Podcasts about Tencent

What if the people telling you AI is going to end the world are the same people selling you the AI that could end the world - and they are doing both at the same time? Erica has been lying awake reading the headlines, thinking about her eight-year-old sleeping down the hall. And today she is naming names, bringing receipts, and taking explicit positions on the AI fear machine, what happens when women are not in the rooms where this technology gets built, and exactly what she thinks we owe the next generation right now.ABOUT THIS EPISODEThis is the spiciest solo episode Erica has ever recorded, and she knew it when she sat down at her kitchen counter to make it. She covers the extinction statement signed by the CEOs of OpenAI, Google DeepMind, and Anthropic - and the fact that they went back to the office and raced each other to build faster. She covers the $140 million super PAC with AI industry money pouring into midterms. She covers NVIDIA, China, and the contradiction at the center of the 'we can't let China win' argument. She covers the Grok image scandal - 4.4 million generated images in nine days, 41% sexualized images of women - and what it tells us about who is not in the product meetings. And she covers the stat that stops her cold: women are 57% of the workers in jobs most likely to be disrupted by AI, and 13% of the C-suite leaders at AI companies. Then she gives you three moves to make this week.INSIDE THE EPISODEShould I Actually Be Concerned About AI? Erica holds both: the AI nerd who sees what this technology can do, and the mom who wants to throw every device in the lake. She sorts the real risks from the manufactured ones and calls out the people making money off of our fear.Who Benefits When We're Scared? When the CEOs selling the product tell you it could end the world, ask one question: who benefits? Fear makes you feel small and leaves it to the tech geniuses. Fear makes them look like the only ones smart enough to save us from the very thing they are selling. That is a damn good business model.The $140 Million Super PAC. Leading the Future has reportedly raised $140 million for the midterms. Its mission: electing people who want AI rules light and one national standard that overrides tougher state laws. Donors include Andreessen Horowitz and the president of OpenAI. The industry warns about extinction. The industry's money builds a nine-figure war chest to keep the rules light.NVIDIA, China, and the Contradiction. For years the rallying cry was that we cannot let China win the AI race - so we have to move fast and keep the rules light. Then the administration said NVIDIA could sell its most powerful chips to Chinese companies, including ByteDance and Tencent, as long as the US gets a 25% cut. The first shipments landed in China this summer.Grok and What Happens When Women Aren't in the Room. xAI rolled out an image editing feature and people immediately used it to digitally undress women and girls from photos found online. In nine days: 4.4 million images generated, 41% sexualized images of women. Any woman in that product meeting could have told them that was coming. Any mom could have told them that.57% Disrupted, 13% in the Room. Women are 57% of the workers in jobs most likely to be disrupted by generative AI. Women hold 13% of C-suite AI roles at AI companies. At the rate we are going, it will take 90 years to reach equal representation in leadership. A girl born today may never see that in her lifetime.Three Moves You Can Make This Week. Start using AI and let people see you use it. Parent it loudly - have the talk with your kids, use it together, tell them what happened with Grok. Follow the money and use your voice - midterms are coming, AI money is pouring into races, opensecrets.org makes it easy. If there is no woman on your company's AI council, get yourself into that room. ERICA'S RESOURCES & LINKSStart small with me inside HER Jumpstart:https://her-collective.mn.co/plans/1985289?bundle_token=60b2c61d62213cdbd16d437cdc0e4204&utm_source=manual

What if the people telling you AI is going to end the world are the same people selling you the AI that could end the world - and they are doing both at the same time? Erica has been lying awake reading the headlines, thinking about her eight-year-old sleeping down the hall. And today she is naming names, bringing receipts, and taking explicit positions on the AI fear machine, what happens when women are not in the rooms where this technology gets built, and exactly what she thinks we owe the next generation right now.ABOUT THIS EPISODEThis is the spiciest solo episode Erica has ever recorded, and she knew it when she sat down at her kitchen counter to make it. She covers the extinction statement signed by the CEOs of OpenAI, Google DeepMind, and Anthropic - and the fact that they went back to the office and raced each other to build faster. She covers the $140 million super PAC with AI industry money pouring into midterms. She covers NVIDIA, China, and the contradiction at the center of the 'we can't let China win' argument. She covers the Grok image scandal - 4.4 million generated images in nine days, 41% sexualized images of women - and what it tells us about who is not in the product meetings. And she covers the stat that stops her cold: women are 57% of the workers in jobs most likely to be disrupted by AI, and 13% of the C-suite leaders at AI companies. Then she gives you three moves to make this week.INSIDE THE EPISODEShould I Actually Be Concerned About AI? Erica holds both: the AI nerd who sees what this technology can do, and the mom who wants to throw every device in the lake. She sorts the real risks from the manufactured ones and calls out the people making money off of our fear.Who Benefits When We're Scared? When the CEOs selling the product tell you it could end the world, ask one question: who benefits? Fear makes you feel small and leaves it to the tech geniuses. Fear makes them look like the only ones smart enough to save us from the very thing they are selling. That is a damn good business model.The $140 Million Super PAC. Leading the Future has reportedly raised $140 million for the midterms. Its mission: electing people who want AI rules light and one national standard that overrides tougher state laws. Donors include Andreessen Horowitz and the president of OpenAI. The industry warns about extinction. The industry's money builds a nine-figure war chest to keep the rules light.NVIDIA, China, and the Contradiction. For years the rallying cry was that we cannot let China win the AI race - so we have to move fast and keep the rules light. Then the administration said NVIDIA could sell its most powerful chips to Chinese companies, including ByteDance and Tencent, as long as the US gets a 25% cut. The first shipments landed in China this summer.Grok and What Happens When Women Aren't in the Room. xAI rolled out an image editing feature and people immediately used it to digitally undress women and girls from photos found online. In nine days: 4.4 million images generated, 41% sexualized images of women. Any woman in that product meeting could have told them that was coming. Any mom could have told them that.57% Disrupted, 13% in the Room. Women are 57% of the workers in jobs most likely to be disrupted by generative AI. Women hold 13% of C-suite AI roles at AI companies. At the rate we are going, it will take 90 years to reach equal representation in leadership. A girl born today may never see that in her lifetime.Three Moves You Can Make This Week. Start using AI and let people see you use it. Parent it loudly - have the talk with your kids, use it together, tell them what happened with Grok. Follow the money and use your voice - midterms are coming, AI money is pouring into races, opensecrets.org makes it easy. If there is no woman on your company's AI council, get yourself into that room. ERICA'S RESOURCES & LINKSStart small with me inside HER Jumpstart:https://her-collective.mn.co/plans/1985289?bundle_token=60b2c61d62213cdbd16d437cdc0e4204&utm_source=manual

China steckt in einer Immobilienkrise und bringt gleichzeitig Unternehmen hervor, die bei künstlicher Intelligenz, Elektromobilität und Robotik vorne mitspielen. Wie passen diese Entwicklungen zusammen – und was übersehen wir, wenn wir aus Deutschland auf China schauen?Zu Gast ist Eric Nebe, Betreiber des YouTube-Kanals China2Invest, Podcaster und Autor von „Das eine China-Buch“. Nach seiner Tätigkeit als Investmentanalyst bei BASF hat er sich selbstständig gemacht und beschäftigt sich heute mit chinesischen Unternehmen, Aktien und wirtschaftlichen Entwicklungen. Er lebt überwiegend in Kuala Lumpur und bringt auch die Perspektive aus Südostasien mit.Gemeinsam sprechen wir darüber, warum China dezentraler und wettbewerbsorientierter ist, als viele vermuten, welche wirtschaftlichen Probleme Eric ernst nimmt und wo er neue Wachstumstreiber sieht.Außerdem geht es um Tencent, Xiaomi und Robotik, die Erwartungen deutscher Anleger und die Frage, warum technologische Fortschritte nicht automatisch steigende Aktienkurse bedeuten. Eric erklärt, weshalb „China Speed“ ein Wettbewerbsvorteil sein kann, aber auch Fehler und hohen Kostendruck mit sich bringt.Zum Abschluss wechseln wir die Perspektive: Während Deutschland über seine Abhängigkeit von China diskutiert, fragt Eric, wie wichtig Deutschland für China künftig noch sein wird. Wir sprechen über sein Buch, darüber, warum Modernisierung nicht automatisch Verwestlichung bedeutet, und was er trotz jahrelanger Beschäftigung mit China bis heute schwer nachvollziehen kann.Eine Folge über Chinas Wirtschaft, die Chancen und Unsicherheiten chinesischer Aktien und darüber, warum ein anderer Blickwinkel neue Fragen eröffnet.Viel Spaß beim Reinhören!Hinweis: Die besprochenen Unternehmen und Aktien dienen der Einordnung und stellen keine Anlageberatung oder Kaufempfehlung dar.Send us Fan Mailasiabits hier abonnieren: asiabits.comDamians Team kontaktieren: www.genuine-asia.comModeratoren & Hosts: Damian Maib & Thomas DerksenSchnitt & Produktion: Nelli Mallmann

