solution? Matching engine, having a healthy ACE pipeline, thanks to partner central agents and the integrations you’re doing. And in addition to being the specializations and doing things like multi-product solutions and ultimately closing opportunities, you dream of LAR and so LAR will help that. [00:30:40] Allison McFadden: It’s more like a nightmare. [00:30:41] Vince Menzione: And so, you know, [00:30:42] Allison McFadden: it’s more like a nightmare, but [00:30:44] Vince Menzione: nightmare. Well, it’s, it’s, yeah. Nightmare of Laura and, and. Nice dreams of PRM, but the, um, but that’s the loop, right? I, I think, uh, increasingly co-sell for us, and in my mind, is largely a hundred percent automated. Yeah. Except for what matters most, those most largest, most strategic, most complex deals. [00:31:01] Vince Menzione: Where our highly paid and very skilled salespeople are most effectively used. [00:31:05] Vince Menzione: Yeah. [00:31:05] Vince Menzione: You know, the days of, you know, this person with 20 years experience selling, clicking, progressing opportunities through a pipeline, uh, should be over. Uh, and, and we need those people out, out selling and, and co-selling. And so that for me. [00:31:19] Vince Menzione: Yeah. That, you know, we talk a lot about co-sell, but I, I’m obsessed with automating as much of the co-sell as possible. [00:31:24] Vince Menzione: I remember going back to the ex Excel spreadsheets and, and that, that seems to be be Viva became spreadsheet jockeys. [00:31:31] Vince Menzione: Yeah. [00:31:32] Vince Menzione: And, and they stopped selling. They forgot how to sell. [00:31:34] Vince Menzione: Yeah. And people spend all this time doing lunch and learns and things like that. [00:31:36] Vince Menzione: And then, you know. Then the salespeople rotate out after 18 months and, and it, that’s, that’s the old days. Uh, you know, the new days are, are AI powered matching algorithms, uh, ag agentic co-sell, using the partner essential agents to get your data and, and putting that data to use automatically and, and what sounded like magic. [00:31:51] Vince Menzione: 12 months ago is being done, you know, by partners at massive scale across thousands of opportunities. You can do it today. And you know, I, there’s a guy named another Mike, right? Mike another Mike who they have, there’s like a guy who’s doing all this and I’m picking on Mike ’cause I, I know their system really well and I know the guy Mike grew easily built it for them. [00:32:08] Vince Menzione: Um, but, you know, I think, yeah, again, in the days of having 10 people sort of doing lunch and learn could be replaced by one or two people, building agents, uh, managing a massive pipeline. And, and that’s the future. [00:32:18] Vince Menzione: Exactly. James, your perspective on what breaks with co-selling? [00:32:22] James Kang: Oh, what breaks co-sell? Um, I would say. [00:32:25] James Kang: It, it starts and finishes with just misalignment and a loss of trust with the customer, especially when you have multiple partners or stakeholders involved. If you’re trying to do a three-way deal with a end customer and you’re not on the same page, you’re not gonna get to a successful outcome on, on the backend. [00:32:44] James Kang: Uh, the fix is a much more complicated story. I would say that to take a step back, um. We’ve talked about the five layer cake. We’ve talked about where NVIDIA kind of fits within the equation. We are invested in the ecosystem and so as different players and application organizations win and see these outcomes for end customers, we celebrate that success. [00:33:07] James Kang: Um, and as part of that kind of ethos of where NVIDIA fits within the ecosystem, we wanna make sure that not only. Our customers, but our partners like ISVs and GSIs are set up for success. Um, we do not as Nvidia sell hardware or GPUs directly to customers We use. Hyperscalers like AWS as kind of our force multiplier. [00:33:31] James Kang: And similarly we think of ISVs and GSIs as the force multipliers in terms of our extensions of how we, we kind of leverage the relationships and build the trust with our end customers. And so going back to kind of the question, Vince, I would say that it all comes back to trust and being able to build that mutual trust. [00:33:48] James Kang: Um, a lot of what we do when we co-sell with AWS is really on the software layer. Um, we actually have more software engineers at NVIDIA than we have hardware engineers, which is a weird thing to say, um, because everyone knows us for our GPUs. But because of that fact, we are heavily invested in Cuda and making sure that Cuda becomes the foundational layer for how not only our ISVs and GSIs, but also our end customers are building. [00:34:12] Vince Menzione: Very cool. So Reiki, you and James together on this production. Versus pilot with the Gentech ai. Tell us a little bit more about that. Where, where