In a special episode of The Negotiation, WPIC CEO Jacob Cooke sits down with Dr. Henry Wang, founder and president of the Center for China and Globalization (CCG), one of China's leading policy think tanks. The conversation takes place fresh off the Trump-Xi summit — a historic meeting that put AI governance, trade stability, and the future of the US-China relationship back at the centre of global attention.Dr. Wang is a former Counselor to China's State Council, a member of the Chinese People's Political Consultative Conference, and one of the most widely cited Chinese foreign policy voices in international media. In this episode, he gives his immediate read on the summit's outcomes, what they mean for the bilateral relationship, and where the US and China go from here.The conversation then turns to AI — the most consequential item on the bilateral agenda. Dr. Wang explains his call for a global no-first-use policy covering AI-enabled autonomous weapons, walks through his argument that the US AI capex boom carries risks that cooperation with China could help mitigate, and assesses what an effective US-China AI governance framework would actually look like in practice. He also breaks down China's AI advantages — not in frontier models, but in application scale, deployment infrastructure, and commercial reach — and what that means for international businesses operating in the region. Discussion Points·       Dr. Wang's immediate reaction to the Trump-Xi summit outcomes and what they signal about the trajectory of the US-China relationship·       Why this summit was a significant anchor for bilateral stability — and what was at stake if it had gone badly·       The areas of US-China cooperation beyond tariffs and technology: people-to-people exchanges, global security challenges, and why they keep getting crowded out·       Assessment of progress on the AI file at the summit — where things moved forward and where they fell short·       The case for a global no-first-use policy on AI-enabled autonomous weapons, modelled on nuclear precedent·       Why Dr. Wang compares the US AI capex boom to an arms race — and the economic risks that cooperation with China could help avoid·       What an effective US-China AI governance framework looks like in practice, and what it would take to get there·       China's AI advantages in application, deployment, and commercial scale — and what that means for international businesses·       Whether the summit leaves Dr. Wang more or less optimistic about the bilateral relationship over the next few years·       The work CCG is doing on US-China relations and global governance, and where listeners can follow Dr. Wang's thinking

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Increasingly, it seems like the corporate names that dominate headlines are much more diverse than they used to be. Samsung, TSMC, and SK Hynix. Alibaba and Tencent; BYD and DeepSeek. Sea and Grab. For twenty-five years, it seems like every major technology was American . That assumption is now under strain as innovation spreads from one place to many. Mehran Gul tries to map out this new network in his book The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Simon and Schuster, 2026). From China's startlingly messy AI ecosystem to Singapore's government-led innovation, from South Korea's decades-old conglomerates still at the frontier to India's world-class talent that too often works abroad; from Europe's struggle to hold on to its best companies, to the deeper role of the state everywhere—including in Silicon Valley—and why the United States, far from declining, is extending its lead into a genuinely bipolar technological order.Mehran is a winner of the Financial Times/McKinsey Bracken Bower Prize. He attended Yale where he was a Fulbright Scholar, Fox International Fellow, and Teaching Fellow. He has been a Lead for the Digital Transformation of Industries at the World Economic Forum and an expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organization. Before Yale, he studied at the Lahore University of Management Sciences. He has been a visiting scholar at the Jawaharlal Nehru University in New Delhi and a Fellow with the Acumen Fund. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The New Geography of Innovation. Follow on Twitter at @BookReviewsAsia.Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hier zum Festgeld-Angebot von Scalable Capital: https://partner.scalable-capital.de/go.cgi?pid=655&wmid=1140&cpid=1&prid=1&subid=&target=Festgeld On will bis 2029 auf 7 Mrd. $ Umsatz. Accelevation plant 5 Mrd. $ IPO für Rechenzentren. Alibaba kündigt 20 Gigawatt KI-Kapazität an. Tencent zeigt neues Bildmodell. Evonik streicht 3.000 Stellen. Viking Therapeutics springt 30% nach Abnehmspritzen-Daten. Pepco (WKN: A3CQ3M) ist Polens Antwort auf Action. 4.000 Filialen, zweistelliges Wachstum, Aktie verdoppelt in einem Jahr. Klappt die Expansion nach Westeuropa oder wird Deutschland zur Warnung? Wisetech (WKN: A2AGET) hat zwei Drittel an Wert verloren. Gründer-Skandal, Wettbewerbsermittlungen und KI-Sorgen treffen auf ein Quasi-Monopol in der Speditionssoftware. Übertreibt die Börse? Diesen Podcast vom 23.09.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

S&P futures are indicating a modestly higher open following a positive Asia session. Taiwan led gains as its benchmark hit an all-time high, and Greater China tech names also advanced, with Alibaba and Tencent in focus following AI announcements. Japan remained closed for a holiday. European equities are mixed in early trading.Companies Mentioned: Alibaba, Tencent, Meritage Hospitality Group, Embraer

The Daily Business and Finance Show - Tuesday, 22 September 2026 We get our business and finance news from Seeking Alpha and you should too! Subscribe to Seeking Alpha Premium for more in-depth market news and help support this podcast. Free for 14-days! Please click here for more info: Subscribe to Seeking Alpha Premium News Today's headlines: SA Asks: Is Nebius a buy, hold, or sell right now? U.S. proposes $5B fund to rebuild Middle East energy sites damaged in Iran war: WSJ Trump approval rating drops to record low 32% as living costs surge: Reuters Kingfisher climbs after raising FY profit guidance Goldman bets on falling yields, rising stocks Biggest stock movers Tuesday: UBS, VICR, and more Stock futures edge lower after Wall Street's tech-led rally On Holding targets CHF 5.6B in sales by 2029, authorizes $1B buyback Tencent unveils AI image model to narrow gap with ByteDance, Alibaba; shares rise Explanations from OpenAI ChatGPT API with proprietary prompts. This podcast provides information only and should not be construed as financial or business advice. This podcast is produced by Klassic Studios Learn more about your ad choices. Visit megaphone.fm/adchoices

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Ein wichtiger abschließender Hinweis: Aus rechtlichen Gründen darf ich keine individuelle Einzelberatung geben. Meine geäußerte Meinung stellt keinerlei Aufforderung zum Handeln dar. Sie ist keine Aufforderung zum Kauf oder Verkauf von Wertpapieren.
 Zum Zeitpunkt der Erstellung dieses Beitrags war der Autor, Lars Erichsen, in folgenden der besprochenen Finanzinstrumente selbst investiert: Tencent, JD.com. Geplante Änderungen: Keine. Weitere Informationen entnehmen Sie bitte unserem Transparenzhinweis zum Umgang mit Interessenskonflikten: https://www.lars-erichsen.de/transparenz-und-rechtshinweis

Two people, a wrong turn into a back alley, a community garden, and about thirty minutes of arguing about protocols on the streets of Tokyo.The guest is Angie Jones, VP of Developer Experience at the Agentic AI Foundation, fresh off launching AGNTCon + MCPCon in China before the Tokyo stop. She opens with what she learned there: a mobile-first, super-app world where the integration problem most of us obsess over barely exists, where every conversation about agents is really a conversation about the model, and where companies are now reaching for MCP and A2A precisely because they want to operate outside that ecosystem.The bulk of it is WebMCP - a protocol with a confusing name and, until recently, almost no attention. The pitch: put tool calling in the page itself, so your agent works inside your logged-in session with only the tools relevant to the page you're on, instead of screenshotting an anonymous browser and burning tokens guessing at the accessibility tree. Angie explains why it went from ignored to urgent the moment agentic browsing got good, and why the fix for computer use being slow and hijacking your machine might be a standard rather than a better model.It closes on agent-to-agent: whether anyone actually wants a marketplace of thousands of agents, or whether the real value is the one agent that has access you'll never get. Plus a well-earned complaint about three-letter acronyms and why researchers are still the only people naming things well.Timestamps:[0:00] Intro[0:59] Launching the conference in China[1:34] What North America gets wrong about agents[2:23] Super apps versus endless integrations[3:14] What happens when they expand beyond China[3:39] Tencent and A2A in production[4:34] A model-first country[5:56] Chinese coding agents and harnesses[6:59] Tokyo and the conference world tour[7:26] What WebMCP actually is[8:56] Why it has nothing to do with MCP[9:20] Page-level tools and your logged-in session[10:36] Why WebMCP sat unnoticed for months[11:25] Token efficiency and reliability[12:23] The moment computer use got good[13:15] Two real grievances with computer use[14:06] Collaborating instead of surrendering your screen[15:24] A short detour into Tokyo signage[16:15] Why web developers should be excited[17:04] Agents and the loss of first-party data[18:27] Why an agent cannot just buy something[19:23] Inside the agentic commerce working group[20:18] Upsells recommenders and an agent that ignores them[23:27] The commerce protocols to watch[24:19] Why A2A is next[25:31] Publishing your agent as a service[27:13] The case against agent marketplaces[27:55] Why access beats capability[29:31] Google's protocol land grab[30:45] Bring back the cool names[31:39] Amsterdam, San Jose, and what comes next

On this week's show Patrick Gray and James Wilson are joined by former US Cyber Command executive director turned PwC's Cyber, Data & Technology Risk leader Morgan Adamski to talk through the week's news, including: More tech guys penned more open letters and AI will destroy us all! Another Wednesday, another congregation of OpenAI agents on wikis… yawn OpenAI agents were behind the headscratching RubyGems hacking campaign in May The FBI will disrupt more adversary operations, NSA is creating more mission centres, lawmakers want sanctions on hackers-for-hire… Release more hounds! So many platforms, so many bugs, so many patches breaking other stuff Much, much more… This week's show is brought to you by Airlock Digital. Its co-founders Daniel Schell and David Cottingham join Patrick to talk about how Airlock has integrated itself with Crowdstrike via its Falcon Foundry platform. This episode is also available on YouTube Show notes AI researcher says there is 'substantial probability' AI could kill all humans in next decade | NBC News Tech Dario Amodei — We Must Pace the Frontier | Social Signals China spy chief points at US AI models in cyber threat warning | therecord.media Weixin Official Accounts Platform | Trump calls concerns over A.I. destroying humanity a “hoax” | NBC News Tech Dr_Gingerballs (@Dr_Gingerballs) on X | X (formerly Twitter) Sam Altman backs Anthropic CEO's call to slow down the global AI race | NBC News Anthropic: Detecting and countering misuse of AI, September 2026 | anthropic.com AI lets small actors run state-level hacking campaigns, Anthropic report finds | cyberscoop.com Users in Houthi-held Yemen tried to develop advanced weapons with AI, Anthropic says | apnews.com Anthropic caught Russia-linked spies using Claude in hacking operations | therecord.media OpenAI's rogue agents used at least 10 more sites for unauthorized comms, researchers say | reuters.com Researchers say OpenAI agents were behind May hacking campaign targeting RubyGems | cyberscoop.com Anthropic claims Moonshot, DeepSeek secretly diverted user requests to Claude | South China Morning Post WeWorm | Social Signals Hackers exploit Tencent app flaw to deploy GrayRabbit malware | BleepingComputer New FBI cyber strategy promises increase in adversary disruptions | Cybersecurity Dive US disrupts Xinbi Guarantee marketplace fueling the cyber scam economy | therecord.media Thorough reorganization at NSA will create five 'mission centers,' including cyber and AI | therecord.media Lawmakers call on Treasury to sanction hackers-for-hire | cyberscoop.com CISA: WatchGuard RCE flaw now exploited in ransomware attacks | BleepingComputer Dutch NCSC: Critical Check Point VPN flaws exploitation is imminent | BleepingComputer GitLab's critical flaw is already drawing internet-wide probes | cyberscoop.com Cisco warns customers of actively exploited zero-day in email gateways | cyberscoop.com 4 groups caught using the same Chrome and Windows exploit kit | Ars Technica September Windows Server updates break Remote Desktop Services | BleepingComputer Microsoft confirms KB5002914 Excel update breaks copy and paste | BleepingComputer Microsoft: September updates break audio on some Windows PCs | BleepingComputer ClickFix attacks infecting PCs and Macs are going viral | arstechnica.com Passkey-themed phishing attacks lead to Microsoft 365 data theft | BleepingComputer

In Part 2 of his conversation with The Negotiation, Zak Dychtwald, founder and CEO of BridgeWorks Global, shifts from diagnosing the hidden tax on global business to addressing what it means for companies operating in or competing against China. Zak regularly briefs executives from Qualcomm, Microsoft, Google, and Walmart when they come through the region, and he shares the framework he uses with them: what to borrow from China's competitive environment, what to leverage, and what to prepare to defend against.He then turns to Chinese companies going global, and why their challenges differ from those of Western multinationals operating in China. The people problem facing Chinese companies as they expand is distinct and underappreciated — and it intersects directly with the global collaboration framework Zak laid out in Part 1.Zak closes with one of his most provocative arguments: today is likely the least competitive Chinese companies will ever be globally, and Western businesses operating in the region should take that seriously as they build their own strategy. He also shares what global companies most consistently get wrong after years of running the Global Collaboration Index — and what actually fixing it looks like. Discussion Points·       The borrow, leverage, and defend framework for Western executives operating in or competing against China·       Why Chinese companies going global face a distinct people problem — and how it differs from the challenges Western companies face in China·       Why today is likely the least competitive Chinese companies will ever be globally, and what Western businesses should do with that insight·       The single thing global companies most consistently get wrong, and what actually fixing it looks like

Is one of the market’s biggest investment narratives starting to shift? Michelle Martin speaks with Alvin Chow, Co-founder of Dr Wealth, about calls to slow frontier AI development, the surge in cybersecurity stocks and where the next AI winners could emerge. They also unpack the Fed’s rate decision, inflation and oil risks, before putting McDonald’s and Tencent under the spotlight.See omnystudio.com/listener for privacy information.

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

The Big Ones

Zak Dychtwald is the founder and CEO of BridgeWorks Global, a cross-geography collaboration lab based in Shanghai. He is the author of Young China: How the Restless Generation Will Change Their Country and the World, has lived in China for over a decade, and speaks Mandarin. His clients include Google, Qualcomm, Microsoft, Medtronic, Walmart, and Ant International. His work has appeared four times in Harvard Business Review, most recently in a piece on how highly effective global teams collaborate across cultures — now running as a feature in HBR Magazine.In Part 1, Zak makes the case that global teams don't break down on culture — they break down on operating design. He lays out a framework of seven elements that determine how well global teams work, split into friction amplifiers (time zones, language, country culture), which leaders can manage but never really change, and trust-and-alignment multipliers (HQ-region power dynamics, company culture, market knowledge, process), which they can redesign. Most leaders pour their energy into the first three and ignore the last four, where all the leverage actually sits.He introduces bridge people — the senior leaders and frontline operators whose real job is holding the seams of a global company together by translating context between headquarters and the market, absorbing the time-zone load, and moving decisions across language and hierarchy — and the global collaboration tax, what those people absorb on the company's behalf. He explains what happens when a crisis hits a team that has never designed for one, and where leaders should start when they recognise the problem in their own organisation. Discussion Points·       Why global team collaboration breaks down at the level of systems, not culture — and what the data from the Global Collaboration Index actually shows·       The seven elements of cross-border collaboration: friction amplifiers (time zones, language, country culture) vs trust-and-alignment multipliers (HQ-region power dynamics, company culture, market knowledge, process)·       Bridge people: who they are, what they carry, and why most companies fail to acknowledge or reward them·       The global collaboration tax: what it costs a business in speed, opportunity, and attrition when bridge people absorb the friction the system creates·       What happens when a crisis hits a global team that hasn't defined decision rights in advance — and where leaders should start when they recognise the problem in their own company

This week's podcast is about WorkBuddy, Tencent's flagship Agent product. It is currently going international. And will likely be followed by Tencent's other "buddy" products:CodeBuddyToastMarvisArdotMioraima CopilotYou can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.Here is the link to TechMoat Consulting.Here is the link to our Tech Tours.Disclosure. I have had a paid consulting relationship with Tencent in the past twelve months.-------I am a consultant & keynote speaker on how to increase digital growth and strengthen digital AI moats.I am the founder of TechMoat Consulting, a consulting firm specialized in increasing digital growth and strengthening digital AI moats. Get in contact here.I write (a lot) about digital growth and digital AI strategy (3 best selling books, +2.9M followers on LinkedIn). There is a free book and email newsletter below.My Moats and Marathons book series is a framework for building and measuring competitive advantages in digital businesses.This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.Support the show