are you in the process? [00:34:24] Rekha Thangellapalli: Yeah. So I mean, in general, what we’re seeing out in the market in, in relation to sort of AI and, and customer’s journeys is that, um, at least from an elastic perspective, um, we’re seeing people very much in production when it comes to, you know, kind of AI assistant co-pilot use cases. [00:34:42] Rekha Thangellapalli: So, you know, things like, um, software development, customer support is a big one. Um, any sort of employee productivity use cases where there’s. Still a human in the loop somewhere. Um, and there’s a very like, clear path to value. And so we see the customers being in production excelling there. Um, no problem. [00:35:01] Rekha Thangellapalli: Where we’re seeing people still kind of in the pilot phase is those fully autonomous workflows where there is no human involved. The agent is reasoning on its own. Um, accessing multiple systems and taking an action on the user’s behalf. And what we’re seeing is that it’s not the intelligence of the agent that’s holding it back. [00:35:26] Rekha Thangellapalli: It’s more about giving the right context to the agent and having the right. Security kind of governance controls in place for the company to feel comfortable in putting these fully autonomous workflows into production. And that’s really the conversation we’re having is all right, what are the controls you need in place? [00:35:47] Rekha Thangellapalli: For you to release this to your business unit. Um, and what is the context that the agent is needed before we can comfortably let the agent make the decision on the user’s behalf? Um, James, I’d be interested to hear what you’re, what you’re seeing in the market [00:36:03] James Kang: plus one on all things context. I, I would even go so far as to say, um. [00:36:09] James Kang: H how many folks in the audience have heard of token maxing? Like this new term? [00:36:13] Rekha Thangellapalli: Yeah. Yeah. [00:36:14] James Kang: Um, I’ll, I’ll give a very specific example of, of Uber that went public. With the example of Claude, like they allowed all of their employees to use as many tokens as possible, and within the span of four months, they exhausted their full budget for the year, and so they had to pull back, and now there’s a cap on every employee. [00:36:33] James Kang: I think the number that’s circulating is $1,500 per month per employee, and so I think that is at least. In this multi-phase evolution of where we’re going to be and where we’re today, cost has become kind of the prohibitive force in terms of agentic AI at scale. Um, I think we are working on some very creative solutions in-house and Nvidia. [00:36:55] James Kang: Um. And we saw some really dynamic announcements this week when it comes to all things agent core, um, where we want to focus on very nimble ways for customers to be able to execute and go to market. And one extreme example of that is our investment within our open model strategy. So Nvidia, not only, again, providing GPUs, we actually offer our own op open models, which we call our Nitron models. [00:37:21] James Kang: And through our Nitron models, we are allowing customers to really develop and fine tune their own proprietary models in a cost effective manner. So right alongside the frontier models like OpenAI and Anthropic. It’s not a if then, it’s not an either or statement. It’s a, it’s a permutation, it’s an and So we’re giving you a cost effective alternative to not only bring your AgTech applications at scale by training on Nibo tron, which is open source, but then once you’ve kind of finished and fine tuned that specific training job to be able to. [00:37:53] James Kang: Go ahead and utilize your frontier models, whether it be OpenAI or Claude. And I know there’s other partners here that are providing those kind of different model capabilities. And so I think for us it’s, it’s a matter of choice. We know that this market is dynamic. It’s gonna be evolving over the next coming months as well as the next coming years. [00:38:10] James Kang: Uh, but we believe that we are positioned for a really unique dynamic expansion of AgTech use cases over the, at least the next three to six months. [00:38:20] Vince Menzione: Allison, for the partners in the room who are glazed over right now going, what do I, what do I do over the next 12 months? [00:38:26] Allison McFadden: Should I wake everybody up by saying, yeah, please. [00:38:27] Allison McFadden: Say go hurricanes. [00:38:28] Vince Menzione: Yes. [00:38:29] Allison McFadden: Is there anyone, anybody? Everyone’s like, boo. I get to leave the parade today to go home to parade. I live in Raleigh, so we’ve got our parade on Saturday. Nice. [00:38:39] Vince Menzione: Nice. [00:38:40] Allison McFadden: All right. Wake up. Um, all right. So for the $50 million partners in the room, um. $50 million is not small. You have something that works. [00:38:50] Allison McFadden: Right. This is great. What I would be thinking about is, you know, we’ve talked about focus before, but really doubling down on, you know, what is, what is your industry, what is your client like, ideal client that you serve. And build, um, almost that kind of community. You know, the, the clients we have move from firm to firm to firm. [00:39:17] Allison McFadden: And if you’ve done good work at one, you’re gonna follow ’em to the next. Um, so build that client demand in a specific place or specific client profile that is just like really knocking it out out of the park for you. Um. Scale with marketplace, right? So if you, I, I love some of the data that you were sharing in your talk earlier, um, because it’s like no overhead scaling mechanism. [00:39:45] Allison McFadden: I mean, it’s, it’s fantastic. Um, Accenture, other GSIs like us, we are investing in marketplace. So we’re investing in resources, um, to help us. Use marketplace more with our clients and we’re gonna capture, right, those storefronts. And if you’re present on marketplace, you’re gonna be able to catch, uh, yourself in that wheel. [00:40:09] Allison McFadden: So I think those are the, the kind of couple of things I would say is focus, focus, focus to drive that client demand and use scaling mechanisms like marketplace to really kind of, uh, accelerate. [00:40:24] Vince Menzione: Matt, anything to add there on the. [00:40:26] Vince Menzione: Well just, you know, Ja, James, you, I love the token maxing reference in Uber and it reminds me, you remember when cloud came out and everyone was like, oh, all these people are, are gonna use the cloud and costs are outta control and. [00:40:39] Vince Menzione: Um, a lot of people pulled back from the cloud and, and a lot of those companies no longer exist. And it’s similar with, with, uh, token maxing, like, oh, these agents are outta control. You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to agent to co-sell, or you can fade and die. [00:40:58] Vince Menzione: And, and that’s, that’s where we’re at. Uh, is, is the, the companies sitting here today embraced the cloud years ago and won. Uh, and and there’s a set of companies here today who are gonna embrace agents in the, for both buyers and sellers, and will win. And there are those who won’t and they won’t win. And so for me, it’s like we’re, we’re at a, we’re at a crossroads. [00:41:18] Vince Menzione: And, and if you’re gonna win, you gotta leap into that, you know? I love it. And, uh, and, and, and it’s, it means the cost of experimentation is so much lower now. Development and, and even business development or software development is, is agent enabled. And so you can take risks, you can experiment and, and you have to, it’s, it’s an existential moment. [00:41:37] Vince Menzione: Agreed. We’ve got a couple minutes left over for any questions. What do you think? Sure. Are there any here. I think there are a couple. Yeah, we’ve got, we’ve got a co-sell question I’m sure coming up here. [00:41:51] Audience Member: Um, I’m Cassandra, I’m the CEO of Partner Tap. And one of the questions I had was, I think, you know, the co-selling between the sellers is where things get. Really, really hard when you’re multi-partner. And so when I was listening, um, with, you know, the Accenture and Elastic together, you talked about how you had, you, you had to get these BD business development people. [00:42:22] Audience Member: Um, is this a new team that is over the client team? And how do these teams interact like with the elastic sellers? Are you doing a lot of coaching to the field and then with if AWS sellers are, are involved, like what is that whole picture? What does look like, [00:42:43] Allison McFadden: like [00:42:44] Audience Member: on the ground? I mean, that is the hardest part, I think, and that’s what we hear. [00:42:48] Allison McFadden: It’s so, it’s so, it’s so tough. Um, and I will, I’ll just say, so our business development leaders that we now have kind of. Expanded their capacity. They have always been, they have always been there. Um, but they have not been well resourced. They haven’t, they haven’t had very clear kind of job description. [00:43:12] Allison McFadden: I’m gonna say I, in the past they have been kind of focused on partner relationship. And so like more like an alliance manager and maybe working on some of the data. Right? So when I say I have nightmares about Lars, because we’re always trying to increase the LAR for Accenture and, and they were focused like in those detailed weeds of like trying to pass ACE and trying to call the PDM and all this stuff. [00:43:39] Allison McFadden: What we are doing is really pivoting them to be proper sales, business development focused on client outcomes and focused on. Technical skills to be able to describe what this solution is to the field. So, um, and because we need, I have many, many questions about, I gotta get agents to work with Eurogen co-sell so that that part somehow goes away. [00:44:05] Allison McFadden: So that’s a, that’s the thing we gotta solve still, but, um, so we’re pivoting them to be kind of driving. More of that co-sell enablement with the field, um, and taking that message to the field rather than being there, waiting for questions to come in from the field, waiting for like our field teams to discover, oh, I saw something that we’re doing with Elastic, like on a press release on LinkedIn. [00:44:30] Allison McFadden: Right. So we’re kind of trying to pivot them to be more proactive. [00:44:33] Vince Menzione: Very cool. [00:44:34] Rekha Thangellapalli: Yeah. And uh, Cassandra, that’s an excellent question because I think. Multi-party, you know, sort of tri-party offerings. The hardest part is operationalizing it at scale, right? Yeah. And so for this particular offering, we are basically having three routes to market. [00:44:51] Rekha Thangellapalli: So one is seeing how this offering fits into our existing elastic go to market. And so I am constantly enabling our field sellers to say, okay, within our three field sales place, here’s exactly where this fits in. Here are, you know, uh. Keywords that you hear in customer conversations where you bring up this offering and here’s a process of how it works. [00:45:14] Rekha Thangellapalli: Um, exactly At what sales stage do I bring in Accenture, how, you know, what are the roles and expectations? Right? So that’s on the elastic side. We’re doing the same thing on the Accenture side. So we’re doing a ton of training enablement and lunch and learns, and we’re also looking at how do we fit into. [00:45:31] Rekha Thangellapalli: Uh, Accenture’s AI transformation projects, we are the semantic layer, right, of their enterprise brain. And so it’s a whole different sales motion, um, and, you know, having the right assets, having the right process again to make sure that that goes smoothly. And then finally, we’re going directly to the customer. [00:45:49] Rekha Thangellapalli: So we are launching multiple external campaigns where, you know, if the customer raises their hand. We will, we will line up immediately. Right. Um, and so, [00:46:01] Allison McFadden: I mean, I can’t, I can’t, I can’t say how important that third leg of the stool is. ’cause the second part, she talked about getting into our catalog is the first thing. [00:46:09] Allison McFadden: ’cause my BU business development leaders have the catalog. Right. And that’s what they’re selling. So what Elastic has done has gotten into one of those offerings and then. If we have a customer that asks for it, that is the fastest way to alignment. That is like the number one thing that we respond to [00:46:26] Vince Menzione: customer at the center. [00:46:27] Vince Menzione: This is great. Well, I think we’re up to time. This was a great session. I want to thank you. This is what a great, what a great group. [00:46:34] Vince Menzione: Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where [00:46:43] Vince Menzione: you listen, and head over to the ultimate partner.com. [00:46:47] Vince Menzione: For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:47:09] I.

AI infrastructure is changing the economy faster than most people understand, and the real story is bigger than Nvidia, SpaceX, TSMC, or data centers. In this video, I'm breaking down the AI boom, chip demand, data center power, water usage, SpaceX valuation, China's AI infrastructure race, tariffs, inflation, grocery debt, and why I believe investing is one of the only ways everyday people can fight back financially.The Dark Side Of The AI Boom Nobody Wants To ExplainJoin our Exclusive Patreon!!! Creating Financial Empowerment for those who've never had it.

- Evercore Questions June-Q Hardware Revenue Due to Memory Crunch - TSMC Pledges More Money and Factories in US - Apple Launches Back-to-School Promo in Canada, Mexico, and US - A17 Pro iPad mini Hits Apple's Refurbished Store - Apple TV Announces August Return for "Stillwater" - A Question About Apple TV (X3) - LEGO Considering Official Bondi Blue iMac G3 Set - Sponsored by OneSkin: Get 15% off OneSkin with the code MACOSKEN at oneskin.co/MACOSKEN #oneskinpod #sponsored - Sponsored by CleanMyMac: Use code MACOSKEN20 for 20% off at clnmy.com/MACOSKEN - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

The company TSMC is riding the artificial intelligence investment wave all the way to the bank, as profits surge at the Taiwanese advanced chip manufacturer. Our correspondent in New York City will dig into the latest jobs and spending data - has the city's big summer of events made consumers feel more confident? And we head to Europe's biggest port - Rotterdam - to hear about a court case locals have taken to force the industrial hub to go greener, faster.World Business Express - Finance, economy and business news from BBC journalists around the world.