GTA VI just proved it can break Netflix, the EU just put Roblox under its strictest rulebook, and one of the best reviewed games of the year is already falling apart behind the scenes.The crew breaks down the numbers behind GTA VI's Netflix trailer drop and what it means for pre-orders, why PlayStation is quietly winning the entire hype cycle, and how rising RAM prices could delay the PS6. They also cover Gamescom's attendance numbers and the shift toward Eastern exhibitors, the EU's new Roblox designation, Star Wars Zero Company's strong launch undone by a founder lawsuit, Brian Ward's exit from Savvy Games Group, Tencent's new AI tool portfolio and NCSoft's $300 million mobile studio buying spree.Topics Covered:• GTA VI's Netflix trailer drop and the pre-order numbers behind it• Why PlayStation is quietly winning the GTA VI hype cycle• Rising RAM prices and what they mean for the PS6• Gamescom 2026 attendance and the shift toward Eastern exhibitors• The EU designates Roblox a very large online platform• Star Wars Zero Company's strong launch and Metacritic scores• The founder lawsuit quietly sinking developer Bit Reactor• Brian Ward steps down as Savvy Games Group CEO• Tencent's new AI tool portfolio for game development• NCSoft's $300 million mobile studio buying spreeCHAPTERS:01:24 Episode 400 Teaser02:08 Shills and New Content03:59 Gamescom Stats Recap07:03 Mishka Gamescom Takeaways09:28 Cologne Logistics and Hotels11:20 GTA VI x Netflix Drop17:14 PlayStation Wins the Cycle21:28 EU Targets Roblox24:56 EU Market Fragmentation28:38 Star Wars Zero Company Launch32:16 Studio Furloughs Lawsuit36:46 Savvy CEO Steps Down49:18 China AI Platform Bets54:36 NCSoft Strategy Skepticism56:10 Episode 400 Tease Wrap

South Korea moves toward AI access as public infrastructure, Tencent releases Hy4 Preview, Nvidia’s leaked DLSS 5 is out in the wild. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you see you canContinue reading "OpenAI Plans To Stop Providing Its Models To Cursor – DTH"

ByteDance consolidates AI teams to compete with Tencent, Amazon and Twitch sued over AI training data, and Taiwan indicts employees of Nvidia and Super Micro over illegal exports to China. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible.Continue reading "ByteDance Merges AI Teams to Compete with Tencent – DTH"

La gauche française est dispersée... Elle serait même « en mode survie », si l'on en croit la Tribune Dimanche. « À huit mois du premier tour de la présidentielle, explique le journal, la gauche non mélanchoniste n'est pas encore parvenue à proposer une candidature assez solide, pour concurrencer le chef des Insoumis (Jean-Luc Mélenchon) ».   Article accompagné d'une photo de l'ancien président socialiste François Hollande (pas encore candidat mais qui pourrait le devenir) discutant avec Raphaël Glucksmann, le député européen de Place Publique qui doit en principe se déclarer officiellement candidat ce dimanche. Candidature qui, nous dit la Tribune Dimanche, « devrait s'inscrire dans le cadre de la primaire des socialistes prévue en octobre ». Mélenchon en tête Mais pour l'heure, c'est bel et bien Jean-Luc Mélenchon qui fait la course en tête. Il est « crédité d'environ 15 % des intentions de vote au premier tour », rappelle la Tribune Dimanche qui ajoute : « Rien dans le paysage actuel, ne semble pouvoir l'arrêter, et c'est bien là le problème ». Il est clair que le journal ne porte pas le candidat insoumis dans son cœur, l'accusant de vivre « de la confusion », ajoutant que son « invraisemblable programme "L'avenir en commun", le même depuis quinze ans, est un slogan qui claque, taillé pour les meetings » mais « qui masque des propositions aussi datées que dangereuses ». Pour la Tribune Dimanche, « l'enjeu pour les socialistes français n'est peut-être plus de gagner mais d'empêcher : empêcher Mélenchon d'atteindre le second tour ».  Une seule union possible Le Journal du Dimanche, lui, s'est rendu à l'université d'été de la France Insoumise, qui se déroule ce week-end dans le sud de la France. « Les Insoumis ont transformé leur université d'été en camp d'entraînement pour la candidature de Jean-Luc Mélenchon. Seule union possible : se ranger derrière leur candidat », annonce le Journal du Dimanche. « On a proposé une nouvelle alliance populaire aux Écologistes, aux communistes, déclare Manuel Bompard, bras droit de Jean-Luc Mélenchon, on n'a pas de temps à perdre dans des palabres sans fin, des réunions dans lesquelles on n'a ni candidat ni programme ». « La porte est ouverte mais la maison est déjà meublée, commente le JDD, l'avenir de la gauche semble déjà écrit : Mélenchon d'abord, l'union ensuite ».  La Chine toujours ambitieuse La Chine est-elle en passe de devenir la première puissance technologique mondiale ? C'est ce que donne à penser le dossier du Point cette semaine. « Vous n'avez encore rien vu », s'exclame l'hebdomadaire, qui publie un dossier sur les progrès de la Chine qui (nous dit-on) « veut doubler l'Amérique pour devenir la première puissance technologique au monde ». Le Point s'enthousiasme et nous parle des « inventeurs géniaux » qui font progresser la Chine… . « Industrie, automobile, informatique. Leurs trouvailles sont en train de bousculer des pans entiers des sciences et de l'économie, dans un pays qui fait de la puissance technologique un levier géopolitique majeur ».  Il y a toutefois une part d'ombre, au-dessus de ces succès technologiques… « Tout n'est pas aussi rose que le proclame le Parti communiste chinois », remarque le Point. « À Schenzen, les révolutions technologiques successives laissent sur le carreau de nombreux talents ». Une entrepreneure raconte que « son mari a été limogé de chez Tencent (géant technologique chinois) parce qu'à 40 ans, ses supérieurs l'ont estimé trop âgé ». « Il faut dire que les bras ne manquent pas », précise le Point. « En 2025, les universités chinoises ont formé environ un million et demi d'ingénieurs. Le chômage reste élevé et le droit du travail est impitoyable ».  Les Haïtiens sans travail aux États-Unis Aux États-Unis, la Floride connaît une pénurie de main-d'œuvre. C'est Courrier International qui nous signale cette pénurie et qui explique : « La Floride regrette déjà ses travailleurs haïtiens… Privés de leur statut de protection temporaire par l'administration Trump, les Haïtiens réfugiés aux États-Unis ne peuvent plus travailler, dans l'hôtellerie, la sécurité ou les services à la personne ». Le Miami Herald a rencontré un employé de ménage haïtien de 70 ans qui travaillait à l'aéroport depuis 19 ans. « Après avoir perdu le statut de protection temporaire accordé aux réfugiés, qui lui permettait de travailler en toute légalité aux États-Unis, son habilitation de sécurité lui a été retirée en juillet. (…) Pour la première fois de sa vie, explique le Miami Herald, il s'est retrouvé sans emploi ». Et « il est loin d'être un cas isolé », précise le journal. Selon une estimation de l'université Princeton, « environ 52 000 Haïtiens bénéficiaires du statut de protection temporaire travaillaient jusqu'à présent dans le sud de la Floride ». Aux États-Unis, ce sont en tout plus de 300 000 Haïtiens qui viennent de perdre leur statut de protection et qui risquent l'expulsion vers leur pays d'origine. 

OpenAI pausa entrenamientos críticos por seguridad; China abre la puerta a 20.000 Nvidia H200 para ByteDance y Tencent; Cerebras presenta el CS-4 con tres chips gigantes; Claude diseña proteínas y analiza química de laboratorio; y Google prueba rutas aéreas con IA para reducir estelas contaminantes.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Bom dia Tech! Tudo bem? Meu nome é Arthur Givigir e hoje é terça-feira, dia 18 de agosto de 2026 e trago para você as principais notícias de tecnologia, vamos lá?No episódio de hoje, a Meta enfrenta um dos processos mais importantes de sua história, com 30 estados americanos buscando mais de US$ 1 trilhão e mudanças profundas em recursos do Instagram e Facebook utilizados por crianças e adolescentes. Também falo sobre a Anatel autorizando a Vivo a manter seu sistema que bloqueia chamadas de spam antes mesmo do telefone tocar, uma grave vulnerabilidade do macOS que já está sendo explorada por criminosos, a Apple propondo cobrar até 15% em compras feitas fora da App Store, a OpenAI encerrando sua equipe dedicada a avaliar riscos de modelos avançados e a Tencent se preparando para assumir o controle da Manus depois do fim do acordo com a Meta.Quer patrocinar ou fazer uma parceria com o Bom dia Tech? Mande um e-mail para contato@bomdia.teche vamos conversar!ApoioAmazon - Semana da casa e cozinha!Notícias00:00: ☀️ Bom dia Tech!00:32: Meta enfrenta processo que pode passar de US$ 1 trilhão02:29: Anatel autoriza Vivo a manter bloqueio automático de chamadas de spam05:33: Apoio: Amazon - Semana da casa e cozinha!05:43: Falha grave do macOS está sendo explorada por criminosos05:49: Apple propõe cobrar até 15% em compras realizadas fora da App Store07:05: OpenAI encerra equipe dedicada a avaliar riscos de modelos avançados08:32: Tencent deve assumir controle da Manus após fim do acordo com a Meta09:50: Inté a próxima!Produtos do EpisódioSmartphones JoviSamsung Galaxy FoldsNintendo Switch OLEDBundle Nintendo Switch OLED com Mario Kart 8PlayStation 5 SlimPlayStation DualSenseApple iPhone 16 (128 GB)Echo Show 5 (3ª geração)Echo Show 8 (3ª geração)Kindle ScribeComprando por esses links, o Bom dia Tech recebe uma pequena comissão e você ajuda no crescimento do podcast.Redes sociais:InstagramThreads

Andrew, Ben, and Tom discuss leaked Anthropic second-quarter results showing revenue of $11.5 billion, up 1300% year-over-year and 143% quarter-over-quarter with an annualized run rate crossing $47 billion in May compared to OpenAI's roughly $40 billion at the same point, SpaceX's AI division contributing $2.6 billion in the quarter while posting positive adjusted EBITDA despite an adjusted operating loss, Chinese memory chipmaker CXMT surpassing Tencent to become China's most valuable company with a market cap over $500 billion as capital increasingly shifts from internet platforms to hardware, and Goldman Sachs pushing back on market pricing that it views as too hawkish on the odds of a Fed rate hike.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Pat, Zach, and Chance get together to talk about Netflix landing the GTA VI special look, Tencent's cancelled GTA competitor, and if now is a good time to build or upgrade your PC.

This week's podcast is about Unitree, which is about to go public.Plus, there is a quick intro to Tencent's Robot Brain (Tairos).You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.Here is the link to TechMoat Consulting.Here is the link to our Tech Tours.Here are my 5 take-aways:A good growth story in robodogs and humanoids. Rapid growth. Highly diversified. In both product categories.Unitree has frontier-level expertise in robot bodies and agility.Unitree was late to embodied AI capabilities.The hands don't appear advanced. Only 10 DoF?Operationally, Unitree is still a relatively small company516 FTEs, versus 1,500 at AgibotDisclosure. I have had a paid consulting relationship with Tencent in the past twelve months.-----------I am a consultant & keynote speaker on how to increase digital growth and strengthen digital AI moats.I am the founder of TechMoat Consulting, a consulting firm specialized in increasing digital growth and strengthening digital AI moats. Get in contact here.I write (a lot) about digital growth and digital AI strategy (3 best selling books, +2.9M followers on LinkedIn). There is a free book and email newsletter below.My Moats and Marathons book series is a framework for building and measuring competitive advantages in digital businesses.This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.Support the show