Benedict Evans, one of tech's most widely-read analysts, joins Jacob Effron. The conversation centers on Benedict's core thesis that comparing AI's scale to past platform shifts (the internet, mobile, PCs) is analytically useless, and that the more productive move is studying how those previous technologies actually evolved economically to reason about where AI's value will accrue. He argues the one genuine difference this time is that we don't know AI's physical or scientific limits, unlike past shifts where the boundaries were at least knowable, and that this uncertainty is what fuels both AGI hype and doomerism without resolving anything. Benedict unpacks why capabilities remain jagged, meaning usage is jagged too, why coding became the first real enterprise use case thanks to scalable verification, and why most consumer and enterprise use cases still have to be invented by entrepreneurs rather than emerging spontaneously once models improve. He also lays out why foundation model labs may end up structurally like TSMC rather than Windows, valuable but bounded rather than owning the entire stack, walks through why automation has historically meant more work rather than less (using a hundred years of rising accountant headcount as evidence), and explains why industries like Uber and Airbnb, or Caterpillar and the internet, show just how unevenly this kind of technology actually lands. Throughout, he offers candid, historically grounded takes on OpenAI's product sprawl versus Anthropic's narrow coding bet, Apple's stumbled AI moment, and why most companies, unlike Silicon Valley, have far bigger priorities than AI on their minds.   (0:00) Intro (1:31) Is AI Bigger Than the Internet? (10:10) Barriers of Getting From Demos to Daily Use (20:15) Why Job Predictions Fail (25:52) Where's the Moat? (33:55) Will Models Eat the App Layer? (39:25) When Average Isn't Enough and Models Don't Work (45:58) Reflections on OpenAI (55:04) Consumer Usage Is Still Shallow (58:51) What's Required for More Enterprise Adoption (1:03:47) Opinion on Sora (1:06:27) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

1부 [텍코노미] 클로드 vs GPT 신모델 전격 비교해봤습니다 - 김덕진 소장(IT커뮤니케이션 연구소) 2부 [글로벌 리포트] TSMC 직원들은 직장 숨기고 선봅니다 - 이혜인 기자(한국경제신문)

The guys kick off the Fourth of July week by arguing that high agency culture is still America's edge, then dive into why AI is officially too big to fail. Marty and John break down FERC forcing grid operators to fast track data center connections, OpenAI floating a five percent stake to the Trump administration, and Marc Andreessen landing on the Pentagon's defense policy board. They also dig into the memory bottleneck squeezing the chip buildout, why frontier models are not getting commoditized by open source, and what Saudi oil flows back at ninety percent mean for Iran's leverage. To close, they look at Trump's fifty million dollar Bitcoin stash, Strategy selling coin to fund dividends, and whether Washington might already be building a strategic position through the public markets.

My guests today are Gavin Uberti and Rob Wachen, the founders of Etched.  A few years ago, when they set out to build a better AI chip than the largest companies in the world, almost everyone I called told me it could not be done. They have since done it, taping out a working chip on their first attempt and becoming the first hardware company founded after ChatGPT to do so. They already have more than a billion dollars of customer demand for their first product, and have raised eight hundred million dollars to build it.  Etched builds chips and systems designed to run AI models faster and at lower cost. They started the company in 2023, and that product is a complete rack for inference, the chip along with the boards, the power delivery, the interconnects, and the manufacturing to produce it all. We talk about the technical bets behind their architecture, how they hired industry legends and paired them with elite 22 year-olds, and why they believe inference will become one of the largest markets in the world. I think you will find the story of what they have built hard to forget. Please enjoy my conversation with Gavin and Rob. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:07) Gavin Uberti and Rob Wachen (00:03:54) Two 21-Year-Olds Taking on NVIDIA (00:07:52) The Two Technical Bets Behind Their Architecture (00:14:15) Why Inference Becomes the Biggest Market (00:20:23) Rob and Gavin's Origins Stories (00:28:38) How They Recruit Industry Legends (00:36:30) Moving a Dozen Engineers to Bangalore for Six Months (00:38:01) Speed Wins (00:43:58) Getting More Concurrency Out of Every Megawatt (00:52:44) Vertical Integration (00:57:43) Hardest Obstacles to Overcome (01:01:09) Raising The Largest AI Chip Series A Ever (01:06:29) TSMC (01:13:20) Designing Gen 2 for Gigawatt-Scale Production (01:16:42) Why Machines Don't Think Like People (01:20:03) A Year of Compute Compressed Into a Month (01:23:44) The Trillion-Dollar Data Center (01:26:19) The Kindest Thing