Amid record numbers of new university graduates and a persistent labor mismatch across China, a fast-growing number of degree holders are turning to vocational training to acquire practical workplace skills, plugging long-standing gaps between theoretical university learning and on-the-job requirements.全国高校毕业生规模创历史新高，劳动力供需错配问题长期存在。越来越多拥有本科学历的毕业生选择参加职业培训，习得职场实用技能，弥补高校理论学习与岗位实操要求之间长期存在的鸿沟。The shift is epitomized by 26-year-old Liu Wenhao, who holds an environmental design degree. His first jobs in interior design in Qingdao, Shandong province, paid under 3,000 yuan ($445) a month, and despite cycling through five employers in a single year, he saw virtually no pay growth. He repeatedly confronted real-world work demands unaddressed by his university education, including being tasked single-handedly with designing a 300-square-meter apartment.26岁的刘文浩便是这一转变的典型代表，他本科专业为环境设计。他早年在山东青岛从事室内设计，首份工作月薪不足3000元（折合445美元）；一年内接连换了五家公司，薪资却几乎毫无涨幅。工作中屡屡遇到大学课程未曾覆盖的实操难题，比如独立完成一套300平米住宅的全套设计。To break his career stagnation, Liu enrolled at Qingdao Technicians College in 2024 in a multimedia training program, a sector booming as social media reshapes corporate communications and brand outreach. His newly acquired practical skills were in such strong demand that he left the two-year program early last year to take a corporate communications role at a local manufacturing plant, with his monthly income increasing to 5,000 yuan.为打破职业发展瓶颈，刘文浩2024年进入青岛市技师学院，报读多媒体专业培训。随着社交媒体重塑企业宣传与品牌推广模式，多媒体行业迎来快速发展。他习得的实操技能市场紧俏，于是去年提前结业，入职当地一家制造企业负责品牌宣传，月薪涨到5000元。"A degree only gets you through the door. What matters afterward is your actual ability," Liu said.刘文浩表示：“学历只是入职敲门砖，真正决定发展上限的是实操能力。”With the number of college graduates nationwide reaching a historic high of 12.7 million this year, vocational "retraining" has evolved from individual career adjustment into a notable social trend, fueled in part by national policy priorities aimed at resolving China's stubborn labor supply-demand imbalance.今年全国高校毕业生规模达到1270万的历史峰值。职业技能回炉培训已从个人职业调整手段，演变为显著社会风潮，国家出台多项重点政策，着力破解长期难以化解的劳动力供需失衡问题，进一步助推这一趋势。During a 2024 study session of the Political Bureau of the 20th Communist Party of China Central Committee, Xi Jinping, general secretary of the CPC Central Committee, identified the mismatch between graduate talent supply and industrial skills demand as the country's foremost employment challenge. He called for adjustments to academic programs and resource allocation in higher education, enhancements to vocational education and improvements in lifelong vocational skills training.2024年，中共二十届中央政治局开展集体学习，习近平总书记指出，高校人才供给与产业技能需求错配是我国就业领域首要难题。总书记提出，要优化高等教育专业设置与资源配置，做强职业教育，完善终身职业技能培训体系。Authorities have moved swiftly to implement these directives, restructuring university course offerings and resource distribution, modernizing vocational training infrastructure and expanding subsidized skill-upgrading programs.各地各部门迅速落实相关部署：调整高校专业布局与资源分配，升级职业培训实训硬件，扩大政府补贴式技能提升培训覆盖面。Official figures show the government delivered 11.53 million subsidized vocational training sessions in 2025 alone, part of a broader push to align graduate capabilities with industrial needs.官方数据显示，仅2025年，政府补贴开展的职业培训累计达1153万人次，这也是全国统筹推进毕业生能力适配产业发展的重要举措。Findings from a 2025 survey covering 105 technician schools show that between 2023 and 2025, the number of schools running courses targeted at college graduates rose from 26 to 45, with total enrollment increasing from 1,821 to 2462.2025年一项覆盖105所技师学院的调研显示：2023至2025年间，开设高校毕业生专项技能班的院校数量从26所增至45所，参训总人数从1821人提升至2462人。Growth is particularly robust among learners with higher academic credentials. Institutions offering retraining for holders of bachelor's degrees and higher jumped 75 percent, from 16 to 28, and enrollment in these programs climbed from 401 to 570 participants.高学历参训群体增长势头尤为迅猛。面向本科及以上学历人群开设回炉课程的院校数量大涨75%，从16所增至28所；对应参训人数从401人升至570人。While highly educated retrainees still account for a modest share of overall vocational students, their rapid growth rate signals fast-rising demand for practical skill upgrades among China's university-educated workforce, experts say.专家表示，尽管高学历回炉学员在全部职校学生中占比仍不高，但其增速迅猛，说明国内本科就业群体对实操技能提升的需求正在快速攀升。National and local policymakers have rolled out targeted initiatives to accelerate the trend.中央及各地政府陆续推出专项扶持举措，持续推动这一发展趋势。In April last year, the Ministry of Human Resources and Social Security launched a nationwide campaign encouraging vocational colleges to open specialized training tracks for university graduates.去年4月，人社部启动全国专项行动，引导各类职业院校开设面向高校毕业生的专属技能培训赛道。The push comes amid acute industry-wide talent shortages. The ministry estimated that China was facing a skilled-labor shortage of 4.5 million in smart manufacturing alone by the end of last year.该行动出台的背景是全行业技能人才缺口突出。人社部测算，截至去年年底，仅智能制造领域，我国技能人才缺口就达450万。To address this, Beijing plans a two-year program this year at six technician colleges in smart manufacturing, multimedia and auto repair.为补齐人才短板，北京今年启动为期两年的专项培养计划，依托六所技师学院开设智能制造、多媒体、汽车维修三大方向培训班。Jiangsu province, a manufacturing powerhouse on China's eastern coast, plans to launch 20 such classes this year in artificial intelligence, advanced manufacturing and modern services.东部制造业大省江苏，计划今年开设20个同类专项培训班，覆盖人工智能、先进制造、现代服务业领域。Vocational educators witness this academic-practical skill divide firsthand. Lu Weiyang, an instructor at a Guangzhou technician college that began offering one-year skills programs to graduates last year, sees the gap daily. Undergraduates arrive with theoretical knowledge but weak hands-on skills — a gap he considers bridgeable. They learn faster, he said, thanks to stronger self-learning and comprehension.职教一线教师直观感受到理论知识与实操能力之间的断层。广州一所技师学院自去年开设一年制毕业生技能专班，教师卢伟阳每天都能见到这类差距：本科生理论基础扎实，但动手实操薄弱，而这一短板完全可以通过培训补齐。他表示，本科生自学与理解能力更强，技能学习速度更快。"Students with an undergraduate background have the ability to extrapolate — they can deduce the next steps after a bit of guidance," he said.他说：“本科毕业生具备知识推演能力，稍加点拨就能举一反三，推导出后续操作流程。”Teaching such mixed cohorts — some with relevant degrees, others from unrelated fields — has required new approaches.培训班学员背景混杂，有人专业对口，有人跨专业转行，这倒逼院校创新教学模式。To accommodate these academically advanced learners, vocational institutions are revamping their teaching frameworks. Li Yang, vice-dean of the college's electromechanical equipment department, said the school has adopted custom modular courses tailored specifically for graduate retrainees.为适配高学历学员的学习特点，各职校重构教学体系。该院机电设备系副主任李阳介绍，学校专门为本科学历回炉学员定制模块化课程。"Modular training is like targeted driving test instruction — each module builds a specific practical skill," he said. Students work through separate skill blocks — electrical systems, pneumatics, installation, programming — at their own pace, focusing only on what they lack rather than repeating what they already know.他解释：“模块化培训类似驾照专项分项教学，每个模块对应一项独立实操技能。”学员分模块学习电气、气动、设备安装、编程等内容，可自主把控学习进度，只补自身短板，无需重复学习已掌握的知识。ntegrated model产教融合一体化培养模式In his speech at the 2024 study session of the Political Bureau, Xi emphasized the need for better integration of vocational and general education, industry and education, and science and education. He also highlighted the importance of allowing the market to play a decisive role in human resource allocation while ensuring that the government fulfills its supportive role.总书记在二十届中央政治局2024年集体学习讲话中强调，要推动职普融通、产教融合、科教融汇。同时要充分发挥市场在人力资源配置中的决定性作用，更好发挥政府扶持保障作用。Guangdong province, an industrial powerhouse, has emerged as a front-runner in implementing this integrated model.制造业大省广东是落地一体化培养模式的先行地区。By the end of last year, the province had built 171 industry-education-evaluation skill chains, partnering with leading companies, including Huawei, Tencent and BYD, to tailor vocational training curricula to real supply chain and industrial needs.截至去年年底，广东建成171条产教评技能产业链，联合华为、腾讯、比亚迪等龙头企业，依据产业链真实需求定制职业培训课程。For Li Shaochang, 29, a graduate of Guangzhou Electromechanical Technician College, the decision to enroll in a technician college was driven by market demand. Despite holding a degree in mold design and manufacturing from Tianjin University, one of China's most prestigious institutions, he spent five years outside his field. Observing the shift toward smart manufacturing and AI, he recognized the need for practical skills.29岁的李少昌毕业于广州机电技师学院，他选择入校学习完全出于市场需求考量。他本科就读顶尖学府天津大学模具设计与制造专业，却有五年时间从事无关行业。目睹智能制造、人工智能产业快速兴起后，他意识到实操技能不可或缺。"The technician college gives me extensive hands-on experience," he said. "That, combined with university training, is exactly what companies want."他表示：“技师学院能提供充足实操练习，再搭配大学打下的理论基础，正是企业最看重的复合能力。”The college's partnership with Guangzhou Jiafan Computer Co, an automation enterprise focused on smart manufacturing, gives students access to the same production line hardware used in the factory, with engineers teaching on campus. Li Shaochang's strong academic background earned him an early placement at Jiafan, where he joined an automated guided vehicle robotics team — a platform that combines a mobile chassis with a robotic arm for tasks like material handling and loading. His team leader was an engineer who had taught him at the college.学院与专注智能制造的自动化企业广州佳帆计算机公司深度合作，校内配备和工厂生产线同款设备，企业工程师驻校授课。李少昌凭借扎实理论基础提前入职佳帆，加入自动导引车机器人研发小组，该设备结合移动底盘与机械臂，可完成物料转运、装卸等作业，小组主管正是在校教过他的工程师。Zhang Kai, 37, holds a business administration degree from Guangdong University of Foreign Studies and now oversees Jiafan's advanced skills training base. Speaking from both sides of the desk — as a manager and a student in the same program — he said the company screens for a willingness to learn and a desire for skills, not credentials.37岁的张凯拥有广东外语外贸大学工商管理学位，现任佳帆高级技能实训基地负责人。他既当过参训学员，如今又是企业管理者，他表示企业招人更看重学习意愿与实操能力，而非一纸文凭。"A bachelor's diploma shows you can handle exam pressure," he said. "But a degree plus real skills — that's where the real competitive edge lies."他说：“本科学历只能证明你具备应试学习能力，学历搭配过硬实操，才是真正的核心竞争力。”At the annual Central Economic Work Conference in December, Xi called for combining investment in physical assets with investment in human capital — a call that has since crystallized into the political catchphrase "investing in people", shorthand for upgrading the workforce through education, skills training, health and social adaptability.去年12月中央经济工作会议上，总书记提出统筹推进物质资本投入与人力资本投入，这一部署凝练为“投资于人”的重要提法，即通过教育、技能培训、健康保障、社会适应能力培育全面提升劳动者综合素养。Embodying that principle is Liu, the environmental design degree holder who enrolled at Qingdao Technicians College. He now earns more than double his previous salary, so his choice to build on his degree with vocational training has already paid off.前文提到的环境设计专业毕业生刘文浩正是这一理念的鲜活案例。如今他薪资较从前翻了一倍有余，依托学历补充职业技能的选择已经收获实实在在的回报。mismatch /ˌmɪsˈmætʃ/错配，供需失衡stagnation /stæɡˈneɪʃn/停滞，发展瓶颈modular /ˈmɒdjələ(r)/模块化的extrapolate /ɪkˈstræpəleɪt/推断，举一反三pneumatics /njuːˈmætɪks/气动技术curriculum /kəˈrɪkjələm/课程体系，教学大纲

Es gibt bis zu 200 €, wenn ihr Friends & Family zu Scalable Capital bringt! Das Ganze geht nur bis zum 31. August. Mehr Infos in der App oder auf scalable.capital/oaws. Cisco, Cerebras & Coherent fallen. Anthropic mit 2.000 Mrd. $ an die Börse? OpenAI-Vorstände gehen. Sandisk & Maersk steigen wegen Prognose. CXMT überholt Tencent. Birkenstock wächst 15%. Ackman kauft Netflix, verkauft Hertz. JD.com schrumpft. Trigano (WKN: 913141) ist einer von Europas größten Wohnmobil-Herstellern und steckt den Lagerabbau weg und wächst wieder. Neue EU-Führerscheinregel könnte den Umsatz pro Fahrzeug pushen. KGV von 10 und fast 3% Dividende. Burger King ist zurück auf Platz 2 in den USA. Restaurant Brands (WKN: A12GMA) investiert 700 Mio. $ in den Umbau und wächst das fünfte Quartal in Folge. KGV von 17, über 3% Dividende. Aber Rindfleischpreise auf Allzeithoch. Diesen Podcast vom 14.08.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's MadTech Daily, we discuss investors betting on a USD$2tn (£1.50tn) IPO valuation for Anthropic and Tencent's AI push sending capex soaring 176%. 

Plus: The AI rally is back after Coreweave shares jump off-hours on bumper profits.  But the AI-buildout is hurting some companies' bottom line, with China's biggest company ending its double-digit earnings streak. Luke Vargas hosts. Sign up for WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sam Vadas takes investors through her top takeaways of the session by focusing on international movers, from record profits in the Norway Sovereign Wealth Fund to Tencent's post-earning sell-off. ======== 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

When U.S. sales teams from Silicon Valley's big data-labeling startups visited this year's International Conference on Machine Learning in Seoul, Korea, they arrived prepped to court the industry's big spenders. The data companies—collectively worth tens of billions of dollars and generating billions in annual revenue supplying training data to customers like OpenAI and Anthropic—are used to chasing AI labs that are notoriously demanding, fickle and difficult to satisfy. They found another eager customer waiting for them: China's AI industry. Some Chinese companies have shopping lists. Tencent—which has previously been designated by the U.S. government as associated with the Chinese military, a characterization the company disputes— circulated with prospective vendors a detailed request for training data spanning finance, cybersecurity and one of AI's most coveted research goals: AI systems capable of improving themselves. By Anna Tong, Forbes Staff Learn more about your ad choices. Visit megaphone.fm/adchoices

Forty years ago, Shenzhen was a small southern Chinese fishing village, just across the border from Hong Kong. Today, it's one of the world's most advanced megacities, with a population of nearly 20 million people and home to many of the tech industry's largest companies, including Huawei, Tencent, DJI, and Transsion, among countless others. That remarkable transformation within a single lifetime offers important lessons for Kenya, Vietnam, and other developing countries seeking to build their own technology hubs. Grace Yuehan Wang, a visiting scholar at the London School of Economics and a research fellow at Stellenbosch University in Cape Town, joins Eric to discuss her new book, which explores how Shenzhen was able to develop so quickly and what lessons its remarkable rise offers countries across the Global South seeking to build their own technology hubs.

Once a small fishing village, Shenzhen has transformed into one of the world's leading innovation hubs. Home to tech giants like Huawei, Tencent, DJI, and BYD, the city has grown from China's first special economic zone into a global center for advanced manufacturing, technology, and ideas. As Shenzhen prepares to host APEC 2026, visitors from across the Asia-Pacific are arriving in record numbers. What makes this city so attractive? How did Shenzhen build its innovation ecosystem? And what does its rise mean for the future of global cooperation?

Jim Fields is an American entrepreneur who has spent over 15 years living and building businesses in China. He speaks fluent Mandarin, began his career at Adobe in Silicon Valley, and moved to China, where he worked at Nokia and Reckitt Benckiser before founding Relay Video in 2016 - a Beijing-based creative marketing agency that helped Chinese tech companies including Xiaomi, Tencent, and Lenovo tell their stories to global audiences. He later founded Relay Club, an influencer management platform for Chinese brands going outbound. He now serves as Chief Growth Officer at Nuon Medical and is involved with Oren Medical, a Shenzhen-based manufacturer within the Kaiyan Medical Group.In this episode, Jim talks about his journey from Silicon Valley to China, what he learned building Relay Video, and the most common mistakes Chinese tech companies make when trying to go global. He explains why he pivoted from marketing into medtech, and breaks down both companies he works with today: Nuon Medical, which embeds clinical technologies like red light therapy, microcurrent, and PEMF directly into cosmetics applicators for brands including Clarins and Vagheggi, and Oren Medical, which manufactures red light therapy and medical wellness devices with over 300 patents across six FDA-cleared facilities.Jim also discusses his TEDx talk arguing that light is a fourth macronutrient alongside oxygen, water, and food - the science behind red and near-infrared light therapy and why he believes it belongs in everyday consumer products. He covers the beauty tech market in China versus the West, how US-China trade tensions and tariffs affect a cross-border business like his, and what he's learned from 15-plus years of building across both markets. Discussion Points·       Jim's journey from Silicon Valley to 15+ years building businesses in China·       Relay Video's thesis: Chinese brands are the brands of the future but need help telling their stories globally·       The most common mistakes Chinese tech companies make when going global, from working with Xiaomi, Tencent, and Lenovo·       The pivot from marketing to medtech and how Jim's China background connects to his work in beauty tech·       Nuon Medical: embedding clinical technologies (light therapy, microcurrent, PEMF) into cosmetics packaging·       Jim's TEDx argument that light is a fourth macronutrient - the science behind red and near-infrared light therapy·       Oren Medical: China's medical device manufacturing sophistication, 300+ patents, six FDA-cleared facilities·       Beauty tech market maturity in China versus the West and where China is leading·       How US-China trade tensions and tariffs affect a cross-border business built on Chinese manufacturing·       Lessons from 15-plus years building businesses that span China and the West

This week, Lotus, Niki, and John discuss Xbox's 7% revenue decrease year over year, Tencent's alleged "Grand Theft Auto killer" Last Sentinel facing major problems, and impressions of one of 2026's most pleasant surprises, Splatoon Raiders.00:00:00 Intro & drug testing00:08:35 Double Fine lays off 23 after going independent00:13:07 Xbox reports $1.7b loss due to critical business failures00:17:07 Tencent's Last Sentinel quietly lays off staff, possibly canceled00:32:50 Super Mario Sunshine coming to Nintendo Switch Online00:38:55 Splatoon Raiders is what Splatoon probably should've always been00:54:25 Endacopia is a surrealist point-and-click jaunt00:58:18 Midnight Moments is a pleasant, but perhaps shallow, cyberpunk diorama-builder01:01:52 Niki's Apple TV came!01:08:53 VGBees Baseball Talk01:20:35 HIVE QUESTIONS02:40:35 OutroThanks for listening!Please leave us a review! We'll read it on the show and it helps us out a lot.VGBees is ad-free, AI-free, and completely supported by you! https://vgbees.com/joinVGBees is a weekly games media podcast hosted by Niki, John, and Lotus.

Mats Steen spent most of his short life indoors in Oslo. To his parents he looked like a young man with no one. When he died, hundreds of people reached out to say they had known him for years, inside World of Warcraft, as a character named Ibelin. His parents had no idea.That story sits at the center of this episode, and so does the question underneath it: how much of what games actually do is invisible to the people measuring them?Recorded on a rooftop in New York at Games for Change 2026, this episode brings together three people running the organization. Susanna Pollack on twenty-plus years of arguing that games carry purpose beyond entertainment. Then Dr. Rachel Kowert and Arana Shapiro on brand-new research that says the panic about kids and gaming is not coming from the evidence.GuestsSusanna Pollack, President, Games for ChangeDr. Rachel Kowert, Research Director, Games for ChangeArana Shapiro, Chief Program/Operating Officer, Games for ChangeInterviews by Lewis Ward.Chapters00:00:00  Cold open: the story of Mats Steen00:00:42  Games for Change 202600:04:38  Susanna Pollack, and this year's awards00:06:30  What Games for Change actually is00:09:05  Rebuilding the festival: workshops and the Playtest Lab00:12:23  The argument the industry keeps failing to make00:14:37  Games are one of the few places failing is fine00:15:41  The education spine, and kids in 92 countries00:18:25  Raising Good Gamers: the 2019 origin00:22:11  Going global, and working with the UN00:27:54  What's coming next00:28:57  Dr. Rachel Kowert and Arana Shapiro00:30:00  The white paper, and 200,000 articles00:31:43  Why bad news about games travels further00:32:52  Stop counting hours, start asking why00:33:55  Most media is passive. Games are not.00:35:17  SPACE: the five moves00:36:25  60% of kids want their parents to play00:36:57  What Raising Good Gamers ships00:41:53  Self-determination theory and the Proteus effect00:43:41  Academy, Intel, and the DiscordWhat you'll take awayThere is more positive coverage of games than negative. The negative just gets shared further, which leaves parents inside what Dr. Kowert calls a biased information ecosystem.A game does not need to be designed for impact to have it. Joy, stress relief, connection, and a space where age, gender and location stop deciding anything.Games are one of very few activities where failing is acceptable and repeatable. That is how mastering a system quietly builds resilience.The parental concerns have not changed in fifty years. What changes is the question: not how many hours, but what purpose the play is serving.SPACE, the framework: Start with curiosity. Play together. Adjust for context. Cultivate digital citizenship. Establish boundaries together.60% of kids want their parents to play with them. What stops most parents is assuming they'll be bad at it.Mentioned in this episodeRaising Good Gamers, and the white paper by Dr. Rachel Kowert. Free, along with a conversation guide and a family gaming code of conduct template. Workshops rolling out through the PTA, Boys and Girls Club, and libraries.The Remarkable Life of Ibelin, the Netflix documentary about Mats Steen. A feature film is in development.The Games for Change Student Challenge, 10+ years, participants from 92 countries.2026 award winners: South of Midnight (Compulsion Games), the Vanguard Award to Jenn Panattoni, and the Giving Award to SpecialEffect for their adaptive controller work.The Proteus effect, first described by Nick Yee, who joined us back in February.Katie Salen, professor of informatics at UC Irvine, co-creator of Raising Good Gamers.Disclosure: the FTI Consulting media analysis underpinning the white paper was commissioned by Tencent.Where to go nextAcademy — free, built in short packs you can finish on a commute. academy.playerdriven.ioIntel — our prediction game. Call what the industry does next and see if you were right. intel.playerdriven.ioDiscord — where this conversation actually continues.The Operator's Briefing — Tuesdays, if you'd rather it come to you.

Plus: chip maker CXMT becomes the most valuable company listed in mainland China in its debut. And some U.S. companies plan to increase headcount despite AI fears. Imani Moise hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tencent is one of China's biggest tech companies, running the popular Chinese messaging app WeChat and the world's largest video game vendor. Now, it's also an up-and-coming force in the field of carbon removal. Xu Hao, the vice president of Sustainable Social Value at Tencent, oversees two of those initiatives: the Carbon Neutrality Lab and CarbonX. He sits down with Sherrell Dorsey, host of the “TED Tech” podcast, to talk about how megacorporation can help advance the climate movement. He also explores the current state of carbon removal technology and how Tencent's video games are becoming an unlikely source of climate education for hundreds of thousands of people. This is episode three of a four-part series airing this month on TED Tech, where host and climate tech journalist Sherrell Dorsey speaks with climate leaders on the technology sparking a greener, more equitable future. Hosted on Acast. See acast.com/privacy for more information.

There's (another) new open source king of AI.

Alice Han and James Kynge look at why, for the first time in decades, China's new Five-Year Plan sets no numerical target for urban job creation. With unemployment at 5.1%, producer prices at a four-year high, and AI anxiety rippling through the labor market, what does it mean when Beijing, a government that treats jobs numbers as sacred, stops setting one? Then: Tencent is in talks to become the largest shareholder in Manus, the Chinese AI agent startup Meta tried to buy for $2 billion before Beijing forced the deal to unwind. Alice and James unpack why Chinese regulators intervened, what happened to Manus's founders, and what this all signals about Beijing's grip on its AI sector. Finally: Gen Z in China is skipping the megacities. Alice and James dig into why more young people are choosing Tier 3 and Tier 4 cities over Beijing and Shanghai, and whether it's a lifestyle choice or a symptom of a tougher economy. Subscribe to China Decode on Substack for weekly analysis, livestreams, and deep dives into the biggest story shaping the global economy: chinadecode.profgmedia.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

PODCAST LAS NOTICIAS CON CALLE DE 10 DE JULIO - Israel dice que Irán iba a asesinar a Trump y por eso cambiaron de avión presidencial - CNN 22 personas y empresas se declaran en quiebra por día - El Nuevo Día Gobernadora entrega 200 títulos de propiedad - NEWSPR Acusan a Gian Carlo Piovanetti por vivir como rico cogiendo de tonto a clientes y fraude - El Nuevo Día Plantean darle alivios a ayunadores y cuidadores, costo de 300 millones - El Nuevo Día  PR es el líder en enfermedades raras en todo USA - El Vocero Ciencias Forenses busca encontrar personas desaparecidas para dar paz a familias que no encuentran a sus seres queridos - El Vocero Politank dice que se va a defender de todo esto sal pa fuera - El Vocero Agricultura y comida en aumento de precio por sequía y fertilizantes - El Nuevo Día Nombran fiscal investigadora en caso de Negrón Reichard - El Nuevo DíaUn momento para WindMar Home — la empresa con más de 20 años protegiendo los hogares puertorriqueños.Solar para bajar tu factura. Techo para proteger tu inversión. Agua para que nunca te quedes sin — especialmente con las sequías que se aproximan. Y batería para total independencia energética.Todo bajo una misma empresa. Un solo llamado. Llama al 787-489-1155 o visita windmarhome.comWindMar Home — los que se preparan hoy , duermen tranquilos mañana.#windmarhome #incluyeauspicioPetróleo/diésel: Brent ~$76; diésel de EE.UU. al alza más rápida en 4 años; Rusia prohíbe exportar diésel (≈30% de su refinación estuvo fuera el mes pasado)Segundo día de ataques Irán–EE.UU.OpenAI y Google vendieron modelos avanzados a subsidiarias en Singapur de Alibaba, Baidu y Tencent — empresas en la lista negra del Pentágono - FT  LOS DATOS DEL DÍA (snapshot Bloomberg, 10 jul) Brent$76.13 (-0.2%) S&P 500 (futuros)7,580.75 (-0.1%) Nasdaq 100 (futuros)29,817.25 (-0.4%) Bono 10 años4.53% (-0.02) Oro$4,104.95 (-0.5%) Diésel EE.UU.alza más rápida en 4 años (nivel s/c)

Alice Han and James Kynge dig into why Apple is lobbying the Trump administration for permission to buy memory chips from a Chinese company on the Pentagon's military blacklist. With DRAM prices up nearly 100% in a single quarter — analysts are calling it "RAMageddon" — Apple already raised MacBook and iPad prices by up to 20%, and iPhones could be next. How far will Apple go to secure its supply chain, and what does it mean if Washington says yes? They also break down DeepSeek's landmark $7.4 billion funding round, which is the first time the Chinese AI startup has ever taken outside money. Tencent, CATL, and China's state-backed National AI Investment Fund are among the backers, and the valuation has jumped six-fold in six weeks to nearly $59 billion. DeepSeek built its reputation on doing more with less — so why does it need the money now? And finally: a new sign that China's middle class is changing what it puts on the table. The Economist calls it the "Californication" of Chinese diets: a growing appetite for organic, health-conscious food. Subscribe to China Decode on Substack for weekly analysis, livestreams, and deep dives into the biggest story shaping the global economy: chinadecode.profgmedia.com. Learn more about your ad choices. Visit podcastchoices.com/adchoices

With Grand Theft Auto VI finally launching this November, we've been left without a key piece of information: Its price. Conjecture and prognostication has run rampant, but now we have confirmation that GTA6's base cost is $80 in the United States, with a $100 version offering players some perks and unlocks. With our long-awaited trip down to Leonida drawing closer on the horizon, we've much to discuss, including our hype level, whether we'll buy the base or special edition, the lack of a real physical version, and -- perhaps most intriguingly -- no new trailer or gameplay videos to help sell a product that (let's be honest) probably doesn't need much help to begin with. Other news this week includes significant layoffs at Sony-owned Bungie, indications that God of War: Laufey may launch as early as Q1 2027, more delisted shovelware tripe removed from PlayStation Store, executive-level issues at FromSoft, and more. Then: Listener inquiries! What's with Steam Machine's absurd price? Should Sony look to invest in any of Xbox's distressed assets? Why did PlayStation even bother publishing Midnight Murder Club if they were only going to ignore it? Will Chris and Colin one day have their shared ex-psychiatrist on the show to discuss our many mental ailments? Timestamps: Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement. 0:00 - Intro22:50 - Happy Birthday nerd35:46 - Trophy hunting37:10 - Chris has a new voice47:15 - Release the files55:38 - The Green Reaper57:31 - GTA VI "plays best" on PlayStation 51:46:15 - Bungie lays of hundreds2:11:09 - Bungie pitched a Destiny dating game2:15:35 - God of War: Laufey might launch in Q1 20272:17:02 - Deborah Ann Woll claims Laufey was pitched in 20182:25:06 - PlayStation Store shovelware developer bites the dust2:30:53 - Kadokawa says Fromsoft is being mismanaged2:39:21 - Tencent exits from several game studio investments2:43:47 - What We're Playing (Pragmata, LEGO Batman, Luna Abyss, GTA: San Andreas)3:05:33 - Steam Machine pricing3:26:19 - Could Sony invest in XBOX?3:31:15 - What's next for Sony?3:40:03 - Midnight Murder Club received its final update3:46:05 - Is gambling software okay?3:54:55 - Should Sony stick to its existing IP catalog? Learn more about your ad choices. Visit podcastchoices.com/adchoices